An electromagnetic environment adaptive path planning sampling method and related device

Abstract

The application discloses an electromagnetic environment self-adaptive path planning sampling method and related equipment, space is filled through Voronoi division, the space distribution sparseness degree and the local change nonlinear degree around each potential moving path are calculated, and the moving weight of each path is obtained. Finally, the Dijkstra path finding algorithm is used for moving path planning, and a sampling moving path which can consider global characteristics and local characteristics of the electromagnetic environment is obtained. The application considers global characteristics and local characteristics of the electromagnetic environment in the selection of the sampling moving path, and reasonably plans the moving path. In the limited sampling points, the shortest path is moved, the information of the area which needs to be focused on is not missed, there is no monitoring blind area in the global range, and the sampling efficiency and accuracy in the complex electromagnetic environment are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of path planning, and particularly relates to an electromagnetic environment adaptive path planning sampling method and related equipment. BACKGROUND

[0002] With the rapid development of wireless technology, the influence of electromagnetic environment on human daily production and life is increasingly prominent, and the development of information communication equipment cannot avoid the research on complex electromagnetic environment. Therefore, it is necessary to monitor the electromagnetic environment in key areas in real time and master the dynamic changes of the electromagnetic environment. According to engineering needs, radiation source data can be obtained through vehicle-mounted patrol measurement, fixed-point measurement, unmanned aerial vehicle sampling and simulation. However, whether simulation or actual measurement, in a wide geographical space, due to the characteristics of high development cost, small number of sample machines, complex system structure, physical condition limitation and the like, it is a very complex and time-consuming work.

[0003] The accuracy of the information contained in the electromagnetic environment reconstruction depends on both the number of sampling points and the specific location of the sampling points. Obviously, the more sampling points, the more accurately the electromagnetic field information of the research area can be expressed, but the work burden also increases. Therefore, how to balance the work efficiency and accuracy is a problem worth considering.

[0004] Most of the current researches are to determine the measurement point position in a fixed or random grid at one time or to use a sequential experimental design method to determine the sampling position, so as to completely cover the research area, without considering the cost limitation and physical limitation of the sampling model in actual measurement. Determining the measurement point position in a fixed or random grid at one time faces the problems of low monitoring efficiency, limited monitoring area range, large monitoring signal interference, and only considering the global coverage ability of the monitoring area, lacking of attention to the local rapid variability of the electromagnetic environment. Obviously, an area with a dramatic change in electromagnetic field distribution needs more sampling points than an area with a uniform electromagnetic field distribution. Although the sequential experimental design sampling method takes into account the global coverage ability and the local rapid variability of the electromagnetic environment, the long sampling moving path does not take into account the actual physical limitation. At present, there is an urgent need to research adaptive path planning sampling algorithm adaptive sampling method to solve the problems of low monitoring efficiency, many blind areas in complex electromagnetic space, to solve the problems of difficult to simultaneously consider global characteristics and local characteristics, and to improve the sampling efficiency and accuracy in complex electromagnetic environment. SUMMARY

[0005] The purpose of the present application is to provide an electromagnetic environment adaptive path planning sampling method and related equipment to solve the technical problems of the existing adaptive path planning sampling algorithm that is difficult to simultaneously consider global characteristics and local characteristics, and low sampling efficiency and accuracy in complex electromagnetic environment.

[0006] To achieve the above object, the present application adopts the following technical solutions:

[0007] An electromagnetic environment adaptive path planning sampling method, comprising the following steps:

[0008] Collecting at least two sampling point information from the starting position along the single path;

[0009] Using the Voronoi diagram method to subdivide the existing sampling points in the monitoring area, constructing a bounded Voronoi diagram, and extracting the vertices and edges of each Voronoi cell;

[0010] Selecting the vertex of the Voronoi cell closest to the last sampling point as the starting point of the sampling movement, and selecting the vertex of the Voronoi cell as the end point of the sampling movement using the Maxmin criterion;

[0011] Calculating the area of the region formed by each edge and the sampling point closest to the edge as a global distribution sparsity evaluation index, and assigning the global distribution sparsity evaluation index as a first weight to each corresponding edge, selecting appropriate neighbor points around the center point of each edge corresponding Voronoi cell, estimating the nonlinearity degree near the center point through the difference between the response and the local linearity between the center point and the neighbor points, and assigning the nonlinearity degree as a second weight to each corresponding edge; based on the first weight and the second weight, obtaining the comprehensive weight of each edge of the Voronoi cell;

[0012] According to the comprehensive weight of each edge, using Dijkstra path finding algorithm to select the movement route with the maximum comprehensive weight between the sampling starting point and the end point along the edge as the optimal sampling movement path;

[0013] Along the optimal sampling movement path, sampling according to the set sampling step, stopping iteration when the sampling accuracy is met or the upper limit of the number of sampling points is reached, otherwise continuing the path planning adaptive sampling iteration.

[0014] Further, before calculating the area of the region formed by each edge and the sampling point closest to the edge, the projection distance of each Voronoi cell center point to each edge of the cell is calculated.

[0015] Further, the calculation formula of the projection distance of each Voronoi cell center point to each edge of the cell is:

[0016]

[0017] In the formula, is the projection distance, is the center point is the projection point position on the edge .

[0018] Further, the first weight is:

[0019]

[0020] wherein, represents the first weight, represents the area of the region, and N represents the total number of edges, represents the total area.

[0021] Further, the evaluation index for the selection of the adjacent point has cohesion and adhesion, and the cohesion function and the adhesion function are aggregated to obtain an evaluation function:

[0022]

[0023] wherein, is the evaluation function, is the direct ratio, is the cohesion function, and the adjacent point with the highest evaluation function value is selected;

[0024] is defined as:

[0025]

[0026] When =1, it represents that all the adjacent points form a direct axis shape; when =0, it represents that all the adjacent points coincide at the same position, represents the adhesion function.

[0027] Further, the second weight is:

[0028]

[0029] wherein, is the non-linear degree;

[0030]

[0031] is to fit a hyperplane covering the center point and the adjacent point to estimate the gradient of the center point corresponding to each edge.

[0032] Further, the comprehensive weight is:

[0033] =

[0034] wherein, is the comprehensive weight, is a first weight, is a second weight.

[0035] In a second aspect, an electromagnetic environment adaptive path planning sampling system comprises a collection module, an extraction module, an endpoint determination module, a weight calculation module, a path selection module, and a path planning module, wherein:

[0036] The collection module is configured to collect information of at least two sampling points from a starting position along a single path.

[0037] The extraction module is configured to divide the monitoring area using the Voronoi diagram method, construct a bounded Voronoi diagram, and extract the vertices and edges of each Voronoi cell.

[0038] The endpoint determination module is configured to select the vertex of the Voronoi cell closest to the last sampling point as the starting point of the sampling movement, and select the vertex of the Voronoi cell as the endpoint of the sampling movement using the Maxmin criterion.

[0039] The weight calculation module is configured to calculate the area of each edge and the region formed by the sampling point closest to the edge as a global distribution sparsity evaluation index, and assign the global distribution sparsity evaluation index as a first weight to each corresponding edge. Suitable neighbor points are selected around the center point of each edge corresponding Voronoi cell, and the nonlinearity degree near the center point is estimated by the difference between the response and the local linearity between the center point and the neighbor points, and the nonlinearity degree is assigned as a second weight to each corresponding edge. Based on the first weight and the second weight, the comprehensive weight of each edge of the Voronoi cell is obtained.

[0040] The path selection module is configured to select the movement route with the maximum comprehensive weight between the sampling starting point and the endpoint along the edge as the optimal sampling movement path using the Dijkstra path finding algorithm according to the comprehensive weight of each edge.

[0041] The path planning module is configured to sample along the optimal sampling movement path according to the set sampling step size, stop iteration when the sampling accuracy is met or the upper limit of the number of sampling points is reached, or continue path planning adaptive sampling iteration otherwise.

[0042] In a third aspect, a terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0043] In a fourth aspect, a computer readable storage medium stores a computer program, wherein the computer program is executed by a processor to implement the steps of the above method.

[0044] Compared with the prior art, the present application has the following beneficial technical effects:

[0045] The present application provides an electromagnetic environment adaptive path planning sampling method, which fills space through Voronoi subdivision, calculates the spatial distribution sparseness and local change nonlinearity around each potential moving path to obtain the moving weight of each path. Finally, the Dijkstra path finding algorithm is used to plan the moving path to obtain a sampling moving path that takes into account the global and local characteristics of the electromagnetic environment. The present application takes into account the global and local characteristics of the electromagnetic environment and reasonably plans the moving path in the selection of the sampling moving path. In the limited sampling points, the shortest path is moved without missing the information of the area that needs to be focused on, and there is no monitoring blind area in the global range, which improves the sampling efficiency and accuracy in the complex electromagnetic environment. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A flow chart of an electromagnetic environment adaptive path planning sampling method in the embodiment of the present application;

[0047] Figure 2 A detailed flow chart of an electromagnetic environment adaptive path planning sampling method in the embodiment of the present application;

[0048] Figure 3 A schematic diagram for global distribution sparseness calculation;

[0049] Figure 4 A half-space dipole antenna field strength distribution diagram;

[0050] Figure 5 A half-space dipole antenna field strength change diagram;

[0051] Figure 6 A path planning sampling diagram;

[0052] Figure 7 A diagram of the relationship between the number of sampling points and the length of the moving path. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0054] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0055] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe the preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, the first preset range can also be referred to as the second preset range, and similarly, the second preset range can also be referred to as the first preset range without departing from the scope of the embodiments of the present application.

[0057] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (a stated condition or event)" can be interpreted to mean "when determined" or "in response to determining" or "when detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)".

[0058] The present application will be described in further detail below with reference to the accompanying drawings:

[0059] As shown in Figure 1 , an electromagnetic environment adaptive path planning sampling method comprises the following steps:

[0060] Step 1: Collect at least two sampling point information from the starting position of the single path:

[0061] Specifically, the sampling point information includes coordinate position information and sampling point electric field intensity, the parameters required by the adaptive path planning sampling algorithm are initialized, at least two initial data samples are collected from the starting position of the single path, the coordinate position information of the sampling node is , and the sampling point electric field intensity is .

[0062] Step 2: The existing sampling points are divided in the monitoring area by using the Voronoi diagram method, a bounded Voronoi diagram is constructed, and the vertex and edge of each Voronoi unit are extracted;

[0063] The vertex of each Voronoi unit is , and the edge is .

[0064] Step three, the vertex of the Voronoi unit closest to the last sampling point is selected as the starting point of the sampling movement, and the Maxmin criterion is used to select the vertex of the Voronoi unit as the end point of the sampling movement;

[0065] Step four, the area of the region formed by each edge and the sampling point closest to the edge is calculated as the global distribution sparsity evaluation index, and the global distribution sparsity evaluation index is given as the first weight to each corresponding edge. In the vicinity of the center point of each edge corresponding Voronoi unit, appropriate neighbor points are selected, and the nonlinearity degree near the center point is estimated by the difference between the response and the local linearity between the center point and the neighbor points, which is given as the second weight to each corresponding edge. Based on the first weight and the second weight, the comprehensive weight of each edge of the Voronoi unit is obtained;

[0066] Step five, according to the comprehensive weight of each edge, the Dijkstra path finding algorithm is used to select the movement route with the maximum comprehensive weight between the sampling starting point and the end point along the edge as the optimal sampling movement path;

[0067] Step six, along the optimal sampling movement path, sampling is carried out according to the set sampling step. When the sampling accuracy is met or the upper limit of the number of sampling points is reached, the iteration is stopped, otherwise the path planning adaptive sampling iteration is continued.

[0068] In an embodiment of the present application, as shown in Figure 2 , an electromagnetic environment adaptive path planning sampling method is provided, comprising the following steps:

[0069] Step S1, the parameters required by the adaptive path planning sampling algorithm are initialized, and at least two initial data samples are collected from the starting position of the set single path, the sampling node coordinate position information is , and the measured electric field intensity amplitude is .

[0070] Wherein the spatial sampling point set , represents the sampling points existing in the space after k sampling iterations, represents the electric field intensity of each sampling point after k sampling iterations.

[0071] Step S2, the existing sampling points are divided in the monitoring area by using the Voronoi diagram method, as shown in Figure 3As shown. The Voronoi diagram method is a discrete partitioning of space, characterized by any position within a polygon being closest to a sample point of that polygon, farthest from samples within adjacent polygons, and each polygon containing exactly one sample point. The vertex positions and edges of each Voronoi cell are extracted, with vertices represented by... It means, while using express.

[0072] Step S3: Select the distance from the last sampling point The vertex of the most recent Voronoi unit This serves as the starting point for the sampling movement. The sampling point and the cell vertex satisfy the following:

[0073]

[0074] Step S4, according to the Maxmin criterion Selecting the vertices of the Voronoi element This serves as the sampling endpoint for the path planning sampling algorithm. The sampling endpoint satisfies:

[0075]

[0076] in For the last sampling point, Represents vertices The nonlinear variation exponent of the n Voronoi units in which it is located.

[0077] Step S5: Calculate the center point of each Voronoi cell. Projected onto each edge of the cell Projection distance .in Calculated by the following formula:

[0078]

[0079] Center point On the side The position of the projection point on the surface.

[0080] Step S6, calculate each edge The area of ​​the region formed by the sampling point closest to the edge is used as a global sparsity evaluation index, where a larger area indicates a sparser global distribution in the current region, and this area is used as a weight. Assign it to each corresponding edge.

[0081] The area is calculated using the following formula:

[0082]

[0083]

[0084]

[0085] where and denote the start and end positions of an edge, denotes the sampling point closest to the edge, denotes the number of sampling points closest to the edge, and N denotes the total number of edges.

[0086] Step S7, the electric field strength at the center point of the Voronoi cell corresponding to each edge is calculated to measure the local nonlinearity of the radiation field.

[0087] First, select the appropriate neighboring points for the sampling point , and T is the number of neighboring points. The ideal neighborhood needs to meet two requirements: cohesion and adhesion.

[0088] Cohesion refers to the neighboring points being close enough to the reference point, which can be measured by calculating the average distance of all neighboring points to the reference point :

[0089]

[0090] Adhesion refers to the neighboring points being far enough apart from each other, which can be measured by calculating the average of the minimum distances between neighboring points:

[0091]

[0092] The cohesion and adhesion functions can be represented by a function, i.e., aggregating the two functions into a function :

[0093]

[0094] where represents the orthodrome ratio, and the most ideal neighborhood distribution is when all neighboring points form an orthodrome. The orthodrome ratio is defined as:

[0095]

[0096] When =1, it means that all neighboring points form an orthodrome; when =0, it means that all neighboring points coincide at the same position. The function value of different points is calculated, and the neighboring points with higher function value are selected.​

[0097] Step S8, fit a coverage sampling point Estimating the hyperplane with neighboring points gradient , The relationship between the gradient and the hyperplane is as follows:

[0098]

[0099] in

[0100]

[0101] Step S9, through sampling points and neighboring points Response and sampling points The difference between local linear responses is used to estimate the sampling points. The degree of nonlinearity in the vicinity .

[0102] Defined as:

[0103]

[0104] Will Used to evaluate the degree of nonlinear change on both sides of each edge; nonlinear weights are used. It means that, among them:

[0105]

[0106] Step S10, calculate each edge Comprehensive evaluation index .

[0107] =

[0108] The above formula and These represent the degree of local nonlinearity and the degree of global sparsity, respectively. and This represents the weights of the local nonlinear evaluation index and the global sparse evaluation index.

[0109] Step S11: Each edge is used as a sampling path for the path planning algorithm. Then, the comprehensive evaluation index of each edge is used as the path weight of that edge. Finally, the Dijkstraz pathfinding algorithm is used to traverse the path between the sampling start point and the end point along the edge. Choose the movement route with the highest total weight.

[0110] Step S12, along the optimal sampling moving path, sampling according to the set sampling step length. When the sampling accuracy is met or the upper limit of the number of sampling points is reached, stop iteration, otherwise continue path planning adaptive sampling iteration.

[0111] The simulation results are as follows:

[0112] Simulation parameters:

[0113]

[0114] The half-space dipole antenna field strength distribution is as shown in Figure 4 The half-space dipole antenna field strength distribution is as shown in Figure 5 The path planning algorithm sampling step length is 15, and the sampling point number is 1100, and the path planning sampling is as shown in Figure 6

[0115] The relationship between the sampling point number and the moving path length is as shown in Figure 7

[0116]

[0117] And The reconstruction parameters of the TVCS are represented by The reconstruction error of the TVCS is represented by using the path planning algorithm sampling step length of 15 and the sampling point of 1500. The reconstruction error of the TVCS is represented by using the random sampling sampling point of 1500. The reconstruction error of the TVCS is represented by using the LOLA-Voronoi sampling sampling point of 1500.

[0118] Compared with the existing electromagnetic environment sampling algorithm, the advantages are that the overall moving path of the sensor can consider both the global characteristics and the local characteristics of the electromagnetic environment, and can plan a more reasonable and shorter sampling path that can be applied to large-scale scenes, and is more in line with actual sampling requirements.

[0119] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as follows: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a hardware and software combined implementation, which can be collectively referred to as "circuit", "module" or "platform" here.

[0120] ​​In still another embodiment of the present application, an electromagnetic environment adaptive path planning sampling system is also provided, comprising a collection module, an extraction module, an endpoint determination module, a weight calculation module, a path selection module and a path planning module, wherein:

[0121] The collection module is configured to collect information of at least two sampling points from the starting position along the single path;

[0122] The extraction module is configured to divide the monitoring area by using the Voronoi diagram method on the existing sampling points, construct a bounded Voronoi diagram, and extract the vertex and edge of each Voronoi unit;

[0123] The endpoint determination module is configured to select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and select the vertex of the Voronoi unit as the endpoint of the sampling movement by using the Maxmin criterion;

[0124] The weight calculation module is configured to calculate the area of each edge and the region formed by the sampling point closest to the edge as a global distribution sparsity evaluation index, and assign the global distribution sparsity evaluation index as a first weight to each corresponding edge, select appropriate neighbor points around the center point of the Voronoi unit corresponding to each edge, estimate the nonlinearity degree near the center point by the difference between the response and the local linearity between the center point and the neighbor points, and assign the nonlinearity degree as a second weight to each corresponding edge; and obtain the comprehensive weight of the edge of each Voronoi unit based on the first weight and the second weight.

[0125] The path selection module is configured to select the movement route with the maximum comprehensive weight between the sampling starting point and the endpoint along the edge as the optimal sampling movement path by using the Dijkstra path finding algorithm according to the comprehensive weight of each edge.

[0126] The path planning module is configured to sample along the optimal sampling movement path according to the set sampling step size. The iteration is stopped when the sampling accuracy is met or the upper limit of the number of sampling points is reached, otherwise the path planning adaptive sampling iteration is continued.

[0127] The terminal device provided in an embodiment of the present application comprises a processor, a memory and a computer program stored in the memory and executable on the processor. The processor implements the steps in each of the method embodiments when executing the computer program. Alternatively, the processor implements the functions of each module / unit in each of the device embodiments when executing the computer program.

[0128] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application.

[0129] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device can include, but is not limited to, a processor and a memory.

[0130] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0131] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory.

[0132] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc.

[0133] The above merely illustrates the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the present application.

Claims

1. A sampling method for adaptive path planning in electromagnetic environments, characterized in that, Includes the following steps: Collect information from at least two sampling points starting from the beginning of a single path; The Voronoi diagram method is used to divide the existing sampling points in the monitoring area, construct a bounded Voronoi diagram, and extract the vertices and edges of each Voronoi unit. The vertex of the Voronoi element closest to the last sampling point is selected as the starting point of the sampling movement, and the vertex of the Voronoi element is selected as the sampling endpoint using the Maxmin criterion, as shown in the formula: in For the last sampling point, Represents vertices The nonlinear variation exponent of the n Voronoi units in which it is located; Calculate the area of ​​the region formed by each edge and the center point closest to that edge, and use it as the global distribution sparsity evaluation index. Assign the global distribution sparsity evaluation index as the first weight to each corresponding edge. Select suitable neighboring points around the center point of the Voronoi unit corresponding to each edge. Estimate the degree of nonlinearity near the center point by the difference between the response between the center point and the neighboring points and the local linearity, and assign it as the second weight to each corresponding edge. Based on the first weight and the second weight, obtain the comprehensive weight of the edge of each Voronoi unit. The first weight is: In the formula, Indicates the first weight. Let N represent the area of ​​the region, and N represent the total number of edges. Indicates the total area; The second weight is: In the formula, The degree of nonlinearity; To fit a coverage center point and neighboring points Hyperplane estimation of the center point corresponding to each edge gradient; Based on the combined weight of each edge, the Dijkstra pathfinding algorithm is used to select the moving route with the largest combined weight between the sampling start point and the end point as the optimal sampling moving path. Sampling is performed along the optimal sampling path according to the set sampling step size. The iteration stops when the sampling accuracy is met or the upper limit of the number of sampling points is reached; otherwise, the path planning adaptive sampling iteration continues.

2. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that, Before calculating the area of ​​the region formed by each edge and the sampling point closest to that edge, the projection distance from the center point of each Voronoi cell to each edge of that cell is first calculated.

3. The electromagnetic environment adaptive path planning sampling method according to claim 2, characterized in that, The formula for calculating the projection distance from the center point of each Voronoi element to each edge of that element is as follows: In the formula, For the projection distance, Center point On the side The position of the projection point on the surface.

4. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that, The evaluation indicators for selecting neighboring points include cohesion and adhesion. The cohesion function and adhesion function are combined to obtain the evaluation function: In the formula, For the evaluation function, It is a positive axis ratio. For the cohesion function, the neighboring nodes are selected based on their higher evaluation function values. The cohesion function is: The adhesion function is: The definition of is: when When =1, it means that all neighboring points form a positive axis shape; when When =0, it means that all neighboring points coincide at the same position. This represents the adhesion force function.

5. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that, The overall weight is: = In the formula, For comprehensive weighting, As the second weight, It is the first weight.

6. An electromagnetic environment adaptive path planning sampling system, characterized in that, It includes a data acquisition module, an extraction module, an endpoint determination module, a weight calculation module, a path selection module, and a path planning module, among which: Acquisition module: Used to collect information from at least two sampling points starting from the beginning of a single path; Extraction module: Used to divide the existing sampling points in the monitoring area using the Voronoi diagram method, construct a bounded Voronoi diagram, and extract the vertices and edges of each Voronoi unit; Endpoint determination module: Used to select the vertex of the Voronoi cell closest to the last sampling point as the starting point of the sampling movement, and the Maxmin criterion is used to select the vertex of the Voronoi cell as the sampling endpoint, as shown in the formula: in For the last sampling point, Represents vertices The nonlinear variation exponent of the n Voronoi cells in which it is located, where ϕ represents the vertex of the Voronoi cell selected as the sampling endpoint; The weight calculation module calculates the area of ​​the region formed by each edge and the nearest center point of that edge, using it as a global sparsity evaluation index. This global sparsity evaluation index is then assigned as the first weight to each corresponding edge. Suitable neighboring points are selected around the center point of the Voronoi unit corresponding to each edge. The degree of nonlinearity near the center point is estimated by the difference between the response between the center point and its neighbors and the local linearity, and this second weight is assigned as the second weight to each corresponding edge. Based on the first and second weights, the comprehensive weight of the edges of each Voronoi unit is obtained. The first weight is: In the formula, Indicates the first weight. Let N represent the area of ​​the region enclosed by each edge and its corresponding center point, and let N represent the total number of edges. This represents the total area of ​​the entire monitoring area; The second weight is: In the formula, To indicate the degree of nonlinearity, This indicates the number of center points corresponding to each edge. Let i be the i-th center point corresponding to each edge. This represents the m-th center point within the entire monitoring area; To fit a coverage center point and neighboring points Hyperplane estimation of the center point corresponding to each edge gradient; Path selection module: Used to select the optimal sampling path by using Dijkstra's pathfinding algorithm based on the comprehensive weight of each edge, along the edge between the sampling start point and the end point. Path planning module: It moves along the optimal sampling path and samples according to the set sampling step size. It stops iterating when the sampling accuracy is met or the upper limit of the number of sampling points is reached; otherwise, it continues to perform path planning adaptive sampling iteration.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-5.

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