Grid deployment method of UAVs for transmission, transformation and distribution based on GIS

Abstract

A grid deployment method for transmission, transformation and distribution drones based on a geographic information system (GIS) belongs to the field of power inspection technology. To address the problems of low drone utilization and low inspection efficiency in existing power inspections, the present invention proposes a new solution: first, the distribution data of power facilities is collected and entered into a GIS platform; then, grid site selection of machine nests is performed based on the distribution of power facilities and the endurance of drones; then, the power facilities are gridded using the GIS spatial analysis function; then, an efficient drone path planning algorithm is proposed to plan the optimal flight path for the drone; finally, drone deployment and scheduling are performed based on the planned path and the location of the machine nests; the present invention achieves efficient and accurate inspection of power facilities through the combination of GIS technology and drone technology, improves inspection efficiency and quality, and ensures the safe and stable operation of the power system.

Description

Technical Field

[0001] The present invention relates to the field of electric power inspection technology, and in particular to a grid deployment method of transmission, transformation and distribution UAVs based on GIS. Background Art

[0002] In the field of power inspection, with the rapid development of drone technology, drones have gradually become an important tool for inspecting power transmission, transformation, and distribution facilities. Traditional power inspection methods rely primarily on manual on-foot or ground vehicles. This approach is not only inefficient but also fails to cover all power facilities, especially those located in remote or difficult-to-reach areas. The introduction of drones has greatly improved the efficiency and coverage of inspections, making power facility inspections more comprehensive and timely.

[0003] At present, the application of drones in power inspections mainly focuses on the following aspects: first, using drones equipped with high-definition cameras to conduct aerial photography and visual inspections of power facilities; second, using drones equipped with infrared thermal imagers and other equipment to monitor the temperature of power facilities and promptly detect potential overheating problems; third, using the drone's flight capabilities to inspect power lines across obstacles, such as across rivers, mountains and other complex terrains.

[0004] However, despite the enormous potential of drones for power plant inspections, existing drone inspection technology still has some significant issues and shortcomings. First, drones have limited endurance and short single-flight times, making them difficult to complete large-scale inspections. This results in the need for frequent battery replacement or charging in practical applications, seriously affecting inspection efficiency. Second, drone path planning is a major challenge. In the complex environment of power facilities, how to plan the optimal flight path to ensure that drones can complete inspection tasks efficiently and safely is an urgent problem that needs to be solved. In addition, existing drone inspection technology lacks intelligent drone nest selection and grid management, resulting in inflexible and inefficient drone deployment and scheduling.

[0005] To address these issues, this paper proposes a grid-based deployment method for transmission, distribution, and UAVs based on a geographic information system (GIS). Through precise drone nest selection, optimized grid division, and efficient UAV path planning, this method aims to address existing UAV inspection technologies, including insufficient endurance, complex path planning, and inflexible deployment and scheduling, thereby improving the efficiency and quality of power inspections. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a GIS-based grid deployment method for transmission, transformation and distribution UAVs, so as to solve the technical problems of insufficient endurance, complex path planning and inflexible deployment and scheduling of UAV inspection technology in the field of power inspection.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a grid deployment method of transmission, transformation and distribution UAVs based on GIS, comprising the following steps:

[0008] Step 1: Calculate the coverage radius based on the maximum flight time and geographical factors of the transmission, transformation and distribution joint inspection drone;

[0009] Step 2: Use the coverage radius and polygon intersection set method PIPS to obtain the candidate area of ​​the machine nest;

[0010] Step 3: Consider the deployment environment constraints of the machine nest to determine the candidate machine nest points in each candidate area and obtain the candidate point set;

[0011] Step 4: Using GIS and convex hull, calculate the Euclidean shortest path ESP between the candidate points of the machine nest in Step 3;

[0012] Step 5: Combine ESP and weighted set coverage model to establish a UAV grid site selection model;

[0013] Step 6: Use Python to call Gurobi to solve the global optimal solution of the model and obtain the location of the machine nest;

[0014] Step 7: Use ArcGIS spatial analysis functions to perform coverage visualization and grid division, and build a UAV leapfrog flight network based on the Euclidean shortest path between the site selection points obtained in Step 4.

[0015] In the preferred solution, in Step 1, the impact factor is introduced to calculate the nest coverage radius , the specific expression is:

[0016] (1)

[0017] Where, To take into account the margin left by the drone due to incomplete charging, battery aging, low temperature, and high-frequency operation, the value is [0, 1]; and are terrain influence factor and wind speed influence factor, respectively, with values ​​ranging from [0, 1]; In a windless environment, the test speed is The longest flight time measured when flying at a constant speed until the remaining battery power is 0%; It is the inspection time of power equipment; The maximum flight time of the drone; divide the calculated distance by 2 to ensure that the drone can return to the starting nest after the inspection.

[0018] In the preferred solution, the specific method and steps of Step 2 are: using the ArcGIS fishnet tool to divide the study area into regular grids, and mapping all transmission and distribution transformation requirements to the corresponding grids to obtain a demand grid; taking the vertex of the demand grid as the center, The intersection of multiple circular areas with a radius of is the coverage boundary of the required grid, and the intersection area of ​​multiple coverage boundaries is the candidate machine nest area.

[0019] In a preferred solution, in step 4, the steps of calculating the Euclidean shortest path ESP between candidate nest points are as follows:

[0020] Step 4.1: Generate a straight line segment between two candidate points and check whether the line intersects with an obstacle. If not, the Euclidean distance between the two points is the shortest distance. If so, proceed to Step 4.2.

[0021] Step 4.2: Obstacles that intersect the straight line are considered direct obstacles, and other obstacles are indirect obstacles;

[0022] Step 4.3: Generate the convex hull of the starting point, end point and each direct obstacle;

[0023] Step 4.4: If any arc in the convex hull intersects another obstacle, replace the arc until there are no arcs that intersect with obstacles;

[0024] Step 4.5: Use Dijkstra's shortest path algorithm to calculate the shortest distance;

[0025] Step 4.6: Repeat the above steps to obtain the distance matrix of the candidate points of the machine nest.

[0026] In the preferred solution, in Step 5, the objective function of the UAV grid site selection model is:

[0027] (2)

[0028] Where, Indicates the center point of the demand grid A collection of represents the total cost of machine nest deployment; The cost of building a nest is an abstract construction difficulty determined by the actual situation. The influencing factors include topography, land cost, power supply network cost, and distance cost. A binary variable indicating whether the facility is chosen.

[0029] In the preferred solution, in Step 5, the constraints of the UAV grid site selection model are:

[0030] (3)

[0031] (4)

[0032] (5)

[0033] (6)

[0034] (7)

[0035] Where, Represents the set of candidate points for the machine nest; Candidate points for the machine nest Construction and development suitability score; To develop suitability for the theoretically highest construction; Representation and candidate points Distance less than Candidate points Collection of; A variable that represents whether the distance between the facility point and the demand point is less than the coverage radius; Indicates candidate points A binary variable of whether or not it was selected;

[0036] In the preferred solution, the constraints include: each demand point is covered by at least one facility, the value of the decision variable, the distance limit of the frog jump inspection, and the calculation of the nest construction cost, and constraint (2) indicates that the economic cost of covering all demands is the minimum; constraint (3) stipulates that each demand point is covered by at least one facility; constraint (4) indicates the value of the decision variable; constraint (5) indicates the binary variable The value of ; Constraint (6) represents the distance limit of frog leap inspection, and constraint (7) is used to calculate the cost of nest construction.

[0037] In the preferred solution, in Step 7, the site selection data obtained in Step 6 is imported into ArcGIS, and a buffer zone tool is established using GIS for coverage visualization. The inspection area is gridded according to the coverage range of the machine nest, and a UAV leapfrog flight network is constructed based on the Euclidean shortest path between the site selection points obtained in Step 4.

[0038] In the preferred solution, in the process of dividing the inspection area into grids according to the coverage of the machine nest, the importance of the power facilities and the inspection capabilities of the drone are also considered to ensure that the power facilities in each grid can be inspected in a timely and effective manner.

[0039] In a preferred solution, the UAV is a combination of one or more multi-rotor UAVs, fixed-wing UAVs or unmanned helicopters to meet the inspection needs of different power facilities.

[0040] The GIS-based grid deployment method for transmission, transformation and distribution UAVs provided by the present invention has the following beneficial effects:

[0041] 1. This invention solves the technical problems of UAV inspection technology in the field of power inspection, such as insufficient endurance, complex path planning, and inflexible deployment and scheduling;

[0042] 2. This invention introduces influencing factors to calculate the coverage radius, taking into account the margin left by factors such as incomplete charging, battery aging, low temperature, high-frequency operation, as well as the influence of terrain and wind speed, making the coverage radius calculation more accurate;

[0043] 3. The present invention combines GIS and convex hull technology to optimize ESP calculations, uses GIS and convex hull technology to efficiently construct the most effective graph, optimizes the solution of the Euclidean shortest path, and improves calculation efficiency and accuracy;

[0044] 4. The present invention establishes a site selection model that comprehensively considers multiple factors. The site selection model not only takes into account the minimization of economic costs, but also combines multiple factors such as the suitability of machine nest construction and the limit of frog-leaping inspection distance, making the site selection results more reasonable and practical;

[0045] 5. Through grid deployment and leapfrog flight network planning, this technology improves the efficiency and coverage of drone inspections, reduces inspection costs, and ensures the safe operation of power facilities;

[0046] 6. This invention solves the problem of insufficient UAV endurance through grid deployment and machine nest construction, and realizes autonomous charging and continuous operation of UAVs;

[0047] 7. Compared with traditional inspection work, the grid deployment and autonomous flight operation of drones in this invention reduce the skill requirements for inspection staff and improve the automation level of inspection operations;

[0048] 8. The present invention solves the problem of redundant investment and resource waste caused by the independent operation of power transmission, transformation and distribution businesses of power grid companies through joint inspection and integrated linkage inspection of transmission, transformation and distribution.

[0049] 9. The present invention reduces inspection costs and improves economic benefits by optimizing the location of the machine nest and reducing redundant investment;

[0050] 10. The implementation of this invention promotes the construction of smart grids and improves the digital management level and operation and maintenance efficiency of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0052] Figure 1 It is a technical flow chart of the present invention;

[0053] Figure 2 A fishnet map created for the study area of ​​this invention;

[0054] Figure 3 This is a grid-based machine nest candidate area judgment map of the present invention;

[0055] Figure 4 For example 2 of the present invention and There is an obstacle between 's picture;

[0056] Figure 5 This is a diagram of the machine nest site selection and grid division results of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The technical solutions of the present invention are further described below with reference to the accompanying drawings and embodiments:

[0058] Example 1

[0059] The GIS-based grid deployment method for transmission, transformation and distribution UAVs includes the following steps:

[0060] Step 1: Calculate the coverage radius based on the maximum flight time and geographical factors of the transmission, transformation and distribution joint inspection drone;

[0061] Step 2: Use the coverage radius and polygon intersection point set method (PIPS) to obtain the candidate area of ​​the machine nest;

[0062] Step 3: Consider the deployment environment constraints of the machine nest to determine the candidate machine nest points in each candidate area and obtain the candidate point set;

[0063] Step 4: Using GIS and convex hull, calculate the Euclidean shortest path ESP between the candidate points of the machine nest in Step 3;

[0064] Step 5: Combine ESP (Euclidean Shortest Path) and the weighted set coverage model to establish a grid-based UAV site selection model.

[0065] Step 6: Use Python to call Gurobi to solve the global optimal solution of the model and obtain the location of the machine nest;

[0066] Step 7: Use ArcGIS spatial analysis functions to perform coverage visualization and grid division, and build a UAV leapfrog flight network based on the Euclidean shortest path between the site selection points obtained in Step 4.

[0067] In this embodiment, in Step 1, the impact factor is introduced to calculate the nest coverage radius. , the specific expression is:

[0068] (1)

[0069] Where, To take into account the margin left by the drone due to incomplete charging, battery aging, low temperature, and high-frequency operation, the value is [0, 1]; and are terrain influence factor and wind speed influence factor, respectively, with values ​​ranging from [0, 1]; In a windless environment, the test speed is The longest flight time measured when flying at a constant speed until the remaining battery power is 0%; It is the inspection time of power equipment; The maximum flight time of the drone; divide the calculated distance by 2 to ensure that the drone can return to the starting nest after the inspection.

[0070] Furthermore, the specific method and steps of Step 2 are to use the ArcGIS fishnet tool to divide the study area into regular grids, and map all transmission and distribution transformation requirements to the corresponding grids to obtain the demand grid; with the vertex of the demand grid as the center, The intersection of multiple circular areas with a radius of is the coverage boundary of the required grid, and the intersection area of ​​multiple coverage boundaries is the candidate machine nest area.

[0071] Furthermore, in the step 4, the steps of calculating the Euclidean shortest path ESP between the candidate nest points are as follows:

[0072] Step 4.1: Generate a straight line segment between two candidate points and check whether the line intersects with an obstacle. If not, the Euclidean distance between the two points is the shortest distance. If so, proceed to Step 4.2.

[0073] Step 4.2: Obstacles that intersect the straight line are considered direct obstacles, and other obstacles are indirect obstacles;

[0074] Step 4.3: Generate the convex hull of the starting point, end point and each direct obstacle;

[0075] Step 4.4: If any arc in the convex hull intersects another obstacle, replace the arc until there are no arcs that intersect with obstacles;

[0076] Step 4.5: Use Dijkstra's shortest path algorithm to calculate the shortest distance;

[0077] Step 4.6: Repeat the above steps to obtain the distance matrix of the candidate points of the machine nest.

[0078] Furthermore, in Step 5, the objective function of the UAV grid site selection model is:

[0079] (2)

[0080] Where, Indicates the center point of the demand grid A collection of represents the total cost of machine nest deployment; The cost of building a nest is an abstract construction difficulty determined by the actual situation. The influencing factors include topography, land cost, power supply network cost, and distance cost. A binary variable indicating whether the facility is chosen.

[0081] Furthermore, in Step 5, the constraints of the UAV grid site selection model are:

[0082] (3)

[0083] (4)

[0084] (5)

[0085] (6)

[0086] (7)

[0087] Where, Represents the set of candidate points for the machine nest; Candidate points for the machine nest Construction and development suitability score; The theoretical maximum construction and development suitability is 100; Representation and candidate points Distance less than Candidate points Collection of; A variable that represents whether the distance between the facility point and the demand point is less than the coverage radius; Indicates candidate points A binary variable of whether or not it was selected;

[0088] Furthermore, the constraints include: each demand point is covered by at least one facility, the value of the decision variable, the distance limit of the frog jump inspection, and the calculation of the nest construction cost, and constraint (2) indicates that the economic cost of covering all demands is the minimum; constraint (3) stipulates that each demand point is covered by at least one facility; constraint (4) indicates the value of the decision variable; constraint (5) indicates the binary variable The value of ; Constraint (6) represents the distance limit of frog leap inspection, and constraint (7) is used to calculate the cost of nest construction.

[0089] Furthermore, in Step 7, the site selection data obtained in Step 6 is imported into ArcGIS, and a buffer zone tool is established using GIS for coverage visualization. The inspection area is gridded according to the coverage range of the machine nest, and a UAV leapfrog flight network is constructed based on the Euclidean shortest path between the site selection points obtained in Step 4.

[0090] Furthermore, in the process of dividing the inspection area into grids according to the coverage of the machine nest, the importance of the power facilities and the inspection capabilities of the drones are also considered to ensure that the power facilities in each grid can be inspected in a timely and effective manner.

[0091] Furthermore, the UAV is a combination of one or more multi-rotor UAVs, fixed-wing UAVs or unmanned helicopters to meet the inspection needs of different power facilities.

[0092] Example 2

[0093] In another preferred embodiment, based on the above embodiment 1, Figures 1 to 5 As shown in the figure, the GIS-based grid deployment method for transmission, transformation and distribution UAVs includes the following steps:

[0094] Step S1: Calculate the coverage radius based on the maximum flight time of the transmission, transformation and distribution joint inspection drone and the geographical factors of the study area. ;

[0095] Step S2: Using coverage radius The candidate area of ​​the machine nest is obtained by using the polygon intersection set method (PIPS);

[0096] Step S3: Consider the restrictions of the machine nest deployment environment to determine the machine nest candidate points in each candidate area and obtain the candidate point set ;

[0097] Step S4: UAV flights do not need to follow a pre-defined traffic network, but obstacles such as high-rise buildings and restricted flight zones may affect their flight paths. Utilizing GIS and convex hulls, the Euclidean shortest path (ESP) between candidate drone nests in step S3 is calculated, providing the necessary basis for the leapfrog distance constraint in the subsequent site selection model.

[0098] Step S5: combining ESP and weighted set coverage model to establish a UAV grid site selection model;

[0099] Step S6: Use Python to call Gurobi to solve the global optimal solution of the model and obtain the location of the machine nest;

[0100] Step S7: Overlay visualization and grid division, and according to the Euclidean shortest path between the site selection points obtained in step 4, obtain a feasible and efficient UAV leapfrog flight network;

[0101] In this embodiment, in step S1, the grid-based inspection mode of transmission and distribution has many inspection targets and relatively complex types, requiring high-frequency operation of drones to ensure that all inspection tasks are completed as required. This type of scenario is usually limited by the maximum flight time of drones. Therefore, the present invention introduces an influencing factor to calculate the nest coverage radius, which is specifically expressed as:

[0102] (1)

[0103] To ensure that the drone can return to the departure nest after the inspection is completed, this embodiment divides the calculated distance by 2.

[0104] Furthermore, in step S2, the study area is divided into regular grids using the ArcGIS fishnet tool, and all transmission and distribution transformation requirements are mapped to the corresponding grids to obtain a demand grid; with the vertex of the demand grid as the center, The intersection of multiple circular areas with a radius of is the coverage boundary of the demand grid. Facilities at any position on or within this boundary can cover the demand object. The intersection area of ​​multiple coverage boundaries is the candidate machine nest area.

[0105] Furthermore, in step S3, when selecting the site for the fixed hangar of the UAV, it is necessary to consider the restrictions on the hangar deployment environment. The ArcGIS clipping tool is used to delete the areas that are not suitable for station construction (lakes and rivers, traffic roads, no-fly zones, and densely populated areas) in the candidate area for the hangar determined in step S2, and obtain the candidate area for the hangar to be built. Then, in the candidate area for the hangar to be built, the expert's experience and knowledge are first used to roughly select the candidate points for the hangar in each area, and then the structural entropy weight method is used for fine selection. A certain number of candidate points for the hangar are selected in proportion, and the construction suitability scores of the candidate points are obtained. .

[0106] Furthermore, step S4 includes the following sub-steps:

[0107] Step S4.1, Euclidean Shortest Path (ESP) problem analysis;

[0108] The Euclidean shortest path problem between two locations is to find the middle point The problem of the number and location of is expressed as:

[0109] (8)

[0110] Where, Starting point The coordinates of For the end The coordinates of is the middle point The coordinates of is the number of intermediate points; and the line between two consecutive points in the path must not intersect any obstacles.

[0111] Step S4.2: Efficiently construct the most effective graph through the convex hull , optimize the solution of ESP;

[0112] In a GIS environment, obstacles in the study area are represented as polygons; the expression of obstacles is as follows:

[0113] (9)

[0114] Where, is the index of the obstacle, there are obstacles; For obstacles The vertex coordinates of Another factor to consider is the regional boundary. Generally, travel outside the regional boundary is prohibited. The expression of the research regional boundary is:

[0115] (10)

[0116] Where, is the vertex coordinate of the study area boundary, indivual.

[0117] Other symbols are defined as follows: is a set of obstacles, for The set of vertices in ,

[0118] (11)

[0119] is the set of all obstacle vertices and the starting and ending points, For the arc segment and the vertex Connected The set of vertices in , and for The index of the vertex in , From the vertex arrive distance.

[0120] The steps of computing the Euclidean shortest path (ESP) between candidate points are as follows, where: , ,and , The candidate point set of the machine nest obtained in step S3:

[0121] 1) Generate a straight line segment between two candidate points , check whether the straight line intersects with the obstacle. If there is no intersection, the Euclidean distance between the two points is the shortest distance; if there is any obstacle If they intersect, go to (12);

[0122] 2) Any obstacle that intersects the straight line is considered a direct obstacle; Other obstacles are considered indirect obstructions;

[0123] 3) Generate starting point ,end And the convex hull of each direct obstacle, the convex hull is an important concept in computational geometry, the convex hull is the The intersection of all convex sets of , or the smallest and unique convex polygon containing all points, the convex hull boundary contains the shortest path around the obstacle;

[0124] 4) If any arc in the convex hull intersects another obstacle, the arc will be replaced by another convex hull until no arc intersects with the obstacle. ;

[0125] 5) Use Dijkstra's shortest path algorithm to calculate the graph The shortest distance in

[0126] The objective function is:

[0127] (12)

[0128] Constraints:

[0129] (13)

[0130] (14)

[0131] (15)

[0132] (16)

[0133] The objective function (12) is to connect the start and end points and The total length of the line segment is minimized; constraints (13) and (14) stipulate that To the end The flow direction; Constraint (15) ensures the flow conservation at each intermediate vertex except the starting and ending points; Constraint (16) restricts the decision variables to binary;

[0134] 6) Repeat the above steps to calculate the Euclidean shortest path (ESP) between the candidate nest points and obtain the distance matrix of the candidate nest points .

[0135] Furthermore, in step S5, the UAV grid site selection model is as follows:

[0136] Objective function:

[0137] (2)

[0138] Constraints:

[0139] (3)

[0140] (4)

[0141] (5)

[0142] (6)

[0143] (7)

[0144] Furthermore, in step S7, the site selection data obtained in step S6 is imported into ArcGIS, and the buffer tool of GIS is used to visualize the coverage effect of the machine nest site selection results on the inspection needs; at the same time, based on the Euclidean shortest path between the site selection points obtained in step S4, a UAV leapfrog flight network for the study area is constructed.

[0145] Example 3

[0146] In another preferred embodiment, based on the above embodiments 1 and 2, a grid deployment method of transmission and distribution UAVs based on GIS is provided, such as Figure 1 As shown, the following steps are included:

[0147] Step S1: Calculate the coverage radius based on the maximum flight time of the transmission, transformation and distribution joint inspection drone and the geographical factors of the study area ;

[0148] The grid-based inspection model for transmission and distribution transformers has many inspection targets and relatively complex types, requiring high-frequency drone operations to ensure that all inspection tasks are completed as required. This type of scenario is usually limited by the maximum flight time of the drone, rather than the flight distance. In actual inspections, drone flight is affected by terrain and wind speed. Large altitude differences or high wind speeds in the inspection area will shorten the drone's flight distance in the same time, thereby reducing the coverage radius. The terrain and wind speed of the inspection area can be obtained through map service platforms and actual surveys. Therefore, this paper introduces an impact factor to calculate the nest coverage radius, specifically expressed as:

[0149] (1)

[0150] The coverage radius of the machine nest in this example is 4km.

[0151] Step S2: Using coverage radius The candidate area of ​​the machine nest is obtained by using the polygon intersection set method (PIPS).

[0152] Further preferably, step S2 includes the following sub-steps:

[0153] Step S2.1: The drone nest site selection problem belongs to the continuous space site selection problem. There are infinite candidate facility locations in the study area. The required objects are the continuously distributed transmission and distribution lines and substation areas. The PIPS method requires the coverage radius Significantly larger than the size of the demand object to achieve full coverage of the demand. Therefore, the study area is divided into a regular grid using the ArcGIS fishnet tool. The length of the fishnet is 1 km. The center of the fishnet is marked with a green dot, and the pink is the study area. All transmission and distribution transformation demands are mapped to the corresponding grid to obtain the demand grid, as shown in the following example: Figure 2 As shown in Table 1, the attribute values ​​of some demand grids include the demand grid number, the corresponding fishing net number, the total length of the transmission and distribution lines in the grid, and the XY coordinates of the center point of the demand grid:

[0154]

[0155] Step S2.2: The required object is the intersection area of ​​the polygon; take the polygon vertex as the center, The intersection of multiple circular areas with a radius of is the coverage boundary of the demand object. Facilities at any position on or within this boundary can cover the demand object. The intersection area of ​​multiple coverage boundaries is the candidate machine nest area. Figure 3 As shown, there are two demand grids and The process of generating candidate regions. and To cover the boundary, cover the boundary intersection area is the equivalent coverage area (candidate area).

[0156] Step S3: Consider the hangar deployment environment constraints to determine the candidate points of the hangar in each candidate area and obtain the candidate point set .

[0157] Step S3.1: When selecting a site for a fixed drone hangar, it is necessary to consider the hangar deployment environment restrictions, such as the hangar power supply network, operation and maintenance management, image transmission occlusion, land cost, safety and other key factors for site selection, and avoid areas that are not suitable for station construction, such as lakes and rivers, traffic roads, no-fly zones, densely populated areas, no network areas, and flammable and explosive hazardous areas; use the ArcGIS clipping tool to delete the areas that are not suitable for station construction on the candidate hangar area determined in step S3 to obtain the candidate area where the hangar can be built.

[0158] Step S3.2: The site selection of the machine nest is flexible. Fixed machine nests can be arranged in substations, power supply stations, and outdoor self-owned assets. Therefore, in the candidate areas where machine nests can be built, the expert experience and knowledge is first used to roughly select the machine nest candidate points in each area. Then, the structural entropy weight method is used for fine selection. The natural terrain conditions, distance conditions, communication conditions, and land costs are selected to establish a suitability evaluation index system, and the construction suitability score of each roughly selected candidate point is calculated. , select a certain number of candidate points for the machine nest according to the proportion. In practical applications, 1~2 points are usually selected for each area where the machine nest can be built, and the candidate point set is obtained. .

[0159] Step 4: Using GIS and convex hull, calculate the Euclidean shortest path (ESP) between the candidate nest points in step S3. UAV flights do not need to follow an established transportation network, but obstacles such as high-rise buildings, mountains, and flight-restricted areas may affect their flight paths. Therefore, calculating the Euclidean shortest path between candidate nest points provides the necessary basis for the leapfrog distance constraint in the subsequent nest site selection model.

[0160] Step S4.1, Euclidean shortest path (ESP) problem analysis;

[0161] The Euclidean shortest path problem between two locations is to find the middle point The problem of the number and location of is expressed as:

[0162] (8).

[0163] Step S4.2: Efficiently construct the most effective graph through the convex hull , optimize the solution of ESP;

[0164] In a GIS environment, obstacles in the study area are represented as polygons; the expression of obstacles is as follows:

[0165] (9)

[0166] Another factor to consider is the regional boundary. Generally, travel outside the regional boundary is prohibited. The expression of the study region boundary is:

[0167] (10).

[0168] Other symbols are defined as follows: is a set of obstacles, for The set of vertices in ,

[0169] (11).

[0170] The steps of computing the Euclidean shortest path (ESP) between candidate points are as follows, where: , ,and , It is the candidate point set of the machine nest obtained in step S3.

[0171] 1) Generate straight line segments , check whether the straight line intersects with the obstacle. If there is no intersection, the Euclidean distance between the two points is the shortest distance; if there is any obstacle If they intersect, go to (12);

[0172] 2) Any obstacle that intersects the straight line is considered a direct obstacle; Other obstacles are considered indirect obstructions;

[0173] 3) Generate starting point ,end And the convex hull of each direct obstacle, the convex hull is an important concept in computational geometry, the convex hull is the The intersection of all convex sets of , or the smallest and unique convex polygon containing all points, the convex hull boundary contains the shortest path around the obstacle;

[0174] 4) If any arc in the convex hull intersects another obstacle, the arc will be replaced by another convex hull until no arc intersects with the obstacle. ;

[0175] 5) Use Dijkstra's shortest path algorithm to calculate the graph The shortest distance in

[0176] The objective function is:

[0177] (12)

[0178] Constraints:

[0179] (13)

[0180] (14)

[0181] (15)

[0182] (16)

[0183] 6) Repeat the above steps to calculate the Euclidean shortest path (ESP) between the candidate nest points and obtain the distance matrix of the candidate nest points ;like Figure 4 As shown, there is an obstacle k between A and B, and the most effective graph is constructed using the convex hull. .

[0184] Step S5: combining ESP and cluster coverage model to establish a UAV grid site selection model;

[0185] The gridding of drones requires that drones can realize “leapfrog inspection” between nests, that is, “one machine, multiple sites, and multiple grids shared”, to improve the efficiency of drone inspections. Therefore, the deployment of drone nests should consider the distance limit of leapfrog inspection, that is, the maximum flight distance of the drone. In this case, the drone does not need to return, and the maximum flight distance of the drone is At the same time, when achieving full coverage of inspection targets, the economic cost is minimized. The UAV grid site selection model is as follows:

[0186] Objective function:

[0187] (2)

[0188] Constraints:

[0189] (3)

[0190] (4)

[0191] (5)

[0192] (6)

[0193] (7).

[0194] Step S6: Solve the nest location model, and convert the above data: the nest candidate point distance matrix Suitability of the construction of candidate sites for the machine nest , substitute it into the weighted set cover site selection model, use Python to call Gurobi to solve the global optimal solution of the model, and obtain the location of the machine nest site.

[0195] Step S7: Overlay visualization and grid division, and according to the Euclidean shortest path between the site selection points obtained in step 4, obtain a feasible and efficient UAV frog-flying network.

[0196] Import the site selection data obtained in step 6 into ArcGIS, and use GIS to create a buffer tool to visualize the coverage effect of the machine nest site selection results on the inspection needs, such as Figure 5 As shown, the yellow circle area is the coverage of the machine nest, and the gray area is the obstacle. Finally, according to the coverage of the machine nest, the inspection area is divided into grids based on the coverage of the machine nest to form a transmission and distribution inspection grid area with solid coverage of the machine nest. At the same time, according to the diagram between the site selection points obtained in step 5, , construct the UAV leapfrog flight network in the study area, such as Figure 5 Indicated by the orange line.

[0197] Example 4

[0198] In another preferred embodiment, based on the above embodiments 1 and 2, as Figures 1 to 5 As shown, this embodiment takes a certain power inspection area as the research object and specifically demonstrates the implementation process of the GIS-based transmission and distribution UAV grid deployment method.

[0199] Step S1: Calculate coverage radius

[0200] First, the coverage radius is calculated based on the maximum flight time of the transmission, transformation and distribution joint inspection drone and the geographical factors of the study area. It is assumed that the maximum flight time of the drone in a windless environment is 60 minutes, the flight speed is 30 kilometers per hour, and the average inspection time of power equipment is 10 minutes. Taking into account the effects of incomplete drone charging, battery aging, low temperature and high-frequency operation, the margin coefficient is set to 0.8. At the same time, the terrain of the study area is complex, with mountainous areas and hills. The terrain influence factor is 0.7 and the wind speed influence factor is 0.9 on average. According to the formula:

[0201] (17)

[0202] The calculated coverage radius R of the machine nest is about 4 kilometers.

[0203] Step S2: Obtaining the candidate area of ​​the machine nest

[0204] Using the ArcGIS Fishnet tool, the study area was divided into a 1 km × 1 km regular grid. All transmission and distribution transformer demands were mapped to the corresponding grid to obtain a demand grid. A circle with a radius of 4 km was drawn with the vertex of the demand grid as the center. The intersection of multiple circles was the candidate machine nest area. Figure 2 As shown in Figure 2, the generation process of some demand grids and their coverage boundaries is demonstrated.

[0205] Step S3: Determine the candidate points of the machine nest

[0206] Taking into account the environmental restrictions of hangar deployment, the ArcGIS clipping tool is used to delete areas that are not suitable for hangar construction, such as lakes and rivers, traffic roads, no-fly zones, and densely populated areas, to obtain candidate areas for hangar construction. Within the candidate areas for hangar construction, experts first conduct a rough selection of each area based on their experience, and then use the structural entropy weight method to fine-select each rough-selected point. Taking into account factors such as natural terrain conditions, distance conditions, communication conditions, and land costs, the construction suitability score of each point is calculated, and finally, candidate hangar points are selected in proportion to obtain a candidate point set.

[0207] Step S4: Calculate the Euclidean Shortest Path (ESP)

[0208] For path planning between candidate nest points, the influence of obstacles such as high-rise buildings and mountains is taken into account. Using GIS and convex hull technology, the ESP between candidate nest points is calculated in the following steps:

[0209] Generate a straight line segment between two candidate points and check whether it intersects with obstacles;

[0210] If they intersect, generate the convex hull of the starting point, end point, and direct obstacles;

[0211] Check whether the arc in the convex hull intersects with other obstacles. If so, replace it with a new convex hull until there are no intersecting arc segments.

[0212] Use Dijkstra algorithm to calculate the shortest path and repeat the above steps to obtain the distance matrix of the candidate points of the machine nest.

[0213] Step S5: Establishing a UAV grid site selection model

[0214] A grid-based drone site selection model was established by combining ESP and the weighted set coverage model. The objective function was to minimize the total cost of drone nest deployment while satisfying constraints such as each demand point being covered by at least one drone nest, reasonable decision variable values, and leapfrog inspection distance limits. The drone nest construction cost took into account factors such as topography, land use costs, power supply network costs, and distance costs.

[0215] Step S6: Solve the model

[0216] Use Python to call the Gurobi solver, input the distance matrix of the candidate machine nest points and the construction suitability score, solve the global optimal solution of the model, and obtain the location of the machine nest.

[0217] Step S7: Coverage visualization and meshing

[0218] The site selection results were imported into ArcGIS, and the buffer tool was used to visualize the coverage of the machine nest sites for inspection needs. The inspection area was gridded based on the coverage of the machine nests, forming a transmission and distribution inspection grid area with solid coverage of the machine nests. Simultaneously, a leapfrog drone flight network was constructed based on the Euclidean shortest path between the selected site points.

[0219] In the preferred solution, the specific method and steps of Step 2 are: using the ArcGIS fishnet tool to divide the study area into regular grids, and mapping all transmission and distribution transformation requirements to the corresponding grids to obtain a demand grid; taking the vertex of the demand grid as the center, The intersection of multiple circular regions with a radius of is the coverage boundary of the required grid, and the intersection of these coverage boundaries is the candidate machine nest area. With these settings, an algorithm automatically calculates and optimizes the location and number of candidate machine nest areas, ensuring comprehensive coverage and rational resource allocation. Subsequently, further screening is performed based on factors such as topography and weather to obtain the final proposed machine nest construction plan.

[0220] In the preferred solution, the constraints include: each demand point is covered by at least one facility, the value of the decision variable, the distance limit of the frog-leaping inspection, and the calculation of the nest construction cost, and constraint (2) indicates the minimum economic cost of covering all demands; constraint (3) stipulates that each demand point is covered by at least one facility; constraint (4) indicates the value of the decision variable; constraint (6) indicates the distance limit of the frog-leaping inspection, and constraint (7) is used to calculate the nest construction cost; the above settings are intended to ensure that the solution is both efficient and economical. Although constraint (5) is not explicitly mentioned, it is implicitly a restriction on the number or type of facilities to balance the coverage breadth and cost input, and work together to build the optimization model to promote the optimization of resource allocation.

[0221] In the preferred solution, in Step 7, the site selection data obtained in Step 6 is imported into ArcGIS, and the buffer tool is established using GIS for coverage visualization, and the inspection area is gridded according to the coverage range of the machine nest. At the same time, a UAV leapfrog flight network is constructed based on the Euclidean shortest path between the site selection points obtained in Step 4. The above settings can ensure that the UAV can efficiently jump between the site selection points when performing inspection tasks, reduce flight path overlap, and improve inspection efficiency. Finally, combined with the environmental assessment results in Step 4, the flight path within the grid is further optimized.

[0222] In the preferred solution, in the process of gridding the inspection area according to the coverage of the machine nest, the importance of the power facilities and the inspection capabilities of the drone are also taken into consideration to ensure that the power facilities in each grid can be inspected in a timely and effective manner; the above settings not only improve the inspection efficiency, but also enhance the safety of power grid operation; at the same time, the solution dynamically adjusts the grid division and flexibly responds to changes in the layout of power facilities, thereby ensuring the continuity and accuracy of the inspection work.

[0223] In the preferred solution, the drone is a combination of one or more multi-rotor drones, fixed-wing drones or unmanned helicopters to meet the inspection needs of different power facilities; the above settings can ensure that the drones can operate flexibly in complex environments, such as multi-rotor drones are suitable for narrow spaces, fixed-wing drones are suitable for long-distance and rapid inspections, and unmanned helicopters can fly stably at high altitudes or in complex terrains, comprehensively improving the efficiency and quality of power inspections.

[0224] In summary, the GIS-based grid deployment method for transmission and distribution UAVs proposed in the present invention solves the technical problems of insufficient endurance, complex path planning, and inflexible deployment and scheduling in the field of power inspection of UAV inspection technology. The present invention combines the geographic information system (GIS) with UAV inspection technology, and uses the powerful spatial analysis and data processing capabilities of GIS to provide a new perspective and method for the site selection and deployment of UAV nests. When calculating the coverage radius of the UAV nest, not only natural factors such as the maximum flight time of the UAV and terrain wind speed are considered, but also human factors such as incomplete charging, battery aging, low temperature, and high-frequency operation are introduced as influencing factors, making the calculation of the coverage radius more accurate and close to reality. The study area is divided into a regular grid through the polygon intersection set method (PIPS), and the transmission and distribution demand is mapped to the grid to determine the candidate nest area. This method is not widely used in the field of UAV inspection. The present invention calculates the Euclidean shortest path (ES) between the candidate nest points in the computer. P), the convex hull technology is used to efficiently construct the most effective graph, optimizing the ESP solution process. This method is highly novel in the field of UAV path planning; the UAV grid site selection model established by the present invention not only takes into account the minimization of economic costs, but also combines multiple factors such as the suitability of machine nest construction and frog-leaping inspection distance restrictions. This site selection model that comprehensively considers multiple factors is highly creative in the field of UAV inspection; through grid deployment and frog-leaping flight network planning, flexible jumping and sharing of UAVs between multiple machine nests are achieved, thereby improving the efficiency and coverage of UAV inspections; the implementation of the technical solution of the present invention not only solves the problems of insufficient endurance and high skill requirements of inspection staff in the process of UAV inspections, but also reduces inspection costs by optimizing machine nest site selection and reducing redundant investment, promotes the construction and development of smart grids, and provides intelligent and efficient inspection solutions for the power industry, promotes the deep integration of UAV technology and smart grids, and injects new vitality into the digital transformation of the power industry.

Claims

1. A GIS-based grid deployment method for transmission, transformation and distribution drones, characterized by: The method comprises the following steps: Step 1: Calculate the coverage radius based on the maximum flight time and geographical factors of the transmission, transformation and distribution joint inspection drone and the study area. , introducing the impact factor, the specific expression is: （1）； Where, To take into account the margin left by the drone due to incomplete charging, battery aging, low temperature, and high-frequency operation, the value is [0, 1]; and are terrain influence factor and wind speed influence factor, respectively, with values ​​ranging from [0, 1]; In a windless environment, the test speed is The longest flight time measured when flying at a constant speed until the remaining battery power is 0%; It is the inspection time of power equipment; The maximum flight time of the drone; divide the calculated distance by 2 to ensure that the drone can return to the starting nest after the inspection is completed; Step 2: Use the coverage radius and polygon intersection set method PIPS to obtain the candidate area of ​​the machine nest; Step 3: Consider the deployment environment constraints of the machine nest to determine the candidate machine nest points in each candidate area and obtain the candidate point set; Step 4: Using GIS and convex hull, calculate the Euclidean shortest path ESP between the candidate points of the machine nest in Step 3; Step 5: Combine ESP and weighted set coverage model to establish a UAV grid site selection model; Step 6: Use Python to call Gurobi to solve the global optimal solution of the model and obtain the location of the machine nest; Step 7: Use ArcGIS spatial analysis functions to perform coverage visualization and grid division, and build a UAV leapfrog flight network based on the Euclidean shortest path between the site selection points obtained in Step 4.

2. The GIS-based transmission, transformation and distribution UAV grid deployment method according to claim 1 is characterized in that: The specific method and steps of Step 2 are: using the fishnet tool of ArcGIS to divide the study area into regular grids, and mapping all transmission and distribution transformation demands to the corresponding grids to obtain the demand grid; Taking the demand grid vertex as the center, The intersection of multiple circular areas with a radius of is the coverage boundary of the required grid, and the intersection area of ​​multiple coverage boundaries is the candidate machine nest area.

3. The GIS-based transmission, transformation and distribution UAV grid deployment method according to claim 1 is characterized in that: In Step 4, the steps of calculating the Euclidean shortest path ESP between candidate points are as follows: Step 4.1: Generate a straight line segment between two candidate points and check whether the line intersects with an obstacle. If not, the Euclidean distance between the two points is the shortest distance. If so, proceed to Step 4.

2. Step 4.2: Obstacles that intersect the straight line are considered direct obstacles, and other obstacles are indirect obstacles; Step 4.3: Generate the convex hull of the starting point, end point and each direct obstacle; Step 4.4: If any arc in the convex hull intersects another obstacle, replace the arc until there are no arcs that intersect with obstacles; Step 4.5: Use Dijkstra's shortest path algorithm to calculate the shortest distance; Step 4.6: Repeat the above steps to obtain the distance matrix of the candidate points of the machine nest.

4. The GIS-based transmission, transformation and distribution UAV grid deployment method according to claim 1 is characterized in that: In Step 5, the objective function of the UAV grid site selection model is: （2）； Where, Indicates the center point of the demand grid A collection of represents the total cost of machine nest deployment; The cost of building a nest is an abstract construction difficulty determined by the actual situation. The influencing factors include topography, land cost, power supply network cost, and distance cost. A binary variable indicating whether the facility is chosen.

5. The GIS-based transmission, transformation and distribution UAV grid deployment method according to claim 4 is characterized in that: In Step 5, the constraints of the UAV grid site selection model are: （3）； （4）； （5）； （6）； （7）； Where, Represents the set of candidate points for the machine nest; Candidate points for the machine nest Construction and development suitability score; To develop suitability for the theoretically highest construction; Representation and candidate points Distance less than Candidate points Collection of; A variable that represents whether the distance between the facility point and the demand point is less than the coverage radius; Indicates candidate points A binary variable indicating whether or not it is selected.

6. The GIS-based transmission, transformation and distribution UAV grid deployment method according to claim 5 is characterized in that: The constraints include: each demand point is covered by at least one facility, the value of the decision variable, the distance limit of the frog jump inspection, and the calculation of the nest construction cost. Constraint (2) indicates that the economic cost of covering all demands is the minimum; constraint (3) stipulates that each demand point is covered by at least one facility; constraint (4) indicates the value of the decision variable; constraint (5) indicates the binary variable The value of ; Constraint (6) represents the distance limit of frog leap inspection, and constraint (7) is used to calculate the cost of nest construction.

7. The GIS-based grid deployment method for transmission, transformation and distribution UAVs according to claim 6 is characterized by: In Step 7, the site selection data obtained in Step 6 is imported into ArcGIS, and a buffer zone tool is used to create coverage visualization in GIS. The inspection area is gridded according to the coverage of the machine nest, and a UAV leapfrog flight network is constructed based on the Euclidean shortest path between the site selection points obtained in Step 4.

8. The GIS-based grid deployment method for transmission, transformation and distribution UAVs according to claim 7 is characterized by: In the process of dividing the inspection area into grids according to the coverage of the machine nest, the importance of the power facilities and the inspection capabilities of the drones are also considered to ensure that the power facilities in each grid can be inspected in a timely and effective manner.

9. The GIS-based grid deployment method for transmission, transformation and distribution UAVs according to claim 8 is characterized by: The UAV is a combination of one or more multi-rotor UAVs, fixed-wing UAVs or unmanned helicopters to meet the inspection needs of different power facilities.

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