Fault processing method, robot and storage medium

Abstract

The invention relates to the technical field of robot testing, and discloses a fault processing method, a robot and a storage medium. The method comprises: acquiring target health data; performing fault diagnosis processing on the target health data to obtain fault information; according to the fault information, executing a path planning process to obtain a target path route; according to the target path route, executing a target moving process to enable the first robot to arrive at a designated place; when the first robot arrives at the designated place, state information of a second robot is obtained, and whether the current state of the second robot accords with the state used for assisting maintenance or not is detected according to the state information; and if the first robot accords with the state for assisting maintenance, the first robot sends the fault information and the target path route to the second robot, so that the second robot executes a fault maintenance process on the first robot. Therefore, the fault processing efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present application relates to the field of robot testing technology, and in particular to a fault handling method, a robot and a storage medium. Background Art

[0002] Current robot testing environments typically involve multiple robots running continuously on closed-loop or open test tracks. Existing technologies rely primarily on manual intervention to handle failures that occur during testing. These methods have the following technical drawbacks: (1) Low efficiency: When a robot fails, manual intervention takes time, causing the test process to pause, affecting overall test efficiency; (2) Waste of resources: Faulty robots occupy the test track, affecting the test progress of other robots; (3) Long fault response time: Relying on the response of a remote monitoring center. Summary of the invention

[0003] The implementation methods of the present application mainly solve the technical problems of low efficiency, waste of resources and long fault response time of existing testing technologies, thereby improving the efficiency of the testing process.

[0004] To solve the above technical problems, a technical solution adopted in the implementation mode of the present application is: providing a fault handling method, the method comprising: obtaining target health data; performing fault diagnosis processing on the target health data to obtain fault information; executing a path planning process according to the fault information to obtain a target path route; executing a target movement process according to the target path route to enable the first robot to arrive at a designated location; when the first robot arrives at the designated location, obtaining status information of the second robot, and detecting whether the current status of the second robot meets the status for assisted maintenance based on the status information; if it meets the status for assisted maintenance, the first robot sends the fault information and the target path route to the second robot, so that the second robot performs the fault repair process on the first robot.

[0005] In some embodiments, the fault diagnosis processing of the target health data to obtain fault information includes: screening the target health data to obtain first fault data for a potential fault of the first robot; fusing the first fault data according to data respectively acquired by different sensors to obtain target fault data; and detecting the target fault data to obtain the fault information.

[0006] In some embodiments, the detecting the target fault data to obtain the fault information includes: dividing the target fault data into fault levels to obtain the fault information; wherein the fault levels include minor, general and severe; if the fault information satisfies the fault level of minor or general, it indicates that the robot can move normally; if the fault information satisfies the fault level of severe, it indicates that the robot cannot move normally.

[0007] In some embodiments, the path planning process is executed based on the fault information to obtain the target path route, including: obtaining current information of the first robot, the current information including position and speed; the first robot performs environmental modeling to obtain current environmental information, the current environmental information including obstacles, fault areas and passable areas; calculating the path route based on the current information and the current environmental information; dynamically adjusting and optimizing the path route to obtain the target path route.

[0008] In some embodiments, the target movement process is executed according to the target path route so that the first robot reaches the designated location, including: when the fault level is minor or general, the first robot communicates and interacts with the second robot, sends the fault information and the target path route to the second robot, so that the first robot avoids the path route of the second robot and moves to the designated location; when the fault level is serious, the first robot communicates and interacts with the second robot, selects a preset number of the second robots, and sends the fault information and the target path route to the second robot, so that the second robot assists the first robot in moving to the designated location.

[0009] In some embodiments, obtaining status information of the second robot and detecting whether the current status of the second robot meets the status for assisted maintenance based on the status information include: obtaining status information of the second robot, the status information including current task information and current health information; inspecting the status information to obtain a first inspection result; if the first inspection result satisfies that the current task information is in a non-important status and the current health information is in a healthy status, then the second robot meets the status for assisted maintenance; if the first inspection result does not satisfy that the current task information is in a non-important status and the current health information is in a healthy status, then the second robot does not meet the status for assisted maintenance.

[0010] In some embodiments, if the state for assisted maintenance is met, the first robot sends the fault information and the target path route to the second robot, so that the second robot performs a fault repair process on the first robot, including: when the state for assisted maintenance is met, sending the fault information and the target path route to the second robot, so that the second robot updates the path route, moves to the designated location of the first robot, and performs plug-in repair on the faulty component.

[0011] In some embodiments, the method further includes: uploading the fault information to a server, so that the server performs data analysis after receiving the fault information, obtains analysis results, and generates a fault detection report according to the analysis results.

[0012] To solve the above technical problems, another technical solution adopted in the embodiment of the present application is: to provide a robot, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0013] In order to solve the above technical problems, another technical solution adopted in the implementation mode of the present application is: providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a robot, the robot executes the method described above.

[0014] Different from the related art, the present application obtains target health data; performs fault diagnosis processing on the target health data to obtain fault information; executes the path planning process according to the fault information to obtain the target path route; executes the target movement process according to the target path route so that the first robot reaches the designated location; when the first robot arrives at the designated location, obtains the status information of the second robot, and detects whether the current state of the second robot meets the state for assisting maintenance according to the status information; if it meets the state for assisting maintenance, the first robot sends the fault information and the target path route to the second robot so that the second robot performs the fault maintenance process on the first robot. Based on this, the present application obtains health data in real time and performs fault diagnosis, so that the first robot can quickly identify and handle faults. Under the guidance of fault information, the path planning process ensures that the first robot selects the optimal route to reach the designated location to avoid interference from obstacles. In addition, by obtaining the status information of the second robot and confirming whether it meets the conditions for assisting maintenance, the system can effectively call resources to ensure that the maintenance task is performed by the appropriate robot, thereby improving the overall maintenance efficiency. In this way, the present application combines real-time monitoring, intelligent path planning and collaborative maintenance to significantly improve the robot's responsiveness, processing efficiency and resource utilization when a fault occurs without relying on remote server scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings, which do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and the figures in the drawings do not constitute proportional limitations unless otherwise stated.

[0016] Figure 1 This is a schematic diagram of a specific application scenario of a fault handling method provided in an embodiment of the present application;

[0017] Figure 2 is a flow chart of a fault handling method provided in an embodiment of the present application;

[0018] Figure 3 is a flowchart of a fault diagnosis process provided by an embodiment of the present application;

[0019] Figure 4 is a flow chart of a path planning process provided by an embodiment of the present application;

[0020] Figure 5 is a flow chart of a target movement process provided by an embodiment of the present application;

[0021] Figure 6It is a schematic diagram of the hardware structure of a robot that executes the fault handling method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0023] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those generally understood by those skilled in the art of the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more related listed items.

[0024] With the rapid development of robotics technology, more and more robots are being used in industries such as industry, logistics, and medical care. In order to ensure the stability and reliability of robots in practical applications, comprehensive testing is particularly important. The design of the test environment needs to take into account various possible failure scenarios so that problems can be identified and solved in a timely manner, thereby improving the overall performance of the robot system.

[0025] See also Figure 1 , Figure 1 1 is a schematic diagram of a specific application scenario of a fault handling method provided in an embodiment of the present application. Specifically, the application scenario includes a first robot 10 and a second robot 20.

[0026] The first robot is a robot that fails during the test, possibly due to hardware failure, software error, or sensor problem and cannot continue to operate normally. In this embodiment, the first robot has a certain self-diagnosis capability and can identify and mark the type of failure so that the second robot can handle the problem more effectively.

[0027] The second robot is a robot that operates normally during the test and is responsible for monitoring the status of the first robot and providing support when it fails. In this embodiment, the second robot quickly moves to the location of the failed robot and performs necessary fault handling measures. For example, physical support (such as pushing, pulling) or software restart is provided to help the failed robot resume normal operation.

[0028] The application scenario process is as follows: Test environment preparation: Set up a closed-loop or open test track and configure multiple robots to ensure that the hardware and software systems of all robots are running normally and perform initialization settings. Start test: Start the test process, and multiple robots perform continuous autonomous operation on the track. Fault detection: The first robot fails during operation, automatically triggering the fault detection mechanism; the faulty robot sends a fault signal to the second robot, marking its location and fault type. Fault response: The second robot receives the fault signal sent by the first robot, identifies the fault status of the first robot, and quickly goes to the faulty robot to prepare for fault handling. Fault handling: The second robot evaluates the status of the faulty robot and performs necessary intervention measures. Recover and continue testing: Once the fault handling is completed, the first robot re-enters the running state, the test process continues, and all robots resume normal operation. Test end and data analysis: After the test is completed, collect and analyze the operating data of all robots; evaluate the frequency and type of faults, analyze the causes of faults, and put forward improvement suggestions; optimize the robot based on the test results to improve future test efficiency and reliability.

[0029] The following describes the fault handling method provided by the embodiment of the present application in conjunction with a specific embodiment. Figure 2 , Figure 2 1 is a flowchart of a fault handling method provided in an embodiment of the present application. Figure 2 As shown, the method includes steps S101-S106:

[0030] S101: Obtain target health data.

[0031] When acquiring data, the robot usually collects the target health data by equipping it with appropriate sensors (for example, pressure sensors, temperature sensors, current sensors, and vibration sensors). According to the test requirements, the appropriate data collection frequency is set. Each sensor continuously monitors the target health indicators through the built-in electronic module, and converts the collected data into electrical signals and transmits them to the robot's central processing unit.

[0032] The robot's central processing unit processes the received raw data, including filtering, denoising and amplification, to improve the availability and accuracy of the data. It is understandable that after the robot obtains the target health data, the central processing unit will perform a series of data preprocessing operations. The specific preprocessing steps are as follows: Data cleaning: Identify and remove invalid data points caused by sensor failure, data transmission errors or environmental interference; fill in missing data, which can be done by using methods such as mean interpolation, linear interpolation or front and back value filling. Filtering: Use a low-pass filter to remove high-frequency noise and retain the main components of the signal, which is particularly effective for relatively stable signals such as temperature and pressure; remove sudden outliers by sorting the values ​​in the data window and taking the median, which is suitable for situations such as vibration signals that may be subject to instantaneous interference. Denoising: Analyze the noise characteristics of the signal, use statistical methods to establish a noise model, and perform data denoising based on the model; use wavelet transform and other technologies to decompose the signal into different frequency components, identify and remove noise components, and then reconstruct the signal. Signal amplification: Amplify the signal to improve its recognizability in subsequent processing and ensure that the data will not be submerged in noise during the analysis process; optimize the amplitude of the signal through technologies such as dynamic range compression to meet the requirements of the data processing system. Standardization and normalization: Convert the data into a zero mean and unit variance form to eliminate the dimensional effects between different sensor data; scale the data to a specific range (such as 0 to 1) for subsequent processing and analysis. Feature extraction: Extract key features such as mean, variance, maximum and minimum values ​​from the preprocessed data to facilitate subsequent analysis and anomaly detection; analyze the features in the time domain, frequency domain or time-frequency domain to extract feature vectors related to health status. Data storage: Store the preprocessed data and extracted features in a database for subsequent analysis, report generation and model training. Feedback and optimization: Adjust the data acquisition strategy in real time according to the preprocessing results to ensure the optimization of data quality and integrity in subsequent tests; dynamically adjust the parameters such as filters and amplifiers to adapt to the signal characteristics under different working conditions.

[0033] Through reasonable sensor installation and configuration, scientific data acquisition strategy setting and preprocessing operations, the robot can achieve efficient real-time monitoring and safety protection, ensure reliable operation in various working environments, and obtain target health data in real time. Among them, the target health data can be the voltage, current and temperature data described above, or other important health data, which is not limited in this embodiment. In addition, new sensors can be added to obtain new health data, and this embodiment does not limit the type of sensor.

[0034] S102: Perform fault diagnosis processing on the target health data to obtain fault information.

[0035] See also Figure 3 , Figure 3 This is a flowchart of a fault diagnosis process provided by an embodiment of the present application. Figure 3 As shown, the target health data is subjected to fault diagnosis processing to obtain fault information, including steps S1021-S1023:

[0036] S1021: Screening and processing the target health data to obtain first fault data for a potential fault of the first robot.

[0037] It is understandable that the target health data collected from multiple sensors needs to be further identified and screened for signals that may indicate potential failures of the first robot. First, the system sets a series of criteria and thresholds to identify normal and abnormal states. With these criteria, the data screening process includes the following key steps: Data filtering: remove irrelevant data and retain information related to the failure, which can be done by applying statistical methods (such as mean and standard deviation) or model-based anomaly detection. Feature extraction: extract key information from the data, such as extreme values, volatility, and rate of change indicators to highlight possible signs of failure. Establish a fault database: compare the characteristics of known faults with the data of normal operating conditions to further determine the data set of potential faults.

[0038] S1022: fusing the first fault data according to the data respectively acquired by different sensors to obtain target fault data.

[0039] Once the potential fault data is screened out, the next step is to fuse the data from different sensors. This step aims to integrate multi-source information to improve the accuracy of fault diagnosis. The specific methods of fusion processing include: Data alignment: Ensure that all sensor data are synchronized in time and space so that the health data collected by different sensors can be effectively compared. Fusion algorithm application: Use advanced data fusion algorithms such as deep learning or ensemble learning, which can effectively process high-dimensional data and mine potential relationships. Deep learning models can identify complex patterns in data through training, while ensemble learning combines the results of multiple models to improve overall prediction performance. Target fault data generation: Finally, target fault data is extracted from the results of the fusion processing, which can reflect the comprehensive characteristics and information of potential faults.

[0040] S1023: Detect the target fault data to obtain the fault information.

[0041] After obtaining the target fault data, the system enters the fault detection phase. The purpose of this step is to identify the actual fault by analyzing the target fault data and generate fault information. The specific process is as follows: Establish a detection model: Based on historical fault data and normal data, a fault detection model is constructed. This can include machine learning-based methods such as support vector machines (SVM), decision trees, or neural networks. Real-time data processing: The robot's edge computing unit is responsible for processing input data from sensors in real time. This processing process is not limited to fault detection, but also includes preliminary data analysis to ensure timely response to the robot status. Fault identification and reporting: By inputting the target fault data into the detection model, the system can determine in real time whether there is a fault behavior or omen. If a fault is detected, the system will generate detailed fault information, including fault type, location, and severity, which is essential for subsequent maintenance and decision support.

[0042] Through the above step S102, the robot can systematically perform fault diagnosis on the target health data. Each step of screening, fusion and detection is aimed at improving the accuracy and efficiency of fault identification. Real-time data processing ensures that the system can respond quickly, thereby reducing the impact of faults on the robot and improving the reliability and operating efficiency of the overall system. In addition, the use of advanced data fusion algorithms not only improves the data processing capabilities, but also provides strong support for potential fault warnings, ensuring that the robot can maintain optimal performance in a complex operating environment. In this way, the robot can achieve self-monitoring, self-diagnosis and self-repair, thereby improving its reliability and intelligence in various application scenarios.

[0043] In some embodiments, the detecting the target fault data to obtain the fault information includes: dividing the target fault data into fault levels to obtain the fault information; wherein the fault levels include minor, general and severe; if the fault information satisfies the fault level of minor or general, it indicates that the robot can move normally; if the fault information satisfies the fault level of severe, it indicates that the robot cannot move normally.

[0044] It is understandable that in the process of detecting target fault data and obtaining fault information, the classification of fault levels is a key step, which directly affects the operation decision of the robot. The following is a detailed introduction to each step of this process:

[0045] First, after detecting the target fault data, it is necessary to classify the fault levels of these data. This process usually includes the following steps: 1) Feature definition: Based on the previously extracted target fault data, define relevant fault indicators, such as vibration amplitude, temperature change rate, current anomaly, etc. Then set reasonable thresholds for each fault indicator to determine the standards for minor, general and major faults. For example: Minor fault: The indicator deviates slightly from the normal range and has no significant impact on the performance of the robot; General fault: The indicator deviates significantly from the normal range, but has not yet caused the robot to fail to operate normally; Major fault: The indicator deviates significantly from the normal range, which may cause the robot to stop running or cause safety hazards. 2) Data analysis: Compare the target fault data with the set threshold to analyze whether the fault indicator exceeds the predetermined standard. Determine the fault level based on the comparison results. For example: If all indicators are within the minor threshold, it is judged as a minor fault; if an indicator exceeds the minor range but does not reach the major, it is judged as a general fault; if multiple indicators or key indicators reach the major threshold, it is judged as a major fault.

[0046] Secondly, after completing the fault level classification, the system generates detailed fault information, including: Fault type: describes the nature of the fault, such as mechanical failure, electronic failure, etc.; Fault level: clearly indicates the level of the fault (minor, general, severe); Impact assessment: Based on the fault level, evaluate its impact on the robot, especially whether it will cause the robot to be unable to move normally.

[0047] Finally, based on the fault information, the system will make the following decisions: If the fault information indicates that the level is minor or general, the system will confirm that the robot can move normally and continue to perform the specified task. At this time, the fault may be recorded for subsequent monitoring and maintenance. If the fault information shows that the fault level is severe, the system will immediately trigger an alarm and take measures, such as stopping all movements of the robot to avoid further damage and sending fault information to surrounding robots.

[0048] This embodiment can timely identify serious faults and take corresponding measures through fault level classification and processing, ensuring the safety of the robot during operation. Minor or general faults will not interfere with the normal operation of the robot, allowing it to continue to perform test tasks, thereby improving the overall test efficiency. In addition, through fault level classification and real-time decision-making, the robot can independently judge the operating status, showing a higher level of intelligence and adaptive ability.

[0049] S103: Execute a path planning process according to the fault information to obtain a target path route.

[0050] See also Figure 4 , Figure 4 : is a flow chart of a path planning process provided by an embodiment of the present application. Figure 4 As shown, according to the fault information, a path planning process is executed to obtain a target path route, including steps S1031-S1034:

[0051] S1031: Acquire current information of the first robot, where the current information includes position and speed.

[0052] Before path planning, the first robot (fault robot) needs to collect its own status information, which includes: Position acquisition: Use the global positioning system (GPS), laser radar (LiDAR) or other sensors (such as ultrasound, infrared) to accurately determine the current position of the robot. These technologies provide real-time position data to ensure accurate navigation of the robot. Speed ​​monitoring: Use encoders, accelerometers or speed sensors to obtain the current movement speed of the robot. This data helps calculate the range of movement of the robot in a specific time.

[0053] S1032: The first robot performs environment modeling to obtain current environment information, where the current environment information includes obstacles, fault areas, and passable areas.

[0054] After obtaining the current information, the robot needs to perform environmental modeling to gain a comprehensive understanding of the surrounding environment. The process includes: Sensor data collection: Use lidar, cameras or other sensors to capture information about the surrounding environment in real time, including obstacles, fault areas and traversable areas. Environmental information analysis: Identify static and dynamic obstacles through sensor data and determine their location and size; based on previous fault information, mark the current fault area to avoid paths passing through these dangerous areas; distinguish the traversable areas in the environment from obstacles and fault areas to form the basis of the environmental model.

[0055] S1033: Calculate a path route according to the current information and the current environment information.

[0056] After obtaining the current information and environmental information, the robot needs to calculate the best path. This step includes: Path planning algorithm: Use an appropriate path planning algorithm (such as A* algorithm, Dijkstra algorithm or RRT (fast random tree)) to calculate the path from the current position to the target position; use the robot's current position information and environmental model as input to calculate the preliminary path. Path evaluation: Evaluate whether the calculated path is safe to ensure that it does not pass through fault areas and obstacles; consider the length of the path and the estimated travel time to find the optimal path.

[0057] It should be noted that this embodiment does not limit the path planning algorithm used, and a genetic algorithm or a reinforcement learning algorithm may also be used to plan the optimal path.

[0058] S1034: Dynamically adjust and optimize the path route to obtain the target path route.

[0059] After calculating the initial path, the robot needs to dynamically adjust and optimize the path. This process includes: Real-time environment update: During driving, the robot continuously monitors changes in the environment and updates the location of obstacles or fault areas in a timely manner; if new obstacles or changes in the environment are detected, the robot can adjust the travel path in real time. Path optimization: If there is a problem with the current path, the environment model is used to replan and find a new feasible path; more complex optimization algorithms can be applied, such as prediction models based on machine learning, to improve the intelligence level of path selection.

[0060] This embodiment uses a systematic path planning process to enable the robot to move safely and efficiently in a complex environment, ensure that the robot can avoid obstacles and fault areas during operation, effectively shorten the time it takes for the robot to reach the target location, and meet various application requirements.

[0061] S104: Execute a target movement process according to the target path route so that the first robot reaches a designated location.

[0062] In the process of executing the target movement process to make the first robot reach the specified location, different levels of faults will lead to different movement strategies. Figure 5 , Figure 5 : is a flow chart of a target movement process provided by an embodiment of the present application. Figure 5 As shown, according to the target path route, the target movement process is executed to enable the first robot to reach the designated location, including steps S1041-S1042:

[0063] S1041: When the fault level is slight or general, the first robot communicates with the second robot to send the fault information and the target path to the second robot, so that the first robot avoids the path of the second robot and moves to the designated location.

[0064] S1042: When the fault level is serious, the first robot selects a preset number of the second robots through communication and interaction with the second robots, and sends the fault information and the target path route to the second robots, so that the second robots assist the first robot in moving to the designated location.

[0065] It is understandable that, first, the first robot will judge its own fault level before moving. When the fault level is slight or general, the first robot can continue to move, but safety needs to be ensured. Secondly, the first robot (faulty robot) establishes contact with the second robot (other normal robots) through wireless communication (such as Wi-Fi, Bluetooth or other communication protocols), and sends fault information and target path route to the second robot to ensure that the second robot understands the status and movement plan of the first robot. Finally, after receiving feedback from the second robot, the first robot adjusts the path to avoid path conflicts with the second robot. The second robot will adjust its own motion trajectory based on the information received to ensure that the first robot can reach the designated location smoothly.

[0066] When the fault level is severe, it means that the first robot cannot move safely alone and other measures must be taken. At this time, the first robot selects a preset number of second robots (for example, 2-3) as auxiliary robots according to the strategy set by its own system. This process can be achieved through pre-defined rules or real-time data analysis. Subsequently, the first robot sends fault information and target path routes to the selected second robots to inform them that they need assistance. After receiving the information, the second robot will calculate the path according to the fault level and target path to help the first robot move safely. Among them, these second robots can monitor the status of the first robot in real time and coordinate through the corresponding communication protocol. Finally, during the execution process, all participating robots (including the first robot and the auxiliary second robot) will work together and move according to their respective paths to ensure that the first robot arrives at the designated location safely. At the same time, the robots maintain real-time data exchange during the movement to ensure that all robots respond quickly to changes in the environment.

[0067] This embodiment enables the first robot to reach the designated location safely and efficiently under different fault levels by executing the target movement process, thereby realizing multi-robot collaboration for fault handling.

[0068] S105: When the first robot arrives at the designated location, the status information of the second robot is obtained, and based on the status information, the current status of the second robot is detected to determine whether it is suitable for assisting maintenance.

[0069] The obtaining of status information of the second robot and detecting whether the current status of the second robot meets the status for performing assisted maintenance based on the status information include: obtaining status information of the second robot, the status information including current task information and current health information; inspecting the status information to obtain a first inspection result; if the first inspection result satisfies that the current task information is in a non-important status and the current health information is in a healthy status, then the second robot meets the status for performing assisted maintenance; if the first inspection result does not satisfy that the current task information is in a non-important status and the current health information is in a healthy status, then the second robot does not meet the status for performing assisted maintenance.

[0070] Specifically, when the first robot arrives at a designated location (eg, a maintenance area or a fault area), before preparing to repair the fault of the first robot, it is necessary to determine whether the second robot is in a state suitable for collaborative repair based on the current state information of the second robot.

[0071] First, it is necessary to clarify the status information that needs to be obtained, including: Current task information: refers to the type of task the second robot is performing and its priority, such as whether it is an important task. Current health information: refers to the current working status of the robot, including the readings of various sensors, fault diagnosis results, etc., to determine its overall health status. It is understandable that the real-time data of the second robot is obtained through various sensors (such as pressure sensors, temperature sensors, status monitoring modules, etc.), and the current task information is extracted from the task management system to understand the current workload and task priority of the second robot.

[0072] Secondly, the current task information and current health information are integrated to form a complete state information data set. The inspection criteria are set, mainly including two key conditions: the current task information must be in a non-critical state (for example, not an emergency repair or a critical task); the current health information must show that the robot is in a healthy state (such as no faults or abnormalities).

[0073] Finally, the integrated status information is logically checked to obtain the first test result. If the current task information is in a non-important state and the current health information is in a healthy state, it means that the second robot meets the conditions for assisting maintenance, and the second robot will make corresponding task preparations. If the current task information is in an important state, or the current health information shows a fault or abnormality, it means that the second robot does not meet the conditions for assisting maintenance, and the first robot will record the status of the second robot and can select other robots or re-evaluate whether other forms of support are needed.

[0074] This embodiment can effectively support the implementation of assisted maintenance tasks, improve maintenance efficiency, optimize resource allocation, and enhance the overall performance and safety of the multi-robot system by obtaining the status information of the second robot and performing inspections.

[0075] S106: If the state is suitable for assisting maintenance, the first robot sends the fault information and the target path to the second robot, so that the second robot performs a fault maintenance process on the first robot.

[0076] If the state is met for assisting maintenance, the first robot sends the fault information and the target path route to the second robot, so that the second robot performs the fault repair process on the first robot, including: when the state is met for assisting maintenance, the fault information and the target path route are sent to the second robot, so that the second robot updates the path route, moves to the designated location of the first robot, and performs plug-in repair on the faulty component.

[0077] It is understandable that first, it is confirmed whether the second robot meets the state for assisting maintenance (as described in the aforementioned step S105). If it meets the conditions, the first robot sends the fault information and the target path route to the second robot through wireless communication (such as Wi-Fi, Bluetooth, etc.). Among them, the fault information includes: the specific fault type, the affected system components and the possible fault impact; the target path route is the path information from the current position of the second robot to the designated location of the first robot.

[0078] Secondly, after receiving the fault information and target path route sent by the first robot, the second robot performs data analysis. Based on the current environment and obstacle status, the path planning is updated to ensure that the first robot's designated location is reached from the current position, and the intelligent routing algorithm is used to optimize the path to ensure that the path is safe and efficient. The second robot starts to move according to the updated path route, monitoring the surrounding environment in real time to avoid obstacles. During the movement, the second robot will feedback the current position and status to the first robot to ensure information synchronization.

[0079] Finally, when the second robot arrives at the location of the first robot, it is ready to perform maintenance operations. It is understandable that the specific device that needs to be repaired is identified based on the fault information provided by the first robot. The second robot performs plug-in repairs on the faulty device through a robotic arm or other operating tools, including replacing, adjusting or reconnecting the faulty component. After the repair is completed, the second robot will test the repaired system to confirm that the fault has been resolved.

[0080] It should be noted that plug-in repair can also modularize the key components. Key components such as batteries, sensors and other core components are designed as detachable modules. These modules can be easily replaced without the need for complex disassembly of the robot. Each module adopts a standardized interface design, which makes the compatibility between different modules higher and convenient for quick replacement. In addition, the entire fault handling process can be completed automatically between robots without relying on remote server scheduling.

[0081] This embodiment provides great convenience for the maintenance of the robot system through quick plug-in repair, modular design and convenient replacement process, and significantly improves the fault handling efficiency of the robot.

[0082] In some embodiments, the method further includes: uploading the fault information to a server, so that the server performs data analysis after receiving the fault information, obtains analysis results, and generates a fault detection report according to the analysis results.

[0083] First, after the first robot completes the fault handling, it organizes the relevant fault information (including fault type, affected components, fault occurrence time, etc.) into structured data, and collects other relevant information about the robot, such as current status, environmental conditions, etc., and organizes it into metadata. The collected fault information and metadata are formatted into a standard format suitable for uploading, such as JSON or XML, to ensure data compatibility and readability. The formatted fault information is uploaded to the central data system (server) via a stable network connection (such as Wi-Fi or wired network). This process may include a retry mechanism to ensure that the data can still be successfully uploaded when the network is unstable.

[0084] Secondly, after receiving the uploaded fault information, the server will confirm the receipt to ensure that the data is complete and correct, and store the received fault information in the database for subsequent query and analysis. Then, the parsing module on the server will parse the received fault information and extract key data, such as fault type, affected components, timestamp, etc. During the parsing process, redundant information and irrelevant data are removed to ensure the accuracy of subsequent analysis.

[0085] Thirdly, based on the parsed fault information, analyze the nature and pattern of the fault, identify possible causes and impacts, and generate a detailed fault detection report, including fault description, impact assessment, recommended solutions and preventive measures, etc. Format the generated report into a form that is easy to read and share (such as PDF or HTML), ensuring that the report is clear and the information is complete.

[0086] Finally, all robot failure data and operation logs uploaded to the server are centralized in the central data system. Data mining and machine learning techniques are used to analyze historical failure data to identify failure modes, trends, and potential risk points. Through these techniques, the system can extract valuable information to help identify potential system bottlenecks or common failure types. Based on the above analysis results, the system will recommend preventive maintenance measures, such as adjusting test parameters, regularly checking specific components, or replacing parts that may have problems. In addition, by continuously collecting new failure data and maintenance results, the fault prediction model will learn and adjust to improve the accuracy of future failure predictions. This dynamic optimization capability enables the system to adapt to new failure modes and improve fault handling efficiency.

[0087] This embodiment forms a systematic fault management process through the process of uploading fault information to the server and its subsequent analysis and decision-making links, thereby improving the ability to predict potential faults.

[0088] The embodiment of the present application obtains target health data; performs fault diagnosis processing on the target health data to obtain fault information; executes a path planning process according to the fault information to obtain a target path route; executes a target movement process according to the target path route so that the first robot reaches the designated location; when the first robot arrives at the designated location, the state information of the second robot is obtained, and the current state of the second robot is detected according to the state information to determine whether it meets the state for assisting maintenance; if it meets the state for assisting maintenance, the first robot sends the fault information and the target path route to the second robot so that the second robot performs the fault maintenance process on the first robot. Based on this, the present application obtains health data in real time and performs fault diagnosis, so that the first robot can quickly identify and handle faults. Under the guidance of the fault information, the path planning process ensures that the first robot selects the optimal route to reach the designated location to avoid interference from obstacles. In addition, by obtaining the state information of the second robot and confirming whether it meets the conditions for assisting maintenance, the system can effectively call resources to ensure that the maintenance task is performed by the appropriate robot, thereby improving the overall maintenance efficiency. Finally, uploading the fault information to the server and performing data analysis can generate a detailed fault detection report, which provides a basis for subsequent fault analysis, optimization and decision-making. This data-driven approach enhances the scientific nature of fault handling. In this way, this application combines real-time monitoring, intelligent path planning, collaborative maintenance and data analysis to significantly improve the robot's responsiveness and processing efficiency when a fault occurs.

[0089] The present application also provides a robot, see Figure 6, which shows a schematic diagram of the hardware structure of a robot capable of executing the method described in the above embodiment. The robot 300 includes: at least one processor 310; and a memory 320 in communication with the at least one processor 310, Figure 6 In the embodiment, a processor 310 is taken as an example. The memory 320 stores instructions that can be executed by the at least one processor 310. The instructions are executed by the at least one processor 310 so that the at least one processor 310 can execute the fault handling method described in the above embodiment. The processor 310 and the memory 320 can be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0090] The memory 320 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the fault handling method in the embodiment of the present application. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, implementing the fault handling method described in the above embodiment.

[0091] The memory 320 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created according to the use of the computing device, etc. In addition, the memory 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 320 may optionally include a memory remotely arranged relative to the processor 310, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] The one or more modules are stored in the memory 320, and when executed by the one or more processors 310, the fault handling method described in the above embodiment is executed.

[0093] The above product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the fault handling method described in the above embodiment of the present application.

[0094] The embodiment of the present application also provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors so that the at least one processor can execute the fault handling method described in the above embodiment. For example, the non-volatile computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0095] It should be noted that the above described embodiments are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0096] Through the description of the above implementation methods, ordinary technicians in this field can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Ordinary technicians in this field can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present application as described above, which are not provided in detail for the sake of simplicity. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A fault handling method, characterized in that: The method comprises: Obtain target health data; Performing fault diagnosis processing on the target health data to obtain fault information; According to the fault information, a path planning process is executed to obtain a target path route; According to the target path route, executing the target movement process so that the first robot reaches the designated location; When the first robot arrives at the designated location, obtaining status information of the second robot, and detecting whether the current status of the second robot meets the status for assisting maintenance according to the status information; If the state is met for assisting maintenance, the first robot sends the fault information and the target path route to the second robot, so that the second robot performs a fault maintenance process on the first robot.

2. The fault handling method according to claim 1, characterized in that: The performing fault diagnosis processing on the target health data to obtain fault information includes: Screening and processing the target health data to obtain first fault data for a potential fault of the first robot; Performing fusion processing on the first fault data according to the data respectively acquired by different sensors to obtain target fault data; The target fault data is detected to obtain the fault information.

3. The fault handling method according to claim 2, characterized in that: The detecting the target fault data to obtain the fault information includes: Classifying the target fault data into fault levels to obtain the fault information; wherein the fault levels include minor, general and severe; If the fault information satisfies the fault level of being slight or general, it indicates that the robot can move normally; If the fault information satisfies the fault level as being serious, it indicates that the robot cannot move normally.

4. The fault handling method according to claim 1, characterized in that: The step of executing a path planning process according to the fault information to obtain a target path route includes: Acquire current information of the first robot, the current information including position and speed; The first robot performs environmental modeling to obtain current environmental information, wherein the current environmental information includes obstacles, fault areas, and traversable areas; Calculate a path route according to the current information and the current environment information; The path route is dynamically adjusted and optimized to obtain the target path route.

5. The fault handling method according to any one of claims 1 to 4, characterized in that: The step of executing the target movement process according to the target path route so that the first robot reaches the designated location includes: When the fault level is slight or general, the first robot communicates with the second robot to send the fault information and the target path to the second robot, so that the first robot avoids the path of the second robot and moves to the designated location; When the fault level is serious, the first robot selects a preset number of the second robots through communication and interaction with the second robots, and sends the fault information and the target path route to the second robots, so that the second robots assist the first robot in moving to the designated location.

6. The fault handling method according to claim 1, characterized in that: The acquiring the state information of the second robot and detecting whether the current state of the second robot meets the state for assisting maintenance according to the state information includes: Acquire status information of the second robot, where the status information includes current task information and current health information; Testing the state information to obtain a first testing result; If the first inspection result satisfies that the current task information is in a non-important state and the current health information is in a healthy state, then the second robot is in a state for assisting maintenance; If the first inspection result does not satisfy that the current task information is in a non-important state and the current health information is in a healthy state, then the second robot is not in a state for assisting maintenance.

7. The fault handling method according to claim 1, characterized in that: If the state for assisting maintenance is met, the first robot sends the fault information and the target path to the second robot, so that the second robot performs a fault maintenance process on the first robot, including: When the state for assisting maintenance is met, the fault information and the target path route are sent to the second robot, so that the second robot updates the path route, moves to the designated location of the first robot, and performs plug-in repair on the faulty component.

8. The fault handling method according to claim 1, characterized in that: The method further comprises: The fault information is uploaded to a server, so that the server performs data analysis after receiving the fault information, obtains analysis results, and generates a fault detection report according to the analysis results.

9. A robot, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by a robot, enable the robot to perform the method according to any one of claims 1 to 8.

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