Robot inspection path optimization method and system applied to extra-high voltage transformer substation

Abstract

The invention relates to the technical field of substation operation and maintenance, in particular to a robot inspection path optimization method and system applied to an extra-high voltage substation, and the method comprises the steps: triggering the dynamic optimization of an inspection path in response to the detected equipment operation state change or inspection environment change of the extra-high voltage substation; based on a preset transformer substation environment space model and real-time equipment states and environment parameters collected in real time, a first target inspection path is generated through path cost analysis, and simulation verification and fine adjustment are carried out; when interference risks, positioning deviation overrun and / or multi-machine conflicts exist in execution of the first target inspection path, path planning is updated based on local environment information collected by the robot in real time, and a second target inspection path is generated; and executing the second target inspection path. According to the invention, the equipment state and the environment change can be responded in real time, the inspection path can be automatically adjusted, key inspection tasks are prevented from being omitted, and the continuity of the inspection process is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation operation and maintenance, and particularly relates to a robot inspection path optimization method and system applied to an extra-high voltage substation. BACKGROUND

[0002] In the operation and maintenance management of an extra-high voltage substation, using a robot to automatically inspect a GIS (gas insulated switchgear) device group has become an important means to improve operation and maintenance efficiency and reliability. Currently, an inspection robot usually performs an inspection task according to a preset fixed path.

[0003] The existing path planning method cannot be adaptively adjusted when facing sudden or local changes in device operation states or inspection environments in an extra-high voltage substation, such as a sudden fault alarm of a specific GIS interval or an original path being blocked due to temporary obstacles in a device area. SUMMARY

[0004] The present application provides a robot inspection path optimization method and system applied to an extra-high voltage substation, which can effectively solve the problems in the background art.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows: The robot inspection path optimization method applied to an extra-high voltage substation comprises the following steps: In response to detecting a change in a device operation state or a change in an inspection environment of the extra-high voltage substation, triggering dynamic optimization of an inspection path; Based on a preset substation environment space model and real-time device states and environment parameters collected in real time, generating a first target inspection path through path cost analysis, and performing simulation verification and fine-tuning; When the first target inspection path has an interference risk, positioning deviation exceeds a limit, and / or multiple robots conflict during execution, updating path planning based on local environment information collected by the robot in real time, and generating a second target inspection path; Executing the second target inspection path, correcting a working posture through visual and positioning information and confirming path tracking deviation, and when the deviation is stable within an allowed range within a preset time, confirming that path optimization is completed.

[0006] Further, the change in the device operation state comprises one or more of GIS device gas leakage, local temperature exceeding a standard, and local discharge exceeding a standard.

[0007] Further, the change in the inspection environment comprises detecting a newly added obstacle and / or a height of a passable space below a GIS device being less than a height required for safe passage of the robot.

[0008] Further, the path cost analysis comprises: For slope and step obstacles, determining impassable areas or calculating passing cost by comparing environmental parameters with robot capability thresholds; For space obstacles, planning detours or calculating comprehensive passing cost by comparing passable spaces with robot size.

[0009] Further, the simulation verification and fine-tuning comprises: Importing a first target inspection path and a three-dimensional model of the robot in a three-dimensional simulation environment; Simulating the robot's full-process travel and mechanical arm movements; Real-time calculating the minimum distance between the robot and GIS equipment; When the minimum distance is less than a preset interference threshold, fine-tuning the path at a preset step size.

[0010] Further, the first target inspection path generation comprises: Accessing a preset substation environmental space model; Accessing real-time collected device abnormal position, terrain parameter, and space size data to form a comprehensive planning environment; Processing slope obstacles, step obstacles, and space obstacles through a multi-dimensional cost evaluation system; Based on the inspection task priority and path cost analysis, generating the first target inspection path using a path search algorithm.

[0011] Further, the second target inspection path generation comprises: Monitoring interference risks, positioning deviation overruns, and / or multi-robot conflicts during execution of the first target inspection path; Triggering local environmental information collection to obtain spatial data and device states of risk areas; Taking the robot's current position as the starting point, constructing a local planning area; Using corresponding path optimization strategies for different risk types; Generating the second target inspection path based on passing cost calculation.

[0012] Further, the path optimization completion confirmation comprises: Correcting the working posture through visual device feature matching; Fusing positioning deviation and visual deviation to obtain path tracking deviation; Setting a deviation monitoring period and stability determination criteria; When the deviation is continuously stable within the allowed range for a preset time, confirming that the optimization is complete.

[0013] Further, the visual equipment feature matching is: comparing the collected GIS equipment features with a preset equipment template library; determining a position deviation of the to-be-detected equipment in the visual field of view; generating a posture adjustment instruction to make the to-be-detected equipment return to the center of the field of view.

[0014] In another aspect, the application also provides a robot inspection path optimization system applied to an extra-high voltage substation, comprising: a dynamic triggering module, configured to trigger dynamic optimization of the inspection path in response to detection of a change in the operation state of equipment of the extra-high voltage substation or a change in the inspection environment; a global path planning module, configured to generate a first target inspection path through path cost analysis based on a preset substation environment space model and real-time collected equipment state and environment parameters, and to perform simulation verification and fine-tuning; a local path optimization module, configured to update path planning and generate a second target inspection path based on real-time collected local environment information of the robot when the first target inspection path has interference risks, positioning deviation out of limits, and / or multi-robot conflicts in execution; a path execution verification module, configured to execute the second target inspection path, correct the working posture through visual and positioning information, and confirm path tracking deviation, and when the deviation is stable within a permitted range within a preset time, confirm that the path optimization is completed.

[0015] Through the technical scheme of the application, the following technical effects can be achieved: through real-time capture of changes in the operation state of GIS equipment such as gas leakage, local temperature exceeding the standard, and local discharge exceeding the standard, and changes in the environment such as newly added obstacles and reduced passable space below the GIS equipment, dynamic optimization of the path is triggered to ensure that key equipment inspection tasks are given priority response; according to the substation environment space model and path cost analysis, combined with the robot capability threshold, the obstacle region and passable cost are determined, and at the same time, the robot advancing and action are simulated through three-dimensional simulation, the distance between equipment is detected, and the path is fine-tuned to effectively avoid the interference collision risk of the robot and the GIS equipment; in the path execution phase, for possible interference risks, positioning deviation out of limits, multi-robot conflicts, and other problems, path planning can be updated based on real-time local environment information to generate a second target inspection path adapted to the current scene, and at the same time, the path tracking accuracy is preserved through visual and positioning correction to improve the efficiency of the inspection task execution.

[0016] The above description is only a summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, which can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the accompanying drawings required by the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0018] Figure 1 A flowchart of a robot inspection path optimization method applied to an extra-high voltage substation according to the present application; Figure 2 A structural block diagram of a robot inspection path optimization system applied to an extra-high voltage substation according to the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments only represent some embodiments of the present application, rather than all the embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0021] As shown in Figure 1 The robot inspection path optimization method applied to an extra-high voltage substation according to the present application specifically includes the following steps: Step S1, in response to detecting a change in the operating state of the equipment of the extra-high voltage substation or a change in the inspection environment, triggering dynamic optimization of the inspection path; the change in the operating state of the equipment includes one or more of GIS equipment gas leakage, local temperature exceeding the standard, and local discharge exceeding the standard; the change in the inspection environment includes detecting a newly added obstacle and / or the height of the passable space below the GIS equipment being less than the required height for safe passage of the robot; Step S2: Based on the preset substation environmental space model and the real-time collected equipment status and environmental parameters, a first target inspection path is generated through path cost analysis, and simulation verification and fine-tuning are performed. The path cost analysis method includes: for slopes and stair obstacles, determining impassable areas or calculating passage costs by comparing environmental parameters with robot capability thresholds; for spatial obstacles, planning detours or calculating comprehensive passage costs by comparing passable space with robot size. The simulation verification and fine-tuning include simulating robot movement and actions in a three-dimensional simulation environment. If the minimum distance between the robot and the GIS equipment is detected to be less than a preset interference threshold, the first target inspection path is fine-tuned with a preset step size. Step S3: When the first target inspection path has interference risk, excessive positioning deviation and / or multi-machine conflict during execution, update the path planning based on the local environmental information collected by the robot in real time, and generate the second target inspection path. Step S4: Execute the second target inspection path, correct the working posture and confirm the path tracking deviation through visual and positioning information. When the deviation stabilizes within the allowable range within a preset time, the path optimization is confirmed to be complete.

[0022] In this embodiment, by capturing real-time changes in the operating status of GIS equipment, such as gas leaks, excessive local temperatures, and excessive partial discharge, as well as environmental changes such as the addition of obstacles and the reduction of passable space under the GIS equipment, dynamic path optimization is triggered to ensure that critical equipment inspection tasks are given priority. Based on the substation environmental spatial model and path cost analysis, obstacle areas and passage costs are determined by combining robot capability thresholds. At the same time, the robot's movement and actions are simulated through 3D simulation to detect equipment spacing and fine-tune the path, effectively avoiding the risk of interference and collision between the robot and GIS equipment. During the path execution phase, for potential interference risks, excessive positioning deviations, and multi-machine conflicts, the path planning can be updated based on real-time local environmental information to generate a second target inspection path adapted to the current scenario. At the same time, visual and positioning corrections are used to ensure path tracking accuracy and improve the efficiency of inspection task execution. By proactively capturing scene changes through triggering mechanisms, the first optimization phase completes basic path planning and risk pre-control, the second optimization dynamically corrects execution deviations, and the verification phase confirms the path execution effect. This enables rapid response to changes in equipment and the environment, proactively anticipating potential risks and calibrating the execution process in real time, ensuring that inspection tasks remain consistent and accurate in dynamic scenarios and avoiding overall inspection interruptions caused by local adjustments. In the first optimization phase, path cost analysis selects the optimal path while ensuring safety; simulation verification only makes local fine adjustments to paths with interference risks without increasing the path length; the second optimization is triggered only in necessary scenarios, reducing the time wasted on meaningless adjustments, thereby controlling the safe distance between the robot and the equipment and minimizing the inspection path and execution time, achieving a balance between safety redundancy and inspection efficiency.

[0023] In a specific implementation, as one example, a triggering system that combines multi-dimensional perception with precise judgment is constructed to ensure accurate detection of changes in equipment and environment, and to avoid invalid optimization caused by false triggers, thereby achieving timeliness and accuracy of dynamic path optimization.

[0024] Specifically, by building a multi-source sensing network covering the collection of equipment and environmental changes, sensing components are configured in the inspection robot and GIS equipment area. The robot body is equipped with sensors for collecting the status of GIS equipment, including sensors for detecting gas parameters, sensors for detecting equipment temperature, and sensors for detecting partial discharge. Environmental scanning equipment is configured in the robot's direction of travel and lateral direction to obtain data on the distribution of obstacles in the equipment area and the spatial dimensions of the space below the GIS equipment. The robot establishes a data interaction channel with the substation operation and maintenance backend to receive GIS equipment fault warning information issued by the backend.

[0025] Simultaneously, it is necessary to set the operating parameters of the sensing components, with the equipment status sensors collecting data at fixed intervals to ensure real-time tracking of equipment operation status; the environmental scanning equipment outputs scanning data at a fixed frequency to promptly capture environmental changes; and the maintenance backend uses a combination of instant push notifications and proactive robot queries to avoid information transmission delays or loss.

[0026] Furthermore, the triggering mechanism identifies valid changes by establishing hierarchical judgment rules. Among these, the judgment of changes in equipment operating status includes gas leakage judgment, local temperature exceeding the standard judgment, and partial discharge exceeding the standard judgment. Specifically, when the gas parameters collected by the equipment status sensor exceed the preset safety range multiple times consecutively, and the deviation between adjacent collected data meets the set requirements, it is judged as gas leakage of the GIS equipment; when the difference between the surface temperature of the GIS equipment collected by the equipment status sensor and the ambient temperature exceeds the preset threshold, and multiple consecutive collections meet this condition, it is judged as local temperature exceeding the standard; when the partial discharge collected by the equipment status sensor exceeds the preset qualified range, and this situation occurs multiple times within a set time, it is judged as partial discharge exceeding the standard.

[0027] The inspection environment change determination includes the determination of new obstacles and the determination of insufficient passable space height. Specifically, the scanning data obtained by the environmental scanning equipment is compared with the preset substation environmental space model. If the outline size of a new object is found to exceed the set value, and the position of the object remains stable in multiple consecutive scans, it is determined that a new obstacle has been detected. If the minimum space height below the GIS equipment calculated by the environmental scanning equipment is less than the height required for safe passage of the robot, which is the sum of the robot's body height and the preset safety margin, and this condition is met in multiple consecutive scans, it is determined that the passable space height below the GIS equipment is insufficient.

[0028] When any of the above-mentioned equipment operating status changes or the inspection environment changes, the robot's local control system immediately generates a change trigger signal, which contains key information such as the type of change, the location of the change, and the degree of change.

[0029] In this embodiment, the multi-source sensing network covers the device status and environmental scene. Combined with fixed-period data acquisition and dual-mechanism data interaction, it avoids missed detection of changes due to sensing blind spots or information delays. The hierarchical judgment rules introduce conditions such as continuous acquisition verification, deviation control, and size limitation to effectively filter out misjudgments caused by sensor errors and environmental interference, reduce invalid triggers, and reduce the resource consumption of path optimization. In implementing the above method, the sensor type is not limited to the sensor devices in the aforementioned embodiments. It can also be replaced with other sensor types that can stably collect equipment status or environmental parameters in an ultra-high voltage electromagnetic environment, as long as they have equivalent functions and can meet the detection accuracy requirements. The preset range, threshold, period, and other parameters in the judgment rules can be set according to the specific substation's safety standards, robot performance parameters, and operation and maintenance requirements. The triggering mechanism can also be adapted according to the substation's operation and maintenance needs. As long as its core is to achieve the detection and path optimization triggering of equipment status or environmental changes through multi-dimensional perception and collaborative judgment, it falls within the protection scope of this method.

[0030] In a specific implementation, as one example, the GIS equipment group in an ultra-high voltage substation is densely arranged in a suspended manner. The inspection path needs to adapt to the dynamic status of the equipment, terrain obstacles, and spatial constraints simultaneously. In this embodiment, a pre-set substation environmental spatial model is used as the basic framework. Cost analysis is performed using real-time equipment status and environmental parameters. Then, the path is verified and corrected in advance through 3D simulation. This ensures that the generated first target inspection path not only meets the requirements for safe passage but also prioritizes the response to critical inspection needs, avoiding inspection interruptions or efficiency reductions due to path defects during the actual execution phase. The specific steps include: Step S211: Retrieve the pre-constructed substation environmental space model. The substation environmental space model needs to include static spatial information such as the three-dimensional coordinates of the GIS equipment group, equipment spacing, ground slope distribution, step location and size, etc., as the basic environmental benchmark for path planning. Then, access the real-time collected data and overlay the dynamic data such as the abnormal location of GIS equipment obtained by the equipment status sensor in step S1, the slope gradient and step elevation difference collected by the environmental scanning device, and the real-time height of the space below the GIS equipment, etc., onto the pre-constructed substation environmental space model to form a comprehensive planning environment that includes static benchmarks and dynamic information. Step S212: Different obstacles are categorized and processed by establishing a multi-dimensional cost assessment system. Specifically, for slope and staircase obstacles, the slope gradient and staircase elevation difference values ​​in the integrated planning environment are extracted and compared with the robot's preset capability thresholds. If the slope gradient exceeds the robot's maximum climbing ability or minimum braking ability, the area is marked as impassable. If it is within the capability range, the passage cost of the path is calculated based on the robot's energy consumption; the higher the energy consumption, the higher the cost. If the staircase elevation difference exceeds the robot's maximum obstacle-crossing height, it is marked as impassable. Conversely, the cost is calculated based on the passage difficulty; the larger the elevation difference, the higher the cost. For handling spatial obstacles, the system obtains the real-time height of the passable space below the GIS equipment and the outline size of newly added obstacles, and compares them with the safe passage size of the robot. If the height of the passable space is less than the safe passage height of the robot, or if the obstacle causes insufficient path width, a detour path is planned. If the space meets the requirements, the system calculates the comprehensive passage cost of the path segment by combining the cost of slopes and steps and the difficulty of space passage. Step S213: Determine the path planning priority, set the abnormal location of GIS equipment as the high priority target point, and set the normal inspection point as the regular priority. Based on the multi-dimensional cost evaluation system, the path search algorithm is adopted to start from the current position of the robot, prioritize connecting the high priority target point, and select the route with the lowest comprehensive travel cost to generate the first target inspection path.

[0031] Furthermore, the implementation methods for 3D simulation verification and path fine-tuning include: Step S221: Build a three-dimensional simulation environment that matches the actual substation environment in scale, and import the first target inspection path and the robot's three-dimensional model. The robot's three-dimensional model includes the motion parameters of the telescopic robotic arm. Step S222: Simulate the robot's full-process inspection. Set the robot's travel speed according to the first target inspection path, and simultaneously simulate the extension, turning and other actions of the robotic arm at each inspection point. Calculate the minimum distance between the robot body, robotic arm and surrounding GIS equipment in real time. Step S223: Perform path fine-tuning. If the minimum spacing is detected to be less than the preset interference threshold during the simulation, adjust the path segment away from the GIS equipment with a fixed step size. After adjustment, re-simulate and detect until the minimum spacing of all road segments is greater than or equal to the interference threshold, thus forming the first target inspection path after fine-tuning.

[0032] It should be noted that the preset substation environment spatial model can be constructed using different methods such as laser scanning modeling and 3D software modeling, as long as it can accurately reflect the spatial relationship between the substation GIS equipment group and the terrain; the energy consumption calculation method and passage difficulty assessment standard in path cost analysis can be set in combination with robot model, battery capacity, and operation and maintenance requirements; the tools for building the 3D simulation environment, interference threshold, and fine-tuning step size can be adjusted according to the equipment accuracy requirements and robot movement range; the path search algorithm can also be replaced with different types of optimization algorithms; in addition, the safety margin in the robot's safe passage size and the hierarchy of path planning priorities can be adjusted according to the substation safety standards and the importance of the equipment.

[0033] In this embodiment, by combining a preset model with real-time data, the problem of static models being detached from the actual environment is avoided. At the same time, through multi-dimensional cost analysis, the path is ensured to balance safety and efficiency, and the path will not fail due to improper handling of a single obstacle. The three-dimensional simulation verification covers the robot's movement and the robotic arm's actions, and detects and eliminates the risk of equipment interference in advance, eliminating the need for temporary adjustments during actual execution and reducing the probability of inspection interruption. The abnormal points of the equipment are set as high priority to ensure that key equipment is inspected first, avoiding the problem of delayed abnormal response caused by the fixed order of traditional paths.

[0034] In a specific implementation, as one example, during the inspection of GIS equipment groups in UHV substations, although the initial inspection path has been verified through simulation, in actual execution, issues such as dynamic environmental changes, accumulated robot positioning deviations, and interference from multi-robot collaboration may still prevent the path from being safely advanced. Therefore, it is necessary to precisely adjust the path to address specific problems encountered during execution to ensure the continuous and safe conduct of the inspection task and avoid efficiency losses or safety hazards caused by a fixed path. Specifically, this includes the following steps: Step S31: During the robot's execution of the first target inspection path, the environmental scanning device captures the changes in the position of surrounding GIS equipment and the addition of temporary obstacles in real time to determine whether there is a risk of interference between the robot body and the robotic arm and the equipment; the positioning component continuously collects the current position and compares it with the preset coordinates of the first target inspection path to calculate the position deviation; through real-time data interaction with the backend, the robot obtains the real-time position and path information of other inspection robots to determine whether there is a risk of conflict due to multiple robots' travel routes intersecting or occupying the same passage space.

[0035] Step S32: When the environmental scanning device detects that the minimum distance between the robot and the GIS device is less than the preset safety value, the position deviation calculated by the positioning component exceeds the allowable range, or the multi-machine position data shows that multiple robots will arrive at the same spatial node within a preset time, it is determined that there is a path execution risk, and the local information collection and path update process is triggered.

[0036] Step S33: After determining that there is a path execution risk, the robot pauses its current movement, controls the environmental scanning device to focus on the risk area, and increases the scanning frequency to obtain denser spatial data; supplements the real-time status of GIS devices in the risk area by using device status sensors to avoid exacerbating path risks due to changes in device status; the positioning component combines the coordinates of surrounding fixed devices to correct its own positioning deviation, ensuring the accuracy of the local environmental data's positional benchmark; filters valid data to remove outliers caused by sensor noise interference, extracts key spatial parameters of the risk area, and integrates them into a local environmental information package as the basis for path updates.

[0037] Step S34: Based on the local environment information package, starting from the robot's current position and ending at the unfinished inspection points in the first target inspection path, construct a local planning area to avoid making meaningless comprehensive adjustments to the overall path. More specifically, optimization paths are implemented for different risk types, including: When there is a risk of interference, prioritize detour routes away from the risky equipment, and combine the size of the passable space in the local environment to ensure that the distance between the robot body and the equipment when the robot arm moves meets the safety requirements. When the positioning deviation exceeds the limit, the path coordinates of the deviation section are adjusted based on the corrected positioning data to make the new path conform to the actual terrain. At the same time, the coordinates of adjacent fixed equipment are incorporated as positioning reference points to avoid the deviation from accumulating again. When multiple robots are in conflict, a conflict-free local path is generated by combining the real-time paths of other robots and either by staggering time or by detouring in space.

[0038] Step S35: Calculate the travel cost of the local path, select the route with the lowest cost that meets the safety requirements, generate the second target inspection path, and simultaneously send the new path information to the substation operation and maintenance backend.

[0039] It should be noted that the risk judgment conditions in the real-time risk monitoring module can be set according to the accuracy requirements of substation equipment and the performance parameters of the robot; the type of equipment for collecting local environmental information can be replaced with other sensing components that can stably acquire spatial data and equipment status, and the collection frequency can also be adjusted according to the urgency of the risk; the path local replanning algorithm can adopt different types of optimization algorithms, as long as it can generate a safe and efficient path based on local information; the solution to multi-machine conflicts can also be different strategies such as time staggering, spatial detour, or priority coordination, depending on the number of robots in the substation and the size of the passage space; in addition, the triggering time for the robot to pause its movement and the scope of the local planning area can be adjusted in combination with the urgency of the inspection task and the scope of risk impact. As long as the second target inspection path is generated based on the logic of the above embodiments, it falls within the protection scope of this method.

[0040] In this embodiment, the real-time monitoring module continuously monitors for sudden risks during execution, avoiding inspection interruptions or equipment collisions due to delayed risk detection; by strengthening the collection of data in risk areas, it avoids data redundancy and processing delays caused by full-area collection, and at the same time, it combines equipment status to supplement the collection, improving the adaptability of path adjustments; it only replans local risk sections without reconstructing the overall path, reducing the consumption of computing resources, ensuring that the inspection task can be quickly resumed, and prioritizing the coverage of high-priority inspection points.

[0041] In a specific implementation, as one example, when actually moving along the second target inspection path, minor terrain changes, robotic arm inertia, and positioning signal interference may still cause the robot's working posture to deviate or its path tracking to go astray, affecting the accuracy of inspection data acquisition or causing safety hazards. Therefore, by establishing dynamic posture correction, real-time deviation confirmation, and stability verification, we can ensure that the robot completes the inspection with precise posture while moving along the optimized path. By judging the stability of deviations, we can avoid subsequent execution risks and ultimately confirm the path optimization effect. The specific implementation is as follows: Step S41: After receiving the second target inspection path, the robot starts execution according to the preset travel speed and inspection sequence of the path. At the same time, it collects the GIS equipment features around the inspection points in real time and continuously collects the robot's current position coordinates and attitude parameters. Step S42: Compare the GIS device features with the preset device template library to determine the positional deviation of the device to be inspected in the visual field of view. If the device features deviate from the center of the field of view, generate a posture adjustment command. The command is transmitted to the robot control system to adjust the vehicle's travel direction or the extension angle of the robotic arm so that the device to be inspected returns to the center of the field of view. At the same time, combined with the coordinates of the surrounding fixed GIS devices, the robot's own positioning deviation is corrected to ensure that the travel direction is consistent with the preset direction of the second target inspection path. Step S43: During the process of the robot executing the second target inspection path, the deviation value between the robot's current position and the preset coordinates of the path is calculated at fixed intervals, including lateral deviation and longitudinal deviation; by identifying fixed reference points around the path, the preset position of the reference points in the visual image is compared with the actual position, and the visual deviation value is calculated; the positioning deviation and visual deviation are fused to obtain a more accurate path tracking deviation result, avoiding misjudgment of deviation caused by interference of a single component. Step S44: Set the deviation monitoring cycle and stability judgment criteria. Record the path tracking deviation results at fixed time intervals. If the deviation values ​​recorded multiple times are all less than the allowable range and the fluctuation amplitude of the deviation values ​​is less than the preset fluctuation threshold, start timing. When the deviation remains stable within the allowable range for a preset time, and no deviation exceeds the allowable value during the period, and the fluctuation amplitude remains within the threshold, it is determined that the path tracking deviation has reached a stable state. The robot sends a path optimization completion signal to the substation operation and maintenance backend and simultaneously uploads the deviation stability record, the attitude correction data of each inspection point, and the inspection data. After the backend receives the signal and verifies the data integrity, it confirms that the path optimization has been completed. The robot can continue to complete the remaining inspection tasks according to the second target inspection path, or return to the charging area after the task is completed.

[0042] In this embodiment, by using vision and positioning to perform coordinated correction, the robot is ensured to travel along the optimized path and its working posture is adjusted to place the device features in the best acquisition position, thereby improving the accuracy of inspection data. By fusing positioning and vision deviation data, misjudgments caused by interference from a single component are reduced. At the same time, stability timing verification is used to avoid subsequent risks caused by short-term deviations being acceptable but long-term fluctuations.

[0043] Based on the same inventive concept as the robot inspection path optimization method for UHV substations described in the foregoing embodiments, this invention also provides a robot inspection path optimization system for UHV substations, such as... Figure 2 As shown, the system includes: The dynamic triggering module is used to dynamically optimize the inspection path in response to changes in the operating status of equipment in the UHV substation or changes in the inspection environment. The global path planning module is used to generate the first target inspection path based on the preset substation environmental space model and the real-time collected equipment status and environmental parameters, and to perform simulation verification and fine-tuning. The local path optimization module is used to update the path planning and generate a second target inspection path based on the local environmental information collected by the robot in real time when the first target inspection path has interference risk, excessive positioning deviation and / or multi-machine conflict during execution. The path execution verification module is used to execute the second target inspection path, correct the working posture through visual and positioning information and confirm the path tracking deviation. When the deviation stabilizes within the allowable range within a preset time, the path optimization is confirmed to be complete.

[0044] The system described above in this invention can effectively implement the robot inspection path optimization method applied to ultra-high voltage substations, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0045] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for optimizing robot inspection paths in ultra-high voltage substations, characterized in that, include: In response to the detection of changes in the operating status of equipment in UHV substations or changes in the inspection environment, dynamic optimization of the inspection path is triggered. Based on the preset substation environmental space model and the real-time equipment status and environmental parameters collected in real time, the first target inspection path is generated through path cost analysis, and then simulation verification and fine-tuning are performed. When the first target inspection path has interference risk, excessive positioning deviation and / or multi-machine conflict during execution, the path planning is updated based on the local environmental information collected by the robot in real time, and a second target inspection path is generated. The second target inspection path is executed, and the working posture is corrected and the path tracking deviation is confirmed through visual and positioning information. When the deviation stabilizes within the allowable range within a preset time, the path optimization is confirmed to be complete.

2. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 1, characterized in that, The changes in the operating status of the equipment include one or more of the following: gas leakage in the GIS equipment, excessive local temperature, and excessive partial discharge.

3. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 2, characterized in that, The changes in the inspection environment include the detection of new obstacles and / or the height of the passable space under the GIS equipment being less than the height required for the robot to pass safely.

4. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 3, characterized in that, The path cost analysis includes: For ramps and staircases, the inaccessible areas are determined or the passage cost is calculated by comparing environmental parameters with the robot's capability threshold. For spatial obstacles, detours or overall passage costs are planned by comparing the passable space with the robot's size.

5. The robot inspection path optimization method applied to UHV substations according to claim 4, characterized in that, The simulation verification and fine-tuning include: Import the first target inspection path and the robot's 3D model into the 3D simulation environment; Simulate the entire process of robot movement and robotic arm actions; Real-time calculation of the minimum distance between robots and GIS equipment; When the minimum spacing is less than the preset interference threshold, the path is fine-tuned with a preset step size.

6. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 4, characterized in that, The generation of the first target inspection path includes: Retrieve the preset substation environmental space model; By integrating real-time data on abnormal device locations, terrain parameters, and spatial dimensions, a comprehensive planning environment can be formed. Slope obstacles, stepped obstacles, and spatial obstacles are categorized and handled using a multi-dimensional cost assessment system. Based on the analysis of inspection task priority and path cost, a path search algorithm is used to generate the first target inspection path.

7. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 6, characterized in that, The generation of the second target inspection path includes: Monitor the risk of interference, excessive positioning deviation, and / or multi-machine conflict during the execution of the primary target inspection path; Trigger local environmental information collection to obtain spatial data and equipment status of the risk area; Starting from the robot's current position, construct a local planning area; Adopt corresponding path optimization strategies for different risk types; The second target inspection path is generated based on the passage cost calculation.

8. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 1, characterized in that, The confirmation path optimization is complete, including: Correcting working posture by matching features from visual devices; The path tracking error is obtained by fusing positioning error and visual error; Establish deviation monitoring cycles and stability judgment criteria; When the deviation remains stable within the allowable range for a preset time, the optimization is considered complete.

9. The method for optimizing robot inspection paths in ultra-high voltage substations according to claim 8, characterized in that, The visual device feature matching is as follows: The collected GIS equipment features are compared with the preset equipment template library; Determine the positional deviation of the device under test in the visual field of view; Generate attitude adjustment commands to bring the device under test back to the center of the field of view.

10. A robot inspection path optimization system applied to ultra-high voltage substations, characterized in that, include: The dynamic triggering module is used to dynamically optimize the inspection path in response to changes in the operating status of equipment in the UHV substation or changes in the inspection environment. The global path planning module is used to generate the first target inspection path based on the preset substation environmental space model and the real-time collected equipment status and environmental parameters, and to perform simulation verification and fine-tuning. The local path optimization module is used to update the path planning and generate a second target inspection path based on the local environmental information collected by the robot in real time when the first target inspection path has interference risk, excessive positioning deviation and / or multi-machine conflict during execution. The path execution verification module is used to execute the second target inspection path, correct the working posture through visual and positioning information and confirm the path tracking deviation. When the deviation stabilizes within the allowable range within a preset time, the path optimization is confirmed to be complete.

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