Control method of GIS bus cylinder internal inspection robot and related device

Abstract

The invention discloses a control method, a control device, control equipment and a computer readable storage medium of a GIS bus cylinder internal inspection robot, and the method comprises the steps that a visual system of the robot obtains shape data and position data of a cylinder wall; matching the wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information; performing angle control on wheels of the robot based on the wheel angle control information; when the robot advances, real-time fault point detection is carried out through a visual system; when a fault point is detected, carrying out photo collection on the fault point to obtain fault photo data; analyzing the fault picture data by adopting a fault feature recognition model to obtain fault feature data; and generating exception handling decision information based on the fault feature data, and sending the exception handling decision information to a preset terminal. The efficiency of checking the interior of the bus cylinder is improved, the influence of the environment in the cylinder on walking of the robot is avoided, and the moving efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of robot control technology, and more specifically, relates to a control method, a control device, a control equipment, and a computer-readable storage medium for a GIS busbar internal inspection robot. Background Art

[0002] GIS (GAS INSULATED SWITCHGEAR) busbar barrel plays a key role as an important carrier of high-voltage busbars in power systems. Its internal structure is complex and the space is relatively small. In actual operation, internal discharge often occurs. When a fault occurs, maintenance personnel need to open the cover of the GIS busbar barrel for inspection in order to determine the cause and location of the fault and take appropriate repair measures.

[0003] In the related art, there are two main ways to inspect GIS busbars. One way is for personnel to directly enter the busbar for inspection. However, for long-distance GIS busbars of more than ten meters or even dozens of meters, it is difficult for personnel to enter. Secondly, although some gases may have been collected in the busbar after the fault, there are still inevitably toxic gases after discharge, and there is a serious risk of suffocation and gas poisoning when personnel enter. In addition, due to the lack of special inspection tools, personnel are less efficient when conducting inspections, which will seriously affect the timeliness of emergency repairs. The other is to inspect through a movable camera device. This method can obtain some information inside the busbar to a certain extent, but for some complex situations and hidden parts, the camera may not be able to accurately capture key information.

[0004] Therefore, how to improve the efficiency of the internal inspection of the busbar barrel and avoid the problem of being blocked by the barrel environment due to the use of movable devices is a key issue that technicians in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide a control method, a control device, a control equipment, and a computer-readable storage medium for a GIS busbar barrel internal inspection robot, which improves the efficiency of the internal inspection of the busbar barrel, avoids the influence of the internal environment of the barrel on the walking of the robot, and improves the efficiency of movement.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a control method for a GIS busbar internal inspection robot, comprising:

[0007] The robot's vision system acquires the shape and position data of the cylinder wall;

[0008] Matching wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information;

[0009] Performing angle control on the wheels of the robot based on the wheel angle control information;

[0010] When the robot moves forward, real-time fault point detection is performed through the visual system;

[0011] When a fault point is detected, a photo of the fault point is collected to obtain fault photo data;

[0012] The fault feature recognition model is used to analyze the fault photo data to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on the fault training data;

[0013] Generate abnormal handling decision information based on the fault characteristic data, and send the abnormal handling decision information to a preset terminal.

[0014] Optionally, also include:

[0015] Performing real-time gas detection by a gas detector installed on the robot to obtain gas composition data;

[0016] The gas composition data is sent to a corresponding mobile terminal so that the mobile terminal generates personnel maintenance protection measures information based on the gas composition data.

[0017] Optionally, also include:

[0018] The robot monitors the tire grip data of the wheels in real time;

[0019] Determining whether the wheel is slipping based on the tire grip data;

[0020] If so, the output torque of the wheel is controlled.

[0021] Optionally, also include:

[0022] The robot obtains obstacle information and its own posture information through sensors;

[0023] Determine whether there is an obstacle in the forward direction based on the obstacle information;

[0024] If yes, the travel direction of the robot is regulated;

[0025] Determine whether there is a rollover situation based on the self-posture information;

[0026] If so, the speed and angle of the wheels of the robot are adjusted.

[0027] Optionally, also include:

[0028] Locally storing the fault photo data and the gas composition data;

[0029] When connected to the network, the fault photo data and the gas composition data are sent to the corresponding server based on a preset encryption algorithm.

[0030] Optionally, also include:

[0031] When the robot fails to receive a signal from the remote controller, the autonomous return function is activated;

[0032] Obtain the return path based on the traveling process;

[0033] Perform return processing based on the return path.

[0034] Optionally, also include:

[0035] Monitor the power quantity according to the preset time period to obtain the power quantity data;

[0036] When the power data is less than the preset power data, a power shortage alarm signal is sent.

[0037] The present application also provides a control device for a GIS busbar internal inspection robot, comprising:

[0038] An environmental perception module is used to obtain the shape data and position data of the cylinder wall;

[0039] A control information generating module, used for matching the wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information;

[0040] A wheel control module, used for controlling the angle of the wheels of the robot based on the wheel angle control information;

[0041] A fault detection module, used for performing real-time fault point detection through the visual system when the robot moves forward;

[0042] A fault photo collection module is used to collect photos of the fault point when a fault point is detected to obtain fault photo data;

[0043] A fault analysis module is used to analyze the fault photo data using a fault feature recognition model to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on fault training data;

[0044] The handling decision sending module is used to generate abnormal handling decision information based on the fault feature data and send the abnormal handling decision information to a preset terminal.

[0045] The present application also provides a control device, comprising:

[0046] Memory for storing computer programs;

[0047] A processor is used to implement the steps of the control method described above when executing the computer program.

[0048] The present application also 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 control method described above are implemented.

[0049] The present application provides a control method for a GIS busbar internal inspection robot, comprising: the robot's visual system acquires shape data and position data of the barrel wall; matches the robot's wheel status data based on the shape data and the position data to obtain wheel angle control information; performs angle control on the robot's wheels based on the wheel angle control information; when the robot moves forward, real-time fault point detection is performed through the visual system; when a fault point is detected, a photo of the fault point is captured to obtain fault photo data; a fault feature recognition model is used to analyze the fault photo data to obtain fault feature data; wherein the fault feature data comprises: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on fault training data; abnormal handling decision information is generated based on the fault feature data, and the abnormal handling decision information is sent to a preset terminal.

[0050] It has the following beneficial effects:

[0051] The data obtained through the visual system provides a basis for wheel control, ensuring that the robot adapts to the barrel wall environment and improves movement efficiency. Real-time fault detection and analysis can accurately identify fault characteristics, generate decision-making information, and effectively improve the accuracy and timeliness of busbar barrel internal inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0053] Figure 1A flow chart of a control method of a GIS busbar internal inspection robot provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a control device for a GIS busbar internal inspection robot provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of the structure of the control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The core of this application is to provide a control method, control device, control equipment, and computer-readable storage medium for a GIS busbar barrel internal inspection robot, which improves the efficiency of internal inspection of the busbar barrel, avoids the impact of the internal barrel environment on the robot's walking, and improves the efficiency of movement.

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0058] The following describes a control method for a GIS busbar internal inspection robot provided by the present application through an embodiment.

[0059] Please refer to Figure 1 , Figure 1 A flow chart of a control method for a GIS busbar internal inspection robot provided in an embodiment of the present application.

[0060] In this embodiment, the method may include:

[0061] S101, the robot's visual system obtains shape data and position data of the cylinder wall;

[0062] In this step, the visual system uses an image acquisition device (such as a camera) to shoot the GIS busbar tube wall, and uses an image processing algorithm to analyze the image to obtain the shape and position information of the tube wall. For example, the contour of the tube wall is determined by an edge detection algorithm to obtain shape data; the position of the tube wall in the robot coordinate system is determined by a feature matching or positioning algorithm.

[0063] Install appropriate visual sensors on the robot and configure corresponding image processing software or algorithm modules. After the sensor collects the image of the cylinder wall, it transmits it to the processor, which runs the image processing algorithm to extract shape and position data.

[0064] It can be seen that this step provides basic data for the subsequent robot motion control and fault detection, enabling the robot to understand its own environment and better adapt to the complex spatial structure inside the cylinder.

[0065] S102, matching the wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information;

[0066] Based on S101, this step aims to calculate the angle that the wheels need to be adjusted based on the shape and position information of the cylinder wall and the robot's kinematic model to ensure that the robot can smoothly drive along the cylinder wall. For example, if the cylinder wall is curved, the steering angle of the wheel needs to be calculated based on parameters such as the degree of curvature and the robot's wheelbase.

[0067] In the robot control system, a model including the relationship between the cylinder wall shape and position data and the wheel angle is established. The acquired shape and position data are input into the model, and the wheel angle control information is obtained through calculation.

[0068] It can be seen that this step can realize precise motion control of the robot, improve the driving stability and adaptability of the robot in a complex cylinder wall environment, and avoid collision with the cylinder wall.

[0069] S103, performing angle control on the wheels of the robot based on the wheel angle control information;

[0070] Based on S102, this step adjusts the angle of the wheel through the motor drive system according to the calculated wheel angle control information. For example, a control signal is sent to the steering motor of the wheel to make the motor rotate to a corresponding angle, thereby driving the wheel to turn.

[0071] Optionally, a wheel steering motor and its driving circuit may be provided in the hardware structure of the robot. The control system converts the wheel angle control information into a motor driving signal and sends it to the driving circuit, which controls the motor to rotate.

[0072] It can be seen that this step ensures that the robot can travel along the predetermined path, thereby improving the efficiency and accuracy of the robot's movement in the busbar barrel.

[0073] S104, when the robot moves forward, real-time fault point detection is performed through the visual system;

[0074] Based on S103, this step aims to continuously collect images of the cylinder wall by the visual system during the robot's forward movement, and use the fault detection algorithm to analyze the images to determine whether there are fault points. For example, by analyzing the color, texture and other features of the image, it is possible to detect whether there are abnormal conditions such as discharge marks, overheated areas or damage.

[0075] Optionally, a fault detection algorithm module can be integrated into the image processing software of the vision system. As the robot moves forward, the vision system continuously acquires images and passes them to the module for analysis.

[0076] It can be seen that in this step, the fault point in the busbar barrel can be discovered in time, the timeliness of fault detection is improved, and timely information is provided for subsequent maintenance and processing.

[0077] S105, when a fault point is detected, taking photos of the fault point to obtain fault photo data;

[0078] Based on S104, once a fault point is detected, the visual system immediately takes a picture of the fault point to obtain more detailed image information as fault photo data, in order to more accurately record the fault situation for subsequent analysis.

[0079] In the control software of the visual system, a trigger mechanism is set. When a fault point is detected, the shooting function is started and the photos taken are stored in the storage device of the robot. This can provide intuitive and detailed image data for fault analysis, which helps to accurately determine the type and degree of the fault.

[0080] S106, using a fault feature recognition model to analyze the fault photo data to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on the fault training data;

[0081] Based on S105, the fault feature recognition model is obtained by machine learning training through a large amount of fault training data. The fault photo data is input into the model, and the model recognizes the features such as discharge marks, overheating areas and damaged locations in the fault photos according to the learned feature patterns, and outputs the corresponding fault feature data.

[0082] In the robot control system, the fault feature recognition model is pre-trained and integrated into the analysis software. After the fault photo data is obtained, it is input into the model for analysis. It can automatically and accurately identify the key features of the fault, improve the efficiency and accuracy of fault diagnosis, and provide a basis for making reasonable disposal decisions.

[0083] S107, generating abnormal handling decision information based on the fault feature data, and sending the abnormal handling decision information to a preset terminal.

[0084] Based on S106, this step generates abnormal handling decision information for the fault based on the identified fault feature data and combined with the pre-set handling rules and strategies. The information is then sent to a preset terminal device, such as a maintenance personnel's mobile phone or a computer in a monitoring center, through a communication module.

[0085] In the robot control system, a fault handling rule base is established and a communication module is set up. The rule base is queried based on the fault feature data to generate decision information, and the information is sent out through the communication module. The fault information is transmitted and processed in a timely manner, so that maintenance personnel can quickly understand the fault situation and take corresponding measures to improve maintenance efficiency.

[0086] In summary, this embodiment obtains data through the visual system to provide a basis for wheel control, ensure that the robot adapts to the barrel wall environment, and improve movement efficiency. Real-time fault detection and analysis can accurately identify fault characteristics, generate decision information, and effectively improve the accuracy and timeliness of busbar barrel internal inspection.

[0087] Optionally, also include:

[0088] Step 1: Perform real-time gas detection using a gas detector installed on the robot to obtain gas composition data;

[0089] Step 2: Send the gas composition data to the corresponding mobile terminal so that the mobile terminal generates personnel maintenance protection measures information based on the gas composition data.

[0090] In this optional solution, the gas detector installed on the robot uses a specific gas detection principle (such as a chemical sensor producing an electrical signal change in response to a chemical reaction of a specific gas) to detect the surrounding gas composition and obtain gas composition data.

[0091] The robot is equipped with a suitable gas detector, whose sensor contacts the surrounding gas, and the detected signal is processed to obtain gas composition data, which is sent to the mobile terminal through the communication module. Maintenance personnel can understand the gas environment in the cylinder in advance based on the gas composition data, formulate scientific personnel maintenance protection measures, and ensure the safety of maintenance personnel.

[0092] Optionally, also include:

[0093] Step 1, the robot monitors the tire grip data of the wheel in real time;

[0094] Step 2, judging whether the wheel is slipping based on the tire grip data;

[0095] Step 3: If yes, the output torque of the wheel is controlled.

[0096] In this optional solution, the robot monitors the tire grip data through sensors installed on the wheels (such as pressure sensors or acceleration sensors), and determines whether the wheels are slipping based on the change in grip. When the grip is lower than a certain threshold, it is judged as slipping, and the wheel output torque is adjusted to improve it.

[0097] In this optional solution, sensors are installed on the relevant parts of the wheels, and the sensors transmit grip data to the control system. The control system determines whether there is slippage based on the set algorithm, and if so, adjusts the motor output torque to control the rotation of the wheel.

[0098] It can effectively prevent wheel slippage, ensure the robot's stable movement in the busbar, and improve the reliability and safety of the robot's movement.

[0099] Optionally, also include:

[0100] Step 1: The robot obtains obstacle information and its own posture information through sensors;

[0101] Step 2: determine whether there is an obstacle in the forward direction based on the obstacle information;

[0102] Step 3: If yes, the robot's travel direction is regulated;

[0103] Step 4, judging whether there is a rollover situation based on the vehicle's own posture information;

[0104] Step 5: If yes, adjust the speed and angle of the robot's wheels.

[0105] In this optional solution, the robot obtains obstacle information and its own posture information through multiple sensors. For obstacle information, a distance sensor (such as an ultrasonic sensor or a laser radar) is used to detect the distance of the object in front to determine whether there is an obstacle; for its own posture information, a gyroscope and accelerometer are used to determine whether it has rolled over.

[0106] In this optional solution, distance sensors and attitude sensors are installed at appropriate locations on the robot. When an obstacle is detected, the control system adjusts the direction of travel; when a rollover risk is detected, the wheel speed and angle are adjusted.

[0107] It can avoid obstacles in time to prevent the robot from collision and damage; at the same time, it can avoid rollover to ensure the robot's own safety and normal operation.

[0108] Optionally, also include:

[0109] Step 1: Locally store the fault photo data and gas composition data;

[0110] Step 2: When connected to the network, the fault photo data and gas composition data are sent to the corresponding server based on a preset encryption algorithm.

[0111] In this optional solution, the fault photo data and gas composition data are stored in the robot's local storage device (such as a hard disk or flash memory). When connected to the network, the data is encrypted using an encryption algorithm and then sent to the corresponding server to ensure secure data transmission and storage.

[0112] In this optional solution, a storage module and a network communication module, as well as an encryption algorithm program, are configured on the robot. The data is first stored locally, and after the network is connected, it is encrypted and sent according to the encryption algorithm.

[0113] It can realize local backup and secure remote transmission of data, which is convenient for subsequent analysis and protects the security and privacy of data.

[0114] Optionally, also include:

[0115] Step 1: When the robot cannot receive the signal from the remote controller, it starts the autonomous return function;

[0116] Step 2, obtaining a return path based on the traveling process;

[0117] Step 3: Perform return processing based on the return path.

[0118] In this optional solution, when the robot cannot receive the remote control signal, it starts the autonomous return function. By recording the path information during the travel process (such as using the odometer and gyroscope to record the position and direction changes), the return path is obtained, and then the robot returns along the path.

[0119] A signal detection module and a path recording module can be set in the robot control system. When the signal is lost, the robot is controlled to return according to the recorded path information.

[0120] It can be seen that the autonomy and reliability of the robot can be improved, the loss of the robot due to remote control signal problems can be avoided, and timely recovery and subsequent use can be facilitated.

[0121] Optionally, also include:

[0122] Step 1, monitor the power consumption according to a preset time period to obtain power consumption data;

[0123] Step 2: When the power data is less than the preset power data, a power shortage alarm signal is sent.

[0124] In this optional solution, the power of the robot is monitored according to a preset time period through a power monitoring circuit (such as using a power detection chip in a battery management system), and when the power is lower than a preset value, a power shortage alarm signal is issued.

[0125] A power monitoring circuit and an alarm module can be set in the power management system of the robot. The circuit regularly detects the power level, and when the power level is lower than the preset level, the alarm module sends a signal.

[0126] It can promptly remind the operator that the robot is low on power so that charging measures can be taken to prevent the robot from shutting down due to power exhaustion and affecting work.

[0127] The following is an introduction to a control device for a GIS busbar internal inspection robot provided in an embodiment of the present application. The control device for a GIS busbar internal inspection robot and a control method for a GIS busbar internal inspection robot described below can be referenced to each other.

[0128] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a control device for a GIS busbar internal inspection robot provided in an embodiment of the present application.

[0129] In this embodiment, the device may include:

[0130] The environment perception module 100 is used to obtain the shape data and position data of the cylinder wall;

[0131] A control information generating module 200 is used to match the wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information;

[0132] The wheel control module 300 is used to control the angle of the wheels of the robot based on the wheel angle control information;

[0133] A fault detection module 400 is used to perform real-time fault point detection through a visual system when the robot moves forward;

[0134] The fault photo collection module 500 is used to collect photos of the fault point when a fault point is detected to obtain fault photo data;

[0135] The fault analysis module 600 is used to analyze the fault photo data using a fault feature recognition model to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on the fault training data;

[0136] The handling decision sending module 700 is used to generate abnormal handling decision information based on the fault characteristic data, and send the abnormal handling decision information to a preset terminal.

[0137] This application also provides control equipment, please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of a control device provided in an embodiment of the present application. The control device may include:

[0138] Memory for storing computer programs;

[0139] The processor is used to implement the steps of any of the above-mentioned control methods of the GIS busbar internal inspection robot when executing the computer program.

[0140] like Figure 3 , which is a schematic diagram of the structure of the control device, the control device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11 and the communication interface 12 communicate with each other through the communication bus 13.

[0141] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices, etc.

[0142] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0143] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0144] The robot's vision system acquires the shape and position data of the cylinder wall;

[0145] Match the robot's wheel state data based on shape data and position data to obtain wheel angle control information;

[0146] The angle of the robot's wheels is controlled based on the wheel angle control information;

[0147] As the robot moves forward, real-time fault point detection is performed through the visual system;

[0148] When a fault point is detected, a photo of the fault point is collected to obtain fault photo data;

[0149] The fault feature recognition model is used to analyze the fault photo data to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on the fault training data;

[0150] Generate abnormal handling decision information based on the fault feature data, and send the abnormal handling decision information to a preset terminal.

[0151] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0152] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0153] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0154] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the control device in the embodiment of the present application. In actual applications, the control device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0155] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the control method of any one of the above-mentioned GIS busbar internal inspection robots can be implemented.

[0156] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0157] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0158] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0159] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0160] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0161] The control method, control device, control equipment, and computer-readable storage medium of a GIS busbar internal inspection robot provided by the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A control method for a GIS busbar internal inspection robot, characterized in that: include: The robot's vision system acquires the shape and position data of the cylinder wall; Matching wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information; Performing angle control on the wheels of the robot based on the wheel angle control information; When the robot moves forward, real-time fault point detection is performed through the visual system; When a fault point is detected, a photo of the fault point is collected to obtain fault photo data; The fault feature recognition model is used to analyze the fault photo data to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on the fault training data; Generate abnormal handling decision information based on the fault characteristic data, and send the abnormal handling decision information to a preset terminal.

2. The control method according to claim 1, characterized in that: Also includes: Performing real-time gas detection by a gas detector installed on the robot to obtain gas composition data; The gas composition data is sent to a corresponding mobile terminal so that the mobile terminal generates personnel maintenance protection measures information based on the gas composition data.

3. The control method according to claim 2, characterized in that: Also includes: The robot monitors the tire grip data of the wheels in real time; Determining whether the wheel is slipping based on the tire grip data; If so, the output torque of the wheel is controlled.

4. The robot system according to claim 3, characterized in that: Also includes: The robot obtains obstacle information and its own posture information through sensors; Determine whether there is an obstacle in the forward direction based on the obstacle information; If yes, the travel direction of the robot is regulated; Determine whether there is a rollover situation based on the self-posture information; If so, the speed and angle of the wheels of the robot are adjusted.

5. The control method according to claim 4, characterized in that: Also includes: Locally storing the fault photo data and the gas composition data; When connected to the network, the fault photo data and the gas composition data are sent to the corresponding server based on a preset encryption algorithm.

6. The control method according to claim 5, characterized in that: Also includes: When the robot fails to receive a signal from the remote controller, the autonomous return function is activated; Obtain the return path based on the traveling process; Perform return processing based on the return path.

7. The control method according to claim 6, characterized in that: Also includes: Monitor the power quantity according to the preset time period to obtain the power quantity data; When the power data is less than the preset power data, a power shortage alarm signal is sent.

8. A control device for a GIS busbar internal inspection robot, characterized in that: include: An environmental perception module is used to obtain the shape data and position data of the cylinder wall; A control information generating module, used for matching the wheel state data of the robot based on the shape data and the position data to obtain wheel angle control information; A wheel control module, used for controlling the angle of the wheels of the robot based on the wheel angle control information; A fault detection module, used for performing real-time fault point detection through the visual system when the robot moves forward; A fault photo collection module is used to collect photos of the fault point when a fault point is detected to obtain fault photo data; A fault analysis module is used to analyze the fault photo data using a fault feature recognition model to obtain fault feature data; wherein the fault feature data includes: discharge trace data, overheating area data, and damage location data; wherein the fault feature recognition model is a recognition model obtained by model training based on fault training data; The handling decision sending module is used to generate abnormal handling decision information based on the fault characteristic data, and send the abnormal handling decision information to a preset terminal.

9. A control device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the control method according to any one of claims 1 to 7 when executing the computer program.

10. 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 control method according to any one of claims 1 to 7 are implemented.

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