Power distribution room intelligent inspection control method and system based on robot cooperation

Abstract

The invention provides a power distribution room intelligent inspection control method and system based on robot collaboration, and the method comprises the steps: obtaining the equipment information of all power distribution equipment in a power distribution room, generating an equipment inspection sequence based on a preset inspection priority algorithm according to the equipment information, and carrying out the inspection according to the movement capability of an unmanned vehicle and a robot dog. Distributing inspection tasks based on the equipment inspection sequence to generate target inspection routes corresponding to the unmanned vehicle and the robot dog, respectively pushing the target inspection routes to the corresponding robot control terminals, and controlling the unmanned vehicle and the robot dog to execute the corresponding inspection tasks by the robot control terminals; and optimizing and adjusting the target inspection route corresponding to the unmanned vehicle and the robot dog until the inspection task is completed. According to the invention, efficient and reliable cooperation of task allocation and path planning among different machines can be realized, environment change is sensed in real time, the inspection route is adaptively adjusted, and the inspection efficiency and the inspection effect are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution room inspection, and in particular to a power distribution room intelligent inspection control method and system based on robot collaboration. Background Art

[0002] The distribution room is a key unit in the power system. The operating status of each distribution equipment in the distribution room will directly affect the stability and safety of power distribution. It is necessary to pay attention to the operating status of each distribution equipment in the distribution room and promptly discover operating hazards to ensure the safe and stable operation of the power system.

[0003] The existing intelligent inspection scenarios of power distribution rooms mainly improve inspection efficiency and reduce inspection costs by introducing inspection robots. However, there are many distribution cabinets in the actual distribution room. The dense electrical equipment makes the passages in the distribution room narrow and there are irregular obstacles. These factors make the mobile path planning of the inspection robot extremely complicated, resulting in reduced inspection efficiency in actual applications. Although in theory, using multiple robots to perform inspection tasks at the same time can further improve inspection efficiency, there are significant differences in the movement ability, perception range and task execution methods of different robots. How to convert vague inspection tasks into specific robot operation instructions to ensure efficient coordination of different robots when performing inspection tasks, thereby effectively improving the intelligent inspection efficiency and inspection effect of the distribution room has become a technical problem that needs to be solved in the field. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent inspection control method for a distribution room based on robot collaboration. By combining a task sorting mechanism that generates an inspection sequence based on a comprehensive analysis of the equipment's operating status and equipment location information, a spatial dimension inspection task allocation mechanism designed based on the difference in motion capabilities between unmanned vehicles and robot dogs and the regional location of power equipment, and a dynamic inspection route adjustment mechanism based on dynamic obstacles and electromagnetic interference information, it can not only achieve efficient and reliable coordination of task allocation and path planning between different machines, but also perceive the dynamic changes of the inspection environment in real time and adaptively adjust the inspection route, thereby effectively improving the inspection efficiency and inspection effect, and providing a reliable decision-making basis for equipment maintenance.

[0005] In order to achieve the above objectives, a method and system for intelligent inspection control of a distribution room based on robot collaboration are provided.

[0006] In a first aspect, an embodiment of the present invention provides a method for intelligent inspection and control of a power distribution room based on robot collaboration, wherein the robot comprises an unmanned vehicle and a robot dog; the method comprises the following steps: Obtaining equipment information of all power distribution equipment in the power distribution room, and generating an equipment inspection sequence based on the equipment information and a preset inspection priority algorithm; the equipment information includes equipment operation status and equipment location information; According to the movement capabilities of the unmanned vehicle and the robot dog, inspection tasks are allocated based on the equipment inspection sequence, target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively, and the target inspection routes corresponding to the unmanned vehicle and the robot dog are pushed to the corresponding robot control terminals respectively; The robot control terminal simultaneously controls the unmanned vehicle and the robot dog to perform corresponding inspection tasks, and optimizes and adjusts the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed.

[0007] Furthermore, the step of generating a device inspection sequence based on the device information and a preset inspection priority algorithm includes: According to the equipment operation status in the equipment information of each power distribution equipment, the corresponding equipment inspection priority is generated based on a preset inspection priority evaluation model; the preset inspection priority evaluation model is constructed based on fuzzy logic; According to the equipment inspection priority of each power distribution equipment, all power distribution equipment are sorted in descending order of priority to obtain an initial equipment inspection sequence; Determine whether there is a power distribution device with the same equipment inspection priority in the initial equipment inspection sequence; If so, according to the equipment location information of the power distribution equipment with the same equipment inspection priority, the shortest path algorithm is used to optimize the sorting of the power distribution equipment with the same equipment inspection priority in the initial equipment inspection sequence to obtain the equipment inspection sequence; If it does not exist, the initial device inspection sequence is used as the device inspection sequence.

[0008] Furthermore, the movement capability includes movement speed, endurance time and minimum turning radius; the steps of allocating inspection tasks based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, and generating target inspection routes corresponding to the unmanned vehicle and the robot dog respectively include: According to the movement capabilities of the unmanned vehicle and the robot dog, a device area classification is obtained; the device area classification includes an open area type and a narrow area type; According to the device location information of all the power distribution devices in the device inspection sequence, spatial clustering is performed based on the device area classification to generate open area device clusters and narrow area device clusters; According to the open area equipment cluster and the narrow area equipment cluster, a corresponding unmanned vehicle inspection equipment set and a robot dog inspection equipment set are respectively constructed; According to the equipment inspection order in the equipment inspection sequence, an unmanned vehicle inspection task sequence and a robot dog inspection task sequence corresponding to the unmanned vehicle inspection equipment set and the robot dog inspection equipment set are generated respectively; The unmanned vehicle inspection task sequence and the robot dog inspection task sequence are respectively used as sequential constraints, based on a preset path optimization algorithm and a distribution room grid map, target inspection routes corresponding to the unmanned vehicle and the robot dog are respectively generated.

[0009] Furthermore, the steps of respectively generating target inspection routes corresponding to the unmanned vehicle and the robot dog based on the preset path optimization algorithm and the grid map of the power distribution room with the unmanned vehicle inspection task sequence and the robot dog inspection task sequence as sequential constraints include: According to preset path planning constraints, based on the A* algorithm and the grid map of the power distribution room, the target inspection route of the unmanned vehicle is generated; the preset path planning constraints include sequence constraints, equipment distribution constraints and channel width constraints; According to preset path planning constraints, based on a breadth-first search algorithm and the grid map of the power distribution room, a target inspection route for the robot dog is generated.

[0010] Furthermore, the step of optimizing and adjusting the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time includes: When it is determined according to the dynamic obstacle information that a dynamic obstacle needs to be avoided, the activity area of ​​the dynamic obstacle is used as a dangerous area of ​​the current robot path; When it is determined according to the electromagnetic interference information that the electromagnetic interference area needs to be avoided, the electromagnetic interference area is used as a dangerous area of ​​the current robot path; According to the current robot path dangerous area information and the current robot position information, the corresponding target inspection route is optimized with the optimization goals of minimizing the path length, minimizing the number of turns and avoiding the path dangerous area, and the adjusted and optimized inspection route is updated to the corresponding robot control terminal.

[0011] Furthermore, the method further comprises: According to the battery capacity and the preset power consumption rate of the robot dog, the remaining power of the robot dog is predicted in real time, and according to the current position information and the remaining inspection route of the robot dog, the remaining power required for the task is obtained; When it is determined that the remaining power is less than the remaining task required power, the corresponding optimal charging path for the robot dog is generated based on the Dijkstra algorithm according to the current position information and the location information of the charging equipment in the distribution room and pushed to the corresponding robot control terminal. After the robot dog is charged to reach the remaining task required power, the corresponding target inspection route is readjusted according to the latest position information of the robot dog to perform the remaining inspection tasks.

[0012] Furthermore, the step of obtaining the preset power consumption rate includes: The current ambient temperature, motion state and workload of the robot dog are obtained, and the preset power consumption rate is obtained according to the current ambient temperature, the motion state, the workload and a preset power consumption rate prediction model; the preset power consumption rate prediction model is obtained based on a linear regression analysis of the historical power consumption rate data of the robot dog under different temperatures, different motion states and different workloads.

[0013] Furthermore, the method further comprises: When performing inspection tasks, the unmanned vehicle and the robot dog obtain the operating status monitoring data of each distribution equipment in the corresponding target inspection route in real time, and when it is determined that the operating status monitoring data is abnormal, generate fault alarm information, increase the inspection priority of the corresponding distribution equipment, and readjust the corresponding target inspection route.

[0014] Furthermore, the method further comprises: In response to the completion of the inspection tasks of the unmanned vehicle and the robot dog, a distribution room equipment status report is generated according to the inspection data corresponding to the unmanned vehicle and the robot dog, and a corresponding distribution room operation and maintenance strategy is generated according to the distribution room equipment status report.

[0015] In a second aspect, an embodiment of the present invention provides a power distribution room intelligent inspection control system based on robot collaboration, wherein the robot includes an unmanned vehicle and a robot dog; the system includes: The inspection sequence generation module is used to obtain the equipment information of all power distribution equipment in the power distribution room, and generate the equipment inspection sequence based on the equipment information and a preset inspection priority algorithm; the equipment information includes the equipment operation status and equipment location information; An inspection task allocation module is used to allocate inspection tasks based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, generate target inspection routes corresponding to the unmanned vehicle and the robot dog, and push the target inspection routes corresponding to the unmanned vehicle and the robot dog to the corresponding robot control terminals; The inspection task execution module is used to control the unmanned vehicle and the robot dog to perform corresponding inspection tasks simultaneously by the robot control terminal, and optimize and adjust the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed.

[0016] The present invention provides a method and system for intelligent inspection control of a power distribution room based on robot collaboration. The method can realize the acquisition of equipment information of all power distribution equipment in the power distribution room, including equipment operation status and equipment location information. According to the equipment information, after generating an equipment inspection sequence based on a preset inspection priority algorithm, inspection tasks are allocated based on the equipment inspection sequence according to the movement capabilities of an unmanned vehicle and a robot dog, target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively, the target inspection routes corresponding to the unmanned vehicle and the robot dog are pushed to the corresponding robot control terminals respectively, and the robot control terminals simultaneously control the unmanned vehicle and the robot dog to perform corresponding inspection tasks, and according to the dynamic obstacle information and electromagnetic interference information monitored in real time, the target inspection routes corresponding to the unmanned vehicle and the robot dog are optimized and adjusted until the inspection tasks are completed. Compared with the existing technology, the intelligent inspection control method of the distribution room based on robot collaboration combines the task sorting mechanism that generates the inspection sequence based on the comprehensive analysis of the equipment operation status and equipment location information, the spatial dimension inspection task allocation mechanism designed based on the difference in the movement capabilities of the unmanned vehicle and the robot dog and the regional location of the power equipment, and the inspection line dynamic adjustment mechanism based on dynamic obstacles and electromagnetic interference information. It can not only realize the efficient and reliable coordination of task allocation and path planning between different machines based on the inspection task priority, but also perceive the dynamic changes of the inspection environment in real time to adaptively adjust and optimize the inspection route, thereby effectively improving the inspection efficiency and inspection effect, and providing a reliable decision-making basis for equipment maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for intelligent inspection and control of a power distribution room based on robot collaboration in an embodiment of the present invention; Figure 2 It is a structural schematic diagram of an intelligent inspection control system for a power distribution room based on robot collaboration in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and beneficial effects of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The intelligent inspection control method for distribution rooms based on robot collaboration provided by the present invention can be understood as a kind of intelligent inspection control scheme that is proposed based on the current application status that only a single robot is used to perform intelligent inspections in distribution rooms, and the inspection efficiency is insufficient. The scheme can effectively improve the inspection efficiency and inspection effect based on the efficient collaboration of task allocation and path planning among different robots. The following embodiments will explain in detail the intelligent inspection control method for distribution rooms based on robot collaboration of the present invention.

[0020] In one embodiment, Figure 1 As shown, a distribution room intelligent inspection control method based on robot collaboration is provided, wherein the robot includes an unmanned vehicle and a robot dog, that is, based on the task collaborative control between the unmanned vehicle and the robot dog, efficient intelligent inspection of the distribution room is realized; the corresponding distribution room intelligent inspection control method includes the following steps: S11. Obtain the equipment information of all power distribution equipment in the distribution room, and generate an equipment inspection sequence based on the preset inspection priority algorithm according to the equipment information; wherein the equipment information includes equipment operating status and equipment location information, and the equipment operating status information can be understood as being obtained through a status monitoring sensor network pre-deployed in the distribution room, and can reflect key parameters of the equipment operating status, such as current, voltage, temperature and other information, which are not specifically limited here; the corresponding equipment location information can be understood as the geographical location coordinate information of each power distribution equipment in the distribution room.

[0021] The preset inspection priority algorithm in this embodiment can be understood as an algorithm for determining the inspection urgency of each power distribution device. In order to ensure the efficiency and scientificity of the generation of the equipment inspection sequence, this embodiment preferably adopts a method for determining the equipment inspection priority based on the equipment operation status and supplemented by the equipment location information. Specifically, the step of generating the equipment inspection sequence based on the preset inspection priority algorithm according to the equipment information includes: According to the equipment operation status in the equipment information of each power distribution equipment, the corresponding equipment inspection priority is generated based on the preset inspection priority evaluation model; wherein, the preset inspection priority evaluation model can be understood as a model based on the equipment operation status data to predict the inspection priority, which is constructed based on the principle that different equipment operation statuses correspond to different equipment health states, and different equipment health states correspond to different equipment inspection priorities; taking into account the diversity and uncertainty of equipment operation status data in actual applications, in order to solve the problem of insufficient judgment of the "intermediate state" by the traditional threshold method (such as "slight overload" or "critical high temperature") to ensure the rationality of the inspection priority evaluation, this embodiment is preferably constructed based on fuzzy logic To the corresponding preset inspection priority evaluation model, that is, through fuzzy analysis of parameters such as current, voltage and temperature, the health status of the equipment is quantified, and then the corresponding inspection priority (for example, high / medium / low, etc.) is output based on the health status of the equipment. The specific model construction process can refer to the existing fuzzy logic theory, for example: first define the fuzzy sets corresponding to each parameter, and design the corresponding membership function (for example, trapezoidal function) to realize the conversion of each parameter value in the equipment operation status into the corresponding fuzzy membership; then formulate fuzzy rules driven by expert experience, and optimize the membership function parameters or rule weights based on relevant historical fault data combined with a neural fuzzy model (such as adaptive-network-based fuzzy inference system, ANFIS), dynamically adjust the rule contribution to perform weighted aggregation on the priority output of the triggering rule to obtain the corresponding fuzzy output; finally, the center of gravity method is used to convert the fuzzy output into an exact value to obtain the required inspection priority.

[0022] According to the equipment inspection priority of each power distribution equipment, all power distribution equipment are sorted based on the priority descending method to obtain the initial equipment inspection sequence; that is, the initial equipment inspection sequence can be understood as the inspection task execution sequence generated by sorting the power distribution equipment on the principle that the higher the equipment inspection priority, the higher the inspection task ranking.

[0023] Determine whether there is any power distribution equipment with the same equipment inspection priority in the initial equipment inspection sequence; that is, check whether there is any situation in the initial equipment inspection sequence where the equipment inspection order is unreasonable due to the same equipment inspection priority.

[0024] If it exists, then according to the equipment location information of the distribution equipment with the same equipment inspection priority, the shortest path algorithm is used to sort and optimize the distribution equipment with the same equipment inspection priority in the initial equipment inspection sequence to obtain the equipment inspection sequence; wherein, the process of sorting and optimizing the distribution equipment with the same equipment inspection priority in the initial equipment inspection sequence using the shortest path algorithm can be understood as the process of finding the shortest non-repetitive open path of all distribution equipment with the same equipment inspection priority according to the equipment location, according to dynamic programming or approximate / heuristic algorithm. For details, please refer to the existing shortest open path acquisition technology based on fixed position path nodes. For example, if the number of distribution equipment with the same equipment inspection priority is too large in actual application (for example, greater than 20), it is preferred to use a heuristic algorithm. Otherwise, a dynamic programming algorithm can be selected, which will not be described in detail here. After obtaining the sub-inspection sequence corresponding to each distribution equipment with the same equipment inspection priority, each sub-inspection sequence is used to replace the inspection sorting of the corresponding distribution equipment in the initial equipment inspection sequence, and finally the required equipment inspection sequence is obtained.

[0025] If not, the initial equipment inspection sequence is used as the equipment inspection sequence; that is, when the inspection priorities of all power distribution devices in the initial equipment inspection sequence are different, the available initial equipment inspection sequence can be used as the required equipment inspection sequence without adjustment.

[0026] S12. According to the movement capabilities of the unmanned vehicle and the robot dog, inspection tasks are allocated based on the equipment inspection sequence, and target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively, and the target inspection routes corresponding to the unmanned vehicle and the robot dog are pushed to the corresponding robot control terminals respectively; wherein, the movement capability can be understood as a feature that can reflect the difference between the applicable movement areas of the unmanned vehicle and the robot dog, preferably including movement speed, endurance and minimum turning radius. For example, the unmanned vehicle has a higher running speed and a longer endurance, and is suitable for equipment inspection in open areas of the distribution room, while the robot dog has a smaller minimum turning radius, is flexible and has the ability to pass through narrow passages, and is suitable for equipment inspection in narrow passage areas of the distribution room.

[0027] In order to ensure that the respective motion capabilities of the unmanned vehicle and the robot dog can be fully utilized, and to better realize the coordination of inspection tasks, so as to maximize the execution efficiency of the overall distribution room inspection tasks, this embodiment preferably reasonably allocates the distribution equipment inspection tasks involved in the equipment inspection sequence based on the inspection area types suitable for the unmanned vehicle and the robot dog, so as to ensure that the two realize spatial task coordination; specifically, the steps of allocating inspection tasks based on the equipment inspection sequence according to the motion capabilities of the unmanned vehicle and the robot dog, and generating target inspection routes corresponding to the unmanned vehicle and the robot dog respectively include: According to the movement capabilities of the unmanned vehicle and the robot dog, the equipment area classification is obtained; wherein, the equipment area classification can be understood as the type of area around the distribution equipment suitable for unmanned vehicle and robot dog inspection respectively, which is determined based on the movement characteristics of the unmanned vehicle and the robot dog, including open area type and narrow area type, and the open area type can be understood as the area type where the width of the movable channel around the distribution equipment is greater than or equal to the preset width threshold, and the corresponding narrow area type can be understood as the area type where the width of the movable channel around the distribution equipment is less than the preset width threshold; it should be noted that the preset width threshold can be determined based on the minimum turning radius of the robot dog. For example, if the minimum turning radius of the robot dog is 0.3 meters, the preset width threshold can be set to 0.8 meters, and no specific limitation is made here.

[0028] According to the equipment location information of all distribution equipment in the equipment inspection sequence, spatial clustering is performed based on the equipment area classification to generate open area equipment clusters and narrow area equipment clusters; wherein, spatial clustering can be understood as DBSCAN clustering based on the spatial distribution density of equipment in the distribution room; the specific process of generating open area equipment clusters and narrow area equipment clusters includes: calculating the Euclidean distance matrix between devices according to the equipment location information (two-dimensional coordinates of the plane) of each distribution equipment; calculating the average distance from each distribution equipment to the nearest several neighboring devices according to the Euclidean distance matrix between the devices, if the average distance is greater than a preset width threshold, it is considered that the distribution equipment is located in an open area, otherwise, it is located in a narrow area; using the preset width threshold as the neighborhood radius, clustering analysis is performed after determining the minimum number of neighbors, and the required open area equipment clusters (high-density clusters) and narrow area equipment clusters (low-density clusters) can be obtained.

[0029] According to the open area equipment cluster and the narrow area equipment cluster, corresponding unmanned vehicle inspection equipment sets and robot dog inspection equipment sets are respectively constructed; wherein, the power distribution equipment in the unmanned vehicle inspection equipment set belongs to the power distribution equipment in the open area equipment cluster, and the corresponding power distribution equipment in the robot dog inspection equipment set belongs to the power distribution equipment in the narrow area equipment cluster, that is, by combining spatial clustering with the characteristics of robot motion capabilities, reliable division of equipment inspection tasks inside the distribution room is achieved, which is convenient for improving the efficiency of inspection task execution.

[0030] According to the equipment inspection sorting in the equipment inspection sequence, an unmanned vehicle inspection task sequence and a robot dog inspection task sequence are generated corresponding to the unmanned vehicle inspection equipment set and the robot dog inspection equipment set, respectively; wherein the unmanned vehicle inspection task sequence can be understood as the priority order of the unmanned vehicle inspection equipment obtained according to the sorting relationship of each power distribution equipment in the unmanned vehicle inspection equipment set in the equipment inspection sequence; the corresponding robot dog inspection task sequence can be understood as the priority order of the robot dog inspection equipment obtained according to the sorting relationship of each power distribution equipment in the robot dog inspection equipment set in the equipment inspection sequence.

[0031] Taking the unmanned vehicle inspection task sequence and the robot dog inspection task sequence as sequential constraints, target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively based on a preset path optimization algorithm and a grid map of a distribution room; wherein, the grid map of a distribution room can be understood as an environmental map obtained by dividing the distribution room into multiple grid cells based on a preset grid cell size (such as 0.5m*0.5m), and the corresponding target inspection routes corresponding to the unmanned vehicle and the robot dog can be understood as equipment inspection task execution paths generated by executing a path optimization algorithm under sequential constraints based on an analysis of the environmental map.

[0032] In order to adapt to different equipment area types to ensure that both the unmanned vehicle and the robot dog can complete all the distribution equipment inspection tasks in the corresponding inspection task sequence in the shortest time, this embodiment preferably adopts different path optimization algorithms for different robot path optimization; specifically, the steps of respectively using the unmanned vehicle inspection task sequence and the robot dog inspection task sequence as sequence constraints, based on the preset path optimization algorithm and the distribution room grid map, to generate the target inspection routes corresponding to the unmanned vehicle and the robot dog include: According to the preset path planning constraints, based on the A* algorithm and the grid map of the distribution room, the target inspection route of the unmanned vehicle is generated; the preset path planning constraints include sequence constraints, equipment distribution constraints and channel width constraints; among which, the equipment distribution constraints can be understood as information for avoiding static obstacles in the distribution room, and the channel width constraints can be understood as information for screening channels suitable for the movement of unmanned vehicles; the specific process of obtaining the target inspection route of the unmanned vehicle can be implemented with reference to the execution process of the existing A* algorithm, and may include: on the grid map of the distribution room, according to the location information of all equipment in the distribution room and the wall and other markers, the obstacle cells (not passable) are fixed, and based on the channel width constraints applicable to the driving of the unmanned vehicle (the channel width is less than 0.8m and cannot be passed); The grid map of the distribution room is further marked with impassable areas to obtain a marked grid map of the distribution room (the information on whether each grid unit is allowed to pass can be stored in the form of a two-dimensional matrix); then, the target inspection route to be found is divided into multiple sub-paths based on the order constraint, and based on the marked grid map of the distribution room, the A* algorithm is independently run on each sub-path with the grid distance required to be moved as the actual cost and the Euclidean distance as the heuristic function to obtain the corresponding initial path, and then the generated initial path can be interpolated by B-spline to generate the required target sub-path; finally, the target sub-paths are merged according to the endpoint information of each sub-path to obtain the required target inspection route for the unmanned vehicle.

[0033] According to the preset path planning constraints, based on the breadth-first search algorithm and the grid map of the distribution room, the target inspection route of the robot dog is generated; wherein, the preset path planning constraints include sequence constraints, equipment distribution constraints and channel width constraints as described above, and the equipment distribution constraints can be understood as information for avoiding static obstacles in the distribution room, and the channel width constraints can be understood as information for screening channels suitable for the movement of the robot dog; the specific process of obtaining the target inspection route of the unmanned vehicle can be implemented with reference to the execution process of the existing breadth-first search algorithm (BFS), and may include: on the grid map of the distribution room, according to the location information of all equipment and walls in the distribution room, fixed obstacle cells (marked as impassable), and based on the channel width constraints applicable to the robot dog's travel (for example, based on the channel width and the robot dog width plus the installation The full threshold determines the inspection width mark) and further marks the impassable area of ​​the distribution room grid map to obtain the marked distribution room grid map (the marking information corresponding to each grid unit can be stored in the form of a two-dimensional matrix); then, based on the order constraint, the target inspection route to be found is divided into multiple sub-paths, and based on the marked distribution room grid map, the breadth-first search algorithm is independently run on each sub-path (it can be allowed to set 8 neighborhood directions including diagonal lines to simulate the flexible movement of the robot dog, and increase the width of the narrow channel to verify the marking inspection width) to obtain the corresponding initial path, and then the generated initial path can be interpolated by B-spline to generate the required target sub-path; finally, the target sub-paths are merged according to the endpoint information of each sub-path to obtain the required target inspection route of the robot dog.

[0034] S13, the robot control terminal simultaneously controls the unmanned vehicle and the robot dog to perform corresponding inspection tasks, and optimizes and adjusts the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed; wherein, the robot control terminal simultaneously controls the unmanned vehicle and the robot dog to perform corresponding inspection tasks, which can be understood as the control terminals of the unmanned vehicle and the robot dog respectively performing corresponding inspection tasks based on the corresponding target inspection routes. In order to ensure the safety and reliability of the unmanned vehicle and the robot dog in performing inspection tasks, this embodiment preferably combines the environmental complexity analysis of the power distribution room, and in the process of the unmanned vehicle and the robot dog performing inspection tasks, the environmental information is perceived in real time based on the laser radar, infrared sensor and electromagnetic interference detector configured by the unmanned vehicle and the robot dog, so as to adaptively optimize and adjust the inspection path. Specifically, the step of optimizing and adjusting the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time includes: When it is determined that a dynamic obstacle needs to be avoided according to the dynamic obstacle information, the activity area of ​​the dynamic obstacle is used as the dangerous area of ​​the current robot path; wherein the dynamic obstacle information may include information such as the position, moving speed and moving direction of the dynamic obstacle, and its motion trajectory can be predicted based on the time series information of the dynamic obstacle, and the shortest distance between the robot and the dynamic obstacle motion trajectory is calculated according to the current position information of the robot; at the same time, the expected collision time is calculated according to the relative speed of the dynamic obstacle and the robot, and the minimum safety distance of the robot is calculated according to the braking ability (maximum deceleration) of the robot, and it is determined whether the expected collision time is greater than the preset safety time threshold and the shortest distance is greater than the minimum safety distance. If so, the activity area of ​​the dynamic obstacle (the activity area can be determined with the dynamic obstacle as the center and the minimum safety distance as the radius) is used as the dangerous area that needs to be avoided, and the path replanning mechanism is immediately triggered. It should be noted that when determining the mobile obstacle information, in order to ensure the accuracy of environmental perception, the Kalman filter can be used to fuse radar monitoring data and infrared sensor data to improve the accuracy and robustness of target state estimation. The specific processing can be implemented by referring to the relevant existing technology, which will not be described in detail here.

[0035] When it is determined that the electromagnetic interference area needs to be avoided based on the electromagnetic interference information, the electromagnetic interference area is used as a dangerous area on the current robot path; wherein the electromagnetic interference information includes the electromagnetic interference intensity. If the electromagnetic interference intensity exceeds a preset intensity threshold, the path replanning mechanism is immediately triggered.

[0036] According to the current robot path danger zone information and the current robot position information, the corresponding target inspection route is optimized with the optimization goals of minimizing the path length, minimizing the number of turns and avoiding the path danger zone, and the adjusted and optimized inspection route is updated to the corresponding robot control terminal; wherein, the path optimization of the target inspection route can be understood as the path optimization of the remaining inspection tasks on the target inspection route based on the current robot position information while keeping the task order unchanged. The specific implementation process may include: constructing the corresponding fitness function with the optimization goals of minimizing the path length (to improve inspection efficiency), minimizing the number of turns (the number of direction changes to avoid the problem of turning in circles) and avoiding the path danger zone (the distance between the path and the obstacle is greater than the safety threshold), and after determining the fitness function, first use the A* algorithm to generate the initial feasible path, and then based on the NSGA-II algorithm, optimize and adjust the initial feasible path according to the fitness function to obtain the required adjusted and optimized inspection route; in this embodiment, the fitness function is preferably expressed as: In the formula, , and Respectively indicate the inspection routes The normalized data corresponding to the corresponding path length, number of turns and proximity to the dangerous area, the specific calculation expressions can be obtained based on the existing related technologies, and are not specifically limited here; , and Represents the weight coefficient, which can be determined according to actual application requirements.

[0037] It should be noted that, based on the NSGA-II algorithm, the process of optimizing and adjusting the initial feasible path according to the fitness function can refer to the existing relevant technical implementation of path optimization based on the NSGA-II algorithm, which will not be described in detail here.

[0038] The embodiment of the present invention provides a technical solution for obtaining equipment information including equipment operating status and equipment location information of all distribution equipment in a distribution room, and generating an equipment inspection sequence based on the equipment information and a preset inspection priority algorithm, and then performing inspection task allocation based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, respectively generating target inspection routes corresponding to the unmanned vehicle and the robot dog, pushing the target inspection routes corresponding to the unmanned vehicle and the robot dog to the corresponding robot control terminals, and controlling the unmanned vehicle and the robot dog to perform corresponding inspection tasks simultaneously by the robot control terminals, and optimizing and adjusting the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed. The technical solution can not only realize efficient and reliable coordination of task allocation and path planning between different machines based on the inspection task priority, but also perceive the dynamic changes of the inspection environment in real time to adaptively adjust and optimize the inspection route, thereby effectively improving the inspection efficiency and inspection effect, and providing a reliable decision-making basis for equipment maintenance.

[0039] Considering that the robot dog has a short battery life, in order to ensure that the robot dog can normally complete the inspection task in the responsible area, the present embodiment preferably predicts the remaining power of the robot dog in real time to evaluate whether energy needs to be replenished; specifically, the method further includes: According to the battery capacity and preset power consumption rate of the robot dog, the remaining power of the robot dog is predicted in real time, and according to the current location information and the remaining inspection route of the robot dog, the remaining power required for the task is obtained; wherein, the remaining power of the robot dog can be understood as the available power value calculated based on the battery capacity, power consumption rate and inspection time. Considering that the actual power consumption rate of the robot dog is related to the ambient temperature, motion state and workload, in order to ensure the accuracy of the remaining power prediction, this embodiment preferably conducts a comprehensive analysis based on the ambient temperature, motion state and load conditions of the robot dog, and obtains a reasonable power consumption rate for the remaining power prediction; specifically, the steps for obtaining the preset power consumption rate include: The current ambient temperature, motion state and workload of the robot dog are obtained, and the preset power consumption rate is obtained according to the current ambient temperature, the motion state, the workload and a preset power consumption rate prediction model; the preset power consumption rate prediction model is obtained based on a linear regression analysis of the historical power consumption rate data of the robot dog under different temperatures, different motion states and different workloads; wherein the motion state includes a fast walking state, a slow walking state and a resting state, etc., and the workload can be understood as the activation status of the sensors on the robot dog, such as using only one of the infrared sensor, the lidar and the electromagnetic interference detector or using any number of sensors in combination, etc. corresponding to different load states. In practical applications, the power consumption rate data of the robot dog under different temperatures, different motion states and different workloads can be pre-collected, and a linear regression analysis can be performed with ambient temperature, motion state and workload as independent variables (the motion state and workload are quantified as numerical variables) and the power consumption rate as the dependent variable to construct the required preset power consumption rate prediction model. During the inspection process, the robot dog can obtain the corresponding ambient temperature, motion state and workload in real time and input the corresponding preset power consumption rate prediction model to predict the power consumption rate, so as to obtain the preset power consumption rate that meets the current state of the robot dog.

[0040] When it is determined that the remaining power is less than the remaining task required power, the corresponding optimal charging path for the robot dog is generated based on the Dijkstra algorithm according to the current position information and the position information of the charging equipment in the distribution room and pushed to the corresponding robot control terminal, and after the robot dog is charged to reach the remaining task required power, the corresponding target inspection route is readjusted according to the latest position information of the robot dog to perform the remaining inspection tasks; wherein, the remaining task required power can be obtained based on the inspection path length of the remaining task combined with the corresponding power consumption rate analysis, such as the estimated required inspection time based on the inspection path length combined with the movement rate of the robot dog, and then, the remaining task required power can be estimated based on the product of the estimated required inspection time and the power consumption rate. When the remaining power of the robot dog is not enough to support the completion of the remaining inspection tasks and needs to be charged, it is necessary to search for the nearest charging device in the distribution room based on the current location information of the robot dog, and use a path planning algorithm to take the nearest charging device as the target point, and avoid obstacles and electromagnetic interference areas as constraints to generate the optimal charging path for the robot dog to move forward to the nearest charging device. When the robot dog is charged to a certain level of power that exceeds the remaining required power, charging can be terminated, and the inspection path corresponding to the remaining inspection tasks can be replanned based on the current charging position and the device location information corresponding to the remaining inspection tasks, so as to optimize the target inspection route stored in the current control terminal, and continue to perform the corresponding inspection tasks based on the adjusted target inspection path. The specific optimal charging path acquisition method and the target inspection path optimization acquisition method can be implemented based on the aforementioned process of optimizing and adjusting the target inspection route based on dynamic obstacle information and electromagnetic interference information, which will not be repeated here.

[0041] It should be noted that, in the present embodiment, during the charging process of the robot dog, the inspection task of the robot dog is in a stagnant state, which will affect the execution efficiency of the overall inspection task to a certain extent. In order to avoid the delay of the execution of the emergency inspection task due to the charging of the robot dog, the present embodiment also obtains the current remaining inspection tasks of the robot dog and the unmanned vehicle respectively after the robot dog starts the charging program, and merges the current remaining inspection tasks of the two based on the task priority (it is necessary to add the task belonging to the robot mark during the merger, and sort and optimize the devices with the same inspection priority according to the location information of the device), and obtains the comprehensive remaining inspection task sequence based on the merger The inspection route of the unmanned vehicle is readjusted until the robot dog is fully charged, and then the remaining inspection tasks in the comprehensive remaining inspection tasks are divided into two remaining inspection task sequences corresponding to the unmanned vehicle and the robot dog according to the corresponding belonging robot tags, and the inspection paths are planned for the two remaining inspection task sequences based on the aforementioned path planning algorithm, and the regenerated inspection paths are pushed to the unmanned vehicle and the robot dog respectively, so that they continue to perform the remaining inspection tasks according to the latest inspection paths, so as to effectively ensure the continuity of the inspection tasks and provide overall inspection efficiency through the seamless connection of the unmanned vehicle and the robot dog in inspection time.

[0042] In addition, considering that there may be sudden abnormalities in power grid equipment that has not been inspected in actual applications, in order to facilitate timely abnormal status confirmation and maintenance of abnormal equipment to ensure power supply safety, in this embodiment, preferably, during the process of the unmanned vehicle and the robot dog collaboratively performing the inspection task, the operating status of the remaining power grid equipment to be inspected in the inspection task will also be monitored, so as to timely perceive the equipment abnormalities and adaptively adjust the corresponding inspection task priority to avoid potential safety risks; specifically, the method also includes: When the unmanned vehicle and the robot dog are performing the inspection task, they obtain the operating status monitoring data of each power distribution equipment in the corresponding target inspection route in real time, and when it is determined that the operating status monitoring data is abnormal, they generate fault alarm information, increase the inspection priority of the corresponding power distribution equipment, and readjust the corresponding target inspection route; wherein the operating status monitoring data can be understood as data including current, voltage, temperature, etc., and the corresponding operating status monitoring data obtained in real time is analyzed to see whether each data is within the preset normal range. If the above conditions cannot be met, for example, the rated current is 10A and the actual collected current value is 15A, Or if the equipment temperature safety threshold is 80°C and the actual collected equipment surface temperature is 80°C, the operation status monitoring data can be analyzed based on the pre-constructed Bayesian network model for abnormality, and the distribution equipment can be determined to have an operation abnormality based on the corresponding output equipment failure probability, so as to trigger the corresponding fault alarm mechanism when it is determined that there is an operation abnormality; at the same time, the inspection priority of the corresponding distribution equipment is adjusted to the highest level, and according to the adjusted inspection priority, the aforementioned shortest path algorithm (such as the Dijkstra algorithm) is used in combination with the real-time environment map to regenerate the remaining inspection task route and push it to the corresponding control terminal. The Bayesian network model in this embodiment can be constructed based on the historical data training of the collected different operation status monitoring data and the corresponding failure probability. The specific construction process can be implemented with reference to the relevant existing technology, which will not be described in detail here. In addition, in order to avoid potential equipment operation risks, this embodiment can also predict potential faulty equipment in advance by learning historical fault data through the support vector machine algorithm. For example, according to the historical data prediction, it is found that a certain disconnector is prone to failure in a high temperature environment, and the inspection priority corresponding to the equipment can be further improved, so that the corresponding equipment inspection task can be executed in advance.

[0043] The method provided by the embodiment of the present invention can ensure that abnormal situations of distribution equipment are responded to and handled in a timely manner, thereby effectively ensuring the stable operation of the power system, by real-time monitoring and analyzing the operating status data of distribution equipment, and timely adjusting the inspection task level and updating the inspection task execution order based on the corresponding equipment status analysis results.

[0044] In addition, in order to further improve the safety and stability of the distribution room operation, this embodiment preferably performs status analysis on each distribution equipment based on the corresponding inspection data of the unmanned vehicle and the robot dog, makes maintenance decisions according to the corresponding analysis results, and generates a corresponding distribution room operation and maintenance strategy; specifically, the method also includes: In response to the completion of the inspection task of the unmanned vehicle and the robot dog, a distribution room equipment status report is generated according to the inspection data corresponding to the unmanned vehicle and the robot dog, and a corresponding distribution room operation and maintenance strategy is generated according to the distribution room equipment status report; wherein the inspection data varies according to the different distribution equipment, for example, for the transformer, the corresponding inspection data includes winding temperature, oil temperature, operating noise, vibration amplitude, oil level, bushing and insulator status, etc.; for the high-voltage switchgear, it includes electrical parameters, mechanical status and insulation performance, etc.; the distribution room equipment status report generated based on the inspection data analysis may include the inspection data of each distribution equipment, the equipment status analysis results generated based on the inspection data analysis, maintenance suggestions and maintenance priorities. The specific process of status analysis of the distribution equipment corresponding to the inspection data analysis can be understood as firstly analyzing the status of each distribution equipment to be divided After the inspection data is preprocessed by cleaning, denoising and normalization, its state is identified using a neural network model built based on relevant historical data. After obtaining the corresponding state analysis results, the state analysis results of each distribution equipment can be analyzed based on the existing association rule analysis algorithm (Apriori algorithm) to generate corresponding maintenance suggestions and maintenance priorities; after generating a distribution room equipment status report based on the inspection data analysis, the maintenance suggestions and maintenance priorities of all distribution equipment in the distribution room equipment status report can be comprehensively analyzed based on the reinforcement learning model pre-built based on relevant historical data to generate a corresponding distribution room operation and maintenance strategy; it should be noted that the association rules used in the association rule analysis algorithm in this embodiment and the construction of the reinforcement learning model can be implemented based on actual application requirements with reference to relevant existing technologies, and will not be described in detail here.

[0045] Through efficient and intelligent analysis of inspection data, this embodiment can not only achieve accurate assessment of the operating status of distribution equipment, but also automatically generate reliable distribution room operation and maintenance strategies based on the operating status of equipment in the entire distribution room to provide guidance for the timely operation and maintenance of the distribution room, thereby providing a strong guarantee for the safe and reliable operation of the entire distribution room.

[0046] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0047] In one embodiment, Figure 2 As shown, a power distribution room intelligent inspection control system based on robot collaboration is provided, wherein the robot includes an unmanned vehicle and a robot dog; the system includes: Inspection sequence generation module 1 is used to obtain the equipment information of all power distribution equipment in the power distribution room, and generate an equipment inspection sequence based on the equipment information and a preset inspection priority algorithm; the equipment information includes equipment operation status and equipment location information; Inspection task allocation module 2, used to allocate inspection tasks based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, generate target inspection routes corresponding to the unmanned vehicle and the robot dog respectively, and push the target inspection routes corresponding to the unmanned vehicle and the robot dog to the corresponding robot control terminals respectively; The inspection task execution module 3 is used to control the unmanned vehicle and the robot dog to perform corresponding inspection tasks simultaneously by the robot control terminal, and optimize and adjust the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed.

[0048] For the specific limitations of the intelligent inspection and control system for distribution rooms based on robot collaboration, please refer to the limitations of the intelligent inspection and control method for distribution rooms based on robot collaboration mentioned above. The corresponding technical effects can also be equivalently obtained, which will not be repeated here. Each module in the above-mentioned intelligent inspection and control system for distribution rooms based on robot collaboration can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0049] In summary, an embodiment of the present invention provides a distribution room intelligent inspection control method and system based on robot collaboration. The distribution room intelligent inspection control method based on robot collaboration can realize the acquisition of equipment information including equipment operation status and equipment location information of all distribution equipment in the distribution room, and after generating an equipment inspection sequence based on the equipment information and a preset inspection priority algorithm, the inspection task is allocated based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, and the target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively. The target inspection routes corresponding to the unmanned vehicle and the robot dog are pushed to the corresponding robot control terminals respectively, and the robot control terminals simultaneously control the unmanned vehicle and the robot dog to perform the corresponding inspection tasks, and according to the real-time monitored dynamic obstacle information and Electromagnetic interference information is used to optimize and adjust the target inspection routes corresponding to the unmanned vehicle and the robot dog until the inspection task is completed. This method combines the task sorting mechanism that generates the inspection sequence based on the comprehensive analysis of the equipment operation status and equipment location information, the spatial dimension inspection task allocation mechanism designed based on the difference in the movement capabilities of the unmanned vehicle and the robot dog and the regional location of the power equipment, and the inspection route dynamic adjustment mechanism based on dynamic obstacles and electromagnetic interference information. It can not only achieve efficient and reliable coordination of task allocation and path planning between different machines based on the inspection task priority, but also perceive the dynamic changes of the inspection environment in real time to adaptively adjust and optimize the inspection route, thereby effectively improving the inspection efficiency and inspection effect, and providing a reliable decision-making basis for equipment maintenance.

[0050] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] The above-mentioned embodiments only express several preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principle of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the protection scope of the claims.

Claims

1. A robot-coordinated intelligent inspection control method for a power distribution room, characterized in that: The robot includes an unmanned vehicle and a robot dog; the method includes the following steps: Obtaining equipment information of all power distribution equipment in the power distribution room, and generating an equipment inspection sequence based on the equipment information and a preset inspection priority algorithm; the equipment information includes equipment operation status and equipment location information; According to the movement capabilities of the unmanned vehicle and the robot dog, inspection tasks are allocated based on the equipment inspection sequence, target inspection routes corresponding to the unmanned vehicle and the robot dog are generated respectively, and the target inspection routes corresponding to the unmanned vehicle and the robot dog are pushed to the corresponding robot control terminals respectively; The robot control terminal simultaneously controls the unmanned vehicle and the robot dog to perform corresponding inspection tasks, and optimizes and adjusts the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed.

2. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1 is characterized in that: The step of generating a device inspection sequence based on a preset inspection priority algorithm according to the device information comprises: According to the equipment operation status in the equipment information of each power distribution equipment, the corresponding equipment inspection priority is generated based on a preset inspection priority evaluation model; the preset inspection priority evaluation model is constructed based on fuzzy logic; According to the equipment inspection priority of each power distribution equipment, all power distribution equipment are sorted in descending order of priority to obtain an initial equipment inspection sequence; Determine whether there is a power distribution device with the same equipment inspection priority in the initial equipment inspection sequence; If so, according to the equipment location information of the power distribution equipment with the same equipment inspection priority, the shortest path algorithm is used to optimize the sorting of the power distribution equipment with the same equipment inspection priority in the initial equipment inspection sequence to obtain the equipment inspection sequence; If it does not exist, the initial device inspection sequence is used as the device inspection sequence.

3. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1, characterized in that: The movement capability includes movement speed, endurance time and minimum turning radius; the steps of allocating inspection tasks based on the equipment inspection sequence according to the movement capability of the unmanned vehicle and the robot dog, and generating target inspection routes corresponding to the unmanned vehicle and the robot dog respectively include: According to the movement capabilities of the unmanned vehicle and the robot dog, a device area classification is obtained; the device area classification includes an open area type and a narrow area type; According to the device location information of all the power distribution devices in the device inspection sequence, spatial clustering is performed based on the device area classification to generate open area device clusters and narrow area device clusters; According to the open area equipment cluster and the narrow area equipment cluster, a corresponding unmanned vehicle inspection equipment set and a robot dog inspection equipment set are respectively constructed; According to the equipment inspection order in the equipment inspection sequence, an unmanned vehicle inspection task sequence and a robot dog inspection task sequence corresponding to the unmanned vehicle inspection equipment set and the robot dog inspection equipment set are generated respectively; The unmanned vehicle inspection task sequence and the robot dog inspection task sequence are respectively used as sequential constraints, based on a preset path optimization algorithm and a distribution room grid map, target inspection routes corresponding to the unmanned vehicle and the robot dog are respectively generated.

4. The intelligent inspection control method for power distribution room based on robot collaboration as claimed in claim 3 is characterized in that: The steps of respectively generating target inspection routes corresponding to the unmanned vehicle and the robot dog based on the preset path optimization algorithm and the grid map of the power distribution room with the unmanned vehicle inspection task sequence and the robot dog inspection task sequence as sequential constraints include: According to preset path planning constraints, based on the A* algorithm and the grid map of the power distribution room, the target inspection route of the unmanned vehicle is generated; the preset path planning constraints include sequence constraints, equipment distribution constraints and channel width constraints; According to preset path planning constraints, based on a breadth-first search algorithm and the grid map of the power distribution room, a target inspection route for the robot dog is generated.

5. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1, characterized in that: The step of optimizing and adjusting the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the real-time monitored dynamic obstacle information and electromagnetic interference information includes: When it is determined according to the dynamic obstacle information that a dynamic obstacle needs to be avoided, the activity area of ​​the dynamic obstacle is used as a dangerous area of ​​the current robot path; When it is determined according to the electromagnetic interference information that the electromagnetic interference area needs to be avoided, the electromagnetic interference area is used as a dangerous area of ​​the current robot path; According to the current robot path dangerous area information and the current robot position information, the corresponding target inspection route is optimized with the optimization goals of minimizing the path length, minimizing the number of turns and avoiding the path dangerous area, and the adjusted and optimized inspection route is updated to the corresponding robot control terminal.

6. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1, characterized in that: The method further comprises: According to the battery capacity and the preset power consumption rate of the robot dog, the remaining power of the robot dog is predicted in real time, and according to the current position information and the remaining inspection route of the robot dog, the remaining power required for the task is obtained; When it is determined that the remaining power is less than the remaining task required power, the corresponding optimal charging path for the robot dog is generated based on the Dijkstra algorithm according to the current position information and the location information of the charging equipment in the distribution room and pushed to the corresponding robot control terminal. After the robot dog is charged to reach the remaining task required power, the corresponding target inspection route is readjusted according to the latest position information of the robot dog to perform the remaining inspection tasks.

7. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 6, characterized in that: The step of obtaining the preset power consumption rate includes: The current ambient temperature, motion state and workload of the robot dog are obtained, and the preset power consumption rate is obtained according to the current ambient temperature, the motion state, the workload and a preset power consumption rate prediction model; the preset power consumption rate prediction model is obtained based on a linear regression analysis of the historical power consumption rate data of the robot dog under different temperatures, different motion states and different workloads.

8. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1, characterized in that: The method further comprises: When performing inspection tasks, the unmanned vehicle and the robot dog obtain the operating status monitoring data of each distribution equipment in the corresponding target inspection route in real time, and when it is determined that the operating status monitoring data is abnormal, generate fault alarm information, increase the inspection priority of the corresponding distribution equipment, and readjust the corresponding target inspection route.

9. The intelligent inspection control method for power distribution room based on robot collaboration according to claim 1, characterized in that: The method further comprises: In response to the completion of the inspection tasks of the unmanned vehicle and the robot dog, a distribution room equipment status report is generated according to the inspection data corresponding to the unmanned vehicle and the robot dog, and a corresponding distribution room operation and maintenance strategy is generated according to the distribution room equipment status report.

10. An intelligent inspection control system for power distribution rooms based on robot collaboration, characterized in that: The robot includes an unmanned vehicle and a robot dog; the system includes: The inspection sequence generation module is used to obtain the equipment information of all power distribution equipment in the power distribution room, and generate the equipment inspection sequence based on the equipment information and a preset inspection priority algorithm; the equipment information includes the equipment operation status and equipment location information; An inspection task allocation module is used to allocate inspection tasks based on the equipment inspection sequence according to the movement capabilities of the unmanned vehicle and the robot dog, generate target inspection routes corresponding to the unmanned vehicle and the robot dog, and push the target inspection routes corresponding to the unmanned vehicle and the robot dog to the corresponding robot control terminals; The inspection task execution module is used to control the unmanned vehicle and the robot dog to perform corresponding inspection tasks simultaneously by the robot control terminal, and optimize and adjust the target inspection routes corresponding to the unmanned vehicle and the robot dog according to the dynamic obstacle information and electromagnetic interference information monitored in real time until the inspection task is completed.

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