A method and system for dispatching electric power inspection drones based on bidding algorithm

Abstract

The present invention discloses a method and system for dispatching electric power inspection drones based on a bidding algorithm, the method comprising: constructing a drone inspection path based on first drone positioning data, and segmenting the drone inspection path to obtain a number of drone segmented inspection paths; calculating the drone power consumption of the corresponding drone segmented inspection path based on the path characteristics of each drone segmented inspection path, and obtaining the total drone power consumption of the drone inspection path according to each drone power consumption; determining a first drone bidding price based on the total drone power consumption, conducting a risk assessment on the drone inspection path, and adjusting the first drone bidding price according to the obtained risk assessment result to obtain a second drone bidding price; determining a first drone scheduling strategy with the goal of matching the second drone bidding price; optimizing the first drone scheduling strategy to obtain a second drone scheduling strategy. The present invention improves the efficiency of drone power inspection.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method and system for dispatching unmanned aerial vehicles for power inspection based on an auction algorithm. Background Art

[0002] Power inspection is crucial to ensuring the safe and stable operation of the power system. The traditional manual inspection method is not only inefficient, but also has many safety hazards and cannot meet the inspection needs of large-scale power grids.

[0003] With the development of drone technology, drone power inspection has gradually become an efficient alternative. Drones can quickly reach areas that are difficult to reach manually, and are equipped with high-definition cameras, infrared thermal imagers and other equipment to conduct all-round inspections of power lines and equipment. However, in actual large-scale power inspection operations, multiple drones are usually required to work together. The existing drone task allocation method often lacks efficiency and flexibility, making it difficult to give full play to the performance advantages of each drone, resulting in high inspection costs and low efficiency.

[0004] Therefore, in the face of complex and changeable power inspection tasks, how to reasonably allocate tasks according to task requirements and the conditions of the drone itself and achieve optimal utilization of resources is an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a method and system for dispatching electric power inspection drones based on an auction algorithm.

[0006] In a first aspect, an embodiment of the present invention provides a method for dispatching a power inspection drone based on an auction algorithm, comprising:

[0007] Constructing a drone inspection path based on the first drone positioning data, and segmenting the drone inspection path to obtain a plurality of drone segment inspection paths, wherein the first drone positioning data includes first three-dimensional coordinate data obtained by the drone at each sampling interval during the power inspection process;

[0008] Based on the path characteristics of each of the drone segmented inspection paths, the drone power consumption corresponding to the drone segmented inspection path is calculated, and the total drone power consumption of the drone inspection path is obtained according to the drone power consumption of each of the drone segmented inspection paths, wherein the path characteristics include the path length and the path height change;

[0009] Determine a first drone auction price based on the total drone power consumption, perform a risk assessment on the drone inspection path, and adjust the first drone auction price according to the obtained risk assessment result to obtain a second drone auction price, wherein the risk assessment includes performing a risk assessment on the path height change of each drone segmented inspection path and performing a risk assessment on the terrain complexity corresponding to each drone segmented inspection path;

[0010] Determine a first drone scheduling strategy with the goal of matching the second drone auction price, wherein the first drone scheduling strategy includes the number of drones and drone models corresponding to each of the drone segmented inspection paths;

[0011] The first drone scheduling strategy is optimized based on the regional characteristics of each of the drone segmented inspection paths to obtain a second drone scheduling strategy, wherein the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods and drone inspection priorities corresponding to each of the drone segmented inspection paths.

[0012] Preferably, the method of constructing a drone inspection path based on the first drone positioning data and segmenting the drone inspection path to obtain a plurality of drone segment inspection paths includes:

[0013] The first UAV positioning data is screened to obtain the second UAV positioning data, and the second UAV positioning data is fitted to obtain the UAV inspection path, wherein the second UAV positioning data includes the second three-dimensional coordinate data obtained by the UAV at each sampling interval exceeding a preset distance threshold during the power inspection process;

[0014] Obtaining segmented point coordinate data of the drone inspection path based on a preset curvature threshold, wherein the segmented point coordinate data includes third three-dimensional coordinate data of each curvature change rate in the drone inspection path exceeding the preset curvature threshold;

[0015] The UAV inspection path is divided based on the segment point coordinate data, and a segmented linear regression is performed on the divided UAV inspection path to obtain a number of UAV segmented inspection paths.

[0016] Preferably, after dividing the UAV inspection path based on the segment point coordinate data and performing segmented linear regression on the divided UAV inspection path to obtain a plurality of UAV segmented inspection paths, the method further includes:

[0017] The height difference value of each of the drone segmented inspection paths is calculated, and each drone segmented inspection path whose height difference value exceeds a preset height threshold is divided twice.

[0018] Preferably, the calculating of the UAV power consumption corresponding to the UAV segmented inspection path based on the path characteristics of each UAV segmented inspection path, and obtaining the total UAV power consumption of the UAV inspection path according to the UAV power consumption of each UAV segmented inspection path, includes:

[0019] Obtaining the path length and the inspection speed of each segmented inspection path of the drone, and performing support vector regression on the path length and the inspection speed of the drone to obtain expected flight time data corresponding to the segmented inspection path of the drone, wherein the path length is calculated based on the change in path height;

[0020] Based on the expected flight time data, real-time power data of each segmented inspection path of the drone is accumulated and calculated to obtain power consumption of the drone corresponding to the segmented inspection path of the drone;

[0021] The power consumption of the drone in each segmented inspection path of the drone is accumulated to obtain the total power consumption of the drone in the inspection path of the drone.

[0022] Preferably, determining the first drone auction price based on the total drone power consumption, performing a risk assessment on the drone inspection path, and adjusting the first drone auction price according to the obtained risk assessment result to obtain the second drone auction price includes:

[0023] Calculating the drone battery loss cost based on the total drone power consumption and the preset unit power loss cost, and obtaining the first drone auction price according to the drone battery loss cost and the drone maintenance cost;

[0024] Performing a risk assessment on the path height change of each segmented inspection path of the drone to obtain a first risk coefficient corresponding to the segmented inspection path of the drone;

[0025] Performing a risk assessment on the terrain complexity corresponding to each segmented inspection path of the UAV to obtain a second risk coefficient corresponding to the segmented inspection path of the UAV;

[0026] The risk compensation amount of each drone segmented inspection path is determined based on the first risk coefficient and the second risk coefficient, and each risk compensation amount and the first drone auction price are weighted to obtain a second drone auction price.

[0027] Preferably, the risk assessment of the path height change of each segmented inspection path of the drone to obtain a first risk coefficient corresponding to the segmented inspection path of the drone includes:

[0028] The path height change amount of each of the drone segmented inspection paths is graded, and a first risk coefficient corresponding to the drone segmented inspection path is obtained based on the grading result.

[0029] Preferably, the determining of the risk compensation amount of each segmented inspection path of the drone based on the first risk coefficient and the second risk coefficient includes:

[0030] The first risk coefficient and the second risk coefficient are summed, and the risk compensation amount of each segmented inspection path of the drone is obtained according to the summed result and the auction price of the first drone.

[0031] Preferably, determining the first drone scheduling strategy with the goal of matching the second drone auction price includes:

[0032] With the goal of matching the second drone auction bid, a support vector machine is used to obtain the number of drones and drone models corresponding to each of the drone segmented inspection paths.

[0033] Preferably, the first drone scheduling strategy is optimized based on the regional characteristics of each segmented inspection path of the drone to obtain the second drone scheduling strategy, including:

[0034] The first UAV scheduling strategy is optimized based on the environmental conditions of the power inspection area corresponding to each UAV segmented inspection path, and the UAV inspection period corresponding to each UAV segmented inspection path is obtained, wherein the environmental conditions of the power inspection area include weather conditions and lighting environment;

[0035] The first drone scheduling strategy is optimized based on the equipment type in the power inspection area corresponding to each of the drone segmented inspection paths to obtain the drone inspection priority corresponding to each of the drone segmented inspection paths, wherein the equipment type in the power inspection area includes trunk lines and branch line equipment.

[0036] In a second aspect, an embodiment of the present invention provides a power inspection drone dispatching system based on an auction algorithm, comprising:

[0037] A path segmentation module, used to construct a drone inspection path based on the first drone positioning data, and segment the drone inspection path to obtain a plurality of drone segment inspection paths, wherein the first drone positioning data includes first three-dimensional coordinate data obtained by the drone at each sampling interval during the power inspection process;

[0038] A power consumption determination module, configured to calculate the power consumption of the drone corresponding to the drone segmented inspection path based on the path characteristics of each drone segmented inspection path, and obtain the total drone power consumption of the drone inspection path according to the drone power consumption of each drone segmented inspection path, wherein the path characteristics include the path length and the path height change;

[0039] A bidding price determination module, used to determine a first drone bidding price based on the total drone power consumption, perform a risk assessment on the drone inspection path, and adjust the first drone bidding price according to the obtained risk assessment result to obtain a second drone bidding price, wherein the risk assessment includes performing a risk assessment on the path height change of each drone segmented inspection path and performing a risk assessment on the terrain complexity corresponding to each drone segmented inspection path;

[0040] An initial scheduling strategy determination module, used to determine a first drone scheduling strategy with the goal of matching the second drone auction price, wherein the first drone scheduling strategy includes the number of drones and drone models corresponding to each of the drone segmented inspection paths;

[0041] The target scheduling strategy determination module is used to optimize the first drone scheduling strategy based on the regional characteristics of each of the drone segmented inspection paths to obtain a second drone scheduling strategy, wherein the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods and drone inspection priorities corresponding to each of the drone segmented inspection paths.

[0042] Compared with the prior art, the method and system for dispatching electric power inspection drones based on an auction algorithm in an embodiment of the present invention have the following beneficial effects: the first drone positioning data is used to construct and segment the inspection path, and the actual flight trajectory of the drone is fully considered. The subsequent tasks can be reasonably arranged according to the characteristics of different segments, thereby improving the accuracy of inspection path planning; the power consumption and total power consumption of each segmented drone are calculated according to the path length and height change, providing a basis for evaluating the drone's endurance, avoiding inspection interruptions due to insufficient power, and thus ensuring the smooth execution of inspection tasks; the initial auction price is determined by the total power consumption, and the price is adjusted by risk assessment based on the path height change and terrain complexity, so that the price is more in line with the actual task difficulty and risk, and the rationality of drone scheduling resource allocation can be ensured; the preliminary scheduling strategy is first determined by matching the quotation, and then combined with the environmental conditions of the power inspection area and the optimization of equipment types, and finally a perfect scheduling strategy is obtained, the power inspection efficiency is improved, and drone resources are reasonably utilized. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for dispatching a power inspection drone based on an auction algorithm according to an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a process of path segmentation according to an embodiment of the present invention;

[0045] Figure 3 is a schematic diagram of a process for determining total UAV power consumption according to an embodiment of the present invention;

[0046] Figure 4 is a schematic diagram of a process of determining a second drone auction bid according to an embodiment of the present invention;

[0047] Figure 5 is a schematic diagram of a process for determining a second UAV scheduling strategy according to an embodiment of the present invention;

[0048] Figure 6 It is a structural schematic diagram of an electric power inspection drone dispatching system based on an auction algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0050] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0051] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a method for dispatching a power inspection drone based on an auction algorithm, comprising the steps of:

[0053] S1. Constructing a drone inspection path based on the first drone positioning data, and segmenting the drone inspection path to obtain a number of drone segment inspection paths;

[0054] The first UAV positioning data includes first three-dimensional coordinate data acquired by the UAV at each sampling interval during the power inspection process.

[0055] Specifically, Figure 2 As shown, step S1 includes:

[0056] S101, filtering the first UAV positioning data to obtain the second UAV positioning data, and fitting the second UAV positioning data to obtain the UAV inspection path;

[0057] In order to reduce data redundancy, the first drone positioning data needs to be screened, that is, the second three-dimensional coordinate data obtained by the drone during each sampling interval exceeding the preset distance threshold during the power inspection process is screened to form the second drone positioning data. In other words, the second drone positioning data includes the second three-dimensional coordinate data obtained by the drone during each sampling interval exceeding the preset distance threshold during the power inspection process. At the same time, the second drone positioning data is subjected to three-dimensional curve fitting to obtain the drone inspection path.

[0058] It should be noted that the preset distance threshold can be flexibly adjusted according to the slope change of the power inspection area. During the power inspection process of the drone, the slope change will cause the load of the drone power system to fluctuate. For power inspection areas with drastic slope changes, the preset distance threshold can be appropriately reduced, while for power inspection areas with gentle slope changes, the preset distance threshold can be appropriately increased.

[0059] S102, obtaining the coordinate data of the segmented points of the inspection path of the UAV based on a preset curvature threshold;

[0060] The drone inspection path obtained by three-dimensional curve fitting reflects the motion trajectory of the drone in space, and the curvature change rate characterizes the smoothness of the motion trajectory. When the curvature change rate exceeds the preset curvature threshold, it means that there is a large turn here, and the path needs to be segmented here. Therefore, the segmentation point coordinate data includes the third three-dimensional coordinate data of each curvature change rate in the drone inspection path that exceeds the preset curvature threshold. Among them, the preset curvature threshold in this embodiment is 0.15 radians per meter.

[0061] S103, dividing the UAV inspection path based on the segmentation point coordinate data, and performing segmented linear regression on the divided UAV inspection path to obtain a number of UAV segmented inspection paths.

[0062] The piecewise linear regression simplifies the complex spatial curve into several linear segments. The slope parameter of each linear segment reflects the inclination of the segment. If the absolute value of the slope is large, it means that the segment corresponds to a large height change area. Therefore, after step S103, if Figure 2 As shown, the steps include:

[0063] S104, calculating the height difference value of each drone segment inspection path, and performing secondary division on each drone segment inspection path whose height difference value exceeds a preset height threshold.

[0064] It should be noted that if there is a segmented inspection path of a drone with a height difference exceeding the preset height threshold, it means that the power inspection area corresponding to the segmented inspection path may have steep slopes or cliffs, and secondary division is required to ensure the flight safety of the drone. It is understandable that the height difference between adjacent sampling points of the segmented inspection path of the drone after secondary division does not exceed the preset height threshold.

[0065] S2. Calculate the UAV power consumption of the corresponding UAV segmented inspection path based on the path characteristics of each UAV segmented inspection path, and obtain the total UAV power consumption of the UAV inspection path according to the UAV power consumption of each UAV segmented inspection path;

[0066] Path characteristics include path length and path height change.

[0067] Specifically, Figure 3 As shown, step S2 includes:

[0068] S201, obtaining the path length and the inspection speed of each drone segmented inspection path, and performing support vector regression on the path length and the inspection speed of the drone to obtain the expected flight time data of the corresponding drone segmented inspection path;

[0069] The path length is calculated based on the path height change. Specifically, the horizontal projection distance and path height change of each drone segment inspection path are obtained through the drone's built-in three-dimensional distance calculator, and the path length of the corresponding drone segment inspection path is calculated based on the horizontal projection distance and path height change. Furthermore, support vector regression is used to establish a mapping relationship between the drone inspection speed and the path length, and the expected flight time data of each drone segment inspection path is calculated. Among them, the drone inspection speed can be obtained from the drone's built-in flight controller.

[0070] S202, based on the expected flight time data, the real-time power data of each UAV segmented inspection path is accumulated and calculated to obtain the UAV power consumption of the corresponding UAV segmented inspection path;

[0071] The real-time power data of each segmented inspection path of the drone is accumulated and calculated based on the expected flight time data by the built-in timing integrator of the drone, and the power consumption of the drone corresponding to the segmented inspection path of the drone is obtained. Among them, the real-time power data can be obtained from the built-in power management unit of the drone.

[0072] S203: Accumulate the power consumption of the drone along each segmented inspection path of the drone to obtain the total power consumption of the drone along the inspection path.

[0073] It should be noted that accurate assessment of power consumption is the basis for formulating reasonable bidding quotations for drones, and calculating the power consumption of drones based on path characteristics takes into account the differences in energy consumption of drones in horizontal flight and vertical climb, which can effectively improve the calculation accuracy of drone power consumption, thereby making the total drone power consumption obtained by accumulation more reliable.

[0074] S3, determining a first drone auction price based on the total drone power consumption, performing a risk assessment on the drone inspection path, and adjusting the first drone auction price according to the obtained risk assessment result to obtain a second drone auction price;

[0075] The risk assessment includes risk assessment of the path height change of each UAV segment inspection path and risk assessment of the terrain complexity corresponding to each UAV segment inspection path.

[0076] Specifically, Figure 4 As shown, step S3 includes:

[0077] S301, calculating the drone battery loss cost based on the total drone power consumption and the preset unit power loss cost, and obtaining the first drone auction price according to the drone battery loss cost and the drone maintenance cost;

[0078] This embodiment presets the unit power loss cost to be 4 yuan per watt-hour, which comes from the fact that the cost of a battery with a capacity of 1,000 watt-hours is about 2,000 yuan, and the battery loss unit price is calculated based on 500 charge and discharge cycle life, so the unit power loss cost is 4 yuan per watt-hour. The maintenance cost of the drone includes the battery replacement time cost and the on-site support cost. This embodiment sets the drone maintenance cost ratio to 15%.

[0079] It should be noted that the first drone auction price is not the sum of the drone battery loss cost and the drone maintenance cost, but a certain percentage higher than the sum. In a specific embodiment, the total drone power consumption of the drone inspection path is 650 watt-hours, the sum of the drone battery loss cost and the drone maintenance cost is 3,000 yuan, and the first drone auction price is 3,600 yuan.

[0080] S302, performing risk assessment on the path height change of each segmented inspection path of the drone to obtain a first risk coefficient of the corresponding segmented inspection path of the drone;

[0081] Specifically, the path height change amount of each UAV segmented inspection path is graded, and a first risk coefficient of the corresponding UAV segmented inspection path is obtained based on the grading result.

[0082] The risk assessment adopts a graded quantitative method. In this embodiment, there are 5 levels for the change in path height. Level 1 is below 50 meters, and the first risk coefficient of Level 1 is 0.1. Every increase of 50 meters increases one level, and the first risk coefficient increases by 0.1 for each increase in risk level. The highest first risk coefficient is 0.5.

[0083] S303, performing risk assessment on the terrain complexity corresponding to each segmented inspection path of the UAV to obtain a second risk coefficient corresponding to the segmented inspection path of the UAV;

[0084] Specifically, the terrain complexity corresponding to each UAV segmented inspection path is graded, and a second risk coefficient of the corresponding UAV segmented inspection path is obtained based on the grading result.

[0085] The risk assessment adopts a graded quantitative method. In this embodiment, there are 5 levels of terrain complexity, which are divided according to the frequency and amplitude of slope changes. The second risk coefficient of level 1 is 0.1, and the second risk coefficient increases by 0.1 for each level of risk increase. The highest second risk coefficient is 0.5.

[0086] S304: Determine the risk compensation amount of each drone segment inspection path based on the first risk coefficient and the second risk coefficient, and weight each risk compensation amount and the first drone auction bid to obtain the second drone auction bid.

[0087] Specifically, the first risk coefficient and the second risk coefficient are summed, and the risk compensation amount of each drone segmented inspection path is obtained according to the summed result and the first drone auction price. In this embodiment, when the sum of the first risk coefficient and the second risk coefficient is 0.6, the risk compensation amount is 30% of the first drone auction price. It can be understood that the risk compensation amount under any summation result can be obtained based on this proportional relationship. Furthermore, each risk compensation amount and the first drone auction price are weighted by a deep neural network to obtain the second drone auction price, which effectively improves the rationality of the auction price.

[0088] S4, determining the first drone scheduling strategy with the goal of matching the second drone auction price;

[0089] With the goal of matching the second drone auction bid, the support vector machine is used to obtain the number of drones and the type of drones corresponding to each drone segmented inspection path. In other words, the first drone scheduling strategy includes the number of drones and the type of drones corresponding to each drone segmented inspection path.

[0090] In the process of drone inspection and scheduling, the selection of models and quantities and the matching of auction bids are the primary links. A support vector machine is used to establish a matching relationship between the number of drones and the drone models and the auction bids of the second drone, and the first drone scheduling strategy is obtained. In a specific embodiment, the first drone scheduling strategy includes 3 quad-rotor drones performing inspection tasks of 500-meter drone segmented inspection paths, 1 fixed-wing drone performing inspection tasks of 2000-meter drone segmented inspection paths, and 1 fixed-wing drone performing inspection tasks of 3000-meter drone segmented inspection paths.

[0091] S5. Optimize the first UAV scheduling strategy based on the regional characteristics of each UAV segmented inspection path to obtain a second UAV scheduling strategy.

[0092] In order to obtain a more robust drone scheduling strategy, the first drone scheduling strategy needs to be optimized in combination with the regional characteristics of each drone segmented inspection path to obtain the second drone scheduling strategy. Specifically, the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods, and drone inspection priorities corresponding to each drone segmented inspection path.

[0093] like Figure 5 As shown, step S5 includes:

[0094] S501, optimizing the first drone scheduling strategy based on the environmental conditions of the power inspection area corresponding to each drone segmented inspection path, and obtaining the drone inspection time period corresponding to each drone segmented inspection path;

[0095] The environmental conditions of the power inspection area include weather conditions and light environment. In the morning, the light is good and the wind is light, which is suitable for detailed inspection. At noon, the light is strong and not suitable for inspection. In the afternoon, the light gradually weakens, which is suitable for large-scale inspection.

[0096] S502: Optimize the first drone scheduling strategy based on the equipment type in the power inspection area corresponding to each drone segmented inspection path to obtain the drone inspection priority corresponding to each drone segmented inspection path.

[0097] The types of equipment in the power inspection area include trunk lines and branch line equipment. It is understandable that the priority of drone inspection of trunk lines is higher than that of drone inspection of branch line equipment.

[0098] The embodiment of the present invention is a method for dispatching electric power inspection drones based on an auction algorithm. The method uses the first drone positioning data to construct and segment the inspection path, fully considers the actual flight trajectory of the drone, can reasonably arrange subsequent tasks according to the characteristics of different segments, and improves the accuracy of inspection path planning; calculates the power consumption and total power consumption of each segmented drone according to the path length and height change, provides a basis for evaluating the drone's endurance, avoids inspection interruptions due to insufficient power, and thus ensures the smooth execution of inspection tasks; determines the initial auction price based on the total power consumption, and adjusts the price based on risk assessment based on the path height change and terrain complexity, so that the price is more in line with the actual task difficulty and risk, and can ensure the rationality of drone scheduling resource allocation; first determines the preliminary scheduling strategy based on matching quotations, and then combines the environmental conditions of the power inspection area and the optimization of equipment types to finally obtain a perfect scheduling strategy, improve power inspection efficiency, and reasonably use drone resources.

[0099] Based on the above-mentioned power inspection drone scheduling method based on the auction algorithm, Figure 6 As shown, an embodiment of the present invention provides a power inspection drone dispatching system based on an auction algorithm, comprising:

[0100] A path segmentation module 1 is used to construct a drone inspection path based on the first drone positioning data, and segment the drone inspection path to obtain a plurality of drone segment inspection paths, wherein the first drone positioning data includes the first three-dimensional coordinate data obtained by the drone at each sampling interval during the power inspection process;

[0101] The power consumption determination module 2 is used to calculate the UAV power consumption of the corresponding UAV segmented inspection path based on the path characteristics of each UAV segmented inspection path, and obtain the total UAV power consumption of the UAV inspection path according to the UAV power consumption of each UAV segmented inspection path, wherein the path characteristics include the path length and the path height change;

[0102] The bidding price determination module 3 is used to determine the first drone bidding price based on the total drone power consumption, perform risk assessment on the drone inspection path, and adjust the first drone bidding price according to the obtained risk assessment result to obtain the second drone bidding price, wherein the risk assessment includes risk assessment on the path height change of each drone segmented inspection path and risk assessment on the terrain complexity corresponding to each drone segmented inspection path;

[0103] The initial scheduling strategy determination module 4 is used to determine the first drone scheduling strategy with the goal of matching the second drone auction bid, wherein the first drone scheduling strategy includes the number of drones and drone models corresponding to each drone segment inspection path;

[0104] The target scheduling strategy determination module 5 is used to optimize the first drone scheduling strategy based on the regional characteristics of each drone segmented inspection path to obtain a second drone scheduling strategy, wherein the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods and drone inspection priorities corresponding to each drone segmented inspection path.

[0105] It should be noted that each module in the above-mentioned power inspection drone dispatching system based on an auction algorithm can be fully or partially implemented through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be 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. For the specific definition of a power inspection drone dispatching system based on an auction algorithm, please refer to the definition of a power inspection drone dispatching method based on an auction algorithm in the above text. The two have the same functions and effects, which will not be repeated here.

[0106] In summary, the embodiment of the present invention is a method and system for dispatching power inspection drones based on an auction algorithm. The method and system utilize the first drone positioning data to construct and segment the inspection path, fully consider the actual flight trajectory of the drone, and can reasonably arrange subsequent tasks according to the characteristics of different segments, thereby improving the accuracy of inspection path planning; the power consumption and total power consumption of each segmented drone are calculated according to the path length and height change, providing a basis for evaluating the drone's endurance, avoiding inspection interruptions due to insufficient power, and thus ensuring the smooth execution of inspection tasks; determining the initial auction price for the total power consumption, and adjusting the price through risk assessment based on the path height change and terrain complexity, so that the price is more in line with the actual task difficulty and risk, and can ensure the rationality of drone scheduling resource allocation; first determine the preliminary scheduling strategy by matching the quotation, and then optimize the environmental conditions and equipment types in the power inspection area, and finally obtain a perfect scheduling strategy, improve the power inspection efficiency, and rationally utilize drone resources.

[0107] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dispatching power inspection drones based on an auction algorithm, characterized in that: include: Constructing a drone inspection path based on the first drone positioning data, and segmenting the drone inspection path to obtain a plurality of drone segment inspection paths, wherein the first drone positioning data includes first three-dimensional coordinate data obtained by the drone at each sampling interval during the power inspection process; Based on the path characteristics of each of the drone segmented inspection paths, the drone power consumption corresponding to the drone segmented inspection path is calculated, and the total drone power consumption of the drone inspection path is obtained according to the drone power consumption of each of the drone segmented inspection paths, wherein the path characteristics include the path length and the path height change; Determine a first drone auction price based on the total drone power consumption, perform a risk assessment on the drone inspection path, and adjust the first drone auction price according to the obtained risk assessment result to obtain a second drone auction price, wherein the risk assessment includes performing a risk assessment on the path height change of each drone segmented inspection path and performing a risk assessment on the terrain complexity corresponding to each drone segmented inspection path; Determine a first drone scheduling strategy with the goal of matching the second drone auction price, wherein the first drone scheduling strategy includes the number of drones and drone models corresponding to each of the drone segmented inspection paths; The first drone scheduling strategy is optimized based on the regional characteristics of each of the drone segmented inspection paths to obtain a second drone scheduling strategy, wherein the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods and drone inspection priorities corresponding to each of the drone segmented inspection paths.

2. The power inspection drone dispatching method according to claim 1 is characterized in that: The step of constructing a drone inspection path based on the first drone positioning data and segmenting the drone inspection path to obtain a plurality of drone segment inspection paths includes: The first UAV positioning data is screened to obtain the second UAV positioning data, and the second UAV positioning data is fitted to obtain the UAV inspection path, wherein the second UAV positioning data includes the second three-dimensional coordinate data obtained by the UAV at each sampling interval exceeding a preset distance threshold during the power inspection process; Obtaining segmented point coordinate data of the drone inspection path based on a preset curvature threshold, wherein the segmented point coordinate data includes third three-dimensional coordinate data of each curvature change rate in the drone inspection path exceeding the preset curvature threshold; The UAV inspection path is divided based on the segment point coordinate data, and a segmented linear regression is performed on the divided UAV inspection path to obtain a number of UAV segmented inspection paths.

3. The power inspection drone dispatching method according to claim 2 is characterized in that: After dividing the UAV inspection path based on the segment point coordinate data and performing segmented linear regression on the divided UAV inspection path to obtain a plurality of UAV segmented inspection paths, the method further includes: The height difference value of each of the drone segmented inspection paths is calculated, and each drone segmented inspection path whose height difference value exceeds a preset height threshold is divided twice.

4. The power inspection drone dispatching method according to claim 1 is characterized in that: The calculating the UAV power consumption corresponding to the UAV segmented inspection path based on the path characteristics of each UAV segmented inspection path, and obtaining the total UAV power consumption of the UAV inspection path according to the UAV power consumption of each UAV segmented inspection path, includes: Obtaining the path length and the inspection speed of each segmented inspection path of the drone, and performing support vector regression on the path length and the inspection speed of the drone to obtain expected flight time data corresponding to the segmented inspection path of the drone, wherein the path length is calculated based on the change in path height; Based on the expected flight time data, real-time power data of each segmented inspection path of the drone is accumulated and calculated to obtain the power consumption of the drone corresponding to the segmented inspection path of the drone; The power consumption of the drone in each segmented inspection path of the drone is accumulated to obtain the total power consumption of the drone in the inspection path of the drone.

5. The power inspection drone dispatching method according to claim 1 is characterized in that: The method of determining a first drone auction price based on the total drone power consumption, performing a risk assessment on the drone inspection path, and adjusting the first drone auction price according to the obtained risk assessment result to obtain a second drone auction price includes: Calculating the drone battery loss cost based on the total drone power consumption and the preset unit power loss cost, and obtaining the first drone auction price according to the drone battery loss cost and the drone maintenance cost; Performing a risk assessment on the path height change of each segmented inspection path of the drone to obtain a first risk coefficient corresponding to the segmented inspection path of the drone; Performing a risk assessment on the terrain complexity corresponding to each segmented inspection path of the UAV to obtain a second risk coefficient corresponding to the segmented inspection path of the UAV; The risk compensation amount of each drone segmented inspection path is determined based on the first risk coefficient and the second risk coefficient, and each risk compensation amount and the first drone auction price are weighted to obtain a second drone auction price.

6. The power inspection drone dispatching method according to claim 5 is characterized in that: The risk assessment of the path height change of each segmented inspection path of the drone is performed to obtain a first risk coefficient corresponding to the segmented inspection path of the drone, including: The path height change amount of each of the drone segmented inspection paths is graded, and a first risk coefficient corresponding to the drone segmented inspection path is obtained based on the grading result.

7. The power inspection drone dispatching method according to claim 5 is characterized in that: The determining of the risk compensation amount of each segmented inspection path of the drone based on the first risk coefficient and the second risk coefficient includes: The first risk coefficient and the second risk coefficient are summed, and the risk compensation amount of each segmented inspection path of the drone is obtained according to the summed result and the auction price of the first drone.

8. The power inspection drone dispatching method according to claim 1 is characterized in that: The step of determining the first drone scheduling strategy with the goal of matching the second drone auction price includes: With the goal of matching the second drone auction bid, a support vector machine is used to obtain the number of drones and drone models corresponding to each of the drone segmented inspection paths.

9. The power inspection drone dispatching method according to claim 1, characterized in that: The first UAV scheduling strategy is optimized based on the regional characteristics of each UAV segmented inspection path to obtain a second UAV scheduling strategy, including: The first UAV scheduling strategy is optimized based on the environmental conditions of the power inspection area corresponding to each UAV segmented inspection path, and the UAV inspection period corresponding to each UAV segmented inspection path is obtained, wherein the environmental conditions of the power inspection area include weather conditions and lighting environment; The first drone scheduling strategy is optimized based on the equipment type in the power inspection area corresponding to each of the drone segmented inspection paths to obtain the drone inspection priority corresponding to each of the drone segmented inspection paths, wherein the equipment type in the power inspection area includes trunk lines and branch line equipment.

10. A power inspection drone dispatching system based on an auction algorithm, characterized in that: include: A path segmentation module, used to construct a drone inspection path based on the first drone positioning data, and segment the drone inspection path to obtain a plurality of drone segmented inspection paths, wherein the first drone positioning data includes first three-dimensional coordinate data obtained by the drone at each sampling interval during the power inspection process; A power consumption determination module, configured to calculate the power consumption of the drone corresponding to the drone segmented inspection path based on the path characteristics of each drone segmented inspection path, and obtain the total drone power consumption of the drone inspection path according to the drone power consumption of each drone segmented inspection path, wherein the path characteristics include the path length and the path height change; A bidding price determination module, used to determine a first drone bidding price based on the total drone power consumption, perform a risk assessment on the drone inspection path, and adjust the first drone bidding price according to the obtained risk assessment result to obtain a second drone bidding price, wherein the risk assessment includes performing a risk assessment on the path height change of each drone segmented inspection path and performing a risk assessment on the terrain complexity corresponding to each drone segmented inspection path; An initial scheduling strategy determination module, used to determine a first drone scheduling strategy with the goal of matching the second drone auction price, wherein the first drone scheduling strategy includes the number of drones and drone models corresponding to each of the drone segmented inspection paths; The target scheduling strategy determination module is used to optimize the first drone scheduling strategy based on the regional characteristics of each of the drone segmented inspection paths to obtain a second drone scheduling strategy, wherein the regional characteristics include the environmental conditions of the power inspection area and the equipment type of the power inspection area corresponding to the drone segmented inspection path, and the second drone scheduling strategy includes the number of drones, drone models, drone inspection time periods and drone inspection priorities corresponding to each of the drone segmented inspection paths.

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