Inspection method and device for power transmission and distribution equipment in power system, computer equipment, readable storage medium and program product

Abstract

The invention relates to an inspection method and device for power transmission and distribution equipment in a power system, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: acquiring comprehensive data of each inspection point in a target inspection area; based on the comprehensive data, planning a plurality of initial routing inspection paths, and controlling the plurality of unmanned aerial vehicles to perform initial line routing inspection through the plurality of initial routing inspection paths; based on the real-time state data of the plurality of unmanned aerial vehicles, generating residual operation durations of the plurality of unmanned aerial vehicles; generating a plurality of residual inspection paths based on the end position of the initial line inspection and the comprehensive data of the inspection points which are not inspected during the initial line inspection; based on the plurality of residual routing inspection paths and the residual operation durations of the plurality of unmanned aerial vehicles, allocating the residual routing inspection paths to the plurality of unmanned aerial vehicles and controlling the unmanned aerial vehicles to perform residual line routing inspection; and when detecting that the inspection of the remaining lines is completed by the preset target, carrying out the next round of inspection. And by dynamically acquiring comprehensive data of the inspection points, inspection can be automatically and flexibly carried out.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for inspecting power transmission and distribution equipment in a power system. Background Technology

[0002] Power transmission and distribution line inspection is a crucial task to ensure the safe and stable operation of the power system. Therefore, power personnel need to regularly conduct outdoor inspections of these lines. Because power transmission and distribution lines are located in the wild, in forests or fields, the terrain for inspection is complex, and the operating environment is harsh and risky. They are typically constructed using multiple power towers connected by high-voltage conductors for long-distance transmission, which can impact the safe operation of the power grid and natural resources such as forests. Therefore, the work of inspection personnel is challenging and carries significant safety risks. Currently, drones have gained increasing attention and are being used in power transmission and distribution line inspections. Equipped with high-definition cameras or infrared thermal imagers, drones can be controlled by certified operators to collect, transmit, and record image data of the lines in real time. This data can be analyzed and evaluated by inspection personnel, facilitating problem identification and handling, and to some extent compensating for the shortcomings of traditional manual inspections.

[0003] However, in current inspection operations, the deployment of drones and the planning of flight routes largely rely on human experience, which means that drones can only perform inspection tasks along fixed routes and lack dynamic adaptability to changes in the actual environment (such as terrain obstacles or sudden equipment malfunctions). This may not only lead to a waste of inspection resources or omissions of key areas, but also make it difficult to meet the refined inspection needs of power transmission and distribution facilities in complex scenarios. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for inspecting power transmission and distribution equipment in a power system that can adaptively plan inspection routes, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for inspecting power transmission and distribution equipment in a power system, including:

[0006] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0007] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0008] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0009] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0010] In one embodiment, the comprehensive data includes the inspection level of each checkpoint; based on the comprehensive data, multiple initial inspection paths are planned, including:

[0011] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0012] In one embodiment, based on multiple remaining inspection paths and the remaining runtime of multiple drones, remaining inspection paths are allocated to multiple drones, including:

[0013] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0014] In one embodiment, determining the predicted power consumption values ​​corresponding to multiple remaining inspection paths includes:

[0015] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0016] In one embodiment, the comprehensive data of each inspection point in the target inspection area is obtained, including:

[0017] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0018] In one embodiment, determining the failure risk value for each checkpoint includes:

[0019] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0020] Secondly, this application also provides an inspection device for power transmission and distribution equipment in a power system, comprising:

[0021] The acquisition module is used to acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0022] The initial planning module is used to plan multiple initial inspection paths based on comprehensive data, and control multiple drones to perform initial line inspections through multiple initial inspection paths; wherein, multiple initial inspection paths end at the same position;

[0023] The remaining planning module is used to generate the remaining runtime of multiple drones based on the real-time status data of multiple drones after completing the initial route inspection; and to generate multiple remaining inspection paths based on the combined data of the end position of the initial route inspection and the checkpoints that were not inspected during the initial route inspection.

[0024] The inspection module is used to allocate remaining inspection paths to multiple drones and control the drones to perform remaining line inspections based on multiple remaining inspection paths and the remaining runtime of multiple drones. When the preset target is detected that the remaining line inspection has been completed, it returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspection.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0027] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0028] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0029] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0030] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0031] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0032] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0033] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0034] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0036] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0037] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0038] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0039] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0040] The aforementioned inspection method, device, computer equipment, computer-readable storage medium, and computer program product for power transmission and distribution equipment in the power system first acquire comprehensive data of each inspection point in the target inspection area; each inspection point is a collection of multiple power transmission and distribution equipment. Then, based on the comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections along these paths; the ending positions of these initial inspection paths are the same. Next, based on the real-time status data of the drones after completing the initial line inspections, the remaining runtime of the drones is generated. Based on the ending positions of the initial line inspections and the comprehensive data of the inspection points not inspected during the initial line inspections, multiple remaining inspection paths are generated. Finally, based on the multiple remaining inspection paths and the remaining runtime of the drones, remaining inspection paths are assigned to the drones, and the drones are controlled to perform the remaining line inspections. When the preset target for the remaining line inspections is detected, the system returns to acquire comprehensive data of each inspection point in the target inspection area and proceeds to the next round of inspections. This application achieves flexible planning of inspection routes by dynamically acquiring comprehensive data from checkpoints and accurately allocating remaining routes by combining the real-time status of drones. This breaks through the limitations of traditional fixed routes and experience-based configuration, improves the coverage efficiency and resource adaptability of drone inspections, and enables all-time, dynamic, and efficient inspection of power transmission and distribution lines and towers. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating an inspection method for power transmission and distribution equipment in a power system, as shown in one embodiment.

[0043] Figure 2 This is a detailed flowchart of an inspection method for power transmission and distribution equipment in a power system, as shown in one embodiment.

[0044] Figure 3 This is a structural block diagram of an inspection device for power transmission and distribution equipment in a power system, as shown in one embodiment.

[0045] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0048] In one embodiment, such as Figure 1 As shown, a method for inspecting power transmission and distribution equipment in a power system is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0049] Step 102: Obtain comprehensive data of each inspection point in the target inspection area; the inspection point is a collection of multiple power transmission and distribution equipment.

[0050] Optionally, the comprehensive data can include the location, number, and inspection level of checkpoints, as well as environmental data.

[0051] The inspection levels are divided into general, special, and key levels from low to high. The inspection level and the number of times the inspection point needs to be inspected in a round of inspection are positively correlated.

[0052] Step 104: Based on comprehensive data, plan multiple initial inspection paths and control multiple drones to perform initial line inspections through these multiple initial inspection paths; wherein, the ending positions of the multiple initial inspection paths are the same.

[0053] Each drone starts from its own storage location and performs an initial route inspection according to its own flight path, with the destination (the end point of the inspection path) being the same location.

[0054] Step 106: Based on the real-time status data of multiple drones after completing the initial route inspection, generate the remaining runtime of multiple drones; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, generate multiple remaining inspection paths.

[0055] The remaining runtime of multiple drones is determined by the drone's current battery level, initial battery level, and runtime. First, the average power consumption rate of the drone is calculated. The formula for calculating the average power consumption rate is as follows:

[0056]

[0057] in, This is the initial battery level. This is the current battery level. It is the runtime; then divide the current power consumption by the average power consumption rate to get the remaining runtime.

[0058] Step 108: Based on multiple remaining inspection paths and the remaining runtime of multiple drones, assign remaining inspection paths to multiple drones and control the drones to perform remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, return to obtain the comprehensive data of each checkpoint in the target inspection area and perform the next round of inspections.

[0059] Optionally, when the preset target is achieved, and 70%-80% of the remaining route inspections have been completed, the remaining uninspected drones can be controlled to start the next round of inspections.

[0060] The aforementioned inspection method, device, computer equipment, computer-readable storage medium, and computer program product for power transmission and distribution equipment in the power system first acquire comprehensive data of each inspection point in the target inspection area; each inspection point is a collection of multiple power transmission and distribution equipment. Then, based on the comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections along these paths; the ending positions of these initial inspection paths are the same. Next, based on the real-time status data of the drones after completing the initial line inspections, the remaining runtime of the drones is generated. Based on the ending positions of the initial line inspections and the comprehensive data of the inspection points not inspected during the initial line inspections, multiple remaining inspection paths are generated. Finally, based on the multiple remaining inspection paths and the remaining runtime of the drones, remaining inspection paths are assigned to the drones, and the drones are controlled to perform the remaining line inspections. When the preset target for the remaining line inspections is detected, the system returns to acquire comprehensive data of each inspection point in the target inspection area and proceeds to the next round of inspections. This application achieves flexible planning of inspection routes by dynamically acquiring comprehensive data from checkpoints and accurately allocating remaining routes by combining the real-time status of drones. This breaks through the limitations of traditional fixed routes and experience-based configuration, improves the coverage efficiency and resource adaptability of drone inspections, and enables all-time, dynamic, and efficient inspection of power transmission and distribution lines and towers.

[0061] In one exemplary embodiment, the comprehensive data includes the inspection level of each checkpoint; based on the comprehensive data, multiple initial inspection paths are planned, including:

[0062] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0063] For example, multiple drone bays are selected at the edge of the target inspection area; within a range of a preset radius centered on the area center, inspection points with special inspection levels are selected. If there are multiple high-level inspection points within this range, the geometric center of these inspection points is used as the unified end position. If there is only a single high-level inspection point, its position is directly set as the end position to ensure that the end position focuses on the core inspection target. When determining multiple initial inspection paths based on multiple drone bays and the unified end position, inspection points located at a perpendicular distance of less than a specified distance threshold from the straight line connecting the drone from its corresponding drone bay to the initial inspection end position are used as the drone's inspection points to generate the initial inspection route.

[0064] In this embodiment, the initial inspection path is determined by selecting the drone storage location and end point from the edge area. This achieves coordinated coverage of multiple drones from the edge to the core area, ensuring priority coverage of key equipment during the initial inspection. At the same time, by using distance thresholds to filter checkpoints, path redundancy is avoided, laying an efficient starting point for planning the remaining inspection paths.

[0065] In an exemplary embodiment, based on multiple remaining inspection paths and the remaining runtime of multiple drones, remaining inspection paths are allocated to multiple drones, including:

[0066] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0067] For example, the predicted power consumption values ​​corresponding to multiple remaining inspection paths are determined; the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones are sorted in descending order; and the drone that ranks first in descending order is assigned the first remaining inspection path in descending order.

[0068] In this embodiment, by sorting the predicted power consumption of the path and the remaining runtime of the drone in descending order, and finally allocating tasks according to the one-to-one correspondence rule of matching long-endurance drones with high power consumption paths, the optimal matching between the remaining runtime of the drone and the energy consumption requirements of the path can be achieved.

[0069] In one exemplary embodiment, determining the predicted power consumption values ​​corresponding to multiple remaining inspection paths includes:

[0070] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0071] For example, power consumption of inspection paths corresponding to multiple remaining inspection paths can be obtained based on historical data. Check the power consumption during the climb. Power consumption of non-inspection routes The environmental parameters obtained include: the average air temperature in the area where the checkpoint was located during a specified sampling period. (Current value can be selected if the fluctuation is not significant) and the average temperature of the drone's heat dissipation location. (Current value can be selected if the fluctuation is not significant), time when the wind direction is inconsistent with the flight direction being checked. With consistent time The number of times the wind direction changes by more than 10° with the start of the specified sampling period as the reference direction. and wind speed value The specified sampling period can be selected multiple times for calculation. The following is an example of obtaining a specified sampling period in a single instance. The specified sampling period can be randomly selected during the execution of various inspection tasks, and can be selected as 20 seconds. The formula for calculating the inspection energy consumption factor is as follows:

[0072]

[0073] in, and These are the fitting coefficients for the corresponding terms, which are useful for later experimental verification and checking of the energy consumption factor. Accuracy assessment involves fitting data to real-world conditions, which is related to factors such as the drone model and usage time, and can be continuously adjusted during subsequent maintenance. To ensure clear video and stable footage, the drone's flight speed is adjusted during the inspection process while performing inspection procedures at checkpoints. It needs to remain constant. Changes in external wind speed will affect the drone. Whether the external wind speed is higher or lower than the drone's speed, the drone needs to overcome external disturbances, thus requiring more energy. Therefore, [the following is introduced] Participate in the inspection of energy consumption factors The quantification is performed. If a specified sampling period is selected multiple times, the average value of the inspection energy consumption factor corresponding to all specified sampling periods is calculated as the final value. , It is the indoor air temperature. Obtain the predicted power consumption for each remaining inspection route. The formula is as follows:

[0074]

[0075] in, The average headwind speed, To average the tailwind speed, this reduces the computational interference from the energy consumption of the drone's heading between checkpoints. The focus is on predicting power consumption for inspection processes requiring continuous attitude and direction changes, resulting in a more accurate prediction of power consumption for the remaining inspection routes. This allows for more accurate sorting.

[0076] Among them, the average air temperature It affects the heat dissipation performance of drones, and the average air temperature The higher the temperature, the greater the heat dissipation pressure on the drone, and the more energy needs to be allocated to the heat dissipation mechanism. This is especially true when the average air temperature... Below the average temperature of the drone's heat dissipation area At that time, the energy consumption of the heat dissipation structure becomes lower, which will (Usually less than 1) Placing it in the exponent position can represent the average air temperature The impact on heat dissipation performance is amplified and differentiated to improve the discriminative power consumption prediction. Furthermore, while drones require more energy to overcome external disturbances regardless of whether the external wind speed is higher or lower than their actual speed, the varying proportions of tailwind and headwind durations also significantly affect the drone's performance. The degree of tailwind can be assessed by measuring the time the wind direction is inconsistent with the flight direction being checked. To estimate the headwind strength, a consistent time frame can be used. Estimations show that in a tailwind, even if the wind speed is greater than the drone's set speed, the drone needs to do extra work. However, if the wind turns into a headwind, the drone needs to increase its power output several times over to overcome the increased workload.

[0077] In this embodiment, the inspection energy consumption factor is calculated by a multi-parameter coupling formula, and the power consumption prediction formula is optimized by combining the average headwind and tailwind speeds. This weakens the heading interference in non-inspection sections and focuses on the energy consumption of attitude changes in inspection sections. It can achieve refined and scenario-based prediction of the power consumption demand of the remaining inspection path, avoiding the deviation caused by ignoring environmental variables in traditional single energy consumption estimation. This provides accurate data support for the subsequent scheduling of long-endurance UAVs for high-power-consuming paths.

[0078] In one exemplary embodiment, acquiring comprehensive data of each inspection point in the target inspection area includes:

[0079] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0080] For example, the location information, quantity information, environmental data and initial level of each checkpoint in the target inspection area are obtained; the fault risk value of each checkpoint is determined; the fault risk value of each checkpoint is multiplied by the initial level to obtain the product result, and the level data of each checkpoint is determined based on the product; the location information, quantity information, environmental data and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0081] In this embodiment, by determining the fault risk value and level data, and simultaneously integrating the location, quantity, and environmental data of checkpoints to form comprehensive data, it avoids over-inspection of low-risk equipment with high inherent levels due to relying solely on the inherent attributes (initial level) of the equipment, and also prevents the neglect of sudden high-risk situations of low-initial-level equipment. This allows the checkpoint level to accurately match the importance of the equipment and the real-time risk status, providing a scientific basis for subsequent inspection path planning and resource scheduling, thereby improving the targeting and response efficiency of power transmission and distribution inspections for key and high-risk equipment.

[0082] In one exemplary embodiment, determining the failure risk value for each checkpoint includes:

[0083] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0084] The load factor is the ratio of the current load of the equipment to its rated capacity.

[0085] Optional electrical quantities include: current, voltage, active / reactive power, power factor, harmonic content, and partial discharge; physical quantities include: temperature (winding, contact, oil temperature), vibration, noise, and pressure (SF6 gas, oil pressure); status quantities include: switch opening and closing positions, protection device action signals, alarm signals, and online monitoring device data (DGA oil chromatography, trace moisture content).

[0086] For example, the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area are obtained; based on the electrical parameters, physical parameters, status parameters, and load rate, the fault risk value of each inspection point is calculated using the fault risk value formula, as follows:

[0087]

[0088] in, Let i be the i-th parameter with a specified normal threshold range, and n be the total number of such parameter items, such as temperature, current, voltage, etc. To specify the median value of the normal threshold range; Maximum threshold The j-th parameter is required, where m is the total number of such parameters, such as vibration, noise, pressure, load rate, etc. This represents the average value of the parameters corresponding to the normal conditions in the previous round of inspections. This indicates a user-defined function, when When greater than 0, ,when When less than or equal to 0, ; The number of anomalies in the state variables. The preset number of exceptions can be set to 2. The compensation index can be set to 1.

[0089] In this embodiment, the fault risk value of each checkpoint is calculated by the fault risk value formula, which avoids the subjectivity and ambiguity of traditional manual experience judgment, and transforms the scattered operating data into a unified risk value through multi-parameter coupling formula, so that the risks of checkpoints of different types and different operating states are comparable.

[0090] In one embodiment, such as Figure 2 As shown, a method for inspecting power transmission and distribution equipment in a power system is provided, including: acquiring the location information, quantity information, environmental data, and initial level of each inspection point in the target inspection area; acquiring the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; and calculating the fault risk value of each inspection point based on the electrical parameters, physical parameters, status parameters, and load rate using a fault risk value formula, as follows:

[0091]

[0092] in, Let i be the i-th parameter with a specified normal threshold range, and n be the total number of such parameter items, such as temperature, current, voltage, etc. To specify the median value of the normal threshold range; Maximum threshold The j-th parameter is required, where m is the total number of such parameters, such as vibration, noise, pressure, load rate, etc. This represents the average value of the parameters corresponding to the normal conditions in the previous round of inspections. This indicates a user-defined function, when When greater than 0, ,when When less than or equal to 0, ; The number of anomalies in the state variables. The preset number of exceptions can be set to 2. The compensation index can be set to 1. The fault risk value of each checkpoint is multiplied by the initial level to obtain the product. The level data of each checkpoint is determined based on this product. The location, quantity, environmental, and level data of each checkpoint constitute the comprehensive data for each checkpoint. Each checkpoint is a collection of multiple power transmission and distribution equipment. Multiple drone bays are selected at the edge of the target inspection area. Within a preset radius centered on the area center, checkpoints with special inspection levels are selected. If multiple high-level checkpoints exist within this range, their geometric center is used as the unified end position. If only a single high-level checkpoint exists, its position is directly set as the end position, ensuring the end position focuses on the core inspection target. When determining multiple initial inspection paths based on multiple drone bays and the unified end position, checkpoints located where the perpendicular distance from the straight line connecting the drone's corresponding drone bay to the initial inspection end position is less than a specified distance threshold are used as the drone's inspection points, generating initial inspection routes. Multiple drones are controlled to perform initial route inspections along multiple initial inspection paths; the end positions of these initial inspection paths are the same. Based on real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; the power consumption of the inspection paths corresponding to the remaining inspection paths is obtained based on historical data. Check the power consumption during the climb. Power consumption of non-inspection routes The environmental parameters obtained include: the average air temperature in the area where the checkpoint was located during a specified sampling period. (Current value can be selected if the fluctuation is not significant) and the average temperature of the drone's heat dissipation location. (Current value can be selected if the fluctuation is not significant), time when the wind direction is inconsistent with the flight direction being checked. With consistent time The number of times the wind direction changes by more than 10° with the start of the specified sampling period as the reference direction. and wind speed value The specified sampling period can be selected multiple times for calculation. The following is an example of obtaining a specified sampling period in a single instance. The specified sampling period can be randomly selected during the execution of various inspection tasks, and can be selected as 20 seconds. The formula for calculating the inspection energy consumption factor is as follows:

[0093]

[0094] in, and These are the fitting coefficients for the corresponding terms, which are useful for later experimental verification and checking of the energy consumption factor. Accuracy assessment involves fitting data to real-world conditions, which is related to factors such as the drone model and usage time, and can be continuously adjusted during subsequent maintenance. To ensure clear video and stable footage, the drone's flight speed is adjusted during the inspection process while performing inspection procedures at checkpoints. It needs to remain constant. Changes in external wind speed will affect the drone. Whether the external wind speed is higher or lower than the drone's speed, the drone needs to overcome external disturbances, thus requiring more energy. Therefore, [the following is introduced] Participate in the inspection of energy consumption factors The quantification is performed. If a specified sampling period is selected multiple times, the average value of the inspection energy consumption factor corresponding to all specified sampling periods is calculated as the final value. , It is the indoor air temperature. Obtain the predicted power consumption for each remaining inspection route. The formula is as follows:

[0095]

[0096] in, The average headwind speed, To average the tailwind speed, this reduces the computational interference from the energy consumption of the drone's heading between checkpoints. The focus is on predicting power consumption for inspection processes requiring continuous attitude and direction changes, resulting in a more accurate prediction of power consumption for the remaining inspection routes. This allows for more accurate sorting. The predicted power consumption values ​​for multiple remaining inspection paths and the remaining runtime of multiple drones are sorted in descending order. The drone ranked first in descending order is assigned its own remaining inspection path. Based on the multiple remaining inspection paths and the remaining runtime of multiple drones, remaining inspection paths are assigned to multiple drones, and the drones are controlled to perform inspections on the remaining routes. When the preset target for the remaining route inspection is detected, the system returns to obtain comprehensive data from each checkpoint in the target inspection area and proceeds to the next round of inspections.

[0097] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0098] In one exemplary embodiment, such as Figure 3 As shown, an inspection device for power transmission and distribution equipment in a power system is provided, comprising: an acquisition module, an initial planning module, a residual planning module, and an inspection module, wherein:

[0099] The acquisition module is used to acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0100] The initial planning module is used to plan multiple initial inspection paths based on comprehensive data, and control multiple drones to perform initial line inspections through multiple initial inspection paths; wherein, multiple initial inspection paths end at the same position;

[0101] The remaining planning module is used to generate the remaining runtime of multiple drones based on the real-time status data of multiple drones after completing the initial route inspection; and to generate multiple remaining inspection paths based on the combined data of the end position of the initial route inspection and the checkpoints that were not inspected during the initial route inspection.

[0102] The inspection module is used to allocate remaining inspection paths to multiple drones and control the drones to perform remaining line inspections based on multiple remaining inspection paths and the remaining runtime of multiple drones. When the preset target is detected that the remaining line inspection has been completed, it returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspection.

[0103] In one embodiment, the initial planning module is further configured to:

[0104] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0105] In one embodiment, the remaining planning module is further configured to:

[0106] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0107] In one embodiment, the initial planning module is further configured to:

[0108] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0109] In one embodiment, the acquisition module is further configured to:

[0110] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0111] In one embodiment, the acquisition module is further configured to:

[0112] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0113] The modules in the inspection device for power transmission and distribution equipment in the aforementioned power system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0114] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores comprehensive data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for inspecting power transmission and distribution equipment in a power system.

[0115] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0117] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0118] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0119] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0120] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0121] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0122] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0123] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0124] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0125] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0126] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0127] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0128] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0129] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0130] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0131] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0132] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0133] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0134] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0135] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0136] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0137] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0138] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0139] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0140] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0141] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0142] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0143] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0144] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0145] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0146] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0147] Acquire comprehensive data from each inspection point in the target inspection area; an inspection point is a collection of multiple power transmission and distribution equipment.

[0148] Based on comprehensive data, multiple initial inspection paths are planned, and multiple drones are controlled to perform initial line inspections through these paths; the ending positions of these multiple initial inspection paths are the same.

[0149] Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated.

[0150] Based on multiple remaining inspection paths and the remaining runtime of multiple drones, the remaining inspection paths are assigned to multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

[0151] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0152] Select multiple drone bays at the edge of the target inspection area; determine the end position of the initial inspection path based on the center position of the target inspection area and the inspection level of each checkpoint; determine multiple initial inspection paths based on the end positions of multiple drone bays and multiple initial inspection paths.

[0153] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0154] Determine the predicted power consumption values ​​corresponding to multiple remaining inspection paths; sort the predicted power consumption values ​​corresponding to multiple remaining inspection paths and the remaining runtime of multiple drones in descending order; match the sorted remaining inspection paths with the multiple drones in descending order and assign remaining inspection paths to multiple drones.

[0155] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0156] Obtain the power consumption of the inspection path, inspection climb power consumption, and non-inspection flight path corresponding to multiple remaining inspection paths; obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; and determine the predicted power consumption value corresponding to multiple remaining inspection paths based on the power consumption of the inspection path, inspection climb power consumption, non-inspection flight path power consumption, environmental parameters, and inspection energy consumption factor.

[0157] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0158] The system acquires the location, quantity, environmental data, and initial level of each checkpoint in the target inspection area; determines the fault risk value of each checkpoint; and determines the level data of each checkpoint based on the initial level and fault risk value. The location, quantity, environmental data, and level data of each checkpoint constitute the comprehensive data of each checkpoint.

[0159] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0160] Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; based on the electrical parameters, physical parameters, status parameters, and load rate, calculate the fault risk value of each inspection point using the fault risk value formula.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0162] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0163] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for inspecting power transmission and distribution equipment in a power system, characterized in that, The methods include: Obtain comprehensive data from each inspection point in the target inspection area; The checkpoint is a collection of multiple power transmission and distribution equipment; Based on the comprehensive data, multiple initial inspection paths are planned, and multiple UAVs are controlled to perform initial route inspections through the multiple initial inspection paths; wherein, the ending positions of the multiple initial inspection paths are the same; Based on the real-time status data of multiple drones after completing the initial route inspection, the remaining runtime of multiple drones is generated; based on the combined data of the end position of the initial route inspection and the checkpoints not inspected during the initial route inspection, multiple remaining inspection paths are generated. Based on the remaining inspection paths and the remaining runtime of the multiple drones, the remaining inspection paths are assigned to the multiple drones and the drones are controlled to perform the remaining line inspections; when the preset target is detected that the remaining line inspection has been completed, the system returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspections.

2. The method according to claim 1, characterized in that, The comprehensive data includes the inspection level of each checkpoint; Based on the comprehensive data, multiple initial inspection paths are planned, including: Select multiple drone bays at the edge of the target inspection area; Based on the center location of the target inspection area and the inspection level of each inspection point, the end position of the initial inspection path is determined. Based on the multiple drone bay locations and the end positions of the multiple initial inspection paths, multiple initial inspection paths are determined.

3. The method according to claim 1, characterized in that, The process of allocating remaining inspection paths to the multiple drones based on the multiple remaining inspection paths and the remaining runtime of the multiple drones includes: Determine the predicted power consumption values ​​for multiple remaining inspection paths; The predicted power consumption values ​​corresponding to the multiple remaining inspection paths and the remaining runtime of the multiple drones are sorted in descending order respectively; The remaining inspection paths, arranged in descending order, are matched one-to-one with the multiple drones, and the remaining inspection paths are assigned to the multiple drones.

4. The method according to claim 3, characterized in that, The determination of the predicted power consumption values ​​corresponding to multiple remaining inspection paths includes: Obtain the power consumption of the inspection path, the power consumption of the inspection climb, and the power consumption of the non-inspection route for multiple remaining inspection paths; Obtain environmental parameters, and determine the inspection energy consumption factor of the UAV based on the environmental parameters; Based on the power consumption of the inspection path, the power consumption of the inspection climb, the power consumption of the non-inspection route, the environmental parameters, and the inspection energy consumption factor corresponding to the multiple remaining inspection paths, the predicted power consumption values ​​corresponding to the multiple remaining inspection paths are determined.

5. The method according to claim 1, characterized in that, The acquisition of comprehensive data for each inspection point in the target inspection area includes: Obtain the location, quantity, environmental data, and initial risk level of each checkpoint in the target inspection area; determine the fault risk value of each checkpoint; Based on the initial level and the fault risk value, the level data of each checkpoint is determined; the location information, quantity information, environmental data and level data of each checkpoint constitute the comprehensive data of each checkpoint.

6. The method according to claim 5, characterized in that, Determining the fault risk value for each checkpoint includes: Obtain the electrical parameters, physical parameters, status parameters, and load rate of each inspection point in the target inspection area; Based on the electrical parameters, physical parameters, status parameters, and load rate, the fault risk value of each checkpoint is calculated using the fault risk value formula.

7. An inspection device for power transmission and distribution equipment in a power system, characterized in that, The device includes: The acquisition module is used to acquire comprehensive data of each inspection point in the target inspection area; the inspection point is a collection of multiple power transmission and distribution equipment. The initial planning module is used to plan multiple initial inspection paths based on the comprehensive data, and control multiple UAVs to perform initial route inspections through the multiple initial inspection paths; wherein, the multiple initial inspection paths end at the same position; The remaining planning module is used to generate the remaining runtime of multiple drones based on the real-time status data of multiple drones after completing the initial route inspection; and to generate multiple remaining inspection paths based on the combined data of the end position of the initial route inspection and the checkpoints that were not inspected during the initial route inspection. The inspection module is used to allocate remaining inspection paths to the multiple drones and control the drones to perform remaining line inspections based on the multiple remaining inspection paths and the remaining runtime of the multiple drones; when it is detected that the remaining line inspection has completed the preset target, it returns to obtain the comprehensive data of each checkpoint in the target inspection area and performs the next round of inspection.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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