Intelligent inspection method for elevator

Abstract

The invention relates to the technical field of elevator safety, and discloses an elevator intelligent inspection method which comprises the following steps: acquiring operation data, state data and fault data of an elevator through a sensor to obtain an elevator data set, and analyzing the data in the elevator data set to generate service data; a maintenance task of the elevator is obtained, a function library of the inspection robot is formed for the maintenance task, a health state analysis result of the elevator is obtained according to the service data, and an inspection task of the elevator is determined; the inspection task of the elevator is issued to the inspection robot, task parameters and task conditions are integrated to obtain task data, the task data are analyzed, and the time required for completing each task is predicted; in combination with the elevator position and the action track of the inspection robot, the inspection route of each inspection robot is planned and decomposed into individual tasks to be issued to the inspection robot, and an inspection report is output through inspection of the inspection robot; the efficiency, performance and effectiveness of elevator inspection are improved, and on-demand inspection is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator safety, and in particular to an intelligent inspection method for elevators. Background Art

[0002] With the increasing number of high-rise buildings, elevators are being used more and more widely. The safe operation of elevators is of vital importance. Traditional elevator inspections mainly rely on regular manual inspections, which have problems such as low efficiency, high risk of missed inspections, and difficulty in real-time monitoring. Therefore, a more intelligent and efficient elevator inspection method is needed to ensure the safe operation of elevators. Summary of the invention

[0003] The purpose of the present invention is to solve the above problems and to design an intelligent elevator inspection method to solve the problems existing in the existing elevator inspection methods.

[0004] The present invention provides an elevator intelligent inspection method, which comprises the following steps:

[0005] Collecting operation data, status data and fault data of the elevator through sensors to obtain an elevator data set, and generating service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components;

[0006] Obtaining maintenance tasks of the elevator, forming a function library of the inspection robot for the maintenance tasks, obtaining health status analysis results of the elevator according to the service data, and determining the inspection tasks of the elevator;

[0007] Send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task;

[0008] Based on the elevator location and the movement trajectory of the inspection robot, the inspection routes of each inspection robot are planned, broken down into individual tasks and sent to the inspection robot, and the inspection robot outputs the inspection report after inspection.

[0009] Optionally, in a first implementation of the present invention, the step of collecting the operation data, status data and fault data of the elevator through the sensor to obtain an elevator data set, and generating service data by analyzing the data in the elevator data set includes:

[0010] The data in the elevator data set are smoothed by using a median filtering method to remove outliers and noise data, and the smoothed data are normalized to obtain a new elevator data set;

[0011] Z-Score standardization is used to process the data in the new elevator data set to obtain standardized data;

[0012] Calculate the covariance matrix of the standardized data, obtain the eigenvalues ​​and eigenvectors of the covariance matrix, sort the eigenvectors according to the size of the eigenvalues, and select the eigenvectors corresponding to the first n eigenvalues ​​to form the principal components;

[0013] Project the data in the new elevator data set onto the principal component to obtain new data after dimensionality reduction. According to the results of principal component analysis, determine the focus direction of maintenance items;

[0014] A clustering algorithm is used to classify the data in the new elevator data set after dimensionality reduction, identify different types of elevator operating states and failure modes, and generate automatic detection results of elevator components based on the clustering results;

[0015] Based on the results of principal component analysis and cluster analysis, the maintenance items of the elevator are determined, the importance and urgency of each maintenance item are evaluated, the priority is determined, the maintenance items are sorted according to the priority, and service data is generated.

[0016] Optionally, in a second implementation of the present invention, the clustering algorithm is used to classify the data in the new elevator data set after dimension reduction, identify different types of elevator operating states and failure modes, and generate automatic detection results of elevator components according to the clustering results, including:

[0017] The first cluster center is randomly selected from the data in the new elevator data set after dimensionality reduction, and the shortest distance sum between the first cluster center and the data in the new elevator data set is obtained through iterative calculation to establish a distance set;

[0018] Normalize the distance set, calculate the sum of all distances in the distance set, add Laplace noise, and obtain the shortest distance sum after adding noise;

[0019] Select seed cluster centers within the normalized range of data in the new elevator data set after dimensionality reduction, and cyclically calculate the shortest distance between data and cluster centers until the selection of cluster centers is completed;

[0020] Based on the obtained shortest distance set and the selection of cluster centers, the data corresponding to the shortest distance set is divided into cluster centers to obtain data categories;

[0021] Calculate the mean of the data category, add noise to the calculation result, and use the result as the new data center point. Repeat the iteration until the cluster center no longer changes or the preset number of iterations is reached, and the data clustering is completed;

[0022] According to the clustering results, different types of elevator operating states and failure modes are identified, including at least normal operation class, minor failure class, and serious failure class;

[0023] For each cluster, the elevator data in the cluster is analyzed to determine the operating status of each component and generate automatic detection results of the elevator components.

[0024] Optionally, in a third implementation of the present invention, the acquiring of the maintenance task of the elevator, forming a function library of the inspection robot for the maintenance task, obtaining the health status analysis result of the elevator according to the service data, and determining the inspection task of the elevator include:

[0025] Classifying elevator maintenance tasks and coding each maintenance task, wherein the task classification at least includes mechanical component inspection, electrical system inspection, and safety device testing;

[0026] According to the classification and coding of maintenance tasks, a function library is built for the inspection robot, wherein the function library contains robot operation instructions corresponding to each maintenance task;

[0027] An elevator health status assessment model is established, and the health status of the elevator is assessed through the elevator health status assessment model based on the service data to obtain an elevator health status analysis result, wherein the elevator health status assessment model is a BP neural network optimized based on a GA algorithm.

[0028] Optionally, in a fourth implementation of the present invention, the BP neural network optimized based on the GA algorithm includes the following steps:

[0029] Select the population size and chromosomes, determine the fitness function, and randomly generate an initial population;

[0030] The cumulative probability and selection probability of each string are calculated by the fitness ratio method, and the individuals with large fitness are returned to the population;

[0031] Determine whether to perform a crossover operation on the two selected individuals based on the crossover probability. If a crossover operation is performed, randomly select one or more positions on the chromosome as crossover points, and exchange parts of the chromosomes of the two individuals at the crossover points to generate two new individuals.

[0032] Determine whether to perform mutation operation on the selected individual according to the mutation probability. If mutation operation is performed, randomly select a position on the chromosome as the mutation point and randomly change the gene value at the mutation point.

[0033] The new population obtained after selection, crossover and mutation operations is used as the next generation population, and a new round of fitness evaluation, selection, crossover and mutation operations are carried out until the preset number of iterations is reached. After the iteration, the individual with the highest fitness is selected from the last generation population as the weights and thresholds of the optimized BP neural network.

[0034] Optionally, in a fifth implementation of the present invention, the step of sending the inspection task of the elevator to the inspection robot, integrating the task parameters and the task status to obtain the task data, analyzing the task data, and predicting the time required to complete each task includes:

[0035] Collect data on historical inspection tasks of the inspection robot, organize the collected data in chronological order to form a time series data set, and divide the time series data set into a training set and a validation set according to a certain ratio;

[0036] Perform a stationarity test on the sorted time series data and select a time series analysis model based on the stationarity test results;

[0037] Use the training set to train the time series analysis model, use the root mean square error to evaluate the model's fitting effect, and verify the trained model on the validation set to obtain a trained time series model;

[0038] The task data is input into the trained time series model, and the model predicts the time required to complete each task based on the patterns and trends of historical data.

[0039] Optionally, in a sixth implementation of the present invention, the inspection routes of the inspection robots are planned in combination with the elevator positions and the movement trajectories of the inspection robots, and the inspection routes are decomposed into individual tasks and sent to the inspection robots. After the inspection by the inspection robots, the inspection reports are output, including:

[0040] Based on the A* algorithm, the elevator position and the area passed by the inspection robot are constructed into a graph, where nodes represent position points, edges represent the connection between two position points, and an indicator weight is assigned to each edge. The shortest path from the current inspection robot position to the target elevator position is searched in the graph, where the indicators include at least distance, time, and energy consumption;

[0041] According to the planned inspection route, the entire inspection task is broken down into multiple subtasks. Each subtask corresponds to an inspection task of an elevator, including checking various indicators of the elevator.

[0042] The decomposed subtasks are sent to the corresponding inspection robots through wireless communication. After receiving the tasks, the inspection robots perform inspections according to the tasks. During the inspection process, the inspection robots collect various data of the elevator, record and store the collected data, and generate inspection reports.

[0043] A second aspect of the present invention provides an elevator intelligent inspection device, the elevator intelligent inspection device comprising a first data analysis module, a health status analysis module, a second data analysis module and an inspection route planning module, wherein:

[0044] A first data analysis module is used to collect operation data, state data and fault data of the elevator through sensors to obtain an elevator data set, and generate service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components;

[0045] A health status analysis module is used to obtain the maintenance tasks of the elevator, form a function library of the inspection robot for the maintenance tasks, obtain the health status analysis results of the elevator according to the service data, and determine the inspection tasks of the elevator;

[0046] The second data analysis module is used to send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task;

[0047] The inspection route planning module is used to plan the inspection routes of each inspection robot based on the elevator position and the movement trajectory of the inspection robot, break it down into individual tasks and send them to the inspection robot, and then output the inspection report after the inspection robot completes the inspection.

[0048] The third aspect of the present invention provides an elevator intelligent inspection device, which includes: a memory and at least one processor, wherein the memory stores instructions; at least one of the processors calls the instructions in the memory so that the elevator intelligent inspection device executes each step of the elevator intelligent inspection method described in any one of the above items.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the various steps of any of the above-mentioned intelligent elevator inspection methods are implemented.

[0050] In the technical solution provided by the present invention, the operation data, status data and fault data of the elevator are collected by sensors to obtain an elevator data set, and service data is generated by analyzing the data in the elevator data set; the maintenance tasks of the elevator are obtained, and a function library of the inspection robot is formed for the maintenance tasks, and the health status analysis results of the elevator are obtained according to the service data, and the inspection tasks of the elevator are determined; the inspection tasks of the elevator are sent to the inspection robot, and the task parameters and task conditions are integrated to obtain task data, and the task data are analyzed to predict the time required for completing each task; the inspection routes of each inspection robot are planned in combination with the elevator position and the movement trajectory of the inspection robot, and the inspection routes are decomposed. The individual tasks are sent to the inspection robot, and the inspection robot outputs the inspection report after inspection. The present invention combines big data to automatically generate inspection tasks and group task planning, thereby improving the efficiency of elevator inspection and reducing the workload of manual inspection. Through the application of big data analysis and intelligent devices, it can more accurately diagnose elevator faults and improve the accuracy of fault diagnosis. According to the health status of the elevator and the preset task requirements, personalized inspection tasks are formulated for each elevator to realize on-demand inspection, improve the pertinence and effectiveness of the inspection, and enhance the intelligence level of the robot. Through collaboration and training, the robot's inspection capability is continuously improved, data security and privacy protection are enhanced, and the safety and reliability of elevator inspection data are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0052] Figure 1 A schematic diagram of a first embodiment of an intelligent elevator inspection method provided by an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a second embodiment of an intelligent elevator inspection method provided by an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the structure of an intelligent elevator inspection device provided by an embodiment of the present invention;

[0055] Figure 4 A schematic diagram of the structure of an intelligent elevator inspection device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 A schematic diagram of a first embodiment of an intelligent elevator inspection method provided by an embodiment of the present invention, the method specifically comprises the following steps:

[0058] Step 101: collect operation data, state data and fault data of the elevator through sensors to obtain an elevator data set, and generate service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components;

[0059] Step 102: Obtain the maintenance tasks of the elevator, form a function library of the inspection robot for the maintenance tasks, obtain the health status analysis results of the elevator according to the service data, and determine the inspection tasks of the elevator;

[0060] Step 103: Send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task;

[0061] Step 104: Based on the elevator position and the movement trajectory of the inspection robot, the inspection routes of each inspection robot are planned, broken down into individual tasks and sent to the inspection robot, and the inspection robot outputs an inspection report after inspection.

[0062] In this embodiment, the elevator data set includes the elevator door switch status, control panel and button operation status, elevator start and stop status, number of operations, waiting time, elevator internal car lighting, elevator internal fan, elevator internal alarm device, elevator internal intercom system, elevator internal maintenance switch, elevator internal stop device, elevator internal fire switch, elevator external elevator door, elevator external pit environment, elevator external shaft lighting, elevator external machine room environment, overload status, and uncivilized elevator riding status.

[0063] In this embodiment, elevator maintenance tasks are classified and each maintenance task is encoded, wherein the task classification includes at least mechanical component inspection, electrical system detection, and safety device testing; based on the classification and coding of maintenance tasks, a function library is constructed for the inspection robot, wherein the function library contains robot operation instructions corresponding to each maintenance task; an elevator health status assessment model is established, and the health status of the elevator is assessed based on the service data through the elevator health status assessment model to obtain the health status analysis result of the elevator, wherein the elevator health status assessment model is a BP neural network optimized based on the GA algorithm.

[0064] In this embodiment, a population size and a chromosome are selected, a fitness function is determined, and an initial population is randomly generated; the cumulative probability and selection probability of each string are calculated by the fitness ratio method, and individuals with large fitness are put back into the population; whether to perform a crossover operation on the two selected individuals is determined according to the crossover probability, and if a crossover operation is performed, one or more positions on the chromosome are randomly selected as crossover points, and part of the chromosomes of the two individuals are exchanged at the crossover points to generate two new individuals; whether to perform a mutation operation on the selected individuals is determined according to the mutation probability, and if a mutation operation is performed, a position on the chromosome is randomly selected as a mutation point, and the gene value at the mutation point is randomly changed; the new population obtained after the selection, crossover and mutation operations is used as the next generation population, and a new round of fitness evaluation, selection, crossover and mutation operations are performed until a preset number of iterations is reached. After the iteration is completed, the individual with the highest fitness is selected from the last generation population as the weight and threshold of the optimized BP neural network.

[0065] In this embodiment, data of historical inspection tasks of the inspection robot are collected, the collected data are sorted in chronological order to form a time series data set, and the time series data set is divided into a training set and a validation set in a certain proportion; a stationarity test is performed on the sorted time series data, and a time series analysis model is selected based on the stationarity test result; the time series analysis model is trained using the training set, the fitting effect of the model is evaluated using the root mean square error, and the trained model is verified on the validation set to obtain a trained time series model; the task data is input into the trained time series model, and the model predicts the time required to complete each task based on the patterns and trends of the historical data.

[0066] In this embodiment, the elevator position and the area passed by the inspection robot are constructed into a graph based on the A* algorithm, where nodes represent position points and edges represent connections between two position points. An indicator weight is assigned to each edge, and the shortest path from the current inspection robot position to the target elevator position is searched in the graph, where the indicators include at least distance, time, and energy consumption. According to the planned inspection route, the entire inspection task is decomposed into multiple subtasks, and each subtask corresponds to an inspection task of an elevator, including checking various indicators of the elevator. The decomposed subtasks are sent to the corresponding inspection robot via wireless communication. After receiving the task, the inspection robot performs inspections according to the task. During the inspection process, the inspection robot collects various data of the elevator, records and stores the collected data, and generates an inspection report.

[0067] See also Figure 2 , a schematic diagram of a second embodiment of an elevator intelligent inspection method provided by an embodiment of the present invention, the method comprising:

[0068] Step 201: Use a median filtering method to smooth the data in the elevator data set, remove outliers and noise data, and normalize the smoothed data to obtain a new elevator data set;

[0069] Step 202: Perform principal component analysis on the data in the new elevator data set to extract the main features, reduce the data dimension, and determine the focus direction of the maintenance items based on the results of the principal component analysis;

[0070] Step 203: using a clustering algorithm to classify the data in the new elevator data set after dimensionality reduction, identifying different types of elevator operating states and failure modes, and generating automatic detection results of elevator components according to the clustering results;

[0071] Step 204: Based on the results of principal component analysis and cluster analysis, determine the maintenance items of the elevator, evaluate the importance and urgency of each maintenance item, determine the priority, sort the maintenance items according to the priority, and generate service data.

[0072] In this embodiment, Z-Score standardization is used to process the data in the new elevator data set to obtain standardized data; the covariance matrix of the standardized data is calculated to obtain the eigenvalues ​​and eigenvectors of the covariance matrix, the eigenvectors are sorted according to the size of the eigenvalues, and the eigenvectors corresponding to the first n eigenvalues ​​are selected to form the principal components; the data in the new elevator data set is projected onto the principal components to obtain new data after dimensionality reduction, and the focus direction of the maintenance items is determined according to the results of the principal component analysis.

[0073] In this embodiment, the first cluster center is randomly selected from the data in the new elevator data set after dimensionality reduction, and the shortest distance sum between the first cluster center and the data in the new elevator data set is obtained through iterative calculation to establish a distance set; the distance set is normalized, all distance sums in the distance set are calculated, and Laplace noise is added to obtain the shortest distance sum after the noise is added; a seed cluster center is selected within the normalized range of the data in the new elevator data set after dimensionality reduction, and the shortest distance between the data and the cluster center is calculated cyclically until the selection of the cluster center is completed; based on the obtained shortest distance set and the selection of the cluster center, the data corresponding to the shortest distance set is divided into the cluster center to obtain the data category; the mean of the data category is calculated, and noise is added to the calculation result, and the result is used as the new data center point, and it is repeated until the cluster center no longer changes or the preset number of iterations is reached, and the data clustering is completed; according to the clustering results, different types of elevator operation status and fault mode are identified, including at least normal operation class, minor fault class, and serious fault class; for each cluster, the elevator data in the class is analyzed, the operation status of each component is determined, and the automatic detection result of the elevator component is generated.

[0074] The present invention collects usage data and electrical status data of each component, analyzes the health status of each component, and combines relevant external equipment such as cameras to obtain the internal and external conditions of the elevator. After system analysis, the system issues tasks to inspection robots, inspects the elevator according to the preset task methods and trajectories, and outputs the final inspection report in the system; combined with the detection of big data and the user's tasks, automatic inspection tasks are issued.

[0075] Based on big data technology, the system automatically inspects elevator conditions: the system collects elevator operation data, status data, and fault data, performs in-depth analysis and calculations through the big data platform, generates automatic inspection results for maintenance items, elevator components, etc., and provides data services for robot inspections;

[0076] 1) Generate the operation status of the elevator equipment, including the opening and closing status of the elevator door, the operation status of the control panel and buttons, the start and stop status of the elevator, the number of operations, and the waiting time for the elevator;

[0077] 2) The internal safety of the elevator, including car lighting, fans, alarm devices, intercom systems, maintenance switches, stop devices, and fire switches;

[0078] 3) External safety of the elevator, including elevator doors, pit environment, shaft lighting, machine room environment, overloading, uncivilized elevator riding, etc.

[0079] Set corresponding functions for each maintenance task to form a function library of the inspection robot. For example, for the lighting anomaly detection task, move from point A to point B, enter the elevator, and use the camera to judge the light on the top of the car to see if the lighting is normal.

[0080] Combined with the system's analysis of the elevator's health status and preset task requirements, each elevator has different inspection tasks and its own task combination.

[0081] The intelligent edge devices of elevators can share computing power and computing power models, deploy part of the robot's intelligence at the edge, and continuously improve the robot's intelligence through collaboration and continuous training. At the same time, edge computing can also support real-time multi-machine collaboration, support real-time knowledge graph extraction, understanding and decision-making, improve the robot's intelligence, and enhance data security and privacy protection.

[0082] Group task planning:

[0083] For multiple elevators, in order to improve the inspection efficiency, multiple robots need to be arranged for inspection;

[0084] According to the results of the 1 big data inspection, we can know the status of each component of the elevator, such as the buttons of the car and each floor button. We only need to assign the floors with possible problems to the robot for inspection to achieve on-demand inspection.

[0085] After the robot completes each task module, it will save the parameters and completion status to the large model system for analysis to predict the time required to complete each task;

[0086] According to 1) and 2), we can get the task objectives that need to be inspected for each elevator and each floor lobby, calculate the time required for the overall task, and then plan the inspection routes of each robot based on the location of the elevator and the robot's movement trajectory, and then break it down into individual tasks and assign them to the robot;

[0087] Relevant collaborative tasks are also simultaneously sent to each intelligent edge device to cooperate with the robot to complete the inspection tasks.

[0088] Based on the analysis of the environment and historical data, the large model is guided to generate task planning that matches the environment. Then, through the multi-level decomposition task planning method, the task is decomposed according to the target level, task level and behavior level to enhance the task planning ability of the large model. Through continuous learning, the task planning can be further improved and optimized.

[0089] Inspection task function:

[0090] Is there any abnormal vibration or noise when the drive mainframe is running?

[0091] Check the trajectory information and electrical condition of the host to see if there are any abnormalities. At the same time, the robot carries a vibration sensor to collect and analyze whether there are any abnormal vibrations or sounds when the elevator is running.

[0092] Are the alarm device and intercom system in the car working properly?

[0093] The system analyzes the communication status and self-check status of the intercom equipment on that day to see if there are any abnormalities. The robot activates the alarm button and communicates with the monitoring center through recording to see if the alarm function is normal.

[0094] Is the car door anti-collision protection device function effective (safety touch panel, light curtain, photoelectric, etc.)

[0095] Regularly judge the action status of the button, and judge that the action times of the light curtain, safety touch panel, photoelectric, etc. are greater than the preset value, which is normal. The robot uses tentacles to block the door from closing, and records the situation of the car door anti-collision protection device by video.

[0096] Elevator leveling detection:

[0097] By collecting records of the elevator's own "leveling failure" and micro-motion abnormality thresholds, the robot is assigned tasks to floors with abnormal leveling data. The robot is equipped with an RGBD camera on a retractable and movable mechanical arm. When the elevator is leveling, it takes a close-up depth map to analyze whether there is a height difference between the car floor and the door sill. Taking the car floor as the reference, a point is taken to fit the plane, and then a point is taken on the door sill. The maximum vertical distance from the point taken on the fitted plane to the car floor is used as the leveling accuracy.

[0098] Through the implementation of the above solution, combined with big data to automatically generate inspection tasks and group task planning, the efficiency of elevator inspections has been improved, and the workload of manual inspections has been reduced. Through big data analysis and the application of intelligent devices, elevator faults can be diagnosed more accurately, improving the accuracy of fault diagnosis. According to the health status of the elevator and the preset task requirements, personalized inspection tasks are formulated for each elevator to achieve on-demand inspections, improve the pertinence and effectiveness of inspections, and enhance the intelligence level of robots. Through collaboration and training, the inspection capabilities of robots are continuously improved, data security and privacy protection are enhanced, and the safety and reliability of elevator inspection data are ensured.

[0099] The above describes the elevator intelligent inspection method in the embodiment of the present invention. The following describes the elevator intelligent inspection device in the embodiment of the present invention. Figure 3 In one embodiment of the present invention, an intelligent inspection device for elevators includes:

[0100] The first data analysis module 301 is used to collect the operation data, state data and fault data of the elevator through sensors to obtain an elevator data set, and generate service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components;

[0101] The health status analysis module 302 is used to obtain the maintenance tasks of the elevator, form a function library of the inspection robot for the maintenance tasks, obtain the health status analysis results of the elevator according to the service data, and determine the inspection tasks of the elevator;

[0102] The second data analysis module 303 is used to send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task;

[0103] The inspection route planning module 304 is used to plan the inspection routes of each inspection robot in combination with the elevator position and the movement trajectory of the inspection robot, decompose it into individual tasks and send them to the inspection robot, and output the inspection report after the inspection robot inspects.

[0104] The following is a detailed description of the intelligent elevator inspection device in the embodiment of the present invention from the perspective of hardware processing.

[0105] Figure 4 6 is a structural diagram of an elevator intelligent inspection device provided by an embodiment of the present invention. The elevator intelligent inspection device 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be short-term storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the elevator intelligent inspection device 600. Furthermore, the processor 610 can be configured to communicate with the storage medium 630, and execute a series of instruction operations in the storage medium 630 on the elevator intelligent inspection device 600 to implement the steps of the elevator intelligent inspection method provided by the above-mentioned method embodiments.

[0106] The elevator intelligent inspection device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input and output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 4 The structure of the elevator intelligent inspection device shown does not constitute a limitation on the elevator intelligent inspection device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0107] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the elevator intelligent inspection method.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0110] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent elevator inspection method, characterized in that: The elevator intelligent inspection method comprises the following steps: Collecting operation data, status data and fault data of the elevator through sensors to obtain an elevator data set, and generating service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components; Obtaining maintenance tasks of the elevator, forming a function library of the inspection robot for the maintenance tasks, obtaining health status analysis results of the elevator according to the service data, and determining the inspection tasks of the elevator; Send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task; Based on the elevator location and the movement trajectory of the inspection robot, the inspection routes of each inspection robot are planned, broken down into individual tasks and sent to the inspection robot, and the inspection robot outputs the inspection report after inspection.

2. An intelligent elevator inspection method according to claim 1, characterized in that: The method collects the operation data, status data and fault data of the elevator through the sensor to obtain the elevator data set, and generates the service data by analyzing the data in the elevator data set, including: The data in the elevator data set are smoothed by using a median filtering method to remove outliers and noise data, and the smoothed data are normalized to obtain a new elevator data set; Z-Score standardization is used to process the data in the new elevator data set to obtain standardized data; Calculate the covariance matrix of the standardized data, obtain the eigenvalues ​​and eigenvectors of the covariance matrix, sort the eigenvectors according to the size of the eigenvalues, and select the eigenvectors corresponding to the first n eigenvalues ​​to form the principal components; Project the data in the new elevator data set onto the principal component to obtain new data after dimensionality reduction. According to the results of principal component analysis, determine the focus direction of maintenance items; A clustering algorithm is used to classify the data in the new elevator data set after dimensionality reduction, identify different types of elevator operating states and failure modes, and generate automatic detection results of elevator components based on the clustering results; Based on the results of principal component analysis and cluster analysis, the maintenance items of the elevator are determined, the importance and urgency of each maintenance item are evaluated, the priority is determined, the maintenance items are sorted according to the priority, and service data is generated.

3. An intelligent elevator inspection method according to claim 2, characterized in that: The clustering algorithm is used to classify the data in the new elevator data set after dimensionality reduction, identify different types of elevator operation states and failure modes, and generate automatic detection results of elevator components according to the clustering results, including: The first cluster center is randomly selected from the data in the new elevator data set after dimensionality reduction, and the shortest distance sum between the first cluster center and the data in the new elevator data set is obtained through iterative calculation to establish a distance set; Normalize the distance set, calculate the sum of all distances in the distance set, add Laplace noise, and obtain the shortest distance sum after adding noise; Select seed cluster centers within the normalized range of data in the new elevator data set after dimensionality reduction, and cyclically calculate the shortest distance between data and cluster centers until the selection of cluster centers is completed; Based on the obtained shortest distance set and the selection of cluster centers, the data corresponding to the shortest distance set is divided into cluster centers to obtain data categories; Calculate the mean of the data category, add noise to the calculation result, and use the result as the new data center point. Repeat the iteration until the cluster center no longer changes or the preset number of iterations is reached, and the data clustering is completed; According to the clustering results, different types of elevator operating states and failure modes are identified, including at least normal operation class, minor failure class, and serious failure class; For each cluster, the elevator data in the cluster is analyzed to determine the operating status of each component and generate automatic detection results of the elevator components.

4. An intelligent elevator inspection method according to claim 1, characterized in that: The obtaining of the maintenance task of the elevator, forming a function library of the inspection robot for the maintenance task, obtaining the health status analysis result of the elevator according to the service data, and determining the inspection task of the elevator, includes: Classifying elevator maintenance tasks and coding each maintenance task, wherein the task classification at least includes mechanical component inspection, electrical system inspection, and safety device testing; According to the classification and coding of maintenance tasks, a function library is built for the inspection robot, wherein the function library contains robot operation instructions corresponding to each maintenance task; An elevator health status assessment model is established, and the health status of the elevator is assessed through the elevator health status assessment model based on the service data to obtain an elevator health status analysis result, wherein the elevator health status assessment model is a BP neural network optimized based on a GA algorithm.

5. An intelligent elevator inspection method according to claim 4, characterized in that: The BP neural network optimized based on GA algorithm comprises the following steps: Select the population size and chromosomes, determine the fitness function, and randomly generate an initial population; The cumulative probability and selection probability of each string are calculated by the fitness ratio method, and the individuals with large fitness are returned to the population; Determine whether to perform a crossover operation on the two selected individuals based on the crossover probability. If a crossover operation is performed, randomly select one or more positions on the chromosome as crossover points, and exchange parts of the chromosomes of the two individuals at the crossover points to generate two new individuals. Determine whether to perform mutation operation on the selected individual according to the mutation probability. If mutation operation is performed, randomly select a position on the chromosome as the mutation point and randomly change the gene value at the mutation point. The new population obtained after selection, crossover and mutation operations is used as the next generation population, and a new round of fitness evaluation, selection, crossover and mutation operations are carried out until the preset number of iterations is reached. After the iteration, the individual with the highest fitness is selected from the last generation population as the weights and thresholds of the optimized BP neural network.

6. An intelligent elevator inspection method according to claim 1, characterized in that: The step of sending the inspection task of the elevator to the inspection robot, integrating the task parameters and the task situation to obtain the task data, analyzing the task data, and predicting the time required to complete each task includes: Collect data on historical inspection tasks of the inspection robot, organize the collected data in chronological order to form a time series data set, and divide the time series data set into a training set and a validation set according to a certain ratio; Perform a stationarity test on the sorted time series data and select a time series analysis model based on the stationarity test results; Use the training set to train the time series analysis model, use the root mean square error to evaluate the model's fitting effect, and verify the trained model on the validation set to obtain a trained time series model; The task data is input into the trained time series model, and the model predicts the time required to complete each task based on the patterns and trends of historical data.

7. An intelligent elevator inspection method according to claim 1, characterized in that: The inspection routes of each inspection robot are planned based on the elevator position and the movement trajectory of the inspection robot, and then decomposed into individual tasks and sent to the inspection robot. After the inspection by the inspection robot, an inspection report is output, including: Based on the A* algorithm, the elevator position and the area passed by the inspection robot are constructed into a graph, where nodes represent position points, edges represent the connection between two position points, and an indicator weight is assigned to each edge. The shortest path from the current inspection robot position to the target elevator position is searched in the graph, where the indicators include at least distance, time, and energy consumption; According to the planned inspection route, the entire inspection task is broken down into multiple subtasks. Each subtask corresponds to an inspection task of an elevator, including checking various indicators of the elevator. The decomposed subtasks are sent to the corresponding inspection robots through wireless communication. After receiving the tasks, the inspection robots perform inspections according to the tasks. During the inspection process, the inspection robots collect various data of the elevator, record and store the collected data, and generate inspection reports.

8. An intelligent elevator inspection device, characterized in that: The elevator intelligent inspection device includes a first data analysis module, a health status analysis module, a second data analysis module and an inspection route planning module, wherein: A first data analysis module is used to collect operation data, state data and fault data of the elevator through sensors to obtain an elevator data set, and generate service data by analyzing the data in the elevator data set, wherein the service data at least includes maintenance items and automatic detection results of elevator components; A health status analysis module is used to obtain the maintenance tasks of the elevator, form a function library of the inspection robot for the maintenance tasks, obtain the health status analysis results of the elevator according to the service data, and determine the inspection tasks of the elevator; The second data analysis module is used to send the inspection task of the elevator to the inspection robot, integrate the task parameters and task status, obtain the task data, analyze the task data, and predict the time required to complete each task; The inspection route planning module is used to plan the inspection routes of each inspection robot based on the elevator position and the movement trajectory of the inspection robot, break it down into individual tasks and send them to the inspection robot, and then output the inspection report after the inspection robot completes the inspection.

9. An intelligent elevator inspection device, characterized in that: The elevator intelligent inspection device comprises: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory so that the elevator intelligent inspection device executes each step of the elevator intelligent inspection method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the intelligent elevator inspection method as described in any one of claims 1 to 7 are implemented.

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