Electric power inspection unmanned aerial vehicle autopilot deployment scheduling method, system, device and medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle scheduling, and provides an electric power inspection unmanned aerial vehicle autopilot deployment scheduling method, system and device and a medium, and the method comprises the steps: issuing an electric power inspection task for an electric power inspection scheduling center, and enabling each unmanned aerial vehicle autopilot to perform deployment scheduling according to the electric power inspection task; calculating task execution estimation cost based on the task execution cost evaluation function, sending the task execution estimation cost to a power inspection scheduling center, and distributing the power inspection task to a target unmanned aerial vehicle autopilot based on a task execution cost minimization principle by the power inspection scheduling center according to a comparison result of all the task execution estimation costs of the power inspection task. And planning a path in real time according to the electric power inspection task and executing the inspection task. According to the method, the inspection tasks can be reasonably allocated based on factors such as the state of the unmanned aerial vehicle autopilot, the weather change and the geographical environment, adaptive scheduling optimization can be performed aiming at emergency situations, the resource utilization rate of the unmanned aerial vehicle autopilot, the task execution flexibility and the inspection efficiency can be effectively improved, and the inspection cost can also be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV scheduling, and particularly to a method, system, device and medium for deploying and scheduling an autopilot of a UAV for power inspection. Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in power inspection and gradually replaced manual inspection. However, most of the existing UAV deployment and scheduling methods adopt static scheduling strategies and cannot dynamically adjust inspection tasks based on factors such as UAV status, weather changes, and geographical environment. This not only leads to low UAV inspection efficiency but also fails to achieve real-time scheduling and task optimization for emergencies, resulting in underutilization of UAV resources. It is very easy to cause problems such as insufficient battery power or task overlap during the inspection process of UAVs, making it impossible to achieve optimal resource allocation and maximize the inspection task effect, while also causing too high inspection costs. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for deploying and scheduling an autopilot of a UAV for power inspection. Through an inspection task allocation mechanism that evaluates the task execution cost based on comprehensively considering the loss cost, risk cost, time cost, and task priority when the UAV executes the inspection task, combined with an inspection path obstacle avoidance mechanism designed based on an improved A* algorithm, it can not only ensure the reasonable allocation of inspection tasks but also achieve real-time dynamic scheduling optimization for emergencies. It can not only effectively improve the utilization rate of UAV autopilot resources, task execution flexibility, and inspection efficiency but also reduce inspection costs.

[0004] To achieve the above purpose, a method, system, device and medium for deploying and scheduling an autopilot of a UAV for power inspection are provided.

[0005] In the first aspect, an embodiment of the present invention provides a method for deploying and scheduling an autopilot of a UAV for power inspection, which is applied to the deployment and scheduling of several UAV autopilots by a power inspection scheduling center. The method includes the following steps: In response to the release of a power inspection task by the power inspection scheduling center, each UAV autopilot calculates the corresponding estimated task execution cost based on the power inspection task using a task execution cost evaluation function, and sends the estimated task execution cost to the power inspection scheduling center; the task execution cost evaluation function is obtained by weighting the loss cost, risk cost, time cost, and task priority of task execution; Based on the principle of minimizing the task execution cost, the power inspection scheduling center distributes the power inspection task to the target UAV autopilot according to the comparison result of all the estimated task execution costs corresponding to the power inspection task; the target UAV autopilot is the UAV autopilot corresponding to the minimum estimated task execution cost; In response to the reception of the power inspection task, the target UAV autopilot plans the path in real time to execute the power inspection task.

[0006] Further, the loss cost includes the electricity cost and the equipment depreciation cost; the steps for obtaining the loss cost include: Based on the cumulative value of the hovering power, climbing power, and air resistance overcoming power of the UAV autopilot, the flight power of the UAV autopilot is obtained, and based on the flight power of the UAV autopilot, the total inspection flight duration of the UAV autopilot, and the unit electricity price, the electricity cost is obtained; Based on the task complexity scoring coefficient, the task load of the UAV autopilot, the task flight altitude of the UAV autopilot, and the total inspection flight duration of the UAV autopilot, the task execution life consumption is obtained, and based on the task execution life consumption, the designed life of the UAV autopilot, and the purchase cost of the UAV autopilot, the equipment depreciation cost is obtained; wherein, the equipment depreciation cost is expressed as: In the formula, represents the equipment depreciation cost; represents the purchase cost of the UAV autopilot; represents the designed life of the UAV autopilot; represents the task execution life consumption; represents the task complexity scoring coefficient; represents the task load of the UAV autopilot, represents the average flight altitude of the UAV autopilot task; represents the total inspection flight duration of the UAV autopilot; and respectively represent correction coefficients; Based on the cumulative value of the electricity cost and the equipment depreciation cost, the loss cost is obtained.

[0007] Further, the risk cost includes the bad weather risk, the altitude risk, and the signal occlusion risk; the steps for obtaining the risk cost include: Obtain the real-time weather data during task execution, and based on the real-time weather data during task execution and a pre-constructed failure probability prediction model, perform UAV autopilot failure prediction to obtain the bad weather risk; Obtain the geographical data of the task execution area, and based on the geographical data of the task execution area and the maximum allowable flight altitude of the UAV autopilot, obtain the altitude risk; Obtain the communication signal coverage data of the task execution area, and perform a disconnection risk prediction based on the communication signal coverage data of the task execution area and a pre-constructed disconnection probability prediction model to obtain the signal occlusion risk.

[0008] Further, the time cost includes the total flight duration of the UAV autopilot for inspection and the total duration of the UAV autopilot for inspection to replenish power; the steps for obtaining the time cost include: Based on the path planning algorithm, obtain the optimal inspection path, and according to the optimal inspection path and the flight speed of the UAV autopilot, obtain the total flight duration of the UAV autopilot for inspection; the flight speed of the UAV autopilot is obtained by correcting the maximum flight speed of the UAV autopilot based on meteorological conditions and load status; According to the total flight duration of the UAV autopilot for inspection, the maximum battery capacity of the UAV autopilot, and the unit flight energy consumption of the UAV autopilot, obtain the power required to be replenished for task execution, and according to the power required to be replenished for task execution and the average charging power of the charging station, obtain the total duration of the UAV autopilot for inspection to replenish power; According to the cumulative value of the total flight duration of the UAV autopilot for inspection and the total duration of the UAV autopilot for inspection to replenish power, obtain the time cost.

[0009] Further, the steps for the target UAV autopilot to plan the path in real time to execute the power inspection task include: When the target UAV autopilot detects a path obstacle, based on the obstacle information of the path obstacle, generate an obstacle avoidance path based on the improved A* algorithm, and according to the obstacle avoidance path, evaluate and obtain the task execution cost after obstacle avoidance; the obstacle information includes the obstacle type, obstacle position, and obstacle speed; the cost function of the improved A* algorithm includes the actual cost, heuristic estimated cost, and obstacle risk cost; the obstacle risk cost is expressed as: Wherein, represents the current path node corresponding obstacle risk cost; represents the current path node the minimum distance between the obstacle; represents the current path node corresponding obstacle speed; and represent the distance influence weight and the speed influence weight; Send obstacle encounter information to the power inspection and dispatch center according to the task execution cost after obstacle avoidance, so that the power inspection and dispatch center makes a decision on whether the target UAV autopilot avoids obstacles and continues to execute the task according to the obstacle encounter information; the obstacle encounter information includes the current position information of the UAV autopilot, the task destination position, and the task execution cost after obstacle avoidance.

[0010] Further, the step that the power inspection and dispatch center makes a decision on whether the target UAV autopilot avoids obstacles and continues to execute the task according to the obstacle encounter information includes: The power inspection and dispatch center issues a new inspection task according to the obstacle encounter information, and compares the estimated cost of executing the new inspection task fed back by the received UAV autopilot with the task execution cost after obstacle avoidance in the obstacle encounter information; When the task execution cost after obstacle avoidance is the minimum value, notify the target UAV autopilot to continue executing the power inspection task based on the obstacle avoidance path; When the task execution cost after obstacle avoidance is not the minimum value, allocate the new inspection task to the UAV autopilot corresponding to the minimum value, and notify the target UAV autopilot to exit the inspection task.

[0011] Further, the method further includes: When the power inspection task is completed, the target UAV autopilot generates the actual task execution cost of the power inspection task, and optimizes and adjusts the weight coefficients of the costs in the task execution cost evaluation function according to the deviation between the actual costs of each item in the task execution actual cost and the estimated costs of the corresponding items in the task execution estimated cost.

[0012] In a second aspect, an embodiment of the present invention provides a power inspection UAV autopilot deployment and dispatch system, the system includes a power inspection and dispatch center and several UAV autopilots; The power inspection and dispatch center is used to issue power inspection tasks, receive the estimated task execution costs fed back by each UAV autopilot, and allocate the power inspection tasks to the target UAV autopilot based on the principle of minimizing the task execution cost according to the comparison result of all the estimated task execution costs corresponding to the power inspection tasks; the target UAV autopilot is the UAV autopilot corresponding to the minimum estimated task execution cost; The UAV autopilot is used to calculate the estimated task execution cost corresponding to the power inspection task issued by the power inspection scheduling center based on the task execution cost evaluation function, and send the estimated task execution cost to the power inspection scheduling center; the task execution cost evaluation function is obtained by weighting the loss cost, risk cost, time cost, and task priority of task execution; it is also used to plan the path in real time according to the power inspection task assigned by the power inspection scheduling center to execute the power inspection task.

[0013] In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0015] The present invention provides a method, system, computer device, and storage medium for deploying and scheduling a UAV autopilot for power inspection. Through the method, the power inspection scheduling center issues power inspection tasks, and each UAV autopilot calculates the corresponding estimated task execution cost based on the task execution cost evaluation function according to the power inspection task and sends it to the power inspection scheduling center. The power inspection scheduling center distributes the power inspection task to the target UAV autopilot based on the comparison result of all the estimated task execution costs corresponding to the power inspection task, following the principle of minimizing the task execution cost. Then, the target UAV autopilot plans the path in real time according to the received power inspection task to execute the power inspection task. Compared with the prior art, the method for deploying and scheduling the UAV autopilot for power inspection can reasonably allocate inspection tasks based on factors such as the UAV autopilot status, weather changes, and geographical environment, and can adaptively schedule and optimize for emergencies. It can not only effectively improve the resource utilization rate, task execution flexibility, and inspection efficiency of the UAV autopilot, but also reduce the inspection cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of the method for deploying and scheduling a UAV autopilot for power inspection in an embodiment of the present invention; Figure 2 is another schematic flowchart of the method for deploying and scheduling a UAV autopilot for power inspection in an embodiment of the present invention; Figure 3 is a schematic structural diagram of the system for deploying and scheduling a UAV autopilot for power inspection in an embodiment of the present invention; Figure 4 is an internal structural diagram of the computer device in an embodiment of the present invention. Detailed implementation manners

[0017] In order to make the objectives, technical solutions, and beneficial effects of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the following described embodiments are part of the embodiments of the present invention and are only used to illustrate the present invention, but not to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0018] The method for deploying and scheduling an electric power inspection UAV autopilot provided by the present invention can be understood as a technical solution that, based on the current application situation where the existing methods for deploying and scheduling electric power inspection UAVs are difficult to meet the optimal allocation of UAV autopilot resources and the maximization of the effects of inspection tasks, rationally allocates inspection tasks based on factors such as the state of the UAV autopilot, weather changes, and geographical environment, and can dynamically adjust the scheduling strategy in real time for emergencies. The following embodiments will describe in detail the method for deploying and scheduling an electric power inspection UAV autopilot of the present invention.

[0019] In one embodiment, as Figure 1 shown, a method for deploying and scheduling an electric power inspection UAV autopilot is provided, which is applied to the deployment and scheduling of a plurality of UAV autopilots by an electric power inspection scheduling center. The electric power inspection scheduling center maintains communication with all UAV autopilots. Among them, the electric power inspection scheduling center is responsible for issuing inspection tasks according to actual electric power inspection requirements, and schedules the UAV autopilots by a method in which the UAV autopilots that can participate in the inspection tasks provide the estimated task execution costs to bid for the inspection tasks. Each UAV autopilot generates the estimated task execution cost to participate in the bidding for the inspection tasks by estimating the loss cost, risk cost, time cost, and task priority during the execution of the inspection task, and plans the path in real time to execute the corresponding inspection task after receiving the inspection task confirmed and allocated by the electric power inspection scheduling center. The specific method for deploying and scheduling an electric power inspection UAV autopilot includes the following steps: S11. In response to the issuance of the electric power inspection task by the electric power inspection scheduling center, each UAV autopilot calculates the corresponding estimated task execution cost based on the electric power inspection task according to the task execution cost evaluation function, and sends the estimated task execution cost to the electric power inspection scheduling center.

[0020] In practical applications, the power inspection and dispatching center generates corresponding power inspection tasks according to power inspection requirements, and publishes the power inspection tasks to the auction task pool shared by all UAV autopilots. All UAV autopilots with the conditions for participating in the auction can receive the inspection task information. To facilitate each UAV autopilot to automatically analyze the estimated cost of task execution, the task information corresponding to the power inspection task may include inspection time, the location of the target inspection area, and corresponding inspection items, etc. After each UAV autopilot receives the power inspection task that can be auctioned, it can obtain the corresponding estimated cost of task execution based on the analysis of the task information and feedback it to the power inspection and dispatching center to update the bidding information in the auction task pool.

[0021] In this embodiment, the estimated costs of task execution reported by each UAV autopilot are used as the main basis for the power inspection and dispatching center to allocate inspection tasks. To ensure the comprehensiveness and reliability of the analysis of the estimated cost of task execution, in this embodiment, considering various factors such as the status of UAV autopilots, weather changes, and geographical environment, a task execution cost evaluation function is preferably designed by weighting the loss cost, risk cost, time cost, and task priority based on task execution.

[0022] Considering that in practical applications, each time a UAV autopilot executes an inspection task, it consumes a certain amount of power and there will be a certain degree of depreciation. To ensure the comprehensiveness of the analysis of the loss cost, in this embodiment, it is preferably set that the loss cost includes electricity cost and equipment depreciation cost. The electricity cost can be understood as the power cost required for the UAV autopilot to execute the inspection task, and the equipment depreciation cost can be understood as the equipment depreciation loss caused by the UAV autopilot executing the inspection task. Specifically, the steps for obtaining the loss cost include: According to the cumulative value of the hover power, climbing power, and power to overcome air resistance of the UAV autopilot, the flight power of the UAV autopilot is obtained, and based on the flight power of the UAV autopilot, the total flight duration of the UAV autopilot for inspection, and the unit electricity price, the electricity cost is obtained; among them, the hover power can be calculated based on the load condition of the UAV autopilot, expressed as: In the formula, and respectively represent the load of the UAV autopilot and the corresponding hover power coefficient, and the hover power coefficient can be set according to actual application requirements; represents the hover power.

[0023] Considering that the climbing power of the UAV autopilot is related to the altitude change and the gravitational potential energy of the UAV autopilot, it can be expressed as: In the formula, Represents the total mass of the UAV autopilot, including the payload weight and the airframe weight; Represents the acceleration due to gravity, taking 9.8 m / s 2 ; Represents the average flight altitude; Represents the total flight duration; Represents the climbing power.

[0024] Considering that the air resistance borne by the UAV autopilot during flight is related to the flight speed and the drag coefficient, then, the climbing power can be expressed as: In the formula, Represents the power to overcome air resistance; 、 、 and Represent the air density, the drag coefficient, the frontal area of the UAV autopilot, and the flight speed of the UAV autopilot, respectively.

[0025] After obtaining the hovering power, the climbing power, and the power to overcome air resistance of the UAV autopilot respectively through the above method, adding them up can obtain the required flight power of the UAV autopilot; the corresponding electricity cost can be calculated through the following formula: In the formula, Represents the electricity cost; Represents the unit electricity price; Represents the flight power of the UAV autopilot; Represents the total flight duration of the UAV autopilot for inspection.

[0026] According to the task complexity scoring coefficient, the UAV autopilot task load, the UAV autopilot task flight altitude, and the total flight duration of the UAV autopilot for inspection, obtain the task execution life consumption amount, and according to the task execution life consumption amount, the UAV autopilot design life, and the UAV autopilot purchase cost, obtain the equipment depreciation cost; among them, the task execution life consumption amount can be understood as the actual consumption amount of life for a single inspection task (hours), and preferably expressed as: In the formula, Represents the task execution life consumption amount; Represents the task complexity scoring coefficient, the higher the score corresponding to the more complex the task, which can be obtained through comprehensive analysis based on the distance between the inspection area location in the task information and the UAV autopilot, the number of inspection items, and the preset operation difficulty corresponding to each inspection item, etc. In principle, the task complexity scoring coefficient is proportional to the distance, the number of inspection items, and the inspection item operation difficulty; Indicates the payload of the UAV autopilot; Indicates the average flight altitude of the UAV autopilot mission; Indicates the total flight duration of the UAV autopilot inspection flight, which can be determined according to the length of the inspection path generated based on the location of the inspection area in the mission information; And Respectively represent correction factors, which respectively measure the impact of flight altitude and flight time on the lifespan, and can be obtained by fitting based on relevant historical data, and are not specifically limited here.

[0027] After obtaining the consumption of the mission execution lifespan through the above method steps, the depreciation cost of the equipment corresponding to the executed mission can be calculated through the following expression: In the formula, Represents the equipment depreciation cost; Represents the purchase cost of the UAV autopilot; Represents the designed lifespan of the UAV autopilot; Represents the consumption of the mission execution lifespan.

[0028] According to the cumulative value of the electricity cost and the equipment depreciation cost, the loss cost is obtained; that is, the loss cost is expressed as: In the formula, Represents the loss cost of the mission execution; And Respectively represent the electricity cost and the equipment depreciation cost.

[0029] The risk cost in this embodiment can be understood as the environmental risks that the UAV autopilot may face when performing the power inspection mission. Considering that the environmental risks include bad weather, altitude, and signal occlusion, etc., preferably, the risk cost is set to include bad weather risk, altitude risk, and signal occlusion risk; specifically, the steps for obtaining the risk cost include: Obtain the real-time weather data for task execution, and perform UAV autopilot fault prediction based on the real-time weather data for task execution and a pre-constructed fault probability prediction model to obtain the severe weather risk; among them, the real-time weather data for task execution can be understood as the weather data situation corresponding to the inspection time in the task information corresponding to the power inspection task obtained through a meteorological platform, including information such as wind speed, precipitation, temperature, and thunderstorm index, etc.; considering that in actual applications, when the wind speed is relatively high, the UAV autopilot is prone to wind disturbance instability, resulting in flight path deviation or even crashing. When the precipitation is relatively high, the rain may affect the normal operation of the UAV autopilot sensor. Temperature and thunderstorm also affect the normal operation of the UAV autopilot sensor. The corresponding fault probability prediction model can be understood as a machine learning model trained and constructed based on the historical data of the flight fault probability of the UAV autopilot under different weather data conditions, which can calculate the potential fault probability of the UAV autopilot under different weather conditions and can be used to reliably determine the risk situation of performing the inspection task under the current task weather conditions to ensure the efficiency and accuracy of the severe weather risk assessment.

[0030] Obtain the geographical terrain data of the task execution area, and obtain the altitude risk based on the geographical terrain data of the task execution area and the maximum allowable flight height of the UAV autopilot; among them, the geographical terrain data of the task execution area can be understood as the terrain data obtained by querying the shared geographic information system according to the target inspection area location in the task information corresponding to the power inspection task, which may include the terrain height information of the target inspection area; among them, the maximum allowable flight height of the UAV autopilot can be understood as the flight height limit determined based on the production design of the UAV autopilot. Different UAV autopilots have different maximum allowable flight heights, which are not specifically limited here. The corresponding altitude risk can be understood as the feasibility of the UAV autopilot performing tasks in different height areas considering factors such as the flight height of the UAV autopilot not meeting the flight requirements of complex terrains and the low air density in high-altitude areas affecting the lift and battery endurance of the UAV autopilot. In actual applications, multiple allowable flight height ranges can be set according to the maximum allowable flight height of the UAV autopilot, and corresponding risk scores can be set for each allowable flight height range based on the principle that the lower the height, the lower the corresponding risk score. The corresponding altitude risk can be determined by judging which allowable flight height range the terrain height of the target inspection area belongs to.

[0031] Obtain the communication signal coverage data of the task execution area, and perform out-of-contact risk prediction based on the communication signal coverage data of the task execution area and the pre-constructed out-of-contact probability prediction model to obtain the signal occlusion risk; among them, the communication signal coverage data of the task execution area can be understood as considering that in practical applications, the communication signals in areas such as high-rise dense areas, valleys, and tunnels may be weakened due to occlusion, which may lead to communication interruption or data loss, and when there are large signal blind spots in the task path, the inspection task may not be completed or even face the risk of losing contact. The mobile communication network signal coverage obtained according to the target inspection area location in the task information corresponding to the power inspection task can include the signal strength distribution of 5G, 4G, or dedicated wireless networks in the target inspection area, etc., and is used to evaluate the reliability of data transmission and remote control. The corresponding out-of-contact probability prediction model can be understood as a neural network model trained and constructed based on the historical data of the communication status of the drone autopilot under different communication signal coverages, and can output the corresponding signal occlusion risk probability value based on the actual input communication signal coverage data to ensure the efficiency and accuracy of signal occlusion risk assessment.

[0032] The time cost in this embodiment can be understood as the total time required for the drone autopilot to execute the inspection task planned based on the target inspection area location and its own current location in the task information, including the total flight time of the drone autopilot for inspection and the total time for the drone autopilot to replenish power during inspection; specifically, the steps for obtaining the time cost include: Obtain the optimal inspection path based on the path planning algorithm, and obtain the total flight time of the drone autopilot for inspection according to the optimal inspection path and the flight speed of the drone autopilot; among them, the path planning algorithm can use the existing shortest path planning algorithm, and the corresponding optimal inspection path can be understood as the shortest path obtained through the path planning algorithm based on the target inspection area location and the current location of the drone autopilot itself. The specific acquisition method can refer to the implementation of relevant existing technologies. The flight speed of the drone autopilot is obtained by correcting the maximum flight speed of the drone autopilot based on the meteorological conditions and load status. The specific implementation process can include comprehensively analyzing the meteorological conditions and load status during the execution of the inspection task to obtain a flight speed correction coefficient (a value greater than 0 and less than 1) or a flight speed correction value (a negative value with an absolute value less than the maximum flight speed), and then using the product of the flight speed correction coefficient and the maximum flight speed of the drone autopilot as the required flight speed of the drone autopilot, or using the sum of the flight speed correction value and the maximum flight speed of the drone autopilot as the required flight speed of the drone autopilot. After determining the flight speed of the drone autopilot, the total flight time of the drone autopilot for inspection can be obtained by dividing the route length of the optimal inspection path by the flight speed of the drone autopilot.

[0033] Based on the total inspection flight duration of the UAV autopilot, the maximum battery capacity of the UAV autopilot, and the unit flight energy consumption of the UAV autopilot, the supplementary power required for task execution is obtained, and based on the supplementary power required for task execution and the average charging power of the charging station, the total supplementary power duration for UAV autopilot inspection is obtained; among them, the route length of the optimal inspection path varies due to the different positions of different UAV autopilots, and at the same time, the maximum battery capacity of the UAV autopilot also varies due to the type or state of the UAV autopilot; the corresponding unit flight energy consumption of the UAV autopilot can be understood as the power consumption per unit time during the flight of the UAV autopilot, which may vary due to the type or state of the UAV autopilot and can be set based on the historical flight experience of the UAV autopilot.

[0034] The supplementary power required for task execution in this embodiment can be understood as the difference between the total power required for task execution and the existing power of the UAV autopilot. Among them, the total power required for task execution can be obtained by multiplying the total inspection flight duration of the UAV autopilot and the unit flight energy consumption of the UAV autopilot; after determining the supplementary power required for task execution, based on the existing power of the UAV autopilot and the maximum battery capacity of the UAV autopilot, the number of times of supplementary power required for UAV autopilot inspection can be calculated; then, after obtaining the single charging duration based on the ratio of the maximum battery capacity of the UAV autopilot to the average charging power of the charging station, the total duration spent on charging due to insufficient power during the power inspection task execution of the UAV autopilot is obtained by multiplying the single charging duration by the number of times of supplementary power required for UAV autopilot inspection, that is, the total supplementary power duration for UAV autopilot inspection.

[0035] Based on the cumulative value of the total inspection flight duration of the UAV autopilot and the total supplementary power duration for UAV autopilot inspection, the time cost is obtained.

[0036] The task priorities in this embodiment are different for different participating UAV autopilots in the auction. It can be understood as the execution priority level (task execution sequence) of the current power inspection task bid by a single UAV autopilot among all the tasks it participates in the auction; in practical applications, the criteria for different UAV autopilots to evaluate the task priority are unified. For example, it can be determined based on the principle that the higher the urgency of the task, the higher the priority (the earlier the execution), etc., and no specific limitation is made here.

[0037] After each UAV autopilot analyzes the corresponding loss cost, risk cost, time cost, and task priority based on the current power inspection task information in the auction, the loss cost, risk cost, time cost, and task priority can be weighted and summed according to the preset weight coefficients to obtain the estimated cost required for task execution, expressed as: In the formula, Indicates the estimated cost of task execution; , , and respectively represent the loss cost, risk cost, time cost, and task priority of task execution; , , and represent weight coefficients, which can use fixed values or be dynamically adjusted.

[0038] In this embodiment, the UAV autopilot determines the estimated cost of task execution by comprehensively evaluating the task information of the power inspection task in terms of loss cost, risk cost, time cost, task priority, etc., and weighted fusion of the evaluation results in various aspects. This method can effectively improve the comprehensiveness and accuracy of the estimated cost of UAV autopilot task execution, and further provide a reliable guarantee for the reasonable scheduling and allocation of UAV autopilot resources by the power inspection dispatch center.

[0039] S12. Based on the principle of minimizing the task execution cost, the power inspection dispatch center allocates the power inspection task to the target UAV autopilot according to the comparison results of all the estimated task execution costs corresponding to the power inspection task; the target UAV autopilot is the UAV autopilot corresponding to the minimum estimated task execution cost. In practical applications, the power inspection dispatch center continuously monitors the estimated task execution costs fed back by the UAV autopilots in the auction task pool, and performs real-time sorting on the bids of the estimated task execution costs. When all the participating UAV autopilots have completed the feedback of the task execution costs, the UAV autopilot corresponding to the minimum estimated task execution cost obtained by sorting is determined as the auction winner, and the allocation work of the power inspection task is completed.

[0040] S13. In response to the receipt of the power inspection task, the target UAV autopilot plans the path in real time to execute the power inspection task. Among them, after receiving the power inspection task allocated by the power inspection dispatch center, the target UAV autopilot can start the inspection task based on the corresponding task information and plan the path in real time to ensure the safe and efficient completion of the inspection task.

[0041] Considering that in practical applications, the target UAV autopilot may encounter obstacles or lose contact during the process of traveling to the target inspection area location and performing area inspections. To ensure the continuous and efficient completion of the power inspection task, this embodiment preferably designs an adaptive scheduling optimization mechanism for emergencies to ensure the continuity, flexibility, and safety of task execution. Specifically, the step of the target UAV autopilot planning the path in real time to execute the power inspection task includes: When the target UAV autopilot detects a path obstacle, based on the obstacle information of the path obstacle, an obstacle avoidance path is generated based on the improved A* algorithm, and according to the obstacle avoidance path, the task execution cost after obstacle avoidance is evaluated; the obstacle information includes obstacle type, obstacle position, and obstacle speed; among them, the improved A* algorithm can be understood as introducing the distance and moving speed of the obstacle, which are risk factors affecting the UAV autopilot, into the cost function design of the A* algorithm for path optimization, so as to realize dynamic optimization and adjustment of the path based on real-time perceived obstacle information and improve the safety of the UAV autopilot during task execution. Specifically, the cost function of the improved A* algorithm includes actual cost, heuristic estimated cost, and obstacle risk cost, where the actual cost represents the actual cost from the starting node to the current node The actual cost, and the heuristic estimated cost represents the heuristic estimated cost from the current node To the target node, which can be calculated using the Euclidean distance formula in three-dimensional space; the obstacle risk cost is expressed as: Where Represents the obstacle risk cost corresponding to the current path node ; Represents the minimum distance between the current path node And the obstacle. When the obstacle is a dynamic obstacle, the existing relevant LSTM (Long Short-Term Memory) prediction algorithm can be used to obtain the motion trajectory of the obstacle, and the minimum distance between the current path node And the obstacle is obtained; Represents the speed of the obstacle corresponding to the current path node . When the obstacle is a static obstacle, this value is 0. When the obstacle is a dynamic obstacle, it can also be obtained by using the existing relevant LSTM prediction algorithm to obtain the motion trajectory of the obstacle; And Represent the distance influence weight and the speed influence weight respectively; the distance influence weight reflects the influence of the distance from the UAV autopilot to the obstacle on the risk. The closer the distance, the higher the risk, which is measured by the minimum distance, that is, the nearest obstacle point on the planned path of the UAV autopilot is used as the basis for risk calculation; the speed influence weight reflects the influence of the moving speed of the dynamic obstacle on the UAV obstacle avoidance. The faster the moving speed, the higher the collision risk, which is applicable to moving obstacles (such as birds, other UAVs, etc.). High-speed moving obstacles are more difficult to avoid and need to be given a higher weight. The improved A* algorithm adopted in this embodiment can effectively ensure the safety of the UAV autopilot by real-time perceiving dynamic obstacles and adjusting the path compared with the traditional A* algorithm that only considers static obstacles.

[0042] During the actual inspection process of the UAV autopilot, relevant sensors are configured to detect the presence of obstacles in real time. The types of obstacles include static obstacles and dynamic obstacles. Static obstacles include buildings, power poles, and trees, etc., and dynamic obstacles include birds and other low-altitude flying vehicles, etc. All are monitored and identified in real time by the UAV autopilot. If the UAV autopilot encounters a temporary obstacle, it will automatically analyze the type, position, and movement characteristics of the obstacle, generate an obstacle avoidance path, and conduct a risk assessment. The specific steps for generating the obstacle avoidance path are as follows: Divide the flight environment of the UAV autopilot into a three-dimensional grid space, with the grid unit being a voxel, and the center point of each voxel is represented by three-dimensional coordinates ; construct a three-dimensional grid map based on the environmental information. The voxel states in the map include free space and obstacle space, where the free space is marked as "0", indicating that it can be passed through, and the obstacle space is marked as "1", indicating an impassable area; define the starting position of the UAV autopilot as the starting node , and the target position is defined as the target node ; adopt a neighborhood model, and the neighborhood of each node includes the voxels directly adjacent to the current node; based on the A* algorithm, construct an improved cost function: In the formula, represents the three-dimensional coordinates of the current node ; , , and respectively represent the cost function of the current node , the actual cost from the starting node to the current node , the heuristic estimated cost from the current node to the target node, and the obstacle risk cost corresponding to the current path node .

[0043] The process of executing path planning is as follows: first, put the starting node S into the open list, set the initial cost , set the initial costs of all other nodes to infinity, and each time select the node with the smallest cost function from the open list As the current node, if the current node is the target node G, end the search and enter the path backtracking step; otherwise, move the current node from the open list to the closed list. Evaluate each neighbor node of the current node one by one, skip the nodes that are already in the closed list, calculate its cost for each neighbor node. If the neighbor node is not in the open list, or the new cost is less than its current cost, update its cost and record the parent node. If the position or speed of the dynamic obstacle changes, update the dynamic obstacle risk cost in real time and recalculate the path. Loop the above steps until the target node is found or the open list is empty (the path does not exist). Starting from the target node G, backtrack to the starting node S according to the parent node recorded for each node to obtain the complete path.

[0044] After the target UAV autopilot generates an obstacle avoidance path based on the improved A* algorithm, it can obtain the task execution cost after obstacle avoidance according to the obstacle avoidance path by using the task execution estimated cost evaluation method given above, and generate obstacle encounter information in real time based on the task execution cost after obstacle avoidance, the current position information of the UAV autopilot, and the task destination position, etc., and feed it back to the power inspection and dispatching center, which decides whether to continue to execute the current inspection task.

[0045] Send obstacle encounter information to the power inspection and dispatching center according to the task execution cost after obstacle avoidance, so that the power inspection and dispatching center decides whether the target UAV autopilot continues to execute the task after obstacle avoidance according to the obstacle encounter information; the obstacle encounter information includes the current position information of the UAV autopilot, the task destination position, and the task execution cost after obstacle avoidance.

[0046] In order to ensure the reliability of the power dispatching center to decide whether the target UAV autopilot continues to execute the task after obstacle avoidance according to the obstacle encounter information, and to make full use of the UAV autopilot resources to ensure the flexibility and efficiency of the inspection task execution, preferably in this embodiment, after the power inspection and dispatching center receives the obstacle encounter information sent by the target UAV autopilot, it will issue a new inspection task according to the obstacle encounter information to determine whether there is a more suitable UAV autopilot to replace the target UAV autopilot to execute the current inspection task; specifically, the steps for the power inspection and dispatching center to decide whether the target UAV autopilot continues to execute the task after obstacle avoidance according to the obstacle encounter information include: The power inspection and dispatching center issues a new inspection task according to the obstacle encounter information, and compares the estimated cost of executing the new inspection task fed back by the received UAV autopilot with the task execution cost after obstacle avoidance in the obstacle encounter information; among them, the estimated cost of executing each new inspection task can also be obtained based on the task execution cost evaluation function, and the specific acquisition process can refer to the relevant description of the aforementioned task execution estimated cost, which will not be elaborated here.

[0047] When the post-obstacle task execution cost is at its minimum value, notify the target UAV autopilot to continue executing the power inspection task based on the obstacle avoidance path; that is, if the post-obstacle task execution cost of the target UAV autopilot is still the minimum cost compared to the estimated execution costs of the new inspection tasks fed back by other UAV autopilots, it will continue to execute the task by avoiding obstacles and detouring.

[0048] When the post-obstacle task execution cost is not the minimum value, allocate the new inspection task to the UAV autopilot corresponding to the minimum value, and notify the target UAV autopilot to withdraw from the inspection task; that is, if the post-obstacle task execution cost of the target UAV autopilot is not the minimum cost compared to the estimated execution costs of the new inspection tasks fed back by other UAV autopilots, the power inspection scheduling center will allocate the new inspection task to the UAV autopilot with the minimum estimated execution cost of the new inspection task. It will take over the inspection task from the target UAV autopilot, and the target UAV autopilot will withdraw from the inspection task and return to the standby state to prepare for the next inspection task.

[0049] In addition, for the situation where a sudden failure or loss of connection occurs during the execution of the inspection task by the UAV autopilot, the power inspection scheduling center can also, based on the last position information and the task destination position of the UAV autopilot, obtain other UAV autopilots that can take over and complete the task. It will preferentially select other UAV autopilots near the faulty UAV autopilot or the lost UAV autopilot to relay and execute the remaining task, and synchronize the relevant inspection task data to the relay UAV autopilot to achieve seamless connection of the inspection task handover.

[0050] The technical solution provided by the embodiment of the present application, where the power inspection scheduling center issues the power inspection task, and each UAV autopilot calculates the corresponding estimated execution cost of the task based on the task execution cost evaluation function according to the power inspection task and sends it to the power inspection scheduling center. The power inspection scheduling center distributes the power inspection task to the target UAV autopilot based on the comparison result of all the estimated execution costs corresponding to the power inspection task, following the principle of minimizing the task execution cost. Then, the target UAV autopilot executes the power inspection task according to the real-time planned path of the received power inspection task. This UAV autopilot deployment and scheduling mechanism, which reasonably distributes the inspection task based on factors such as the UAV autopilot status, weather changes, and geographical environment and realizes the adaptive scheduling and optimization of the inspection task for emergencies, can not only effectively improve the resource utilization rate and inspection efficiency of the UAV autopilot, but also ensure the continuity and flexibility of the execution of the inspection task, and effectively reduce the inspection cost.

[0051] In addition, in order to better improve the accuracy of the estimated execution cost evaluation of the task and provide reliable guidance for the subsequent power inspection task auction, this embodiment preferably corrects the task execution cost evaluation function based on the error feedback mechanism; specifically, such as Figure 2As shown, the method further includes: S14. When the power inspection task is completed, the target UAV autopilot generates the actual task execution cost of the power inspection task, and optimally adjusts the weight coefficients of the costs in the task execution cost evaluation function according to the deviation between the actual costs in the actual task execution cost and the corresponding estimated costs in the estimated task execution cost. That is, after the power inspection task is completed, the target UAV autopilot will count the costs consumed for executing the inspection task and compare and analyze them with the corresponding estimated task execution costs. If the deviation of a certain cost is large (exceeding the preset deviation threshold), the weight coefficient corresponding to the task execution cost evaluation function will be automatically adjusted through the following formula: In the formula, and respectively represent the current weight coefficient and the corrected weight coefficient of the i-th cost; and respectively represent the estimated value and the actual value corresponding to the i-th cost; represents the learning rate, which can be set as a fixed value or optimally adjusted based on the reinforcement learning method to improve the reliability of the cost weight coefficient optimization. For example, the state space can be defined based on the parameters required for calculating the actual costs actually collected during the task execution and the task priority, and based on the learning rate , the correction coefficient used for calculating the equipment depreciation cost and , the hovering power coefficient In the formula, represents the reward value; and respectively represent the estimated task execution cost and the actual task execution cost; represents the mean absolute error function; and respectively represent the actual task execution duration and the expected duration; represents the efficiency factor constant, which can be set according to actual application requirements.

[0052] It should be noted that after the inspection task is completed, the drone autopilot will transmit the actual consumption cost of the power inspection obtained through statistics back to the power inspection dispatching center to complete the task settlement. At the same time, the power inspection dispatching center will share the actual cost-related data of all drone autopilots collected in various inspection tasks within the drone autopilot cluster. Each drone autopilot can obtain the inspection task execution data of other drone autopilots to optimize and adjust its own task execution cost evaluation function, enabling each drone autopilot to optimize the cost evaluation function based on collective experience and effectively improve the accuracy of cost evaluation. Among them, the optimization and adjustment of the task execution cost evaluation function can not only adjust the weight coefficients of the loss cost, risk cost, and time cost, but also update and adjust the models and parameters involved in the loss cost and risk cost evaluation based on regression analysis methods to improve the accuracy of obtaining the estimated task execution cost as much as possible.

[0053] It should be noted that although the steps in the above flow chart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders.

[0054] In one embodiment, as Figure 3 shown, a power inspection drone autopilot deployment and scheduling system is provided. The system includes a power inspection dispatching center and a number of drone autopilots; The power inspection dispatching center 1 is used to issue power inspection tasks, receive the estimated task execution costs fed back by each drone autopilot, and based on the comparison results of all the estimated task execution costs corresponding to the power inspection tasks, allocate the power inspection tasks to the target drone autopilot according to the principle of minimizing the task execution cost; the target drone autopilot is the drone autopilot corresponding to the minimum estimated task execution cost; The drone autopilot 2 is used to calculate the corresponding estimated task execution cost based on the task execution cost evaluation function according to the power inspection task issued by the power inspection dispatching center, and send the estimated task execution cost to the power inspection dispatching center; the task execution cost evaluation function is weighted by the loss cost, risk cost, time cost, and task priority of the task execution; it is also used to plan the path in real time according to the power inspection task allocated by the power inspection dispatching center to execute the power inspection task.

[0055] In one embodiment, a power inspection UAV autopilot deployment and scheduling system is provided. The UAV autopilot 2 is further configured to generate the actual task execution cost of the power inspection task when the power inspection task is completed, and optimize and adjust the weight coefficients of the costs in the task execution cost evaluation function according to the deviation between the actual costs in the task execution actual cost and the corresponding estimated costs in the task execution estimated cost.

[0056] For the specific limitations of the power inspection UAV autopilot deployment and scheduling system, reference can be made to the limitations of the power inspection UAV autopilot deployment and scheduling method in the foregoing text, and the corresponding technical effects can also be equivalently obtained, which will not be elaborated here. Each module in the above power inspection UAV autopilot deployment and scheduling system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0057] Figure 4 The internal structure diagram of a computer device in one embodiment is shown. The computer device can specifically be a terminal or a server. As Figure 4 shown, the computer device includes a processor, a memory, a network interface, a display, a camera, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program can implement the power inspection UAV autopilot deployment and scheduling method when executed by the processor. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0058] Those of ordinary skill in the art can understand that Figure 4 the structure shown in

[0059] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0060] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0061] In summary, for the power inspection UAV autopilot deployment and scheduling method and system provided by the embodiments of the present invention, the power inspection UAV autopilot deployment and scheduling method realizes that the power inspection scheduling center issues power inspection tasks, and each UAV autopilot calculates the corresponding estimated task execution cost based on the task execution cost evaluation function according to the power inspection tasks and sends it to the power inspection scheduling center. The power inspection scheduling center distributes the power inspection tasks to the target UAV autopilot based on the comparison results of all the estimated task execution costs corresponding to the power inspection tasks, and in accordance with the principle of minimizing the task execution cost. Then, the target UAV autopilot plans the path in real time according to the received power inspection tasks to execute the power inspection tasks. The technical solution can reasonably allocate inspection tasks based on factors such as the state of the UAV autopilot, weather changes, and geographical environment, and can adaptively schedule and optimize for emergencies. It can not only effectively improve the resource utilization rate, task execution flexibility, and inspection efficiency of the UAV autopilot, but also reduce the inspection cost.

[0062] Each embodiment in this specification is described in a progressive manner. For parts that are the same or similar in each embodiment, reference can be made to each other. The key point of each embodiment is to illustrate 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 for the relevant parts, reference can be made to the description of the method embodiment. It should be noted that the above technical features of the embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the above technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0063] The above-described embodiments only represent several preferred embodiments of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the technical principle of the present invention, several improvements and substitutions can still be made, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the protection scope of the claims.

Claims

1. A method for deploying and dispatching an electric power inspection drone autopilot, characterized in that: Applied to the deployment and dispatching of several UAV autopilots by a power inspection and dispatching center, the method comprises the following steps: In response to the power inspection task released by the power inspection dispatching center, each unmanned aerial vehicle autopilot calculates the corresponding task execution estimated cost based on the task execution cost evaluation function according to the power inspection task, and sends the task execution estimated cost to the power inspection dispatching center; the task execution cost evaluation function is obtained based on the loss cost, risk cost, time cost and task priority of the task execution; Based on the principle of minimizing the task execution cost, the power inspection dispatching center allocates the power inspection task to the target UAV autopilot according to the comparison result of all task execution estimated costs corresponding to the power inspection task; the target UAV autopilot is the UAV autopilot corresponding to the minimum task execution estimated cost; In response to receiving the power inspection task, the target UAV autopilot plans a path in real time to perform the power inspection task.

2. The method for deploying and dispatching the power inspection drone autopilot according to claim 1, characterized in that: The loss cost includes electricity cost and equipment depreciation cost; the steps of obtaining the loss cost include: The flight power of the UAV autopilot is obtained according to the accumulated value of the hovering power, climbing power and power to overcome air resistance of the UAV autopilot, and the electricity cost is obtained according to the flight power of the UAV autopilot, the total inspection flight time of the UAV autopilot and the unit electricity price; According to the task complexity score coefficient, the UAV autopilot task load, the UAV autopilot task flight altitude and the total inspection flight time of the UAV autopilot, the task execution life consumption is obtained, and according to the task execution life consumption, the UAV autopilot design life and the UAV autopilot purchase cost, the equipment depreciation cost is obtained; wherein the equipment depreciation cost is expressed as: In the formula, represents the depreciation cost of equipment; Indicates the purchase cost of the drone autopilot; Indicates the design life of the drone autopilot; Indicates the task execution life consumption; represents the task complexity scoring coefficient; Indicates the UAV autopilot mission load, It represents the average flight altitude of the UAV autopilot mission; Indicates the total duration of the drone autopilot inspection flight; and They represent correction coefficients respectively; The loss cost is obtained according to the accumulated value of the electricity cost and the equipment depreciation cost.

3. The method for deploying and dispatching the power inspection drone autopilot according to claim 1, characterized in that: The risk cost includes bad weather risk, altitude risk and signal blocking risk; the steps of obtaining the risk cost include: Acquire real-time weather data for mission execution, and perform UAV autopilot failure prediction based on the real-time weather data for mission execution and a pre-built failure probability prediction model to obtain the severe weather risk; Obtaining geographic data of the mission execution area, and obtaining the altitude risk according to the geographic data of the mission execution area and the maximum allowable flight altitude of the UAV autopilot; The communication signal coverage data of the task execution area is obtained, and the loss of connection risk is predicted according to the communication signal coverage data of the task execution area and a pre-built loss of connection probability prediction model to obtain the signal blocking risk.

4. The method for deploying and dispatching the power inspection drone autopilot according to claim 1, characterized in that: The time cost includes the total flight time of the UAV autopilot inspection and the total time of the UAV autopilot inspection and power replenishment; the steps for obtaining the time cost include: The optimal inspection path is obtained based on the path planning algorithm, and the total inspection flight time of the UAV autopilot is obtained according to the optimal inspection path and the flight speed of the UAV autopilot; the flight speed of the UAV autopilot is obtained by correcting the maximum flight speed of the UAV autopilot based on meteorological conditions and load status; According to the total inspection flight time of the UAV autopilot, the maximum battery capacity of the UAV autopilot and the unit flight energy consumption of the UAV autopilot, the required amount of power to be replenished for the task execution is obtained, and according to the required amount of power to be replenished for the task execution and the average charging power of the charging station, the total inspection replenishment time of the UAV autopilot is obtained; The time cost is obtained according to the accumulated value of the total flight time of the UAV autopilot inspection and the total time of the UAV autopilot inspection and power replenishment.

5. The method for deploying and dispatching the power inspection drone autopilot according to claim 1, characterized in that: The target UAV autopilot plans a path in real time to perform the power inspection task, including: When the target UAV autopilot detects a path obstacle, an obstacle avoidance path is generated based on the improved A* algorithm according to the obstacle information of the path obstacle, and the task execution cost after obstacle avoidance is evaluated according to the obstacle avoidance path; the obstacle information includes obstacle type, obstacle position and obstacle speed; the cost function of the improved A* algorithm includes actual cost, heuristic estimation cost and obstacle risk cost; the obstacle risk cost is expressed as: in, Indicates the current path node The corresponding obstacle risk cost; Indicates the current path node Minimum distance to obstacles; Indicates the current path node The corresponding obstacle speed; and Indicates the distance influence weight and speed influence weight; According to the task execution cost after obstacle avoidance, obstacle information is sent to the power inspection and dispatching center, so that the power inspection and dispatching center decides whether the target UAV autopilot should avoid obstacles and continue to execute the task according to the obstacle information; the obstacle information includes the current position information of the UAV autopilot, the task destination position and the task execution cost after obstacle avoidance.

6. The method for deploying and dispatching the power inspection drone autopilot according to claim 5, characterized in that: The step of the power inspection dispatching center deciding whether the target UAV autopilot should avoid obstacles and continue to perform the task according to the obstacle information includes: The power inspection dispatch center issues a new inspection task according to the obstacle information, and compares the estimated execution cost of the new inspection task received from the drone autopilot with the post-obstacle avoidance task execution cost in the obstacle information; When the task execution cost after obstacle avoidance is the minimum value, notifying the target UAV autopilot to continue to execute the power inspection task based on the obstacle avoidance path; When the task execution cost after obstacle avoidance is not the minimum value, the new inspection task is assigned to the UAV autopilot corresponding to the minimum value, and the target UAV autopilot is notified to exit the inspection task.

7. The method for deploying and dispatching the power inspection drone autopilot according to claim 1, characterized in that: The method further comprises: When the power inspection task is completed, the target UAV autopilot generates the actual cost of executing the power inspection task, and optimizes and adjusts the weight coefficient of each cost in the task execution cost evaluation function based on the deviation between the actual cost of each item in the actual cost of task execution and the estimated cost of the corresponding item in the estimated cost of task execution.

8. A deployment and dispatching system for an electric power inspection drone autopilot, characterized in that: The system includes a power inspection and dispatching center and several UAV autopilots; The electric power inspection dispatching center is used to issue electric power inspection tasks, receive the estimated task execution costs fed back by each unmanned aerial vehicle autopilot, and allocate the electric power inspection tasks to target unmanned aerial vehicle autopilots based on the principle of minimizing task execution costs according to the comparison results of all task execution estimated costs corresponding to the electric power inspection tasks; the target unmanned aerial vehicle autopilot is the unmanned aerial vehicle autopilot corresponding to the minimum task execution estimated cost; The unmanned aerial vehicle autopilot is used to calculate the corresponding task execution estimated cost based on the task execution cost evaluation function according to the power inspection task issued by the power inspection dispatching center, and send the task execution estimated cost to the power inspection dispatching center; the task execution cost evaluation function is obtained based on the loss cost, risk cost, time cost and task priority of the task execution; it is also used to plan the path in real time according to the power inspection task assigned by the power inspection dispatching center to execute the power inspection task.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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