A method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data

Abstract

The application discloses a kind of based on multi-source data's wireless channel three-dimensional electromagnetic environment reconstruction method, it is related to wireless channel research technical field, specific steps are as follows: 1) building information is extracted using electronic map, and the first stage of building reconstruction is carried out;2) satellite map is used to carry out the second stage of geomorphic environment reconstruction, image semantic segmentation technology is introduced to satellite map is carried out environment perception, and segmented mask optimization processing method is designed, the precision problem of semantic segmentation is made up, extracts key geomorphic environment information, according to segmentation result, environment three-dimensional surface reconstruction is carried out;3) the third stage of electromagnetic parameter matching and ray tracing simulation is carried out, and the offline channel map of target area is constructed.The method realizes the three-dimensional electromagnetic environment reconstruction of wireless communication scene, obtains richer electromagnetic environment information than traditional building model, completes the construction of offline channel map, and can efficiently meet the technical needs of 6G digital twin online channel model.

Description

Technical Field

[0001] This invention relates to the field of wireless channel research technology, and in particular to a method for reconstructing the three-dimensional electromagnetic environment of a wireless channel based on multi-source data. Background Technology

[0002] With the advancement of wireless communication technology, 6th Generation Wireless Communication Technology (6G) is expected to achieve "full coverage, full application, full digitalization, full spectrum, full sensing, and strong security." Digital Twin Online Channel Models (DTOCMs) can dynamically and accurately reflect the channel characteristics of real-world communication environments, improving the overall performance of 6G networks. Currently, the construction of DTOCMs for known scenarios involves four steps: identifying and initializing the communication scenario, reconstructing the offline channel map, online sensing of the physical environment and dynamic updating of the digital twin channel model, and the application of the digital twin channel model. Among these, the effective reconstruction of the three-dimensional (3D) twin electromagnetic environment is the key foundation for DTOCM construction, supporting the accuracy of the entire digital twin model and laying a solid foundation for the subsequent real-time sensing and efficient operation of DTOCM.

[0003] Currently, building the DTOCM environment relies on manual creation using software like Blender, which is both time-consuming and labor-intensive. Additionally, open-source databases such as OpenStreetMap (OSM) are used to create 3D building models. However, these databases may be outdated and lack information for many areas. Another approach is to use drone mapping for scene reconstruction; however, this method is also costly and labor-intensive.

[0004] To address these challenges, researchers have applied artificial intelligence (AI) technology to scene reconstruction. Many deep learning models have been used in 3D reconstruction. However, these studies primarily focus on extracting and reconstructing building information, neglecting environmental factors such as vegetation, roads, and water bodies, which significantly impact wireless channel propagation. Currently, the construction of DTOCM (Digital Transmission Twin) scenarios is complex and labor-intensive. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method for reconstructing the three-dimensional electromagnetic environment of wireless channels based on multi-source data. The present invention enables DTOCM to reconstruct the three-dimensional electromagnetic environment quickly and efficiently. The present invention realizes the initialization of communication scenarios and the construction of offline channel maps, thereby improving the accuracy of the entire digital twin model.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for reconstructing the three-dimensional electromagnetic environment of a wireless channel based on multi-source data, according to the present invention, includes:

[0008] Acquire electronic and satellite maps of the target area for 3D electromagnetic environment reconstruction;

[0009] Extract the coordinates and height information of buildings from the electronic map of the target area;

[0010] Based on the coordinate and height information of the building, a 3D reconstruction of the building is performed using triangular meshes to obtain a 3D building model;

[0011] The satellite map of the target area is segmented and preprocessed to obtain the preprocessed satellite map.

[0012] An environment perception module is composed of a semantic segmentation network and a fully connected conditional random field. The environment perception module is used to perform environment perception classification on the preprocessed satellite map to obtain the classification result. The classification result is then stitched together to obtain the complete environment perception classification result of the target area.

[0013] The environmental perception classification results are optimized and processed to obtain the optimized environmental perception classification results.

[0014] Extract ground feature coordinate information from the optimized environmental perception classification results, convert the ground feature coordinate information into actual coordinates, and assign height information according to the optimized environmental perception classification results;

[0015] Based on the actual coordinates and height information of the transformed ground features, a three-dimensional reconstruction of the ground features is performed using triangular meshes to obtain a three-dimensional ground feature environment model.

[0016] The obtained 3D building model and 3D terrain environment model are matched and fused to obtain a 3D twin environment model;

[0017] Based on the final optimized environmental perception classification results, electromagnetic parameters are assigned to the three-dimensional twin environment model, and channel characteristics are simulated using ray tracing technology.

[0018] As a further optimization scheme for the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data described in this invention, the format, accuracy, and environmental information that can be obtained from the electronic map and satellite map used are first determined.

[0019] As a further optimization scheme of the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data described in this invention, the electronic map format is plantet, the satellite map is a TIFF image, the accuracy of the electronic map is 5 meters, and the accuracy of the satellite map is 0.3 meters.

[0020] As a further optimization scheme of the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data described in this invention, the three-dimensional reconstruction of the building is performed using triangular meshes based on the coordinate and height information of the building to obtain a three-dimensional building model; including the following steps:

[0021] Step 3.1: When generating the side of a building, the longitude and latitude information and height data of each building vertex are used to create rectangular side faces; these rectangular side faces are divided into two triangular meshes to form each side face of the building, thus obtaining the side face model of the building.

[0022] Step 3.2, Generation of the building's roof model: Before generating the building's roof model, determine the concavity or convexity of the roof polygon; if the roof polygon is convex, connect the vertices of the building's roof polygon to the center point of the polygon to obtain the building's roof model; if the roof polygon is concave, divide the roof polygon into multiple triangles or convex polygons, and then connect the vertices of each polygon to their respective center points to obtain the building's roof model.

[0023] Step 3.3: Combine the obtained side and roof models of the building to create a complete three-dimensional building model.

[0024] As a further optimization of the wireless channel 3D electromagnetic environment reconstruction method based on multi-source data described in this invention, an environment perception module is composed of a semantic segmentation network and a fully connected conditional random field. This module is used to perform environment perception classification on the preprocessed satellite map to obtain classification results. These classification results are then stitched together to obtain a complete environment perception classification result for the target area. Specifically, the following steps are included:

[0025] Step 5.1: Determine the dataset for the environment perception module, and select the LoveDa dataset as the semantic segmentation dataset;

[0026] Step 5.2: Perform environmental perception on the preprocessed satellite images to obtain preliminary classification results of the environmental perception.

[0027] Step 5.3: Optimize the preliminary classification results of environmental perception using a fully connected conditional random field, and then stitch the classification results together to obtain the complete environmental perception classification results for the target area.

[0028] As a further optimization scheme for the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data described in this invention, the environmental perception classification results are optimized and processed to obtain optimized environmental perception classification results; including the following:

[0029] Step 6.1: Optimize the environmental perception classification results to obtain the classification results of local environmental features;

[0030] Step 6.2: After performing masking optimization on the classification results of local environmental features, obtain environmental perception classification results that conform to the real environment.

[0031] As a further optimization scheme for the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data described in this invention, electromagnetic parameters are assigned to the three-dimensional twin environment model according to the results of the final optimized environmental perception classification, and channel characteristic simulation is performed using ray tracing technology; specifically, it includes the following:

[0032] Step 10.1: Import the obtained 3D twin environment model into the ray tracing simulation software Wireless InSite;

[0033] Step 10.2: In Wireless InSite, based on the optimized environmental perception classification results, assign electromagnetic parameters to the environmental information of buildings, roads, forests, and water bodies in the 3D twin environment model;

[0034] Step 10.3: Configure the antenna and transceiver settings in Wireless InSite, and perform ray tracing simulation.

[0035] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0036] (1) This invention makes comprehensive use of electronic map and satellite map data, combined with deep learning and image processing, to reconstruct the three-dimensional twin electromagnetic environment for DTOCM, thereby accelerating the construction of offline channel maps;

[0037] (2) First, accurate building information is extracted from the electronic map for 3D model reconstruction. Second, environmental perception is achieved by applying a semantic segmentation network to the satellite map. Then, the environmental perception results are enhanced using a fully connected conditional random field and mask optimization methods. After extracting ground feature information, 3D environment reconstruction is performed, providing more environmental information for the twin scene and significantly improving the accuracy of channel characteristic simulation. Finally, based on the 3D reconstruction results from the previous two stages, multi-source modeling is implemented to assign electromagnetic parameters to the 3D twin environment model for channel characteristic simulation. In summary, this invention is an effective 3D electromagnetic environment reconstruction method, providing a prerequisite for DTOCM to achieve efficient offline channel map construction. Attached Figure Description

[0038] Figure 1 This is a block diagram of the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data provided in Embodiment 1 of the present invention;

[0039] Figure 2 This is a schematic diagram of a portion of the electronic map in Embodiment 1 of the present invention;

[0040] Figure 3 This is a partial satellite map schematic diagram from Embodiment 1 of the present invention;

[0041] Figure 4 This is a schematic diagram of a three-dimensional model of some buildings in Embodiment 1 of the present invention;

[0042] Figure 5 This is a schematic diagram of the environmental perception module in Embodiment 1 of the present invention;

[0043] Figure 6 This is a schematic diagram of the road mask edge connection result in Embodiment 1 of the present invention;

[0044] Figure 7 This is a schematic diagram of the three-dimensional electromagnetic environment reconstruction result in Embodiment 1 of the present invention;

[0045] Figure 8 This is a schematic diagram comparing path loss results in Embodiment 1 of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Example 1:

[0048] See Figure 1 This embodiment provides a method for reconstructing the three-dimensional electromagnetic environment of a wireless channel based on multi-source data, which mainly includes the following steps:

[0049] Step 1: Obtain electronic and satellite maps of the target area for 3D electromagnetic environment reconstruction, and determine the format, accuracy, and available environmental information of the electronic and satellite maps to be used.

[0050] Specifically, in this embodiment, the electronic map format is determined to be "plantet," and the satellite map is an "TIFF" image. The electronic map has a resolution of 5 meters, and the satellite map has a resolution of 0.3 meters. Key layers of the electronic map are analyzed, including but not limited to the Buildings layer, Height layer, and Vector layer, to obtain accurate building information from the electronic map. The geographic environment in the satellite map includes buildings, roads, water bodies, wasteland, forests, and farmland. These geographic environments have a significant impact on the wireless channel. A schematic diagram of the electronic map and satellite map of the target area is shown below. Figure 2 , Figure 3 As shown.

[0051] Step 2: Extract the coordinates and height information of buildings from the electronic map of the target area and save them in a txt file.

[0052] Specifically, in this embodiment, the Buildings layer, Height layer, and Vector layer in the electronic map provide the coordinate and height information of the buildings. The coordinate and height information of the buildings in the target area are extracted and stored in txt files. The coordinate files are stored as sets for the next step of three-dimensional surface reconstruction of the buildings.

[0053] Step 3: Based on the building's txt file, read the vertex coordinates and height data of the building, and use triangular mesh to reconstruct the three-dimensional surface of the building.

[0054] Specifically, in this embodiment, the vertex coordinates and height data of the building are read from a txt file. The side of the building is generated by iterating through the vertex coordinates to form the vertices of a polygon, and each side rectangle is constructed using two triangular meshes. The roof of the building generates a center point based on the polygon vertices, and connects the vertices and the center point to construct a corresponding triangular mesh. The generated 3D model of the building includes the side and roof, and is saved as an obj file.

[0055] In the process of generating the roof, it is necessary to determine whether the polygon is convex. If it is a concave polygon, the center point of the generated vertex will appear outside the polygon, leading to incorrect roof generation. Therefore, it is necessary to determine the concavity or convexity of the polygon and divide it into several convex triangles and polygons, including the following steps:

[0056] 1) Vertex check: Check the geometric properties of the polygon to ensure that its boundaries are closed (the start and end points are the same) and there is no self-intersection, and sort the vertices;

[0057] 2) Ear segmentation initialization: Identify the ear-shaped region (convex vertex) of the polygon to prepare for the ear segmentation process;

[0058] 3) Partitioning execution: Cut off the convex area one by one, generate triangles, remove the processed vertices, and update the vertex list of the remaining part of the polygon;

[0059] 4) Boundary update: After each subdivision, re-examine the remaining polygon boundaries and repeat the ear-cutting operation until the polygon is completely subdivided into triangles;

[0060] 5) Output Results: After the partitioning is complete, obtain the coordinates or vertex indices of all the partitioned triangles.

[0061] For use in subsequent geometric reconstruction processing.

[0062] Finally, height information is assigned to the segmented roof polygons, and a 3D model of the building is generated. Figure 4 The example displays 3D models of some of the buildings generated in the instance.

[0063] Step 4: Perform segmentation and preprocessing on the satellite map of the target area.

[0064] The specific steps are as follows: the satellite map is segmented into images of size 1024×1024 pixels.

[0065] Step 5: An environment perception module, composed of a semantic segmentation network and a fully connected conditional random field, is used to perform environment perception classification on the preprocessed satellite map. The results of the environment perception module are then stitched together to obtain the complete environment perception classification result for the target area. A schematic diagram of the environment perception module is shown below. Figure 5 As shown. In this example, the semantic segmentation network used is the UNetFormer network, trained using the training set in the dataset. UNetFormer consists of a ResNet18 encoder and a Transformer-based decoder. The encoder comprises ResBlocks arranged in four stages, each downsampling the feature map. The decoder consists of three Global-Local Transformer Blocks (GLTB), three Weighted Summation Modules (WS), and a Feature Refinement Head (FRH). GLTB introduces a global-local attention mechanism, using a dual-branch structure to capture global and local contextual information. The local branch extracts local information using parallel convolutional layers with kernel sizes of 3 and 1, and applies batch normalization before the final summation operation. The global branch captures global context through a window-based multi-head self-attention mechanism. Each stage of the feature map generated by the encoder is connected to the corresponding feature map in the decoder via a 1×1 convolution. WS is responsible for feature aggregation, thereby learning more general multi-source features. The formula for WS can be expressed as:

[0066] BF = α·RBF + (1-α)·GLBF

[0067] In the formula, BF represents multi-source features, RBF represents features generated by Resblocks, and GLBF represents features generated by GLTB. α is a weighting coefficient.

[0068] Fully connected conditional random fields improve overall segmentation quality by smoothing segmentation results and reducing noise and misclassification. The goal of a fully connected conditional random field is to maximize the conditional probability P(y|x) of the label configuration y:

[0069]

[0070] In the formula, y={y1,y2,…,y N}, x={x1,x2,…,x N}, where x represents the observed feature, y i Let Z(x) represent the label of the i-th pixel, x represent the feature vector of the i-th pixel, i∈[1,N], and N be the total number of pixels in the image. y exp(-E(y|x)) is the partition function that normalizes the probability distribution, where E(y|x) is the energy function, composed of univariate and paired potential functions, and can be expressed as:

[0071]

[0072] In the formula, ψ u (y i |x) is a univariate potential used to calculate the label y based on the observed feature x. i The cost, ψ, allocated to pixel i p (y i ,y j |x) is a binary potential used to measure the interaction between pixel pairs i and j, such that spatially similar pixels share the same label.

[0073] The initial predictions of the neural network are processed by a fully connected conditional random field to make them more consistent with the actual physical boundaries.

[0074] In the environment perception module, the LoveDa dataset is selected. In the trained environment perception module, the segmentation accuracy is evaluated by pixel accuracy, where pixel accuracy is:

[0075]

[0076] In the formula, TP represents the number of pixels correctly predicted as positive, and FP represents the number of pixels incorrectly predicted as positive. The higher the pixel accuracy, the higher the accuracy of the model's perception.

[0077] The specific steps are as follows:

[0078] Step 501: Dataset Training. Prepare the training dataset, which consists of a training set, a validation set, and a test set, to train the UNetFormer model.

[0079] Step 502: Preliminary classification using the UNetFormer module. In the trained environment perception module, the segmented satellite images are processed for environment perception to obtain preliminary classification results.

[0080] Step 503: Fully Connected Conditional Random Field Optimization. Calculate ψ based on the classification result graph. u (y i |x) and ψ p (y i y j The algorithm minimizes the energy function E(y|x) to maximize P(y|x). By performing multiple inferences, iteratively optimizing the label assignment, it finds the most likely category for each pixel and saves the final processed image. The number of inferences can be adjusted appropriately according to accuracy requirements.

[0081] Step 504: Evaluation and Tuning. Finally, the module's performance needs to be evaluated, typically using cross-validation or other evaluation metrics (such as accuracy, precision, recall, etc.). This example uses pixel accuracy for evaluation. The simulation results are shown in Table 1. The segmentation accuracies for roads, buildings, water bodies, bare land, woodland, and cultivated land are 68%, 84%, 84%, 66%, 61%, and 92%, respectively, with an overall accuracy of approximately 70%. Satisfactory results were achieved through the environment perception module. After introducing a fully connected conditional random field, the boundary segmentation of various ground environments was significantly improved.

[0082] Table 1 Simulation Results

[0083] category the way building water body bare land forest arable land Overall accuracy accuracy 68％ 84％ 84％ 66％ 61％ 92％ 70％

[0084] Step 6: Perform masking optimization on the environmental perception classification results to obtain perception classification results that better reflect the actual environment. Because Google Earth satellite images from different regions were used, their quality differs from the images in the dataset, affecting the performance of the segmentation network. Furthermore, occlusion caused by vegetation and other geospatial features in the satellite images impacts accurate road segmentation. In this embodiment, a series of image processing steps were designed and implemented to enhance the continuity and integrity of road boundaries in the perception segmentation output. The specific steps are as follows:

[0085] Step 601, Morphological Operations. Apply morphological operations to smooth and expand the edges of the road mask, eliminating breaks and noise caused by segmentation;

[0086] Step 602, Line Segment Detection. A line detection algorithm based on Hough transform is used to fit straight lines along the road boundary to ensure their straightness and consistency.

[0087] Step 603, Edge Connecting. The result of edge connecting is shown in the image below. Figure 6 As shown, when multiple lines are detected or certain areas lack edge detection, an edge connection algorithm is used to connect these lines to ensure edge continuity. Specifically, if the current pixel is a road edge (pixel value 255), the neighboring pixels around each pixel are determined based on the center size of the neighborhood, and their surrounding pixels are checked. If only one neighboring pixel is 255: the current pixel is marked as the endpoint of the line, and the unique neighboring pixel is recorded. The distance between the current endpoint and its neighboring pixels is calculated. If the distance is less than or equal to a threshold, the two points are connected by linear interpolation. The optimal size for the neighborhood center size and the connection threshold can be determined experimentally.

[0088] Step 7: Extract coordinate information from the environmental perception results and mask optimization results, convert them into actual coordinates, and assign height information based on the environmental perception classification results. Specifically, both the environmental perception results and the mask optimization results are images, with each pixel representing a real-world resolution of 0.3 meters. The image pixel coordinates are mapped to real-world coordinates, and the precise coordinates of buildings in the electronic map are used as anchor points to correct and match the coordinates of the ground feature information. Based on the environmental perception classification results, height values ​​are assigned to the ground feature information, and both coordinate and height information are saved as txt files.

[0089] Step 8: Based on the coordinate and height information of the terrain environment, use triangular meshes to perform 3D reconstruction of the terrain environment to obtain a 3D terrain environment model. Specifically, in this embodiment, similar to the method for generating 3D models of buildings, the vertex coordinates and height data of the terrain information in the txt file are read, and the corresponding triangular meshes are constructed to generate a 3D model of the terrain environment, which is then saved as an obj file.

[0090] Step 9: Match and fuse the obtained 3D building model and 3D terrain environment model to obtain a 3D twin environment model. Specifically, match and fuse the building model and terrain environment model generated in the first two stages to obtain a more comprehensive 3D twin environment model of the target area. In this example, the 3D electromagnetic environment reconstruction result is as follows: Figure 7 As shown.

[0091] Step 10: Based on the environmental perception classification results, assign electromagnetic parameters to the 3D twin environment model and perform channel characteristic simulation using ray tracing technology. Specifically...

[0092] Step 1001: Import the obtained 3D twin environment model into the ray tracing simulation software Wireless InSite;

[0093] Step 1002: In Wireless InSite, based on the optimized environmental perception classification results, assign electromagnetic parameters to the environmental information of buildings, roads, forests, and water bodies in the 3D twin environment model.

[0094] Step 1003: In Wireless InSite, simulate and set up the antenna, transceiver, etc., and perform ray tracing simulation to obtain the required channel characteristics;

[0095] Step 1004: Evaluate the channel characteristics and compare them with actual channel measurement data to ensure the accuracy of the 3D twin electromagnetic environment and verify the model's improvement in channel characteristic simulation. In Example 1, the channel measurement environment is a suburban scene near a building. The transmitter is placed at a fixed location near the building, and the receiver changes location continuously. The measurement frequency is 2.6 GHz, and there are two scenarios. The channel characteristic comparison results in this example are shown in the figure. Figure 8 Compared to simulations based solely on building models, the simulation results derived from the three-dimensional electromagnetic twin environment show good consistency with channel measurement data. This consistency stems from the rich environmental perception information provided by the three-dimensional electromagnetic twin environment model.

[0096] In summary, the wireless channel three-dimensional electromagnetic environment reconstruction method based on multi-source data established in this invention effectively extracts environmental information from electronic maps and satellite maps, thereby effectively reconstructing the wireless communication environment, promoting and accelerating the construction of offline channel maps, and improving the overall accuracy of digital twin channel models.

[0097] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for reconstructing the three-dimensional electromagnetic environment of a wireless channel based on multi-source data, characterized in that, include: Acquire electronic and satellite maps of the target area for 3D electromagnetic environment reconstruction; Extract the coordinates and height information of buildings from the electronic map of the target area; Based on the coordinate and height information of the building, a 3D reconstruction of the building is performed using triangular meshes to obtain a 3D building model; The satellite map of the target area is segmented and preprocessed to obtain the preprocessed satellite map. An environment perception module is composed of a semantic segmentation network and a fully connected conditional random field. The environment perception module is used to perform environment perception classification on the preprocessed satellite map to obtain the classification result. The classification result is then stitched together to obtain the complete environment perception classification result of the target area. The environmental perception classification results are optimized and processed to obtain the optimized environmental perception classification results. Extract ground feature coordinate information from the optimized environmental perception classification results, convert the ground feature coordinate information into actual coordinates, and assign height information according to the optimized environmental perception classification results; Based on the actual coordinates and height information of the transformed ground features, a three-dimensional reconstruction of the ground features is performed using triangular meshes to obtain a three-dimensional ground feature environment model. The obtained 3D building model and 3D terrain environment model are matched and fused to obtain a 3D twin environment model; Based on the final optimized environmental perception classification results, electromagnetic parameters are assigned to the three-dimensional twin environment model, and channel characteristics are simulated using ray tracing technology.

2. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 1, characterized in that, First, determine the format, accuracy, and available environmental information of the electronic and satellite maps to be used.

3. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 2, characterized in that, The electronic map is in Plantet format, and the satellite map is in TIFF format. The electronic map has an accuracy of 5 meters, and the satellite map has an accuracy of 0.3 meters.

4. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 1, characterized in that, Based on the building's coordinate and height information, a 3D reconstruction of the building is performed using triangular meshes to obtain a 3D building model; this includes the following steps: Step 3.1: When generating the side of a building, the longitude and latitude information and height data of each building vertex are used to create rectangular side faces; these rectangular side faces are divided into two triangular meshes to form each side face of the building, thus obtaining the side face model of the building. Step 3.2, Generation of the building's roof model: Before generating the building's roof model, determine the concavity or convexity of the roof polygon; if the roof polygon is convex, connect the vertices of the building's roof polygon to the center point of the polygon to obtain the building's roof model; if the roof polygon is concave, divide the roof polygon into multiple triangles or convex polygons, and then connect the vertices of each polygon to their respective center points to obtain the building's roof model. Step 3.3: Combine the obtained side and roof models of the building to create a complete three-dimensional building model.

5. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 1, characterized in that, An environment perception module is composed of a semantic segmentation network and a fully connected conditional random field. The environment perception module is used to perform environment perception classification on the preprocessed satellite map to obtain the classification result. The classification result is then stitched together to obtain the complete environment perception classification result of the target area. Specifically, the following steps are included: Step 5.1: Determine the dataset for the environment perception module, and select the LoveDa dataset as the semantic segmentation dataset; Step 5.2: Perform environmental perception on the preprocessed satellite images to obtain preliminary classification results of the environmental perception. Step 5.3: Optimize the preliminary classification results of environmental perception using a fully connected conditional random field, and then stitch the classification results together to obtain the complete environmental perception classification results for the target area.

6. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 1, characterized in that, The environmental perception classification results are optimized and processed to obtain the optimized environmental perception classification results, including the following: Step 6.1: Optimize the environmental perception classification results to obtain the classification results of local environmental features; Step 6.2: After performing masking optimization on the classification results of local environmental features, obtain environmental perception classification results that conform to the real environment.

7. The method for reconstructing a three-dimensional electromagnetic environment of a wireless channel based on multi-source data according to claim 1, characterized in that, Based on the final optimized environmental perception classification results, electromagnetic parameters are assigned to the 3D twin environment model, and channel characteristics are simulated using ray tracing technology; specifically, the following are included: Step 10.1: Import the obtained 3D twin environment model into the ray tracing simulation software Wireless InSite; Step 10.2: In Wireless InSite, based on the optimized environmental perception classification results, assign electromagnetic parameters to the environmental information of buildings, roads, forests, and water bodies in the 3D twin environment model; Step 10.3: Configure the antenna and transceiver settings in Wireless InSite, and perform ray tracing simulation.

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