Electromagnetic environment adaptive path planning sampling method and related equipment

Abstract

The invention discloses an electromagnetic environment adaptive path planning sampling method and related equipment, and the method comprises the steps: carrying out the space filling through Voronoi subdivision, calculating the spatial distribution sparse degree and local change nonlinear degree around each potential moving path, and obtaining the moving weight of each path; finally, a Dijkstra routing algorithm is used for planning a moving path, and a sampling moving path capable of considering the global characteristics and the local characteristics of the electromagnetic environment is obtained. The global characteristics and the local characteristics of the electromagnetic environment are considered in the aspect of selection of the sampling moving path, and the moving path is reasonably planned; the shortest path is moved under the limited sampling points, information of areas needing to be focused is not missed, no monitoring blind area exists in the global range, and the sampling efficiency and precision under the complex electromagnetic environment are improved.

Description

Background Art

[0002] With the rapid development of radio technology, the impact of electromagnetic environment on human daily production and life has become increasingly prominent, and the development of information and communication equipment cannot avoid the study of complex electromagnetic environment. The comprehensive control and efficient use of electromagnetic space is also a major strategic need of the country. Various types of radio equipment are emerging in an endless stream, and electromagnetic safety issues are gradually being taken seriously. Therefore, it is very necessary to monitor the electromagnetic environment in key areas in real time and grasp the dynamic changes of the electromagnetic environment. According to engineering needs, radiation source data can be obtained through vehicle-mounted patrol, fixed-point measurement, drone sampling and simulation. However, whether it is simulation or actual measurement, in a wide geographical space, it is limited by its high development cost, small number of prototypes, complex system structure, physical conditions and other characteristics. It is a very complicated and time-consuming task.

[0003] The accuracy of the information contained in the electromagnetic environment reconstruction depends not only on the number of sampling points, but also on the specific location of the sampling points. Obviously, the more sampling points there are, the more accurate the electromagnetic field information of the study area can be expressed, but the workload also increases. Therefore, how to balance work efficiency and accuracy is a question worth thinking about. Most of the current research still determines the measurement point locations in a fixed or random grid at one time or uses a sequential experimental design method to determine the sampling locations so that they completely cover the study area, without considering the cost and physical limitations of the sampling model in actual measurements. Determining the measurement point locations in a fixed or random grid at one time faces the problems of low monitoring efficiency, limited monitoring area, large monitoring signal interference, and only considering the global coverage capability of the monitoring area, and lacking attention to the local agility of the electromagnetic environment. Obviously, an area with drastic changes in electromagnetic field distribution requires more sampling points than an area with uniform electromagnetic field distribution. Although the sequential experimental design sampling method takes into account both global coverage capability and local agility of the electromagnetic environment, the sampling moving path is too long and does not take into account actual physical limitations. At present, it is urgent to study the adaptive path planning sampling algorithm and adaptive sampling method to address the problems of low efficiency and many blind spots in complex electromagnetic space monitoring, solve the problem of difficulty in taking into account both global and local characteristics at the same time, and improve the sampling efficiency and accuracy in complex electromagnetic environments. Summary of the invention

[0004] The purpose of the present invention is to provide an electromagnetic environment adaptive path planning sampling method and related equipment to solve the technical problems that the existing adaptive path planning sampling algorithm is difficult to take into account both global and local characteristics at the same time, and has low sampling efficiency and accuracy in complex electromagnetic environments.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: An electromagnetic environment adaptive path planning sampling method comprises the following steps: Collect information of at least two sampling points along the starting position 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; Select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; The area of ​​the region formed by each edge and the sampling point closest to the edge is calculated as the global distribution sparse evaluation index, and the global distribution sparse evaluation index is assigned to each corresponding edge as the first weight, and appropriate neighboring points are selected around the center point of the Voronoi unit corresponding to each edge, and the nonlinear degree near the center point is estimated by the difference between the response between the center point and the neighboring points and the local linearity, and the second weight is assigned to each corresponding edge; based on the first weight and the second weight, the comprehensive weight of the edge of each Voronoi unit is obtained; According to the comprehensive weight of each edge, the Dijkstra path finding algorithm is used to select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Along the optimal sampling moving path, sampling is performed according to the set sampling step size. When the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, the iteration is stopped. Otherwise, the path planning adaptive sampling iteration is continued.

[0006] Furthermore, 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 unit center point to each edge of the unit is first calculated.

[0007] Furthermore, the calculation formula for the projection distance of each Voronoi unit center point to each edge of the unit is:

[0008] In the formula, is the projection distance, Center point On the side The projection point position on .

[0009] Furthermore, the first weight is:

[0010] In the formula, represents the first weight, represents the area of ​​the region, N represents the total number of edges, Indicates the total area.

[0011] Furthermore, the evaluation indexes for neighbor point selection include cohesion and adhesion, and the cohesion function and the adhesion function are aggregated to obtain the evaluation function:

[0012] In the formula, is the evaluation function, is the positive axis ratio, is the cohesion function, and the points with high ranking of evaluation function values ​​are selected as neighboring points; is defined as:

[0013] when =1, it means that all neighboring points form a positive axis; when =0, it means that all neighboring points overlap at the same position. represents the adhesion function.

[0014] Furthermore, the second weight is:

[0015] In the formula, is the degree of nonlinearity;

[0016] To fit a coverage center point and neighboring points The hyperplane estimates the center point corresponding to each edge gradient.

[0017] Furthermore, the comprehensive weight is: =

[0018] In the formula, is the comprehensive weight, is the first weight, is the second weight.

[0019] In a second aspect, an electromagnetic environment adaptive path planning sampling system includes a collection module, an extraction module, an endpoint determination module, a weight calculation module, a path selection module and a path planning module, wherein: Collection module: used to collect information of at least two sampling points starting from the starting position of a single path; Extraction module: used to use the Voronoi diagram method 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; Endpoint determination module: used to select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; Weight calculation module: used to calculate the area of ​​the region formed by each edge and the sampling point closest to the edge as the global distribution sparse evaluation index, and assign the global distribution sparse evaluation index as the first weight to each corresponding edge, select appropriate neighboring points around the center point of the Voronoi unit corresponding to each edge, estimate the 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 to each corresponding edge as the second weight; based on the first weight and the second weight, obtain the comprehensive weight of the edge of each Voronoi unit; Path selection module: used to use the Dijkstra path-finding algorithm according to the comprehensive weight of each edge, and select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Path planning module: used to move along the optimal sampling path, sample according to the set sampling step size, and stop iteration when the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, otherwise continue the path planning adaptive sampling iteration.

[0020] In a third aspect, a terminal device includes 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.

[0021] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides an electromagnetic environment adaptive path planning sampling method, which performs space filling through Voronoi subdivision, calculates the spatial distribution sparsity and local variation nonlinearity around each potential moving path, and obtains the moving weight of each path. Finally, the Dijkstra path finding algorithm is used to plan the moving path, and a sampling moving path that can take into account both the global and local characteristics of the electromagnetic environment is obtained. The present invention not only takes into account both the global and local characteristics of the electromagnetic environment in the selection of the sampling moving path, but also reasonably plans the moving path. The shortest path is moved under limited sampling points, and the information of the area that needs to be focused on is not missed, and there is no monitoring blind spot in the global range, which improves the sampling efficiency and accuracy in a complex electromagnetic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an electromagnetic environment adaptive path planning sampling method in an embodiment of the present invention; Figure 2 A detailed flow chart of an electromagnetic environment adaptive path planning sampling method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of global distribution sparsity calculation; Figure 4 is the field strength distribution diagram of half-space dipole antenna; Figure 5 This is the field strength variation diagram of the half-space dipole antenna; Figure 6 Sampling graph for path planning; Figure 7 This is a graph showing the relationship between the number of sampling points and the length of the moving path. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0027] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0028] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0029] The present invention is further described in detail below in conjunction with the accompanying drawings: like Figure 1 As shown, a method for sampling an electromagnetic environment adaptive path planning includes the following steps: Step 1: Collect information of at least two sampling points along the starting position of a single path: Specifically, the sampling point information includes the coordinate position information and the electric field strength of the sampling point. The parameters required for the adaptive path planning sampling algorithm are initialized. At least two initial data samples are collected along the starting position of a single path. The sampling node coordinate position information is , the electric field strength at the sampling point is .

[0030] Step 2: Use the Voronoi diagram method 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 vertices of each Voronoi cell are , edge .

[0031] Step 3: Select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; Step 4, calculate the area of ​​the region formed by each edge and the sampling point closest to the edge as the global distribution sparse evaluation index, and assign the global distribution sparse evaluation index as the first weight to each corresponding edge, select appropriate neighboring points around the center point of the Voronoi unit corresponding to each edge, and 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 to each corresponding edge as the second weight; based on the first weight and the second weight, obtain the comprehensive weight of the edge of each Voronoi unit; Step 5: According to the comprehensive weight of each edge, the Dijkstra path finding algorithm is used to select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Step 6: Sampling is performed along the optimal sampling moving path according to the set sampling step length. The iteration is stopped when the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, otherwise the path planning adaptive sampling iteration is continued.

[0032] In one embodiment of the present invention, Figure 2 As shown, a method for sampling an electromagnetic environment adaptive path planning is provided, comprising the following steps: Step S1, initialize the parameters required by the adaptive path planning sampling algorithm, collect at least two initial data samples along the starting position of the set single path, and the sampling node coordinate position information is and the measured electric field strength amplitude .

[0033] The spatial sampling point set , represents the sampling points in the space after k sampling iterations, Represents the electric field intensity at each sampling point after k sampling iterations.

[0034] Step S2, using the Voronoi diagram method to divide the existing sampling points in the monitoring area, such as Figure 3 The Voronoi diagram is a discrete partitioning of space. Its characteristics are that any position in a polygon is closest to the sample point of the polygon, far away from the sample point in the adjacent polygon, and each polygon contains and only contains one sample point. Extract the vertex position and edge of each Voronoi unit, and use Indicates that the side express.

[0035] Step S3, select the distance from the last sampling point The vertex of the nearest Voronoi cell As the starting point of the sampling movement. The sampling point and the unit vertex satisfy:

[0036] Step S4, according to the Maxmin criterion Select the vertices of the Voronoi cell As the sampling end point of the path planning sampling algorithm. The sampling end point satisfies:

[0037] in is the last sampling point, Represents a vertex The nonlinear change index of the n Voronoi cells.

[0038] Step S5, calculate the center point of each Voronoi cell Projected onto each edge of the unit Projection distance .in Calculated by the following formula:

[0039] Center point On the side The projection point position on .

[0040] Step S6, calculate each edge The area of ​​the region formed by the sampling point closest to the edge is used as the global distribution sparse evaluation index. The larger the area, the sparser the global distribution of the current region is, and it is used as the weight Assign to each corresponding edge.

[0041] The area is calculated by the following formula:

[0042]

[0043]

[0044] in and Indicates the starting and ending positions of the edge, represents the sampling point closest to the edge, represents the number of sampling points closest to the edge, and N represents the total number of edges.

[0045] Step S7: at the center point of the Voronoi cell corresponding to each edge The electric field strength The gradient of is calculated to measure the local nonlinearity of the radiation field.

[0046] First, for the sampling point Choose the right neighbor , T is the number of neighboring points. An ideal neighborhood needs to meet two requirements: cohesion and adhesion.

[0047] Cohesion means that the neighboring points should be close enough to the reference point, which can be achieved by calculating the To reference point Measured by the average distance:

[0048] Adhesion means that the distance between neighboring points should be far enough apart from each other, which can be measured by calculating the average of the minimum distance between neighboring points:

[0049] The two functions of cohesion and adhesion are expressed as one function, that is, the two functions are aggregated into a function :

[0050] in Represents the positive axis ratio. The most ideal neighbor point distribution is when all neighbor points form a positive axis shape. The positive axis ratio is defined as:

[0051] when =1, it means that all neighboring points form a positive axis; when =0, it means that all adjacent points overlap at the same position. Function value, select the point with higher function value as the neighboring point.

[0052] Step S8: Fitting a coverage sampling point and the hyperplane of the neighboring points to estimate Gradient , The relationship between the gradient and the hyperplane is:

[0053] in

[0054] Step S9, through the sampling point and neighboring points The response and sampling points between The difference between the local linear responses is used to estimate the sampling point The degree of nonlinearity near .

[0055] Defined as:

[0056] Will Used to evaluate the degree of nonlinear changes on both sides of each edge, and the nonlinear weight is represents, where:

[0057] Step S10, calculate each edge Comprehensive evaluation index .

[0058] =

[0059] The above and Represent the degree of local nonlinearity and global sparsity respectively, where and Represents the weight of the local nonlinear evaluation index and the global sparse evaluation index.

[0060] Step S11, each edge is used as a sampling path for the path planning algorithm, and then the comprehensive evaluation index of each edge is used as the path weight of the edge, and then the Dijkstraz path finding algorithm is used to find the path between the sampling start point and the end point along the edge. Select a moving route with the largest total weight.

[0061] Step S12: sampling along the optimal sampling moving path according to the set sampling step length. The iteration is stopped when the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, otherwise the path planning adaptive sampling iteration is continued.

[0062] The following are the simulation results: Simulation parameters:

[0063] The field strength distribution of the half-space dipole antenna is as follows: Figure 4 As shown, the field strength of the half-space dipole antenna varies as follows Figure 5 As shown, the sampling step of the path planning algorithm is 15, and the number of sampling points is 1100. The path planning sampling obtained is as follows Figure 6 As shown; Compare the random sampling, LOL-Voronoi, path planning step size 15, path planning step size 5, and the sampling moving path. The relationship between the number of sampling points and the moving path length is shown in the figure below. Figure 7 shown.

[0064]

[0065] and represents the reconstruction parameters of TVCS, It means that the path planning algorithm is used with a sampling step of 15 and sampling points of 1500, and TVCS is used to reconstruct the error. It indicates that the random sampling points are 1500 and the TVCS is used to reconstruct the error. It means that the sampling points are 1500 using LOLA-Voronoi sampling and the error is reconstructed using TVCS.

[0066] Compared with the existing electromagnetic environment sampling algorithms, the advantages are that the overall moving path of the sensor can take into account both the global and local characteristics of the electromagnetic environment, and can be planned more reasonably and shorter, which can be suitable for sampling in large-scale scenes and is more in line with actual sampling needs.

[0067] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0068] In yet another embodiment of the present invention, there is also provided an electromagnetic environment adaptive path planning sampling system, 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: Collection module: used to collect information of at least two sampling points starting from the starting position of a single path; Extraction module: used to use the Voronoi diagram method 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; Endpoint determination module: used to select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; Weight calculation module: used to calculate the area of ​​the region formed by each edge and the sampling point closest to the edge as the global distribution sparse evaluation index, and assign the global distribution sparse evaluation index as the first weight to each corresponding edge, select appropriate neighboring points around the center point of the Voronoi unit corresponding to each edge, estimate the 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 to each corresponding edge as the second weight; based on the first weight and the second weight, obtain the comprehensive weight of the edge of each Voronoi unit; Path selection module: used to use the Dijkstra path-finding algorithm according to the comprehensive weight of each edge, and select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Path planning module: used 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 preset upper limit of the number of sampling points is reached, otherwise the path planning adaptive sampling iteration is continued.

[0069] A terminal device provided in one embodiment of the present invention comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0070] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0071] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0072] The processor can be a central processing unit (CPU), or 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.

[0073] The memory may be used to store the computer program and / or module, and the processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0074] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0075] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An electromagnetic environment adaptive path planning sampling method, characterized in that: The following steps are involved: Collect information of at least two sampling points along the starting position 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; Select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; The area of ​​the region formed by each edge and the sampling point closest to the edge is calculated as the global distribution sparse evaluation index, and the global distribution sparse evaluation index is assigned to each corresponding edge as the first weight, and appropriate neighboring points are selected around the center point of the Voronoi unit corresponding to each edge, and the nonlinear degree near the center point is estimated by the difference between the response between the center point and the neighboring points and the local linearity, and the second weight is assigned to each corresponding edge; based on the first weight and the second weight, the comprehensive weight of the edge of each Voronoi unit is obtained; According to the comprehensive weight of each edge, the Dijkstra path finding algorithm is used to select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Along the optimal sampling moving path, sampling is performed according to the set sampling step size. When the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, the iteration is stopped. Otherwise, the path planning adaptive sampling iteration is continued.

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 the edge, the projection distance of each Voronoi unit center point to each edge of the unit is first calculated.

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

4. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that: The first weight is: In the formula, represents the first weight, represents the area of ​​the region, N represents the total number of edges, Indicates the total area.

5. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that: The evaluation indexes for neighbor point selection include cohesion and adhesion. The cohesion function and adhesion function are aggregated to obtain the evaluation function: In the formula, is the evaluation function, is the positive axis ratio, is the cohesion function, and the points with high ranking of evaluation function values ​​are selected as neighboring points; is defined as: when =1, it means that all neighboring points form a positive axis; when =0, it means that all neighboring points overlap at the same position. represents the adhesion function.

6. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that: The second weight is: In the formula, is the degree of nonlinearity; To fit a coverage center point and neighboring points The hyperplane estimates the center point corresponding to each edge gradient.

7. The electromagnetic environment adaptive path planning sampling method according to claim 1, characterized in that: The comprehensive weight is: = In the formula, is the comprehensive weight, is the first weight, is the second weight.

8. An electromagnetic environment adaptive path planning sampling system, characterized in that: It includes a collection module, an extraction module, an endpoint determination module, a weight calculation module, a path selection module and a path planning module, wherein: Collection module: used to collect information of at least two sampling points starting from the starting position of a single path; Extraction module: used to use the Voronoi diagram method 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; Endpoint determination module: used to select the vertex of the Voronoi unit closest to the last sampling point as the starting point of the sampling movement, and use the Maxmin criterion to select the vertex of the Voronoi unit as the sampling end point; Weight calculation module: used to calculate the area of ​​the region formed by each edge and the sampling point closest to the edge as the global distribution sparse evaluation index, and assign the global distribution sparse evaluation index as the first weight to each corresponding edge, select appropriate neighboring points around the center point of the Voronoi unit corresponding to each edge, estimate the 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 to each corresponding edge as the second weight; based on the first weight and the second weight, obtain the comprehensive weight of the edge of each Voronoi unit; Path selection module: used to use the Dijkstra path-finding algorithm according to the comprehensive weight of each edge, and select the moving route with the largest comprehensive weight along the edge between the sampling starting point and the end point as the optimal sampling moving path; Path planning module: used to move along the optimal sampling path, sample according to the set sampling step size, and stop iteration when the sampling accuracy is met or the preset upper limit of the number of sampling points is reached, otherwise continue the path planning adaptive sampling iteration.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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