A path planning method, apparatus, electronic device, and storage medium

Abstract

This invention discloses a path planning method, apparatus, electronic device, and storage medium. The method includes: acquiring terrain data of a target area; standardizing the terrain surface based on the terrain data to obtain a terrain height matrix of the target area; interpolating and expanding the terrain height matrix to obtain a terrain numerical surface matrix; determining the terrain height extrema points based on the terrain numerical surface matrix to obtain basic path data; performing energy allocation on the ground numerical surface matrix based on the path basic data to obtain an element edge condition matrix; acquiring a preset endpoint and using the target planning point as the starting point; performing path planning on the terrain numerical surface matrix based on the preset endpoint and starting point, using the element edge condition matrix as constraints, to obtain the next planning point as the target planning point; returning to the step of acquiring the preset endpoint and using the target planning point as the starting point, until path planning is completed. This invention can perform path planning efficiently and can be widely applied in the field of data processing technology.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a path planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] Promoting the transformation of mines towards green and intelligent operations aligns with the needs of the times, and establishing a mine environmental industrial data local area network (LAN) is the foundation for building a smart mine. However, the mining production environment is harsh, and with the continuous advancement of working faces, the traditional fixed base station networking model, with its long construction cycle and high operation and maintenance costs, is becoming increasingly unacceptable to production enterprises. Existing solutions using mobile base stations to build industrial data LANs rely on manual labor, resulting in high work intensity, poor working conditions, and accident risks. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, this invention proposes a path planning method, apparatus, electronic device, and storage medium capable of efficient path planning.

[0004] On one hand, embodiments of the present invention provide a path planning method, including:

[0005] Acquire terrain data for the target area, perform terrain surface normalization based on the terrain data, and obtain the terrain height matrix of the target area;

[0006] The terrain height matrix is ​​interpolated and extended to obtain the terrain numerical surface matrix;

[0007] The extreme points of terrain elevation are determined based on the terrain numerical surface matrix to obtain the basic path data;

[0008] Energy allocation is performed on the ground numerical surface matrix based on the path data to obtain the element edge condition matrix;

[0009] Obtain the preset endpoint and use the target planning point as the starting point;

[0010] Based on the preset endpoint and starting point, path planning is performed on the terrain numerical surface matrix with the element edge condition matrix as the constraint condition, and the next planning point is obtained as the target planning point.

[0011] Return to the step of obtaining the preset destination and using the target planning point as the starting point, until the path planning is completed.

[0012] Optionally, the terrain data includes the length, width, and height data of the target area; the terrain surface is normalized based on the terrain data to obtain the terrain height matrix of the target area, including:

[0013] The target area is gridded based on a preset spacing;

[0014] The horizontal coordinate of the grid is determined based on the length data, and the vertical coordinate of the grid is determined based on the width data;

[0015] Based on the grid's horizontal and vertical coordinates, and combined with the preset spacing, the coordinates of the intersection points of each grid are determined. Based on the intersection point coordinates, the corresponding height data is extracted from the terrain data as the grid intersection point value, thus obtaining the terrain height matrix of the target area.

[0016] Optionally, the terrain height matrix is ​​interpolated and extended to obtain a terrain numerical surface matrix, including:

[0017] The per-unit height values ​​are obtained from the terrain height matrix, and then the insertion matrix is ​​determined by combining them with a preset first weighting coefficient; the expression for the insertion matrix is:

[0018]

[0019] In the formula, H represents the insertion matrix; m represents the number of permametric height values ​​in the column direction of the terrain height matrix; n represents the number of permametric height values ​​in the row direction of the terrain height matrix; ω xy Z represents the first weighting coefficient; xy This represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix;

[0020] The terrain height matrix is ​​interpolated and extended using the interpolation matrix to obtain the terrain numerical surface matrix.

[0021] Optionally, the method further includes:

[0022] Based on the insertion matrix, determine the element-related functions and initial values ​​of the interpolation relationships.

[0023] Based on the values ​​of the straight-line distances between elements in the terrain height matrix, combined with the element correlation functions, the initial values ​​of the interpolation relationship, and the preset interpolation attenuation coefficient, the first weighting coefficient is obtained;

[0024] The expression for the first weighting coefficient is:

[0025]

[0026] In the formula, ρ0 represents the initial value of the interpolation relationship; ρ(h) represents the element correlation function; a represents the interpolation attenuation coefficient; and h represents the straight-line distance between elements in the terrain height matrix.

[0027] Optionally, the method further includes:

[0028] The element correlation function of the insertion matrix is ​​determined based on the per-unit height value and the straight-line distance between elements in the terrain height matrix, combined with the preset element relationship correction coefficient.

[0029] The expression for the element-related function is:

[0030]

[0031] In the formula, k represents the element relationship correction coefficient.

[0032] Optionally, the path base data includes the path start point and path points, and the path points include the path midpoint and path end point; energy allocation is performed on the ground numerical surface matrix based on the path base data to obtain the element edge condition matrix, including:

[0033] Based on path-related data, determine the energy function;

[0034] The energy function is subjected to exponential accumulation to obtain the energy partition function;

[0035] Based on the energy partition function and the path starting point, the first edge condition function is obtained; and based on the energy partition function and the path point, the second edge condition function is obtained.

[0036] The first and second edge condition functions are combined to obtain the element edge condition matrix.

[0037] Optionally, based on path-based data, the energy function is determined, including:

[0038] Based on path-based data, an energy function is determined by combining preset coefficients; among which, the preset coefficients include a second weighting coefficient, a first element bias coefficient, and a second element bias coefficient.

[0039] The expression for the energy function is:

[0040]

[0041] In the formula, ε(s,HI|θ) represents the energy function; m′ represents the number of path starting points in the path base data; n′ represents the number of path points in the path base data; ω′ xy Indicates the second weighting coefficient; s j HI represents the j-th path starting point in the basic path data. i P represents the starting point of the i-th path in the basic path data; j Q represents the paranoia coefficient of the first element; i This represents the paranoia coefficient of the second element.

[0042] Optionally, path planning is performed on the terrain numerical surface matrix using the element edge condition matrix as a constraint, including:

[0043] The target plane is determined by taking the positive height direction of the target area as the first direction and the column direction of the terrain numerical surface matrix as the second direction.

[0044] The target plane is divided into grids, and then the element edge condition matrix is ​​used as a constraint. The planning points are optimized along the row direction of the terrain numerical surface matrix using a biomimetic algorithm heuristic function.

[0045] Optionally, in path planning, the method also includes:

[0046] When the terrain data of the target area is updated, the terrain data of the target area is reacquired, and then the process of performing terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area is repeated until the path planning is completed.

[0047] On the other hand, embodiments of the present invention provide a path planning device, comprising:

[0048] The first module is used to acquire terrain data of the target area, perform terrain surface normalization based on the terrain data, and obtain the terrain height matrix of the target area.

[0049] The second module is used to interpolate and extend the terrain height matrix to obtain the terrain numerical surface matrix.

[0050] The third module is used to determine the extreme points of terrain height based on the terrain numerical surface matrix and obtain the basic path data.

[0051] The fourth module is used to perform energy allocation on the ground numerical surface matrix based on the path baseline data to obtain the element edge condition matrix;

[0052] The fifth module is used to obtain the preset endpoint and use the target planning point as the starting point;

[0053] The sixth module is used to perform path planning on the terrain numerical surface matrix based on the preset endpoint and starting point, with the element edge condition matrix as the constraint, and to obtain the next planning point as the target planning point.

[0054] The seventh module is used to return to the fifth module until the path planning is complete.

[0055] Optionally, the terrain data includes the length, width, and height data of the target area. The first module is specifically used for:

[0056] The target area is gridded based on a preset spacing;

[0057] The horizontal coordinate of the grid is determined based on the length data, and the vertical coordinate of the grid is determined based on the width data;

[0058] Based on the grid's horizontal and vertical coordinates, and combined with the preset spacing, the coordinates of the intersection points of each grid are determined. Based on the intersection point coordinates, the corresponding height data is extracted from the terrain data as the grid intersection point value, thus obtaining the terrain height matrix of the target area.

[0059] Optionally, the second module is specifically used for:

[0060] The per-unit height values ​​are obtained from the terrain height matrix, and then the insertion matrix is ​​determined by combining them with a preset first weighting coefficient; the expression for the insertion matrix is:

[0061]

[0062] In the formula, H represents the insertion matrix; m represents the number of permametric height values ​​in the column direction of the terrain height matrix; n represents the number of permametric height values ​​in the row direction of the terrain height matrix; ω xy Z represents the first weighting coefficient; xy This represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix;

[0063] The terrain height matrix is ​​interpolated and extended using the interpolation matrix to obtain the terrain numerical surface matrix.

[0064] Optionally, the device further includes:

[0065] The eighth module is used to determine the element-related functions and initial values ​​of the interpolation relationships based on the interpolation matrix.

[0066] The ninth module is used to obtain the first weighting coefficient based on the straight-line distance between elements in the terrain height matrix, combined with the element correlation function, the initial value of the interpolation relationship, and the preset interpolation attenuation coefficient.

[0067] The expression for the first weighting coefficient is:

[0068]

[0069] In the formula, ρ0 represents the initial value of the interpolation relationship; ρ(h) represents the element correlation function; a represents the interpolation attenuation coefficient; and h represents the straight-line distance between elements in the terrain height matrix.

[0070] Optionally, the device further includes:

[0071] The tenth module is used to determine the element correlation function of the insertion matrix based on the per-unit height value and the straight-line distance between elements in the terrain height matrix, combined with the preset element relationship correction coefficient;

[0072] The expression for the element-related function is:

[0073]

[0074] In the formula, k represents the element relationship correction coefficient.

[0075] Optionally, the basic path data includes the path start point and path points, and the path points include path intermediate points and path end points; the fourth module is specifically used for:

[0076] Based on path-related data, determine the energy function;

[0077] The energy function is subjected to exponential accumulation to obtain the energy partition function;

[0078] Based on the energy partition function and the path starting point, the first edge condition function is obtained; and based on the energy partition function and the path point, the second edge condition function is obtained.

[0079] The first and second edge condition functions are combined to obtain the element edge condition matrix.

[0080] Optionally, the fourth module includes a first unit for determining the energy function based on path-based data. Specifically, the first unit is used for:

[0081] Based on path-based data, an energy function is determined by combining preset coefficients; among which, the preset coefficients include a second weighting coefficient, a first element bias coefficient, and a second element bias coefficient.

[0082] The expression for the energy function is:

[0083]

[0084] In the formula, ε(s,HI|θ) represents the energy function; m′ represents the number of path starting points in the path base data; n′ represents the number of path points in the path base data; ω′ xy Indicates the second weighting coefficient; s j HI represents the j-th path starting point in the basic path data. i P represents the starting point of the i-th path in the basic path data; j Q represents the paranoia coefficient of the first element; i This represents the paranoia coefficient of the second element.

[0085] Optionally, the fifth module is specifically used for:

[0086] The target plane is determined by taking the positive height direction of the target area as the first direction and the column direction of the terrain numerical surface matrix as the second direction.

[0087] The target plane is divided into grids, and then the element edge condition matrix is ​​used as a constraint. The planning points are optimized along the row direction of the terrain numerical surface matrix using a biomimetic algorithm heuristic function.

[0088] Optionally, the device further includes:

[0089] The eleventh module is used to reacquire the terrain data of the target area when the terrain data of the target area is updated, and then return to the first module to perform terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area, until the path planning is completed.

[0090] On the other hand, embodiments of the present invention provide an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above-described path planning method.

[0091] On the other hand, embodiments of the present invention provide a computer storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described path planning method.

[0092] This invention first acquires terrain data of the target area, then performs terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area. This normalization eliminates the influence of orders of magnitude and units, facilitating subsequent path analysis of the target area. The terrain height matrix is ​​then interpolated to obtain a terrain numerical surface matrix. This interpolation enhances the understanding of hidden patterns in the terrain data, enabling more accurate analysis of data characteristics and trends, and providing a data foundation for subsequent path planning. The terrain height extrema are determined based on the terrain numerical surface matrix to obtain basic path data. Energy allocation is performed on the ground numerical surface matrix based on the basic path data to obtain an element edge condition matrix. A preset endpoint is obtained, and the target planning point is used as the starting point. Based on the preset endpoint and starting point, path planning is performed on the terrain numerical surface matrix using the element edge condition matrix as constraints to obtain the next planning point as the target planning point. The process of obtaining the preset endpoint and using the target planning point as the starting point is repeated until path planning is complete. This invention uses the element edge condition matrix as a constraint and iteratively updates the planning points, enabling real-time adaptation to terrain information and efficient and accurate path planning. Attached Figure Description

[0093] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0094] Figure 1 This is a schematic diagram of an implementation environment for path planning provided in an embodiment of the present invention;

[0095] Figure 2This is a flowchart illustrating a path planning method provided in an embodiment of the present invention;

[0096] Figure 3 This is a schematic diagram of the process for per-unit scaling of terrain surfaces provided in an embodiment of the present invention;

[0097] Figure 4 This is a schematic diagram of the process for obtaining the element edge condition matrix according to an embodiment of the present invention;

[0098] Figure 5 This is a schematic diagram of the path planning process on a terrain numerical surface matrix provided in an embodiment of the present invention;

[0099] Figure 6 This is a flowchart illustrating the path planning method for autonomous driving of mobile base stations provided in an embodiment of the present invention.

[0100] Figure 7 This is a schematic diagram of an example of autonomous driving path planning for a mobile base station in a mine, provided in an embodiment of the present invention.

[0101] Figure 8 This is a schematic diagram of the structure of a path planning device provided in an embodiment of the present invention;

[0102] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0103] Figure 10 A computer system architecture block diagram suitable for implementing electronic devices according to embodiments of the present invention is provided. Detailed Implementation

[0104] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0105] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100," "second / S200," etc., in the specification, claims, and the aforementioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0106] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0107] It is understood that the path planning method provided in this embodiment of the invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet computer, laptop computer, or desktop computer, but it is not limited to these.

[0108] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0109] Server 101 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0110] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0111] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0112] Exemplary based on Figure 1 The implementation environment shown in this embodiment of the invention provides a path planning method. The following description uses the application of this path planning method in server 101 as an example. It can be understood that this path planning method can also be applied to terminal 102.

[0113] Reference Figure 2 , Figure 2 This is a flowchart illustrating a path planning method applied to a server, provided in an embodiment of the present invention. The execution subject of this path planning method can be any of the aforementioned computer devices. (Refer to...) Figure 2 The method includes the following steps:

[0114] S100. Obtain the terrain data of the target area, perform terrain surface normalization based on the terrain data, and obtain the terrain height matrix of the target area;

[0115] It should be noted that in some embodiments, the terrain data includes the length, width, and height data of the target area; such as... Figure 3 As shown, the terrain surface is normalized based on the terrain data to obtain the terrain height matrix of the target area, which may include: S101, performing gridding processing on the target area based on a preset spacing; S102, determining the grid horizontal coordinate based on the length data, and determining the grid vertical coordinate based on the width data; S103, determining the intersection coordinates of each grid point based on the grid horizontal and vertical coordinates and the preset spacing, and extracting the corresponding height data from the terrain data based on the intersection coordinates as the grid intersection value to obtain the terrain height matrix of the target area.

[0116] In some specific embodiments, terrain surface normalization can be achieved by reading a topographic map of the mining area, setting an equally spaced coordinate grid, where the horizontal and vertical coordinates of the grid correspond to the length and width of the selected mining area, respectively, and the values ​​of each intersection point in the grid are taken as the terrain height values ​​at the corresponding locations, thereby obtaining the terrain height matrix of the mining area. The grid density can be manually set according to the required accuracy control.

[0117] S200. Interpolate and extend the terrain height matrix to obtain the terrain numerical surface matrix;

[0118] It should be noted that in some embodiments, interpolating and expanding the terrain height matrix to obtain a terrain numerical surface matrix may include: obtaining the per-unit height values ​​at various points in the terrain height matrix, and then determining the interpolation matrix by combining it with a preset first weighting coefficient; wherein, the expression for the interpolation matrix is:

[0119]

[0120] In the formula, H represents the insertion matrix; m represents the number of permametric height values ​​in the column direction of the terrain height matrix; n represents the number of permametric height values ​​in the row direction of the terrain height matrix; ω xy Z represents the first weighting coefficient; xy This represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix;

[0121] The terrain height matrix is ​​interpolated and extended using the interpolation matrix to obtain the terrain numerical surface matrix.

[0122] In some embodiments, the method may further include: determining the element correlation function and initial values ​​of the interpolation relationship of the insertion matrix based on the insertion matrix;

[0123] Based on the values ​​of the straight-line distances between elements in the terrain height matrix, combined with the element correlation functions, the initial values ​​of the interpolation relationship, and the preset interpolation attenuation coefficient, the first weighting coefficient is obtained;

[0124] The expression for the first weighting coefficient is:

[0125]

[0126] In the formula, ρ0 represents the initial value of the interpolation relationship; ρ(h) represents the element correlation function; a represents the interpolation attenuation coefficient; and h represents the straight-line distance between elements in the terrain height matrix.

[0127] In some embodiments, the method may further include: determining the element correlation function of the insertion matrix based on the per-unit height value and the straight-line distance between elements in the terrain height matrix, combined with a preset element relationship correction coefficient; wherein the expression of the element correlation function is:

[0128]

[0129] In the formula, k represents the element relationship correction coefficient.

[0130] In some specific embodiments, the terrain height matrix of the mining area is interpolated and extended to generate a numerical terrain surface matrix M′. The extended formula for matrix interpolation H is:

[0131]

[0132] In equation (1), H represents the estimated value (i.e., the extended value) of the insertion matrix, and Z xy ω represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix. xy The weighting coefficients are m, which represents the known Z values ​​involved in the calculation of the matrix column directions. xy The number of known Z values ​​involved in the calculation in the row direction of the matrix, where n represents the number of matrix rows involved in the calculation. xy The quantity. Where the weighting coefficient ω xy The calculation method is as follows:

[0133]

[0134] In equation (2), ρ(h) represents the interpolation matrix element correlation function, ρ0 is the initial value of the interpolation relationship, which can be directly determined by the element correlation function; h represents the straight-line distance between the elements of the terrain height matrix; a represents the interpolation attenuation coefficient, which is set manually depending on the geological conditions. The matrix element correlation function ρ is calculated as follows:

[0135]

[0136] In equation (3), k represents the element relationship correction coefficient, which is set manually depending on the geological conditions.

[0137] S300. Determine the extreme points of terrain height based on the terrain numerical surface matrix to obtain the basic path data;

[0138] In some specific embodiments, step S300 can automatically determine the terrain height extreme points based on the aforementioned standardized terrain surface (i.e., terrain numerical surface matrix) to determine the optimal signal coverage location, serving as the basis for path optimization, through boundary condition calculation. S400: Energy allocation is performed on the ground numerical surface matrix based on the path baseline data to obtain the element edge condition matrix;

[0139] It should be noted that in some embodiments, the basic path data includes the path start point and path points, and the path points include path intermediate points and path end points; for example... Figure 4 As shown, step S400 may include: S401, determining the energy function based on the path base data; S402, performing exponential accumulation on the energy function to obtain the energy distribution function; S403, obtaining the first edge condition function based on the energy distribution function and the path starting point; and obtaining the second edge condition function based on the energy distribution function and the path point; S404, performing union processing on the first edge condition function and the second edge condition function to obtain the element edge condition matrix.

[0140] The determination of the energy function based on path data can include: determining the energy function based on path data and preset coefficients; wherein the preset coefficients include a second weighting coefficient, a first-element bias coefficient, and a second-element bias coefficient; the expression for the energy function is:

[0141]

[0142] In the formula, ε(s,HI|θ) represents the energy function; m′ represents the number of path starting points in the path base data; n′ represents the number of path points in the path base data; ω′ xy Indicates the second weighting coefficient; s j HI represents the j-th path starting point in the basic path data. i P represents the starting point of the i-th path in the basic path data; j Q represents the paranoia coefficient of the first element; i This represents the paranoia coefficient of the second element.

[0143] In some specific embodiments, the elements of the interpolated terrain height matrix are used as network nodes, and the energy function is defined as follows:

[0144]

[0145] In equation (4), ω′ xy The weighting coefficients for the interpolated terrain height matrix M′ are: s represents the path start point, m′ represents the number of potential path start points; HI represents path points (including path end points), n′ represents the total number of HI points in the terrain height matrix, and P... j Q is the bias coefficient of the starting element of the path. i This represents the offset coefficient for path point elements (including the path endpoint). Where θ (i.e., the preset coefficient) can be expressed as:

[0146] θ={ω′ xy P j Q i :1≤i≤m′,1≤j≤n′} (5)

[0147] Among them, the calculation methods for the edge condition matrix M′0 of the numerical surface matrix M′ of the designed terrain are as follows:

[0148] M′0=M′(s|θ)∪M′(HI|θ) (6)

[0149] Where M′(s|θ) represents the edge condition function of the path starting element (i.e., the first edge condition function); M′(HI|θ) represents the edge condition function of the path point (including the path ending element) element (i.e., the second edge condition function), and the specific calculation method is as follows:

[0150]

[0151]

[0152] In equations (7) and (8), Z(θ) is the energy partition function, which is calculated as follows:

[0153]

[0154] In equations (7), (8), and (9), exp or exp represents an exponential function.

[0155] S500: Obtain the preset endpoint and use the target planning point as the starting point;

[0156] Among them, the preset endpoint and starting point are determined from the path starting point and path point in the relevant path base data determined in the aforementioned steps.

[0157] S600. Based on the preset endpoint and starting point, perform path planning on the terrain numerical surface matrix with the element edge condition matrix as the constraint condition, and obtain the next planning point as the target planning point.

[0158] It should be noted that in some embodiments, such as Figure 5 As shown, path planning on a terrain numerical surface matrix with the element edge condition matrix as a constraint can include: S601, taking the positive height direction of the target area as the first direction and the column direction of the terrain numerical surface matrix as the second direction, and then determining the target plane based on the first and second directions; S602, dividing the target plane into grids, and then using the element edge condition matrix as a constraint and a biomimetic algorithm heuristic function to optimize the planning points along the row direction of the terrain numerical surface matrix.

[0159] In some specific embodiments, path planning involves manually setting the endpoint at the mining face, using the current location of the mobile base station as the starting point, and automatically planning the optimal route with M′0 as a constraint. The positive direction of terrain height is designated as the Δ direction, and the column direction of the interpolated terrain height matrix M′ is designated as the y direction. In three-dimensional space, the Δ-y plane is uniformly divided into grids of equal size. Optimization is performed along the row direction of the terrain height matrix M′, i.e., the x direction. The biomimetic algorithm heuristic function includes:

[0160] f(x,y,Δ)=A×L(x,y,Δ) α ×(abs(Δ)) β ×Bas(x,y,Δ)×ρ(δ+1) (10)

[0161] In equation (10), A, α, and β are optimization coefficients used to adjust the optimization bias, which is manually set; L(x,y,Δ) represents the optimal straight-line distance between the current path node and the node within the optimization range; abs(Δ) is the height coordinate of the node within the optimization range; V(x,y,Δ) is the guarantee function used to ensure that the path always remains above the material surface without crossing it: it is 1 when the height of the optimization node is greater than the height of the material surface, and 0 if the condition is not met; ρ(δ) is the pheromone update constraint condition. Further, we have:

[0162]

[0163]

[0164] In equation (11), x1, y1, and Δ1 represent the spatial coordinates of the current path point, and x2, y2, and Δ2 represent the spatial coordinates of the next point. In equation (12), μ and ∈ are constant coefficients, ρ(δ) represents the current pheromone of particle m with the shortest path, ρ(δ+1) is the enhanced shortest path pheromone, and further, ρ(0) = 1.

[0165] S700, return to the step of obtaining the preset endpoint and using the target planning point as the starting point, until the path planning is completed;

[0166] In some embodiments, the method may further include: when the terrain data of the target area is updated, reacquiring the terrain data of the target area, and then returning to step S100 to perform terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area, until path planning is completed.

[0167] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0168] The technical solution of this invention mainly includes three steps: terrain surface normalization, boundary condition calculation, and spatial three-dimensional path planning. For example... Figure 6 As shown, the specific steps are as follows:

[0169] S1) This invention standardizes the terrain surface by reading a topographic map of the mining area and setting an equally spaced coordinate grid. The horizontal and vertical coordinates of the grid correspond to the length and width of the selected mining area, respectively. The values ​​of each intersection point in the grid are taken as the terrain height values ​​at the corresponding locations, thus obtaining the terrain height matrix M of the mining area. The grid density is manually set according to the required accuracy.

[0170] S2) Further, the topographic height matrix of the mining area is interpolated and extended to generate a numerical topographic surface matrix M′. The extended formula for matrix interpolation H is:

[0171]

[0172] In equation (13), H represents the estimated value (i.e., the extended value) of the insertion matrix, and Z xy ω represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix. xy The weighting coefficients are m, which represents the known Z values ​​involved in the calculation of the matrix column directions. xy The number of known Z values ​​involved in the calculation in the row direction of the matrix, where n represents the number of matrix rows involved in the calculation. xy The quantity. Where the weighting coefficient ω xy The calculation method is as follows:

[0173]

[0174] In equation (14), ρ(h) represents the interpolation matrix element correlation function, ρ0 is the initial value of the interpolation relationship, which can be directly determined by the element correlation function; h represents the straight-line distance between the elements of the terrain height matrix; a represents the interpolation attenuation coefficient, which is set manually depending on the geological conditions. The matrix element correlation function ρ is calculated as follows:

[0175]

[0176] In equation (15), k represents the element relationship correction coefficient, which is set manually depending on the geological conditions.

[0177] S3) The boundary condition calculation of this invention automatically identifies the extreme points of terrain height based on the aforementioned standardized terrain surface (i.e., terrain numerical surface matrix) to determine the optimal location for signal coverage, serving as the basis for path optimization.

[0178] Furthermore, using each element of the interpolated terrain height matrix as a network node, the energy function is defined as follows:

[0179]

[0180] In equation (16), ω′ xy The weighting coefficients for the interpolated terrain height matrix M′ are: s represents the path start point, m′ represents the number of potential path start points; HI represents path points (including path end points), n′ represents the total number of HI points in the terrain height matrix, and P... j Q is the bias coefficient of the starting element of the path. i θ represents the bias coefficients of the path point elements (including the path endpoint). θ can be expressed as:

[0181] θ={ω′ xy P j Q i :1≤i≤m′,1≤j≤n′} (17)

[0182] S4) Further, the calculation methods for the edge condition matrix M′0 of the topographic numerical surface matrix M′ are as follows:

[0183] M′0=M′(s|θ)∪M′(HI|θ) (18)

[0184] Where M′(s|θ) represents the edge condition function of the path start element; M′(HI|θ) represents the edge condition function of the path point (including the path end element), and the specific calculation method is as follows:

[0185]

[0186]

[0187] In equations (19) and (20), Z(θ) is the energy partition function, which is calculated as follows:

[0188]

[0189] S5) Further, the path planning endpoint is manually set at the mining face, and the optimal route is automatically planned using the current location of the mobile base station as the starting point and M′0 as the constraint. Taking the positive direction of terrain height as the Δ direction and the column direction of the interpolated terrain height matrix M′ as the y direction, the Δ-y plane is uniformly divided into grids of equal size in three-dimensional space. Optimization is performed along the row direction of the terrain height matrix M′, i.e., the x direction. The biomimetic algorithm heuristic function is set as follows:

[0190] f(x,y,Δ)=A×L(x,y,Δ) α ×(abs(Δ)) β ×Bas(x,y,Δ)×ρ(δ+1) (22)

[0191] In equation (22), A, α, and β are optimization coefficients used to adjust the optimization bias, which is manually set; L(x,y,Δ) represents the optimal straight-line distance between the current path node and the node within the optimization range; abs(Δ) is the height coordinate of the node within the optimization range; V(x,y,Δ) is the guarantee function used to ensure that the path always remains above the material surface without crossing it: it is 1 when the height of the optimization node is greater than the height of the material surface, and 0 if the condition is not met; ρ(δ) is the pheromone update constraint condition. Further, we have:

[0192]

[0193]

[0194] In equation (23), x1, y1, and Δ1 represent the spatial coordinates of the current path point, and x2, y2, and Δ2 represent the spatial coordinates of the next point. In equation (24), μ and ∈ are constant coefficients, ρ(δ) represents the current pheromone of particle m with the shortest path, ρ(δ+1) is the enhanced shortest path pheromone, and further, ρ(0) = 1.

[0195] S6) Based on the progress of the mining face, manually intervene to determine whether to re-perform the terrain surface normalization step. If the digital terrain surface is regenerated, repeat steps S1) to S6); if the terrain does not need to be regenerated, substitute the data from the previous steps S1) to S4) and repeat steps S5) to S6).

[0196] It should be noted that in some specific embodiments, taking equation (16) as an example, the energy function is defined in the design. Coefficients not specifically explained by the formula need to be set manually based on experience. Under normal circumstances, this coefficient can be modified or left unchanged; during the path planning process, as the tunnel face advances, the starting and ending positions will change, and can be reset each time optimization is performed.

[0197] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the accompanying drawings are provided. Figure 7 The present invention will be further described below. For example... Figure 7 As shown, a certain mine actually covers an area of ​​18 square kilometers. The selected area is chosen to ensure that the mining area (including the ore vein, i.e., the future development area) completely covers the working area. To ensure intuitive display, the selected area is converted to an area within the region, i.e., the rank of the terrain height matrix is ​​200. Further expansion yields an interpolated terrain height matrix rank of 600. The starting point coordinates are set to 0, and the ending point coordinates to 200, to extend the base station's autonomous driving trajectory as much as possible to facilitate the demonstration of the planning effect. The results of the two path planning methods are shown below. Figure 7 As shown by the solid and dashed lines, both automatic path planning methods effectively avoided obstacles and adapted to the terrain information, proving the effectiveness of the method. Furthermore, the automatic avoidance of deep pits and peaks demonstrated the effective value of the invention.

[0198] On the other hand, such as Figure 8As shown, this embodiment of the invention provides a path planning device 800, comprising: a first module 810, used to acquire terrain data of a target area, perform terrain surface normalization based on the terrain data, and obtain a terrain height matrix of the target area; a second module 820, used to interpolate and expand the terrain height matrix to obtain a terrain numerical surface matrix; a third module 830, used to determine the terrain height extreme points based on the terrain numerical surface matrix to obtain basic path data; a fourth module 840, used to perform energy allocation on the ground numerical surface matrix based on the basic path data to obtain an element edge condition matrix; a fifth module 850, used to acquire a preset endpoint and use a target planning point as the starting point; a sixth module 860, used to perform path planning on the terrain numerical surface matrix based on the preset endpoint and starting point, using the element edge condition matrix as a constraint, to obtain the next planning point as the target planning point; and a seventh module 870, used to return to the fifth module 850 until path planning is completed.

[0199] It should be noted that, in some embodiments, the apparatus may further include:

[0200] The eighth module is used to determine the element-related functions and initial values ​​of the interpolation relationships based on the interpolation matrix.

[0201] The ninth module is used to obtain the first weighting coefficient based on the straight-line distance between elements in the terrain height matrix, combined with the element correlation function, the initial value of the interpolation relationship, and the preset interpolation attenuation coefficient.

[0202] The expression for the first weighting coefficient is:

[0203]

[0204] In the formula, ρ0 represents the initial value of the interpolation relationship; ρ(h) represents the element correlation function; a represents the interpolation attenuation coefficient; and h represents the straight-line distance between elements in the terrain height matrix.

[0205] In some embodiments, the apparatus may further include:

[0206] The tenth module is used to determine the element correlation function of the insertion matrix based on the per-unit height value and the straight-line distance between elements in the terrain height matrix, combined with the preset element relationship correction coefficient;

[0207] The expression for the element-related function is:

[0208]

[0209] In the formula, k represents the element relationship correction coefficient.

[0210] In some embodiments, the apparatus may further include:

[0211] The eleventh module is used to reacquire the terrain data of the target area when the terrain data of the target area is updated, and then return to the first module to perform terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area, until the path planning is completed.

[0212] The content of the method embodiments of the present invention is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0213] On the other hand, such as Figure 9 As shown, this embodiment of the invention also provides an electronic device 900, which includes at least one processor 910 and at least one memory 920 for storing at least one program; taking one processor 910 and one memory 920 as an example.

[0214] The processor 910 and memory 920 can be connected via a bus or other means.

[0215] Memory 920, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory 920 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 920 may optionally include memory remotely located relative to the processor, and this remote memory can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0216] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0217] Specifically, Figure 10 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present invention is shown.

[0218] It should be noted that, Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0219] like Figure 10As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1002 or programs loaded from storage section 1008 into random access memory (RAM). The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output interface 1005 (I / O interface) is also connected to the bus 1004.

[0220] The following components are connected to the input / output interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a local area network card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1010 as needed so that computer programs read from it can be installed into the storage section 1008 as needed.

[0221] In particular, according to embodiments of the present invention, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions defined in the system of the present invention.

[0222] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0223] The content of the method embodiments of the present invention is applicable to the system embodiments. The specific functions implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0224] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the aforementioned method.

[0225] The content of the method embodiments of the present invention is applicable to the computer-readable storage medium embodiments. The specific functions implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0226] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0227] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0228] It should be noted that although several modules for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0229] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0230] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0231] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0232] If a function is implemented as 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 technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0233] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution means, apparatus, or device (such as a computer-based device, a processor-including device, or other means that can fetch and execute instructions from, or in conjunction with, an instruction execution means, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution means, apparatus, or device.

[0234] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0235] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution device. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0236] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0237] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0238] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A path planning method characterized by, include: Acquire terrain data of the target area, and perform terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area; The terrain height matrix is ​​interpolated and extended to obtain a terrain numerical surface matrix; Based on the terrain numerical surface matrix, the extreme points of terrain height are determined to obtain the basic path data; Based on the path-based data, energy allocation is performed on the terrain numerical surface matrix to obtain the element edge condition matrix; Obtain the preset endpoint and use the target planning point as the starting point; Based on the preset endpoint and the starting point, path planning is performed on the terrain numerical surface matrix using the element edge condition matrix as a constraint to obtain the next planning point as the target planning point. Return to the step of obtaining the preset endpoint and using the target planning point as the starting point, until the path planning is completed.

2. The path planning method of claim 1, wherein, The terrain data includes the length, width, and height data of the target area; the step of performing terrain surface normalization based on the terrain data to obtain the terrain height matrix of the target area includes: The target area is gridded based on a preset spacing; The horizontal coordinate of the grid is determined based on the length data, and the vertical coordinate of the grid is determined based on the width data; Based on the grid's horizontal and vertical coordinates, and combined with the preset spacing, the coordinates of the intersection points of each grid are determined. Based on the intersection point coordinates, the corresponding height data is extracted from the terrain data as the grid intersection point value, thereby obtaining the terrain height matrix of the target area.

3. The path planning method of claim 1, wherein, The step of interpolating and expanding the terrain height matrix to obtain the terrain numerical surface matrix includes: The per-unit height values ​​at each point in the terrain height matrix are obtained, and then an insertion matrix is ​​determined by combining these values ​​with a preset first weighting coefficient; wherein, the expression for the insertion matrix is: In the formula, H represents the insertion matrix; m represents the number of per-unit height values ​​of the terrain height matrix in the column direction; n represents the number of per-unit height values ​​of the terrain height matrix in the row direction; ω xy Z represents the first weighting coefficient; xy This represents the per-unit height value corresponding to the x-th row and y-th column of the terrain height matrix; The terrain height matrix is ​​interpolated and extended based on the interpolation matrix to obtain the terrain numerical surface matrix.

4. The path planning method of claim 3, wherein, The method further includes: Based on the insertion matrix, determine the element correlation function and initial values ​​of the interpolation relationship of the insertion matrix; Based on the values ​​of the straight-line distances between elements in the terrain height matrix, combined with the element correlation function, the initial value of the interpolation relationship, and the preset interpolation attenuation coefficient, the first weighting coefficient is obtained; The expression for the first weighting coefficient is as follows: In the formula, ρ0 represents the initial value of the interpolation relationship; ρ(h) represents the element correlation function; a represents the interpolation attenuation coefficient; and h represents the straight-line distance between elements in the terrain height matrix.

5. The path planning method of claim 4, wherein, The method further includes: The element correlation function of the insertion matrix is ​​determined based on the normalized height value and the straight-line distance between the elements in the terrain height matrix, combined with a preset element relationship correction coefficient. The expression for the element-related function is as follows: In the formula, k represents the element relationship correction coefficient.

6. The path planning method of claim 1, wherein, The basic path data includes the path start point and path points, and the path points include path intermediate points and path end points. The step of performing energy allocation on the ground numerical surface matrix based on the path baseline data to obtain the element edge condition matrix includes: Based on the aforementioned path data, the energy function is determined; The energy function is subjected to exponential accumulation to obtain the energy partition function; Based on the energy allocation function and the path starting point, a first edge condition function is obtained; and based on the energy allocation function and the path point, a second edge condition function is obtained. The first edge condition function and the second edge condition function are combined to obtain the element edge condition matrix.

7. The path planning method of claim 6, wherein, The determination of the energy function based on the path data includes: Based on the aforementioned path data, the energy function is determined using preset coefficients; wherein the preset coefficients include a second weighting coefficient, a first element bias coefficient, and a second element bias coefficient. The expression for the energy function is: In the formula, ε(s,HI|θ) represents the energy function; m′ represents the number of path starting points in the path base data; n′ represents the number of path points in the path base data; ω′ xy Indicates the second weighting coefficient; s j HI represents the j-th path starting point in the path base data; i P represents the starting point of the i-th path in the basic path data; j Q represents the paranoia coefficient of the first element; i This represents the paranoia coefficient of the second element.

8. The path planning method of claim 1, wherein, The method of using the element edge condition matrix as a constraint to perform path planning on the terrain numerical surface matrix includes: The target plane is determined by taking the positive height direction of the target area as the first direction and the column direction of the terrain numerical surface matrix as the second direction. The target plane is divided into grids, and then the element edge condition matrix is ​​used as a constraint and a biomimetic algorithm heuristic function is used to optimize the planning points along the row direction of the terrain numerical surface matrix.

9. The path planning method of claim 1, wherein, In the path planning, the method further includes: When the terrain data of the target area is updated, the terrain data of the target area is reacquired, and then the process returns to the step of performing terrain surface permutation based on the terrain data to obtain the terrain height matrix of the target area, until path planning is completed.

10. A path planning device characterized by comprising: include: The first module is used to acquire terrain data of the target area, perform terrain surface normalization based on the terrain data, and obtain the terrain height matrix of the target area. The second module is used to interpolate and expand the terrain height matrix to obtain a terrain numerical surface matrix. The third module is used to determine the extreme points of terrain height based on the terrain numerical surface matrix and obtain the basic path data. The fourth module is used to perform energy allocation on the terrain numerical surface matrix based on the path basic data to obtain the element edge condition matrix; The fifth module is used to obtain the preset endpoint and use the target planning point as the starting point; The sixth module is used to perform path planning on the terrain numerical surface matrix based on the preset endpoint and the starting point, using the element edge condition matrix as a constraint, and obtain the next planning point as the target planning point. The seventh module is used to return to the fifth module until the path planning is completed.

11. An electronic device, comprising: Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 9.

12. A computer storage medium having stored thereon a program that is executable by a processor, the program comprising instructions for causing the processor to perform the method of any one of claims 1-11. The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1 to 9.

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