A method and system for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs)

Abstract

This invention discloses a method and system for on-site surveying of uninterrupted power distribution network operations based on unmanned aerial vehicles (UAVs). The method includes: calculating the distance cost of vehicle movement to the work site, the associated risk of critical equipment, and the road traffic efficiency factor based on the Terrain Feature Index (TFI), the Power Grid Correlation Index (GCR), and the Road Traffic Index (RPI); allocating weights to the distance cost of the work site, the associated risk of critical equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; constructing an optimization model for the location planning of the work vehicle based on preset constraints, using the weighted distance cost of the work site, the weighted associated risk of critical equipment, and the weighted road traffic efficiency factor as objective functions; and solving the optimization model to obtain the work plan for the work vehicle. This method enables rapid and reasonable planning of the work vehicle's location, improving the efficiency and safety of uninterrupted power distribution network operations.

Description

Technical Field

[0001] This invention belongs to the field of power system distribution network maintenance technology, and particularly relates to a method and system for on-site survey of distribution network uninterrupted power supply operations based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Insulated boom lifts and other work vehicles are easily limited by terrain slope, obstacle density, and road width. Traditional manual site selection is inefficient and carries high safety risks. Distribution network maintenance requires consideration of both power grid operation data and the on-site environment (such as road accessibility and work space). Traditional methods rely on static GIS data, which cannot perceive real-time weather changes (such as sudden winds causing insufficient safety distances), and have not established a quantitative coupling model of the power grid, terrain, and roads. Using drones for live-line work reconnaissance improves data collection efficiency and flexibility, but in existing technologies, the reconnaissance data is not fully applied to the parking location and route planning of non-energized work vehicles, resulting in insufficient adaptability to dynamic environments.

[0003] Chinese patent application number 2024103560899 discloses a method and device for intelligent identification of power lines and environment based on image recognition. Based on the three-dimensional structural data of the environment and power distribution lines, it constructs a three-dimensional model of the power distribution lines, analyzes the structural and positional features of various structural information of the power distribution lines, and filters target uninterrupted power supply (UPS) feature information that meets the conditions for UPS operations. This patent mainly determines suitable UPS operation scenarios based on the three-dimensional structural information of the power lines and environmental information, but it does not consider the power grid operating parameters or the application scenarios of UPS operations performed by the work vehicles.

[0004] Chinese patent application No. 2024113085158 discloses a digital survey method for live-line power distribution network operations based on unmanned aerial vehicles (UAVs). By scientifically dividing the survey area, monitoring the flight trajectory in real time, evaluating data quality, generating image quality coefficients using machine learning models, and optimizing flight strategies, it can achieve efficient and safe digital surveying for live-line power distribution network operations. However, it mainly addresses the accuracy and reliability issues of UAV survey data and does not solve the problems of planning the location of live-line operation vehicles and quantifying the safe distance between vehicle parking points and live equipment. Summary of the Invention

[0005] This invention provides a method and system for on-site surveying of power distribution network operations based on unmanned aerial vehicles (UAVs), which solves the technical problem that survey data is not fully applied to the parking location and route planning of vehicles performing non-energized operations, resulting in insufficient adaptability to dynamic environments.

[0006] In a first aspect, the present invention provides a method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs), comprising:

[0007] Acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data;

[0008] Obtain road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data.

[0009] Acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data;

[0010] The distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor are calculated based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI).

[0011] The distance cost of the work site, the associated risk of the key equipment, and the road traffic efficiency factor are assigned weights according to the preset dynamic weight allocation rules.

[0012] Based on preset constraints, a work vehicle location planning optimization model is constructed with weighted work point distance cost, weighted key equipment association risk, and weighted road traffic efficiency factor as objective functions. The model is then solved to obtain the work vehicle's operational plan. The expression for the objective function is:

[0013] ,

[0014] In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0015] Secondly, the present invention provides a UAV-based on-site survey system for uninterrupted power distribution network operations, comprising:

[0016] The first acquisition module is configured to acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data.

[0017] The second acquisition module is configured to acquire road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data.

[0018] The third acquisition module is configured to acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data.

[0019] The calculation module is configured to calculate the distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI), respectively.

[0020] The allocation module is configured to allocate weights to the distance cost of the work point, the associated risk of the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule.

[0021] The solution module is configured to construct an optimization model for work vehicle location planning based on preset constraints, using the weighted distance cost of work points, the weighted associated risk of key equipment, and the weighted road traffic efficiency factor as objective functions. The module then solves this optimization model to obtain the work plan for the work vehicles. The expression for the objective function is:

[0022] ,

[0023] In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0024] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the on-site survey method for unmanned aerial vehicle-based power distribution network operation according to any embodiment of the present invention.

[0025] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the on-site survey method for unmanned aerial vehicle-based power distribution network operation according to any embodiment of the present invention.

[0026] This application presents a method and system for on-site reconnaissance of unmanned aerial vehicle (UAV) power distribution network live-line work. The method calculates the distance cost of the vehicle to the work site, the associated risk of critical equipment, and the road traffic efficiency factor based on the Terrain Feature Index (TFI), the Power Grid Correlation Rate (GCR), and the Road Traffic Index (RPI). Weights are allocated to these factors according to a preset dynamic weighting rule. Based on preset constraints, a vehicle location planning optimization model is constructed using the weighted distance cost, weighted associated risk of critical equipment, and weighted road traffic efficiency factor as objective functions. The model is then solved to obtain the work plan for the vehicle, enabling rapid and rational planning of vehicle locations and improving the efficiency and safety of unmanned aerial vehicle (UAV) power distribution network work. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart of a method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention;

[0029] Figure 2 This is a structural block diagram of a UAV-based on-site survey system for uninterrupted power distribution network operations, provided in one embodiment of the present invention.

[0030] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 The diagram shows a flowchart of a method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to this application.

[0033] like Figure 1 As shown, the method for on-site survey of power distribution network uninterrupted operation based on drones specifically includes the following steps:

[0034] Step S101: Obtain terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data and ground flatness data.

[0035] In this step, the UAV uses a multimodal sensor integration to collect data, extracting slope, obstacle density, and ground flatness data. The expression for calculating the Terrain Feature Index (TFI) is as follows:

[0036] ,

[0037] In the formula, , All are terrain weighting factors. The range of elevations within the grid. This represents the standard deviation of the elevation within the grid.

[0038] It should be noted that the drone takes off, flies automatically along a planned flight path, and uses a multi-modal sensor integration for data collection, specifically including:

[0039] LiDAR point cloud scanning: The UAV is equipped with LiDAR (wavelength 905nm, accuracy ±3cm) to scan an area with a working radius of 50m. It continuously emits lasers and receives reflected signals at a measurement frequency of 100,000 points per second to generate three-dimensional point cloud data including terrain elevation, vegetation density and power grid structure.

[0040] Infrared thermal imager: resolution 640×512, temperature measurement range -20℃~150℃, performs thermal imaging scans on the device at a frame rate of 25 frames per second, identifies overheating of adjacent lines (>80℃) in real time and triggers vehicle avoidance.

[0041] Multispectral camera: Simultaneously acquires visible and near-infrared bands, combined with slope sensor (range ±30°) to detect ground bearing capacity (threshold: soft soil area <10t / m²).

[0042] Step S102: Obtain road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather influence factors and preset temporary event influence factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data.

[0043] In this step, corresponding digital drawings are obtained through the power company's internal Geographic Information System (GIS) platform, urban road structure drawings are obtained through the local municipal planning department, or road data of the survey area can be obtained through the Gaode Map API interface. After the above data is format converted, map registered and digitally processed, a corresponding road domain database is established.

[0044] The expression for calculating the Road Traffic Index (RPI) is as follows:

[0045] ,

[0046] In the formula, For dynamic correction, the following criteria will be disqualified: vehicle length Lv+2m < minimum straight section length, vehicle height Hv+0.5m < minimum overhead line height, and vehicle axle load Wv > road bearing capacity limit.

[0047] The expression for calculating the dynamic correction score is:

[0048] ,

[0049] In the formula, Basic pass points, Traffic flow influencing factors This is a weather influencing factor, with a value of 0.7 during rain, fog, or snow; and a value of 1 at other times. This is a temporary event impact factor, with a value of 0.6 during construction or accidents.

[0050] The expression for calculating the traffic flow influencing factor is as follows:

[0051] ,

[0052] In the formula, Traffic flow;

[0053] The expression for calculating the basic pass points is:

[0054] ,

[0055] In the formula, The effective width for vehicle travel. This is a reference value for the industry standard road width. The turning radius, This refers to the minimum turning radius of insulated boom trucks and other special vehicles. The longitudinal slope of the road. This refers to the barrier density.

[0056] in, ,

[0057] In the formula, For road width, For safety margin;

[0058] ,

[0059] In the formula, The minimum turning radius of the vehicle is determined based on the operating vehicle.

[0060] ,

[0061] In the formula, Slope;

[0062] ,

[0063] In the formula, The two-dimensional projected area of ​​the effective traffic zone of the road. The number of fixed obstacles identified by the drone.

[0064] Step S103: Obtain power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data.

[0065] In this step, the expression for calculating the grid correlation coefficient (GCR) is:

[0066] ,

[0067] In the formula, Let i be the voltage level of the i-th device to be operated. Let i be the load rate of the i-th device to be operated. The key weight for the i-th device to be operated is... The reference voltage value. This represents the system's maximum allowable load rate.

[0068] Step S104: Calculate the distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI).

[0069] In this step, the expression for calculating the cost of the distance the vehicle travels to the work point is:

[0070] ,

[0071] In the formula, This represents the actual distance traveled by the vehicle. The slope gradient of the vehicle parking point. Penalty for frequent turning The weighting of the actual distance traveled by the vehicle. The weights for the gradient of the vehicle stopping point's slope. Weights for the frequency penalty of switching;

[0072] The expression for calculating the associated risk of the critical equipment is as follows:

[0073] ,

[0074] In the formula, The straight-line spatial distance between the vehicle's boom and the nearest live wire;

[0075] The expression for calculating the road traffic efficiency factor is as follows:

[0076] ,

[0077] In the formula, To achieve the maximum saturation of the road design, The maximum saturation of the real-time traffic road design is determined by the flow saturation.

[0078] Step S105: Assign weights to the distance cost of the work site, the associated risk of the key equipment, and the road traffic efficiency factor according to the preset dynamic weight allocation rules.

[0079] In this step, the dynamic weight allocation rule is as follows: based on GCR to judge grid risk and RPI to judge road congestion risk, dynamically set the weight allocation of work point distance cost, road traffic efficiency factor and key equipment associated risk, and set grid emergency event priority coverage rule. When the load rate is >90%, the weight of key equipment associated risk is forcibly locked to 0.5.

[0080] Specifically, in a typical work scenario, the weight allocation is set as follows:

[0081] Topography:Roads:Power Grid = 0.35:0.35:0.3;

[0082] Based on the GCR (Global Grid Risk Assessment), when the GCR > 0.7, it typically involves main lines. The weight allocation is set as follows:

[0083] Topography:Roads:Power Grid = 0.2:0.3:0.5;

[0084] Based on RPI to determine road congestion risk, when RPI < 0.6, the weight allocation is set as follows:

[0085] Topography:Roads:Power Grid = 0.4:0.4:0.2;

[0086] Based on real-time feedback parameters and priority settings, the weights are adaptively adjusted, specifically:

[0087] Real-time feedback mechanism: The weights are updated every 5 seconds and dynamically adjusted based on sensor data (wind speed, traffic flow, power grid load);

[0088] Priority coverage rule: In the event of a power grid emergency (such as load > 90%), the power grid weight is forcibly locked by 0.5.

[0089] Step S106: Based on preset constraints, construct an operation vehicle location planning optimization model with the weighted distance cost of the operation point, the weighted associated risk of key equipment, and the weighted road traffic efficiency factor as objective functions, and solve the operation vehicle location planning optimization model to obtain the operation plan of the operation vehicle.

[0090] In this step, the vehicle position coordinates and the boom's operating envelope are used as decision variables:

[0091] Vehicle location coordinates: (Plane coordinates + heading angle)

[0092] Bucket arm operation envelope: (Dynamic safety radius, wind speed) and insulation class ).

[0093] The constraints include terrain constraints, power grid security constraints, road access constraints, and operating radius constraints, specifically:

[0094] Terrain constraints, expressed as:

[0095] ,

[0096] In the formula, Indicates vehicle parking location The terrain slope gradient vector at that location;

[0097] The power grid security constraint is expressed as:

[0098] ,

[0099] In the formula, The straight-line spatial distance between the vehicle's boom and the nearest live wire. The current wind speed monitoring value in the work area;

[0100] Road traffic constraints, expressed as:

[0101] ,

[0102] In the formula, For road width, The total width of the special operation vehicle including the extended rearview mirrors;

[0103] The operating radius constraint is expressed as follows:

[0104] ,

[0105] In the formula, The coordinates of the tower center are obtained by calibrating the GIS drawings and point cloud data. This is the maximum effective working radius of the bucket arm.

[0106] Using the cost of vehicle movement to the work site, the associated risks of critical equipment, and the road traffic efficiency factor as the objective function, the expression is:

[0107] ,

[0108] In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0109] In practical applications, multi-objective optimization algorithms such as NSGA-II can be used to solve the above multi-objective model, output the parking position of the work vehicle, and formulate a detailed uninterrupted power supply operation plan. For those skilled in the art, the solution algorithm is not limited to the above limitations.

[0110] In summary, the method of this application calculates the distance cost of vehicle movement to the work site, the critical equipment association risk, and the road traffic efficiency factor based on the terrain feature index (TFI), power grid correlation coefficient (GCR), and road traffic efficiency index (RPI), respectively. It then assigns weights to the work site distance cost, critical equipment association risk, and road traffic efficiency factor according to a preset dynamic weight allocation rule. Based on preset constraints, it constructs a work vehicle location planning optimization model with the weighted work site distance cost, weighted critical equipment association risk, and weighted road traffic efficiency factor as objective functions. The method then solves the work vehicle location planning optimization model to obtain the work vehicle's operation plan, achieving rapid and reasonable planning of work vehicle locations and improving the efficiency and safety of uninterrupted power distribution network operations.

[0111] Please see Figure 2 The diagram shows a structural block diagram of a UAV-based on-site survey system for uninterrupted power distribution network operations according to this application.

[0112] like Figure 2As shown, the power distribution network live-line work site survey system 200 includes a first acquisition module 210, a second acquisition module 220, a third acquisition module 230, a calculation module 240, an allocation module 250, and a solution module 260.

[0113] The system includes a first acquisition module 210 configured to acquire terrain domain data and calculate the Terrain Feature Index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data; a second acquisition module 220 configured to acquire road domain data and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather influence factors and preset temporary event influence factors, wherein the road domain data includes geographic information system (GIS) map data of the power distribution network and road structure map data; a third acquisition module 230 configured to acquire power grid domain data and calculate the Power Grid Correlation Rate (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load data, and equipment operating status data; and a calculation module 240 configured to... To calculate the distance cost of vehicle movement to the work site, the critical equipment association risk, and the road traffic efficiency factor based on the Terrain Feature Index (TFI), the Power Grid Correlation Rate (GCR), and the Road Traffic Index (RPI), an allocation module 250 is configured to allocate weights to the work site distance cost, the critical equipment association risk, and the road traffic efficiency factor according to a preset dynamic weight allocation rule. A solution module 260 is configured to construct a work vehicle location planning optimization model based on preset constraints, using the weighted work site distance cost, weighted critical equipment association risk, and weighted road traffic efficiency factor as objective functions, and solve the work vehicle location planning optimization model to obtain the work vehicle's operation plan. The expression for the objective function is: In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0114] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0115] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the on-site survey method for unmanned aerial vehicle-based power distribution network operation in any of the above method embodiments.

[0116] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0117] Acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data;

[0118] Obtain road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data.

[0119] Acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data;

[0120] The distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor are calculated based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI).

[0121] The distance cost of the work site, the associated risk of the key equipment, and the road traffic efficiency factor are assigned weights according to the preset dynamic weight allocation rules.

[0122] Based on preset constraints, a work vehicle location planning optimization model is constructed with weighted work point distance cost, weighted key equipment association risk, and weighted road traffic efficiency factor as objective functions. The model is then solved to obtain the work vehicle's operational plan. The expression for the objective function is:

[0123] ,

[0124] In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0125] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the UAV-based live-line power distribution work site survey system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected to the UAV-based live-line power distribution work site survey system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0126] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the UAV-based power distribution network live-line work site survey method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the UAV-based power distribution network live-line work site survey system. The output device 340 may include a display screen or other display device.

[0127] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0128] In one implementation, the above-described electronic device is applied to a drone-based power distribution network uninterrupted operation site survey system, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0129] Acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data;

[0130] Obtain road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data.

[0131] Acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data;

[0132] The distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor are calculated based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI).

[0133] The distance cost of the work site, the associated risk of the key equipment, and the road traffic efficiency factor are assigned weights according to the preset dynamic weight allocation rules.

[0134] Based on preset constraints, a work vehicle location planning optimization model is constructed with weighted work point distance cost, weighted key equipment association risk, and weighted road traffic efficiency factor as objective functions. The model is then solved to obtain the work vehicle's operational plan. The expression for the objective function is:

[0135] ,

[0136] In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the associated risks of key equipment, and the weighting coefficients for the road traffic efficiency factor.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs), characterized in that, include: Acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data; Obtain road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data. Acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data; The distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor are calculated based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI). The distance cost of the work site, the associated risk of the key equipment, and the road traffic efficiency factor are assigned weights according to the preset dynamic weight allocation rules. Based on preset constraints, a work vehicle location planning optimization model is constructed with weighted work point distance cost, weighted key equipment association risk, and weighted road traffic efficiency factor as objective functions. The model is then solved to obtain the work vehicle's operational plan. The expression for the objective function is: ， In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the risk associated with key equipment, and the weighting coefficients for the road traffic efficiency factor, respectively. The expression for calculating the cost of the distance the vehicle travels to the work point is as follows: ， In the formula, This represents the actual distance traveled by the vehicle. The slope gradient of the vehicle parking point. Penalty for frequent turning The weighting of the actual distance traveled by the vehicle. The weights for the gradient of the vehicle stopping points. Weights for the frequency penalty of switching; The expression for calculating the associated risk of the critical equipment is as follows: ， In the formula, The straight-line spatial distance between the vehicle's boom and the nearest live wire; The expression for calculating the road traffic efficiency factor is as follows: ， In the formula, To achieve the maximum saturation of the road design, The maximum saturation of the real-time traffic road design is determined by the flow saturation.

2. The method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, in, The expression for calculating the Terrain Feature Index (TFI) is as follows: ， In the formula, , All are terrain weighting factors. The range of elevations within the grid. This represents the standard deviation of the elevation within the grid.

3. The method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, in, The expression for calculating the Road Traffic Index (RPI) is as follows: ， In the formula, For dynamic correction, the following criteria will be disqualified: vehicle length Lv+2m < minimum straight section length, vehicle height Hv+0.5m < minimum overhead line height, and vehicle axle load Wv > road bearing capacity limit. The expression for calculating the dynamic correction score is: ， In the formula, Basic pass points, Traffic flow influencing factors This is a weather influencing factor, with a value of 0.7 during rain, fog, or snow. The value is 1 at other times. This is a temporary event impact factor, with a value of 0.6 during construction or accidents. The expression for calculating the traffic flow influencing factor is as follows: ， In the formula, Traffic flow; The expression for calculating the basic pass points is: ， In the formula, The effective width for vehicle travel. This is a reference value for the industry standard road width. The turning radius, This refers to the minimum turning radius of insulated boom trucks and other special vehicles. The longitudinal slope of the road. This refers to the barrier density.

4. The method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The expression for calculating the power grid correlation coefficient (GCR) is as follows: ， In the formula, Let i be the voltage level of the i-th device to be operated. Let i be the load rate of the i-th device to be operated. Let i be the key weight of the i-th equipment to be operated. The reference voltage value. This represents the system's maximum allowable load rate.

5. The method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The dynamic weight allocation rule is as follows: Based on GCR to assess grid risk and RPI to assess road congestion risk, the system dynamically sets the weight allocation of work point distance cost, road traffic efficiency factor, and key equipment associated risk, and sets grid emergency event priority coverage rules. When the load rate is >90%, the weight of key equipment associated risk is forcibly locked to 0.

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6. The method for on-site survey of power distribution network uninterrupted operation based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The constraints include: Terrain constraints, expressed as: ， In the formula, Indicates vehicle parking location The terrain slope gradient vector at that location; The power grid security constraint is expressed as: ， In the formula, The straight-line spatial distance between the vehicle's boom and the nearest live wire. The current wind speed monitoring value in the work area; Road traffic constraints, expressed as: ， In the formula, For road width, The total width of the special operation vehicle including the extended rearview mirrors; The operating radius constraint is expressed as follows: ， In the formula, The coordinates of the tower center are obtained by calibrating the GIS drawings and point cloud data. This is the maximum effective working radius of the bucket arm.

7. A UAV-based on-site survey system for uninterrupted power distribution network operations, characterized in that, include: The first acquisition module is configured to acquire terrain domain data and calculate the terrain feature index (TFI) based on the terrain domain data, wherein the terrain domain data includes slope data, obstacle density data, and ground flatness data. The second acquisition module is configured to acquire road domain data, and calculate the Road Traffic Index (RPI) based on the road domain data and using preset weather impact factors and preset temporary event impact factors. The road domain data includes geographic information system drawing data of the power distribution network and road structure drawing data. The third acquisition module is configured to acquire power grid domain data and calculate the power grid correlation coefficient (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load status data, and equipment operating status data. The calculation module is configured to calculate the distance cost of vehicle movement to the work site, the risk of critical equipment association, and the road traffic efficiency factor based on the terrain feature index (TFI), the power grid correlation coefficient (GCR), and the road traffic index (RPI), respectively. The allocation module is configured to allocate weights to the distance cost of the work point, the associated risk of the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule. The solution module is configured to construct an optimization model for work vehicle location planning based on preset constraints, using the weighted distance cost of work points, the weighted associated risk of key equipment, and the weighted road traffic efficiency factor as objective functions. The module then solves this optimization model to obtain the work plan for the work vehicles. The expression for the objective function is: ， In the formula, Cost of the distance the vehicle travels to the work site. Risks associated with critical equipment This is the road traffic efficiency factor. , , These are the weighting coefficients for the distance cost of vehicle movement to the work site, the weighting coefficients for the risk associated with key equipment, and the weighting coefficients for the road traffic efficiency factor, respectively. The expression for calculating the cost of the distance the vehicle travels to the work point is as follows: ， In the formula, This represents the actual distance traveled by the vehicle. The slope gradient of the vehicle parking point. Penalty for frequent turning The weighting of the actual distance traveled by the vehicle. The weights for the gradient of the vehicle stopping points. Weights for the frequency penalty of switching; The expression for calculating the associated risk of the critical equipment is as follows: ， In the formula, The straight-line spatial distance between the vehicle's boom and the nearest live wire; The expression for calculating the road traffic efficiency factor is as follows: ， In the formula, To achieve the maximum saturation of the road design, The maximum saturation of the real-time traffic road design is determined by the flow saturation.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform 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 program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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