Methods and systems for optimizing wireless sensor network coverage on 3D real terrain surfaces

Abstract

This invention discloses a method and system for optimizing wireless sensor network coverage on a three-dimensional realistic terrain surface, belonging to the field of sensor network optimization technology. Taking maximum planar coverage as the first constraint, a tangent search quadratic interpolation optimization algorithm is used to optimize the planar coordinates of sensor nodes, determining the planar deployment strategy of sensor nodes in the monitoring area; the initial spatial coordinates of the sensor nodes are determined; the rotation angle of the sensor nodes is calculated using the grid points matched with the sensors as centers; based on the initial spatial coordinates and rotation angles, the spatial coordinates of the sensor nodes are determined, and taking maximum overall coverage as the second constraint, a tangent search quadratic interpolation optimization algorithm is used to optimize the spatial coordinates of the sensor nodes, determining the spatial deployment strategy of the sensor nodes; sensors are deployed accordingly on the monitoring area based on the spatial deployment strategy of the sensor nodes. This method can improve the coverage of wireless sensor networks on three-dimensional realistic surfaces.

Description

Technical Field

[0001] This invention relates to the field of sensor network optimization technology, and in particular to a method and system for optimizing wireless sensor network coverage on a three-dimensional real terrain surface. Background Technology

[0002] Wireless Sensor Networks (WSNs), with their distributed, self-organizing, low-power, and easy-to-deploy characteristics, can achieve real-time monitoring and collection of terrain and environmental parameters, providing data support for early warning and prevention of natural disasters. In the field of natural disaster early warning and prevention, such as volcano detection, landslide detection, and disaster monitoring, accurate collection and in-depth analysis of environmental data can effectively prevent and mitigate disaster losses. In recent years, the problem of surface cover on real terrain has become a widely concerned research topic.

[0003] Sensor nodes can sense various physical and chemical parameters of the surrounding environment and transmit the data wirelessly to the user terminal for further processing and analysis. However, due to the large number of sensor nodes and their random deployment, some areas suffer from node redundancy, leading to resource waste, while other areas remain uncovered, creating coverage gaps. Therefore, researching WSN node coverage optimization strategies in real terrain is crucial for improving network coverage quality and ensuring efficient data acquisition. Currently, most coverage optimization research focuses on two-dimensional regions, typically assuming the sensing area is an ideal plane. Coverage strategies in two-dimensional environments cannot be directly applied to complex three-dimensional terrain environments. Furthermore, two-dimensional sensor node models have significant limitations when sensing complex terrain, failing to meet the monitoring needs of three-dimensional environments.

[0004] Therefore, there is an urgent need for a method to address the low coverage of wireless sensor networks on real three-dimensional surfaces. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and system for optimizing wireless sensor network coverage on a three-dimensional real terrain surface to address the aforementioned technical problems. This method can improve the coverage rate of wireless sensor networks on a three-dimensional real surface.

[0006] The present invention adopts the following technical solution:

[0007] This invention provides a method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface, including:

[0008] Taking maximum planar coverage as the first constraint, the planar coordinates of the sensor nodes are optimized using the tangent search quadratic interpolation optimization algorithm to determine the planar deployment strategy of the sensor nodes in the monitoring area. The tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update methods of exploration, development and tangent search.

[0009] The sensor node positions obtained from the planar deployment strategy are matched with the grid point positions in the monitoring area, and the initial height of the sensor nodes is fixed to determine the initial spatial coordinates of the sensor nodes.

[0010] The rotation angle of the sensor node is calculated by taking the grid point that matches the sensor node as the center.

[0011] Based on the initial spatial coordinates and rotation angle of the sensor nodes, the spatial coordinates of the sensor nodes are determined. With the maximum comprehensive coverage rate as the second constraint, the spatial coordinates of the sensor nodes are optimized using a tangent search quadratic interpolation optimization algorithm to determine the spatial deployment strategy of the sensor nodes in the monitoring area. The comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor nodes.

[0012] Sensors are deployed in the monitoring area according to the spatial deployment strategy of sensor nodes.

[0013] Optionally, taking maximum planar coverage as the first constraint, a tangent search quadratic interpolation optimization algorithm is used to optimize the planar coordinates of the sensor nodes, determining the planar deployment strategy of the sensor nodes in the monitoring area, including:

[0014] Initialize the population size of the search agent; the population position in the population size represents the planar coordinates of the sensor node;

[0015] Based on the population location, calculate the planar coverage of the search agent population and use the planar coverage as the fitness value;

[0016] Compare the fitness values ​​of the search agent population and take the search agent with the highest fitness value as the best historical position;

[0017] An adaptive switching probability is used to determine the population update method and update the population position of the search agent;

[0018] Based on the updated population position of the search agent, the historical best position is recalculated until the maximum number of iterations is reached. Then, the historical best position and the planar deployment strategy of the sensor node corresponding to the historical best position are output.

[0019] Optionally, an adaptive switching probability determination method is used to update the population position of the search agent, including:

[0020] Get random numbers;

[0021] If the random number is less than or equal to the adaptive switch probability, the population position of the search agent is updated using an exploratory approach.

[0022] If the random number is greater than the adaptive switching probability model, then determine whether the random number is greater than or equal to a specific value;

[0023] If the random number is greater than or equal to a specific value, the population position of the search agent is updated using an open-source approach.

[0024] If the random number is less than a specific value, the population position of the search agent is updated using a tangent search method.

[0025] Optionally, before calculating the planar deployment strategy of sensor nodes in the monitoring area, the method further includes:

[0026] The number of sensor nodes that need to be deployed in the monitoring area is determined based on the sensing radius of the sensor nodes and the size of the monitoring area.

[0027] Optionally, using the grid points matched with the sensor as the center, the rotation angle of the sensor node is calculated, including:

[0028] For any grid point that matches a sensor node, the matched grid point is used as the center, and data is acquired in four directions (up, down, left, and right) according to a preset data length to obtain grid area data of a preset size.

[0029] Obtain the x-coordinate values ​​of the first and last columns, and the y-coordinate values ​​of the first and last rows from the grid area data;

[0030] Based on the z-coordinate values ​​of all data in the first column, first row, middle column, middle row, last column, and last row of the grid area data, calculate the mean z-coordinate of the first column, first row, middle column, middle row, last column, and last row respectively;

[0031] The rotation angle of the corresponding sensor node is determined based on the x-coordinate values ​​of the first and last columns, the y-coordinate values ​​of the first and last rows, and the average z-coordinate values ​​of the first column, first row, middle column, middle row, last column, and last row.

[0032] Optionally, the spatial coordinates of the sensor node are determined based on its initial spatial coordinates and rotation angle, including:

[0033] Based on the initial spatial coordinates and rotation angle of the sensor node, determine the coordinates of the center of the cone base of the monitoring area covered by the sensor node;

[0034] The sensing direction of the sensor node is determined based on the coordinates of the center of the cone's base and the initial spatial coordinates.

[0035] The spatial coordinates of the sensor node are determined based on the sensing direction, scaling factor, and initial spatial coordinates. Optionally, the formula for calculating the spatial coordinates of the sensor node is:

[0036] New nc = Direction*τ + nc;

[0037] Direction = cc-nc;

[0038]

[0039] Where Newnc represents the spatial coordinates of the sensor node, Direction represents the sensing direction, τ represents the scaling factor, nc represents the initial spatial coordinates, and cc represents the coordinates of the center of the cone base of the sensor node. x cc y and cc z Let nc represent the coordinates of the center of the cone's base on the x-axis, y-axis, and z-axis, respectively. x and nc y Represent the x and y coordinates in the initial spatial coordinate system, respectively; Rs represents the sensing radius; θ represents half of the sensing range; degree results_α degree results_β This indicates the rotation angle of the sensor node in the horizontal direction and the rotation angle in the vertical direction.

[0040] Optionally, in the process of optimizing the spatial coordinates of the sensor node using the tangent search quadratic interpolation optimization algorithm, the algorithm optimizes the scaling factor τ.

[0041] Optionally, the calculation method for the overall coverage rate includes:

[0042] For any grid point, calculate the distance sensing of each sensor to the grid point based on the spatial coordinates of the sensor nodes and the coordinates of the grid point.

[0043] Calculate the angle perception of each sensor node relative to the grid point based on the horizontal and vertical deflection angles between the sensor node and the grid point;

[0044] Based on the distance and angle perception of each sensor node to the grid point, the overall perception probability of each grid point is obtained, and the comprehensive coverage rate is determined based on the overall perception probability of each grid point.

[0045] This invention provides a wireless sensor network coverage optimization system for three-dimensional real terrain surfaces, comprising:

[0046] The planar optimization algorithm module is used to optimize the planar coordinates of sensor nodes using the tangent search quadratic interpolation optimization algorithm with the maximum planar coverage as the first constraint, thereby determining the planar deployment strategy of sensor nodes in the monitoring area. The tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update methods, including exploration, development and tangent search methods.

[0047] An angle optimization module is used to match the sensor node position obtained by the planar deployment strategy of the sensor node with the grid point position of the monitoring area, fix the initial height of the sensor node, determine the initial spatial coordinates of the sensor node, and calculate the rotation angle of the sensor node using the grid point matched with the sensor node as the center.

[0048] The high-level optimization module is used to determine the spatial coordinates of the sensor nodes based on their initial spatial coordinates and rotation angles. Using the maximum overall coverage rate as a second constraint, it employs a tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor nodes and determine the spatial deployment strategy of the sensor nodes in the monitoring area. The overall coverage rate is obtained by considering both the distance and angle perception of the sensor nodes.

[0049] The deployment module is used to deploy sensors in the monitoring area according to the spatial deployment strategy of the sensor nodes.

[0050] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface.

[0051] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface.

[0052] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects:

[0053] In this invention, the algorithm optimizes the planar coordinates of sensor nodes with the maximum planar coverage as the first constraint. This algorithm integrates exploration, development, and tangent search methods. The exploration method allows the algorithm to search for possible high-quality solutions over a larger area, avoiding limitation to local regions. The development method focuses on discovered potential high-quality areas, performing a refined search to find better solutions. The tangent search method utilizes the characteristics of the tangent function to search the solution space along unique paths. The combination of these three methods enables the algorithm to efficiently explore different position combinations when searching for the planar coordinates of sensor nodes, thereby finding a node deployment strategy that maximizes planar coverage. With the maximum overall coverage as the second constraint, the tangent search quadratic interpolation optimization algorithm is used again to optimize the spatial coordinates of sensor nodes. The overall coverage considers distance perception, angle perception, and line-of-sight perception, comprehensively measuring the sensor's perception capability in space. Through this optimization, the position and orientation of sensor nodes in space are further adjusted, enabling the sensors to more effectively cover the monitoring area in three-dimensional space, thereby improving the coverage of the entire sensor network. In this way, from optimizing the planar deployment strategy to determining the spatial deployment strategy, the entire process gradually considers various factors of the sensors in the plane and space. Through two optimization processes based on the tangent search quadratic interpolation optimization algorithm, the position and orientation of the sensor nodes are continuously adjusted, so that the sensor network can be more reasonably distributed in the plane and space, and ultimately the coverage of the sensor network to the monitoring area is effectively improved. Attached Figure Description

[0054] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0055] Figure 1 A schematic flowchart of a wireless sensor network coverage optimization method for a three-dimensional real terrain surface provided by the present invention;

[0056] Figure 2 A flowchart illustrating a tangent search quadratic interpolation optimization algorithm provided by this invention;

[0057] Figure 3 A schematic diagram of the sensing range of a sensor node provided by the present invention;

[0058] Figure 4 A schematic diagram illustrating the sensing range of another sensor node provided by the present invention;

[0059] Figure 5 A schematic diagram of another method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface provided by the present invention;

[0060] Figure 6 This is a schematic diagram illustrating the coverage effect of a sensor node over a monitored area.

[0061] Figure 7 This is a schematic diagram showing the change in coverage of the method of this invention and the conventional method as a function of iteration;

[0062] Figure 8 A schematic diagram of a wireless sensor network coverage optimization system for a three-dimensional real terrain surface provided by the present invention;

[0063] Figure 9 This is a schematic diagram of a computer device used in the present invention to implement a method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] Currently, most coverage optimization research focuses on two-dimensional regions, typically assuming the sensing area is an ideal plane. Coverage strategies in two-dimensional environments cannot be directly applied to complex three-dimensional terrain environments. Furthermore, two-dimensional sensor node models have significant limitations in sensing complex terrain, failing to meet the monitoring needs of three-dimensional environments. Existing technologies mention a three-dimensional directed sensor network deployment strategy based on an improved differential evolution algorithm. The basic principle of this strategy is that it combines crossover rate sorting and polynomial-based mutation operations to enhance the search capability, population diversity, and optimization performance of the differential evolution algorithm, while simultaneously avoiding the algorithm from getting trapped in local optima to improve optimization efficiency.

[0066] The existing node deployment strategies described above address the coverage deployment problem on realistic 3D terrain surfaces, employing heuristic algorithms to seek solutions by optimizing node positions and angles in a single step. However, this approach faces significant challenges in finding the optimal solution when dealing with large-scale scenes, and it also places high demands on algorithm performance. Furthermore, heuristic algorithms typically exhibit high complexity when solving 3D problems. In summary, existing optimization strategies suffer from low network coverage and high complexity, and the heuristic algorithms used are relatively outdated, requiring improvement in their optimization capabilities.

[0067] Based on this, the present invention provides a method and system for optimizing wireless sensor network coverage on a three-dimensional real terrain surface, which can improve the optimization capability and achieve higher network coverage with shorter computation time, thereby optimizing network coverage and network lifetime.

[0068] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0069] Figure 1 This is a schematic diagram of a wireless sensor network coverage optimization method for a three-dimensional real terrain surface according to the present invention, which specifically includes the following steps:

[0070] S101, with the maximum planar coverage as the first constraint, uses the tangent search quadratic interpolation optimization algorithm to optimize the planar coordinates of the sensor nodes and determine the planar deployment strategy of the sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update methods of exploration, development and tangent search.

[0071] First, before calculating the planar deployment strategy of sensor nodes in the monitoring area, the number of sensor nodes that need to be deployed in the monitoring area is determined based on the sensing radius of the sensor nodes and the size of the monitoring area.

[0072] The formula for calculating the number of sensor nodes is:

[0073] R ref =R s ×sin(θ) (1)

[0074]

[0075] Among them, R ref R is the reference radius. s Let R be the node sensing radius, θ be half the sensing angle, N be the number of sensor nodes to be deployed in the monitoring area, AreaX be the length of the monitoring area, and AreaY be the width of the monitoring area. The sensing capability of the node model decreases with increasing distance and angle; therefore, the sensing radius R is selected when the distance sensing capability and angle sensing capability are both 0.9. s Calculate R using (19.74m) and the sensing angle θ (22.5°). ref .

[0076] In one embodiment, taking maximum planar coverage as the first constraint, a tangent search quadratic interpolation optimization algorithm is used to optimize the planar coordinates of the sensor nodes to determine the planar deployment strategy of the sensor nodes in the monitoring area, including:

[0077] S201, Initialize the population size of the search agent; the population position in the population size represents the planar coordinates of the sensor node.

[0078] Initialize the population size of the search agent, use the initialized sensor node position as the position of any individual in the initial search agent population, and generate the positions of the remaining individuals around the individual.

[0079] The initial population can be represented as: Pos = Lb + rand × (Ub - Lb); Pos represents the population position, and Lb and Ub represent the lower and upper bounds of the population, respectively.

[0080] S202, calculate the planar coverage of the search agent population based on the population location, and use the planar coverage as the fitness value.

[0081] First, calculate the distance between the sensor node and the grid points in the monitoring area:

[0082]

[0083] Among them, Dis(S) i ,P j (x) represents the distance between the i-th sensor node and the j-th grid point. i y i Let (x) be the planar coordinates of the i-th sensor node. j y i ) represents the planar coordinates of the j-th grid point.

[0084] For any sensor node and any grid point, determine Dis(S) i ,P j ) and reference radius R ref The relationship, if Dis(S) i ,P j )<=R ref If a grid point is detected, it is marked; otherwise, it is not marked. The number of grid points marked at least once is counted. Based on this number and the total number of grid points in the monitored area, the planar coverage rate is calculated.

[0085]

[0086] S203, compare the fitness values ​​of the search agent population, and take the search agent with the largest fitness value as the historical best position.

[0087] S204, an adaptive switching probability determination method is used to update the population position of the search agent.

[0088] Specifically, an adaptive switching probability is used to determine the population update method and update the population position of the search agent, including: obtaining a random number; if the random number is less than or equal to the adaptive switching probability, then an exploration method is used to update the population position of the search agent; if the random number is greater than the adaptive switching probability model, then it is determined whether the random number is greater than or equal to a specific value; if the random number is greater than or equal to the specific value, then an exploration method is used to update the population position of the search agent; if the random number is less than the specific value, then a tangent search method is used to update the population position of the search agent.

[0089] The adaptive switching probability can be determined based on the current iteration number, the maximum iteration number, and the fitness value of the population at the current iteration number. The formula for calculating the adaptive switching probability is as follows:

[0090]

[0091] in, Let T represent the adaptive switching probability of the i-th population in the t-th iteration, and let T represent the maximum number of iterations. This represents the fitness value of the i-th population in the t-th iteration. and Let represent the maximum and minimum fitness in the t-th iteration, respectively.

[0092] Explore location update formula:

[0093]

[0094] Where, x i (t+1) represents the position of the i-th population in the t-th iteration, γ1 is the first weight coefficient, and x rand1 (t), x rand2 (t) and x rand3 (t) represents the position of a different individual randomly selected in the population during the t-th iteration, r1, r2, and r3 are random numbers between (0,1), fit(·) represents the value of the fitness function, and the function GQI(·) outputs the minimum value of the interpolation function based on the three input positions and their fitness values. The value of the first weight coefficient γ1 is determined by the following formula:

[0095] γ1=3n1b (8)

[0096]

[0097] Where n1 follows a standard normal distribution, and t is the current iteration number.

[0098] Development location update formula:

[0099]

[0100] Where γ2 is the second weighting coefficient, n² follows a standard normal distribution, rD is a random integer in the interval [1, d], where d represents the problem dimension, and Lb rD and Ub rD γ1 and γ2 represent the lower and upper bounds of the rD-th generation, respectively. The weight coefficient γ2 is an adaptive development weight coefficient, whose value gradually decreases as the number of iterations increases. This indicates the population position in generation rD.

[0101] Tangent search position update formula:

[0102]

[0103] Where, x i (t) represents the position of the i-th population in the t-th iteration, norm() represents the Euclidean norm, and d is the problem dimension. exist Select randomly within the range, x best (t) represents the historical best position in the t-th iteration.

[0104] S205. Based on the updated population position of the search agent, recalculate the historical best position until the number of iterations reaches the maximum number of iterations. Then, output the historical best position and the planar deployment strategy of the sensor node corresponding to the historical best position.

[0105] Based on the updated search agent population position, calculate the fitness value, and update the assigned search agent population position according to the updated fitness value, resulting in a new search agent population. Compare the new search agent population fitness value with the initial search agent fitness value, and select the search agent position with the largest fitness value as the historical best position for this iteration. Determine if the iteration count has reached the maximum iteration count. If the iteration count has reached the maximum iteration count, output the historical best position and the optimal planar deployment strategy for the sensor nodes corresponding to the historical best position. If the iteration count has not reached the specified iteration count, return to the step of "compare the fitness values ​​of the search agent population and select the search agent individual with the largest fitness value as the historical best position".

[0106] Specifically, updating the assigned search agent population position based on the fitness value of the search agent population after the update position includes: comparing the fitness value of the search agent population after the update position with the fitness value of the current generation search agent; if it is better than the fitness value of the current generation, then the fitness value of the search agent population and the population position of the search agent are retained; otherwise, no update is performed.

[0107] like Figure 2 As shown, Figure 2This is a flowchart of the tangent search quadratic interpolation optimization algorithm.

[0108] S102, Match the sensor node positions obtained from the planar deployment strategy of the sensor nodes with the grid point positions of the monitoring area, fix the initial height of the sensor nodes, and determine the initial spatial coordinates of the sensor nodes.

[0109] Specifically, for any given sensor node, the grid point with the closest plane distance to that sensor node is selected as the matching grid point for that sensor node, and the initial height of the sensor node can be fixed at 23 meters, specifically including:

[0110]

[0111] [X,Y,Z]=[X,Y,Z point +23] (16)

[0112] Among them, X point ,Y point and Z point Represents the relationship with the i-th sensor node (X) i ,Y i The coordinates of the matched grid points, X, Y and Z represent the initial spatial coordinates of the i-th sensor node. In the initial state, the node height is 23 meters above the terrain surface, which meets the initial coverage requirements.

[0113] S103, using the grid points matched with the sensor as the center, calculates the rotation angle of the sensor node.

[0114] Using the grid point matched with the sensor as the center, the rotation angle of the sensor node is calculated as follows: for any grid point matched with the sensor node, the matched grid point is used as the center to divide a grid area of ​​a preset size; based on the grid area data, the rotation angle of the sensor node is calculated using the node rotation angle calculation method.

[0115] Specifically, using the grid points matched with the sensor as the center, the rotation angle of the sensor node is calculated, including:

[0116] For any matched grid point of a sensor node, the matched grid point is used as the center, and data is acquired in four directions (up, down, left, and right) according to a preset data length to obtain a grid area of ​​the preset size. The x-coordinate values ​​of the first and last columns, and the y-coordinate values ​​of the first and last rows are obtained from the grid area data. Based on the z-coordinate values ​​of all data in the first column, first row, middle column, middle row, last column, and last row of the grid area data, the average z-coordinate values ​​of the first column, first row, middle column, middle row, last column, and last row are calculated respectively. Based on the x-coordinate values ​​of the first and last columns, the y-coordinate values ​​of the first and last rows, and the average z-coordinate values ​​of the first column, first row, middle column, middle row, last column, and last row, the rotation angle of the corresponding sensor node is determined.

[0117] Specifically, dividing the grid area into a preset size means taking the matched grid point as the center and spreading outwards by m data lengths to generate grid point data (grid area data) with a side length of (2*m+1). The number of grid areas is equal to the number of sensor nodes.

[0118] Optionally, the rotation angle of the corresponding sensor node is determined based on the x-coordinate values ​​of the first column and the last column, the y-coordinate values ​​of the first row and the last row, and the average z-coordinate values ​​of the first column, the first row, the middle column, the middle row, the last column, and the last row. This includes determining whether the average z-coordinate values ​​of the middle column and the middle row are significantly higher or lower than the average z-coordinate values ​​of the first column, the first row, the last column, and the last row, while ensuring that the difference between the average z-coordinate values ​​of the first column, the first row, the last column, and the last row is within an allowable range. Specifically, "significantly higher or significantly lower" can indicate that the difference between the average z-coordinate values ​​of the middle column and the middle row and the average z-coordinate values ​​of the first column, the first row, the last column, and the last row is greater than a first preset threshold or less than a second preset threshold.

[0119] If the above conditions are met, the rotation angle of the sensor node is determined to be 0, indicating no significant rotation. Otherwise, the slope angles in the x and y directions are calculated based on the difference between the mean z coordinates of the first row and the first column, and these differences are converted into angle values ​​using the arctangent function.

[0120] These differences can be converted into angle values ​​using the arctangent function, as follows:

[0121]

[0122] Where α and β are the angle values ​​of the sensor node in the x and y directions, respectively, x first x end These represent the x-coordinate values ​​of the first and last columns, respectively, and the y-coordinate values ​​of the last column. first y endThese represent the y-coordinates of the first and last rows, respectively, and the z-coordinates. first_x z end_x z first_y and z end_y These represent the mean Z-coordinates of the first column, last column, first row, and last row, respectively.

[0123] The corrected rotation angles in the x and y directions are scaled according to a certain ratio and then stored in the result array, corresponding to the data unit being processed.

[0124] degree results_α =0.3·α (19)

[0125] degree results_β =0.3·β (20)

[0126] Among them, degree results_α The degree represents the rotation angle of the sensor node in the x-direction. results_β This represents the rotation angle of the sensor node in the y-direction, with 0.3 as the scaling factor, the size of which is determined by the terrain type.

[0127] In one embodiment, it specifically includes:

[0128] The data receiving and initialization unit receives a structure containing multiple datasets, each dataset is stored in an independent data unit, and each data unit contains two-dimensional coordinate information. It determines the total number of units in the dataset and initializes the corresponding result array for storing the calculation results.

[0129] It should be clarified that the size of the grid area data obtained in the previous step is different (the side length of the grid area generated near the boundary of the monitoring area may not be 2*m+1). In addition, the grid area data obtained in the previous step is stored centrally. The role of the data receiving unit is to split the centrally stored grid area data obtained in the previous step into regions. Subsequent operations process the data of each region separately. That is, each dataset is stored in an independent unit, and the unit contains the coordinate information of a grid area (Note: The two-dimensional coordinate data in this invention refers to the dimension of the array, that is, an array of size m*n, not the specific data. The data is actually three-dimensional coordinates).

[0130] The purpose of the initialization unit is to store the node rotation angle data for this stage. The number of grid regions = the number of nodes = equal to the size of the initialization data unit. Therefore, it is necessary to determine the total number of units in the dataset and initialize the corresponding result array for storing the calculation results.

[0131] The data units are processed one by one. For each data unit, its two-dimensional coordinate data is extracted, and the number of rows and columns of the data is determined to obtain the overall size of the data.

[0132] As mentioned above, "the size of the grid area data is different (the side length of the generated grid area may not be 2*m+1 for nodes near the boundary of the monitoring area)", so this operation is required and is defined as: processing data units one by one.

[0133] The key coordinate point extraction unit extracts the x and y coordinates of the key start and end points from the current data unit, obtaining the specific values ​​of the start and end points for subsequent calculations.

[0134] This step and the next step obtain the coordinates in order to calculate the rotation angle. This step is an operation on the x and y coordinates.

[0135] The characteristics of this grid area data are: the X coordinates of each column are the same, and the Y coordinates of each row are the same; extracting the x and y coordinates of the start and end points from the current data unit means extracting the x coordinates of the first and last columns and selecting the values ​​at arbitrary positions (taking the first column as an example, the value of the first position of the first column is selected in this paper), and extracting the y coordinates of the first and last rows. For a grid area, the number of start and end points are 2 each (1 row start point, 1 column start point, totaling 2; 1 row end point, 1 column end point, totaling 2). The specific values ​​of the start and end points are the extracted coordinate values.

[0136] The intermediate point coordinate extraction unit extracts the coordinates of the intermediate position of the data in the x and y directions to obtain the intermediate point information of the data. It also extracts the z coordinate values ​​of the start, middle and end positions, corresponding to the x and y directions, respectively.

[0137] This step involves operating on the z-coordinate. Since the z-coordinate values ​​are irregular, we need to calculate the average. Therefore, we extract the z-coordinate values ​​from the first column and row, the middle column and row, and the last column and row. The number of coordinates extracted in this extraction unit is different from the previous unit. Taking the first row as an example, the extracted z-coordinates are the z-coordinate values ​​of the entire first row.

[0138] Extract the coordinates of the midpoint of the data in the x and y directions to obtain the midpoint information of the data. The x and y directions are the column and row directions, respectively. Extracting the coordinates of the midpoint of the data in the x and y directions is to locate the position of the middle column and row so as to extract the z coordinate of the middle column and row.

[0139] The z-coordinate value calculation and storage unit calculates the average z-coordinates of the starting, middle, and ending positions in the x and y directions to reflect the overall height change trend.

[0140] The angle judgment and calculation unit determines whether the average z-coordinate of the middle position is significantly higher or lower than the average value of the starting and ending positions, while ensuring that the difference between the average values ​​of the starting and ending positions is within the allowable range. If the above conditions are met, the rotation angle of the node is set to zero, indicating no significant rotation; otherwise, the slope angles in the x and y directions are calculated based on the difference in z-coordinates between the starting and ending positions, and these differences are converted into angle values ​​using the arctangent function.

[0141] The result storage and output unit scales the corrected rotation angles in the x and y directions according to a certain ratio and stores them in the result array, corresponding to the data unit being processed.

[0142] After processing all data units, output a result array containing the rotation angles of all nodes.

[0143] S104. Based on the initial spatial coordinates and rotation angle of the sensor node, determine the spatial coordinates of the sensor node. With the maximum comprehensive coverage rate as the second constraint, use the tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor node and determine the spatial deployment strategy of the sensor node in the monitoring area. The comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor node.

[0144] Optionally, the spatial coordinates of the sensor node are determined based on the initial spatial coordinates and rotation angle of the sensor node, including: determining the center coordinates of the cone base of the monitoring area covered by the sensor node based on the initial spatial coordinates and rotation angle of the sensor node; determining the sensing direction of the sensor node based on the center coordinates of the cone base and the initial spatial coordinates; and determining the spatial coordinates of the sensor node based on the sensing direction, scaling factor, and initial spatial coordinates.

[0145] The formula for calculating the spatial coordinates of a sensor node is:

[0146] New nc = Direction*τ + nc (21)

[0147] Direction = cc-nc (22)

[0148]

[0149] Where Newnc represents the spatial coordinates of the sensor node, Direction represents the sensing direction of the sensor node, τ represents the scaling factor, nc represents the initial spatial coordinates, and cc represents the coordinates of the center of the cone base of the sensor node. x cc y and cc z Let nc represent the coordinates of the center of the cone's base on the x-axis, y-axis, and z-axis, respectively. xand nc y Represent the x and y coordinates in the initial spatial coordinate system, respectively; Rs represents the sensing radius; θ represents half of the sensing range; degree results_α degree results_β This indicates the rotation angle of the sensor node in the horizontal direction and the rotation angle in the vertical direction.

[0150] like Figure 3 As shown, Figure 3 This is a schematic diagram of the sensing range of a sensor node, where the sensing direction of the sensor is... Since the sensing direction of the sensor node is vertically downward, the coordinates of the center of the cone's base are the coordinates of the intersection between the sensor node's vertically downward direction and the monitoring area.

[0151] In the high-level optimization stage, the tangent search quadratic interpolation optimization algorithm is used for optimization. The solution space of the population is between the initial position coordinates of the node and the center coordinates of the bottom surface of the node model cone (i.e., the node only moves in the current sensing direction after rotation). This can reduce the activity space of the population and improve the convergence of the solution. In the process of using the tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor node, the algorithm optimizes the scaling factor τ.

[0152] Optionally, the calculation method for the overall coverage rate specifically includes:

[0153] For any given grid point, the distance sensing of each sensor to the grid point is calculated based on the spatial coordinates of the sensor nodes and the coordinates of the grid point.

[0154]

[0155] in, This represents the distance perceived by the i-th sensor node to the j-th grid point, where dist represents the Euclidean distance between the i-th sensor node and the j-th grid point, (x i y i , z i Let (x) be the spatial coordinates of the i-th sensor node. j y j , z j Let R be the spatial coordinates of the j-th grid point. l R m R u These are custom threshold factors, which can be determined based on historical experience; parameters ν1, ν2, ω1, and ω2 are used to represent the sensing characteristics of the node. Different types of sensors can be simulated by adjusting the values ​​of these parameters, satisfying ν1+ν2=1, and ν1, ν2∈[0,1].

[0156] The angle perception of each sensor node relative to the grid point is calculated based on the horizontal and vertical deflection angles between the sensor node and the grid point.

[0157]

[0158] in, This indicates the angle perceived by the i-th sensor node relative to the j-th grid point. The deflection angle represents the combined deflection angle of the horizontal and vertical directions. Indicates the horizontal deflection angle. θ represents the vertical deflection angle. l θ m θ u These are custom threshold factors, with parameters λ1, λ2, μ1, μ2, and σ. PAN and σ TILT The sensing characteristics of the nodes can be represented by adjusting the values ​​of these parameters to simulate different types of sensors, satisfying λ1+λ2=1, and λ1, λ2∈[0,1]; the larger the angle between the sensing direction of the node and the vector direction determined by the two coordinate points of the node and the grid point, the smaller its sensing probability.

[0159]

[0160] Among them, new nc x (S i ), new nc y (S i ) and New nc z (S i ) represent sensor nodes S respectively i In spatial coordinates, the x, y, and z coordinates, X point (P j ), Y point (P j ) and Z point (P j ) represent grid points P respectively j The x, y, and z coordinate values ​​of the monitored area. x (S i ),cc y (S i ) and cc z (S i ) represent sensor nodes S respectively i The coordinates of the center of the base of the cone on the x-axis, y-axis, and z-axis are given by Direction(S). i Sensor node S i The direction of perception.

[0161] Based on the distance and angle perception of each sensor node to the grid points, the overall perception probability of each grid point is obtained; the formula for calculating the overall perception probability is:

[0162] P OV (S i ,P j ) = P Dist (S i ,P j )×P Agl (S i ,P j (32)

[0163]

[0164] Among them, P state (P j P represents the overall perception probability of the j-th grid point. th The threshold factor is represented and can be determined based on historical experience; N represents the number of sensor nodes.

[0165] The overall coverage rate is determined based on the overall perception probability of each grid point; the formula for calculating the overall coverage rate is as follows:

[0166]

[0167] Where CR represents the overall perception probability, and M represents the total number of grid points in the monitoring area.

[0168] It should be noted that the sensing range of a sensor node may be obstructed. Therefore, when calculating the overall sensing probability of a sensor node, line-of-sight perception can also be considered. Figure 4 As shown, let the i-th sensor node S i The set of perceptible target area points is P. Area Since regions A and B are not adjacent, their coordinate information differs, while the coordinates within each region are numerically continuous. Therefore, set P can be grouped based on the differences in coordinate information. Area Divided into P AreaA ={P1, P2, ..., P u} and P AreaB ={P1, P2, ..., P v Two subsets represent point sets in two regions. The ability of a region to be monitored is determined by calculating the average distance from the two subsets to the nodes. The subset with the closer average distance can be monitored and is considered as LOS; otherwise, it is NLOS, as shown in formula (36).

[0169]

[0170] Therefore, considering visual perception, the formula for calculating the overall perception probability is:

[0171] P OV (S i ,P j ) = P LOS (S i ,P j )×P Dist (S i ,P j )×P Agl (S i ,P j (37)

[0172] In one embodiment, the maximum comprehensive coverage rate is used as the fitness function. Under the constraint of the fitness function, the position of the search agent population is updated iteratively a specific number of times using the tangent search quadratic interpolation optimization algorithm. After the iteration is completed, the optimal spatial deployment strategy of the sensor nodes corresponding to the search agents is output by the tangent search quadratic interpolation optimization algorithm. The optimal spatial deployment strategy of the sensor nodes includes the spatial coordinates of each sensor node in the monitoring area.

[0173] The tangent search quadratic interpolation optimization algorithm in this embodiment is the same as the tangent search quadratic interpolation optimization algorithm for plane coordinate optimization described above, and will not be repeated here.

[0174] S105 deploys sensors in the monitoring area according to the spatial deployment strategy of sensor nodes.

[0175] The coverage rate of the sensor network for the covered area is used as an optimal fitness function. By maximizing this function, an ideal deployment method is obtained. To more effectively find the global optimum, an adaptive switching probability increases the exploration effort in the initial stage of the algorithm, ensuring that the population can broadly cover the entire search space, thereby enhancing the diversity of solutions. In the later stages of the algorithm, local searches are performed near known optimal solutions. Simultaneously, tangent search improves the convergence of the algorithm and accelerates the convergence speed, making it easier to find the optimal solution.

[0176] This invention employs a distributed optimization strategy based on a tangent search quadratic interpolation optimization algorithm, combining three key steps: planar optimization, angle optimization, and height optimization. First, the quadratic interpolation optimization algorithm is improved by proposing a tangent search quadratic interpolation optimization algorithm. By introducing adaptive switching probabilities and tangent search techniques, the algorithm's search speed and convergence are significantly improved. This algorithm is used to optimize the planar distribution of nodes within the monitoring area. Second, a scheme for calculating the horizontal and vertical deflection angles of nodes is proposed for node angle optimization. Finally, the tangent search quadratic interpolation optimization algorithm is used again to adjust the node height to achieve optimal coverage. This invention effectively solves the problems of low deployment efficiency and insufficient coverage of wireless sensor network nodes in three-dimensional realistic terrain.

[0177] In one embodiment, the present invention also provides a method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface, such as... Figure 5 As shown, this embodiment includes the following steps:

[0178] S501 determines the number of sensors based on the size of the monitored area and calculates the planar coverage based on the sensor nodes and grid points.

[0179] S502 uses the maximum planar coverage as the fitness function.

[0180] S503, under the constraint of the fitness function, uses the tangent search quadratic interpolation optimization algorithm to update the position of the search agent population a specific number of times.

[0181] S504 matches the sensor node positions obtained from the optimal planar deployment strategy with the grid point positions, and fixes the initial height of the sensor nodes.

[0182] S505 uses the matched grid points as the center to divide a grid area of ​​a specific size.

[0183] S506, based on the grid area data, calculates the sensor node rotation angle using the node rotation angle calculation method.

[0184] S507 calculates the overall coverage rate based on the location, angle, and grid points of the sensor nodes.

[0185] S508 uses the maximum overall coverage rate as the fitness function.

[0186] S509, under the constraint of the fitness function, uses the tangent search quadratic interpolation optimization algorithm to update the position of the search agent population a specific number of times.

[0187] S510, after the iteration is completed, outputs the optimal spatial deployment strategy for the sensor nodes corresponding to the agent searched by the tangent search quadratic interpolation optimization algorithm.

[0188] This invention employs a tangent search quadratic interpolation optimization algorithm, a node rotation angle calculation method, and a comprehensive coverage evaluation method to effectively optimize the coverage of wireless sensor networks on three-dimensional real terrain surfaces, achieving higher network coverage while significantly shortening computation time and improving optimization efficiency. In summary, this invention provides an efficient, reliable, and highly adaptable method and system for optimizing wireless sensor network coverage, significantly improving network coverage and possessing broad application prospects and practical value.

[0189] To further illustrate the above issues, a simulation is performed on the wireless sensor network coverage problem using the distributed optimization strategy based on the Tangent Search Quadratic Interpolation Optimization Algorithm (TS-QIODOS) employed in this invention. Consider 64 sensors deployed in an area of ​​100m × 100m. The population size is 50, and the sensor sensing radius R... S =25m. The number of iterations is 100. The grid size is considered to be 1m×1m, that is, there are 10,000 grid points used to calculate the coverage.

[0190] When the monitored area is 100m × 100m and there are 64 nodes, the coverage effect is as follows: Figure 6 As shown, the solid red circles represent sensor nodes. Figure 6 This is a coverage convergence curve graph compared to the comparison algorithm. From Figure 6 The distribution of nodes shows that the sensor nodes are more evenly distributed and have better coverage after implementing TS-QIODOS.

[0191] To compare the convergence performance of the algorithms, the coverage iterations of TS-QIODOS, the Quadratic Interpolation Optimization Algorithm (QIO), the Differential Evolution Algorithm with CR-sort and polynomial-based mutation (CCDEXSPM), and the Archimedes Optimization Algorithm (AOA) are compared. Figure 7As shown, the horizontal axis represents the number of iterations, and the vertical axis represents the coverage rate. Figure 7 As can be seen, the convergence speed of TS-QIODOS is faster and the coverage is significantly improved compared to the previous version.

[0192] Table 1 compares the running times of the four algorithms under different terrains. TS-QIODOS took 16396.35 seconds on plains, 20683.22 seconds on hills, and 14821.26 seconds on mountains. The running time of TS-QIODOS was 61.89% lower than that of the QIO algorithm and 27.5% lower than that of the CCDEXSPM algorithm, significantly lower than both QIO and CCDEXSPM algorithms.

[0193] Table 2 compares the energy consumption of the four algorithms under different terrains. In plains, hills, and mountains, the energy consumption of TS-QIODOS is 34.8016 J, 42.0692 J, and 43.1638 J, respectively. Taking plains as an example, compared to the QIO algorithm, TS-QIODOS reduces energy consumption by 67.66%; compared to the CCDEXSPM algorithm, by 97.37%; and compared to the AOA algorithm, by 36.15%. These data indicate that TS-QIODOS effectively shortens the node's movement distance and reduces the rotation angle, thereby reducing movement energy consumption and allowing more energy to be used for monitoring tasks, thus extending the overall network lifespan.

[0194] Table 1

[0195]

[0196] Table 2

[0197]

[0198] When applying the wireless sensor network coverage optimization method for three-dimensional real terrain surfaces provided by this invention, it is not necessary to consider... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0199] The above describes a method for optimizing wireless sensor network coverage on a three-dimensional real terrain surface, provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding system for optimizing wireless sensor network coverage on a three-dimensional real terrain surface, such as... Figure 8 As shown.

[0200] Figure 8A schematic diagram of a wireless sensor network coverage optimization system for a three-dimensional real terrain surface provided by the present invention is shown. The system 800 includes:

[0201] The planar optimization algorithm module 801 is used to optimize the planar coordinates of sensor nodes using the maximization of planar coverage as the first constraint, and to determine the planar deployment strategy of sensor nodes in the monitoring area by employing the tangent search quadratic interpolation optimization algorithm. The tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update methods of exploration, development and tangent search.

[0202] Angle optimization module 802 is used to match the sensor node position obtained by the planar deployment strategy of the sensor node with the grid point position of the monitoring area, fix the initial height of the sensor node, and determine the initial spatial coordinates of the sensor node; and calculate the rotation angle of the sensor node by taking the grid point matched with the sensor node as the center.

[0203] The height optimization module 803 is used to determine the spatial coordinates of the sensor node based on the initial spatial coordinates and rotation angle of the sensor node, and to optimize the spatial coordinates of the sensor node using a tangent search quadratic interpolation optimization algorithm with the maximum comprehensive coverage rate as the second constraint, thereby determining the spatial deployment strategy of the sensor node in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor node.

[0204] The deployment module 804 is used to deploy sensors in the monitoring area according to the spatial deployment strategy of the sensor nodes.

[0205] Optionally, the system 800 also includes a data processing module and a control and management module.

[0206] The data processing module is used to receive and import three-dimensional terrain data, sensor node parameters, and grid point distribution information of the monitoring area, and divide the terrain data into grid areas of a specific size to provide a basis for subsequent coverage calculations.

[0207] The control and management module is used to manage the number of iterations and termination conditions of the optimization algorithm, ensuring that the algorithm runs within the predetermined number of iterations, recording the optimal solution of each iteration, and ensuring that the final output deployment strategy is optimal.

[0208] Specific limitations regarding the wireless sensor network coverage optimization device for 3D realistic terrain surfaces can be found in the limitations of the wireless sensor network coverage optimization method for 3D realistic terrain surfaces described above, and will not be repeated here. Each module in the aforementioned wireless sensor network coverage optimization device for 3D realistic terrain surfaces can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0209] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for optimizing wireless sensor network coverage on a 3D real terrain surface is provided.

[0210] The present invention also provides Figure 9 The schematic diagram of the computer device shown is as follows: Figure 9 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 A method for optimizing wireless sensor network coverage on a 3D real terrain surface is provided.

[0211] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0212] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. A method for optimizing wireless sensor network coverage on a three-dimensional realistic terrain surface, characterized in that, include: Taking maximum planar coverage as the first constraint, the planar coordinates of the sensor nodes are optimized using the tangent search quadratic interpolation optimization algorithm to determine the planar deployment strategy of the sensor nodes in the monitoring area. The tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update methods of exploration, development and tangent search. The sensor node positions obtained by the planar deployment strategy of the sensor nodes are matched with the grid point positions of the monitoring area, and the initial height of the sensor nodes is fixed to determine the initial spatial coordinates of the sensor nodes. The rotation angle of the sensor node is calculated by taking the grid point that matches the sensor node as the center. Based on the initial spatial coordinates and rotation angle of the sensor node, the spatial coordinates of the sensor node are determined. With the maximum comprehensive coverage rate as the second constraint, the spatial coordinates of the sensor node are optimized using a tangent search quadratic interpolation optimization algorithm to determine the spatial deployment strategy of the sensor node in the monitoring area. The comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor node. Sensors are deployed in the monitoring area according to the spatial deployment strategy of the sensor nodes; The process, which uses maximum planar coverage as the first constraint and employs a tangent search quadratic interpolation optimization algorithm to optimize the planar coordinates of sensor nodes and determine their planar deployment strategy within the monitoring area, includes: initializing the population size of the search agent; the population position in the population size represents the planar coordinates of the sensor nodes; calculating the planar coverage of the search agent population based on the population position, and using the planar coverage as the fitness value; comparing the fitness values ​​of the search agent population, and selecting the search agent with the highest fitness value as the historical optimal position; using an adaptive switching probability to determine the population update method and updating the population position of the search agent; recalculating the historical optimal position based on the updated population position of the search agent until the maximum number of iterations is reached, and then outputting the historical optimal position and the planar deployment strategy of the sensor nodes corresponding to the historical optimal position. The method of using adaptive switching probability to determine the population update mode and update the population position of the search agent includes: obtaining a random number; if the random number is less than or equal to the adaptive switching probability, then updating the population position of the search agent using an exploration mode; if the random number is greater than the adaptive switching probability model, then determining whether the random number is greater than or equal to a specific value; if the random number is greater than or equal to the specific value, then updating the population position of the search agent using an exploration mode; if the random number is less than the specific value, then updating the population position of the search agent using a tangent search mode.

2. The method according to claim 1, characterized in that, Before calculating the planar deployment strategy of sensor nodes in the monitoring area, the method further includes: The number of sensor nodes that need to be deployed in the monitoring area is determined based on the sensing radius of the sensor nodes and the size of the monitoring area.

3. The method according to claim 1, characterized in that, The step of calculating the rotation angle of the sensor node, using the grid point matched with the sensor as the center, includes: For any grid point that matches a sensor node, the matched grid point is used as the center, and data is acquired in four directions (up, down, left, and right) according to a preset data length to obtain grid area data of a preset size. Obtain the first and last columns from the grid region data. x Coordinate values, and the first and last rows y Coordinate values; According to the coordinate values of all the data in the first column, the first row, the middle column, the middle row, the last column and the last row in the grid area data, calculate the z coordinate means of the first column, the first row, the middle column, the middle row, the last column and the last row respectively; z coordinate means; Based on the first and last columns x Coordinate values, and the first and last rows y Coordinate values, and the first column, first row, middle column, middle row, last column, and last row. z The average coordinates are used to determine the rotation angle of the corresponding sensor node.

4. The method according to claim 1, characterized in that, Determining the spatial coordinates of the sensor node based on its initial spatial coordinates and rotation angle includes: Based on the initial spatial coordinates and rotation angle of the sensor node, determine the coordinates of the center of the cone base of the monitoring area covered by the sensor node; The sensing direction of the sensor node is determined based on the coordinates of the center of the cone's base and the initial spatial coordinates. The spatial coordinates of the sensor node are determined based on the sensing direction, scaling factor, and initial spatial coordinates.

5. The method according to claim 4, characterized in that, The formula for calculating the spatial coordinates of the sensor node is: ； ； ； ； ； in, Represents the spatial coordinates of the sensor node. Indicates perceived direction. Indicates the scaling factor. Indicates the initial spatial coordinates. This indicates the coordinates of the center of the cone base of the sensor node. , and These represent the centers of the cone's base at... x axis, y shaft and z The coordinates of the axis, and These represent the initial spatial coordinates. x coordinate values ​​and y Coordinate values Indicates the sensing radius. This represents half of the perceptual range. , This indicates the rotation angle of the sensor node in the horizontal direction and the rotation angle in the vertical direction.

6. The method according to claim 5, characterized in that, In the process of optimizing the spatial coordinates of the sensor nodes using the tangent search quadratic interpolation optimization algorithm, the algorithm optimizes the scaling factor. .

7. The method according to claim 1, characterized in that, The calculation method for the overall coverage rate includes: For any grid point, calculate the distance sensing of each sensor to the grid point based on the spatial coordinates of the sensor nodes and the coordinates of the grid point. Calculate the angle perception of each sensor node relative to the grid point based on the horizontal and vertical deflection angles between the sensor node and the grid point; Based on the distance and angle perception of each sensor node to the grid point, the overall perception probability of each grid point is obtained, and the comprehensive coverage rate is determined based on the overall perception probability of each grid point.

8. A wireless sensor network coverage optimization system for three-dimensional realistic terrain surfaces, characterized in that, include: The planar optimization algorithm module is used to optimize the planar coordinates of sensor nodes using a tangent search quadratic interpolation optimization algorithm with maximizing planar coverage as the first constraint, thereby determining the planar deployment strategy of sensor nodes in the monitoring area. The tangent search quadratic interpolation optimization algorithm is an algorithm that integrates exploration, development, and tangent search methods for population updates. The step of optimizing the planar coordinates of sensor nodes using the tangent search quadratic interpolation optimization algorithm with maximizing planar coverage as the first constraint to determine the planar deployment strategy of sensor nodes in the monitoring area includes: initializing the population of search agents. The population size is defined as follows: the population position in the population size represents the planar coordinates of the sensor nodes; based on the population position, the planar coverage of the search agent population is calculated, and the planar coverage is used as the fitness value; the fitness values ​​of the search agent population are compared, and the search agent with the largest fitness value is taken as the historical best position; an adaptive switching probability is used to determine the population update method and update the population position of the search agent; based on the updated population position of the search agent, the historical best position is recalculated until the number of iterations reaches the maximum number of iterations, then the historical best position and the planar deployment strategy of the sensor nodes corresponding to the historical best position are output; The method of using adaptive switching probability to determine the population update mode and update the population position of the search agent includes: obtaining a random number; if the random number is less than or equal to the adaptive switching probability, then updating the population position of the search agent using an exploration mode; if the random number is greater than the adaptive switching probability model, then determining whether the random number is greater than or equal to a specific value; if the random number is greater than or equal to the specific value, then updating the population position of the search agent using an exploration mode; if the random number is less than the specific value, then updating the population position of the search agent using a tangent search mode. An angle optimization module is used to match the sensor node position obtained by the planar deployment strategy of the sensor node with the grid point position of the monitoring area, fix the initial height of the sensor node, determine the initial spatial coordinates of the sensor node, and calculate the rotation angle of the sensor node using the grid point matched with the sensor node as the center. The high-level optimization module is used to determine the spatial coordinates of the sensor nodes based on their initial spatial coordinates and rotation angles. Using the maximum overall coverage rate as a second constraint, it employs a tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor nodes and determine the spatial deployment strategy of the sensor nodes in the monitoring area. The overall coverage rate is obtained by considering both the distance and angle perception of the sensor nodes. The deployment module is used to deploy sensors in the monitoring area according to the spatial deployment strategy of the sensor nodes.

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