Substation inspection optimization method and system based on fusion of robots and unmanned aerial vehicles

Abstract

This invention discloses a substation inspection optimization method and system based on the integration of robots and drones. The method includes: acquiring inspection task instructions, which include information on the location of the equipment to be inspected, a set of limited access areas, the location of charging piles, and the initial power status of the equipment; generating optimized inspection task instructions based on a weighted and minimized objective of total inspection time and the robot's historical maximum energy consumption, and constraining the access sequence, collaborative take-off and landing points, and charging plan of the drone and robot according to the inspection task instructions; and distributing the optimized inspection task instructions to the robot and drone to achieve collaborative substation inspection by robots and drones. This invention can improve inspection efficiency and reduce inspection time for complex substation environments and specific inspection needs. As a substation inspection optimization method and system based on the integration of robots and drones, this invention can be widely applied in the field of intelligent power grid inspection technology.

Description

Technical Field

[0001] This invention relates to the field of intelligent power grid inspection technology, and in particular to an optimized method and system for substation inspection based on the fusion of robots and drones. Background Technology

[0002] In power systems, substations play a crucial role in power transformation, distribution, and protection, and their operational status directly impacts the safety and stability of the power grid. However, traditional substation inspections rely primarily on manual labor or single equipment (drones or ground robots only), which has the following shortcomings: First, manual inspections are arduous and risky: substations contain numerous and densely distributed devices, and are situated in complex environments with high voltage, high temperature, and strong electromagnetic fields. Manual inspections are not only labor-intensive but also pose safety hazards such as electric shock and falls. Second, a single platform cannot meet all inspection needs: When using only drones, there is a lack of close-range, high-precision inspection capabilities for low-level equipment (such as switchgear and relay protection panels), and battery capacity limitations result in short flight times. When using only ground robots, it is impossible to perform overhead inspections of high-altitude components such as busbars and insulator strings, easily creating blind spots. Third, there is a significant conflict between inspection efficiency and energy consumption: to cover all equipment, existing solutions often employ fixed routes or manual point-to-point confirmation, leading to repetitive walking (flying) and empty round trips, which both prolongs operation time and increases energy consumption. Fourth, there is a lack of coordination between path planning and energy replenishment in complex environments: substations have limited access areas such as high-voltage isolation zones and areas with dense GIS equipment; drones have restricted flight paths under strong electromagnetic interference, and energy replenishment facilities (charging piles / robot platforms) have fixed locations. Current methods lack joint optimization for these real-world constraints, making it difficult to guarantee safety and continuous operation. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide a substation inspection optimization method and system based on the integration of robots and drones. This method can provide a collaborative inspection path for drones and robots to address the complex environment and specific inspection needs of substations, thereby reducing the risks and costs of manual operations, improving inspection efficiency, and reducing inspection time.

[0004] The first technical solution adopted in this invention is: an optimized method for substation inspection based on the fusion of robots and drones, comprising the following steps: Obtain inspection task instructions, which include information on the location of the equipment to be inspected, a set of limited access areas, the location of the charging pile, and the initial power status of the equipment. Based on the weighted and minimization objective of total inspection time and robot's historical maximum energy consumption, and according to the inspection task instructions, the visit sequence, coordinated take-off and landing points, and charging plan constraints of the UAV and robot are implemented to generate optimized inspection task instructions. The optimized inspection task instructions are sent to robots and drones to enable collaborative inspection of substations by robots and drones.

[0005] Furthermore, the step of generating optimized inspection task instructions based on the weighted and minimized objective of total inspection time and the robot's historical maximum energy consumption, and constraining the access sequence, coordinated take-off and landing points, and charging plan of the UAV and robot according to the inspection task instructions, specifically includes: Based on the location information of the equipment to be inspected, the location of the charging pile, and the initial power status of the equipment in the inspection task instruction, determine the earliest time constraint, the shortest path constraint, and the maximum energy consumption constraint that can leave the equipment to be inspected, and construct a weighted sum minimization objective of the total inspection time and the robot's historical maximum energy consumption. Determine the constraints that all equipment to be inspected must be inspected and the constraints that prioritize the robot in performing inspection tasks; To ensure that the drone maintains sufficient power to return to the robot during the inspection process, a power consumption constraint for the drone is established. Based on the set of limited access areas in the inspection task instruction, establish areas accessible only by drones and areas accessible only by robots. For areas accessible only by drones, the inflow of robots is 0. For areas accessible only by drones, the inflow and outflow of drones are less than or equal to the inflow and outflow of robots. Construct the constraints for heterogeneous access areas. Define the cooperative motion state of the drone and the robot, and construct the constraints for the drone's take-off, landing and synchronization logic; Based on the weighted and minimization objective of total inspection time and robot's historical maximum energy consumption, and combined with constraints such as all devices to be inspected needing to be inspected, robot priority for executing inspection tasks, drone power consumption, heterogeneous access area constraints, and drone take-off, landing, and synchronization logic constraints, the inspection task instructions are optimized to generate optimized inspection task instructions.

[0006] Furthermore, the expression for the maximum energy consumption constraint is as follows: ; ; ; ; ; ; ; ; ; In the above formula, Indicates arrival at node The current maximum energy consumption, This indicates that you are about to leave the node. The current maximum energy consumption, Representing an edge Whether a robot passed by, Indicates that the robot passes through the edge Energy consumption during driving This indicates that a sufficiently large constant is used as a constraint. Represents all edge sets, This indicates that you are about to leave the node. The current maximum energy consumption, Indicates arrival at node Furthermore, the current maximum energy consumption when no node inspection task is performed and when charging is not performed at the inspection point. Represents a node Are there charging stations? Represents the set of all inspection nodes. Indicates robot execution node Energy consumption for the task Indicates the drone's arrival at the node The amount of electricity, Represents a node The binary variable for assigning inspection tasks at the location. This indicates the maximum power consumption of the ground robot in historical records. and Setting it to 0 indicates the initialization starting point. The maximum energy consumption at this location is 0.

[0007] Furthermore, the expression for the drone's power consumption constraint is as follows: ; ; ; ; ; ; ; ; ; ; ; ; ; In the above formula, Indicates arrival at node The drone's battery level at that location, This indicates the drone's battery capacity. Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Indicates leaving the node The drone's battery level at that location, Indicates the drone passed by the edge , Indicates leaving the node The drone's battery level at that time This indicates the drone's battery level at the starting position.

[0008] Furthermore, the expression for the robot inflow being 0 in areas accessible only by drones is as follows: In the above formula, Representing an edge Whether a robot passed by, This represents the set of nodes that are accessible only to drones. Representation and Node The set of other nodes that have direct edges.

[0009] Furthermore, the specific expression for the inflow and outflow of drones being less than or equal to the inflow and outflow of robots in areas accessible only by drones is as follows: ; ; In the above formula, Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Represents the set of nodes that can be accessed independently by only the robot, where Let j represent the set of nodes that have a direct edge to node j. Indicates from node The set of nodes that can be reached from the starting point.

[0010] The second technical solution adopted in this invention is: a substation inspection optimization system based on the fusion of robots and drones, comprising: The first module is used to obtain inspection task instructions, which include the location information of the equipment to be inspected, the set of limited access areas, the location of the charging pile, and the initial power status of the equipment. The second module is used to generate optimized inspection task instructions based on the weighted and minimized objectives of total inspection time and robot's historical maximum energy consumption, and according to the inspection task instructions, to constrain the access sequence, cooperative take-off and landing points, and charging plan of the UAV and robot. The third module is used to send optimized inspection task instructions to robots and drones, enabling robots and drones to conduct collaborative inspections of substations.

[0011] The beneficial effects of the method and system of this invention are as follows: This invention acquires inspection task instructions, which include information on the location of the equipment to be inspected, a set of limited access areas, the location of charging piles, and the initial power status of the equipment. Then, based on the weighted and minimized objective of the total inspection time and the robot's historical maximum energy consumption, and according to the inspection task instructions, it generates optimized inspection task instructions by constraining the access sequence of the UAV and the robot, the collaborative take-off and landing points, and the charging plan. This fully considers factors such as terrain and complex electromagnetic environment within the substation, and improves the reliability of the inspection by allocating and executing collaborative tasks between the ground robot and the UAV. Furthermore, incorporating the maximum energy consumption of the ground robot into the optimization objective can serve as a reference for the robot's battery capacity, promoting efficient management of the robot's power. Finally, the optimized inspection task instructions are sent to the robot and the UAV, enabling collaborative inspection of the substation by both robots and UAVs. This allows for the provision of collaborative inspection paths for UAVs and robots tailored to the complex environment and specific inspection needs of the substation, reducing the risks and costs of manual operations, improving inspection efficiency, and reducing inspection time. Attached Figure Description

[0012] Figure 1 This is a flowchart of the steps of the substation inspection optimization method based on the integration of robots and drones in this invention; Figure 2 This is a structural block diagram of the substation inspection optimization system based on the integration of robots and drones of the present invention; Figure 3 This is a schematic diagram of the inspection mode of the substation equipment collaborative inspection optimization model provided in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the operation process of the substation equipment collaborative inspection optimization model provided in a specific embodiment of the present invention; Figure 5 This is a schematic diagram of the optimized implementation of substation inspection provided in a specific embodiment of the present invention. Detailed Implementation

[0013] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0014] This invention addresses the complex environment and specific inspection needs of substations by providing a collaborative inspection path for drones and robots, reducing the risks and costs of manual operations, improving inspection efficiency, and reducing inspection time.

[0015] Reference Figure 1 This invention provides an optimized substation inspection method based on the fusion of robots and drones, which includes the following steps: S100. Obtain inspection task instructions, the inspection task instructions include the location information of the equipment to be inspected, the set of limited access areas, the location of the charging pile, and the initial power status of the equipment. In this embodiment, the maintenance personnel submit an inspection task instruction, provide the location to be inspected, and provide relevant equipment information and environmental information of the substation.

[0016] Specifically, maintenance personnel submit inspection task instructions through the central control system, providing information on the locations to be inspected. The instructions include a list of equipment to be inspected, restricted access areas (such as high-voltage areas / high-altitude areas / strong electromagnetic areas), and the location of charging piles.

[0017] S200: Based on the weighted and minimization objective of total inspection time and robot's historical maximum energy consumption, and according to the inspection task instructions, the UAV and robot's access sequence, coordinated take-off and landing points, and charging plan constraints are performed to generate optimized inspection task instructions. S210. Based on the location information of the equipment to be inspected, the location of the charging pile, and the initial power status of the equipment in the inspection task instruction, determine the earliest time constraint, the shortest path constraint, and the maximum energy consumption constraint that can leave the equipment to be inspected, and construct a weighted sum minimization objective of the total inspection time and the robot's historical maximum energy consumption. Specifically, based on real-time equipment status data and geographic information, the optimization objective is to minimize the weighted sum of the total inspection time and the robot's historical maximum energy consumption (without charging).

[0018] In this embodiment, the objective is further defined as follows: ;

[0019] in For each node requiring inspection, the time required to complete all tasks and reach the destination is specified. , The earliest time that can leave this node is determined by the maximum time for both the drone and the robot to leave the node, and is expressed by the constraint as follows: ； ; To ensure that the path does not contain loops and is thus the shortest path, all paths are subject to the following constraints: ； ； ； ； ； ； ； ; For each node, the historical maximum energy consumption is updated by recording the drone's energy consumption and the robot's energy consumption when the two meet, as well as the distribution status of charging stations, where the constraints are expressed as follows: ; ; ; ; ; ; ; ; ; In the above formula, Indicates arrival at node The current maximum energy consumption, This indicates that you are about to leave the node. The current maximum energy consumption, Representing an edge Whether a robot passed by, Indicates that the robot passes through the edge Energy consumption during driving This indicates that a sufficiently large constant is used as a constraint. Represents all edge sets, This indicates that you are about to leave the node. The current maximum energy consumption, Indicates arrival at node Furthermore, the current maximum energy consumption when no node inspection task is performed and when charging is not performed at the inspection point. Represents a node Are there charging stations? Represents the set of all inspection nodes. Indicates robot execution node Energy consumption for the task Indicates the drone's arrival at the node The amount of electricity, Represents a node The binary variable for assigning inspection tasks at the location. This indicates the maximum power consumption of the ground robot in historical records. and Setting it to 0 indicates the initialization starting point. The maximum energy consumption at this location is 0.

[0020] S220. Determine the constraints that all equipment to be inspected must be inspected and the constraints that the robot prioritizes the execution of inspection tasks. Specifically, the constraint that all nodes to be inspected are served is determined: a node is visited by at least one of the drones or robots, and the robot takes priority in performing the inspection task for nodes that are reached by both.

[0021] In this embodiment, the constraint that all nodes must be inspected is: ; The priority constraints for the robot are: ； ; ; In the above formula, Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passes through.

[0022] S230. Determine that the drone always maintains enough power to return to the robot during the inspection process, and construct constraints on the drone's power consumption. Specifically, it is determined that the drone always maintains enough power to return to the robot during the inspection process, and a precise battery consumption and charging logic is established.

[0023] In this embodiment, the drone's power consumption constraint is as follows: ; ; In the above formula, Indicates arrival at node The drone's battery level at that location, This indicates the drone's battery capacity. Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Indicates leaving the node The drone's battery level at that location, Indicates the drone passed by the edge , Indicates leaving the node The drone's battery level at that time This indicates the drone's battery level at the starting position.

[0024] S240. Based on the set of limited access areas in the inspection task instruction, establish areas accessible only by drones and areas accessible only by robots. For areas accessible only by drones, the inflow of robots is 0. For areas accessible only by drones, the inflow and outflow of drones are less than or equal to the inflow and outflow of robots. Construct the constraints for heterogeneous access areas. Specifically, heterogeneous access area constraints are established: the substation environment is divided into sets with different access permissions, including a set that only allows drones to access it. (such as high-altitude equipment) and collections that only allow access to ground robots. (such as areas with strong electromagnetic interference or no-fly zones).

[0025] In this embodiment, the constraints regarding heterogeneous access regions are expressed as follows: For sets For any node in the map, the inflow of ground robots must be 0, meaning the robot cannot reach it. ; In the above formula, Representing an edge Whether a robot passed by, This represents the set of nodes that are accessible only to drones. Representation and Node The set of other nodes that have direct edges.

[0026] For sets For any node in the system, the inflow and outflow of drones must be less than or equal to the inflow and outflow of robots. In other words, drones must be mounted on robots to move within that area and cannot fly independently. ; ; In the above formula, Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Represents the set of nodes that can be accessed independently by only the robot, where Let j represent the set of nodes that have a direct edge to node j. Indicates from node The set of nodes that can be reached from the starting point.

[0027] S250. Define the cooperative motion state of the drone and the robot, and construct the constraints for the drone's take-off, landing and synchronization logic. Specifically, establish coordinated takeoff and landing and synchronization logic: Define the coordinated motion states of the drone and the robot, including simultaneous movement in the same direction, drone takeoff, and drone landing, and constrain them within a specific area (set). Independent take-off and landing operations of drones are prohibited within the area.

[0028] In this embodiment, the constraints for takeoff, landing, and synchronization logic are expressed as follows: First, define the synchronization variables. Only when the robot and the drone pass by simultaneously Time is 1: ; Define nodes For landing point (variable) This means that both arrive at the point, but not simultaneously (the drone arrives by flying in the air, while the robot arrives by landing on the ground). ; Define nodes Takeoff point (variable) That is, both start from that point but not at the same time: ; In particular, in the set Within this area, drones are prohibited from taking off or landing. ; In the above formula, Indicates synchronization variables, Representing an edge Whether it was a drone passing by, Representing an edge Whether the robot passed by is indicated by a value of 1 if it did, and 0 if it did not.

[0029] S260. Based on the weighted and minimization objective of the total inspection time and the robot's historical maximum energy consumption, and combined with the constraints that all devices to be inspected need to be inspected, the constraints that the robot prioritizes the execution of inspection tasks, the constraints that the drone's power consumption, the constraints that the heterogeneous access area is subject to, and the constraints that the drone's take-off, landing and synchronization logic are subject to, the inspection task instructions are optimized to generate optimized inspection task instructions.

[0030] In this embodiment, the system calculates the optimal inspection plan based on real-time device status data and map topology information through a collaborative inspection model. The plan information includes the robot and drone's subsequent access sequence under limited access restrictions and drone power limitations, charging and rendezvous points, and instructions such as whether to wait. This achieves a weighted minimization of the total inspection time and the robot's historical maximum energy consumption (without charging). A single inspection location can be inspected by a maximum of one device.

[0031] The S300 sends optimized inspection task instructions to robots and drones, enabling robots and drones to conduct collaborative inspections of substations.

[0032] In this embodiment, the system sends optimized instructions to the robot and drone, continuously monitoring the device's battery status and environmental feedback information. The drone or robot waits at a designated waiting location for the other device to rendezvous, and then continues to perform the remaining tasks until all tasks are completed and the robot returns to the warehouse.

[0033] The robot performs equipment inspections along the ground path and replenishes its energy at the charging station; after completing the equipment inspection, the drone replenishes its energy at the rendezvous point with the robot; the drone and robot return to the warehouse after completing all the required inspection points.

[0034] Finally, the following description is provided in conjunction with the accompanying drawings of this embodiment: like Figure 3 As shown, this paper describes an inspection method that integrates ground robots and drones for collaborative inspection of substation equipment. Figure 4 As shown, the operation process of the substation equipment collaborative inspection optimization model integrating ground robots and drones is described.

[0035] In a substation, there are 10 nodes, including warehouses, numbered from 0 to 9. 0 represents the starting point, i.e., the warehouse, and 9 represents the final station of the inspection, as shown in Table 1 and Table 2.

[0036] Table 1. Distribution of Nodes and Access Characteristics in the Example Implementation ; Table 2 Node Distribution in Example ; 1) Maintenance personnel submit inspection task instructions through the central control system, providing information on the locations to be inspected. The instructions include a list of equipment to be inspected, restricted access areas (such as high-voltage areas / high-altitude areas / strong electromagnetic areas), and the location of charging piles. 2) The model combines inspection requirements, environmental data, and inspection equipment parameters to obtain the optimal driving route for drones and robots.

[0037] The optimal route for the robot, obtained from the model solution, is as follows: The optimal flight path for the drone is: Nodes 0, 3, 4, 7, and 9 represent the points where the two separate or reunite. Node 1 is a location inaccessible to the robot, and node 2 is a location inaccessible to the drone. Node 2 is also the robot's charging point.

[0038] 3) The driving route is distributed to the drones and robots.

[0039] 4) According to the issued driving route, the robot performs equipment inspections along the ground path and replenishes energy at the charging point when passing through the charging point; after the drone completes the equipment inspection, it is recharged by the robot at the rendezvous point; after the drone and robot complete all the inspection points, they return to the warehouse.

[0040] Assuming there are no access restrictions, meaning all nodes can be accessed by a single device, this invention has significant advantages in meeting constraints compared to this ideal assumption. The optimization effect is shown in Table 3, which significantly reduces the objective function.

[0041] Table 3 Comparison of optimization results between this embodiment and existing methods ; In summary, this embodiment of the invention addresses the complex environment of substations by dividing the inspection area into heterogeneous access spaces and dynamically planning the collaborative paths and task allocation of heterogeneous devices using a mixed-integer programming method. Based on the model solution results, the inspection system autonomously decides the robot's movement path, the drone's flight trajectory, and the collaborative actions (takeoff, landing, and loading) of both at specific nodes.

[0042] like Figure 5 The operational steps of this collaborative inspection are shown below: 1) Maintenance personnel submit inspection task instructions through the central control system, which include a list of equipment to be inspected and substation environmental constraints. 2) Based on the real-time device status and map topology, the collaborative inspection model determines the access sequence, charging plan and take-off and landing points of the robot and the drone, and gives the optimal collaborative driving / flight route to achieve the weighted sum and minimization of the total inspection time and the robot's peak energy consumption. 3) The system will issue optimized instructions and continuously monitor the equipment status; 4) The equipment operates autonomously according to the plan: the robot serves as a mobile platform and refueling station, the drone performs high-altitude / blind spot inspections, and carries the robot to move or refuel in specific areas.

[0043] Therefore, compared with traditional single-equipment inspection, this invention analyzes the substation equipment status and environmental constraints, plans inspection paths and task allocation strategies for heterogeneous equipment, and senses the power status of inspected equipment in real time, achieving coordinated optimization of safe inspection and resource consumption. Based on the model solution results, the inspection system autonomously decides on robot movement paths, drone flight trajectories, equipment charging nodes, and inspection strategies for limited access areas. This invention adapts to complex constraints such as high-voltage areas and no-fly zones within substations. Through a "vehicle-machine collaboration" mode, it solves the robot's blind spot problem and overcomes the drone's endurance and no-fly zone limitations. This invention optimizes task allocation logic, significantly reduces inspection time and peak energy consumption, improves the intelligent operation and maintenance level of substations, and has practical application value.

[0044] In summary, the embodiments of the present invention have the following advantages compared with the prior art: 1) This embodiment is more closely aligned with and adaptable to the actual conditions of substations. Firstly, automated inspection can solve the problem of manpower requirements for substation inspections. Secondly, compared to substation inspections using a single drone or robot, this invention fully considers factors such as terrain and complex electromagnetic environments within the substation, improving inspection reliability through the collaborative task allocation and execution of ground robots and drones. Furthermore, the drone's power supply via ground robots places lower demands on substation infrastructure and is more suitable for the actual conditions of more substations.

[0045] 2) Regarding the optimization objectives, this embodiment incorporates the maximum energy consumption of the ground robot into the optimization objective, which can serve as a reference for the robot's battery capacity and promote the efficient management of the robot's electrical energy. Therefore, this invention more comprehensively considers the complex conditions within substations, has lower requirements for substation infrastructure, and can efficiently perform inspection tasks within substations, thus possessing practical application value.

[0046] Reference Figure 2 A substation inspection optimization system based on the integration of robots and drones includes: The first module 201 is used to obtain inspection task instructions, which include the location information of the equipment to be inspected, the set of limited access areas, the location of the charging pile, and the initial power status of the equipment. The second module 202 is used to generate optimized inspection task instructions based on the weighted and minimized objectives of the total inspection time and the robot's historical maximum energy consumption, and according to the inspection task instructions, to constrain the access sequence, cooperative take-off and landing points and charging plan of the UAV and the robot. The third module 203 is used to send the optimized inspection task instructions to the robot and the drone, so as to realize the collaborative inspection of the substation by the robot and the drone.

[0047] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0048] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An optimized method for substation inspection based on the fusion of robots and drones, characterized in that, Includes the following steps: Obtain inspection task instructions, which include information on the location of the equipment to be inspected, a set of limited access areas, the location of the charging pile, and the initial power status of the equipment. Based on the weighted and minimization objective of total inspection time and robot's historical maximum energy consumption, and according to the inspection task instructions, the visit sequence, coordinated take-off and landing points, and charging plan constraints of the UAV and robot are implemented to generate optimized inspection task instructions. The optimized inspection task instructions are sent to robots and drones to enable collaborative inspection of substations by robots and drones.

2. The substation inspection optimization method based on fusion robot and UAV as described in claim 1, characterized in that, The step of generating optimized inspection task instructions based on the weighted and minimized objective of total inspection time and the robot's historical maximum energy consumption, and constraining the access sequence, coordinated take-off and landing points, and charging plan of the UAV and robot according to the inspection task instructions, specifically includes: Based on the location information of the equipment to be inspected, the location of the charging pile, and the initial power status of the equipment in the inspection task instruction, determine the earliest time constraint, the shortest path constraint, and the maximum energy consumption constraint that can leave the equipment to be inspected, and construct a weighted sum minimization objective of the total inspection time and the robot's historical maximum energy consumption. Determine the constraints that all equipment to be inspected must be inspected and the constraints that prioritize the robot in performing inspection tasks; To ensure that the drone maintains sufficient power to return to the robot during the inspection process, a power consumption constraint for the drone is established. Based on the set of limited access areas in the inspection task instruction, establish areas accessible only by drones and areas accessible only by robots. For areas accessible only by drones, the inflow of robots is 0. For areas accessible only by drones, the inflow and outflow of drones are less than or equal to the inflow and outflow of robots. Construct the constraints for heterogeneous access areas. Define the cooperative motion state of the drone and the robot, and construct the constraints for the drone's take-off, landing and synchronization logic; Based on the weighted and minimization objective of total inspection time and robot's historical maximum energy consumption, and combined with constraints such as all devices to be inspected needing to be inspected, robot priority for executing inspection tasks, drone power consumption, heterogeneous access area constraints, and drone take-off, landing, and synchronization logic constraints, the inspection task instructions are optimized to generate optimized inspection task instructions.

3. The substation inspection optimization method based on fusion robot and UAV as described in claim 2, characterized in that, The expression for the maximum energy consumption constraint is as follows: ； ； ； ； ； ； ； ； ； In the above formula, Indicates arrival at node The current maximum energy consumption, This indicates that you are about to leave the node. The current maximum energy consumption, Representing an edge Whether a robot passed by, Indicates that the robot passes through the edge Energy consumption during driving This represents a sufficiently large constant used as a constraint. Represents all edge sets, This indicates that you are about to leave the node. The current maximum energy consumption, Indicates arrival at node Furthermore, the current maximum energy consumption when no node inspection task is performed and when charging is not performed at the inspection point. Represents a node Are there charging stations? Represents the set of all inspection nodes. Indicates robot execution node Energy consumption for the task Indicates the drone has arrived at the node. The amount of electricity, Represents a node The binary variable for assigning inspection tasks at the location. This indicates the maximum power consumption of the ground robot in historical records. and Setting it to 0 indicates the initialization starting point. The maximum energy consumption at this location is 0.

4. The substation inspection optimization method based on the fusion of robots and drones as described in claim 3, characterized in that, The specific expression for the drone's power consumption constraint is as follows: ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, Indicates arrival at node The drone's battery level at that location, This indicates the drone's battery capacity. Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Indicates leaving the node The drone's battery level at that location, Indicates the drone passed by the edge , Indicates leaving the node The drone's battery level at that time This indicates the drone's battery level at the starting position.

5. The substation inspection optimization method based on the fusion of robots and drones as described in claim 4, characterized in that, The specific expression for the robot inflow being 0 in areas accessible only by drones is as follows: ； In the above formula, Representing an edge Whether a robot passed by, This represents the set of nodes that are accessible only to drones. Representation and Node The set of other nodes that have direct edges.

6. The substation inspection optimization method based on the fusion of robots and drones as described in claim 5, characterized in that, The specific expression for the inflow and outflow of drones being less than or equal to the inflow and outflow of robots in areas accessible only by drones is as follows: ； ； In the above formula, Representing an edge Whether it was a drone passing by, Representing an edge Whether a robot passed by, Represents the set of nodes that can be accessed independently by only the robot, where Let j represent the set of nodes that have a direct edge to node j. Indicates from node The set of nodes that can be reached from the starting point.

7. A substation inspection optimization system based on the integration of robots and drones, characterized in that, Includes the following modules: The first module is used to obtain inspection task instructions, which include the location information of the equipment to be inspected, the set of limited access areas, the location of the charging pile, and the initial power status of the equipment. The second module is used to generate optimized inspection task instructions based on the weighted and minimized objectives of total inspection time and robot's historical maximum energy consumption, and according to the inspection task instructions, to constrain the access sequence, cooperative take-off and landing points, and charging plan of the UAV and robot. The third module is used to send optimized inspection task instructions to robots and drones, enabling robots and drones to conduct collaborative inspections of substations.

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