Simulation method for regional environment electromagnetic radiation
By acquiring and processing geographic information system data of the target area, performing format conversion and propagation model configuration, the problems of high cost and long cycle of traditional field measurement methods are solved, and high-precision and high-efficiency assessment of regional electromagnetic radiation is achieved, which is suitable for electromagnetic radiation assessment in complex urban environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-07
AI Technical Summary
Traditional measurement methods are costly, time-consuming, and incomplete in assessing electromagnetic radiation in target areas, making it difficult to meet the needs for high-precision and high-efficiency assessments, especially in complex urban environments where they are severely affected by building obstruction and meteorological factors.
By acquiring geographic information system data of the target area, performing format conversion and propagation model configuration, and combining data preprocessing and adaptation parameters, a high-precision and high-efficiency assessment of regional electromagnetic radiation is achieved, including data preprocessing, coordinate system transformation, format mapping and integration, propagation model selection, and result generation.
It achieves high-precision and high-efficiency assessment of regional electromagnetic radiation, reduces assessment costs, shortens the cycle, and improves simulation accuracy and coverage.
Smart Images
Figure CN121809072A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of electromagnetic radiation simulation, in particular to a simulation method for regional environmental electromagnetic radiation. BACKGROUND
[0002] Under the background of large-scale deployment of wireless communication technology (5G / 6G, Internet of Things, broadcast, radar, etc.) and industrial, medical, scientific research electronic equipment, the density of electromagnetic radiation sources is showing an increasing trend. This trend not only results from the intensive construction of communication base stations, but also involves the widespread penetration of multiple radiation sources such as industrial frequency conversion equipment, medical imaging systems, and scientific research instruments, leading to a significant increase in the complexity of the environmental electromagnetic background. The demand for electromagnetic environmental safety assessment of target areas is increasingly prominent, and the radiation exposure level and spatial influence range need to be quantified systematically. Such assessment needs to consider radiation field strength, frequency characteristics, and other multidimensional parameters, and set safety thresholds in combination with international standards and domestic regulations.
[0003] Traditional measurement methods rely on field sampling, which has limitations such as high cost, long cycle, and geographical and meteorological conditions. For example, in complex urban environments, building obstructions lead to enhanced spatial heterogeneity of field distribution, and meteorological factors may interfere with the accuracy of measurement equipment. SUMMARY
[0004] The present application provides a simulation method for regional environmental electromagnetic radiation, which solves the problems of high cost, long cycle, and incomplete coverage of traditional measurement methods, and realizes high-precision and high-efficiency evaluation of regional electromagnetic radiation.
[0005] The present application provides a simulation method for regional environmental electromagnetic radiation, which includes the following steps: S1. Obtain geographic information system data of the target area, including digital elevation model data, land cover / land use type data, vector building outline data, and radiation source data; S2. Format conversion of the geographic information system data, including coordinate system conversion, format mapping and integration, and data structure standardization; S3. Based on the converted geographic information system data, select an appropriate propagation model and configure parameters; S4. Based on the selected propagation model, obtain the electromagnetic radiation results of the target area.
[0006] The above-mentioned embodiments have the beneficial effect of realizing high-precision and high-efficiency evaluation of regional electromagnetic radiation through full-process automated modeling and simulation, solving the problems of high cost, long cycle, and incomplete coverage of traditional measurement methods.
[0007] Based on the above-mentioned embodiments, the present application can be further improved as follows: In one embodiment of the present application, the S1 geographic information system data needs to be preprocessed, including: De-noising processing: Gaussian filtering or median filtering is performed on remote sensing images and digital elevation model data (DEM data); Target data preprocessing: according to the latitude and longitude range of the target area, the original data is preprocessed to retain the effective area; Preliminary coordinate alignment: all data are unified to the same reference coordinate system; Attribute annotation: electromagnetic characteristic parameters (such as vegetation attenuation coefficient, water reflectivity) are added to the ground object type. Technical effect: Through data preprocessing, noise interference is eliminated, spatial consistency is ensured, high-fidelity basic data is provided for subsequent simulation, and simulation accuracy is improved.
[0008] In one embodiment of the present application, the S1 radiation source data includes transmit power, operating frequency, antenna gain, polarization mode, installation height and direction angle, and the data is derived from an operator or a public data set. Technical effect: Ensure the accuracy of the radiation source parameters, provide real input for simulation, and improve the reliability of the radiation intensity calculation.
[0009] In one embodiment of the present application, the coordinate system conversion in S2 uses a projection conversion algorithm (such as Gauss-Kruger, UTM) to convert latitude and longitude coordinates to planar rectangular coordinates, and preserves the spatial topological relationship of the data. Technical effect: Through standardized coordinate system conversion, data compatibility problems are eliminated, and distance calculation efficiency is improved.
[0010] In one embodiment of the present application, the format mapping and integration in S2 includes converting raster data to TIN or vector format, converting vector building data to OBJ / GIS compatible format, and associating material electromagnetic parameters. Technical effect: Reduce data redundancy, improve data processing efficiency, and make electromagnetic reflection / absorption effects more realistic, improving simulation accuracy.
[0011] In one embodiment of the present application, the selected propagation model in S3 is the ITM model, and the parameter configuration includes: Ground object parameter adaptation: adjusting the diffraction coefficient, attenuation coefficient, and reflectivity for buildings, vegetation, and water; Terrain parameter adaptation: adjusting terrain fluctuation loss based on DEM data; Radiation source parameter adaptation: adjusting the spatial distribution of radiation intensity according to the antenna pattern and polarization mode.
[0012] Technical effect: Through scenario-based parameter configuration, the deviation between simulation results and measured values is reduced, the model accuracy is improved, and the calculation efficiency is considered.
[0013] In one of the embodiments of the present application, the electromagnetic radiation result generation in S4 includes: Sub-regional refinement calculation: calculate the radiation intensity based on the topographic segmentation results (building area, vegetation area, water area); Shadow effect processing: modify the radiation intensity of the shadow area through ray tracing or visibility analysis; Multi-source superposition calculation: calculate the total radiation intensity of multiple radiation sources using linear superposition or coherent superposition algorithm. Technical effect: realize the refined evaluation of regional electromagnetic radiation, accurately identify the radiation hotspots, and provide quantitative basis for urban planning and base station site selection.
[0014] In one of the embodiments of the present application, the electromagnetic radiation result output in S4 includes generating the spatial distribution thermal map, contour map and three-dimensional rendering map of electric field intensity, power density and path loss, and performing error analysis (such as RMSE, MAE) by comparing with the measured data. Technical effect: through multi-dimensional visual output, the radiation distribution characteristics are intuitively displayed, and the simulation reliability is verified through error analysis, which improves the credibility of the evaluation results.
[0015] One or more technical solutions provided in the embodiments of the present application refine the key links of the simulation process (data acquisition, format conversion, model configuration, result generation, etc.), and construct a complete regional electromagnetic radiation simulation method. Each claim is limited to the core steps of data preprocessing, coordinate system conversion, model adaptation, result output, etc., to ensure the operability and innovativeness of the method, and finally realize the evaluation target of "low cost, high precision and fast cycle" of electromagnetic radiation. BRIEF DESCRIPTION OF DRAWINGS
[0016] In order to more clearly illustrate the technical solutions in the specific embodiments or prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual proportion.
[0017] Figure 1 A flowchart of a simulation method for regional environmental electromagnetic radiation in an embodiment of the present application; Figure 2 A target regional electromagnetic radiation result schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION
[0018] The present application will be further illustrated below in conjunction with the specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application. After reading the present application, those skilled in the art can modify various equivalent forms of the present application, which all fall within the scope defined by the claims attached to the present application.
[0019] Embodiment: As Figure 1 shown, a simulation method for regional environmental electromagnetic radiation, comprising the following steps: S1, obtaining geographic information system data of the target region; Specifically, using authoritative data sources and public data sets such as national geographic information public service platform and geographic spatial data cloud, Landsat remote sensing images of the target region and digital elevation model (DEM) data of different accuracy are extracted, so as to obtain the following data: Digital elevation model data is used to accurately represent the three-dimensional elevation information of the target region and the surrounding terrain; the digital elevation model resolution selection standard (such as 30 meters / 90 meters resolution) needs to include continuous grid data of elevation; Land cover / land use type data is used to describe the distribution of different electromagnetic scattering and absorption characteristics of ground objects such as vegetation, water, building group and road; land cover / land use type data adopts standardized classification system (such as bare soil, grassland, forest, water, building area, road, etc.), and needs to associate electromagnetic scattering / absorption characteristic label of ground object; Accurate vector building outline data is used to build a three-dimensional city environment model, and the vector building outline data needs to include geometric shape (three-dimensional outline), position information, wall material (such as dielectric constant and loss factor of concrete, glass, etc.); Radiation source data: the emission power, working frequency, antenna gain, polarization mode, installation height and direction angle of the radiation source need to be obtained.
[0020] Further, the obtained data needs to be preprocessed, including: De-noising: Gaussian filtering or median filtering is performed on remote sensing images and digital elevation model data (DEM data); Target data preprocessing: according to the latitude and longitude range of the target region, the original data is preprocessed to retain the effective area; Preliminary coordinate alignment: all data are unified to the same reference coordinate system (such as WGS84) to ensure spatial position consistency; Attribute annotation: electromagnetic characteristic parameters (such as attenuation coefficient of vegetation and reflection coefficient of water) are added to the ground object type.
[0021] S2, format conversion of geographic information system data; S2.1, coordinate system conversion: The latitude and longitude coordinates are converted into plane rectangular coordinates (x, y) by using projection conversion algorithm (such as Gauss-Kruger, UTM); the conversion basic formula is: ; ; ; x, y x, y are the longitudinal and latitudinal coordinates of the plane rectangular coordinate system; K φ R is the radius of curvature of the prime vertical circle at the point; λ, φ are the longitude and latitude difference, respectively; S φ is the length of the central meridian intercepted by the parallel of the point from the equator; N R is the radius of curvature of the prime vertical circle; where e is the second eccentricity of the earth.
[0022] Coordinate system conversion is batch processed using professional tools (such as GDAL library), and the coordinate conversion error needs to be controlled, and the spatial topological relationship of the data (such as the relative position of buildings and terrain) is preserved.
[0023] S2.2, format mapping and integration: Raster data conversion: convert DEM, remote sensing image and other raster data into TIN (irregular triangle network) or vector format, reduce data redundancy; Vector data conversion: convert building CAD contour to OBJ / GIS compatible format, associate material electromagnetic parameters; Land cover data conversion: convert classification results into raster mask and map to the ground object loss coefficient of the propagation model; Attribute information integration: associate attributes (such as ground object type, height, material) of data from different sources to a unified spatial unit (such as a surface element).
[0024] S2.3, data structure standardization: Output data needs to include fields such as spatial coordinates, ground object type, electromagnetic characteristic parameters, and radiation source parameters; Ensure that the data format is compatible with the input requirements of the subsequent propagation model (such as supporting grid division and parameter calling).
[0025] S3, based on geographic information system data, select the appropriate propagation model; In wireless propagation environment modeling, according to the characteristics of the target area (such as urban dense area, open field, indoor environment or tunnel, etc.) and the precision requirement, select the appropriate model (such as ITM, ray tracing, FDTD, etc.). In this embodiment, the most widely used ITM propagation model is selected to ensure the correctness of the electromagnetic radiation results. The basic formula of ITM propagation model is as follows: ; ; ; In the formula: L ref The additional signal attenuation caused by factors such as terrain, reflection, scattering, etc. in the actual environment, i.e. additional loss; L free The free space loss, the attenuation of the signal with distance propagation in free space without obstruction and reflection; d The propagation distance, unit is km ; f The radio wave frequency, unit is MHz ; d Ls The distance on smooth ground; d x Indicates that the diffraction loss and scattering loss here are equal; L bed 、L bes Respectively represent the propagation loss values when diffraction and scattering in free space; k 1 and k 2 are the propagation loss coefficients; m d And m s Respectively are the diffraction and scattering loss coefficients; d min ≤ d ≤ d Ls The line-of-sight propagation distance, d Ls ≤d≤ d x The diffraction propagation distance, d x ≤d The scattering propagation distance.
[0026] After the model is selected, the parameters need to be adjusted adaptively: Ground object parameter adaptation: adjust the diffraction coefficient, attenuation coefficient, and reflection coefficient of the model for different ground object types (buildings, vegetation, water bodies); Terrain parameter adaptation: adjust the terrain fluctuation loss of the propagation path based on DEM data (such as adding diffraction loss calculation for terrain shielding); Radiation source parameter adaptation: adjust the spatial distribution of radiation intensity according to the antenna pattern and polarization mode of the radiation source.
[0027] Then calibrate and optimize the model: Combine historical measured data or public data sets to fine-tune the model parameters (such as controlling the path loss error within 5 dB); Support dynamic parameter updating (such as time-varying radiation source power and antenna angle).
[0028] S4. Based on the selected propagation model, obtain the electromagnetic radiation results for the target area; Input the geographic information system data into the selected propagation model, and obtain the electromagnetic radiation result map by solving the model, as shown below: S4.1, Refined Calculation by Region: Based on the results of landform segmentation (such as building areas, vegetation areas, and water areas), the electromagnetic radiation intensity is calculated separately for different areas; the electromagnetic interactions between areas are taken into account (such as the reflection and diffraction of electromagnetic waves by buildings, and the absorption of electromagnetic waves by vegetation).
[0029] S4.2, Handling of occlusion effect: Identify the shielded area between the radiation source and the receiving point through ray tracing or visualization analysis; Correct the radiation intensity in the shielded area (e.g., by adding diffraction loss and multipath scattering loss). The computational logic distinguishes between visible and non-visual areas (indirect propagation paths need to be considered for non-visual areas).
[0030] S4.3 Multi-source superposition calculation: If multiple radiation sources exist, the total radiation intensity is calculated using a linear superposition or coherent superposition algorithm. Consider the interference effects between radiation sources (such as intermodulation products and co-frequency interference).
[0031] S4.4 Result Output and Verification: Generate multi-dimensional results: spatial distribution of electric field intensity, power density, and path loss; Visualization outputs: heat maps, contour maps, and 3D renderings (which must include radiation differences between different land cover types); Results verification: Compare with measured data to perform error analysis (such as calculating RMSE and MAE) and evaluate the simulation accuracy.
[0032] Electromagnetic radiation results in the target area are as follows Figure 2 As shown, Figure 2 The diagram shows the power distribution near the target area, measured in dBm. "Site 1 Antenna 1" marks the power emission point, from which power diffuses outwards. Color-coded areas (e.g., red, yellow) indicate higher power, while cool colors (e.g., blue, green) indicate lower power. Different colors correspond to different power value ranges on the right-hand color scale, allowing for an assessment of the approximate power value for each area. The scale bar in the lower right corner displays a 10 km length, aiding in understanding the relationship between distance and power attenuation.
[0033] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: 1. The simulation method is based on electromagnetic propagation theory and constructs a three-dimensional space field distribution model combined with numerical algorithms. By integrating geographic information system data, building material parameters and radiation source characteristics, it can simulate electromagnetic wave propagation paths, reflection / diffraction effects and multi-source interference superposition in different scenarios, realize dynamic visualization of regional field strength and hotspot identification. It provides scientific basis for base station site optimization, environmental safety and other fields, and ultimately promotes the overall planning and effective control of electromagnetic safety and technology development.
[0034] 2. The simulation method significantly reduces the time required for environmental modeling by integrating geographic information system data and using a format conversion mechanism.
[0035] 3. Compared with the traditional scheme that relies on manual processing of terrain, building and other vector data, the simulation method directly converts the original data into a format vector database suitable for the simulation platform, eliminating the need for repeated modeling, thereby significantly reducing the time-consuming of the pre-simulation preparation work.
[0036] 4. The simulation method uses a widely applicable propagation model strategy, which can effectively improve the calculation efficiency while ensuring the simulation accuracy, making it particularly suitable for large-scale regional electromagnetic radiation simulation analysis tasks.
[0037] Although embodiments of the present application have been shown and described above, it is to be understood that the above-described embodiments are exemplary, and are not to be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.
Claims
1. A simulation method for regional environmental electromagnetic radiation, characterized in that, Includes the following steps: S1. Obtain geographic information system data for the target area, including digital elevation model data, land cover / land use type data, vector building outline data, and radiation source data; S2. Perform format conversion on the geographic information system data, including coordinate system conversion, format mapping and integration, and data structure standardization; S3. Based on the transformed GIS data, select the appropriate propagation model and configure the parameters; S4. Based on the selected propagation model, obtain the electromagnetic radiation results for the target area.
2. The simulation method according to claim 1, characterized in that: The geographic information system data in S1 needs to be preprocessed, including: Denoising: Gaussian filtering or median filtering is applied to remote sensing images and digital elevation model data; Target data preprocessing: The raw data is preprocessed according to the latitude and longitude range of the target area, retaining the effective area; Preliminary coordinate alignment: unify all data to the same reference coordinate system; Attribute annotation: Add electromagnetic characteristic parameters to ground feature types.
3. The simulation method according to claim 1, characterized in that: The radiation source data in S1 includes transmission power, operating frequency, antenna gain, polarization, installation height, and azimuth angle, and the data comes from operators or public datasets.
4. The simulation method according to claim 1, characterized in that: The coordinate system transformation in S2 uses a projection transformation algorithm to convert latitude and longitude coordinates into plane rectangular coordinates while preserving the spatial topology of the data.
5. The simulation method according to claim 1, characterized in that: The format mapping and integration in S2 includes converting raster data into TIN or vector format, converting vector building data into OBJ / GIS compatible format, and associating material electromagnetic parameters.
6. The simulation method according to claim 1, characterized in that: The propagation model selected in S3 is the ITM model, and the parameter configuration includes: Ground feature parameter adaptation: Adjust diffraction coefficient, attenuation coefficient, and reflection coefficient for buildings, vegetation, and water bodies; Terrain parameter adaptation: Adjusting terrain undulation loss based on DEM data; Radiation source parameter adaptation: Adjust the spatial distribution of radiation intensity according to the antenna pattern and polarization.
7. The simulation method according to claim 1, characterized in that: The generation of electromagnetic radiation results in S4 includes: Regional refined calculation: Radiation intensity is calculated separately based on the results of terrain segmentation; Masking effect handling: Correcting the radiation intensity of the masked area through ray tracing or visibility analysis; Multi-source superposition calculation: The total radiation intensity of multiple radiation sources is calculated using linear superposition or coherent superposition algorithms.
8. The simulation method according to claim 1, characterized in that: The electromagnetic radiation results output in S4 include generating spatial distribution heatmaps, contour maps, and 3D rendering maps of electric field strength, power density, and path loss, and performing error analysis by comparing them with measured data.