MAP-BUILDING METHOD FOR AUTOMATIC POOL CLEANING DEVICE, AUTOMATIC POOL CLEANING DEVICE, AND STORAGE MEDIUM

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No. 63/691,512, filed on Sep. 6, 2024, the subject matter of which is hereby incorporated by reference in its entirety.

FIELD

This disclosure relates to the field of pool cleaning technology, and more particularly to a map-building method for an automatic pool cleaning device, an automatic pool cleaning device, and a storage medium.

BACKGROUND

Automatic pool cleaning devices can automatically perform cleaning operations to pools, thereby reducing the burden of manual cleaning and improving pool cleaning performance and user experience. To realize accurate cleaning path planning and efficient cleaning operations, the automatic pool cleaning device needs to build an accurate pool map.

Map-building along pool edge is a commonly used map-building method, through which, the automatic pool cleaning device completes a mapping task by traveling along the pool edge for one circle. However, due to the insufficient positioning accuracy and cumulated errors, during the map-building process, the built map usually cannot be a closed map, in particular the starting point of the map being unable to connect with the ending point of the map.

SUMMARY

In one aspect of the present disclosure, a map-building method for an automatic pool cleaning device is provided. The map-building method comprises: controlling the automatic pool cleaning device to travel one circle or longer than one circle along a pool edge on a pool bottom or a water surface, and during the travel along the pool edge, collecting pool edge information in real-time to obtain point cloud data for a first path, wherein a path of the travel along the pool edge is the first path; controlling the automatic pool cleaning device to continue traveling along the pool edge from an ending point of the first path and continue to collect pool edge information in real time until a first predetermined condition is met, thereby obtaining point cloud data for a second path, wherein a path along the pool edge starting from the ending point of the first path is the second path; and generating a closed pool-edge map based on the second path.

In another aspect of the present disclosure, an automatic pool cleaning device is provided. The automatic pool cleaning device comprises: a sensor and a processor, wherein the sensor is capable of collecting point cloud data, and the processor is configured to implement the above-mentioned map-building method.

In another aspect of the present disclosure, a computer-readable non-volatile storage medium, comprising computer program instructions, which, when executed by a processor, perform the above-mentioned map-building method.

The map-building method for an automatic pool cleaning device provided herein optimizes point cloud data so that the optimized point cloud data can be used to generate a closed pool-edge map, thereby reducing the impact of the insufficient positioning accuracy and cumulated errors, improving the path planning and navigation accuracy of the automatic pool cleaning device during subsequent operations, and enhancing the cleaning performance of the automatic pool cleaning device.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings that form a part of the present application are used to provide further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the accompanying drawings:

FIG. 1 is a flowchart of a map-building method for the automatic pool cleaning device provided by the present disclosure.

FIG. 2 is a schematic drawing showing a driving path of the automatic pool cleaning device provided by the present disclosure.

FIG. 3 is another schematic drawing showing a driving path of the automatic pool cleaning device provided by the present disclosure.

FIG. 4 is a schematic drawing of a pool-edge map generated based on point cloud data of a driving path provided by the present disclosure.

FIG. 5 is a schematic drawing of a pool-edge map generated based on updated point cloud data of a first path provided by the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The technical solutions in the present application will be clearly and completely described below in combination with the accompanying drawings. Obviously, the embodiments described are only partial embodiments in the present application and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor shall fall within the protection scope of the present application. It should be illustrated that the embodiments in the present application and the features in the embodiments may be combined with each other without contradicting each other.

FIG. is a flowchart of a map-building method for the automatic pool cleaning device provided by the present disclosure; FIG. 2 is a schematic drawing showing a driving path of the automatic pool cleaning device provided by the present disclosure; FIG. 3 is another schematic drawing showing a driving path of the automatic pool cleaning device provided by the present disclosure; FIG. 4 is a schematic drawing of a pool-edge map generated based on point cloud data of a driving path provided by the present disclosure; and FIG. 5 is a schematic drawing of a pool-edge map generated based on updated point cloud data of a first path provided by the present disclosure.

The present application provides an automatic pool cleaning device. It can be appreciated that the automatic pool cleaning device can clean the pool. The pool is, for example, a pool-shaped building. The pool-shaped building may be a swimming pool, a water storage pool, a hydrotherapy pool, a water storage tank, a water storage channel, and so on. The automatic pool cleaning device may be a device such as an automatic cleaning device, a pool cleaning robot, which can clean the pool-shaped building. The present application does not limit the specific presentation of the automatic pool cleaning device or the pool-shaped building, as long as the principle of the present disclosure can be realized. Hereinafter, unless otherwise described, the terms “pool bottom”, “bottom surface of the swimming pool”, and “bottom of the swimming pool” all refer to the surface of the pool bottom in the swimming pool.

While performing cleaning operations in a pool, an automatic pool cleaning device needs to build a pool map to improve path-planning accuracy and cleaning efficiency. For example, the automatic pool cleaning device may be controlled to travel along an edge of the pool. For example, the automatic pool cleaning device may be controlled to travel, along the junction between the bottom and the pool wall, on the bottom surface of the pool, or the automatic pool cleaning device may be controlled to travel, along the junction between the water surface and the pool wall, on the water surface. This travel may be referred to as “travel along the edge” or “travel along the pool edge”. During the travel, the pool edge information is collected in real-time to obtain the contours and shape of the pool bottom. As shown in FIG. 2, a travel path of the automatic pool cleaning device is illustrated. The dotted line in FIG. 2 represents a portion of the path's end. Due to limitations in positioning accuracy and the influence of accumulated errors during traveling, after the automatic pool cleaning device's traveling a circle of the pool edge, the travel path's ending point cannot coincide with its starting point. This results in inaccurate pool-map building and it is unable to generate a closed-loop map of the pool edge. Consequently, the accuracy of the automatic pool cleaning device's path planning and navigation is reduced in subsequent operations, thereby impacting cleaning effectiveness.

This disclosure provides a map-building method for an automatic pool cleaning device. The embodiments disclosed are provided below with reference to the accompanying drawings. FIG. 1 shows a flowchart of a map-building method 100 for an automatic pool cleaning device. The map-building method 100 includes steps S101 to S103, which are described below.

In Step S101, controlling the automatic pool cleaning device to travel one circle or longer than one circle along the edge on the pool bottom or the water surface, and during this travel along the edge, collecting the pool edge information in real-time to obtain point cloud data for a first path, wherein the path of the travel along the edge is the first path.

The automatic pool cleaning device can travel along the pool bottom or the water surface to clean the pool bottom or the water surface. During the map-building along the pool edge, the automatic pool cleaning device can be controlled to travel along the edge for one circle or longer than one circle, and during this travel, the pool edge information is collected in real time to obtain point cloud data for the first path. Here, the term “point cloud data of the first path” refers to the point cloud data obtained during the first path by the automatic pool cleaning device. This point cloud data can be used to generate a map of the pool edge corresponding to the first path. The point cloud data consists of the coordinates of multiple spatial points and can reflect the geometric characteristics of the pool edge. For instance, the geometric characteristics of the pool edge may comprise: the contours of the pool edge, and one or more features (e.g., corners, curved surfaces, and obstacles) at the pool edge.

For example, the pool edge information may be collected through a distance sensor or radar.

Distance sensors typically measure the distance between an object (such as the pool edge) and the sensors through signals such as ultrasonic waves, lasers, or infrared light. A distance sensor may transmit an ultrasonic, laser, or infrared signal, which is reflected by the sidewalls at the pool edge and returned to the distance sensor. The distance sensor may receive the reflected signal and converts it into the point cloud data, thereby collecting the pool edge information.

Radar (e.g., lidar) may measure distance through electromagnetic waves. The radar transmits an electromagnetic wave signal, which, upon reaching the sidewalls at the pool edge, is reflected and returned to the radar. The radar can receive the reflected signal and converts it into the point cloud data, thereby collecting the pool edge information.

The distance sensor or radar may be located on the front or side of the automatic pool cleaning device. It may be appreciated that any place, where the distance sensor or radar may collect the pool edge information while the automatic pool cleaning device is moving along the edge, is within the scope of the disclosure.

It may be appreciated that the above-mentioned method for collecting the pool edge information is merely exemplary, and the scope of protection provided by this disclosure is not limited to the above-mentioned method. A person having ordinary skill in the art (POSITA) may select and configure the distance sensor and radar based on actual circumstances. POSITA may also adopt other methods which can be used for collecting the pool edge information, as long as the technical principles of this disclosure are implemented.

For example, the controlling the automatic pool cleaning device to travel one circle or longer than one circle along the edge on the pool bottom or the water surface may comprise: determining, through a positioning device or an IMU (Inertial Measurement Unit), whether the automatic pool cleaning device has traveled one circle or longer than one circle along the edge.

The positioning device may track the position and movement of the automatic pool cleaning device in real time to determine whether the automatic pool cleaning device has traveled one circle or longer than one circle along the pool edge. For instance, the positioning device may determine the position of the automatic pool cleaning device through GPS, LiDAR, or visual positioning.

The positioning device may comprise, for example, GPS, which may receive satellite signals and calculate three-dimensional coordinates of the automatic pool cleaning device. By comparing the coordinates of the current position of the automatic pool cleaning device with the coordinates of its starting position, it may be determined whether the automatic pool cleaning device has traveled one circle or longer than one circle along the pool edge. The positioning device may comprise, for example, LiDAR, which transmits laser beams and receives reflected signals to generate point cloud data for the surrounding environment. By analyzing this point cloud data, the pool edge features may be identified. By determining whether repeated edge features are identified, it may be determined whether the automatic pool cleaning device has traveled one circle or longer than one circle along the pool edge. The positioning device may comprise, for example, an image-collecting sensor (such as a camera), which may collect images of the pool's surroundings and use image processing techniques to identify features of the pool edge (e.g., color and shape). Based on the identified features, the position change of the automatic pool cleaning device is calculated so as to determine whether the automatic pool cleaning device has traveled one circle or longer than one circle along the pool edge.

The IMU may obtain the yaw angle of the automatic pool cleaning device in real time during its travel along the edge and accumulate yaw angle values. Depending on the position and pose of the automatic pool cleaning device, the yaw angle may be positive or negative. When the automatic pool cleaning device is traveling along a straight line, the accumulated yaw angle is relatively small; when the automatic pool cleaning device turns, the accumulated yaw angle is relatively large, approximately corresponding to the angle of the turn. When the automatic pool cleaning device completes one-circle travel along the edge, the accumulated yaw angle value (the absolute value of the accumulated value) is close to 360 degrees. Therefore, the accumulated yaw angle value may be used to determine whether the automatic pool cleaning device has traveled one circle or longer than one circle along the pool edge.

It should be noted that the above description of the method for determining whether the automatic pool cleaning device has traveled one circle or more than one circle along the edge is merely exemplary. POSITA may select and configure the IMU or positioning device based on their experience or the actual scenarios of the automatic pool cleaning device. As long as the technical principles of this disclosure are implemented, no specific limitations are imposed in this disclosure.

A closed pool-edge map may not be generated due to the deviations between the starting point and ending point of the first path, or an end portion of the pool edge in the generated closed pool-edge map may have a relatively large deviation from the actual pool edge.

As can be seen from the above description, due to limitations in positioning accuracy and the influence of cumulated errors, the starting point and ending point of the automatic pool cleaning device's travel along the edge may not coincide, i.e., there is a positioning deviation between the starting point and ending point of the first path. In this example, a pool-edge map corresponding to the first path may be generated based on the point cloud data of the first path. Under the circumstance that there is a positioning deviation between the starting point and ending point of the first path, the pool-edge map generated based on the point cloud data of the first path may not be closed.

Furthermore, due to the accumulated IMU errors, while traveling along the edge, the automatic pool cleaning device experiences a relatively large deviation at the end portion of its travel path. Even if the ending point of the first path can be adjusted so that it coincides with the starting point, the end portion of the first path will still exhibit a relatively large deviation, causing the end portion of the pool edge in the closed pool-edge map generated based on the point cloud data of the first path to relatively greatly deviate from the actual edge.

The relatively large deviation from the actual edge at the end portion of the pool edge in the generated pool-edge map can be identified as one of the following features at the end portion: a map distortion, a continuous bending, or a bending with an angle greater than a predetermined angle. It may be understood that the criteria for determining whether the end portion of the pool edge in the generated pool-edge map relatively greatly deviates from the actual edge may be determined based on the experience of POSITA or may be summarized based on the contours of a limited number of pools. As long as the technical principles of this disclosure are implemented, the criteria for determining whether the end portion of the pool edge in the generated pool-edge map relatively greatly deviates from the actual edge are not specifically defined in this disclosure.

It may be appreciated that the positioning deviation between the starting point and the ending point of the first path may be identified as a positioning deviation at the end portion of the first path; or may be identified as positioning deviations throughout the first path, with the deviation at the end portion of the first path being greater than the deviation at the starting portion of the first path.

For example, while the automatic pool cleaning device is traveling along the edge, it may also be determined whether the starting point and the ending point of the first path can coincide, whether a closed pool-edge map can be generated, or whether the end portion of the pool edge in the generated closed pool-edge map is free of distortion or free of relatively large errors. If it is determined that the starting point and the ending point of the first path can coincide, a closed pool-edge map can be generated, or the end portion of the pool edge in the generated closed pool-edge map is free of distortion or free of relatively large errors, then the automatic pool cleaning device can be controlled to stop traveling after traveling along the edge for one circle or longer than one circle, and generate a closed pool-edge map. Otherwise, the method proceeds to Step S102.

For example, point cloud registration may be used to determine whether the pool-edge map corresponding to the point cloud data of the first path is closed or the starting point and the ending point of the first path coincide. Alternatively, after generating the corresponding pool-edge map based on the point cloud data of the first path, image processing methods may be used to determine whether the vector line segment is closed or whether the starting point and the ending point of the vector line segment coincide. It may be appreciated that the above-mentioned method of determining whether the vector line segment is closed or whether the starting point and the ending point of the vector line segment coincide is merely exemplary. The specific determination method may be configured based on the experience of POSITA, or may be configured based on the components and controllers actually installed in the automatic pool cleaning device. As long as the technical principles of this disclosure can be implemented, the specific determination method is not specifically limited here in this disclosure.

In Step S102, controlling the automatic pool cleaning device to continue traveling along the edge from the ending point of the first path and continue to collect pool edge information in real time until a first predetermined condition is met, thereby obtaining point cloud data for a second path, wherein the path along the edge starting from the ending point of the first path is the second path.

The term “point cloud data of the second path” refers to the point cloud data obtained during the second path by the automatic pool cleaning device. This point cloud data may be used to generate a portion of the pool-edge map corresponding to the second path.

For example, in FIG. 3, the path represented by the solid line is a portion of the first path, the path represented by the dash-dotted line is the ending portion of the first path, and the path represented by the dashed line is the second path. In FIG. 4, the solid line represents a portion of the pool-edge map generated based on the point cloud data of a portion of the first path; the dash-dotted line represents a portion of the pool-edge map generated based on the point cloud data of the ending portion of the first path; and the dashed line represents a portion of the pool-edge map generated based on the point cloud data of the second path.

As shown in FIG. 3 and FIG. 4, the intersection of the dotted line and the dash-dotted line represents the ending point of the first path and also the starting point of the second path. That is to say, the ending point of the first path is the starting point of the second path, i.e., the starting point of the second path connects with the ending point of the first path. It should be noted that in practice, the automatic pool cleaning device may not detect the ending point of the first path. Instead, after traveling one circle or nearly one circle along the first path, the automatic pool cleaning device may continue traveling along the predetermined direction, thereby smoothly transitioning to the second path.

For example, the first predetermined condition is one or more of the following conditions: specific feature data is detected; the automatic pool cleaning device has traveled beyond a predetermined distance; and the automatic pool cleaning device has traveled for a predetermined duration.

The automatic pool cleaning device continues to travel along the edge from the ending point of the first path and stops when the travel distance exceeds a predetermined distance or the travel duration exceeds a predetermined duration. This increases the length or travel duration of the second path, allowing the above-mentioned sensor or radar to collect more pool edge information, reducing the lack of pool edge information due to short travel distance or insufficient travel duration, and ensuring that the second path is of sufficient length to correspond to a portion of the first path.

It may be appreciated that the predetermined distance and predetermined duration may be configured based on the experience of POSITA, or through a limited number of experiments, as long as the technical principles of this disclosure are implemented. The actual data for the predetermined distance and predetermined duration are not specifically limited in this disclosure.

For example, the specific feature data may comprise one or more of the following types: corner feature data, curved surface feature data, and obstacle feature data.

While traveling along the second path, the automatic pool cleaning device continues to collect pool edge information in real time. When the automatic pool cleaning device collects specific feature data (e.g., one or more of the data types: corner feature data, surface feature data, and obstacle feature data), it may compare and match the collected specific feature data with the specific feature data collected during the first path, so that the second path corresponds to a portion of the first path. The corner feature data represents the feature of corners on the pool wall, such as the angle of the corners; the surface feature data represents the feature of the curved surfaces on the pool wall, such as the size and curvature of the surfaces; and the obstacle feature data represents the feature of obstacles on the pool wall, such as the size and orientation of the obstacles.

For example, the point cloud for the second path meets a second predetermined condition. For example, the second predetermined condition includes one or more of the following conditions: the density of the points in the point cloud for the second path is greater than a predetermined density threshold; the noise of the point cloud for the second path is less than a predetermined noise threshold; and the number of points in the point cloud for the second path is greater than a predetermined number threshold.

The density of the points in the point cloud for the second path is greater than a predetermined density threshold, and thus it is enabled the automatic pool cleaning device to improve its capability to capture details and the quality of reconstructing the pool-edge map, thereby ensuring that the generated pool-edge map contains more geometric information. The point cloud noise is lower than a predetermined noise threshold, which improves the reliability of the point cloud data, reduces measurement errors, and reduces the complexity of processing point cloud data. The number of points in the point cloud must be greater than a predetermined number threshold to improve the second path's coverage of the pool edge. This second predetermined condition may improve the accuracy, reliability, and usability of the map generated based on the second path.

It may be appreciated that the specific values of the predetermined density threshold, the predetermined noise threshold, and the predetermined number threshold can be determined based on the experience of POSITA, or through a limited number of experiments, as long as the technical principles of this disclosure are implemented.

In step S103, generating a closed pool-edge map based on the second path.

In this example, the automatic pool cleaning device is controlled to travel along the pool bottom or the water surface, and during such travel, the automatic pool cleaning device collects real-time pool edge information to obtain the point cloud data for the first path and the point cloud data for the second path. The closed pool-edge map is generated based on the second path. In other words, the point cloud data for the first path can be optimized based on the point cloud data for the second path, so that the optimized point cloud data can be used to generate a closed pool-edge map. This reduces the impact of insufficient positioning accuracy and cumulated errors, improves the path planning and navigation accuracy of the automatic pool cleaning device in subsequent operations, and enhances the cleaning performance of the automatic pool cleaning device.

For example, Step S103 may comprise: optimizing at least a portion of the first path based on the second path to generate the closed pool-edge map.

The optimizing at least a portion of the first path based on the second path refers to registering the point cloud data of the second path with the point cloud data of the corresponding first path to generate a point cloud transformation matrix or pose transformation relationship (i.e., the pose transformation relationship between the point cloud on the first path and the point cloud on the second path), and then applying the point cloud transformation matrix or pose transformation relationship to at least a portion of the first path, thereby optimizing the first path.

When optimizing the first path based on the second path, if there is positioning deviation at the end portion of the first path, the entire end portion of the first path or a part of the end portion may be optimized; if there is positioning deviation throughout the first path, the entire first path or a portion of the first path may be optimized. It may be appreciated that the length of the first path optimized based on the second path may be determined based on the length of the first path where the deviation exists or based on the processing power of the processor actually installed in the automatic pool cleaning system, as long as the technical principles of this disclosure are implemented.

For example, the said at least a portion of the first path may comprise the end portion of the first path. The optimizing at least a portion of the first path based on the second path may comprise: optimizing the end portion of the first path based on the second path, thereby enabling the first path to be closed.

As can be seen from the above description, due to positioning accuracy limitations and the influence of cumulated errors, the end portion of the first path exhibits a relatively large deviation. The said at least a portion of the first path comprises the end portion of the first path. This means that the end portion of the first path may be optimized based on the second path to reduce the deviation at the end portion of the first path, thereby allowing the optimized first path to be closed and generating a closed pool-edge map.

For example, Step S103 may comprise: selecting at least one first segment on the first path and at least one second segment on the second path; calculating a pose transformation relationship between the point cloud on the first segment and the point cloud on the second segment corresponding to the first segment; and generating the pool-edge map based on the pose transformation relationship.

The selected first segment and the selected second segment may correspond to the same portion of the pool edge. That is to say, the automatic pool cleaning device, when traveling on the first segment, may collect information within a first range of the pool edge in real time, and may also collect, when traveling on the second segment, information within the first range of the pool edge in real time. For example, the first segment may be segment A in FIG. 3, and the second segment may be segment B in FIG. 3.

The step of calculating the pose transformation relationship between the point cloud on the first segment and the point cloud on the second segment corresponding to the first segment may comprise: calculating the position and orientation of the point cloud on the first segment relative to the point cloud on the second segment, or calculating the position and orientation of the point cloud on the second segment relative to the point cloud on the first segment.

For example, the generating the pool-edge map based on the pose transformation relationship may comprise: at least updating the coordinates of the point cloud at the end portion of the first path based on the pose transformation relationship, so that the starting point of the first path coincides with the ending point of the first path.

Due to positioning accuracy limitations and the influence of accumulated errors, the point cloud data corresponding to the end portion of the first path has relatively large deviations. At least updating the coordinates of the point cloud at the end portion of the first path based on the pose transformation relationship may reduce the deviation of the point cloud data corresponding to the end portion of the first path, thereby allowing the optimized first path to be closed and generating a closed pool-edge map. For example, the dash-dotted line in FIG. 5 may represent the part of the pool edge map generated from the updated point cloud data of the end portion of the first path, while the solid line in FIG. 5 may represent the part of the pool edge map generated from the point cloud data of other portions of the first path that have not been updated.

Illustratively, the generating the pool-edge map based on the pose transformation relationship may comprise: generating the closed pool-edge map based on the pose transformation relationship with the smallest deviation.

It may be appreciated that, due to the influence of cumulated errors, as the automatic pool cleaning device continues to travel, the deviation of the automatic pool cleaning device's travel path will gradually increase, and the deviation of the second path will be greater than or equal to the deviation of the first path, and the deviation of the calculated pose transformation relationship will also gradually increase or remain constant. When updating the coordinates of the point cloud of the first path based on the pose transformation relationship, the pose transformation relationship with the smallest deviation may be applied to reduce the error of the updated first path and improve the accuracy of generating a closed pool-edge map. For example, on the second path, there is a segment or position where the deviation of the second path's pose change relationship relative to the first path is the smallest. At this segment or position, the deviation between the first path and second path is the smallest, indicating that the two paths are relatively close. The pose transformation relationship corresponding to this smallest deviation is used as the basis for pose transformation to perform the pose transformation operation on at least a portion of the first path to generate a closed pool-edge map.

This disclosure provides an automatic pool cleaning device, comprising a sensor and a processor. The sensor is capable of collecting the point cloud data, and the processor is configured to implement any of the above-mentioned map-building methods.

Because the automatic pool cleaning device provided herein is capable of implementing any of the above-mentioned map-building methods, it possesses all the beneficial effects of any of the above-mentioned map-building methods, the detailed description of which is omitted here.

For example, the sensor may comprise a distance sensor and/or a radar.

This disclosure provides a computer-readable non-volatile storage medium comprising computer program instructions, which, when executed by a processor, perform any of the above-mentioned map-building methods.

In the description of the present specification, the reference terms “an embodiment”, “some embodiments”, “examples”, “specific examples”, or “some examples”, etc., refer to that the specific features, structures, materials, or characteristics described in combination with this embodiment or example are included in at least one embodiment or example of the present disclosure. Further, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more of embodiments or examples. In addition, without contradicting each other, persons skilled in the art may combine and assemble the different embodiments or examples and the features of different embodiments or examples described in the present specification.

Moreover, the terms “first” and “second” are only used to described purposes and are not to be understood as indicating or implying relative importance or as implicitly indicating the quantity of technical features indicated. In view of this, a feature defined as “first” or “second” may explicitly or implicitly include at least one feature. In the description of the present disclosure, “multiple” means two or more, unless otherwise expressly and specifically defined.

In the present disclosure, without the opposite explanation, the used positional words such as “up and down” are in terms of the directions shown in the accompanying drawings, or the vertical, perpendicular or gravitational directions; similarly, for the convenience of understanding and description, “left and right” usually refers to the left and right shown in the accompanying drawings; “inside and outside” refers to the inside and outside relative to the outline of each part itself. However, the positional words above described are not used to limit the present disclosure.

The above displays and describes the basic principles, main features and beneficial effects of the present disclosure. It should be understood by persons skilled in the art that the present disclosure is not limited by the above-mentioned embodiments. The above-mentioned embodiments and the descriptions therein merely represent preferred examples of the present disclosure and are not intended to limit the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and all such changes and improvements fall within the protection scope of the present disclosure as claimed. The protection scope of the present disclosure is defined by the claims and their equivalents.