Weighing device fault diagnosis method, device, equipment, storage medium and system

Abstract

The application relates to a weighing device fault diagnosis method, device, equipment, storage medium and system. The method comprises the following steps: acquiring point cloud data and a weighing weight of a plurality of target objects in a target detection range, wherein the target detection range refers to a containing space of a containing device corresponding to the weighing device; determining a pre-estimated weight of the plurality of target objects according to the point cloud data of the plurality of target objects and a preset weight estimation model, wherein the preset weight estimation model represents a mapping relationship between the point cloud data and the pre-estimated weight; and determining a fault diagnosis result of the weighing device according to a deviation condition of the pre-estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects. The weighing accuracy of the weighing device can be accurately calibrated by using the method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of elevator fault diagnosis, and in particular to a weighing device fault diagnosis method, device, equipment, storage medium and system. BACKGROUND

[0002] With the development of elevator technology, the weighing device in the elevator as an important safety device of the elevator, the calibration of the weighing device is particularly important.

[0003] At present, in order to solve the problem of reduced weighing accuracy caused by aging of the spring-like weighing device in the elevator, the weighing value and torque current value when the elevator starts are usually corrected, or a two-dimensional picture taken by a two-dimensional camera in the elevator is used for manual evaluation of the actual weight in the elevator to correct the weighing value. However, the current calibration method of the weighing device still has the problem of inaccurate calibration. SUMMARY

[0004] Therefore, it is necessary to provide a weighing device fault diagnosis method, device, equipment, storage medium and system capable of improving the calibration accuracy of the weighing device to solve the above technical problems.

[0005] In a first aspect, the present application provides a weighing device fault diagnosis method, comprising:

[0006] obtaining point cloud data of a plurality of target objects in a target detection range and a weighing weight, the target detection range being a containing space of a containing device corresponding to the weighing device;

[0007] determining the estimated weight of the plurality of target objects according to the point cloud data of the plurality of target objects and a preset weight estimation model, wherein the preset weight estimation model is used to represent the mapping relationship between the point cloud data and the estimated weight;

[0008] determining a fault diagnosis result of the weighing device according to the deviation of the estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects.

[0009] In one embodiment, the point cloud data of the plurality of target objects in the target detection range and the weighing weight are obtained, comprising:

[0010] obtaining the point cloud data of the plurality of target objects in the target detection range collected by the time-of-flight ranging sensor.

[0011] In one embodiment, the fault diagnosis result of the weighing device is determined according to the deviation of the estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects, comprising:

[0012] calculating the mean square error between the estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects;

[0013] determine the fault diagnosis result of the weighing device according to the mean square error.

[0014] In one of the embodiments, the fault diagnosis result of the weighing device is determined according to the mean square error, comprising:

[0015] In the case that the mean square error is greater than the deviation threshold, it is determined that the weighing device is abnormal.

[0016] In one of the embodiments, the mean square error between the estimated weights of the target objects and the weighing weights of the target objects is calculated, comprising:

[0017] The mean square error is determined according to the estimated weights of the target objects and the weighing weights of the target objects, and the following expression:

[0018]

[0019] wherein, σ is the mean square error; i = 1, 2, …, n, n is the total number of the target objects, W i is the estimated weight of the i-th target object in the target objects; M i is the weighing weight of the i-th target object in the target objects.

[0020] In one of the embodiments, the weighing device fault diagnosis method further comprises:

[0021] In the case that the weighing device is determined to be abnormal, an alarm information is generated and sent to the terminal.

[0022] In a second aspect, the present application further provides a weighing device fault diagnosis device, comprising:

[0023] An information acquisition module is configured to acquire point cloud data of a plurality of target objects and weighing weights of the plurality of target objects within a target detection range;

[0024] A weight determination module is configured to determine estimated weights of the plurality of target objects according to the point cloud data of the plurality of target objects and a preset weight estimation model, wherein the preset weight estimation model is used to represent a mapping relationship between the point cloud data and the estimated weights.

[0025] A fault diagnosis module is configured to determine a fault diagnosis result of the weighing device according to a deviation condition of the estimated weights of the plurality of target objects and the weighing weights of the plurality of target objects.

[0026] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0027] Obtain point cloud data and a weighing weight of a plurality of target objects in a target detection range, the target detection range being a containing space of a containing device corresponding to the weighing device;

[0028] Determine a pre-estimated weight of the plurality of target objects according to the point cloud data and a pre-set weight estimation model, the pre-set weight estimation model being used to represent a mapping relationship between the point cloud data and the pre-estimated weight;

[0029] Determine a fault diagnosis result of the weighing device according to a deviation condition of the pre-estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects.

[0030] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0031] Obtain point cloud data and a weighing weight of a plurality of target objects in a target detection range, the target detection range being a containing space of a containing device corresponding to the weighing device;

[0032] Determine a pre-estimated weight of the plurality of target objects according to the point cloud data and a pre-set weight estimation model, the pre-set weight estimation model being used to represent a mapping relationship between the point cloud data and the pre-estimated weight;

[0033] Determine a fault diagnosis result of the weighing device according to a deviation condition of the pre-estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects.

[0034] In a fifth aspect, the present application further provides a weighing device fault diagnosis system, comprising:

[0035] A time-of-flight ranging sensor is installed on the containing device and is used to collect point cloud data of a plurality of target objects in a target detection range; the target detection range is a containing space of the containing device;

[0036] A weighing device is used to obtain a weighing weight of the plurality of target objects;

[0037] The computer device is in communication with the time-of-flight ranging sensor and the weighing device, respectively.

[0038] The application has at least the following beneficial effects: the weighing device fault diagnosis method, device, equipment, storage medium and system, point cloud data of the target object is obtained in the target detection range through a time-of-flight ranging sensor or the like, and the estimated weight of the target object can be determined according to the obtained point cloud data. Meanwhile, the weighing weight of the target object is obtained through the weighing device, and whether the weighing state of the weighing device is normal can be determined according to the deviation of the estimated weight and the weighing weight, thereby providing a more accurate reference standard for fault diagnosis and calibration of the weighing device, and further ensuring the weighing accuracy of the weighing device. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 An application environment schematic diagram of the weighing device fault diagnosis method in an embodiment is shown in the figure.

[0040] Figure 2 A flowchart of the weighing device fault diagnosis method in an embodiment is shown in the figure.

[0041] Figure 3 A flowchart of the weighing device fault diagnosis method in another embodiment is shown in the figure.

[0042] Figure 4 A flowchart of the weighing device fault diagnosis method in another embodiment is shown in the figure.

[0043] Figure 5 A flowchart of the weighing device fault diagnosis method in another embodiment is shown in the figure.

[0044] Figure 6 A structural block diagram of the weighing device fault diagnosis device in an embodiment is shown in the figure.

[0045] Figure 7 An internal structure diagram of the computer equipment in an embodiment is shown in the figure. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0047] The weighing device fault diagnosis method provided by the embodiments of the application can be applied to, for example, Figure 1The application environment shown. Among them, the TOF (Time of flight) ranging sensor 104 is installed in the elevator 102 car, the weighing device 106 is installed at the bottom or top of the elevator 102 car, and the terminal 110 communicates with the server 108 through the network. The data storage system can store the data required by the server 108 to process. The data storage system can be integrated on the server 108, or placed on the cloud or other network servers. The server 108 obtains the point cloud data of several target objects obtained by the TOF ranging sensor 104 in the elevator 102 car, and determines the estimated weight of the several target objects according to the point cloud data and the preset weight estimation model. At the same time, the weighing weight of the several target objects is obtained through the weighing device 106. Finally, according to the deviation of the estimated weight and the weighing weight obtained above, the fault diagnosis result of the weighing device 106 is determined. The server 108 can send the fault diagnosis result to the terminal 110, and the user can view the fault diagnosis result through the terminal 110, and timely maintenance work when the weighing device 106 fails.

[0048] Among them, the terminal 110 can be but not limited to various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 108 can be implemented by an independent server or a server cluster composed of multiple servers.

[0049] In one embodiment, as shown in Figure 2 , a fault diagnosis method of a weighing device is provided. The method is applied to the server 108 in Figure 1 for example, including the following steps:

[0050] Step S202, obtaining the point cloud data and weighing weight of several target objects in the target detection range, the target detection range refers to the accommodation space of the weighing device corresponding to the accommodation device.

[0051] The target objects can be living objects such as people or animals, or non-living objects such as goods. The point cloud data is a set of point data of the surface of the object obtained by a measuring instrument, and is used to represent the surface characteristics of the target object. The point cloud data carries information such as the orientation and distance of the target object. The measuring instrument can be a three-dimensional coordinate measuring machine, a three-dimensional laser scanner, a photographic scanner, or a TOF distance sensor. The point cloud data can be composed of sparse point clouds with a small number of points and a large distance between points, or dense point clouds with a large number of points and a small distance between points. The sparsity of the point cloud data can be configured according to the calculation accuracy and speed requirements. For example, in an embodiment, the target objects are people in the application scenario of the weighing device applied to the elevator. The point cloud data is a set of three-dimensional point data of each person, which can include three-dimensional point cloud data representing the head, torso, and limbs of the human body. The weighing weight is the weight of the target object measured by the weighing device such as a pressure sensor built-in the containing device. The weighing weight can be the sum of the weights of the target objects, or can include the weight of each target object. The weighing device can be an electronic scale or a weighbridge.

[0052] Specifically, the point cloud data can be obtained in real time by the measuring instrument when the target object enters the target detection range, or can be obtained by the data storage system. Similarly, the weighing weight can be obtained in real time by the weighing device installed in the containing device when the target object enters the target detection range, or can be obtained by the data storage system. For example, in an embodiment, the weighing device is applied to the elevator, and the target objects are people. When people enter the elevator car one after another, the measuring instrument installed in the elevator car emits laser light to the surface of the target object, and the reflected laser light carries information such as the orientation and distance of the laser points. The laser beam is scanned according to a certain trajectory, and the reflected laser point information is recorded, thereby obtaining the point cloud data of the target object. At the same time, the point cloud data can be stored in the data storage system as historical data for subsequent calculation, or can be processed in real time. Similarly, the weighing weight can also be obtained in the above manner, which will not be described here.

[0053] In step S204, the estimated weight of the target object is determined according to the point cloud data of the target object and the preset weight estimation model.

[0054] The preset weight estimation model is used to represent the mapping relationship between the point cloud data and the estimated weight. Specifically, according to a large amount of data experiments, for the same type of object, for example, for the human body, different volume sizes correspond to different weights, and the volume size of the target object is determined according to the point cloud data of the target object, so as to determine the weight of the target object. The estimated weight refers to a calculated value about the weight of the target object obtained by substituting the point cloud data into the above-mentioned estimated weight estimation model after obtaining the point cloud data of the target object. The estimated weight estimation model can be obtained by training in advance. The estimated weight is determined by the estimated weight estimation model, and the estimated weight is used as a reference standard to evaluate the accuracy of the weighing weight of the weighing device.

[0055] Specifically, in a certain time period, if only one target object enters the target detection range, the estimated weight at this time is the estimated weight of the single target object determined by the point cloud data and the estimated weight estimation model; in the same time, if a plurality of target objects enter the target detection range, the estimated weight at this time is the overall estimated weight of the plurality of target objects as a set determined by the point cloud data and the estimated weight estimation model. For example, in a specific embodiment, taking the application scenario of the weighing device applied in the elevator as an example, the estimated weight in a certain time period is considered to be the weight of a single target object determined by the point cloud data and the estimated weight estimation model. For example, the total estimated weight of a plurality of people entering the car in the time period from when the elevator car door is opened to when it is closed.

[0056] In step S206, the fault diagnosis result of the weighing device is determined according to the deviation of the estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects.

[0057] The deviation refers to a data dispersion condition between the estimated weight and the weighing weight, which can be the mean square error or variance between the two, or other measurement values that can be used to represent the error between the data.

[0058] Specifically, after obtaining the estimated weight of the plurality of target objects and the weighing weight of the plurality of target objects, in order to ensure the timeliness of fault diagnosis, the deviation can be calculated in real time; or, in order to avoid occupying too many resources when calculating the deviation, causing the server to overheat and affecting the safety of the elevator during operation, the above-mentioned obtained data can be first stored in the database, and the deviation can be calculated when the elevator is in idle time, such as at night, to reduce the performance requirements of the server and reduce the cost.

[0059] In the aforementioned fault diagnosis method for weighing devices, point cloud data of several target objects are acquired within the target detection range. Based on this point cloud data and a pre-defined weight estimation model established using the point cloud data, the estimated weight of the target objects is obtained. This estimated weight is used as the standard value for the weighing device, providing a high-precision reference standard for its weighing accuracy. This standard value is then compared with the weighing weight obtained by the weighing device, further enabling high-precision calibration of the weighing device when a weighing accuracy fault occurs.

[0060] In one embodiment, such as Figure 3 As shown, point cloud data and weight of several target objects within the target detection range are obtained, including:

[0061] Step S302: Acquire point cloud data of several target objects within the target detection range collected by the time-of-flight ranging sensor.

[0062] Specifically, time-of-flight ranging sensors emit infrared or laser light using a tiny transmitter. The generated light bounces off any object and returns to the sensor. Based on the time difference between the emission of the light and its return to the sensor, the sensor can measure the distance between the object and the sensor, thus acquiring point cloud data of the target object. The orientation of each point cloud element can also be determined. For example, in an elevator application using a weighing device, the time-of-flight ranging sensor can be placed inside the elevator car. Over a period of time, passengers enter the elevator car. The sensor emits light and calculates the return time of the light from the object's surface to determine the distance between the target object and the sensor, as well as the target object's position, thereby determining the target object's point cloud data. A coordinate system can be predefined; for example, with the location of the time-of-flight ranging sensor as the origin, an X-axis and a Y-axis can be defined through this origin. The plane defined by the X-axis and Y-axis is parallel to the horizontal plane, and a Z-axis is defined perpendicular to the horizontal plane and upwards through this origin. Based on this coordinate system, the coordinates of each point on the target object can be determined, and the aforementioned point cloud data can be used as coordinate data.

[0063] In the above embodiments, by using a long-time-of-flight ranging sensor to acquire point cloud data of the target object, compared with the existing technology of using a regular camera to acquire point cloud data of the target object on a plane from only a two-dimensional perspective, the actual volume of the target object can be determined more comprehensively and accurately from the three-dimensional structure, further ensuring the accuracy of the estimated weight.

[0064] In one embodiment, such as Figure 3 As shown, based on the estimated weight of several target objects and the deviation of the weighed weight of several target objects, the fault diagnosis results of the weighing device are determined, including:

[0065] Step S304: Calculate the standard deviation between the estimated weight of several target objects and the weighed weight of several target objects.

[0066] Step S306: Determine the fault diagnosis result of the weighing device based on the standard deviation.

[0067] Specifically, the mean squared error is calculated using the following formula:

[0068]

[0069] Where σ is the mean square error; i = 1, 2, ..., n, n is the total number of target objects, and W i M represents the estimated weight of the i-th target object among a set of target objects; i Let be the weighing weight of the i-th target object among a number of target objects.

[0070] In the above embodiments, when calculating the deviation between the estimated weight and the weighed weight, the mean square error between the estimated weight and the weighed weight can more accurately reflect the degree of dispersion between the two sets of data, that is, it can more intuitively present the weighing accuracy of the weighing device, thereby intuitively judging whether the weighing device is faulty.

[0071] In one embodiment, such as Figure 4 As shown, the fault diagnosis results of the weighing device are determined based on the root mean square error, including:

[0072] Step S402: If the root mean square error is greater than the deviation threshold, the weighing device is determined to be abnormal.

[0073] The deviation threshold refers to the value at which the standard deviation between the weighed weight and the estimated weight meets the weighing accuracy requirements. It is used to assess whether the weighing weight of the weighing device is accurate during actual use. Taking the weighing device applied to an elevator as an example, the deviation threshold can be 10%. If the standard deviation exceeds this deviation threshold, the weighing accuracy of the elevator's weighing device is determined to be abnormal, and calibration is required.

[0074] In the above embodiments, by setting a certain deviation threshold to measure the abnormal condition of the weighing device, the judgment of the abnormal weighing condition of the weighing device is made more accurate.

[0075] In one embodiment, such as Figure 5 As shown, the fault diagnosis method for the weighing device also includes:

[0076] Step S502: If the weighing device is determined to be malfunctioning, an alarm message is generated and sent to the terminal. The alarm message is a prompt indicating that the weighing device's weighing accuracy is abnormal.

[0077] In the above embodiments, when an abnormality is detected in the weighing device, an alarm message is promptly generated and sent to the terminal to remind maintenance personnel to perform repairs. This ensures that the weighing device can be maintained in a timely manner when a malfunction occurs, guaranteeing its normal operation. Specifically, when the weighing device is used in elevator applications, timely calibration of the elevator's weighing device ensures that the elevator does not experience excessive starting force due to weighing errors, thus avoiding starting shocks and improving the user experience for passengers.

[0078] To further illustrate the solution of this application, a specific example is provided below, using an elevator scenario as an example. In this case, the target detection range refers to the internal space of the elevator car; the target objects specifically refer to people; the preset weight estimation model is a weight estimation model obtained from human body point cloud data. The point cloud data can be acquired through one or more Time-of-Flight (TOF) sensors installed inside the elevator car, and the weighing weight can be obtained through a weighing device installed at the bottom or top of the elevator car.

[0079] Specifically, a Time-of-Flight (TOF) sensor emits a beam of light onto the surface of an object. The reflected light carries information such as orientation and distance. By scanning the beam along a specific trajectory, the sensor records the reflected light points as it scans. Simultaneously, based on the head position and a projection image, it acquires 3D point cloud data of the passenger, specifically including 3D point cloud data of the head, torso, and limbs. From this 3D point cloud data, the passenger's weight can be estimated using a pre-analyzed weight estimation model. This weight estimation model is calculated by determining the proportion of the volume occupied by head, torso, and limb features to the passenger's weight using a large sample of collected data. The specific point cloud data acquisition method is as follows:

[0080] After the first passenger enters the elevator, the Time-of-Flight (TOF) sensor acquires the passenger's 3D point cloud data. Based on a human body weight estimation model, the estimated weight W1 of the first passenger is calculated; the weighing device then obtains the passenger's weight M1. After the second passenger enters, the TOF sensor calculates and obtains the second passenger's estimated weight W2. At this point, the total weighing weight is M, and the second passenger's weight is M2 = M - M1, and so on, obtaining the weight W of the nth passenger using TOF. n The weight of the nth passenger obtained by the weighing device is M. n =M-M1-M2-…M n-1 The root mean square error between the total estimated weight W calculated by the TOF sensor and the total weighed weight M obtained by the weighing device for these n passengers is:

[0081]

[0082] Wherein, σ is the aforementioned mean square error, and the deviation threshold is 10%. When the aforementioned mean square error is greater than the deviation threshold, it is determined that the weighing accuracy of the elevator's weighing device is abnormal. At this time, an alarm message is generated and sent to the terminal to notify maintenance personnel to maintain the elevator, reweigh the elevator to correct the weighing deviation, reduce the elevator's starting impact, and improve operating comfort. When the aforementioned mean square error is less than or equal to the deviation threshold, it is determined that the elevator's weighing accuracy is normal. At this time, it is not necessary to generate and send an alarm message to the terminal.

[0083] By installing Time-of-Flight (TOF) sensors in the elevator car and using the point cloud data and weight estimation model obtained from the TOF sensors to derive the estimated weight of the human body, a new calibration standard is provided for the calibration of elevator weighing accuracy from a completely new perspective. This ensures the accuracy of elevator weighing, reduces elevator start-up shock, and improves operating comfort.

[0084] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0085] Based on the same inventive concept, this application also provides a weighing device fault diagnosis device for implementing the weighing device fault diagnosis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the weighing device fault diagnosis device provided below can be found in the limitations of the weighing device fault diagnosis method described above, and will not be repeated here.

[0086] In one embodiment, such as Figure 6 As shown, a weighing device fault diagnosis device is provided, including: an information acquisition module 602, a weight determination module 604, and a fault diagnosis module 606, wherein:

[0087] The information acquisition module 602 is used to acquire point cloud data of several target objects within the target detection range and the weighing weight of several target objects.

[0088] The weight determination module 604 is used to determine the estimated weight of several target objects based on the point cloud data of several target objects and a preset weight estimation model, wherein the preset weight estimation model is used to characterize the mapping relationship between the point cloud data and the estimated weight.

[0089] The fault diagnosis module 606 is used to determine the fault diagnosis result of the weighing device based on the estimated weight of several target objects and the deviation of the weighed weight of several target objects.

[0090] In one embodiment, the information acquisition module 602 includes:

[0091] The point cloud data acquisition unit is used to acquire point cloud data of several target objects collected by the time-of-flight ranging sensor within the target detection range.

[0092] In one embodiment, the fault diagnosis module 606 includes:

[0093] The mean square error determination unit is used to calculate the mean square error between the estimated weight of several target objects and the weighed weight of several target objects.

[0094] The diagnostic result determination unit is used to determine the fault diagnosis result of the weighing device based on the mean square error.

[0095] In one embodiment, the diagnostic result determination unit includes:

[0096] The weighing anomaly determination unit is used to determine that the weighing device is abnormal when the root mean square error is greater than the deviation threshold.

[0097] In one embodiment, the mean square error determination unit includes:

[0098] The root mean square error calculation unit is used to determine the root mean square error based on the estimated weight and weighed weight of several target objects, and the following expression:

[0099]

[0100] Where σ is the mean square error; i = 1, 2, ..., n, n is the total number of target objects, and W i M represents the estimated weight of the i-th target object among a set of target objects; i Let be the weighing weight of the i-th target object among a number of target objects.

[0101] In one embodiment, the above-mentioned weighing device fault diagnosis device further includes:

[0102] The alarm module is used to generate and send alarm information to the terminal when the weighing device is found to be abnormal.

[0103] Each module in the aforementioned weighing device fault diagnosis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0104] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database is used for estimating weighing and measuring weight. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for diagnosing faults in a weighing device.

[0105] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0106] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0107] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0108] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0109] In one embodiment, a weighing device fault diagnosis system is provided, comprising a time-of-flight ranging sensor, a weighing device, and a computer device, wherein:

[0110] A time-of-flight ranging sensor, mounted on a housing device, is used to collect point cloud data of several target objects within the target detection range; the target detection range is the housing space of the housing device.

[0111] A weighing device used to obtain the weight of several target objects;

[0112] The computer equipment communicates with the time-of-flight ranging sensor and the weighing device, respectively.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0115] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for diagnosing faults in a weighing device, characterized in that, Applied to elevators, the method includes: The point cloud data and weighing weight of several target objects within the target detection range are obtained. The target detection range refers to the accommodating space of the accommodating device corresponding to the weighing device. The target objects include people, and the point cloud data includes 3D point cloud data of human head features, torso features, and limb features. Based on the point cloud data of the target objects and a preset weight estimation model, the estimated weight of the target objects is determined, wherein the preset weight estimation model is used to characterize the mapping relationship between the point cloud data and the estimated weight. Based on the estimated weight of the target objects and the deviation of the weighed weight of the target objects, the fault diagnosis result of the weighing device is determined. The acquisition of point cloud data and weighing of several target objects within the target detection range includes: The system acquires point cloud data of several target objects within the target detection range collected by time-of-flight ranging sensors; the time-of-flight ranging sensors are multiple and installed inside the elevator.

2. The method according to claim 1, characterized in that, The step of determining the fault diagnosis result of the weighing device based on the deviation between the estimated weight of the plurality of target objects and the weighed weight of the plurality of target objects includes: Calculate the root mean square error between the estimated weight of the target objects and the weighed weight of the target objects; The fault diagnosis result of the weighing device is determined based on the mean square error.

3. The method according to claim 2, characterized in that, Determining the fault diagnosis result of the weighing device based on the mean square error includes: If the mean square error is greater than the deviation threshold, the weighing device is determined to be malfunctioning.

4. The method according to claim 2, characterized in that, The calculation of the root mean square error between the estimated weight of the plurality of target objects and the weighed weight of the plurality of target objects includes: The root mean square error is determined based on the estimated weight and the weighed weight of the target objects, and the following expression: Where σ is the mean square error; i = 1, 2, ..., n, n is the total number of the target objects, W i M is the estimated weight of the i-th target object among the plurality of target objects; i The weight is the measured weight of the i-th target object among the plurality of target objects.

5. The method according to claim 3, characterized in that, The method further includes: If the weighing device is determined to be malfunctioning, an alarm message is generated and sent to the terminal.

6. A fault determination device for a weighing apparatus, characterized in that, The device, used in elevators, includes: The information acquisition module is used to acquire point cloud data of several target objects within the target detection range and the weighing weight of the target objects; the target objects include a person, and the point cloud data includes 3D point cloud data of human head features, torso features, and limb features. A weight determination module is used to determine the estimated weight of the target objects based on the point cloud data of the target objects and a preset weight estimation model, wherein the preset weight estimation model is used to characterize the mapping relationship between the point cloud data and the estimated weight. The fault diagnosis module is used to determine the fault diagnosis result of the weighing device based on the estimated weight of the target objects and the deviation of the weighed weight of the target objects. The information acquisition module includes: The point cloud data acquisition unit is used to acquire point cloud data of several target objects within the target detection range collected by the time-of-flight ranging sensor; the time-of-flight ranging sensor is multiple and is installed inside the elevator.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A fault diagnosis system for a weighing device, characterized in that, Applied to elevators, including: A time-of-flight ranging sensor, mounted on a housing device, is used to collect point cloud data of several target objects within a target detection range; the target detection range is the housing space of the housing device; the target objects include a person, and the point cloud data includes 3D point cloud data of human head features, torso features, and limb features; the number of time-of-flight ranging sensors is multiple. A weighing device for obtaining the weighing weight of the plurality of target objects; The computer device of claim 7, wherein the computer device communicates with the time-of-flight ranging sensor and the weighing device respectively.

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