Distribution network non-power-cut operation site investigation method and system based on unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle-based distribution network uninterruptible operation site investigation method and system, and the method comprises the steps: respectively calculating the distance cost of a vehicle moving to an operation point, the key equipment association risk and a road traffic efficiency factor according to a topographic feature index TFI, a power grid association degree GCR and a road traffic index RPI; according to a preset dynamic weight distribution rule, weight distribution is carried out on the operation point distance cost, the key equipment association risk and the road traffic efficiency factor; based on a preset constraint condition, taking the operation point distance cost after the weight distribution, the key equipment association risk after the weight distribution and the road traffic efficiency factor after the weight distribution as objective functions to construct an operation vehicle position planning optimization model, and solving the operation vehicle position planning optimization model. And obtaining an operation scheme of the operation vehicle. The rapid and reasonable planning of the position of the operation vehicle is realized, and the power-off-free operation efficiency and safety of the distribution network are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system distribution network maintenance, and in particular relates to a method and system for on-site survey of distribution network non-stop operations based on drones. Background Art

[0002] Insulated boom trucks and other operational vehicles are susceptible to restrictions due to terrain slope, obstacle density, and road width. Traditional manual site selection is inefficient and carries high safety risks. Distribution network maintenance requires a balanced consideration of grid operating data and the on-site environment (such as road accessibility and workspace). Traditional methods rely on static GIS data, are unable to perceive real-time meteorological changes (such as insufficient safety distances due to gusts of wind), and lack a quantitative coupling model for the grid, terrain, and roads. The use of drones for non-stop operational surveys improves data collection efficiency and flexibility. However, existing technologies do not fully utilize these survey data for parking locations and route planning for non-powered operational vehicles, resulting in insufficient adaptability to dynamic environments.

[0003] Chinese patent application number 2024103560899 discloses a method and apparatus for intelligent identification of power lines and environments based on image recognition. Based on the three-dimensional structural data of the environment and distribution lines, a three-dimensional distribution line model corresponding to the distribution line is constructed. The structural and positional characteristics of each structural information of the distribution line are analyzed, and target non-stop operation characteristic information that meets the conditions for non-stop operation is selected. This patent primarily determines working scenarios suitable for non-stop operation based on the three-dimensional structural and environmental information of the power lines, but does not consider the application scenarios of grid operating parameters and non-stop operation of working vehicles.

[0004] Chinese patent application number 2024113085158 discloses a drone-based digital survey method for non-stop power supply operations in distribution networks. By scientifically dividing the survey area, monitoring the flight trajectory in real time, evaluating data quality, using machine learning models to generate image quality coefficients, and optimizing flight strategies, efficient and safe digital surveys for non-stop power supply operations in distribution networks can be achieved. However, it is mainly used to solve the accuracy and reliability problems of drone survey data, and also does not solve the problems of vehicle location planning for non-stop power supply operations and quantitative decision-making on the safe distance between vehicle stops and live equipment. Summary of the Invention

[0005] The present invention provides a method and system for on-site survey of distribution network non-stop operations based on drones, which is used to solve the technical problem that survey data is not fully applied to the parking position and path planning of non-powered operation vehicles, resulting in insufficient adaptability to dynamic environments.

[0006] In a first aspect, the present invention provides a method for on-site survey of distribution network without power outages based on a drone, comprising: Acquire terrain domain data, and calculate a 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; Obtaining road domain data, and calculating a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; Obtaining grid domain data, and calculating a grid correlation degree (GCR) based on the grid domain data, wherein the grid domain data includes voltage level data, load condition data, and equipment operation status data; Calculate the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; Allocating weights to the distance cost of the operation point, the risk associated with the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; Based on the preset constraints, a work vehicle location planning optimization model is constructed with the weighted work point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function. The work vehicle location planning optimization model is solved to obtain the work vehicle operation plan, wherein the expression of the objective function is: , Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0007] In a second aspect, the present invention provides a distribution network non-stop operation site survey system based on a drone, comprising: A first acquisition module is configured to acquire terrain domain data and calculate a 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 configured to acquire road domain data, and calculate a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; a third acquisition module configured to acquire power grid domain data and calculate a power grid correlation degree (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load condition data, and equipment operation status data; a calculation module configured to calculate the distance cost of moving a vehicle to a work point, the risk associated with key equipment, and a road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; an allocation module configured to allocate weights to the distance cost of the operation point, the risk associated with 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 operation vehicle location planning optimization model based on preset constraints with the weighted operation point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function, and solve the operation vehicle location planning optimization model to obtain an operation plan for the operation vehicle, wherein the expression of the objective function is: , Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0008] According to a third aspect, 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, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for on-site survey of distribution network without power outages based on a drone of any embodiment of the present invention.

[0009] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program instructions are executed by a processor, the processor executes the steps of the method for on-site investigation of distribution network without power outages based on drones of any embodiment of the present invention.

[0010] The drone-based on-site survey method and system for distribution network non-stop power supply operations of the present application calculates the distance cost of the vehicle moving to the operation point, the key equipment associated risk and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR and the road traffic index RPI respectively, assigns weights to the operation point distance cost, the key equipment associated risk and the road traffic efficiency factor according to the preset dynamic weight allocation rule, constructs an operation vehicle position planning optimization model based on preset constraints with the weighted operation point distance cost, the weighted key equipment associated risk and the weighted road traffic efficiency factor as the objective function, and solves the operation vehicle position planning optimization model to obtain the operation plan of the operation vehicle, realizes rapid and reasonable planning of the operation vehicle position, and improves the efficiency and safety of distribution network non-stop power supply operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 A flowchart of a method for on-site survey of a distribution network without power outages based on a drone according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a distribution network non-stop operation site survey system based on a drone provided by one embodiment of the present invention; Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] See also Figure 1 , which shows a flow chart of a method for on-site survey of distribution network without power outage based on drones of the present application.

[0015] like Figure 1 As shown in the figure, the on-site survey method for distribution network non-stop power supply operation based on drone specifically includes the following steps: Step S101 : acquiring terrain domain data, and calculating a 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.

[0016] In this step, the UAV uses multimodal sensor integration to collect data, extract slope, obstacle density and ground flatness data, and calculate the terrain feature index TFI expression as follows: , Where, 、 are terrain weight factors, is the range of elevation within the grid, is the standard deviation of the elevation mark within the grid.

[0017] It should be noted that the drone takes off, flies automatically according to the planned flight path, and uses multimodal sensor integration to collect data, including: LiDAR point cloud scanning: The drone is equipped with LiDAR (wavelength 905nm, accuracy ±3cm) to scan an area with a 50m radius. It continuously emits laser light and receives reflected signals at a measurement frequency of 100,000 points per second, generating 3D point cloud data including terrain elevation, vegetation density, and power grid structure. Infrared thermal imager: 640×512 resolution, temperature measurement range -20°C to 150°C, thermal imaging scans the equipment at a frame rate of 25 frames per second, identifying overheated adjacent lines (>80°C) in real time and triggering vehicle avoidance; Multispectral camera: Synchronously collects visible light and near-infrared bands, and combines with a slope sensor (range ±30°) to detect ground bearing capacity (threshold: <10t / m² in soft soil areas).

[0018] Step S102: 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, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data.

[0019] In this step, the 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, and road data in the survey area can be obtained through the AutoNavi Map API interface. After format conversion, map registration, and digitization of the above data, the corresponding road domain database is established.

[0020] Among them, the expression for calculating the road traffic index RPI is: , Where, For dynamic correction points, the veto items are vehicle length Lv+2m<minimum straight length of the road section, vehicle height Hv+0.5m<minimum height of overhead wire, and vehicle axle weight Wv>road bearing limit; The expression for calculating the dynamic correction score is: , Where, As the basic pass score, is the traffic flow influencing factor, is the weather factor. When it is rainy, foggy, snowy or snowy, the value is 0.7; at other times, the value is 1. is the temporary event impact factor, which is 0.6 during construction or accidents; The expression for calculating the traffic flow impact factor is: , Where, For traffic flow; The expression for calculating the basic pass points is: , Where, is the effective width of the vehicle, is the industry standard road width reference value, is the turning radius, The minimum turning radius of special vehicles such as insulated bucket trucks. is the longitudinal slope of the road, is the obstacle density.

[0021] in, , Where, is the road width, is the safety margin; , Where, The minimum turning radius of the vehicle is determined based on the operating vehicle; , Where, is the slope; , Where, is the two-dimensional projection area of ​​the effective road passage area, The number of fixed obstacles identified for the drone.

[0022] Step S103 : acquiring grid domain data, and calculating a grid correlation degree GCR based on the grid domain data, wherein the grid domain data includes voltage level data, load condition data, and equipment operation status data.

[0023] In this step, the expression for calculating the grid correlation GCR is: , Where, is the voltage level of the i-th device to be operated, is the load rate of the i-th equipment to be operated, is the key weight of the i-th device to be operated, is the reference voltage value, The maximum allowable load rate of the system.

[0024] Step S104 , respectively calculating the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI.

[0025] In this step, the expression for calculating the distance cost of the vehicle moving to the operation point is: , Where, is the actual distance traveled by the vehicle, is the slope gradient of the vehicle stop point, To penalize the frequency of turning, is the weight of the vehicle’s actual driving distance, is the weight of the slope gradient of the vehicle stop, The weight of the penalty for frequent turning; The expression for calculating the risk associated with the key equipment is: , Where, It is the straight-line distance between the vehicle bucket arm and the nearest live wire; The expression for calculating the road traffic efficiency factor is: , Where, Designing maximum saturation for roads, Design maximum saturation flow saturation for real-time traffic roads.

[0026] Step S105 , allocating weights to the distance cost of the operation point, the risk associated with the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule.

[0027] In this step, the dynamic weight allocation rules are as follows: grid risk is determined based on GCR and road congestion risk is determined based on RPI. The weights of the distance cost to the operation point, the road traffic efficiency factor, and the risks associated with key equipment are dynamically set. In addition, the grid emergency priority override rules are set. When the load rate is greater than 90%, the weight of the risks associated with key equipment is forcibly locked to 0.5.

[0028] Specifically, in a conventional operation scenario, the weight distribution is set as: Terrain:Road:Grid=0.35:0.35:0.3; The grid risk is judged based on GCR. When GCR>0.7, it usually involves trunk lines. The weight distribution is set as follows: Terrain:Road:Grid=0.2:0.3:0.5; Road congestion risk is determined based on RPI. When RPI < 0.6, the weight distribution is set as: Terrain:Road:Grid=0.4:0.4:0.2; Based on real-time feedback parameters and priority settings, weights are adaptively adjusted. Specifically: Real-time feedback mechanism: weights are updated every 5 seconds and dynamically adjusted based on sensor data (wind speed, traffic flow, grid load); Priority override rule: In case of grid emergency (such as load > 90%), the grid weight is forcibly locked to 0.5.

[0029] In step S106, based on the preset constraints, a work vehicle location planning optimization model is constructed with the weighted work point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function, and the work vehicle location planning optimization model is solved to obtain the work plan of the work vehicle.

[0030] In this step, the vehicle position coordinates and bucket arm operation envelope are used as decision variables: Vehicle location coordinates: , (plane coordinates + heading angle) Bucket arm operation envelope: , (dynamic safety radius, wind speed and insulation grade ).

[0031] The constraints set include terrain constraints, power grid security constraints, road access constraints, and operation radius constraints. Specifically: Terrain constraint, the expression is: , Where, Indicates a vehicle stop The terrain slope gradient vector at ; The grid security constraint is expressed as: , Where, is the straight-line distance between the vehicle bucket arm and the nearest live wire, The current wind speed monitoring value in the operating area; Road access constraints, expressed as: , Where, is the road width, The total width of the special operation vehicle including the rearview mirror extended; Working radius constraint, the expression is: , Where, The center coordinates of the tower after calibration of the GIS drawing and point cloud. It is the maximum effective operating radius of the bucket arm.

[0032] Taking the distance cost from the vehicle to the operation point, the risk associated with key equipment, and the road traffic efficiency factor as the objective function, the expression is: , Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0033] In practical applications, a multi-objective optimization algorithm such as NSGA-II can be used to solve the multi-objective model, output the parking positions of the work vehicles, and compile a detailed non-stop operation plan. For those skilled in the art, the solution algorithm is not limited to the above definition.

[0034] In summary, the method of the present application calculates the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road access efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road access index RPI, and assigns weights to the operation point distance cost, the risk associated with key equipment, and the road access efficiency factor according to the preset dynamic weight allocation rules. Based on the preset constraints, an operation vehicle position planning optimization model is constructed with the weighted operation point distance cost, the weighted key equipment associated risk, and the weighted road access efficiency factor as the objective function, and the operation vehicle position planning optimization model is solved to obtain the operation plan of the operation vehicle, thereby realizing rapid and reasonable planning of the operation vehicle position and improving the efficiency and safety of the distribution network without power outages.

[0035] See also Figure 2 , which shows a structural block diagram of a distribution network non-stop operation field survey system based on drones in this application.

[0036] like Figure 2 As shown, the distribution network non-stop operation field 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 .

[0037] Among them, the first acquisition module 210 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 220 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, wherein the road domain data includes geographic information system drawing data and road structure drawing data of the distribution network; the third acquisition module 230 is configured to acquire power grid domain data and calculate the power grid correlation GCR based on the power grid domain data, wherein the power grid domain data includes voltage level data, load condition data, and equipment operation status data; the calculation module 240 is configured The distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor are calculated based on the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; the allocation module 250 is configured to assign weights to the operation point distance cost, the risk associated with key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; the solution module 260 is configured to construct an operation vehicle position planning optimization model based on preset constraints with the weighted operation point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function, and solve the operation vehicle position planning optimization model to obtain an operation plan for the operation vehicle, wherein the expression of the objective function is: , where is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0038] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects also apply to Figure 2 The modules in it will not be described in detail here.

[0039] In other embodiments, embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the method for on-site survey of distribution network without power outage based on a drone in any of the above method embodiments; As an embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows: Acquire terrain domain data, and calculate a 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; Obtaining road domain data, and calculating a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; Obtaining grid domain data, and calculating a grid correlation degree (GCR) based on the grid domain data, wherein the grid domain data includes voltage level data, load condition data, and equipment operation status data; Calculate the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; Allocating weights to the distance cost of the operation point, the risk associated with the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; Based on the preset constraints, a work vehicle location planning optimization model is constructed with the weighted work point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function. The work vehicle location planning optimization model is solved to obtain the work vehicle operation plan, wherein the expression of the objective function is: , Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0040] The computer-readable storage medium may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the drone-based non-stop power distribution network site survey system. Furthermore, the computer-readable storage medium may include high-speed random access memory and may also include storage, 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 storage remote from the processor, which may be connected to the drone-based non-stop power distribution network 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.

[0041] Figure 3 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. 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, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 The example uses a bus connection. Memory 320 is the aforementioned computer-readable storage medium. Processor 310 executes the various server functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in memory 320, thereby implementing the aforementioned method embodiment for the drone-based on-site survey method for power distribution network operations without power outages. Input device 330 can receive input digital or character information and generate key signal input related to user settings and function control of the drone-based on-site survey system for power distribution network operations without power outages. Output device 340 may include a display device such as a display screen.

[0042] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0043] As an embodiment, the electronic device is applied to a drone-based distribution network non-stop operation field survey system, and is used for a client, including: 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, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire terrain domain data, and calculate a 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; Obtaining road domain data, and calculating a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; Obtaining grid domain data, and calculating a grid correlation degree (GCR) based on the grid domain data, wherein the grid domain data includes voltage level data, load condition data, and equipment operation status data; Calculate the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; Allocating weights to the distance cost of the operation point, the risk associated with the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; Based on the preset constraints, a work vehicle location planning optimization model is constructed with the weighted work point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function. The work vehicle location planning optimization model is solved to obtain the work vehicle operation plan, wherein the expression of the objective function is: , Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficient of the distance cost from the vehicle to the operation point, the weight coefficient of the risk associated with key equipment, and the weight coefficient of the road traffic efficiency factor.

[0044] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods of each embodiment or certain portions of the embodiments.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for on-site survey of distribution network without power outage based on drone, characterized in that: include: Acquire terrain domain data, and calculate a 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; Obtaining road domain data, and calculating a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; Obtaining grid domain data, and calculating a grid correlation degree (GCR) based on the grid domain data, wherein the grid domain data includes voltage level data, load condition data, and equipment operation status data; Calculate the distance cost of the vehicle moving to the operation point, the risk associated with key equipment, and the road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; Allocating weights to the distance cost of the operation point, the risk associated with the key equipment, and the road traffic efficiency factor according to a preset dynamic weight allocation rule; Based on the preset constraints, a work vehicle location planning optimization model is constructed with the weighted work point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function. The work vehicle location planning optimization model is solved to obtain the work vehicle operation plan, wherein the expression of the objective function is: ， Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficients of the distance cost from the vehicle to the work point, the weight coefficients of the risks associated with key equipment, and the weight coefficients of the road traffic efficiency factor.

2. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: in, The expression for calculating the terrain feature index TFI is: ， Where, 、 are terrain weight factors, is the range of elevation within the grid, is the standard deviation of the elevation mark within the grid.

3. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: in, The expression for calculating the road traffic index RPI is: ， Where, For dynamic correction points, the veto items are vehicle length Lv+2m<minimum straight length of the road section, vehicle height Hv+0.5m<minimum height of overhead wire, and vehicle axle weight Wv>road bearing limit; The expression for calculating the dynamic correction score is: ， Where, As the basic pass score, is the traffic flow influencing factor, is the weather influence factor, which is 0.7 in rain, fog, ice or snow; At other times, the value is 1. is the temporary event impact factor, which is 0.6 during construction or accidents; The expression for calculating the traffic flow impact factor is: ， Where, For traffic flow; The expression for calculating the basic pass points is: ， Where, is the effective width of the vehicle, is the industry standard road width reference value, is the turning radius, The minimum turning radius of special vehicles such as insulated bucket trucks. is the longitudinal slope of the road, is the obstacle density.

4. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: The expression for calculating the grid correlation GCR is: ， Where, is the voltage level of the i-th device to be operated, is the load rate of the i-th equipment to be operated, is the key weight of the i-th device to be operated, is the reference voltage value, The maximum allowable load rate of the system.

5. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: in, The expression for calculating the distance cost of the vehicle moving to the operation point is: ， Where, is the actual distance traveled by the vehicle, is the slope gradient of the vehicle stop point, To penalize the frequency of turning, is the weight of the vehicle’s actual driving distance, is the weight of the slope gradient of the vehicle stop, The weight of the penalty for frequent turning; The expression for calculating the risk associated with the key equipment is: ， Where, It is the straight-line distance between the vehicle bucket arm and the nearest live wire; The expression for calculating the road traffic efficiency factor is: ， Where, Designing maximum saturation for roads, Design maximum saturation flow saturation for real-time traffic roads.

6. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: The dynamic weight allocation rule is: Based on GCR to judge grid risk and RPI to judge road congestion risk, the weight distribution of operation point distance cost, road traffic efficiency factor and key equipment associated risks is dynamically set, and the grid emergency event priority coverage rules are set. When the load rate is greater than 90%, the weight of key equipment associated risks is forcibly locked to 0.

5.

7. The method for on-site survey of distribution network without power outage based on drone according to claim 1, characterized in that: The constraints include: Terrain constraint, the expression is: ， Where, Indicates a vehicle stop The terrain slope gradient vector at ; The grid security constraint is expressed as: ， Where, is the straight-line distance between the vehicle bucket arm and the nearest live wire, The current wind speed monitoring value in the operating area; Road access constraints, expressed as: ， Where, is the road width, The total width of the special operation vehicle including the rearview mirror extended; Working radius constraint, the expression is: ， Where, The center coordinates of the tower after calibration of the GIS drawing and point cloud. It is the maximum effective operating radius of the bucket arm.

8. A distribution network non-stop operation site survey system based on drones, characterized by: include: A first acquisition module is configured to acquire terrain domain data and calculate a 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 configured to acquire road domain data, and calculate a road traffic index (RPI) based on the road domain data and using a preset weather impact factor and a preset temporary event impact factor, wherein the road domain data includes geographic information system drawing data of the distribution network and road structure drawing data; a third acquisition module configured to acquire power grid domain data and calculate a power grid correlation degree (GCR) based on the power grid domain data, wherein the power grid domain data includes voltage level data, load condition data, and equipment operation status data; a calculation module configured to calculate the distance cost of moving a vehicle to a work point, the risk associated with key equipment, and a road traffic efficiency factor according to the terrain feature index TFI, the grid correlation GCR, and the road traffic index RPI; an allocation module configured to allocate weights to the distance cost of the operation point, the risk associated with 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 operation vehicle location planning optimization model based on preset constraints with the weighted operation point distance cost, the weighted key equipment associated risk, and the weighted road traffic efficiency factor as the objective function, and solve the operation vehicle location planning optimization model to obtain an operation plan for the operation vehicle, wherein the expression of the objective function is: ， Where, is the distance cost of the vehicle moving to the operation point, Risks associated with key equipment, is the road traffic efficiency factor, 、 、 They are the weight coefficients of the distance cost from the vehicle to the work point, the weight coefficients of the risks associated with key equipment, and the weight coefficients of the road traffic efficiency factor.

9. 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, and the instructions are executed 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 7.

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

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