RECONFIGURATION OF ASSETS IN CONTROLLED ENVIRONMENT

Description

BACKGROUND

A controlled environment may incorporate various assets configured or deployed to carry out specific functions. For instance, Fan Coil Units (FCUs) are commonly employed to maintain desired environmental conditions in controlled spaces or zones, which may include residential, commercial, and industrial settings. These units are generally designed to circulate air and maintain desired thermal conditions within the controlled environments or zones. Consequently, monitoring the operation of the assets, for example, FCUs may be important for various reasons.

BRIEF DESCRIPTION OF DRAWINGS

The detailed description is described with reference to the accompanying figures. It should be noted that the description and figures are merely examples of the present subject matter and are not meant to represent the subject matter itself.

FIGS. 1A to 1D illustrate a block diagram of a computing environment having a system, according to an example implementation of the present subject matter.

FIG. 2 illustrates a block diagram of the system, according to one example implementation of the present subject matter.

FIG. 3 illustrates a block diagram of a computing environment comprising the system, according to another example implementation of the present subject matter.

FIGS. 4A and 4B illustrate a visual indicator, according to one example implementation of the present subject matter.

FIG. 5 illustrates an exemplary graphical representation of evaluated statistical variables derived for each of a plurality of assets, according to one example implementation.

FIG. 6 illustrates a block diagram of a method for reconfiguring at least one asset of a controlled environment, according to one implementation of the present subject matter.

FIG. 7 illustrates a block diagram of a method for reconfiguring the at least one asset of the controlled environment, according to another example implementation of the present subject matter.

FIG. 8 illustrates a non-transitory computer-readable medium for reconfiguring an asset of a controlled environment, in accordance with an example of the present subject matter.

Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements. The figures are not necessarily to scale, and the size of some parts may be exaggerated to more clearly illustrate the example shown. Moreover, the drawings provide examples and/or implementations consistent with the description; however, the description is not limited to the examples and/or implementations provided in the drawings.

DETAILED DESCRIPTION

A controlled environment may encompass various types of environments or spaces. For example, the controlled environment may be an enclosed area or space. Examples of the controlled environment may include, but are not limited to, a building, a room, an industrial facility or premises, a partially open or exposed enclosing or space, a storage facility, a warehouse, a data center, and an access-controlled space or area.

Generally, the controlled environment may include one or more assets configured or deployed to perform in a specific manner to implement designated operations or functionalities. For example, a controlled environment may include one or more Fan Coil Units (FCUs) to achieve or maintain desired environmental conditions in controlled spaces or zones. The FCUs may typically include a fan, a coil, and a control valve that may regulate the flow of a working fluid or refrigerant within the coil. Such units are generally designed to circulate air and achieve or sustain desired thermal conditions within the controlled environment. Additionally, the controlled environment may include multiple FCUs, where different set of FCUs may enable localized temperature control and air circulation in distinct zones of the controlled environment. Similarly, the controlled environment may include one or more other assets that may be used to maintain or achieve desired thermal conditions in the controlled environment.

Monitoring operation of the assets may be important for various reasons. For example, malfunctioning of the one or more FCUs may lead to failure or disruption in the circulation of air and/or maintenance of desired localized temperatures in the controlled environment. As a result, the comfort and well-being of occupants located within the controlled environment may be affected. Further, monitoring the operation of the assets may be necessary to protect perishable or sensitive materials or processes. For example, it may be necessary to maintain temperature and circulation of air to prevent any undesired physical, chemical, and/or biological change in the properties of goods or materials located or stored in the controlled environment. In another example, it may be necessary to maintain a cool environment within a controlled environment to prevent malfunctioning or damaging of equipment. Also, in controlled environments, for example, laboratories, manufacturing facilities, or other specialized environments, precise control of temperature, air circulation, and air quality may be critical for maintaining product integrity or ensuring process consistency.

Further, monitoring the functioning of the assets allows for early detection of potential issues or malfunctioning and, thereby enables proactive maintenance and reduced risks of unexpected or unintended scenarios, for example, undesirable temperature conditions within the controlled environment. Thus, proper functioning and efficiency of the assets are critical for ensuring well-being of occupants and materials or equipment located within the controlled environment. Also, monitoring the performance or functioning of the assets may be important to optimize energy efficiency and reduce operational costs. Further, monitoring the functioning of the assets may also be required to ensure compliance with regulatory or business rules and requirements. For example, in some industries, maintaining specific environmental conditions may be mandated by law or industry standards. Other reasons may also be possible that may necessitate monitoring of operations or functioning of the assets.

With advancements in technology, various solutions have been developed for monitoring the performance or functionalities of the assets. Generally, monitoring performance or functionalities of the assets relies on a combination of periodic manual inspections and installation of various sensors. These sensors may measure different parameters associated with the assets and the control environment. For example, the sensors may be used to monitor temperature being maintained in the controlled environment, speed of fans of the FCUs, and position of control valve regulating flow of the working fluid in the coil of the FCUs. The data generated by these sensors is generally used to assess the performance of the FCUs, or assets.

However, the implementation of comprehensive monitoring of the assets often encounters significant challenges. For instance, physical and/or spatial limitations may restrict the number of sensors that can be deployed in the controlled environment and in/on the assets. These constraints impact the ability to achieve thorough and effective monitoring coverage. Thus, only a limited number of sensors are generally deployed to monitor the performance of the assets. Such incomplete coverage may result in blind spots where critical asset conditions may go undetected, potentially leading to unexpected failures or performance issues.

Further, a reduced number of sensors leads to generation of limited data. That is, the reduced number of sensors may provide less granular data, making it challenging to accurately assess the overall performance of the assets. Additionally, the restricted sensor placement may limit the types of parameters that can be monitored, potentially overlooking important indicators of asset's health and performance. Less granular data may lead to derivation of outcomes without consideration of multiple other important factors or parameters that could not be directly determined from such limited data. There may be lack of information having considerable significance, for example, data indicating aspects about the performance of the FCUs, which may not be directly indicated by the limited data. As a result, it may be difficult to reach a meaningful conclusion or make fully informed decisions about the performance, maintenance, and optimization of the assets.

The present subject matter relates to techniques for detecting deterioration in ability or performance of assets in a controlled environment, and cause reconfiguration of the assets. The deterioration, in one example, may be related to the incapability of an asset in maintaining or achieving a thermal condition within the controlled environment. Further, the reconfiguration may include, for example, reconfiguring or modifying the operational parameters of the assets. The reconfiguration may be, for example, to optimize the performance of the assets, enhance efficiency, and maintain or achieve desired conditions within the controlled environment. The techniques may also cause enhancement of the overall operation and management of assets across diverse controlled environments.

According to one example implementation of the present subject matter, a set of characteristic indicators may be received for the controlled environment having a plurality of assets. In one example, an asset may be a unit comprising one or more ventilation devices, thermal regulation devices, and control devices. For example, the asset may be a Fan Coil Unit (FCU) having the one or more ventilation devices, thermal regulation devices, and control devices. The one or more ventilation devices may include devices that regulate flow of a circulation fluid, for example, air, in the controlled environment. Further, the thermal regulation device, operationally linked with the one or more of the ventilation devices, may include devices that influence thermal characteristics of the circulation fluid. For example, the thermal regulation device may be coils or heat exchangers that may affect the temperature of the air being regulated by the one or more ventilation devices. Further, one or more control devices may be operationally linked with at least one of the thermal regulation devices to regulate flow of a working fluid in the thermal regulation device. In one example, the working fluid may be a refrigerant.

Further, in one example, the received set of characteristic indicators may include, in one example, a thermal state indicator indicating a measure of thermal condition of the controlled environment. The thermal condition may be, for example, temperature or an average temperature of the controlled environment. The set of characteristic indicators may further include a thermal setpoint indicator indicating a threshold operational range for the thermal state indicator. The threshold operational range may indicate, for example, an optimal or desired temperature range to be maintained or achieved for the controlled environment. For example, in case of maintaining low or cool temperatures in the controlled environment, the thermal operational range may define or indicate a lower temperature limit and an upper temperature limit. In one example, the lower temperature limit may be a setpoint or a first threshold and the upper temperature limit may be a second threshold defining an allowed deviation of thermal condition from the first threshold. Whereas, in the case of maintaining high or hot temperatures in the controlled environment, the upper temperature limit may be a setpoint or a first threshold and the lower temperature limit may be a second threshold defining an allowed deviation of thermal condition from the first threshold.

The set of characteristic indicators may also include a speed indicator for each of the plurality of assets. For example, each asset may have a speed indicator corresponding to it. The speed indicator may indicate a measure of an operational speed of the ventilation device associated with the asset. Furthermore, the set of characteristic indicators may include a position indicator for each of the plurality of assets. The position indicator may indicate, for example, a measure of opening of the control device associated with the asset. The measure of opening, in one example, may indicate an extent or percentage of opening of the control device upon which regulation or flow of the working fluid may depend.

Based on the set of characteristics indicators, a set of intermediate statistical variables may be derived for each of the plurality of assets. In one example, each set of intermediate variables may include key performance indicators (KPIs). For example, the set of intermediate statistical variables may include a first KPI, i.e., first statistical variable, and a second KPI, i.e., second statistical variable. The first statistical variable may indicate performance of the ventilation device, associated with an asset, in maintaining the thermal condition within the threshold operating range during a total period of its operation. That is, the first statistical variable may indicate how well a specific ventilation device, of an asset, maintained the thermal condition within the threshold range during its operation. Thus, a higher value of the first statistical variable may indicate that the asset, or the ventilation device, is performing well in maintaining the thermal condition. Each of the first statistical variables may thus indicate the reliability and effectiveness of individual assets. The first statistical variables may thus assist in assessing, for instance, the performance, consistency, and potential issues of individual assets, or at least the ventilation device of the assets, of the controlled environment. In one example, each of the first statistical variables, corresponding to a unique asset, may be computed based on a period of continuous operation of the ventilation device of the asset and a duration of compliance of the threshold operational range by the thermal condition. In one example, the first statistical variable may be computed based on a series of such periods or durations. For example, an average or total duration may be determined and utilized for the computation of the first statistical variable. In one example, the first statistical variable may be a ratio of the duration of compliance of the threshold operational range and the total period of operation, or total operating time, of the ventilation device of the asset.

Further, each of the second statistical variables, linked with a corresponding asset for which the first statistical variable was derived, may be determined based on thermal power metric and deviation of the thermal condition from the threshold operational range. In one example, the thermal power metric, for an asset, may be determined based on the operational speed of the ventilation device of the asset and the measure of opening of the control device associated with at least one thermal regulation device operationally linked with the ventilation device of the asset. In one example, the second statistical variable may indicate a relation between change in the deviation of the thermal condition from the threshold operational range based on a change in the thermal power metric of the asset. That is, the second statistical variable may represent how changes in thermal power metric affect, for instance, temperature deviations from the threshold operational range. The second statistical variable may thus indicate the effect of cooling adjustments on the temperature deviations and may assist in assessing the responsiveness and efficiency of the ventilation devices, the thermal regulation devices, and/or the control devices in maintaining desired thermal conditions. Thus, the first and the second statistical variables may be derived for each of the plurality of assets of the controlled environment.

Further, in one example, an evaluated statistical variable may also be derived based on the set of intermediate statistical variables. In one example, the evaluated statistical variable may be determined based on a weighted average of scalar values of each of the first statistical variable and the second statistical variable derived for an asset. Similarly, evaluated statistical variables may be derived for each of the plurality of assets of the controlled environment.

Based on at least one of the evaluated statistical variable and the set of intermediate statistical variables, a reconfiguration signal may be generated. In one example, the reconfiguration signal may cause reconfiguration of one or more operational parameters associated with at least one asset from amongst the plurality of assets. The reconfiguration of the one or more operational parameters may cause, for example, the thermal condition, or the thermal state indicator, of the controlled environment to at least comply with the threshold operational range, or the thermal setpoint indicator. The one or more operational parameters may be, for example, control parameters associated with each of the assets. For example, the one or more operational parameters may include operational speed of a ventilation device of the at least one asset and a measure of opening of the control device of the at least one asset. In another example, the one or more parameters may also include control signals that may control performance of the at least one asset. For example, the control signal may control operational speed of a ventilation device of an asset.

In one example, modification of the one or more operational parameters may help in complying with the threshold operational range. Generation of the reconfiguration signal may thus trigger adjustment of the one or more operational parameters of the at least one asset of the controlled environment. Such adjustments may aim, in one example, to align the thermal condition with the specified threshold operational range or the thermal setpoint indicator.

Further, in one example, rendering of a recommendation, hereinafter referred to as an actionable item, may also be caused to indicate a probable action with respect to the at least one asset. For example, the recommendation may indicate one or more actions that may be opted to comply with the threshold operational range. For example, the actionable item may indicate to replace an asset to optimize cooling in the controlled environment. In another example, the actionable item may indicate reconfiguration or reset of one or more operational parameters of at least one asset. For example, the recommendation may indicate to reconfigure the operational speed of a ventilation device of an asset. Thus, in one example, generation and display of actionable items may be triggered to suggest potential interventions for the at least one asset. These recommendations may aim, in one example, to ensure compliance with the threshold operational range. Therefore, the actionable items may provide specific guidance on steps that can probably be taken to optimize the performance of the assets and maintain desired environmental conditions in the controlled environment.

Further, in one example, a graphical representation, hereinafter referred to as a visual indicator, may also be rendered for each of the assets. In one example, the visual indicator may indicate a relation between the speed indicator associated with a corresponding ventilation device of an asset and a thermal deviation from the threshold operational range. Each visual indicator may also indicate a relation between the thermal power metric determined for an asset and deviation of the thermal condition from the threshold operational range. Each visual indicator may further indicate one or more non-compliance durations, each indicating a period, amongst the total period of operation of an asset which the thermal state indicator is non-compliant with the thermal setpoint indicator. In one example, the reconfiguration signal may be generated based on the at least one of the set of intermediate statistical variables, the evaluated statistical variable, and the visual indicated associated with each of the ventilation devices.

Thus, the present subject matter offers techniques that may optimize the thermal management of controlled environments by analyzing characteristic indicators, generating the statistical variables, and causing reconfiguration of operational parameters of various assets. The provided techniques may assist in efficiently maintaining desired thermal conditions by leveraging limited data to derive valuable insights which are not directly indicated. For example, the characteristic indicators may include the limited data generated by the existing sensors or derived from control signals that control the operations of the assets. From such limited or low granular data or signals, different statistical variables may be derived, each providing valuable insight that may not be indicated directly. For example, each of the first statistical variables may assess individual asset's or ventilation device's performance by indicating how well each asset maintains thermal conditions below or within the threshold range during operation. Similarly, each of the second statistical variables may assess the effectiveness of cooling adjustments by indicating how changes in thermal power metric affect temperature deviations from the threshold operational range. The second statistical variables may help in assessing the responsiveness and efficiency of the assets. Thus, even by using the lesser granular data, different valuable and practical insights may be derived. The existing data or control signals may thus be utilized with improved efficiency and up to an increased extent to derive practical and meaningful insights about the functioning of the assets and, thereby cause manipulation of the thermal conditions within the controlled environment. That is, signal and data utilization is enhanced by extracting multiple layers of insights, thereby leading to efficient data utilization.

Additionally, such insights may assist in assessing the overall health and performance of the assets in a more informed manner, thereby assisting in enhancing the accuracy of taking actions with respect to the assets. Further, by deriving such insights, significant indicators of asset health or environmental conditions may be brought to one's attention. Further, the present subject matter may assist in enhanced monitoring and performance assessment of the assets. For example, generation of the statistical variables may help in quantitatively assessing the functioning and performance of the assets and evaluating the effectiveness of the assets in maintaining desired thermal conditions in the controlled environment over periods of operation.

Additionally, the present subject matter may assist in energy efficiency improvements by identifying patterns of overcooling or overheating, based on the statistical variables, allowing for fine-tuning of asset operations. Further, the statistical variables may reveal subtle performance degradations, enabling proactive interventions. Furthermore, the insights derived from the statistical variables may aid in capacity planning, helping to determine when additional assets may be needed or when existing assets are underutilized.

The above techniques are further described with reference to FIGS. 1A to 8. It would be noted that the description and the figures merely illustrate the principles of the present subject matter along with examples described herein and would not be construed as a limitation to the present subject matter. It is thus understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present subject matter. Moreover, all statements herein reciting principles, aspects, and implementations of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof.

FIGS. 1A to 1D illustrate a block diagram of a computing environment 100 comprising a system 102, according to an example implementation of the present subject matter. FIGS. 1A to 1D may be discussed in conjunction with each other.

The computing environment 100 may be any environment comprising the system 102 having a processor 104. The computing environment 100 may hereinafter interchangeably be referred to as a controlled environment 100. The controlled environment 100 may include various types of environments, enclosures, spaces, and portions or zones thereof, including residential, commercial, and industrial settings. For example, the controlled environment 100 may be a fully enclosed space. In another example, the controlled environment 100 may be any enclosed space that may be partially exposed to external factors or conditions. Additionally, in some instances, the controlled environment 100 may comprise a combination of fully enclosed zones and other zones which may at least partially be exposed to external factors. Examples of the controlled environment 100 may include, but are not limited to, environments associated with a building, a room, an industrial facility or premises, a partially open or exposed enclosing or space, a storage facility, a warehouse, a data centre, a laboratory, and an access-controlled space or area. The controlled environment 100 could also be a computing environment associated with a smart home system, a building management system, or an industrial control system.

In one example, the controlled environment 100 may be an industrial process environment where raw materials may be processed into finished products through a series of chemical, physical, mechanical, or biological operations. For example, the controlled environment 100 may be an industrial process environment related to a metal processing industry. In another example, the controlled environment 100 may be related to food storage and/or processing industry. In yet another example, the computing environment 100 may be related to a building or a residential unit. Similarly, other examples of the controlled environment 100 may also be possible. Further, in one example, the controlled environment 100 may be a combination of one or more units or environments. For example, the controlled environment 100 may include manufacturing units, assembling units, testing units, and material processing units or plants.

In one example, the computing environment 100 may include one or more assets 105 communicably coupled with the system 102. The plurality of assets may hereinafter interchangeably be referred to as assets 105 and individually be referred to as asset 105. In one example, the one or more assets may be Fan Coil Units (FCUs). Other examples of the assets 105 may include, but are not limited to, roof top units, Heating, Ventilation, and Air Conditioning (HVAC) units and Heating, Ventilation, Air Conditioning and Refrigeration (HVACR) units.

In one example, the asset 105 may be any device or unit, or a collection thereof, comprising at least one of a ventilation device 106, a thermal regulation device 108, and a control device 110, as illustrated in FIG. 1A. The ventilation device 106 may be any apparatus, system, or mechanism designed to facilitate the movement, circulation, exchange, or conditioning of a circulation fluid in an enclosed space, structure, or environment. Examples of the ventilation device 106 may include, but are not limited to, a fan, an air conditioning device, and a blower. Further, in one example, the circulation fluid may be air. The ventilation device 106 may regulate flow of the circulation fluid in the controlled environment 100. For example, if the ventilation device 106 is a fan, rotation of the fan may cause circulation of air in the controlled environment 100.

Further, the thermal regulation device 108 may be operationally linked with the ventilation device 106, as illustrated in FIG. 1A. For example, the thermal regulation device 108 and the ventilation device 106 may be so arranged in the controlled environment 100 that the circulation fluid being regulated by the ventilation device 106 interacts with the thermal regulation device 108. In one example, the ventilation device 106 and the thermal regulation device 108 may be operationally linked through one or more ducts that may convey the circulation fluid from the ventilation device 106 to the thermal regulation device 108. In another example, the thermal regulation device 108 may be located proximate to the ventilation device 106 so that the circulation fluid interacts with the thermal regulation device 108. Similarly, other arrangements and configurations may also be possible that may lead to interaction between the circulation fluid, being regulated by the ventilation device 106, and the thermal regulation device 108.

The thermal regulation device 108 may be any device capable of influencing thermal characteristics of the circulation fluid. The thermal characteristics may be, for example, temperature and/or moisture content of the circulation fluid. Examples of the thermal regulation device 108 devices may include, but are not limited to, heat exchangers, coils, radiators, chillers, heat sinks, thermoelectric coolers, phase change materials, heat pipes, liquid cooling devices, air cooling devices, thermal interface materials, condensers, evaporators, heat-dissipating fins, and cold plates. In some aspects, the thermal regulation device 108 may incorporate multiple components working in conjunction to regulate temperature and manage heat transfer within the controlled environment 100. Thus, the thermal regulation device 108 may refer to any apparatus, system, component, or combination thereof designed to control, regulate, transfer, or dissipate thermal energy within the controlled environment 100. The thermal regulation device 108 may operate through various mechanisms including, but not limited to, conduction, convection, radiation, phase change, or active cooling techniques. The thermal regulation device 108 may hereinafter be individually referred to as thermal regulation device 108 and multiple thermal regulation devices may collectively be referred to as thermal regulation devices 108.

Further, the thermal regulation device 108 may be operationally linked with the control device 110. In one example, the control device 110 may regulate flow of a working fluid in the thermal regulation device 108. The working fluid may be any heat transfer fluid that may be circulated in the thermal regulation device 108, and the circulation may be controlled or regulated by the control device 110. Examples of the working fluid may include, but are not limited to, a coolant, a refrigerant, a glycol-based solution, chlorine-based fluid, and fluids derived based on mixtures of hydrogen and chlorine. Further, examples of the control device 110 may include, but are not limited to, gate valves, globe valves, butterfly valves, ball valves, needle valves, diaphragm valves, pinch valves, plug valves, piston valves, and angle valves. Further, the control device 110 may be manually operatable, pneumatically actuatable, or electrically controllable/actuatable.

In one example, a measure of opening and closing of the control device 110 may affect or control flow of the working fluid in the thermal regulation device 108. For example, when the control device 110 is in a fully open state, the control device 110 may allow flow of the working fluid with a higher flow rate as compared to another state in which the control device 110 is partially open. Whereas, in a closed state, the control device 110 may restrict the flow of the working fluid in the thermal regulation device 108. As the measure of opening may affect the regulation of the working fluid in the thermal regulation device 108, the measure of opening may influence the thermal characteristics of the circulation fluid via the thermal regulation device 108.

Further, though it has been illustrated that the asset 105 includes the ventilation device 106 operationally linked with the thermal regulation device 108, and the thermal regulation device 108 is operationally linked with the control device 110, other arrangements may also be possible. Further, the control device 110 may hereinafter be individually referred to as control device 110 and multiple control devices may collectively be referred to as control devices 110.

In one example, the controlled environment 100 may include a plurality of assets 105 comprising one or more ventilation devices, thermal regulation devices, and control devices as illustrated in FIGS. 1B to 1D. For example, the control environment 100 may include the asset 105 comprising ventilation devices 106-1, 106-2, 106-3, . . . 106-N, where N is a natural number. The ventilation devices 106-1, 106-2, 106-3, . . . 106-N may individually be referred to as ventilation device 106 and collectively as ventilation devices 106. As illustrated in FIG. 1B, the ventilation devices 106 may be operationally linked with the thermal regulation device 108. The thermal regulation device 108 may influence the thermal characteristics of the circulation fluid being regulated by the ventilation devices 106.

In one example, it may also be possible that one or more thermal regulation devices 108 may be operationally linked with one or more ventilation devices 106, as illustrated in FIG. 1C. For example, a thermal regulation device 108-1 may be operationally linked with a first set of ventilation devices 106-1 to 106-4 and a second set of ventilation devices 106-5 to 106-N. The thermal regulation device 108-1 may influence the thermal characteristics of the circulation fluid being regulated by the first and the second set of ventilation devices 106, respectively. In another example, it may also be possible that a plurality of thermal regulation devices may be operationally coupled with one or more ventilation devices 106. For example, the thermal regulation device 108-1 and another thermal regulation device 108-2 may be operationally linked with the first and the second set of ventilation devices 106, as illustrated in FIG. 1C. The control device 110 may be operationally linked with the one or more thermal regulation devices 108, as illustrated in FIGS. 1A to 1C. Further, in one example, it may also be possible that the thermal regulation devices 108-1 and 108-2 are operationally linked with at least one of the ventilation devices 106-1 to 106-N.

Further, in one example, it may also be possible that more than one control device is operationally linked with one or more thermal regulation devices. For example, as illustrated in FIG. 1D, a control device 110-1 and another control device 110-2 may be operationally linked with the thermal regulation device 108-1. The control devices 110-1 and 110-2 may, in one example, regulate flow for different working fluids in the thermal regulation device 108-1. For example, the control device 110-1 may regulate flow of a first working fluid in the thermal regulation device 108-1 and the control device 110-2 may regulate flow of a second working fluid in the thermal regulation device 108-1. In one example, the flow may be regulated either simultaneously or alternatively by the control devices 110-1 and 110-2. In one example, the control devices may also be operably coupled with each other. For instance, as illustrated in FIG. 1D, the control devices 110-1 and 110-2 may be operably coupled with each other. In one example, the coupling may be to exchange the working fluid(s) therebetween.

Though the ventilation devices 108, the thermal regulation devices, and the control devices have been illustrated as separate devices that may be operationally interlinked with each other, it may also be possible that there may be a combination of two or more such devices. For example, it may be possible that a thermal regulation device may be integrated in the ventilation device 106, and the thermal regulation device may have a control valve operationally coupled with it. Similarly, other different combinations may also be possible.

Further, in one example, the controlled environment 100 may include multiple sub-environments having the one or more assets, as illustrated in FIG. 1D. For example, the controlled environment 100 may include a first sub-environment 100-1, . . . , and an Mth sub-environment 100-M, where M is a natural number. The first sub-environment 100-1 may include the asset 105-1 having the ventilation devices 106-1 to 106-4, the thermal regulation device 108-1, and the control devices 110-1 and 110-2. The Mth sub-environment 100-M may include the asset 105-P, where P is a natural number, having the ventilation devices 106-5 to 106-N, the thermal regulation device 108-2, and a control device 110-3. Further, each of the sub-environments may be different zones in the controlled environment 100 and may operate either on similar parameters or different parameters and may function differently from each other as per thermal requirements of their respective zones.

Similarly, other different architectures between the ventilation devices 106, the thermal regulation devices 108, and the control devices 110 may also be possible, though not illustrated. For example, the controlled environment 100 may include a plurality of assets 105. Each of the plurality of assets may include one or more ventilation devices 106, one or more thermal regulation devices 108, and one or more control devices 110. Such devices may be operationally linked with each other in multiple ways. For example, each of the assets 105 may include only one ventilation device 106, one thermal regulation device 108, and one control device 110. In another example, it may also be possible that each asset 105 may have more than one ventilation device 106, more than one thermal regulation device 108, and more than one control device 110. Also, in one example, it may be possible that the controlled environment 100 may include a single asset 105. However, in another example, it may also be possible that the controlled environment 100 may include multiple assets 105. In such an example, the assets 105 may work either for the same zone or different zones in the controlled environment 100.

Further, in one example, the computing environment 100 may also include one or more sensors 112 that may be, in one example, operationally linked with the one or more assets 105 and the controlled environment 100 to generate data or indicators indicating performance, state, and/or functionalities of the assets 105 and about the controlled environment 100. For example, one or more sensors may be deployed in the controlled environment 100, for instance at strategic points, to monitor or measure thermal conditions, such as temperature, of the controlled environment 100.

In one example, one or more of the sensors 112 may be located in path of the circulation fluid, for being operationally coupled, being regulated by the one or more ventilation devices 106 of the assets 105. For example, one or more sensors may be located within ducts associated with each of the ventilation devices 106 and may measure flow, or flow rate, of the circulation fluid being regulated by each of the ventilation devices 106. In another example, the one or more of the sensors 112 may be operationally coupled with each of the ventilation devices 106 in such a manner that they may be able to monitor or detect operational speed, for instance, Rotation Per Minute (RPM), of each of the ventilation devices 106. The one or more of the sensors 112 may generate data indicating a value of the RPM.

In one example, the one or more of the sensors 112 may be operationally linked with the control devices 110 of the assets 105. The one or more sensors 112 may be operationally coupled in such a manner that they may be able to monitor the measure of opening of each of the control devices 110. For example, one or more sensors may be integrated into the control devices 110, or adjustable components of the control devices 110, to monitor accurate feedback on their current state and degree of opening or closing. In another example, one or more of the sensors 112 may be operationally linked with input and output and of the control devices 110 to monitor or detect flow, or flow rate, of the working fluid being caused by the control devices 110. From the flow, or the flow rate, measure of opening of each of the control devices 110 may be determined. Similarly, the one or more sensors 112 may be operationally linked with the control devices 110 in other known methods to determine the measure of opening or closing of the control devices 110.

Though it has been illustrated that the one or more sensors 112 are operationally linked with the assets 105, or their components, it may also be possible, in one example, that the one or more sensors 112 may be included in each, or one or more, assets 105 to detect and measure functioning, state, and operations of the assets 105 or their components. Thus, in one example, each of the assets 105 may include the one or more sensors 112.

Further, in one example, the one or more sensors 112 may be sensors that may generally exist in the computing environment 100. Examples of the sensors 112 may include, but are not limited to, temperature sensors, a flow meters or sensors, and airflow sensors. In one example, the sensors 112 may also incorporate wired or wireless communication capability to exchange data and/or signals with external entities, for instance, the processor 104. Further, one or more sets of the sensors 112 may be deployed in the controlled environment 100, or one or more zones thereof. For example, a first set of sensors may be operationally linked with the sub-environment 100-1 and the assets 105-1 associated therewith, whereas a second set of sensors may be operationally linked with the sub-environment 100-M and the assets 105-P associated therewith. Such deployment of sensors 112 may help in monitoring conditions in the controlled environment 100, or different zones thereof, and performance or operation of the assets 105.

In one example, the controlled environment 100 may include a controller 111. In one example, the controller 111 may control performance and functioning of the assets 105 of the controlled environment 100 by generating one or more control signals. In one example, the controller 111 may be operationally linked with the assets 105 and may generate control commands or signals that may be communicated to the assets 105 to operate them in a desired manner. For example, the controller 111 may generate a control signal that cause the one or more control devices 110 to open upto a certain amount or extent. The control signal, in one example, may thus be indicative of the measure of opening with which the one or more control devices 110 are to operate. Similarly, the controller 111 may generate a control signal that cause the one or more ventilation devices 106 to operate at a particular speed. The control signal may thus be indicative of the operational speed at which the one or more ventilation devices 106 should operate. Thus, the control signal may be indicative of, and may therefore be referred to as indicators, the operational speed and the measure of opening.

In one example, the controller 111 may either be associated with a control system or device of the controlled environment 100 or may be a part of the control system and may control the functioning and operations of the assets 105 of the controlled environment 100. In another example, though the controller 111 has been illustrated separately, it may also be possible that the processor 104 may be the controller 111, or vice versa. In yet another example, the controller 111 may be a controller located in the assets 105. For example, each of the assets 105 may have the controller 111 that may generate signals to control the functioning, performance, and operations of the components of the assets 105, such as the one or more ventilation devices 106, the thermal regulation devices 108, and the control devices 110.

Further, the computing environment 100 may include the system 102 having the processor 104. The system 102 may, in one example, assist in reconfiguration of the one or more assets 105 in the controlled environment 100. The reconfiguration may include, for example, reconfiguring or modifying operational parameters of the one or more assets 105. The reconfiguration may be, for example, to optimize performance of the one or more assets 105, enhance efficiency, and maintain or achieve desired conditions within the controlled environment 100, or any of the sub-environments 100-1 to 100-M. In one example, the system 102 may also cause generation of one or more actionable items for recommending one or more actions with respect to the one or more assets 105. The actions may be recommended, for example, to resolve a potential lapse or degradation in the controlled environment 100. For instance, the actions may be indicated to cause the control environment 100, or any of the sub-environments 100-1 to 100-M of the computing environment 100 to comply with thermal necessities or requirements.

In one example, the system 102 may be implemented in the computing environment 100 as a set of one or more hardware devices or modules. For example, the system 102 may be implemented as a set of one or more hardware devices, comprising at least the processor 104. The processor 104 may be implemented as a dedicated processor, a shared processor, or a plurality of individual processors, some of which may be shared. Examples of the processor 104 may include, but are not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, Artificial Intelligence (AI) based processors, machine learning-based processors, deep learning-based processors, system on chip (SOC), processing circuitries including one or more modules or engines, and/or any other devices that manipulate signals and data based on computer-readable instructions, and/or any other devices.

In another example, the system 102 may be implemented as a set of computer-executable instructions. In this example, the processor 104 may be an engine capable of executing the set of computer-executable instructions. Examples of the system 102, according to this example, may include, but are not limited to, software applications, cloud-based platforms, and Software as a Service (Saas). In yet another example, the system 102 may be implemented as a combination of the one or more hardware devices and the set of computer-executable instructions. In this example, the set of computer-executable instructions may be executed by the processor 104.

Further, the system 102 may be communicably coupled with the one or more assets 105, the controller 111, and/or the sensors 112 to exchange data, commands, and/or signals therebetween. In one example, the system 102 may be in direct communication, as illustrated in FIGS. 1A to 1C. The connection or coupling may be through a wired connection or a wireless connection. In another example, the system 102 may be in indirect communication, as illustrated in FIG. 1D. For example, the system 102, the one or more assets 105, the controller 111, and the sensors 112 may be communicably coupled with each other through a communication network 114. Examples of the communication network 114 may include, but are not limited to LAN, WAN, the internet, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access (TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NGN), Public Switched Telephone Network (PSTN), and Integrated Services Digital Network (ISDN). Depending on the technology, the communication network 114 may include various network entities, such as transceivers, gateways, and routers. In an example, the communication network 114 may include any communication network that uses any of the commonly used protocols, for example, Hypertext Transfer Protocol (HTTP), and Transmission Control Protocol/Internet Protocol (TCP/IP).

The arrangements of different assets 105, the sensors 112, and the controller 111 of the controlled environment 100 illustrated in FIGS. 1A to 1D may only be for illustration purposes. Other architectures and arrangements of the assets 105, the controller 111, and the sensors 112 may also be possible. Also, the controlled environment 100 may include other assets that may include multiple other devices in addition to the ventilation devices 106, the thermal regulation devices 108, and the control devices 110. For example, the controlled environment 100 may include process controllers, such as the controller 111, that may dedicatedly monitor and control different processes being executed in the controlled environment 100. The controlled environment 100 could also include controllers, such as the controller 111, that may dedicatedly be associated with different assets 105 or sensors 112 to control or modify their operations or operational parameters. Examples of such other assets may also include, but are not limited to, conveyor belts and assembly lines. Other examples of the components or entities that may generally exist in the computing or controlled environment 100 may also be possible.

Further, FIGS. 1A to 1D illustrate that the system 102 is operationally coupled with the one or more assets 105, the sensors 112, and the controller 111. However, in one example, the one or more assets 105, the sensors 112, and the controller 111 may be a part of the system 102 and may be operationally and communicably linked with the processor 104. Similarly, different combinations, arrangements, and architectures of the system 102, the assets 105, the sensors 112, and the controller 111 may be implemented, though not illustrated.

FIG. 2 illustrates a block diagram of the system 102, according to one example implementation of the present subject matter. FIG. 2 will be discussed in conjunction with FIGS. 1A to 1D for the sake of brevity. In one example, the system 102 may cause reconfiguration of operational parameters of one or more assets 105 of the controlled environment 100. The system 102 may include the processor 104 that may monitor different types of data or indicators and accordingly determine whether to reconfigure the operational parameters of the one or more assets 105.

In one example operation, the processor 104 may receive a set of characteristic indicators for a controlled environment, such as the controlled environment 100 having the plurality of assets 105. Each of the plurality of assets 105 may include, in one example, at least one ventilation device, such as the ventilation device 106, for regulating the flow of the circulation fluid in the controlled environment 100. Each of the plurality of assets 105 may also include at least one control device, such as the control device 110. The at least one control device 110 may be operationally linked with a thermal regulation device, such as the thermal regulation device 108, associated with the at least one ventilation device 106, as illustrated in FIGS. 1A to 1D, to regulate flow of the working fluid in the thermal regulation device 108 for influencing thermal characteristics of the circulation fluid. In one example, the thermal characteristics may be temperature.

The set of characteristic indicators may include, in one example, a thermal state indicator, a thermal setpoint indicator, a speed indicator for each of the plurality of assets 105, and a position indicator for each of the plurality of assets 105. The thermal state indicator, in one example, may quantitatively indicate a thermal condition of the controlled environment 100. The thermal condition may be, for example, a temperature within the controlled environment 100. Further, the thermal setpoint indicator may indicate a threshold operational range for the thermal state indicator. Furthermore, a speed indicator may be uniquely associated with each of the plurality of assets 105. The speed indicator, for each of the plurality of assets 105, may quantitatively indicate an operational speed of the at least one ventilation device 106 of a corresponding asset from amongst the plurality of assets 105. Further, a position indicator may be uniquely associated with each of the plurality of assets 105. The position indicator, for each of the plurality of assets, may indicate a measure of opening of the at least one control valve 110 of a corresponding asset from amongst the plurality of assets 105.

Based on the set of characteristic indicators, the processor 104 may derive a set of intermediate statistical variables for each of the plurality of assets 105. For example, a set of intermediate statistical variables may be derived for the asset 105-1. Similarly, another set of intermediate statistical variables may be derived for another asset, say the asset 105-P. Thus, for each of the plurality of assets 105 in the controlled environment 100, a corresponding set of intermediate statistical variables may be derived. In one example, each set of intermediate statistical variables may include a first statistical variable and a second statistical variable.

The first statistical variable may be determined based on a total period of operation of at least one ventilation device 106 of a corresponding asset 105 and a duration of compliance of the threshold operational range by the thermal condition within the controlled environment 100. In one example, the first statistical variable may quantitatively indicate performance of the associated or corresponding asset 105 in maintaining the thermal condition in compliance with the threshold operational range during the total period of operation of the corresponding asset 105, or the at least one ventilation device 106 associated with the corresponding asset 105.

Further, the second statistical variable, in one example, may be determined based on a thermal power metric of the corresponding asset 105 and deviation, also referred to as thermal deviation, of the thermal condition from the threshold operational range. The second statistical variable may indicate, for example, a relation between the deviation of the thermal condition from the threshold operational range based on the thermal power metric. Based on the set of intermediate statistical variables, derived for each of the plurality of assets 105, the processor 104 may generate a reconfiguration signal to cause reconfiguration of operational parameters associated with at least one asset from amongst the plurality of assets 105.

Thus, the present subject matter offers several key technical advantages, enhancing thermal control in managed or controlled environments through advanced data analysis and maximizing the utility of existing data to maintain optimal thermal conditions. By extracting deeper insights from limited data inputs, the present subject matter may enable the evaluation of performance and identification of issues with individual assets, or components thereof. Further, the significance and value of data may increase by obtaining multiple layers of insights by deriving the statistical variables, improving decision-making accuracy for asset management and enabling more comprehensive and quantitative asset performance tracking. Further, the entire system may be optimized by analyzing relationships between multiple assets, reducing energy waste by detecting inefficient heating or cooling patterns, and allowing preventative maintenance by identifying subtle performance declines.

FIG. 3 illustrates a block diagram of a computing environment 300 comprising the system 102, according to another example implementation of the present subject matter. FIG. 3 will be discussed in conjunction with FIGS. 1A to 2 for the sake of brevity. The subject matter disclosed in the description of FIGS. 1A to 2 will be incorporated herein as references for the sake of brevity.

In one example, the computing environment 300 may be similar to the computing environment 100 discussed with reference to FIGS. 1A to 1D. The computing environment 300, similar to the computing environment 100, may include the system 102, the one or more assets 105, the controller 111, and the one or more sensors 112. In addition to the examples discussed above in FIGS. 1A to 1D, other examples of the controlled environment 100 may include, but are not limited to, manufacturing plants, chemical processing plants, food processing facilities, steel mills, paper mills, textile mills, petrochemical plants, glass manufacturing plants, power generation plants, semiconductor manufacturing labs, and biotechnology labs.

Further, in one example, the computing environment 300 may also include one or more datastores 302 and one or more workstations 304. In one example, the one or more datastores 302 may include one or more sets of storage devices capable of storing data, signals, and/or information. The sets of storage devices may be virtual storage devices, physical storage devices, a cloud-based storage service, or a combination thereof. For example, the one or more datastores 302 may be any repository or storage unit implemented by physical, logical, and/or virtual storage devices. In one example, the one or more datastores 302 may include a set of physical storage devices. In another example, the one or more datastores 302 may include virtual storage devices being implemented on physical storage devices. In another example, the one or more datastores 302 may include one or more physical or logical storage units that may either be located at the same location or distributed geographically. In another example, the one or more datastores 302 may be implemented over a cloud-based storage service. Further, the one or more datastores 302 may be communicably coupled with the system 102, the controller 111, the one or more sensors 112, the assets 105, and the one or more workstations 304 through the communication network 114 to exchange data and/or signals. The computing environment 300 may thus be a network of such entities that may be communicably coupled with each other, for example, over the communication network 114.

Further, in one example, the one or more workstations 304 may be used by one or more users for monitoring the data being generated by the sensors 112, for configuring the sensors 112, and monitoring and configuring the state, functioning, and operational parameters associated with the assets 105. The one or more workstations 304 may also be used for configuring the controller 111 and exchanging signals and commands with the controller 111, the processor 104, and the one or more assets 105. The one or more workstations 304 may also provide access to the data and information stored in the one or more datastores 302. The one or more workstations 304 may also enable defining the thermal setpoint indicator, or the threshold operation range indicated by the thermal setpoint indicator. Further, examples of the one or more users may include, but are not limited to, one or more operators associated with the controlled environment 100. In one example, the one or more users may define the thermal setpoint indicator, which may then be stored in the one or more datastores 302.

Further, in one example, the one or more workstations 304 may render one or more graphical user interfaces that may indicate different information about the controlled environment 100, the assets 105, the sensors 112, the controller 111, the processor 104 or data generated by them, and the one or more datastores 302 or the data stored therein. The graphical user interface may be rendered, in one example, on a display device associated with the one or more workstations 304. The graphical user interface may be, for example, an interactive interface with which the one or more users may be able to interact and view different information. For example, the user may be able to view, interact, modify, and customize the information being rendered via the display device. In one example, the graphical user interface may be a dashboard that may be rendered on the display device. Examples of the one or more workstations 304 may include, but are not limited to, a computing device, a desktop, a computer system, a user device, a mobile, a laptop, and a tablet.

As discussed above, the computing environment 300 may include the system 102, having the processor 104, to reconfigure assets 105 in the controlled environment 100, for example, by reconfiguring the operational parameters of the one or more assets 105. The reconfiguration may be, for example, to optimize performance of the assets 105, enhance efficiency, and maintain or achieve desired conditions within the controlled environment 100, or any of the sub-environments 100-1 to 100-M. In one example, the system 102 may also cause generation of one or more actionable items for indicating one or more actions with respect to the one or more assets. The actions may be indicated, for example, to overcome a potential lapse in the controlled environment 100. For instance, the actions may be indicated to cause the control environment 100, or any of the sub-environments 100-1 to 100-M of the computing environment 100, to comply with thermal necessities or requirements. In another example, the actions may indicate necessary steps that the one or more users may opt to overcome the potential lapse or to comply with the threshold operational range. For example, the actions may indicate steps with respect to one or more assets 105 that may help in overcoming the potential lapse or complying with the threshold operational range.

The system 102 may further include, in one example, interface(s) 306 that may allow communicably coupling the system 102, and/or the processor 104, with one or more other entities, such as the communication network 114, the assets 105, the one or more datastores 302, the one or more workstations 304, the controller 111, and the sensors 112. The connection or coupling may be through a wired connection or a wireless connection.

In one example, the system 102 may further include other unit(s) 308. The other unit(s) 308 may include, in one example, a power supply unit and a communication unit. The power supply unit may, for example, manage the distribution or supply of electrical current within the system 102 for functioning of the system 102. Further, the communication unit may be, in one example, a wireless communication unit. Examples of the communication unit may include, but are not limited to, Global System for Mobile communication (GSM) modules, Code-division multiple access (CDMA) modules, Bluetooth modules, network interface cards (NIC), Wi-Fi modules, dial-up modules, Integrated Services Digital Network (ISDN) modules, Digital Subscriber Line (DSL) modules, and cable modules. In one example, the communication unit may also include one or more antennas to enable wireless transmission and reception of data and signals. The communication unit may allow the system 102 to be communicably coupled with the communication network 114, the assets 105, the one or more datastores 302, the one or more workstations 304, the controller 111, and the sensors 112. Also, the communication unit may allow the system 102, or the processor 104, to transmit and receive data and signals. Further, in one example, the processor 104 may be the controller 111, or vice versa, though illustrated separately.

In one example operation, the processor 104, or a data acquisition unit 310, may receive the set of characteristic indicators for the controlled environment 300 having the plurality of assets 105. The processor 104 may receive the set of characteristic indicators, in one example, from the plurality of assets 105. For example, the processor 104 may be operationally linked, either directly or through the communication network 114, with the plurality of assets 105 and may receive the set of characteristic indicators from one or more of the assets 105 of the controlled environment 100. Each of the plurality of assets 105 may include, for example, at least one ventilation device 106, at least one the thermal regulation device 108, and/or at least one control device 110, as also discussed in FIGS. 1A to 1D. In another example, the processor 104 may receive the set of characteristic indicators from the sensors 112 operationally linked with the assets 105 and the processor 104. In another example, the processor 104 may receive the set of characteristic indicators from the one or more datastores 302. For example, the sensors 112 may generate data, referred to as the set of characteristic indicators. The set of characteristic indicators may then be stored in the one or more datastores 302, from where the processor 104 may receive or obtain the set of characteristic indicators.

In yet another example, the set of characteristic indicators may be received or derived from the control signals generated by the controller 111. For example, the controller 111 may generate a signal for an asset 105 to operate a ventilation device 106 of the asset 105 at a particular operational speed. In another example, the controller 111 may generate a signal that may indicate the measure of opening with which a control device 110 of the asset 105 should operate. Thus, such control signal may be indicative of the operational speed and the measure of opening, and may therefore be referred to as indicators, and may be included in the set of characteristic indicators.

In one example, a set of characteristic indicators may be received for each of the plurality of assets 105 of the controlled environment 300. In another example, a single set of characteristic indicators may be received for the plurality of assets 105, where each of the speed indicators and the position indicators may be associated with, or correspond to, a unique asset from amongst the plurality of assets 105. For example, each of the speed indicators and the position indicators may have at least one identifier associated therewith to indicate the asset 105 with which they are associated. The at least one identifier may be, for example, a unique address associated with each of the plurality of assets 105.

Further, in one example, the set of characteristic indicators may be received by the processor 104 in response to a request made by the processor 104. For example, the processor 104, or the data acquisition unit 310, may be operationally linked with the plurality of assets 105, controller 111, the sensors 112, and/or the one or more datastores 302. The processor 104 may generate and send one or more requests, or calls, for the set of characteristic indicators to at least one of the assets 105, the controller 111, sensors 112, and/or the one or more datastores 302. In response to such requests or calls, the processor 104 may receive the set of characteristic indicators.

In another example, the processor 104 may automatically receive the set of characteristic indicators. For example, the processor 104 may receive the set of characteristic indicators at regular intervals, for instance, after every minute. In yet another example, the processor 104 may receive the set of characteristic indicators based on a configuration defined by the one or more users through the one or more workstations 304. For example, the one or more users may define that the set of characteristic indicators may only be provided to the processor 104 during working/business hours and/or working days. Through the one or more workstations 304, the one or more users may also configure the intervals at which the set of characteristic indicators may be fed to, or received by, the processor 104. In yet another example, the processor 104 may receive the set of characteristic indicators upon the commencement of asset's operation. For example, the processor 104 may receive the set of characteristic indicators in response to initiation, startup, or setup of one or more of the assets 105. Similarly, other methodologies may also be implemented that may cause the processor 104 to receive the set of characteristic indicators.

Further, the set of characteristic indicators may include, in one example, the thermal state indicator, the thermal setpoint indicator, the speed indicator for each of the plurality of assets 105, and the position indicator for each of the plurality of assets 105. In one example, the thermal state indicator may quantitatively indicate the thermal condition of the controlled environment 300. For example, the thermal state indicator may indicate a measure or degree of temperature, say 24 degrees Celsius, within the controlled environment 300, or a zone or section of interest thereof. In one example, the thermal state indicator may indicate an average measure of the temperature over a period of time. For example, the thermal state indicator may indicate an average measure of the temperature over a period of 5 minutes. In another example, the thermal state indicator may indicate an instant measure of the temperature.

The thermal condition, in one example, may be measured by the one or more sensors 112 deployed in the controlled environment 300 or a zone or section of interest. The one or more sensors 112 may generate data, or the thermal state indicator, quantitatively indicating the thermal condition of the controlled environment 300. The data may be provided to the processor 104 or may be stored in the datastores 302, from where the processor may 104 receive or obtain the data. In another example, the data generated by the sensors 112 may be conditioned or processed, by the processor 104, to derive or determine the thermal condition. For example, the data may be filtered and/or normalized to determine information being indicated by the data generated by the one or more sensors 112.

In another example, the thermal condition of the controlled environment 300 may be determined based on the control signals generated by the controller 111. For example, based on the control signals indicating the speed indicator, being indicative of operational speed of the ventilation device 106, and/or the position indicator, being indicative of measure of opening of the control device 110, actual thermal conditions may be estimated within the controlled environment 300. The operational speed may indicate or provide information about the rate of air circulation within the controlled environment 300. Thus, higher operational speeds may indicate lesser temperatures within the environment 300. Similarly, the position indicator may provide information about the rate of the working fluid, for example, coolant, regulating in the thermal regulation device 108 associated with the ventilation device 106. A larger measure of openings may indicate a higher flow of the coolant and, thereby lesser temperatures within the environment 300. Similarly, lower operational speed and/or lesser measure of openings may indicate higher temperatures in the environment 300. Thus, based on the control signals, the speed indicator and the position indicator, for each of the plurality of assets 105, may be obtained or received.

Further, the thermal setpoint indicator may indicate the threshold operational range for the thermal state indicator. In one example, the threshold operational range may be a desired temperature range to be maintained or achieved for the controlled environment 300. For example, the thermal operational range may define or indicate a lower and an upper temperature limit within which the thermal state indicator, or the thermal condition indicated by the thermal state indicator, should be maintained. For example, the thermal setpoint indicator may indicate 23 degrees Celsius to 25 degrees Celsius as the threshold operational range for the thermal state indicator. That is, the thermal setpoint indicator may indicate that the temperature in the controlled environment 300, or a zone thereof, is required to be between 23 degrees Celsius to 25 degrees Celsius. In one example, the lower temperature limit may be a setpoint or a first threshold and the upper temperature limit may be a second threshold defining an allowed deviation of thermal condition from the first threshold. For example, considering the limits in the above example, 23 degrees Celsius may be the setpoint or the first threshold and 25 degrees Celsius may be the second threshold up to which deviation of thermal condition may be allowed. The temperature in the room should equal the setpoint. However, some amount of increase in temperature may still be acceptable, i.e., up to the second threshold, but may be monitored. However, if the temperature exceeds the allowed deviation, i.e., exceeds the second threshold, any of the assets 105 may not be operating in the desired manner, and attention may be required.

Similarly, in another example, the thermal setpoint indicator may indicate that the temperature in the controlled environment 300, or a zone thereof, is required to be between 20 degrees Celsius to 22 degrees Celsius. In one example, the upper temperature limit may be a setpoint or a first threshold and the lower temperature limit may be a second threshold defining an allowed deviation of thermal condition from the first threshold. Considering the limits in the above example, 22 degrees Celsius may be the setpoint or the first threshold and 20 degrees Celsius may be the second threshold up to which deviation of thermal condition may be allowed. The temperature in the controlled environment 300, or a zone thereof, is required to be in compliance with the setpoint. However, some amount of decrease in temperature may still be acceptable, i.e., down to the second threshold, but may be monitored. In the case of heating, a temperature above the first threshold may therefore indicate uneconomical and undesired heating. If the temperature exceeds the allowed deviation, any of the assets 105 may not be operating in the desired manner, and attention may be required.

In another example, the threshold operational range may be a range to cover±errors in the temperatures that may be possible, for instance, due to the performance or calibration of the one or more sensors 112. For example, the threshold operational range may be from 25 degrees Celsius to 25.5 degrees Celsius. In yet another example, the threshold operational range may be a range of more than two limits. For instance, the threshold operational range may define or indicate a specific value as a setpoint and two further values as allowable deviations from the setpoint. For example, the setpoint may be defined as 25 degrees Celsius, the value for a first allowable deviation may be 24.5 degrees Celsius and the value for a second allowable deviation may be 25.5 degrees Celsius.

In one example, the thermal setpoint indicator, or the threshold operational range, may be defined by the user through the one or more workstations 304. The defined thermal setpoint indicator, or the threshold operational range, may be stored in the one or more datastores 302. The processor 104 may receive the thermal setpoint indicator, indicating the threshold operational range, either from the one or more workstations 304 or from the one or more datastores 302.

Further, the set of characteristic indicators may include the speed indicator, for each of the plurality of assets 105. The speed indicator may quantitatively indicate an operational speed of the at least one ventilation device of a corresponding asset from amongst the plurality of assets 105. For example, the set of characteristic indicators may include a first speed indicator quantitatively indicating an operational speed of a ventilation device 106 of the asset 105. Similarly, a second speed indicator may quantitatively indicate an operational speed of a ventilation device of another asset. Accordingly, a speed indicator for each of the plurality of assets 105 in the controlled environment 100 may be included in the set of characteristic indicators. In another example, multiple sets of characteristic indicators, each being associated with a unique asset of the controlled environment 100, could also be received. For example, a first set of characteristic indicators may be received for the asset 105-1 and a Pth set of characteristic indicators may be received for the asset 105-P. In one example, the set of characteristic indicators may include multiple sets of characteristic indicators, as sub-sets, where each sub-set may include characteristic indicators for a corresponding asset from amongst the plurality of assets 105.

In one example, each of the plurality of speed indicators may be control signals generated by the controller 111 quantitatively indicating the operational speed of the ventilation device 106, of the corresponding asset 105, based on the working or operating speed of the ventilation device 106. For example, a speed indicator may indicate the operational speed in RPMs, indicating speed of rotation of the ventilation device 106. In another example, the speed indicator may indicate the operational speed as a speed or flow rate of the circulation fluid being regulated by the ventilation device 106. The speed or flow rate of the circulation fluid may itself be the operational speed, or the RPM of the associated ventilation device 106 may be derived based on the speed or flow rate of the circulation fluid. In one example, the operational speed may be detected or derived from the control signals or commands generated by the controller 111, as discussed above. For example, based on the control signals that cause a ventilation device of an asset to operate at a particular speed, the operational speed may be determined. In another example, the operational speed may be detected by the one or more sensors 112. As discussed above in FIGS. 1A to 1D, the one or more of the sensors 112 may be located within ducts associated with the ventilation device 106 and may measure flow, or flow rate, of the circulation fluid being regulated the ventilation device 106. In another example, the one or more sensors 112 may be operationally coupled with the ventilation device 106 in such a manner that the one or more sensors may be able to monitor or detect the operational speed, such as RPM, of each of the ventilation devices 106.

Further, the processor 104 may receive the plurality of speed indicators either from one or more of the sensors 112 or from the one or more datastores 302. In another example, it may also be possible that the processor 104 may receive the plurality of speed indicators directly from the ventilation devices 106. In yet another example, the processor 104 may receive the operational speed from the control signals generated by the controller 111 for operating the ventilation devices 106 at a particular speed.

In yet another example, the processor 104 may receive indications about operational commands or control signals provided to the ventilation devices 106, for instance, from the one or more workstations 304, or electrical power being consumed by the ventilation devices 106. Based on such received indicators, the processor 104 may derive or determine the operational speeds of the ventilation device 106.

Further, the set of characteristic indicators may include a position indicator, for each of the plurality of assets 105, indicating a measure of opening of at least one control device 110 of the corresponding asset 105. In one example, the set of characteristic indicators may include a plurality of position indicators, each corresponding to a control device of an asset of the controlled environment 300. Thus, the set of characteristic indicators may include a plurality of position indicators, each being associated with a unique asset. Further, as discussed above in FIGS. 1A to 1D, the control devices 110 may regulate flow of the working fluid in the at least one thermal regulation device 108 operationally linked or associated therewith. For example, as illustrated in FIG. 1A, the control device 110 may be operationally linked with the thermal regulation device 108 and may regulate or control flow of the working fluid in the thermal regulation device 108. As also illustrated in FIG. 1C, as another example, the control device 110 may be operationally linked with the thermal regulation devices 108-1 and 108-2, and may regulate or control flow of the working fluid in each of the thermal regulation devices 108-1 and 108-2. The controlled environment 100 or 300 may include more control devices 106, though not illustrated.

In one example, the processor 104 may receive or derive the position indicator from the control signals generated by the controller 111 for operating the control devices 110 with a particular measure of opening. In another example, one or more of the sensors 112 may be operationally linked with the control devices 110 in such a manner that the one or more sensors 112 may be able to monitor the measure of opening of each of the control devices 110. For example, one or more sensors 112 may be integrated into the control devices 110, or adjustable components of the control devices 110, to monitor feedback on their current state and degree of opening or closing. The one or more sensors 112 may also be operationally linked with the assets in other known methods.

Further, each of the plurality of control devices 110 may have an associated or corresponding position indicator. For example, the control device 110-1 may have a first position indicator associated therewith, the control device 110-2 may have a second position indicator associated therewith, and the control device 110-3 may have a third position indicator associated therewith. Each of the plurality of position indicators may indicate the measure of opening of the associated control device 110. For example, a position indicator may indicate an extent or percentage of opening of the associated control device 110. For instance, the first position indicator may indicate that the control device 110-1 may be 80% open. The processor 104 may receive each of the plurality of position indicators either from the control devices 110, from the controller 111, or from one or more of the sensors 112 operationally linked with the control devices 110. In another example, the position indicators may be stored in the one or more datastores 302, by the one or more sensors 112, and may be received by the processor 104 from the one or more datastores 302.

In one example, flow of the working fluid may influence the thermal characteristics of the circulation fluid. For example, each of the control devices 110 may be operationally linked with at least one of the thermal regulation devices 108, which in turn are operationally linked with at least one ventilation device 106, as illustrated in FIGS. 1A to 1D. In one example, the working fluid may be a coolant. Thus, the circulation fluid being regulated by the ventilation devices 106 may interact with at least one of the thermal regulation devices 108, in which the working fluid may be regulated, thereby affecting the thermal characteristics, say reducing or increasing the temperature of the circulation fluid regulated by the ventilation devices 106. Supply of the working fluid may thus affect the thermal characteristics of the circulation fluid. For example, the circulation fluid may be cooler when the control device 110 is in a more open state as compared to a lesser open state. For instance, the circulation fluid may be cooled more if the control device 110 is in an 80% open state or position as compared to the 60% open position. Similarly, the circulation fluid may be warmer if the control device 110 is in an 60% open state or position as compared to the 70% open position. Thus, by regulating flow of the working fluid, the control device 110 may influence the thermal characteristics of the circulation fluid. For example, temperature of the circulation fluid may be increased or reduced by controlling the opening of the control devices 110.

Further, based on the set of characteristic indicators, the processor 104, or a data processing unit 312 of the processor 104, may derive a set of intermediate statistical variables for each of the plurality of assets 105. In one example, each of the intermediate set of statistical variables, corresponding to a unique asset from amongst the plurality of assets 105, may include a first statistical variable and a second statistical variable. Each set of intermediate statistical variables may indicate performance of the corresponding asset 105 of the controlled environment 300. In one example, the set of intermediate statistical variables may be a set of KPIs.

In one example, based on the set of characteristic indicators, the processor 104, or the data processing unit 312, may derive the first statistical variable, each corresponding to a unique asset from amongst the plurality of assets 105 of the controlled environment 300. In another example, the processor 104 may derive a first statistical variable for each of the ventilation devices 106 of the corresponding asset 105 that may be associated with one or more specific zones, such as one or more of the sub-environments 100-1 to 100-M of the controlled environment 300. Thus, a first statistical variable, for each of the assets 105, may be derived by the processor 104. For example, the processor 104 may derive a first statistical variable for the asset 105 and another first statistical variable for another asset of the controlled environment 300. Similarly, the processor 104 may determine the plurality of first statistical variables, each being associated with a unique asset from amongst the plurality of assets 105.

The processor 104 may derive or determine the first statistical variable for each asset, in one example, based on a total period of operation of at least one ventilation device 106 of that asset and a duration of compliance of the threshold operational range by the thermal condition within the controlled environment 300, or a zone thereof. In one example, the total period of operation, for a ventilation device of a corresponding asset, may be determined based on the operational speed indicated by the speed indicator associated with that ventilation device or the corresponding asset 105. For example, if the speed indicator is received at regular intervals, a significant decrease in the operational speed of the ventilation device could be determined by the processor 104. Until the operational speed becomes zero, or falls below a pre-engineered or pre-defined threshold operational speed, the ventilation device may be considered to be in a state of operation. Accordingly, the processor 104 may maintain or initiate, in one example, a counter or timer that may indicate a duration in which the operational speed of the ventilation device 106 did not become zero or reduced below the threshold operational speed. Such a duration, in one example, may be the total period of operation of the ventilation device 106.

In another example, the processor 104 may analyze the speed indicators to detect when the ventilation device 106 may be operating above the threshold operational speed indicative of active operation of the ventilation device 106. The processor 104 may track continuous time periods where the speed indicators may indicate that the ventilation device 106 is operating above the threshold operational speed. The total duration of operation may be determined by measuring the time period where the operational speed of the ventilation device remains above the threshold operational speed. Thus, for each of the ventilation devices 106, or for the required ventilation devices 106, the processor 104 may be able to determine the total duration of their operation.

Further, the duration of compliance may indicate a duration for which the thermal condition of the controlled environment 300, or a zone thereof, was detected to be within, or in compliance with, the threshold operational range defined for the controlled environment 300 or the zone thereof. For example, as the processor 104 may receive the thermal state indicator at regular intervals or as per other different configurations or conditions, the processor 104 may compare the thermal state indicator with the thermal setpoint indicator. When the thermal state indicator, and thereby the thermal condition of the controlled environment 300 or a zone thereof, comply with the thermal setpoint indicator, the processor 104 may initiate a timer, counter, or record a timestamp. The processor 104 may continue monitoring subsequent thermal state indicators, maintaining the timer or updating the recorded duration as long as the thermal condition complies with the thermal setpoint indicator. If the thermal state indicator indicates a thermal condition that fails to comply with the thermal setpoint indicator, the processor 104 may stop the timer. Thus, the processor 104 may be able to determine or derive the duration of compliance of the threshold operational range by the thermal condition, that is, the duration for which the thermal condition complied with the thermal setpoint indicator.

Based on the total period of operation of the ventilation device 106 of the corresponding asset 105, and the duration of compliance, the processor 104 may derive the first statistical variable for each of the assets 105. In one example, each of the first statistical variables may be derived based on a ratio of (a) the duration of compliance of the threshold operational range by the thermal condition within the controlled environment 300 and (b) the total period of operation of the ventilation device 106 of the corresponding asset 105. For example, based on a ratio of (a) and (b), the first statistical variable may be derived. Similarly, a first statistical variable may be determined for each ventilation device 106 corresponding to the asset 105. That is, a first statistical variable may be derived for each of the assets 105 of the controlled environment 300 similarly. In one example, the first statistical variable may be computed when the assets are operating at maximum operational capacity. For example, the first statistical variable may be computed based on the total period in which the ventilation device 106 may be operating at maximum operational speed. Thus, in such a scenario, the first statistical variable may indicate performance of the ventilation device 106, or the asset 105, when operating at the maximum possible capacity. Similarly, the first statistical variable may be determined for any operational capacity or rating.

In one example, each first statistical variable may indicate performance of the one or more ventilation devices 106 of the corresponding asset 105. For example, if the ratio has a value closer to 1, say 0.97, it may indicate that the thermal conditions complied with the threshold operational range for most of the portion (97%) of the total period of operation of the ventilation device 106. This may indicate that during operation of the ventilation device 106 of the asset 105, the thermal conditions remained within the acceptable range, for instance, the threshold operational range for majority of time of its operation. On the other hand, a ratio having a value farther than 1, say 0.75, may indicate that during the total period of operation of the ventilation device 106 of the asset 105, the thermal conditions failed to comply with the threshold operational range for considerable duration (75% of the total period of operation). Thus, the first statistical variable may indicate the performance of the asset 105, or at least the ventilation device 106 of the asset 105.

Thus, the first statistical variables, each corresponding to an asset, may indicate performance of the asset 105 in maintaining the thermal condition within the threshold operating range during the period of its continuous operation at the maximum value of the thermal power metric. That is, each first statistical variable may indicate how well a specific asset 105 could maintain the thermal condition within the threshold range during its maximum performance. Thus, each of the first statistical variables may indicate the reliability and effectiveness of individual assets 105. Also, each of the first statistical variables may provide a quantitative measure to assess the effectiveness of the asset 105 in maintaining desired thermal conditions in the controlled environment 300, or one or more sub-environments thereof. By comparing the duration of compliance to the total duration of operation, situations where the current ventilation or air conditioning strategy is insufficient could also be identified.

Further, the processor 104 may determine a second statistical variable for each of the plurality of assets 105 of the controlled environment 300. In one example, for each of the assets 105 for which the first statistical variable was derived, the processor 104 may derive a second statistical variable corresponding to that asset 105. The second statistical variable may be determined, in one example, based on a relation between the thermal power metric of the corresponding asset and deviation, or thermal deviation, of the thermal condition from the threshold operational range, as discussed in FIGS. 4A and 4B.

In one example, the processor 104 may determine the thermal power metric of the corresponding asset 105 based on the corresponding speed indicator and the corresponding position indicator. For example, for the asset 105, as illustrated in FIG. 1A, the thermal power metric may be determined based on the speed indicator associated with the ventilation device 106 of the asset 105 and the position indicator associated with the control device 110 operationally linked with that ventilation device 106, through the thermal regulation device 108. In one example, the processor 104 may determine the thermal power metric, for the asset 105, based on a product of the operational speed of the ventilation device 106 of the corresponding asset 105 and the measure of opening of the control device(s) 110 associated with that asset 105. Further, the processor 104 may determine the deviation of the thermal condition from the threshold operational range by determining a difference between the thermal condition and the threshold operational range. For example, the processor 104 may determine the difference between the thermal condition and the allowed deviation indicated by the threshold operational range.

Further, each of the second statistical variables may indicate, for example, a relation between the deviation of the thermal condition from the threshold operational range based on the thermal power metric. In one example, each of the second statistical variables may represent how much the deviation, from the threshold operational range, is when a combined operation of a ventilation device and a control device, of an asset 105, is considered. For example, more deviation from the threshold operational range may indicate that the thermal power metric, or any of the operational speed of the ventilation device 106 and/or measure of opening of the associated control device(s) 110, needs to be modified to reduce the deviation. In other words, each of the second statistical variables may quantify the deviation from the threshold operational range when considering the combined operation of a ventilation device and a corresponding control device. A larger deviation from the threshold operational range may indicate that the cooling power needs to be adjusted to reduce or minimize the deviation. For instance, if the deviation is significant, the current cooling power may be insufficient or excessive, and may therefore require modification of at least one of the operational speed of the ventilation device or measure of opening of the control device to bring the threshold condition within the threshold operational range.

Further, as the second statistical variable may indicate a relation between the thermal power metric and deviation of the thermal condition from the threshold operational range, the second statistical variable may indicate an effect or impact on the thermal condition with respect to the thermal power metric. For example, the second statistical variable may help in assessing how well the asset 105 may be performing relative to the thermal conditions. It may help identify situations where the thermal conditions are worsening despite the cooling efforts, potentially signalling a need for adjustments or improvements in the assets 105, or operational parameters thereof.

In one example, each of the second statistical variables may also indicate whether the combined performance of the ventilation device 106 and the corresponding control device 110 is able to comply with the threshold operational range. For example, if no or zero deviation from the threshold operational range is determined with a thermal power metric, it may be ascertained that the asset 105, or operation of the ventilation device 106 and the control device 110 of the corresponding asset 105, is able to comply with the threshold operational range or maintain the thermal conditions within the threshold operational range. Further, by deriving the second statistical variable, it may also be determined whether the operation of the asset 105 is leading to deviation, for example, more than the second threshold, as discussed above. In such cases, the operational speed of the ventilation device 106 of the asset 105, for example, may be increased to bring the thermal conditions within the threshold operational range. As a result, the assets 105 may be utilized in a proper manner to maintain the required thermal conditions in the controlled environment 300.

Thus, each of the second statistical variables may represent how changes in thermal power metric may impact or affect, for instance, deviations from the threshold operational range. In other words, each of the second statistical variables may indicate a relationship between thermal power metric adjustments and their impact on maintaining operational parameters within a desired range. Thus, each of the second statistical variables may quantify, for each of the assets 105, how changes in thermal power metric affect deviations from the threshold operational range. By analyzing these relationships, insights into the effectiveness and efficiency of cooling power adjustments may be gained.

Therefore, by deriving the set of intermediate statistical variables, multiple meaningful insights may be derived for the controlled environment 300 using the generally available data. By leveraging the intermediate statistical variables, more value and insights may be derived from existing data without requiring additional sensors or data collection infrastructure. This may reduce the costs and complexity associated with data acquisition. The present subject matter may therefore not only lead to efficient and better utilization of data but also provide meaningful and helpful insights about different assets 105 and the controlled environment 300. Also, derivation of the intermediate statistical variables may lead to efficient utilization of the assets 105 and energy-efficient operation. For example, over cooling or over heating situations may be determined based on at least one of the characteristic indicators and the statistical variables. Necessary actions, as also discussed below, may therefore be opted for utilization of the assets in an optimized and energy-efficient manner.

In one example, each of the first and the second statistical variables may indicate numerical values. For example, each of the first statistical variables may have a value between 0 and 1. In one example, such values may be scaled by multiplying with a common or weighted variable. For example, each of the first and the second statistical variables may be multiplied by 100 and the resulting value may be the value of each of the first and the second statistical variables.

In another example, the set of statistical variables, for each of the plurality of assets 105, may be determined based on a visual indicator, as illustrated in FIGS. 4A and 4B. FIGS. 4A and 4B illustrate the visual indicator 400, according to one example implementation of the present subject matter. In one example, the visual indicator 400 may be a graphical representation of the performance of an asset from amongst the plurality of assets 105. For example, based on the set of characteristic indicators, the processor 104, or an indication generation unit 316 of the processor 104, may generate and/or cause rendering of the visual indicator 400 for an asset 105. Similarly, the processor 104 may cause rendering of multiple visual indicators, each corresponding to an asset of the controlled environment 300. For the sake of brevity, visual indicator has been illustrated for only one asset, however, multiple visual indicators may be generated, each corresponding to a unique asset of the controlled environment 300.

In one example, the visual indicator 400 may include multiple axes. As exemplarily illustrated in FIGS. 4A and 4B, the visual indicator 400 may include an axis representing the thermal power metric, and another axis indicating the thermal deviation. In one example, points for different operation speeds may indicate scale in RPMs, such as 200 RPM, 400 RPM, . . . , and 1000 RPM. Further, the thermal deviation axis may indicate the scale for thermal deviation in degree Celsius, such as −2 degree Celsius, −1 degree Celsius, 0 degree Celsius . . . , and 6 degree Celsius. Further, the thermal power metric axis may indicate a scale for the measure of the thermal power metric. The scales and the values have been indicated only for illustration purposes and other values and scales may also be used for graphical representation.

The visual indicator 400 may also include, in one example, a first marker 402 indicating an extent or measure of allowed thermal deviation. In one example, the thermal deviation may be determined based on the thermal state indicator and the thermal setpoint indicator. For example, the thermal deviation may be a difference between the thermal setpoint indicator and the thermal state indicator. In such a case, the first marker 402 may indicate, for example, that 0.5 degree Celsius of thermal deviation (difference between the thermal setpoint indicator and the thermal state indicator) may be allowed or acceptable. In case the threshold operational range is a range of values, such as the first threshold and the second threshold, the thermal deviation may be determined based on a difference between the second threshold and the thermal condition. Any thermal deviation beyond the first marker 402 may be undesired and may indicate, for example, that the thermal condition of the controlled environment 300 has become undesirable or out of control, and some attention or action may probably be required for an asset for which the visual indicator 400 is rendered.

In another example, the first marker 402 may itself indicate the thermal setpoint indicator or the threshold operational range. In such a case, the axis may be thermal condition instead of thermal deviation and the first marker 402 may indicate the threshold operational range. Any deviation beyond the first marker 402 may be undesired and may indicate, for example, that the thermal condition of the controlled environment 300 has exceeded the threshold operational range, and some attention or action may probably be required for an asset for which the visual indicator 400 is rendered.

Further, the visual indicator 400 may include multiple second marker, collectively referred to as marker 404. Each of the second markers 404 may indicate a relation between the axes for each interval or period of operation of an asset, for which the visual indicator 400 is rendered. For example, the second marker 404 may indicate the thermal power metric achieved with the operational speed and the thermal deviation observed for a 5 minute period of operation of the asset 105. This may indicate the thermal deviation observed when the asset 105 is operated at such operating parameters, i.e. the operational speed and the cooling power metric. Thus, the second markers 404 may indicate a relation between the speed indicator, indicating the operational speed, associated with the at least one ventilation device of the corresponding asset 105 and the thermal deviation. For example, the second markers 404 may indicate the thermal deviation that was observed when the at least one ventilation device was operated with a particular operation speed. In one example, the operational speed may be operated with different type of markers. For example, circular markers 404-1 may indicate an effect on the thermal deviation when an asset 105, or the ventilation device 106 of the asset 105, may be operating at an operational speed from 0 to 33.3% of its maximum operational speed capacity.

Similarly, the square markers 404-2 may indicate an effect on the thermal deviation when an asset 105, or the ventilation device 106 of the asset 105, may be operating at an operational speed between 33.4% to 66.6% of its maximum operational speed capacity. Also, markers 404-3 may indicate an effect on the thermal deviation when an asset 105, or the ventilation device 106 of the asset 105, may be operating at an operational speed between 66.4 to maximum operational capacity (100%). The markers 404-1, 404-2, and 404-3 may collectively be referred to as second markers 404 and individually be referred to as second marker 404. Thus, the second markers 404 may assist in evaluating the relationship between the operational speed and the thermal deviation. Such visualization may also be beneficial for human evaluation of the asset's performance.

The second markers 404 may also indicate a relation between the thermal power metric determined for the corresponding asset and the thermal deviation. For example, the second markers 404 may indicate the thermal deviation that was observed when the at least one ventilation device 106 was operated with a particular thermal power metric. Similarly, the visual indicator 400 may include multiple second markers, each for a particular interval, as indicated. For example, each second marker 404 may indicate the thermal power metric achieved with the operational speed and the thermal deviation observed for every 5 minute period of operation of the asset 105. That is, a new second marker 404 may be rendered after every 5 minutes. In one example, the interval may be adjustable and may be defined through the one or more workstations 304.

The visual indicator 400 may also indicate a third marker 406. The third marker 406 may indicate, or may be derived from, a median or mean of the second markers 404. In one example, the third marker 406 may indicate a linear statistical approximation of all the second markers 404. The third marker 406 may thus indicate a trend, for example, the thermal deviation observed with an increase or decrease in the thermal power metric and the operational speed. For example, as indicated in FIG. 4A, the thermal deviation may be observed to not exceed the thermal setpoint indicator or the first marker 402 as none of the second markers 404 exceed the first marker 402. The visual indicator 400 may thus indicate functioning working conditions that prevailed in the evaluated time period. Thus, it may be determined that the asset, with which the visual indicator 400 is associated with, is operating or functioning in the desired manner or as per the defined operational parameters. Another insight that may be derived is whether the asset 105 is capable of maintaining the thermal conditions within or below the threshold operational range and therefore comply with the cooling requirements or demands.

However, as indicated in FIG. 4B, if the thermal deviation may be observed to exceed the thermal setpoint indicator or the first marker 402 as some of the second markers 404 exceed the first marker 402, it may be determined that the asset, with which the visual indicator 400 is associated, is not operating or functioning in the desired manner or as per the defined operational parameters. Other insights may also be derived from the visual indicator 400. In one example, another insight that may be derived is that with increase in cooling demands or the operational speed, the asset 105 fails to maintain the thermal conditions within or below the threshold operational range. For example, if the thermal conditions within the controlled environment 300 degrade, the thermal deviation may increase which may indicate that the cooling demands have increased. To reduce the thermal deviation, and comply with the cooling demands and the threshold operational range, the thermal power metric and the operational speed of the asset 105 may have to be increased. However, as illustrated in FIG. 4B, some of the second markers 404 may exceed the threshold operational range or the first marker 402 when the cooling power metric and/or the operational speed has been increased. Thus, it may be determined that the asset 105 may not be able to comply with the cooling requirements or demands.

In one example, the visual indicator 400 may also indicate one or more non-compliance durations, each indicating a period, amongst the total period of operation of the at least one ventilation device 106 in which the thermal state indicator is incompliant with the thermal setpoint indicator. In one example, the visual indicator 400 may indicate the fourth marker 408 to indicate the duration of non-compliance. In one example, the fourth marker 408 may include one or more bars, where each bar may indicate a specific amount of thermal deviation. For example, each bar may indicate a band of 0.5 degree Celsius width of the thermal deviation. Thus, for the total period of operation of the at least one ventilation device 106 of the corresponding asset 105 associated with the visual indicator 400, each bar may indicate a duration for which thermal deviation was observed to be within a band 0.5 degree Celsius wide, based on the set of characteristic indicators. Further, the height of each bar may indicate the duration of deviation within an interval, for example, 1 to 1.5 degree Celsius, relative to the total duration of operation. More will be the duration of deviation, more will be the height of the bar. Also, each bar may indicate a successive or increased thermal deviation as compared to the previous bar. For example, the bar 408-1 may indicate a duration for which the thermal deviation was observed to be between 1 to 1.5 degree Celsius, thus indicating a duration for which the allowed thermal deviation limit was exceeded and referred to as non-compliance duration. Further, the bar 408-2 may indicate a duration for which the thermal deviation was observed to be between 1.5 to 2.0 degrees Celsius and the bar 408-3 may indicate a duration for which the thermal deviation was observed to be between 2.0 to 2.5 degrees Celsius. Thus, each of the bars 408 may indicate the non-compliance duration.

In one example, the non-compliance duration may be indicated as a percentage derived based on the total duration of operation of the asset 105. In one example, the percentage may be derived from the total duration of operation of the asset 105 with maximum thermal power metric. For example, the bar 408-3 may indicate that out of 100% of total time of operation with maximum thermal power metric, the thermal deviation of 2 to 2.5 degrees Celsius was observed for the 50% of the that total duration of operation. Thus, different types of insights may be drawn about the thermal conditions in the controlled environment 300 and performance or capabilities of the assets 105. For example, from the bar 408-3, it may be determined that the asset 105 failed to comply with the cooling demands and the thermal conditions exceeded the threshold operational range for 50% of the time during its total period of operation. Similarly, different insights may be derived from the visual indicator 400.

In one example, a visual indicator (not shown) may similarly be generated by the processor 104 when compliance with heating requirements is required to be determined. In such a case, a marker, similar to the marker 402, may be rendered on left or before “0” (say at −0.5) on the thermal deviation axis, indicating the allowed amount of thermal deviation. Accordingly, bars, similar to bars 408, may be rendered from the marker and towards the axis indicating the thermal power metric. Such visual indicators may indicate different insights about an asset, for example, compliance of an asset with the heating requirements when being operated at different operational speeds or with thermal power metrics. Deviations outside the allowed range are undesirable. For instance, for cooling, values above the range indicate insufficient cooling, while those below the range indicate waste. Thus, such visual indicators may be indicative of inefficient cooling or heating.

In one example, similar visual indicators may be generated and rendered by the processor 104 for each of the plurality of assets 105 based on the set of characteristic indicators. Also, in one example, the processor 104 may analyze the generated visual indicators to derive different insights and observations, as discussed above.

Further, in one example, the set of intermediate statistical variables may be determined based on the set of characteristic indicators, as discussed above. However, in another example, the processor 104 may determine the set of intermediate statistical variables based on the visual indicators, such as the visual indicator 400, associated with each of the assets 105 of the controlled environment 300.

For example, the processor 104 may select points or second markers 404 where the thermal power metric may be between a specific range, as per required insight to be drawn. For example, to determine key performance indicators or statistical variables for maximum operational capacity of the asset 105, the range for the thermal power metric may be defined to be around maximum. Accordingly, different ranges may be defined for deriving statistical variable indicating the required insights. The range, in one example, may be defined through the one or more workstations 304.

In one example, the range may be defined between a maximum value of the thermal power metric and a slightly lower than the maximum thermal power metric. For example, if the thermal power metric is normalized to an integer range of 100, the maximum thermal power metric may be 100 and the minimum thermal power metric may be 0. Thus, in one example, the range of 98 to 100 may be defined or selected for deriving the statistical variables for an asset 105 operating in that range. The processor 104 may filter the points or second markers 404 that may fall between the normalized thermal power metric (Cp) of 98 to 100. Further, as each marker 404 may also indicate the relation between the thermal power metric (Cp) and the thermal deviation, each of the second markers 404 may be distributed in the bars 408. For example, for markers indicating 2 to 2.5 degrees Celsius of deviation, the markers may be distributed or added to generate the bar 408-3. With increase in the number of such markers 404, the bar 408-3 may rise. Further, the processor 104 may assign a value to each of the so-generated bars, interchangeably referred to as bins 408. In one example, the processor 104 may assign the value using the below function:

Dt ⁡ ( i ) = NOS ⁡ ( bin_i ) NOS ⁡ ( Max_Cp )

Where i may be the bar or bin number (for example 1 for the first bin 408-1, 2 for the second bin 408-2), Dt may be the value for the i^th bin, NOS may be the number of samples in bins and Max_Cp may be a set of points (second markers 404) sufficiently close to the maximum value of thermal power metric, that fall, for example, between the normalized thermal power metric (Cp) of 98 to 100.

Once the value has been determined, the processor 104 may determine the first statistical variable for an asset 105. In one example, the processor 104 may determine the first statistical variable using the below function:

First ⁢ statistical ⁢ variable = 100 · Dt ⁡ ( 0 ) ( Dt ⁡ ( 0 ) + ∑ i = 1 N ( 1 . 0 + w · i · bw 2 ) · Dt ⁡ ( i ) )

where

( w · i · bw 2 )

is a weight of the deviation bin_i and where bw is bin width. For example, each bin indicates a thermal deviation of 0.5 degree Celsius, as discussed above. So, the value of bw, for a bin, is 0.5. Further, i is the bin number, and w is the averaging or scaling weight.

Further, in one example, the processor 104 may derive the second statistical variable, for each of the plurality of assets 105, based on the corresponding visual indicators. For example, for an asset having the visual indicator 400 associated therewith, the processor 104 may determine the moving average by considering all the markers 404. In one example, the processor 104 may order the pairs of (Cp, deviation) according to increasing Cp. A moving average may then be applied to the ordered series of values. Further, through the moving averaged points, or markers 404, a line (marker 406) may be inserted for connecting the moving averaged points or markers 404. The processor 104 may then determine the slope between the marker 406 and the thermal deviation axis. In one example, the slope may indicate a relation between the change in thermal power metric and effect of that change on the thermal deviation. In another example, the slope may also be indicative of a relation between change in the thermal deviation and effect of the change on the thermal power metric. In one example, the steeper the slope or angle of the marker 406, more favourable the thermal conditions may be, thereby indicating that the asset 105 may be performing or functioning in a desired manner. For example, a slope of 90 degrees, as indicated by numeral 410 in FIG. 4A, may indicate that even with increasing cooling demands, performance of the asset 105 remained consistent and the thermal conditions did not exceed the threshold operational range or the marker 402. It may also indicate that during performance of the assets, the thermal deviation was not observed. However, a slope of value lesser than 90, as indicated by numeral 412 in FIG. 4B, may indicate that with increasing cooling demands, performance of the asset 105 becomes inconsistent and the thermal conditions exceeded the threshold operational range or the marker 402. For example, when the asset is operated at maximum operational speed (marker 404-3) to achieve increased cooling, thermal deviation may be observed. Thus, the marker 406 may be tilted towards the marker 402. Though the above examples have been discussed with respect to cooling, performance of the assets may also be assessed for increasing temperatures in the controlled environment in a similar/analogous manner.

In one example, as the slope may indicate relation between deviation of the thermal condition from the threshold operational range based on the thermal power metric, the processor 104 may determine the second statistical variable for the asset 105 based on the slope. In one example, the processor 104 may determine the second statistical variable using the below function:

second ⁢ statistical ⁢ variable = abs ⁢ ( tan - 1 ⁢ ( 1. s · a scale ) · 2 . 0 π ) · 100

where s is the slope of the line or marker 406 and a_scale is the angle or slope scaling factor or parameter. In one example, the slope may be normalized by the scaling factor a_scale. The slope may then be converted into angle units and mapped to a range of −90 degrees to 90 degrees to values between −100 to 100.

Thus, in one example, the processor 104 may determine the set of intermediate statistical variables, using the above-discussed examples, for each of the assets 105. Based on the derived intermediate statistical variables, the processor 104, or a signal generation unit 314 of the processor 104, may generate a reconfiguration signal to cause reconfiguration of one or more operational parameters of at least one asset from amongst the plurality of assets 105. In one example, the reconfiguration may be to modify the performance of at least one asset from amongst the plurality of assets 105.

In one example, to generate the reconfiguration signal, the processor 104 may derive an evaluated statistical variable for each of the plurality of assets 105 based on the pair of first and second statistical variables corresponding to the assets. For example, the processor 104 may derive an evaluated statistical variable for an asset 105-1 based on the pair of first and second statistical variables derived for the asset 105-1. Similarly, the processor 104 may derive an evaluated statistical variable for an asset 105-2 based on the pair of first and second statistical variables derived for the asset 105-2. Similarly, the processor 104 may derive the evaluated statistical variables, each corresponding to an asset of the controlled environment 300. In one example, the evaluated statistical variable may be a scalar weighted average of the first and the second statistical variables. For example, a weight w_o may be used for averaging each of the first statistical variables and a weight w_a may be used for scalar averaging each of the statistical variables. For example, the first and the second statistical variables derived for the asset 105-1 may be scalar weighted averaged to determine the evaluated statistical variable for the asset 105-1. Similarly, an evaluated statistical variable may be derived for each of the plurality of assets. In one example, w_o may be 0.66 and w_a may be 0.34. In one example, the weight may be determined so that sum of the weights may be equal to 1.

The processor 104, in one example, may compare each evaluated statistical variable with a threshold statistical variable. In one example, the threshold statistical variable may be pre-defined by the one or more users through the one or more workstations 304. The threshold statistical variable may be a scalar numerical value. Based on the comparison, the processor 104 may determine whether to generate the reconfiguration signal. For example, if the evaluated statistical variable is more than the threshold statistical variable, the processor 104 may determine to generate the reconfiguration signal. However, if the evaluated statistical variable is less than or equal to the threshold statistical variable, the processor 104 may refrain from generating the reconfiguration signal. Further, in one example, the threshold statistical variable may be a modifiable threshold and may be dynamically modified, for instance through the workstations 304, in order to change conditions in which the reconfiguration signals may be generated.

In another example, to generate the reconfiguration signal, the processor 104 may cause deployment of a computational model. Based on at least one of the evaluated statistical variables and the set of intermediate statistical variables, the computation model may determine whether the reconfiguration signal is to be generated. In one example, the computation model may be one of a Machine Learning (ML) model or an Artificial Neural Network (ANN).

In one example, the computation model may be trained or modelled based on an input dataset and output dataset. The training process may involve several steps to ensure the computational model can effectively determine the conditions, or value of the intermediate statistical variables, for which the reconfiguration signals may be generated by the processor 104. Also, the training process may involve several steps to ensure the computational model can generate recommendations based on the provided data.

Initially, the input dataset, which may include historically evaluated statistical variables, historically derived sets of intermediate statistical variables, and historic visual indicators, may undergo preprocessing. This step may involve cleaning, normalizing, and preparing the data for model ingestion. Feature engineering may also be performed to extract or create relevant features from the input dataset. The computational model may then enter the training phase, where the model may learn the relationships between the input features and the corresponding output features, such as conditions on which reconfiguration signals were historically generated and recommendations, in the output dataset.

Further, a validation phase may be conducted using a separate validation dataset. The final model may then be evaluated on a test dataset to assess its performance. Once trained, the computational model may be capable of generating recommendations for new input data, such as the one of the evaluated statistical variables and the intermediate statistical variables. The model may also be capable of suggesting reconfigurations of operational parameters or actionable items related to assets to maintain compliance with threshold operational ranges.

Thus, in one example, the computational model may be deployed over the processor 104 or may be operationally or communicably linked with the processor 104. Using the computational model, the processor 104 may determine whether to generate the reconfiguration signal for the derived set of intermediate statistical variables or the evaluated statistical variable. In one example, based on the training and mappings so created by the computation model using the historical data, the computational model may determine whether one or more operational parameters are required to be reconfigured for such values of the set of intermediate statistical variables or the evaluated statistical variable. For example, based on the set of intermediate statistical variables or the evaluated statistical variable, the computation model may refer to the mapping so created during the training phase to determine whether one or more operational parameters were modified for similar set of intermediate statistical variables or the evaluated statistical variable. The computational model may accordingly determine whether the reconfiguration of the operational parameters is required.

In case the computational model determines that reconfiguration is required, the computational model may generate a signal indicating such requirement and share the signal with the processor 104. In response to receiving such signal, the processor 104 may determine to generate the reconfiguration signal. In one example, the computation model, based on at least one of the evaluated statistical variable and the set of intermediate statistical variables, may also be trained to indicate what operational parameters may be modified or reconfigured and for which of the assets 105. For example, upon noticing a significant drop in value of the first statistical variable (say more than 10) when the asset is operating at its maximum capacity, the computational model may determine that the asset 105 may be unable to maintain the temperature within the threshold operational range. This may indicate asset degradation, and the computational model may suggest or indicate maintenance of the asset 105. For instance, the computational model may indicate to clean the thermal regulation device 108.

In another example, the computational model may also be configured to indicate or suggest modification of the operational parameters based on the visual indicators. For example, the slope of marker 406 may be feedback of the measure of opening of the control device 110 and/or operational speed of the ventilation device 106 of the asset 105. If the slope/angle of the line or marker 406 is lower than a pre-defined threshold angle or the marker crosses the acceptable or allowed deviation (indicated by the marker 402), the computational model may suggest or indicate increasing at least one of the operational speed and the measure of opening. Thus, based on signals or indications received from the computational model, the processor 104 may determine to generate the reconfiguration signal to reconfigure the one or more operational parameters of the one or more assets from amongst the plurality of assets 105.

Thus, based on at least one of the evaluated statistical variable and the set of intermediate statistical variables, the processor 104 may determine to generate the reconfiguration signal to reconfigure the one or more operational parameters associated with the at least one asset, from amongst the plurality of assets 105. For example, based on at least one of the evaluated statistical variables and the set of intermediate statistical variables derived for an asset 105-1, the processor 104 may determine whether to generate the reconfiguration signal for the asset 105-1. Similarly, based on at least one of the evaluated statistical variables and the set of intermediate statistical variables derived for each of the assets 105, the processor 104 may determine whether to generate the reconfiguration signal for one or more of the assets for reconfigure the one or more operational parameters of the one or more assets.

In one example, the one or more operational parameters may include at least one of the operational speed of at least one ventilation device 106 of the at least one asset 105, the measure of opening of the at least one control device 110 of the at least one asset 105, and a control signal, associated with the controller 111, to control performance of the at least one asset from amongst the plurality of assets 105. For example, based on at least one of the evaluated statistical variables and the set of intermediate statistical variables, the processor 104 may determine whether the operational speed of an asset needs to be increased to comply with the threshold operational range. The processor 104 may accordingly generate the reconfiguration signal to cause reconfiguration of the operational speed of that asset. In one example, the magnitude of change may be determined by the processor 104 or the computational model based on historical data indicating operational parameter modifications that may have been previously done in view of the estimated statistical variables and/or the intermediate set of statistical variables to improve functioning of the assets or comply with the threshold operational range.

In one example, to cause reconfiguration of the operational parameters, the processor 104 may generate the reconfiguration signal (for example, a control signal) that may cause the one or more assets 105 to operate their ventilation devices at a particular speed and/or operate control devices of that asset with a particular measure of opening. In another example, the processor 104 may communicate the reconfiguration signal, indicating the modified speed and/or measure of opening for on or more assets, to the controller 111. The controller 111 may accordingly generate control signals for the one or more assets to operate them at a particular speed and/or their control devices with a particular measure of opening.

Further, in one example, the processor 104 may also trigger generation of one or more recommendations. The one or more recommendations may include at least one of an indication to reconfigure the one or more operational parameters and an actionable item indicating an action with respect to the at least asset to comply with the threshold operational range.

In one example, the processor 104 may determine the one or more operational parameters to be reconfigured based on the computational model. In another example, the processor 104 may refer to a mapping stored in the one or more datastores 302 indicating the one or more operational parameters that may be changed for the value of the evaluated statistical variable or the set of intermediate statistical variables. For example, the mapping may indicate adjusting the operational speed of a ventilation device 106 when a certain value of the evaluated statistical variable or the set of intermediate statistical variables is determined for an asset 105. In one example, to adjust the operational speed, the processor 104 may cause modification of the control signal being provided to the asset 105 by the controller 111. Similarly, the mapping may indicate increment or decrement of the measure of opening of a control device 110 when a certain value of the evaluated statistical variable or the set of intermediate statistical variables is determined for an asset or to comply with the threshold operational range. In another example, the mapping may suggest to modify flow rate of the working fluid by adjusting the measure of opening of the control device 110. Similarly, multiple recommendations can be generated. In one example, to adjust the flow rate and/or the measure of opening, the processor 104 may cause modification of the control signal being provided to the asset 105 by the controller 111.

Further, in one example, the mapping may also include multiple actionable items, each indicating an action with respect to the at least one asset to comply with the threshold operational range. In one example, the mapping may indicate that for a certain value of the evaluated statistical variable or the set of intermediate statistical variables, the following action may be opted as a remedy for complying with the threshold operational range. For example, the actionable item may indicate actions such as cleaning, maintenance, or replacement of an asset based on the value of the evaluated statistical variable or the set of intermediate statistical variables.

Thus, the processor 104, in one example, may also provide recommendations, in addition to causing reconfiguration of the operational parameters. The recommendation may indicate the operational parameters that may probably be reconfigured or modified and/or one or more actions that may be opted to comply with the threshold operational range and optimize the functioning and performance of the assets. In one example, the recommendation, including at least one of the indications and the actionable item, and the visual indicators may be rendered by the processor 104 on one or more workstations 304. The one or more users may thus be indicated of different insights and guided in reconfiguring the operational parameters and the actions that may be opted.

Further, in one example, the processor 104 may also cause rendering of the evaluated statistical variables and/or the set of intermediate statistical variables for each of the plurality of assets 105. In one example, the evaluated statistical variables and/or the set of intermediate statistical variables for each of the plurality of assets 105 may be rendered on the one or more workstations 304. Further, in one example, each of the evaluated statistical variables may be arranged in a particular order based on the values of the statistical variables. For example, the values of the evaluated statistical variables, derived for each of the plurality of assets 105, may be arranged in ascending order, as illustrated in FIG. 5, thereby clearly indicating the assets with highest and lowest values of evaluated statistical variables. FIG. 5 illustrates an exemplary graphical representation of the evaluated statistical variables derived for each of the plurality of assets, according to one example implementation. For example, bar 505-1 may indicate value of the evaluated statistical variable derived for the asset 105-1, bar 505-2 may indicate the value of the evaluated statistical variable derived for the asset 105-2, . . . , and bar 505-P may indicate the value of the evaluated statistical variable derived for the asset 105-P. Similarly, in one example, rendering of the set of intermediate statistical variables may also be caused by the processor 104, though not illustrated.

FIGS. 6 and 7 illustrate block diagrams of an exemplary methods 600 and 700, respectively, for reconfiguration of at least one asset. FIGS. 6 and 7 will be discussed in conjunction with FIGS. 1A to 5. The description of FIGS. 1A to 5 has been incorporated for reference for the sake of brevity.

Further, the order in which the methods 600 and 700 are described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method, or an alternative method. Furthermore, methods 600 and 700 may be implemented by processing resource(s) or computing device(s) through any suitable hardware, non-transitory machine-readable instructions, or combination thereof.

It may also be understood that methods 600 and 700 may be performed by programmed computing device(s), such as the processor 104, as depicted in FIGS. 1A to 3. Furthermore, the methods 600 and 700 may be executed based on instructions stored in a non-transitory computer-readable medium, as will be readily understood. The non-transitory computer-readable medium may include, for example, digital memories, magnetic storage media, such as one or more magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. While the methods 600 and 700 are described below with reference to the processor 104 and the system 102 as described above; other suitable systems for the execution of these methods may also be utilized. Additionally, the implementation of the method is not limited to such examples.

FIG. 6 illustrates a block diagram of the method 600 for reconfiguring at least one asset of a controlled environment, according to one implementation of the present subject matter.

At block 602, a set of intermediate statistical variables may be determined for a controlled environment based on a plurality of characteristic indicators linked with the controlled environment. In one example, the controlled environment may be the controlled environment 100 or 300, as discussed above.

Further, in one example, the set of characteristic indicators may include a thermal state indicator, a thermal setpoint indicator, a flow indicator, and a fluid regulation indicator. In one example, the thermal state indicator may quantitatively indicate the thermal condition in the controlled environment and the thermal setpoint indicator may define a threshold operational range for the thermal state indicator. Further, the flow indicator may indicate a measure of flow of the circulation fluid in the controlled environment. The flow of the circulation fluid may be caused by at least one asset, such as the asset 105, of the controlled environment. Furthermore, the fluid regulation indicator may indicate a measure of flow of the working fluid. The flow of the working fluid being caused by the at least one asset. In one example, the measure of flow of the working fluid may influence the thermal condition in the controlled environment.

Further, the set of intermediate statistical variables may include a first statistical variable and a second statistical variable. In one example, the first statistical variable may be determined based on the total period of operation of the at least one asset and a duration of compliance of the threshold operational range by the thermal condition in the controlled environment. The first statistical variable may quantitatively indicate, in one example, performance of the at least one asset in maintaining the thermal condition in compliance with the threshold operational range during the total period of operation of the at least one asset. In one example, the first statistical variable may be determined when the at least one asset may be operating at maximum capacity or maximum thermal power metric. In another example, the first statistical variable may be determined when the at least one asset may be operating normally, i.e., below maximum capacity or maximum thermal power metric, to manage the thermal conditions in the controlled environment.

The second statistical variable, in one example, may be determined based on a thermal power metric of the at least one asset and deviation of the thermal condition from the threshold operational range. The second statistical variable may indicate a relation between change in the deviation of the thermal condition from the threshold operational range upon change in the thermal power metric.

At block 604, a reconfiguration signal may be generated, based on the set of intermediate statistical variables, to cause modification of one or more operational parameters for reconfiguring functioning of the at least one asset.

FIG. 7 illustrates a block diagram of the method 700 for reconfiguring the at least one asset of the controlled environment, according to another example implementation of the present subject matter.

At block 702, a plurality of characteristic indicators may be received for the controlled environment. As discussed above in different examples, the plurality of characteristic indicators may be received or obtained from multiple sources. For example, the characteristic indicators may be received from at least one of the one or more sensors 112, the control signals generated by the controller 111, and the one or more datastores 302. Further, the plurality of characteristic indicators may include the thermal state indicator, the thermal setpoint indicator, the flow indicator and the fluid regulation indicator, as discussed above. Further, in one example, the plurality of characteristic indicators may be received for at least one asset, such as the asset 105 discussed above in FIGS. 1A to 3.

At block 704, a set of intermediate characteristic variables may be determined for the controlled environment based on the plurality of characteristic indicators linked with the controlled environment. In one example, the set of intermediate characteristic variables may be determined for the at least one asset and may include the first statistical variable and the second statistical variable.

In one example, the first statistical variable may be determined based on the total period of operation of the at least one asset and the duration of compliance of the threshold operational range by the thermal condition in the controlled environment, as discussed above. The total period of operation may be, for example, a period in which the at least one asset may be operating at maximum output or capacity (i.e., at maximum thermal power metric). In one example, the first statistical variable may be a ratio of (a) the duration of compliance of the threshold operational range by the thermal condition within the controlled environment and (b) the total period of operation of the at least one asset. The first statistical variable may indicate, for example, performance of the asset in maintaining the thermal condition within the threshold operating range during the total period of its operation. That is, the first statistical variable may indicate how well the asset maintained the thermal condition within the threshold range during its operation.

Further, the second statistical variable may be determined based on the thermal power metric of the at least one asset and deviation of the thermal condition from the threshold operational range, as discussed above. The cooling power metric may be, in one example, the maximum thermal power metric on which the at least one asset can operate and generate maximum output. The second statistical variable indicates a relation between change in the deviation of the thermal condition from the threshold operational range upon change in the thermal power metric. In one example, the second statistical variable may indicate the deterioration of the thermal conditions as the cooling demands increase, as discussed above. For example, based on the relation between the thermal power metric and the thermal deviation, an insight about the effect on performance of the at least one asset may be determined. Further in one example, the cooling power metric of at least one asset may be determined based on the flow indicator and fluid regulation indicator. In one example, the thermal power metric may be a result of the product of the flow indicator and fluid regulation indicator.

At block 706, an evaluated statistical variable may be derived for the at least one asset by determining a scalar weighted average of the first statistical variables and the second statistical varriable. For example, each of the first and the second statistical variables may be multiplied with corresponding weights, such as w_o and w_a, as discussed above.

At block 708, a reconfiguration signal may be generated based on at least one of the evaluated statistical variable and the set of intermediate statistical variables. That is, in one example, based on the evaluated statistical variable, it may be determined that the reconfiguration signal may be generated. In another example, based on the set of intermediate statistical variables, it may be determined that the reconfiguration signal may be generated. However, in one example, the reconfiguration signal may be generated based on the evaluated statistical variable and the set of intermediate statistical variables.

In one example, to generate the reconfiguration signal, deployment of a computational model may be caused. The computational model may be configured based on an input dataset and an output dataset, as discussed above. The output dataset may include, in one example, a plurality of recommendations, each corresponding to a data in the set of input data. Each of the plurality of recommendations may indicate reconfiguration of one or more operational parameters of at least one asset and an actionable item indicating an action with respect to at least one asset to comply with the threshold operational range. Further, the input dataset may include, historically derived sets of intermediate statistical variables and historic evaluated statistical variables. The computational model may be configured, as discussed above.

Further, in one example, the reconfiguration signal may be generated to cause modification of one or more operational parameters for reconfiguring functioning of the at least one asset. In one example, the one or more operational parameters may include an operational speed of a ventilation device, such as the ventilation device 106, of the at least one asset, a measure of opening of a control device, such as the control device 110 of the at least one asset, and a control signal, associated with the controller 111, to control the functioning of at least one of the ventilation device (for example, the operational speed) and the control device (for example, the measure of opening) of the at least one asset. Thus, based on at least one of the evaluated statistical variable and the set of intermediate statistical variables, the reconfiguration signal may be generated to cause modification of the one or more operational parameters for reconfiguring functioning of the at least one asset. In one example, the modification of the one or more operational parameters may be for complying the thermal conditions with the threshold operational range. In another example, the modification may be to operate the assets with particular operating parameters to optimize their functioning.

FIG. 8 illustrates a non-transitory computer-readable medium for reconfiguring an asset of a controlled environment, in accordance with an example of the present subject matter. FIG. 8 will be discussed with reference to FIGS. 1A to 5. The description of FIGS. 1A to 5 has been incorporated for reference for the sake of brevity.

In an example, the computing environment 800 includes a processor 802 communicatively coupled to a non-transitory computer-readable medium 804 through communication link 806. In one example, the processor 802 may include one or more processing resources for fetching and executing computer-readable instructions from the non-transitory computer-readable medium 804. The processor 802 and the non-transitory computer-readable medium 804 may be implemented, for example, in the system 102.

The non-transitory computer-readable medium 804 may be, for example, an internal memory device or an external memory. In an example implementation, the communication link 806 may be a network communication link, or other communication links, such as a PCI (Peripheral component interconnect) Express, USB-C (Universal Serial Bus Type-C) interfaces, I2C (Inter-Integrated Circuit) interfaces, etc. In an example implementation, the non-transitory computer-readable medium 804 includes a set of computer-readable instructions 808 which may be accessed by the processor 802 through the communication link 806. The processor 802 and the non-transitory computer-readable medium 804 may also be communicatively coupled to the assets 105, the sensors 112, and the controller 111.

Referring to FIG. 8, in one example, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to obtain a set of characteristic indicators for a controlled environment having an asset, such as the asset 105 discussed in FIGS. 1A to 3. In one example, the asset may include a plurality of ventilation devices and a plurality of ventilation device. FIGS. 1B to 1D illustrate assets 105 having the plurality of ventilation devices 106. Further, FIG. 1C illustrates an asset having the plurality of ventilation devices 106 and the plurality of thermal regulation devices 108. In one example, at least one thermal regulation device 108 may be operationally linked with at least one of the plurality of ventilation devices 106 to influence thermal characteristics of the circulation fluid, as discussed above. Further, each of the plurality of thermal regulation devices 108 may have a control device, such as the control device 110 discussed above, operationally linked therewith to regulate flow of the working fluid in the at least one thermal regulation device 108.

Further, the set of characteristic indicators for the asset 105 may include, the thermal state indicators, the thermal setpoint indicators, a plurality of speed indicators and a plurality of position indicators. In one example, each of the plurality of speed indicators may be associated with a unique ventilation device from amongst the plurality of ventilation devices 106 of the asset. For example, each of the ventilation devices 106-1 to 106-N may have a speed indicator associated therewith and may indicate a measure of the operational speed of that ventilation device. Similarly, each of the plurality of position indicators may be associated with a unique control device operationally linked with at least one thermal regulation device of the asset, and may indicate a measure of the opening of that control device 110.

Further, in one example, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to derive a set of intermediate statistical variables based on the set of characteristic indicators. The set of intermediate statistical variables may include a plurality of first statistical variables and a plurality of second statistical variables.

In one example, each of the plurality of first statistical variables may be linked with a unique ventilation device and determined based on a ratio of a duration of compliance of the threshold operational range by the thermal condition and a total period of operation of that ventilation device. Each of the plurality of first statistical variables indicates performance of that ventilation device in maintaining the thermal condition in compliance with the threshold operational range during the total period of operation. Thus, in an asset having multiple ventilation devices, a statistical variable indicating performance of each individual ventilation device may be derived.

Further, each of the plurality of second statistical variables may be linked with a unique ventilation device and may be determined based on the thermal power metric of that ventilation device and deviation of the thermal condition from the threshold operational range. Each of the plurality of second statistical variables may indicate variation in the deviation of the thermal condition from the threshold operational range based on variation in the thermal power metric. Further, in one example, the thermal power metric, for an associated ventilation device, may be determined based on a product of the speed indicator of the associated ventilation device and the position indicator of a control device operationally linked with that associated ventilation device.

Further, in one example, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to generate, based on the set of intermediate statistical variables, a reconfiguration signal to cause modification of one or more operational parameters associated with at least one of the ventilation devices and control devices of the asset. In one example, the reconfiguration may be caused to comply with the threshold operational range.

Further, in one example, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to derive an evaluated statistical variable by determining a weighted average of each pair of the first statistical variable and the second statistical variable corresponding to a ventilation device. That is, a weighted average of the first and the second statistical variable may be determined for a ventilation device 106-1 of the asset. The result of the weighted average may be referred to as the evaluated statistical variable. Similarly, an evaluated statistical variable may be determined for each of the ventilation devices of the asset. In one example, based on the evaluated statistical variable, the processor 802 may determine to generate the reconfiguration signal.

Further, to generate the reconfiguration signal, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to cause deployment of a computational model. The computational model may be modelled based on the input dataset and the output dataset, as discussed above. The output dataset may include a plurality of recommendations, each corresponding to a data in the set of input data. Each of the plurality of recommendations may indicate at least one of (a) reconfiguration of one or more operational parameters associated with at least one of the plurality of ventilation devices and control devices and (b) an actionable item indicating an action with respect to at least one of the ventilation devices and control devices to comply with the threshold operational range, as discussed above. Further, the input dataset comprises at least one of historically evaluated statistical variables and historically derived set of intermediate statistical variables, as discussed above.

Further, in one example, the non-transitory computer-readable medium 804 includes computer-readable instructions 808 that may cause the processor 802 to trigger the generation of a recommendation. The recommendation may include at least one of an indication to reconfigure the one or more operational parameters and the actionable item indicating an action with respect to at least one of the ventilation devices and control devices to at least comply with the threshold operational range.

Although examples of the present subject matter have been described in language specific to methods and/or structural features, it is to be understood that the present subject matter is not limited to the specific methods or features described. Rather, the methods and specific features are disclosed and explained as examples of the present subject matter.