Robot diagnosis method, robot diagnosis system, electronic equipment and storage medium

Abstract

The invention provides a robot diagnosis method, a robot diagnosis system, electronic equipment and a storage medium, and relates to the technical field of robots. The method comprises the steps that a server side responds to an operation instruction of a user on an operation interface, generates a monitoring request and sends the monitoring request to a client side; the client side inquires the running state of the robot according to the monitoring request, responds to robot running, executes a monitoring task according to task execution information and generates a high-frequency data packet, and the high-frequency data packet comprises state parameters of at least one component of the robot; the client sends monitoring data to the server according to the high-frequency data packet; and the server obtains a diagnosis result of the robot according to the monitoring data. Through interaction between the server and the client, real-time monitoring and fault detection are performed on the operation state of the robot, and the real-time health state of the robot is mastered, so that the robot is effectively identified and replaced in time before having a fault, and normal operation of a production line is ensured.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a robotic diagnosis method, a robotic diagnosis system, an electronic device, and a storage medium. Background Art

[0002] With the rapid development of intelligent manufacturing and industrial automation, robots have become key equipment in modern production lines. Their efficient and stable operation is crucial to ensuring production efficiency and product quality. The robot Internet of Things platform (Over-the-Air, OTA) can realize functions such as remote monitoring of robots, data collection and analysis, and software upgrades by integrating Internet of Things technology, greatly improving the manageability and flexibility of the robot system.

[0003] However, since most robot IoT platform OTAs rely on post-failure analysis, that is, the robot can only be discovered when it fails or its performance has significantly degraded, it cannot be effectively identified and replaced in advance before the robot reaches the end of its service life or is about to fail, which greatly affects the normal operation of the production line. Summary of the invention

[0004] The purpose of the present application is to provide a robot diagnostic method, a robot diagnostic system, an electronic device and a storage medium to address the deficiencies in the above-mentioned prior art, so as to solve the problem in the prior art that the robot cannot be effectively identified and replaced in advance before it reaches the end of its service life or is about to fail, thereby greatly affecting the normal operation of the production line.

[0005] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, an embodiment of the present application provides a robot diagnosis method, which is applied to a robot diagnosis system. The system includes a server and a client. The server provides an operation interface. The method includes:

[0007] The server generates a monitoring request in response to an operation instruction of the user on the operation interface, and sends the monitoring request to the client, wherein the monitoring request includes task execution information corresponding to a monitoring task for performing a health check on the robot;

[0008] The client queries the operation status of the robot according to the monitoring request, and in response to the operation of the robot, performs the monitoring task according to the task execution information, and generates a high-frequency data packet, wherein the high-frequency data packet includes a state parameter of at least one component of the robot;

[0009] The client sends monitoring data to the server according to the high-frequency data packet;

[0010] The server obtains a diagnosis result of the robot according to the monitoring data.

[0011] As a possible implementation manner, the client sends monitoring data to the server according to the high-frequency data packet, including:

[0012] The client sends the high-frequency data packet to the server as the monitoring data;

[0013] The server obtains the diagnosis result of the robot according to the monitoring data, including:

[0014] The server performs a health analysis on the robot based on the monitoring data to obtain a diagnosis result of the robot.

[0015] As a possible implementation manner, the client sends monitoring data to the server according to the high-frequency data packet, including:

[0016] The client performs health analysis on the robot based on the state parameter of at least one of the components in the high-frequency data packet to obtain a diagnosis result of the robot;

[0017] The client sends the diagnosis result of the robot to the server as the monitoring data.

[0018] As a possible implementation manner, performing health analysis on the robot based on the monitoring data to obtain a diagnosis result of the robot includes:

[0019] Parsing the high-frequency data packet, extracting the state parameters of each of the components in the high-frequency data packet, wherein the state parameters are used to reflect the real-time operating state of the components;

[0020] Determine the status score of each component according to the status parameters of each component and the preset scoring strategy, and determine the comprehensive status score of the robot according to the status score of each component;

[0021] The comprehensive status score of the robot is used as the overall health of the robot to obtain a diagnosis result of the robot.

[0022] As a possible implementation manner, before performing health analysis on the robot based on the high-frequency data packet to obtain a diagnosis result of the robot, the method further includes:

[0023] The server sends a log collection request to the client, wherein the log collection request includes a control cabinet code of a control cabinet associated with the robot;

[0024] The client responds to the log collection request, collects error log data in the control cabinet corresponding to the control cabinet code, and sends the error log data to the server;

[0025] The performing health analysis on the robot based on the high-frequency data packet to obtain a diagnosis result of the robot includes:

[0026] A health analysis is performed on the robot based on the high-frequency data packet and the error log data to obtain a diagnosis result of the robot.

[0027] As a possible implementation, the task execution information includes: task name, robot number, control cabinet code, task running days, number of collections per day, and maximum single collection duration;

[0028] The querying of the running state of the robot, responding to the running of the robot, executing the monitoring task according to the task execution information, and generating a high-frequency data packet include:

[0029] Initialize the monitoring task according to the task name, the robot number and the control cabinet code;

[0030] Query the current running status of the robot in real time to determine whether the robot is currently in a non-offline state;

[0031] If so, in response to the robot running, the monitoring task is executed according to the task execution information to generate a high-frequency data packet; otherwise, a prompt message is sent to the server, and a preset time is waited until the robot runs to execute the monitoring task. The prompt message is used to remind the user that the robot is currently offline.

[0032] As a possible implementation, the responding to the robot running, executing the monitoring task according to the task execution information, and generating a high-frequency data packet include:

[0033] The monitoring task is performed according to the number of days the task is run, the number of collections per day and the maximum duration of a single collection to collect state parameters of at least one of the components, and the high-frequency data packet is generated based on the state parameters of at least one of the components, wherein the components include at least one robot joint and / or at least one key component in a control cabinet associated with the robot identified by the control cabinet code.

[0034] As a possible implementation manner, the sending of monitoring data to the server includes:

[0035] Compressing and formatting the monitoring data to obtain processed monitoring data;

[0036] Sending a data transmission request to the server based on the hypertext transfer protocol, so that the server responds to the data transmission request, parsing the processed monitoring data included in the data transmission request, and obtaining the high-frequency data packet;

[0037] After the monitoring data is sent to the server, the method further includes:

[0038] Sending upload information of the monitoring data to the server, the upload information including an upload result indicating whether the monitoring data is successfully uploaded and a reason for upload failure;

[0039] The uploaded information is displayed on the operation interface.

[0040] In a second aspect, an embodiment of the present application provides a robot diagnosis system, the system comprising a server and a client, the server providing an operation interface;

[0041] The server is used to execute the method steps performed by the server in the robot diagnosis method described in any one of the first aspects above;

[0042] The client is used to execute the method steps executed by the client in the robot diagnosis method described in any one of the first aspects above.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the robot diagnostic method as described in any one of the first aspects above.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the robot diagnostic method as described in any one of the first aspects above are executed.

[0045] According to the robot diagnosis method, robot diagnosis system, electronic device and storage medium of the embodiment of the present application, the user triggers the monitoring instruction on the operation interface, and the server generates a monitoring request, which includes the task execution information corresponding to the monitoring task for the health check of the robot, and sends the monitoring request to the client. According to the monitoring request, the client queries the running status of the robot, and executes the monitoring task according to the task execution information, generates a high-frequency data packet, and the high-frequency data packet includes the state parameters of at least one component of the robot. The client determines the monitoring data according to the high-frequency data packet and sends the monitoring data to the server. The monitoring data can be a high-frequency data packet, or it can be a diagnosis result of the robot obtained by health analysis based on the high-frequency data packet. Accordingly, the server can obtain the diagnosis result of the robot based on the health analysis of the high-frequency data packet, or directly obtain the diagnosis result of the robot, so as to timely grasp the real-time health status of the robot. According to the embodiment of the present application, based on the interaction between the server and the client, the running status of the robot is monitored and fault detected in real time, and the real-time health status of the robot is grasped, so that the robot can be effectively identified and replaced in time before the robot fails, so as to avoid the production line shutdown caused by the robot failure, thereby ensuring the normal operation of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A schematic diagram of the architecture of a robot diagnostic system provided by an embodiment of the present application is shown;

[0048] Figure 2 A schematic diagram of a process flow of a robot diagnosis method provided by an embodiment of the present application is shown;

[0049] Figure 3 An interactive flow chart of a robot diagnosis and prediction method provided by an embodiment of the present application is shown;

[0050] Figure 4 A schematic diagram of a process flow of a method for determining a diagnosis result of a robot provided in an embodiment of the present application is shown;

[0051] Figure 5 An interactive flow chart of another robot diagnosis and prediction method provided by an embodiment of the present application is shown;

[0052] Figure 6A schematic diagram of a process flow of a high-frequency data packet generation method provided in an embodiment of the present application is shown;

[0053] Figure 7 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0054] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0055] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0056] In order to enable those skilled in the art to use the content of this application, the following implementation is given in combination with the specific application scenario "dynamic remote diagnosis and predictive maintenance based on the robot Internet of Things platform". For those skilled in the art, the general principles defined here can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application is mainly described around the robot diagnosis method, it should be understood that this is only an exemplary embodiment.

[0057] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0058] Figure 1 FIG. 1 is a schematic diagram showing the architecture of a robot diagnostic system provided by an embodiment of the present application. Figure 1As shown, the robot diagnostic system includes a client and a server. The client is usually installed inside the control cabinet of the robot. The server is usually a server in the cloud, and a robot Internet of Things platform OTA is deployed. In an embodiment of the present application, the server provides an operation interface for the user, and the server is composed of a collection service, a data processing service and a prediction service, wherein the collection service is responsible for remote communication with the client, collecting and parsing the data reported by the client, the data processing service is responsible for receiving the data sent by the user in the operation interface to generate monitoring tasks, and parsing the data reported by the client, and the prediction service is responsible for parsing the high-frequency data packets uploaded by the client, and performing health analysis on the robot based on the high-frequency data packets to obtain the robot's diagnostic results, which are, for example, the health of the robot. The client includes two services, the client main program and the collection middleware. The client main program is responsible for receiving the monitoring tasks of the server, and sending them to the collection middleware, as well as collecting the high-frequency data packets generated by the collection middleware, and forwarding the status of the collection middleware and reporting it. The collection middleware is responsible for executing high-frequency data collection requests according to the monitoring tasks and generating high-frequency data packets.

[0059] Optionally, based on the robot diagnosis system provided in the embodiment of the present application, the client can periodically collect high-frequency data of the running robot according to the monitoring tasks issued by the server, generate high-frequency data packets, and report the high-frequency data packets to the remote cloud server. The prediction service in the server performs a health analysis of the robot based on the high-frequency data packets to obtain the diagnosis results of the robot, thereby realizing remote diagnosis of the robot.

[0060] Optionally, after the client generates a high-frequency data packet, it is not necessary to send the high-frequency data packet to the remote server for remote diagnosis. The client can directly collect high-frequency data and directly perform health analysis to obtain the robot's diagnostic results, and only report the diagnostic results to the cloud server. In this way, the amount of data uploaded will be greatly reduced, thereby reducing the pressure on the server. It should be noted that when the client performs health prediction, the server does not include a prediction service, which means that there are two situations in the composition structure of the server in the embodiment of the present application, one is including collection services, data processing services and prediction services, and the other is only including collection services and data processing services.

[0061] Based on this, remote diagnosis and predictive maintenance of the robot are realized through the interaction between the server and the client. The client can collect high-frequency data of the running robot according to the monitoring tasks issued by the server, analyze and predict on the client, and report the diagnosis results to the server in the cloud. The client can also report high-frequency data packets to the server, and the server will perform health analysis based on the high-frequency data packets to obtain the diagnosis results of the robot. Through the robot diagnosis system of the embodiment of the present application, users can easily issue monitoring tasks based on the robot Internet of Things platform OTA to remotely diagnose and analyze and predict the health status of the robot, so as to timely understand and grasp the real-time health status of the robot.

[0062] The following is combined with the above Figure 1 The contents described in the robot diagnostic system shown are used to explain in detail the robot diagnostic method provided in the embodiment of the present application.

[0063] Figure 2 A flow chart of a robot diagnosis method provided in an embodiment of the present application is shown.

[0064] Reference Figure 2 As shown, the method specifically comprises the following steps:

[0065] S201. The server generates a monitoring request in response to an operation instruction of the user on the operation interface, and sends the monitoring request to the client. The monitoring request includes task execution information corresponding to a monitoring task for performing a health check on the robot.

[0066] Optionally, the operation interface can be a web page or an application, etc., which allows the user to input instructions or choose to perform certain operations, such as providing a "start monitoring" button or a selection button for selecting a robot for health check, or configuring some configuration items of parameters required for health check, etc. When the user issues an operation instruction through the operation interface, the server generates a monitoring request according to the content of the operation instruction. The purpose of the monitoring request is to perform a health check on the robot, and the monitoring request includes task execution information corresponding to the monitoring task for performing a health check on the robot.

[0067] Optionally, the task execution information is used to instruct the client on how to execute the monitoring task. The task execution information includes: task name, robot number, control cabinet code, task running days, number of collections per day, and maximum single collection duration. Among them, the task name refers to the name used to identify the monitoring task, such as "daily health check", "fault diagnosis and maintenance", etc. The robot number is used to uniquely identify the robot to be monitored. Each robot has a unique number to ensure that the monitoring task can be accurately sent to the correct robot. The control cabinet code refers to the code used to identify the robot control cabinet, which helps to accurately identify and locate the robot. The control cabinet is part of the robot control system and usually contains key components such as controllers and drivers. The number of days the task runs refers to the number of days the monitoring task will continue to run. The number of collections per day refers to the number of times data needs to be collected per day. For example, if the number of collections per day is set to 10 times, the client will collect data 10 times a day. The maximum single collection duration refers to the maximum duration of each data collection, which is used to control the time of each collection to prevent excessive occupation of resources.

[0068] S202. The client queries the operation status of the robot according to the monitoring request, responds to the robot operation, performs the monitoring task according to the task execution information, and generates a high-frequency data packet.

[0069] Optionally, based on the task execution information carried in the monitoring request, the client can determine the robot whose running status needs to be queried based on the robot number in the task execution information. The robot's running status includes offline status and non-offline status. The offline status indicates that the robot is disconnected from the network, and the non-offline status indicates that the robot is connected to the network. The non-offline status includes idle, running, paused, faulty, alarm and other states.

[0070] Optionally, the high-frequency data packet includes state parameters of at least one component of the robot, and the state parameters include but are not limited to parameters such as voltage, current, temperature, pressure, speed, position, load, vibration, etc. It should be noted that high-frequency data can only be obtained when the robot is in an online state, that is, high-frequency data is only collected during the operation of the robot. On this basis, after receiving the monitoring request sent by the server, the client will not directly perform the monitoring task according to the task execution information in the monitoring request, but will first query the running status of the robot. If the robot is in an online state and the robot is in operation, the client can perform the monitoring task according to the task execution information to collect the running data of the robot in the running state, thereby generating a high-frequency data packet. If the robot is offline, that is, the robot is not online, data cannot be collected, and it is necessary to wait for the robot to be online and the robot to start running to trigger the execution of the monitoring task.

[0071] S203: The client sends monitoring data to the server according to the high-frequency data packet.

[0072] Optionally, the monitoring data may be the original high-frequency data packet or the robot's diagnostic result obtained by health analysis based on the high-frequency data packet. The client collects the state parameters of at least one key component of the robot according to the task execution information in the monitoring request and generates a high-frequency data packet. If the client performs local predictive analysis, the robot's diagnostic result obtained by health analysis based on the high-frequency data packet is sent to the server as monitoring data. If the client only collects high-frequency data without predictive analysis, the high-frequency data packet is directly sent to the server as monitoring data.

[0073] S204: The server obtains the robot's diagnostic results based on the monitoring data.

[0074] Optionally, the server receives monitoring data sent by the client, and the monitoring data may be an original high-frequency data packet or a robot diagnosis result obtained by health analysis based on the high-frequency data packet. If the monitoring data received by the server is a high-frequency data packet, the prediction service in the server performs a health analysis on the robot based on the high-frequency data packet to obtain the robot diagnosis result. If the monitoring data received by the server is the robot diagnosis result obtained by the client based on the high-frequency data packet, the server can directly obtain the robot diagnosis result.

[0075] Based on this, according to the robot diagnosis method provided by the embodiment of the present application, the user triggers the monitoring instruction on the operation interface, and the server generates a monitoring request, which includes the task execution information corresponding to the monitoring task for the health check of the robot, and sends the monitoring request to the client. According to the monitoring request, the client queries the running status of the robot, and executes the monitoring task according to the task execution information, generates a high-frequency data packet, and the high-frequency data packet includes the state parameters of at least one component of the robot. The client determines the monitoring data according to the high-frequency data packet and sends the monitoring data to the server. The monitoring data can be a high-frequency data packet, or it can be a diagnosis result of the robot obtained by health analysis based on the high-frequency data packet. Accordingly, the server can obtain the diagnosis result of the robot based on the health analysis of the high-frequency data packet, or directly obtain the diagnosis result of the robot, so as to timely grasp the real-time health status of the robot. According to the embodiment of the present application, based on the interaction between the server and the client, the running status of the robot is monitored and fault detected in real time, and the real-time health status of the robot is grasped, so that the robot can be effectively identified and replaced in time before the robot fails, so as to avoid the production line shutdown caused by the robot failure, thereby ensuring the normal operation of the production line.

[0076] As a possible implementation, in step S203, the client sends monitoring data to the server according to the high-frequency data packet, including: the client sends the high-frequency data packet as monitoring data to the server. Correspondingly, the server obtains the diagnosis result of the robot according to the monitoring data, including: the server performs health analysis on the robot based on the monitoring data to obtain the diagnosis result of the robot.

[0077] Optionally, if the client only collects high-frequency data without performing predictive analysis, the client collects the status parameters of at least one key component of the robot according to the task execution information in the monitoring request, generates a high-frequency data packet, and then sends the high-frequency data packet directly to the server as monitoring data. The server's predictive service performs a health analysis of the robot based on the high-frequency data packet.

[0078] Figure 3 The following is an interactive flow chart of a robot diagnosis prediction method provided by an embodiment of the present application. Figure 3 As shown, the server receives the user's operation instruction based on the data processing service, generates a monitoring request to request high-frequency data collection, and writes the task execution information carried in the monitoring request into the first data table, which is used to record the relevant information of the monitoring task. Then, based on the acquisition service, the server communicates with the client to send the monitoring request to the client's client main program. At the same time, the server will issue a request to query the status of the robot, and accordingly, the client will return the status of the robot to inform the server that the robot is online or offline. Further, the client main program receives the monitoring request issued by the server, waits for the robot to run and performs high-frequency data collection, writes the state parameters of at least one component of the robot collected into the log, and compresses it to generate a high-frequency data packet, and then uploads the high-frequency data packet to the acquisition service and data processing service of the server, and reports the upload result of the high-frequency data packet to the acquisition service and data processing service, and stores the upload result in the second data table, which is used to record the task log information. Combined with the aforementioned first data table, it is convenient to view all executed acquisition tasks and the specific execution status of each task, such as which stage is executed. After the collection service and data processing service on the server perform preliminary analysis and decompression on the high-frequency data packets, the processed high-frequency data packets are sent to the prediction service. The prediction service performs a health analysis on the robot based on the high-frequency data packets, obtains the diagnosis results of the robot, and displays the diagnosis results on the operation interface for users to view.

[0079] Based on this, the client collects the high-frequency data of the running robot according to the monitoring tasks sent by the server, generates high-frequency data packets, and sends the high-frequency data packets to the cloud server for analysis and prediction, so as to achieve remote diagnosis effects.

[0080] Figure 4The following is a flow chart of a method for determining a diagnosis result of a robot provided in an embodiment of the present application. Figure 4 As shown, the method specifically comprises the following steps:

[0081] S401, parsing the high-frequency data packet, and extracting the status parameters of each component in the high-frequency data packet.

[0082] Exemplarily, the state parameters are used to reflect the real-time operating status of the components. When parsing the high-frequency data packets, the communication protocol used by the high-frequency data packets is first determined in order to understand the information structure in the high-frequency data packets. Then, the key components are identified based on factors such as the robot function and design, and the state parameters of each key component are extracted from the data stream using parsing tools, including voltage, current, temperature, pressure, etc. On this basis, the extracted state parameters of each component can be verified to ensure the validity and accuracy of the state parameters and eliminate the influence of noise or erroneous data.

[0083] S402: Determine the status score of each component according to the status parameters of each component and a preset scoring strategy, and determine the comprehensive status score of the robot according to the status score of each component.

[0084] Exemplarily, in an embodiment of the present application, a scoring rule is defined, and a scoring interval and scoring logic are set for each state parameter, so as to calculate the state score according to the state parameters actually collected. For example, if the components include a motor, a battery, and a sensor, for the motor, the corresponding state parameter is, for example, the motor temperature, and the scoring strategy corresponding to the motor temperature is, for example: the motor temperature is between 0°C and 50°C, the score is 100 points, the motor temperature is between 50°C and 60°C, and 2 points are deducted for every 1°C increase, and the motor temperature exceeds 60°C, and 5 points are deducted for every 1°C increase. For the battery, the corresponding state parameter is, for example, the battery voltage, and the scoring strategy corresponding to the battery voltage is, for example: the battery voltage is between 11.5V and 12.5V, the score is 100 points, the battery voltage is between 11.0V and 11.5V or between 12.5V and 13.0V, and 2 points are deducted for every deviation of 0.1V, and the battery voltage is lower than 11.0V or higher than 13.0V, and 5 points are deducted for every deviation of 0.1V. For the sensor, the corresponding state parameter is, for example, the sensor reading error, and the scoring strategy corresponding to the sensor reading error is, for example: if the reading error is between 0% and 2%, the score is 100 points; if the reading error is between 2% and 5%, 5 points will be deducted for every 1% increase; if the reading error exceeds 5%, 10 points will be deducted for every 1% increase.

[0085] For example, according to the importance and influence of each component on the overall performance of the robot, a corresponding weight is assigned to the status score of each component, and the status scores of each component are weighted and summed to obtain the comprehensive status score of the robot. On this basis, since the key components corresponding to different types of robots may be different, after obtaining the comprehensive status score of the robot, a standardized processing strategy such as "maximum-minimum normalization" can be used to standardize the comprehensive status score of the robot to a specific range, for example, quantify the comprehensive status score to 0 to 100 points.

[0086] Exemplarily, continuing to take the above components including motors, batteries and sensors as an example, if the state parameters of each component extracted from the high-frequency data packet are motor temperature 55°C, battery voltage 12.2V, and sensor reading error 3%, then according to the scoring strategy of the above example, the motor state score is 90 points, the battery state score is 100 points, and the sensor reading error state score is 95 points. On this basis, assuming that the component weights assigned to the motor, battery and sensor are 40%, 30%, and 30%, the weighted state score of the motor is 36 points, the weighted state score of the battery is 30 points, and the weighted state score of the sensor is 28.5 points, then the comprehensive state score of the robot can be obtained as 94.5 points.

[0087] S403: Taking the comprehensive status score of the robot as the overall health of the robot, and obtaining a diagnosis result of the robot.

[0088] Exemplarily, the comprehensive status score of the robot is used as the overall health of the robot. For example, taking the comprehensive status score of the above-mentioned robot as 94.5 points as an example, the overall health of the corresponding robot is 94.5. In an embodiment of the present application, a grading standard for health is pre-set. For example, if the overall health is above 90, the robot is determined to be in a healthy state. If the overall health is between 70-89, the robot is determined to be in a sub-healthy state. If the overall health is between 60-69, the robot is determined to be in a state of focus. If the overall health is below 60, the robot is determined to be in an unhealthy state and there is a hidden danger of failure. On this basis, the corresponding health level of the robot is determined according to the overall health of the robot, and then the diagnosis result of the robot is obtained, so that the user can determine whether to replace the robot's components, etc. according to the corresponding health level of the robot, so as to avoid failures in advance.

[0089] Based on this, status parameters are extracted from high-frequency data packets to evaluate the health of the robot, so as to provide users with targeted maintenance suggestions to avoid possible safety hazards in advance, thereby ensuring the stable operation of the robot and the normal operation of the production line.

[0090] As a possible implementation method, in the above step S203, the client sends monitoring data to the server according to the high-frequency data packet, including: the client performs a health analysis on the robot based on the status parameters of at least one component in the high-frequency data packet to obtain a diagnosis result of the robot; and sends the diagnosis result of the robot as monitoring data to the server.

[0091] Optionally, if the client performs local predictive analysis, after the client collects the status parameters of at least one key component of the robot according to the task execution information in the monitoring request and generates a high-frequency data packet, the client will perform a health analysis on the robot based on the high-frequency data packet to obtain the robot's diagnostic results, and send the robot's diagnostic results as monitoring data to the server. At this time, the server no longer performs health analysis and prediction and can directly obtain the robot's diagnostic results.

[0092] Figure 5 FIG. 2 shows an interactive flow chart of another robot diagnosis prediction method provided by an embodiment of the present application. Figure 5 As shown, the server receives the user's operation instruction based on the data processing service, generates a monitoring request to request high-frequency data collection, and writes the task execution information carried in the monitoring request into the first data table. Then, based on the collection service, the server communicates with the client to send the monitoring request to the client's client main program. At the same time, the server will issue a request to query the status of the robot, and accordingly, the client will return the status of the robot to inform the server that the robot is online or offline. Further, the client main program receives the monitoring request issued by the server, waits for the robot to run and perform high-frequency data collection, writes the state parameters of at least one component of the robot obtained by the collection into the log, and compresses it to generate a high-frequency data packet, and then directly performs health analysis on the robot based on the high-frequency data packet to obtain the robot's diagnosis result, and then uploads the robot's diagnosis result to the collection service and data processing service of the server, and reports the upload result of the robot's diagnosis result to the collection service and data processing service, and stores the upload result in the second data table. On this basis, the server can directly obtain the robot's diagnosis result, and display the diagnosis result on the operation interface for the user to view.

[0093] Based on this, the client collects the high-frequency data of the running robot according to the monitoring tasks issued by the server, and directly analyzes and predicts on the client, and finally reports the robot's diagnostic results obtained from the prediction analysis to the cloud server, so as to achieve the effect of remote diagnosis. And because only the robot's diagnostic results are sent to the server, the amount of uploaded data is greatly reduced, thereby reducing data transmission pressure and network pressure, and predictive analysis by the client can reduce the computing pressure on the server.

[0094] As a possible implementation method, before performing a health analysis on the robot based on high-frequency data packets and obtaining a diagnostic result of the robot, the method also includes: the server sends a log collection request to the client, and the log collection request includes a control cabinet code of a control cabinet associated with the robot; the client responds to the log collection request, collects error log data in the control cabinet corresponding to the control cabinet code, and sends the error log data to the server.

[0095] Exemplarily, the server sends a log collection request to the client, which includes the control cabinet code of the control cabinet associated with the robot. The client collects error log data from the corresponding control cabinet according to the control cabinet code and sends the error log data back to the server.

[0096] Accordingly, a health analysis of the robot is performed based on the high-frequency data packet to obtain a diagnosis result of the robot, including: a health analysis of the robot is performed based on the high-frequency data packet and error log data to obtain a diagnosis result of the robot.

[0097] Exemplarily, the server receives the error log data sent by the client, parses the error log data, extracts key information therein, combines the error log data with the status parameters in the high-frequency data packet, and performs a comprehensive analysis. Specifically, weights may be assigned to the high-frequency data packet and the error log data, and based on the analysis results obtained from the high-frequency data packet and the analysis results obtained from the error log data, a comprehensive analysis of the health status of the robot is performed to obtain a comprehensive diagnostic result of the robot.

[0098] Based on this, the server can initiate a log collection request, the client responds to the request and collects error log data, and then sends the error log data to the server. The server combines high-frequency data packets and error log data to perform a comprehensive health analysis and generate a diagnostic report for the robot, thereby more comprehensively evaluating the robot's health status.

[0099] Figure 6 The flowchart of a method for generating a high-frequency data packet provided in an embodiment of the present application is shown. Figure 6 As shown, the above step S202 queries the running status of the robot, and in response to the robot running, performs the monitoring task according to the task execution information, and generates a high-frequency data packet, including:

[0100] S601, initializing the monitoring task according to the task name, robot number and control cabinet code.

[0101] Exemplarily, on the operation interface provided by the server, the task name, robot number and control cabinet code are used as input parameters, the monitoring task is initialized according to the input parameters, and the operating parameters of the monitoring task are further set, such as the number of days the task runs, the number of collections per day and the maximum single collection time.

[0102] S602: Query the current running status of the robot in real time to determine whether the robot is currently in a non-offline state.

[0103] Exemplarily, the running state of the robot includes an offline state and a non-offline state, the offline state means that the robot is disconnected from the network, the non-offline state means that the robot is connected to the network, and the non-offline state includes idle, running, paused, faulty, alarm, etc. In the embodiment of the present application, the current running state of the robot can be queried in real time through an API interface or a network interface to determine whether the robot is online, so as to determine whether to execute the monitoring task and when to start executing the monitoring task.

[0104] S603. If yes, respond to the robot running, perform the monitoring task according to the task execution information, and generate a high-frequency data packet. Otherwise, send a prompt message to the server and wait for a preset time until the robot runs to perform the monitoring task. The prompt message is used to remind the user that the robot is currently offline.

[0105] Optionally, if the robot is online, the monitoring task is performed according to the number of days the task is run, the number of collections per day, and the maximum single collection duration, and the state parameters of at least one component are collected, including at least one robot joint and / or at least one key component in the control cabinet associated with the robot identified by the control cabinet code, and the state parameters of at least one component collected are generated into a high-frequency data packet, and the high-frequency data packet is sent to the server. If the robot is offline, a prompt message is sent to the server and retry after waiting for a preset time, so as to realize automatic retry of the offline state through looping and delay.

[0106] Based on this, the monitoring task is initialized according to the task name, robot number and control cabinet code, the current operating status of the robot is queried in real time, the robot operating status is responded to, the monitoring task is executed and high-frequency data packets are generated, and the robot offline status is processed at the same time, so as to ensure the smooth execution of the monitoring task and be able to promptly discover and handle problems such as robot offline.

[0107] As a possible implementation method, the client, based on generating a high-frequency data packet, either sends the high-frequency data packet as monitoring data to the server, or obtains the robot's diagnostic results based on the high-frequency data packet and sends the robot's diagnostic results as monitoring data to the server. When sending the monitoring data to the server, the monitoring data needs to be compressed and format converted to obtain processed monitoring data, and a data transmission request is sent to the server based on the Hypertext Transfer Protocol so that the server responds to the data transmission request, parses the processed monitoring data included in the data transmission request, and obtains the high-frequency data packet.

[0108] Exemplarily, before sending the monitoring data to the server, the client may preprocess the monitoring data, including but not limited to data cleaning, removing invalid or redundant data, and converting the preprocessed data into a suitable transmission format. In the embodiment of the present application, the HyperText Transfer Protocol (HTTP) may be used to transmit data, so the preprocessed data may be format converted according to the HTTP protocol, and then the data after the format conversion may be compressed using a compression algorithm to obtain processed monitoring data.

[0109] For example, in the process of sending a data transmission request to the server based on the HTTP protocol to transmit the processed monitoring data, the processed monitoring data can be encrypted to ensure the transmission security of the processed monitoring data. Accordingly, after receiving the encrypted processed monitoring data, the server performs decryption processing to obtain the processed monitoring data, and parses the processed monitoring data to obtain the original high-frequency data packet.

[0110] Correspondingly, after sending the monitoring data to the server, the method further includes: sending upload information of the monitoring data to the server, and displaying the upload information on the operation interface.

[0111] Exemplarily, the upload information includes an upload result indicating whether the monitoring data has been successfully uploaded, and the reason for the upload failure. The upload information can be a simple message or a more detailed report, such as the date and time of the upload, the amount of data uploaded, and metadata related to the monitoring data. After receiving the upload information, the server displays the upload information on an operation interface that can be accessed and operated by the user, such as displaying a pop-up window on the operation interface. If the upload is successful, it clearly indicates that the monitoring data has been successfully uploaded. If the upload fails, the reasons for the upload failure, such as grid error, data format error, server error, etc., are displayed to help users diagnose the problem and take relevant measures to solve the problem in a timely manner.

[0112] Based on this, the uploaded information is displayed on the operation interface, ensuring the transparency and reliability of data transmission between the client and the server.

[0113] The present application embodiment also provides an electronic device 700, such as Figure 7 As shown, it is a schematic diagram of the structure of an electronic device 700 provided in an embodiment of the present application, including: a processor 701, a memory 702, and optionally, a bus 703. The memory 702 stores machine-readable instructions executable by the processor 701. When the electronic device 700 is running, the processor 701 communicates with the memory 702 through the bus 703. When the machine-readable instructions are executed by the processor 701, the method steps in any of the above robot diagnosis methods are performed.

[0114] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method steps in any of the above robot diagnosis methods are executed.

[0115] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0116] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the function 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 is essentially or part of the technical solution that contributes to the prior art or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including 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, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program code.

[0117] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A robot diagnosis method, characterized in that: Applied to a robot diagnosis system, the system includes a server and a client, the server provides an operation interface, and the method includes: The server generates a monitoring request in response to an operation instruction of the user on the operation interface, and sends the monitoring request to the client, wherein the monitoring request includes task execution information corresponding to a monitoring task for performing a health check on the robot; The client queries the operation status of the robot according to the monitoring request, and in response to the operation of the robot, performs the monitoring task according to the task execution information, and generates a high-frequency data packet, wherein the high-frequency data packet includes a state parameter of at least one component of the robot; The client sends monitoring data to the server according to the high-frequency data packet; The server obtains a diagnosis result of the robot according to the monitoring data.

2. The method according to claim 1, characterized in that The client sends monitoring data to the server according to the high-frequency data packet, including: The client sends the high-frequency data packet to the server as the monitoring data; The server obtains the diagnosis result of the robot according to the monitoring data, including: The server performs a health analysis on the robot based on the monitoring data to obtain a diagnosis result of the robot.

3. The method according to claim 1, characterized in that The client sends monitoring data to the server according to the high-frequency data packet, including: The client performs health analysis on the robot based on the state parameter of at least one of the components in the high-frequency data packet to obtain a diagnosis result of the robot; The client sends the diagnosis result of the robot to the server as the monitoring data.

4. The method according to claim 2, characterized in that: The performing health analysis on the robot based on the monitoring data to obtain a diagnosis result of the robot includes: Parsing the high-frequency data packet to extract state parameters of each of the components in the high-frequency data packet, wherein the state parameters are used to reflect the real-time operating state of the components; Determine the status score of each component according to the status parameters of each component and the preset scoring strategy, and determine the comprehensive status score of the robot according to the status score of each component; The comprehensive status score of the robot is used as the overall health of the robot to obtain a diagnosis result of the robot.

5. The method according to claim 2, characterized in that: Before performing health analysis on the robot based on the high-frequency data packet to obtain a diagnosis result of the robot, the method further includes: The server sends a log collection request to the client, wherein the log collection request includes a control cabinet code of a control cabinet associated with the robot; The client responds to the log collection request, collects error log data in the control cabinet corresponding to the control cabinet code, and sends the error log data to the server; The performing health analysis on the robot based on the high-frequency data packet to obtain a diagnosis result of the robot includes: A health analysis is performed on the robot based on the high-frequency data packet and the error log data to obtain a diagnosis result of the robot.

6. The method according to claim 1, characterized in that The task execution information includes: task name, robot number, control cabinet code, task running days, number of collections per day, and maximum single collection duration; The querying of the running state of the robot, responding to the running of the robot, executing the monitoring task according to the task execution information, and generating a high-frequency data packet include: Initialize the monitoring task according to the task name, the robot number and the control cabinet code; Query the current running status of the robot in real time to determine whether the robot is currently in a non-offline state; If so, in response to the robot running, the monitoring task is executed according to the task execution information to generate a high-frequency data packet; otherwise, a prompt message is sent to the server, and a preset time is waited until the robot runs to execute the monitoring task. The prompt message is used to remind the user that the robot is currently offline.

7. The method according to claim 6, characterized in that The step of responding to the robot running and executing the monitoring task according to the task execution information to generate a high-frequency data packet includes: The monitoring task is performed according to the number of days the task is run, the number of collections per day and the maximum duration of a single collection to collect state parameters of at least one of the components, and the high-frequency data packet is generated based on the state parameters of at least one of the components, wherein the components include at least one robot joint and / or at least one key component in a control cabinet associated with the robot identified by the control cabinet code.

8. The method according to claim 1, characterized in that: The sending of monitoring data to the server includes: Compressing and formatting the monitoring data to obtain processed monitoring data; Sending a data transmission request to the server based on the hypertext transfer protocol, so that the server responds to the data transmission request, parsing the processed monitoring data included in the data transmission request, and obtaining the high-frequency data packet; After the monitoring data is sent to the server, the method further includes: Sending upload information of the monitoring data to the server, the upload information including an upload result indicating whether the monitoring data is successfully uploaded and a reason for upload failure; The uploaded information is displayed on the operation interface.

9. A robot diagnostic system, characterized in that: The system includes a server and a client, and the server provides an operation interface; The server is used to execute the method steps performed by the server in the robot diagnosis method described in any one of claims 1 to 8; The client is used to execute the method steps executed by the client in the robot diagnosis method according to any one of claims 1-8.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor executes the machine-readable instructions to perform the steps of the robot diagnosis method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the robot diagnosis method according to any one of claims 1 to 8 are executed.

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