Communication node automatic deployment method and system applied to complex restricted space

Abstract

This disclosure relates to the field of wireless communication, providing a method and system for automatic deployment of communication nodes in complex and confined spaces. The method includes: constructing a grid map based on radar-visual perception data from an unmanned vehicle in the target space; constructing a passive node deployment scheme based on signal strength and key point markers in the grid map, quantifying the coverage area of ​​communication nodes using the grid map, and constructing an active node deployment scheme by combining a maximum rectangle coverage filling algorithm and a maximum resource reuse algorithm; constructing an electromagnetic environment map based on signal data and the grid map, evaluating the coverage performance of each communication node and optimizing the deployment location of communication nodes to generate an optimized node deployment scheme; and controlling an unmanned vehicle to autonomously explore the target space based on the grid map and the optimized node deployment scheme to complete the deployment of communication nodes. This disclosure can quickly construct temporary communication networks, solve the problem of establishing communication links in complex and confined spaces, and achieve rapid deployment and monitoring of unknown spaces.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a method and system for automatic deployment of communication nodes in complex and confined spaces. Background Technology

[0002] Disaster relief, industrial production monitoring, and other scenarios often involve numerous complex and confined spaces such as tunnels, storage tanks, and pipelines. These spaces are characterized by complex environmental structures, dense obstacles, limited signal propagation paths, and significant multipath effects, making it difficult to quickly establish stable, comprehensive communication links.

[0003] Wired communication typically relies on transmission media such as optical fibers and cables. While it has advantages such as high transmission speed and strong anti-interference ability, its deployment depends on physical cabling. In complex and confined spaces, it faces problems such as blocked cabling paths, long deployment cycles, and poor flexibility. It is especially unsuitable for scenarios that require rapid network setup, such as emergency rescue.

[0004] Traditional wireless communication methods such as WiFi and Bluetooth do not require physical wiring, but their signal penetration is weak. Obstacles such as walls, steel bars, and rocks in complex and confined spaces can cause significant signal attenuation. At the same time, wireless communication usually relies on fixed base stations or nodes, but complex and confined spaces often lack infrastructure support, making it difficult to directly form a network.

[0005] Emergency wireless communication methods typically include satellite communication and microwave relay communication. Satellite communication is easily limited by the overhead structure of confined spaces and cannot penetrate enclosed or underground spaces. Microwave relay communication requires line-of-sight propagation, and relay links are difficult to establish in complex, confined spaces with multiple obstructions, making it unsuitable for small or hazardous confined environments.

[0006] While manually deploying communication nodes can supplement local coverage, manual deployment can threaten the safety of operators, is inefficient, makes it difficult to accurately plan node locations, and can easily lead to signal blind spots or node redundancy, resulting in unstable network communication quality.

[0007] In summary, existing methods for deploying communication nodes in complex and confined spaces suffer from insufficient flexibility, incomplete signal coverage, low networking efficiency, and poor security, making it difficult to meet the communication requirements of "fast, secure, ubiquitous, and adaptive". Summary of the Invention

[0008] This disclosure aims to address at least one of the problems existing in the prior art, and to provide a method and system for automatic deployment of communication nodes in complex and confined spaces.

[0009] One aspect of this disclosure provides a method for automatically deploying communication nodes in complex and confined spaces, comprising: Based on the radar-visual perception data of the unmanned vehicle in the target space, a grid map of the propagation environment is constructed; A passive node deployment scheme is constructed based on the signal strength of the received signal data and the key point markers in the propagation environment grid map. The coverage of communication nodes is quantified by the propagation environment grid map. An active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm. An electromagnetic environment map is constructed based on signal data and a raster map of the propagation environment. The electromagnetic environment map is used to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, and to optimize the deployment position of the communication nodes, thereby generating an optimized node deployment scheme. Based on the propagation environment grid map and the optimized node deployment scheme, the unmanned vehicle is controlled to autonomously explore the target space and complete the deployment of communication nodes.

[0010] Optionally, based on the autonomous vehicle's radar-visual perception data of the target space, a raster map of the propagation environment is constructed, including: The target space is scanned using sensors on the autonomous vehicle to obtain radar vision perception data; Preprocessing of radar-visual perception data from unmanned vehicles; The target space is divided into several grids of equal size, and the pre-processed autonomous vehicle radar vision perception data is mapped to the corresponding grids. Based on the Bayesian update formula and the radar vision perception data of the unmanned vehicle, the grid state is derived to obtain the propagation environment grid map.

[0011] Optionally, the preprocessed autonomous vehicle radar-visual perception data is mapped to the corresponding grid, including: The data points in the preprocessed autonomous vehicle radar-visual perception data are projected onto the corresponding grid using the following formula: ; ; ; in, These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data within the raster map of the propagation environment. These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data. These are the x-axis, y-axis, and z-axis coordinates of the origin in the raster map of the propagation environment. It is the size of each grid cell. Points to the floor.

[0012] Optionally, a passive node deployment scheme is constructed based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, including: By combining the signal strength of the signal data and the detection results of key point markers in the propagation environment grid map, the deployment locations of communication nodes are determined, and a passive node deployment scheme is constructed.

[0013] Optionally, the deployment locations of communication nodes are determined by combining the signal strength of the signal data and the detection results of key point markers in the propagation environment grid map, including: When the signal strength of the signal data is lower than a predetermined threshold, analyze the signal coverage of the current area and use the geometric center point of the current area as the location for the deployment of communication nodes. If the raster map of the propagation environment contains marked key points, the location of the marked key point is selected as the deployment location of the communication node.

[0014] Optionally, the coverage area of ​​communication nodes is quantified by propagating an environmental raster map, and an active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm, including: The idle areas not yet covered by the current communication node are extracted using a raster map of the propagation environment. The idle areas are then processed using the maximum rectangle coverage and filling algorithm to determine the maximum filling problem for the idle areas. The idle area is simplified by using the concepts of horizontal slicing and maximal matrix. A greedy selection strategy that prioritizes the interval with the largest coverage area is adopted to solve the problem of maximizing the filling of the idle area and determine the placement of communication nodes. Based on the communication sensing requirements, the coverage area of ​​the communication node is divided into several sensing cells. The resource reuse scheme of adjacent sensing cells is determined by maximizing resource reuse algorithm in order to reduce the number of communication nodes deployed.

[0015] Optionally, an electromagnetic environment map is constructed based on signal data and a raster map of the propagation environment. This electromagnetic environment map is then used to evaluate the coverage performance of each communication node in both passive and active node deployment schemes, optimize the deployment locations of communication nodes, and generate an optimized node deployment scheme, including: Based on signal data and a raster map of the propagation environment, an electromagnetic environment map is constructed by simulating the propagation of electromagnetic waves in the corresponding environment using the ray tracing method. Based on electromagnetic environment map analysis of the current network coverage area, key indicators including energy efficiency, network coverage quality, and network connectivity are calculated. The particle swarm optimization algorithm is used to optimize the placement of each communication node in the passive node placement scheme and the active node placement scheme, generating an optimized node placement scheme.

[0016] Optionally, the placement of each communication node in the passive and active node deployment schemes can be optimized using a particle swarm optimization algorithm, including: The objective function of the particle swarm optimization algorithm is expressed as: ; in, Let Particle Swarm Optimization be the objective function. For energy efficiency, For network coverage quality, For network connectivity, These are the weights corresponding to energy efficiency, network coverage quality, and network connectivity, respectively. The velocity and position of the particles corresponding to the distribution locations of each communication node are updated according to the following formula: ; ; in, It is a particle In the The speed of generation It is a particle In the The speed of generation It is a particle In the The position of the generation, It is a particle In the The position of the generation, It is a particle The best historical location It is the globally optimal solution. It is inertial weight. and Both are acceleration constants. and All are random numbers.

[0017] Optionally, based on the propagation environment grid map and the optimized node deployment scheme, the unmanned vehicle is controlled to autonomously explore the target space to complete the deployment of communication nodes, including: Based on the propagation environment grid map and the optimized node deployment scheme, exploration target points are generated. Based on the exploration target points and the communication node deployment positions determined by the optimized node deployment scheme, the global path for the autonomous vehicle is planned. The autonomous vehicle is controlled to travel along a global path in the target space. When the autonomous vehicle reaches the communication node deployment location, the communication node is released, and the driving path is replanned to deploy the communication node again until all communication nodes are deployed.

[0018] Another aspect of this disclosure provides an automatic deployment system for communication nodes applied in complex and confined spaces, comprising: The map building module is used to construct a raster map of the propagation environment based on the radar vision perception data of the unmanned vehicle in the target space. The deployment scheme construction module is used to construct a passive node deployment scheme based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, and to quantify the coverage of communication nodes through the propagation environment grid map, and to construct an active node deployment scheme by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm. The optimization module is used to construct an electromagnetic environment map based on signal data and a raster map of the propagation environment. It uses the electromagnetic environment map to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, optimizes the deployment position of the communication nodes, and generates an optimized node deployment scheme. The deployment module is used to control the unmanned vehicle to autonomously explore the target space and complete the deployment of communication nodes based on the raster map of the propagation environment and the optimized node deployment scheme.

[0019] Compared with existing technologies, this disclosure enables unmanned vehicles to autonomously decide the deployment location of communication nodes, quickly build temporary communication networks, solve the problem of establishing communication links in complex and confined spaces, and achieve rapid deployment and monitoring of unknown spaces. Attached Figure Description

[0020] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0021] Figure 1 A flowchart illustrating an automatic deployment method for communication nodes in a complex and confined space, provided as an embodiment of this disclosure; Figure 2 This is a flowchart illustrating an automatic deployment method for communication nodes in complex and confined spaces, provided as another embodiment of this disclosure. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the various embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this disclosure to facilitate a better understanding of the disclosure. However, the technical solutions claimed in this disclosure can be implemented even without these technical details and with various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of this disclosure. The various embodiments can be combined with and referenced by each other without contradiction.

[0023] One embodiment of this disclosure provides an automatic deployment method for communication nodes in complex and confined spaces, the process of which is as follows: Figure 1 As shown, it includes the following steps S1 to S4. The following is in conjunction with... Figure 2 Steps S1 to S4 will be explained in detail.

[0024] Step S1: Construct a raster map of the propagation environment based on the radar vision perception data of the unmanned vehicle in the target space.

[0025] Specifically, step S1 is used to construct a raster map of the propagation environment, which includes the following steps S1.1 to S1.4. The target space can be any space where communication nodes need to be deployed, especially complex and confined spaces.

[0026] Step S1.1, Perception Data Acquisition: Use the sensors on the unmanned vehicle to scan the target space and acquire the unmanned vehicle's radar vision perception data.

[0027] Specifically, step S1.1 involves using onboard sensing hardware, i.e., sensors on the autonomous vehicle, to perform a large-scale scan of the target space to acquire autonomous vehicle radar-visual perception data. These sensors may include LiDAR and vision sensors. Step S1.1 uses sensors on the autonomous vehicle to collect environmental data of the target space and transforms the collected environmental data from the sensor coordinate system to the autonomous vehicle coordinate system. Based on the autonomous vehicle's position and attitude in the world coordinate system, the sensor information, i.e., the environmental data in the autonomous vehicle coordinate system, is mapped to the world coordinate system, thereby obtaining the autonomous vehicle radar-visual perception data.

[0028] For example, autonomous vehicle radar-visual perception data includes timestamps and radar-visual point cloud data. The radar-visual point cloud data comprises point cloud information from LiDAR and visual sensors. In the radar-visual point cloud data, each point is typically represented by three-dimensional spatial coordinates, including x-axis, y-axis, and z-axis coordinates, to indicate the point's precise location in the environment. Point cloud information from LiDAR provides spatial coordinates based on distance and angle, while visual sensors generate three-dimensional coordinates through image processing algorithms or depth sensors. In this way, LiDAR data and visual data can be effectively fused to provide accurate 3D environmental modeling.

[0029] Step S1.2, Perception Data Preprocessing: Preprocess the radar-visual perception data of the unmanned vehicle.

[0030] Specifically, step S1.2 is used to preprocess the radar vision perception data of the unmanned vehicle after receiving it, use filtering methods to remove noise, and then segment, model and recombine the radar vision perception data of the unmanned vehicle.

[0031] The acquisition of radar-based perception data for autonomous vehicles is often affected by factors such as object occlusion and uneven lighting, which can easily create blind spots and holes in the scanning of complex-shaped objects. Furthermore, due to the limited scanning range, large objects or large-scale scenes cannot be fully measured in one go and must be scanned multiple times. Therefore, the scanning results often contain multiple pieces of perception data with different coordinate systems and noise, which cannot fully meet the requirements of realism and real-time performance of digital models. Therefore, preprocessing such as denoising, simplification, registration, and hole filling is necessary for the 3D radar-based perception data of autonomous vehicles.

[0032] First, the autonomous vehicle's radar-sensing data undergoes denoising processing. Specifically, a nonlinear median smoothing filter is first used to calculate the median of three or more adjacent data points for a given point in the radar-sensing data. This result replaces the original value, effectively removing jagged noise while preserving edge features. Next, the average curvature flow method is used for denoising. Curvature is estimated before denoising, which is effective for data with simple models and few noise points. These two filtering methods—nonlinear median smoothing and average curvature flow—can remove systematic and random errors in the autonomous vehicle's radar-sensing data caused by weak reflected signals due to object surface materials, people passing between the scanner and objects, scanner accuracy, and camera resolution.

[0033] Subsequently, duplicate data in the autonomous vehicle's radar-visual perception data was removed. Specifically, a gridded downsampling method was used to divide the 3D autonomous vehicle radar-visual perception data into fixed-size 3D grids, such as 1m×1m×1m grids, with only the center point retained in each grid. This significantly reduces the amount of data while preserving the overall spatial structure of the data. Furthermore, a multi-resolution downsampling strategy can be used for regions at different distances. High resolution is maintained in close-range areas, such as those within a 5-meter radius of the autonomous vehicle, while larger grid sizes and downsampling are used in distant areas, thus achieving a reasonable allocation of computing resources.

[0034] Finally, for more complex scanning objects, directly modeling the perception data would make the modeling process extremely difficult. Therefore, we first segment the perception data, then model each segment separately, and finally combine them. We use an edge-based segmentation method to segment the autonomous vehicle's radar-visual perception data. First, we find feature lines, i.e., lines connecting feature points. We then extract feature points using curvature and normal vector extraction methods, typically using points with abrupt changes in curvature or normal vectors, such as inflection points or corners. We then connect the extracted feature points to obtain the feature lines. After extracting the feature lines, we segment the areas enclosed by these feature lines. This completes the preprocessing of the autonomous vehicle's radar-visual perception data.

[0035] Step S1.3, Perception Data Raster Mapping: Divide the target space into several grids of equal size, and map the preprocessed autonomous vehicle radar perception data to the corresponding grids.

[0036] Specifically, step S1.3 first divides the real environment of the target space into several grids of equal size. Each grid contains characteristic information of the map area. Then, using a transformation formula, the points in the preprocessed autonomous vehicle radar perception data are projected onto the propagation environment grid map.

[0037] In other words, the real environment of the target space is first divided into several equally sized grids, each containing characteristic information of the map area. Then, points in the preprocessed unmanned vehicle radar perception data are projected onto the propagation environment grid map using a transformation formula. For example, mapping the preprocessed unmanned vehicle radar perception data to the corresponding grids includes: projecting data points in the preprocessed unmanned vehicle radar perception data to the corresponding grids using the following formula: ; ; ; in, These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data within the raster map of the propagation environment. These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data. These are the x-axis, y-axis, and z-axis coordinates of the origin in the raster map of the propagation environment. It is the size of each grid cell. Points to the floor.

[0038] Through the projection process, points in the preprocessed autonomous vehicle radar-sensing data are mapped onto specific grid cells of the propagation environment grid map. After projection, the map area characteristics contained in each grid cell of the propagation environment grid map are categorized into three states: occupied, free, and unknown. An occupied state indicates that the grid cell contains autonomous vehicle radar-sensing data (i.e., it is occupied). A free state indicates that the grid cell does not contain autonomous vehicle radar-sensing data (i.e., it is idle). An unknown state indicates that the state of the grid cell is unknown and is used to mark areas outside the sensor range, i.e., areas not scanned by the autonomous vehicle.

[0039] Step S1.4, Raster Map Update: Based on the Bayesian update formula and the radar vision perception data of the unmanned vehicle, the raster state is derived to obtain the propagation environment raster map.

[0040] Specifically, step S1.4 is used to derive the grid state update map based on the Bayesian update formula and the measurement values ​​in the autonomous vehicle's radar vision perception data, and the data can be visualized using tools.

[0041] Because autonomous vehicle radar-visual perception data needs to be collected periodically to update the environmental map, the grid states in the propagation environment grid map need to be updated when new preprocessed autonomous vehicle radar-visual perception data is projected onto the propagation environment grid map. At this point, it is necessary to calculate the state probability of each grid in the propagation environment grid map.

[0042] The dynamic update of the grid state is based on the Bayesian update formula, which adjusts the occupancy probability of each grid by fusing current observation data with prior information. The Bayesian update formula is as follows: ; in, Let be the posterior probability of the grid. Let be the prior probability of the grid. Let be the likelihood function under sensor observation conditions. This is the normalization factor. During the state update process, if a grid cell is observed to be idle multiple times, its occupancy probability gradually approaches 0; if a grid cell is observed to have obstacles multiple times, its occupancy probability gradually approaches 1. Through continuous observation fusion, the accuracy of the grid state is continuously improved, ultimately generating a high-precision 3D grid map reflecting the actual environment as a propagation environment grid map.

[0043] Finally, the propagation environment grid map is visualized using tools to facilitate analysis and navigation by external command centers. For example, occupied grids can be displayed in black to represent environmental boundaries; free grids can be displayed in white; and grids in unknown states can be displayed in gray or other colors. In addition to grid states, the propagation environment grid map also provides the coordinates of key points. These key points include spatial entrances and exits, intersections, and steep slopes, which typically represent important geographical locations or signal processing areas requiring close monitoring.

[0044] Step S2: Based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, a passive node deployment scheme is constructed. The coverage range of communication nodes is quantified through the propagation environment grid map. An active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm.

[0045] Specifically, step S2 is used to construct a node deployment scheme, which includes the following steps S2.1 to S2.4. Among them, constructing a passive node deployment scheme based on the signal strength of the received signal data and the key point markers in the propagation environment grid map includes: combining the signal strength of the signal data and the detection results of key point markers in the propagation environment grid map to determine the deployment location of the communication nodes and construct the passive node deployment scheme.

[0046] For example, the deployment location of communication nodes is determined by combining the signal strength of the signal data and the detection results of key point markers in the propagation environment grid map. Specifically, this includes: when the signal strength of the signal data is lower than a predetermined threshold, analyzing the signal coverage of the current area and taking the geometric center point of the current area as the deployment location of the communication node; detecting whether the propagation environment grid map contains marked key points, and if so, prioritizing the location of the marked key points as the deployment location of the communication node.

[0047] The following section, in conjunction with steps S2.1 and S2.2, details the construction process of the passive node deployment scheme.

[0048] Step S2.1. Signal strength monitoring: Based on the signal strength of the signal data, analyze the signal coverage of the current area and initially construct a passive node deployment scheme.

[0049] Specifically, the autonomous vehicle periodically subscribes to signal data from wireless communication hardware. When the signal strength in the received signal data falls below a predetermined threshold, it triggers a deployment decision process. First, the signal coverage of the current area is analyzed, and the geometric center of the current area is calculated as a potential deployment location, i.e., the communication node deployment location. Based on this deployment location, and according to regional needs and communication objectives, the geometric center point of the current area is chosen as the target point for the deployment node.

[0050] Step S2.2. Key point identification detection: Combine the key point markings in the propagation environment grid map to further optimize the node deployment strategy, obtain a passive node deployment scheme, and ensure priority coverage of important areas.

[0051] Specifically, the autonomous vehicle continuously reads real-time updated propagation environment grid map data, which includes the coordinates of key points in the target spatial environment. Upon receiving the propagation environment grid map data, it first analyzes whether the current propagation environment grid map contains any marked key points, that is, it detects whether the propagation environment grid map contains marked key points. If so, it prioritizes the locations of these marked key points as the communication node deployment locations, i.e., the target points of the deployment nodes, to ensure priority coverage in key areas.

[0052] By combining the communication node deployment locations determined by signal strength with those determined by key point marker detection results, a passive node deployment scheme is constructed.

[0053] For example, in step S2, the coverage area of ​​communication nodes is quantified by the propagation environment grid map, and an active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm. This includes: extracting the uncovered areas of the current communication nodes using the propagation environment grid map, processing the uncovered areas using the maximum rectangle coverage filling algorithm, and determining the maximum filling problem of the uncovered areas; simplifying the uncovered areas using the concepts of horizontal slicing and maximal matrix, and solving the maximum filling problem of the uncovered areas using a greedy selection strategy that prioritizes the interval with the largest coverage area, thereby determining the deployment location of the communication nodes; and dividing the coverage area of ​​the communication nodes into several sensing cells according to the communication sensing requirements, and determining the resource reuse scheme of adjacent sensing cells using the maximum resource reuse algorithm to reduce the number of communication nodes deployed.

[0054] The construction process of the active node deployment scheme will be explained in detail below, in conjunction with steps S2.3 and S2.4.

[0055] Step S2.3. Empty Area Extraction and Filling: Use the raster map of the propagation environment to extract the empty areas that are not yet covered by the current communication node, process the empty areas using the maximum rectangle coverage filling algorithm, and pre-distribute the nodes into these empty areas to determine the maximum filling problem of the empty areas in order to maximize the coverage efficiency.

[0056] Specifically, in the process of generating the active node deployment scheme, the propagation environment grid map is first spatially analyzed to identify unoccupied or uncovered areas as idle areas, which are then extracted from the overall propagation environment grid map to provide basic data for subsequent coverage optimization, ensuring that node deployment or other coverage operations can be carried out efficiently in the idle areas.

[0057] Then, the maximum rectangle cover-fill algorithm is used to decompose the free area into several rectangles. Random points are used as the starting points for decomposition to quickly generate a rectangle covering scheme. The maximum rectangle cover-fill algorithm assumes that the given free area is a polygonal region composed of multiple unit grids, and the goal is to cover it using the minimum number of rectangles possible. This problem, as the free area maximization filling problem, can be formally described as: Maximize perception coverage: ; in, Indicates the first The coverage area of ​​the rectangle, This represents the total number of rectangles.

[0058] Since optimizing the maximum rectangle filling problem within a polygonal region is difficult, this implementation proposes a strategy to simplify the polygonal region using horizontal slices and the concept of maximal rectangles. Specifically, the empty regions are simplified using horizontal slices and maximal rectangles. First, by sweeping a horizontal line across the polygonal region from top to bottom and recording the intersection points of the horizontal line and the vertical boundary of the polygonal region, equivalent horizontal slices are deleted, resulting in a set of unequal horizontal slices. These horizontal slices can be considered as the source of a unique maximal rectangle within the polygonal region. Then, the set of unequal horizontal slices is projected onto... On the axis, we obtain a set of real number intervals. By dividing the rectangle into horizontal slices, the original three-dimensional covering problem can be simplified into multiple one-dimensional interval problems. Using intervals to represent the horizontal projection of each rectangle, a new covering representation is obtained. This simplified form is helpful for subsequent optimal covering solutions.

[0059] Next, a greedy selection strategy that prioritizes the interval with the largest coverage area is adopted to solve the problem of maximizing the filling of the idle area and determine the placement of communication nodes.

[0060] Specifically, through the simplification described above, the rectangular coverage problem can be transformed into an interval optimization problem, the goal of which is to find a method to achieve optimal coverage by reducing the number of intervals. This can be formalized as the following optimization objective: ;in, The goal of this minimum generating set is to select the minimum number of intervals such that each unit grid is covered by at least one interval. Represents the set of all possible intervals. express The elements in the interval represent a possible range. Indicates an unused area.

[0061] To achieve the above optimization objectives, a greedy selection strategy is adopted, which prioritizes selecting the interval with the largest coverage area until the entire free area is covered, in order to achieve the goal of optimal coverage and minimal node usage.

[0062] For example, the input idle area is first analyzed to determine if its area exceeds the preset maximum sensor cell range. If so, the maximum rectangle coverage and filling algorithm is used to iteratively calculate the idle area multiple times. In each iteration, an arbitrary point is selected as the decomposition starting point, the maximum inscribed rectangle of the current area is extracted, and the remaining idle area is updated. After multiple iterations, the coordinates of the largest rectangle that can be obtained are output. This iterative filling method can achieve efficient coverage of idle areas while minimizing resource consumption.

[0063] Step S2.4. Sensitive Cell Division and Resource Reuse: Based on the communication sensing requirements, the coverage area of ​​the communication nodes is divided into several sensitive cells. The resource reuse scheme of adjacent sensitive cells is determined by maximizing resource reuse algorithm to reduce the number of communication nodes deployed, construct an active node deployment scheme, avoid resource waste, and ensure that the coverage quality is not affected.

[0064] Specifically, based on communication sensing requirements, after coverage filling is completed, the coverage area of ​​the communication nodes needs to be divided into several sensing cells to achieve partitioned management and allocation of communication resources. This step is based on Wi-Fi sensing technology and uses Fresnel zone theory to determine the geometric boundaries of the sensing cells. In a system consisting of one transmitter and two receivers (the transmitter and receivers are the communication nodes), the effective propagation range of the signal is the superimposed area between two Fresnel zones, which can be approximated as a rectangular area in practical applications. Specifically, when calculating the sensing cell, the channel state information data of the multipath signal is first extracted based on the propagation path between the transmitter and the two receivers. Then, the superimposed area of ​​the Fresnel zones on the two signal propagation paths is discretized, and its circumscribed rectangle is defined as the geometric boundary of a sensing cell. This approximation simplifies the modeling of the sensing range while preserving the accuracy of environmental information.

[0065] Specifically, when the area of ​​the rectangle output by the maximum rectangle coverage and fill algorithm is less than the preset maximum sensing cell range, the rectangle coordinates are directly output; when the area of ​​the rectangle output by the maximum rectangle coverage and fill algorithm exceeds the preset maximum sensing cell range, these rectangles are further divided into multiple sub-rectangles that meet the maximum sensing cell limit, and the coordinates of the sub-rectangles are output.

[0066] According to the rules for dividing interconnected cells, two adjacent rectangular areas can reuse resources by sharing transmitters and receivers, thereby significantly reducing system resource consumption while meeting network coverage and target detection requirements. Specifically, the coordinates of the rectangles output by the maximum rectangle coverage filling algorithm are received, and it is determined whether different rectangles are adjacent. If different rectangles are adjacent, they share transmitters and receivers. All rectangles are traversed to maximize the reuse of all transmitters and receivers. Finally, the deployment positions of all communication nodes are output, thus obtaining the active node deployment scheme.

[0067] After obtaining the passive node deployment scheme and the active node deployment scheme, the two schemes are combined to form a preliminary node deployment scheme.

[0068] Step S3: Construct an electromagnetic environment map based on signal data and a raster map of the propagation environment. Use the electromagnetic environment map to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, optimize the deployment position of the communication nodes, and generate an optimized node deployment scheme.

[0069] Specifically, step S3 is used to optimize the node deployment scheme, which includes the following steps S3.1 and S3.2.

[0070] Step S3.1, Electromagnetic Environment Map Construction: Based on signal data and a raster map of the propagation environment, an electromagnetic environment map is constructed by simulating the propagation of electromagnetic waves in the corresponding environment using ray tracing. That is, based on the input signal data and raster map, an electromagnetic environment map of the real environment of the target space is constructed, and ray tracing technology is used to simulate the propagation path of signals in the complex environment of the target space, analyzing the coverage performance of different nodes at their respective locations.

[0071] After obtaining the raster map of the propagation environment and the locations of the communication nodes, these locations are considered as potential transmitter sites, and ray tracing is used to simulate the propagation of electromagnetic waves in the specific environment of the target space. This simulation process considers multiple electromagnetic wave propagation phenomena, including reflection, refraction, scattering, and diffraction, to construct a detailed electromagnetic environment map.

[0072] Specifically, when constructing the electromagnetic environment map, a more detailed and comprehensive environmental context is first built by integrating the previously obtained propagation environment grid map and the geometric shape, material properties, and reflectivity information of buildings in the target space. This environmental context covers various elements in the target space environment, such as buildings, terrain, obstacles, and vegetation, and takes into account their location, shape, and characteristics within the environment.

[0073] Next, the locations of the communication nodes are identified as potential transmitter locations. Based on the antenna type and radiation characteristics of objects at these locations, rays are created from each transmitter antenna to simulate the propagation path of electromagnetic waves. Propagation calculations are performed on all rays to analyze electromagnetic wave propagation phenomena, including reflection, refraction, scattering, and diffraction. During this process, information on the material properties, reflectivity, and absorptivity of objects in the target space scene is integrated to simulate electromagnetic wave behavior.

[0074] During the propagation of the ray, intersection detection with objects in the scene is performed to determine whether the ray is blocked, reflected, or refracted by the object, thus identifying the propagation path of the electromagnetic wave at different locations. When each ray reaches a different location, the power and phase of the ray are calculated to reflect the propagation loss and signal characteristics of the electromagnetic wave along the corresponding propagation path.

[0075] Finally, all the calculation results from the simulation process are integrated to generate an electromagnetic environment map, which includes electromagnetic environment information such as signal strength, phase information, multipath effects, and signal-to-noise ratio.

[0076] Step S3.2, Node Deployment Scheme Optimization: Based on the electromagnetic environment map analysis of the current network coverage area, calculate key indicators including energy efficiency, network coverage quality, and network connectivity. Combine the particle swarm optimization algorithm to optimize the deployment positions of each communication node in the passive node deployment scheme and the active node deployment scheme, and generate the optimized node deployment scheme.

[0077] Specifically, step S3.2 can evaluate the performance of the current node deployment scheme, including passive node deployment scheme and active node deployment scheme, based on indicators such as coverage range and signal strength uniformity. The incremental displacement adjustment algorithm is used to optimize and iterate the deployment position of communication nodes based on the evaluation results, prioritizing the correction of poor-performing areas, and generating an optimized communication node deployment scheme as the final node deployment scheme.

[0078] After obtaining an electromagnetic environment map, the impact of communication node deployment on network performance can be assessed using electromagnetic environment information, leading to optimization of the communication node deployment. In the optimization of node deployment schemes based on electromagnetic environment maps, the goal is to achieve maximum network coverage with minimal energy consumption and cost through effective network planning and optimization strategies. The core objective of communication node deployment is to optimize the following three key indicators: energy efficiency, network coverage quality, and network connectivity.

[0079] (1) Energy efficiency: By rationally arranging communication nodes and controlling power allocation, energy efficiency can be optimized to reduce unnecessary energy consumption. For example, communication nodes in the network Transmission power It can be dynamically adjusted based on distance and channel conditions to ensure that the transmission power does not exceed the required minimum value: ; in, This is the path loss index. It is a low power threshold. This is a reference distance. It is a communication node The actual transmission distance.

[0080] (2) Network coverage quality: Network coverage quality is mainly used to measure the coverage range of a network within a given area, and is usually evaluated by signal strength. For example, for communication nodes... The strength of the signal it receives It can be represented as: ; in, and These are the transmit antenna gain and the receive antenna gain, respectively. It is a communication node The distance from the corresponding receiving point to the transmitter. It is the number of communication nodes. It is the reflection coefficient of the signal. It is an attenuation factor that occurs during transmission and reflection.

[0081] (3) Network connectivity: Communication nodes in the network need to be connected to avoid dead zones or communication blind spots. By optimizing the deployment location and power allocation of communication nodes, the overall connectivity of the network can be improved, ensuring that the signal coverage and communication quality between nodes meet the predetermined standards.

[0082] After identifying the core targets for communication node deployment, the deployment locations of communication nodes are optimized and corrected based on the electromagnetic environment map. This mainly involves analyzing the current network coverage area, identifying areas with insufficient signal coverage or strong interference, and adjusting the distribution and location of communication nodes to optimize the performance of the entire network.

[0083] To achieve balanced optimization among multiple objectives, a particle swarm optimization algorithm is used to optimize the node positions. The objective function of the particle swarm optimization algorithm is expressed as: ; in, Let be the objective function of the particle swarm optimization algorithm; For energy efficiency, For network coverage quality, For network connectivity, The specific values ​​are determined based on the evaluation results of existing technologies on energy efficiency, network coverage quality, and network connectivity. The weights for energy efficiency, network coverage quality, and network connectivity are used to control the importance of each objective.

[0084] The velocity and position of the particles corresponding to each communication node's placement location are updated according to the following formula, where each particle corresponds to a communication node, the initial position of each particle is the placement location of each communication node in the current node placement scheme, including both passive and active node placement schemes, and the final position of each particle is the final placement location of each communication node: ; ; in, It is a particle In the The speed of generation It is a particle In the The speed of generation It is a particle In the The position of the generation, It is a particle In the The position of the generation, It is a particle The best historical location It is the globally optimal solution. It is inertial weight. and Both are acceleration constants; and All of these are random numbers, used to increase the randomness of the algorithm.

[0085] After optimizing the deployment locations of communication nodes, the final node deployment plan is output, including the specific location coordinates of the communication nodes, the coverage range and coverage effect evaluation of each communication node, resource reuse, and node count statistics.

[0086] In the optimization of communication node deployment locations, the electromagnetic environment map provides crucial information, and the communication node deployment optimization strategy based on the particle swarm optimization algorithm effectively improves network performance. First, the optimized communication node layout effectively expands the network coverage area, reduces blind spots or areas with weak signals, thereby improving overall coverage. Second, a reasonable distribution of communication nodes reduces interference between nodes and lowers energy consumption, thus optimizing network energy efficiency. Finally, the correction of communication node deployment locations helps improve the connection quality between communication nodes in the network, reducing connection drops and communication delays, thereby enhancing network robustness and stability.

[0087] Step S4: Based on the propagation environment grid map and the optimized node deployment scheme, control the unmanned vehicle to autonomously explore the target space and complete the deployment of communication nodes.

[0088] Specifically, step S4 is used for autonomous exploration planning and node deployment of unmanned vehicles, which includes the following steps S4.1 and S4.2.

[0089] Step S4.1, Autonomous Exploration Planning of Unmanned Vehicle: Based on the propagation environment grid map and the optimized node deployment scheme, control the unmanned vehicle to drive autonomously in the unknown environment of the target space, generate exploration target points, and plan and generate the global path for the unmanned vehicle to drive according to the exploration target points and the communication node deployment positions determined by the optimized node deployment scheme.

[0090] Specifically, the autonomous vehicle uses a combination of environmental grid maps and LiDAR for autonomous exploration and planning. This process is mainly divided into two parts: autonomous exploration and navigation planning. The autonomous exploration part focuses on autonomously identifying and mapping in unknown environments, while the navigation planning part focuses on how to adjust the driving path in real time in dynamic environments to ensure that the autonomous vehicle can reach its destination safely and efficiently.

[0091] The main workflow for the independent exploration portion is as follows: (1) Boundary Raster Generation: Based on the propagation environment raster map, all rasters are first traversed. Unexplored rasters and rasters occupied by obstacles are excluded by setting a threshold, thus obtaining all passable rasters. Next, a search is performed within a certain area around these passable rasters, and rasters adjacent to unexplored rasters are marked as boundary rasters. This process provides basic data for subsequent exploration and path planning.

[0092] (2) Boundary Generation: After the boundary grid is determined, the boundary grids within a certain range are classified to form the same boundary. Through this process, a series of clear boundary lines can be generated. Subsequently, the length of each boundary is calculated, and boundaries with a length less than a set threshold are deleted. This measure aims to retain boundaries with practical significance, thereby improving the efficiency and accuracy of subsequent processing.

[0093] (3) Target point generation: After the boundary is generated, the weight of each point on the boundary is calculated based on the current pose of the autonomous vehicle, the size of the boundary, and the relative distance. The point with the largest weight is selected as the target point for this exploration. This strategy aims to ensure the exploration efficiency of the autonomous vehicle, minimize unnecessary movement, and optimize the overall exploration process.

[0094] The main workflow for navigation planning is as follows: (1) Target point acquisition: Target points are obtained from two sources: target points generated through autonomous exploration and communication node placement positions in the optimized node placement scheme provided by the host computer. Among them, the communication node placement positions provided by the host computer have higher priority and need to be verified. If the communication node placement position is too close to an obstacle or located in a position that the planning algorithm considers unreachable, a certain range around it is searched to find a suitable placement point. If a suitable placement point is found, it is used as the target point; otherwise, the task is abandoned.

[0095] (2) Global path generation: A global cost map is generated based on the propagation environment grid map. In this global cost map, the closer the grid is to the obstacle, the higher the value, and the farther the grid is from the obstacle, the lower the value. Then, a heuristic algorithm is used to perform path search on the global cost map to obtain a path with the lowest cost from the current point to the target point.

[0096] (3) Local trajectory generation: A local cost map is constructed based on lidar data, and its principle is similar to that of the global cost map. Points on the global path are selected sequentially as local target points for trajectory planning, and the trajectory and speed command to reach the local target point are generated.

[0097] Step S4.2, Node Deployment: Control the unmanned vehicle to travel along the global path in the target space. When the unmanned vehicle travels to the target point of the communication node deployment location, release the communication node, replan the driving path and deploy the communication node again until all communication nodes are deployed.

[0098] Specifically, once the unmanned vehicle moves to the designated area of ​​the target point, if the target point was obtained through autonomous exploration, the vehicle will immediately stop its current path planning and wait for the generation of the next target point. If the target point is a location deployed by a communication node assigned by a host computer, the drone needs to adjust its position before the communication node is deployed. In essence, the process from the starting point to the target point is divided into three stages: normal movement, orientation fine-tuning, and position fine-tuning.

[0099] (1) Normal Movement Phase: When the autonomous vehicle receives a new target point of a communication node to be deployed, it enters the normal movement phase. In this phase, the autonomous vehicle uses the Timed Elastic Band (TEB) algorithm to navigate from the current starting point to the target point, while also implementing obstacle avoidance. When the distance between the autonomous vehicle and the target point is less than a preset distance threshold, this phase ends and the vehicle enters the orientation fine-tuning phase.

[0100] (2) Orientation fine-tuning stage: In this stage, the unmanned vehicle does not move horizontally, but only rotates in place. At this time, the unmanned vehicle rotates towards the target point in the desired orientation, thereby reducing the orientation deviation. This stage uses a PID algorithm, and theoretically the orientation error is 0. In actual use, in order to reduce the adjustment time and speed up the positioning, the orientation deviation threshold is set to a preset radian, such as 0.1 radian. When the orientation deviation of the unmanned vehicle is less than the orientation deviation threshold, i.e., the preset radian, such as 0.1 radian, the orientation fine-tuning stage ends and the position fine-tuning stage begins.

[0101] (3) Position fine-tuning stage: In this stage, the unmanned vehicle only moves horizontally and does not change its orientation. The PID algorithm is also used in this stage, and the theoretical position error is 0. In actual use, the position deviation threshold is set to a preset distance, such as 0.2 meters. The position fine-tuning stage ends when the position deviation of the unmanned vehicle is less than the position deviation threshold, i.e., the preset distance, such as 0.2 meters.

[0102] When the autonomous vehicle's location meets the requirements for orientation and position, it will remain at the target location, release a communication node, and await the completion of the deployment task. This fine-tuning process ensures that the autonomous vehicle can accurately reach the predetermined location, thereby improving the accuracy and efficiency of task execution.

[0103] Compared with the prior art, the present invention can complete the deployment of communication nodes by autonomously deciding the deployment location of communication nodes through unmanned vehicles, quickly build a temporary communication network, solve the problem of establishing communication links in complex and confined spaces, and realize rapid deployment and monitoring of unknown spaces.

[0104] Another embodiment of this disclosure provides an automatic deployment system for communication nodes in complex and confined spaces, including a map building module, a deployment scheme building module, an optimization module, and a deployment module.

[0105] The map building module is used to construct a raster map of the propagation environment based on the radar-visual perception data of the unmanned vehicle in the target space.

[0106] The deployment scheme construction module is used to construct a passive node deployment scheme based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, and to quantify the coverage of communication nodes through the propagation environment grid map, and to construct an active node deployment scheme by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm.

[0107] The optimization module is used to construct an electromagnetic environment map based on signal data and a raster map of the propagation environment. It uses the electromagnetic environment map to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, optimizes the deployment position of the communication nodes, and generates an optimized node deployment scheme.

[0108] The deployment module is used to control the unmanned vehicle to autonomously explore the target space and complete the deployment of communication nodes based on the raster map of the propagation environment and the optimized node deployment scheme.

[0109] The specific implementation method of the automatic communication node deployment system for complex and confined spaces provided in this disclosure can be found in the automatic communication node deployment method for complex and confined spaces provided in this disclosure, and will not be repeated here.

[0110] Those skilled in the art will understand that the above embodiments are specific implementations of this disclosure, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this disclosure.

Claims

1. A method for automatically deploying communication nodes in complex and confined spaces, characterized in that, include: Based on the radar-visual perception data of the unmanned vehicle in the target space, a grid map of the propagation environment is constructed; A passive node deployment scheme is constructed based on the signal strength of the received signal data and the key point markers in the propagation environment grid map. The coverage of communication nodes is quantified by the propagation environment grid map. An active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm. An electromagnetic environment map is constructed based on signal data and a raster map of the propagation environment. The electromagnetic environment map is used to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, and to optimize the deployment position of the communication nodes, thereby generating an optimized node deployment scheme. Based on the propagation environment grid map and the optimized node deployment scheme, the unmanned vehicle is controlled to autonomously explore the target space and complete the deployment of communication nodes.

2. The automatic deployment method for communication nodes in complex and confined spaces according to claim 1, characterized in that, Based on the radar-visual perception data of the unmanned vehicle in the target space, a raster map of the propagation environment is constructed, including: The target space is scanned using sensors on the autonomous vehicle to obtain radar vision perception data; Preprocessing of radar-visual perception data from unmanned vehicles; The target space is divided into several grids of equal size, and the pre-processed autonomous vehicle radar vision perception data is mapped to the corresponding grids. Based on the Bayesian update formula and the radar vision perception data of the unmanned vehicle, the grid state is derived to obtain the propagation environment grid map.

3. The automatic deployment method for communication nodes in complex and confined spaces according to claim 2, characterized in that, The preprocessed autonomous vehicle radar-visual perception data is mapped to the corresponding grid, including: The data points in the preprocessed autonomous vehicle radar-visual perception data are projected onto the corresponding grid using the following formula: ； ； ； in, These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data within the raster map of the propagation environment. These represent the x-axis, y-axis, and z-axis coordinates of data points in the preprocessed autonomous vehicle radar-visual perception data. These are the x-axis, y-axis, and z-axis coordinates of the origin in the raster map of the propagation environment. It is the size of each grid cell. Points to the floor.

4. The automatic deployment method for communication nodes in complex and confined spaces according to claim 1, characterized in that, A passive node deployment scheme is constructed based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, including: By combining the signal strength of the signal data and the detection results of key point markers in the propagation environment grid map, the deployment locations of communication nodes are determined, and a passive node deployment scheme is constructed.

5. The automatic deployment method for communication nodes in complex and confined spaces according to claim 4, characterized in that, By combining the signal strength data and the detection results of key point markers in the propagation environment grid map, the deployment locations of communication nodes are determined, including: When the signal strength of the signal data is lower than a predetermined threshold, analyze the signal coverage of the current area and use the geometric center point of the current area as the location for the deployment of communication nodes. If the raster map of the propagation environment contains marked key points, the location of the marked key point is selected as the deployment location of the communication node.

6. The automatic deployment method for communication nodes in complex and confined spaces according to claim 1, characterized in that, The coverage area of ​​communication nodes is quantified by propagating an environmental raster map. An active node deployment scheme is constructed by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm, including: The idle areas not yet covered by the current communication node are extracted using a raster map of the propagation environment. The idle areas are then processed using the maximum rectangle coverage and filling algorithm to determine the maximum filling problem for the idle areas. The idle area is simplified by using the concepts of horizontal slicing and maximal matrix. A greedy selection strategy that prioritizes the interval with the largest coverage area is adopted to solve the problem of maximizing the filling of the idle area and determine the placement of communication nodes. Based on the communication sensing requirements, the coverage area of ​​the communication node is divided into several sensing cells. The resource reuse scheme of adjacent sensing cells is determined by maximizing resource reuse algorithm in order to reduce the number of communication nodes deployed.

7. The automatic deployment method for communication nodes in complex and confined spaces according to claim 1, characterized in that, An electromagnetic environment map is constructed based on signal data and a raster map of the propagation environment. This map is then used to evaluate the coverage performance of each communication node in both passive and active node deployment schemes, optimize the node deployment locations, and generate an optimized node deployment scheme, including: Based on signal data and a raster map of the propagation environment, an electromagnetic environment map is constructed by simulating the propagation of electromagnetic waves in the corresponding environment using the ray tracing method. Based on electromagnetic environment map analysis of the current network coverage area, key indicators including energy efficiency, network coverage quality, and network connectivity are calculated. The particle swarm optimization algorithm is used to optimize the placement of each communication node in the passive node placement scheme and the active node placement scheme, generating an optimized node placement scheme.

8. The automatic deployment method for communication nodes in complex and confined spaces according to claim 7, characterized in that, The particle swarm optimization algorithm is used to optimize the placement of communication nodes in both passive and active node deployment schemes, including: The objective function of the particle swarm optimization algorithm is expressed as: ； in, Let Particle Swarm Optimization be the objective function. For energy efficiency, For network coverage quality, For network connectivity, These are the weights corresponding to energy efficiency, network coverage quality, and network connectivity, respectively. The velocity and position of the particles corresponding to the distribution locations of each communication node are updated according to the following formula: ； ； in, It is a particle In the The speed of generation It is a particle In the The speed of generation It is a particle In the The position of the generation, It is a particle In the The position of the generation, It is a particle The best historical location It is the globally optimal solution. It is inertial weight. and Both are acceleration constants. and All are random numbers.

9. The automatic deployment method for communication nodes in complex and confined spaces according to claim 1, characterized in that, Based on the propagation environment grid map and the optimized node deployment scheme, the unmanned vehicle is controlled to autonomously explore the target space and complete the deployment of communication nodes, including: Based on the propagation environment grid map and the optimized node deployment scheme, exploration target points are generated. Based on the exploration target points and the communication node deployment positions determined by the optimized node deployment scheme, the global path for the autonomous vehicle is planned. The autonomous vehicle is controlled to travel along a global path in the target space. When the autonomous vehicle reaches the communication node deployment location, the communication node is released, and the driving path is replanned to deploy the communication node again until all communication nodes are deployed.

10. An automatic deployment system for communication nodes applied in complex and confined spaces, characterized in that, include: The map building module is used to construct a raster map of the propagation environment based on the radar vision perception data of the unmanned vehicle in the target space. The deployment scheme construction module is used to construct a passive node deployment scheme based on the signal strength of the received signal data and the key point markers in the propagation environment grid map, and to quantify the coverage of communication nodes through the propagation environment grid map, and to construct an active node deployment scheme by combining the maximum rectangle coverage filling algorithm and the maximum resource reuse algorithm. The optimization module is used to construct an electromagnetic environment map based on signal data and a raster map of the propagation environment. It uses the electromagnetic environment map to evaluate the coverage performance of each communication node in the passive node deployment scheme and the active node deployment scheme, optimizes the deployment position of the communication nodes, and generates an optimized node deployment scheme. The deployment module is used to control the unmanned vehicle to autonomously explore the target space and complete the deployment of communication nodes based on the raster map of the propagation environment and the optimized node deployment scheme.

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