A method for generating complex electromagnetic environments based on multi-source data fusion

Abstract

This invention relates to the field of electromagnetic testing technology, and more particularly to a method for generating complex electromagnetic environments based on multi-source data fusion. The method includes: preprocessing multi-source electromagnetic data collected by a UAV to output a fused feature set reflecting the electromagnetic situation; inputting the fused feature set into an initial model for training to obtain an electromagnetic environment generation model; inputting preset scene conditions into the electromagnetic environment generation model to generate electromagnetic environment samples; optimizing the electromagnetic environment generation model based on the electromagnetic environment samples and actual scene conditions; determining whether to increase the conditional constraint weights of the electromagnetic environment samples; determining whether to increase the field uniformity correction coefficient of the generated electromagnetic environment; and determining the frequency hopping rate of multi-source electromagnetic data transmission based on the signal-to-noise ratio of the multi-source electromagnetic data. This invention improves the realism of generating complex electromagnetic environments.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic testing technology, and in particular to a method for generating complex electromagnetic environments based on multi-source data fusion. Background Technology

[0002] In the context of the rapid popularization of 5G communication, IoT, and intelligent equipment, the electromagnetic environment exhibits complex characteristics of multi-source interference superposition and spatiotemporal dynamic fluctuations. The demand for accurate reproduction of complex electromagnetic environments in high-value scenarios such as electronic system simulation, electromagnetic compatibility testing, and communication anti-interference assessment is increasingly prominent. The core value of complex electromagnetic environment generation methods based on multi-source data fusion lies in their ability to integrate multi-dimensional data sources such as observational electromagnetic data, geographic information data, and equipment operation data. Through spatiotemporal alignment, feature fusion, and model optimization, it achieves physically reasonable and statistically consistent reproduction of complex electromagnetic environments. This effectively solves the shortcomings of traditional generation methods, such as incomplete coverage of single data sources, large deviations between simulated scenarios and real environments, and low dynamic interference reproduction. It promotes the upgrade of electromagnetic environment simulation from static single simulation to dynamic and accurate reproduction, providing reliable support for performance evaluation and intelligent decision-making of related systems, and has significant engineering practical significance and application prospects. However, existing methods still generally suffer from problems such as fusion deviations caused by inconsistent spatiotemporal benchmarks of multi-source data, insufficient consideration of physical laws and business semantics in feature extraction, and insufficient real-time adaptability of models to dynamic interference, making it difficult to meet the needs of high-precision simulation and decision support.

[0003] Chinese Patent Publication No. CN113433401A discloses a method for simulating and generating complex electromagnetic environments using multiple sources and channels. The method includes: Step 1: Adjusting the main beam of the transmitting antenna to point towards key parts of the device under test: The test antenna is an open waveguide antenna, with a frequency range covering the signal frequencies to be constructed. The number of test antennas is determined based on the number of channels in the environment to be constructed. The bottom of the test antenna is fixed with a front-end structure to adjust the angle and polarization direction of the transmitting antenna, so that the main beam of the antenna points towards key parts of the device under test; Step 2: Constructing an electromagnetic environment physical simulation system: The electromagnetic environment physical simulation system includes interconnected ultra-wideband electromagnetic signal transmitting units, complex electromagnetic environment simulation devices, wideband up-converters, ultra-wideband data recording and playback units, power amplifiers, directional couplers, power meters, test antennas, and switches; Step 3: Real-time monitoring of complex electromagnetic environment generation: By connecting a coupler at the output of the power amplifier, the power at the coupling end of the physical simulation channel is read to monitor the generation of the electromagnetic environment in each channel. Therefore, it can be seen that the multi-source, multi-channel complex electromagnetic environment simulation generation method has the problem that it cannot simulate and generate a realistic and reasonable electromagnetic environment because it only judges the generation of the channel electromagnetic environment by monitoring the power of the coupling terminal at the output of the power amplifier, resulting in insufficient realism in the generation of complex electromagnetic environments. Summary of the Invention

[0004] To address this, the present invention provides a method for generating complex electromagnetic environments based on multi-source data fusion, which overcomes the problem in the prior art that the generation of the electromagnetic environment of the channel is not realistically simulated because the power of the coupling terminal at the output of the power amplifier is only used to determine the generation of the electromagnetic environment.

[0005] To achieve the above objectives, the present invention provides a method for generating complex electromagnetic environments based on multi-source data fusion, comprising: The multi-source electromagnetic data collected by the UAV is preprocessed to output a fusion feature set that reflects the electromagnetic situation. The fused feature set is input into the initial model for training to obtain the electromagnetic environment generation model. Preset scene conditions are input into the electromagnetic environment generation model to generate electromagnetic environment samples. The electromagnetic environment generation model is optimized based on the electromagnetic environment samples and the actual scene conditions. Obtain the spatial power distribution similarity rate between electromagnetic environment samples and actual scenes, and determine whether the authenticity of the generated complex electromagnetic environment meets the requirements based on the spatial power distribution similarity rate between the electromagnetic environment samples and actual scene conditions. If the realism of the generated complex electromagnetic environment does not meet the requirements, then it is determined whether the conditional constraint weights of the electromagnetic environment samples need to be increased. If it is not necessary to increase the conditional constraint weight of the electromagnetic environment sample, then obtain the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines to determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements. If the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements, then determine whether it is necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment. If it is not necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment, the frequency hopping rate of multi-source electromagnetic data transmission is determined based on the signal-to-noise ratio of multi-source electromagnetic data.

[0006] Furthermore, based on the similarity rate of the spatial power distribution between the electromagnetic environment samples and the actual scene, the determination of whether the authenticity of the generated complex electromagnetic environment meets the requirements includes: The similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is compared with a preset second similarity rate; If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset second similarity rate, then the authenticity of the generated complex electromagnetic environment is determined to meet the requirements. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset second similarity rate, it is determined that the authenticity of the generated complex electromagnetic environment does not meet the requirements.

[0007] Further, determine whether it is necessary to increase the conditional constraint weights of the electromagnetic environment samples, including: The similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is compared with the preset first similarity rate and the preset second similarity rate, respectively. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset first similarity rate, then it is determined that the conditional constraint weight of the electromagnetic environment sample needs to be increased. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset first similarity rate and less than or equal to the preset second similarity rate, then it is determined that there is no need to increase the condition constraint weight of the electromagnetic environment sample.

[0008] Furthermore, the increase in the conditional constraint weights of the electromagnetic environment sample is determined by the difference between the preset first similarity rate and the spatial power distribution similarity rate between the electromagnetic environment sample and the actual scene.

[0009] Furthermore, based on the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines, it is determined whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements, including: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset second overlap. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset second overlap, then it is determined that the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements, and it is determined whether the condition constraint weights of the electromagnetic environment sample meet the requirements. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset second overlap, then the spatial consistency between the electromagnetic environment sample and the actual scene is determined to be non-compliant.

[0010] Further, determine whether it is necessary to increase the field uniformity correction factor generated by the electromagnetic environment, including: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset first overlap and a preset second overlap, respectively. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset first overlap and less than or equal to the preset second overlap, then it is determined that the field uniformity correction coefficient generated by the electromagnetic environment needs to be increased, and the field uniformity correction coefficient generated by the electromagnetic environment needs to be increased. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset first overlap, then it is determined that there is no need to increase the field uniformity correction coefficient generated by the electromagnetic environment.

[0011] Furthermore, the increase in the field uniformity correction coefficient generated by the electromagnetic environment is determined by the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap.

[0012] Furthermore, the frequency hopping rate of multi-source electromagnetic data transmission is determined based on the signal-to-noise ratio of multi-source electromagnetic data, including: The signal-to-noise ratio of the multi-source electromagnetic data is compared with a preset signal-to-noise ratio; If the signal-to-noise ratio of the multi-source electromagnetic data is greater than the preset signal-to-noise ratio, then it is determined that the data transmission anti-interference capability meets the requirements, and there is no need to increase the frequency hopping rate of the multi-source electromagnetic data transmission. It is also determined whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements. If the signal-to-noise ratio of the multi-source electromagnetic data is less than or equal to the preset signal-to-noise ratio, it is determined that the collaborative dynamics of knowledge injection and model training do not meet the requirements, and the frequency hopping rate of multi-source electromagnetic data transmission needs to be increased.

[0013] Furthermore, the multi-source electromagnetic data signal-to-noise ratio is the ratio of the signal power of the available data to the overall noise power under multi-source data acquisition conditions.

[0014] Furthermore, the reduction in the frequency hopping rate of the multi-source electromagnetic data transmission is determined by the difference between the preset signal-to-noise ratio and the signal-to-noise ratio of the multi-source electromagnetic data.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: The method of this invention adjusts the conditional constraint weights of the electromagnetic environment samples based on the spatial power distribution similarity between the electromagnetic environment samples and actual scene conditions. Because real electromagnetic scenes involve dynamic interference source fluctuations, propagation losses due to terrain occlusion, and the energy superposition effect of multiple interference sources, the generation process does not fully reproduce the physical characteristics and dynamic constraints of these real scenes, resulting in the simulated energy distribution failing to match the actual energy propagation laws of the real scene, and the energy distribution similarity coefficient being lower than a preset threshold. By increasing the conditional constraint weights of the electromagnetic environment samples, the generation process is forced to conform to the physical propagation characteristics and dynamic interference laws of the real scene, reducing the energy distribution difference between the simulated scene and the actual scene. The method also adjusts the average field strength of the generated electromagnetic environment based on the overlap between the electromagnetic environment samples and actual electromagnetic contour lines. The uniformity correction coefficient is adjusted because the terrain is irregular and the data acquisition path has slight offsets in real electromagnetic scenarios. By increasing the field uniformity correction coefficient, the spatial distribution adaptability of the sample field strength can be optimized in a targeted manner, reducing the deviation from the actual electromagnetic contour lines. The frequency hopping rate of multi-source electromagnetic data transmission is adjusted according to the signal-to-noise ratio of multi-source electromagnetic data. Due to interference sources such as electromagnetic radiation from equipment start-up and shutdown and the superposition of multiple signal frequency bands in complex industrial environments, the signal-to-noise ratio of the data transmission link is lower than the anti-interference qualification threshold, and the signal is prone to distortion and packet loss, affecting the transmission stability. By increasing the transmission frequency hopping rate, the residence time of the signal in a single frequency band can be shortened, reducing the probability of continuous influence by interference sources, quickly switching to interference-free or low-interference frequency bands, improving the anti-interference of data transmission, and improving the realism of complex electromagnetic environment generation.

[0016] Furthermore, the method of the present invention adjusts the conditional constraint weights of electromagnetic environment samples by setting preset first similarity rates and preset second similarity rates. Since real electromagnetic scenes have dynamic interference source fluctuations, propagation loss caused by terrain occlusion, and the energy superposition effect of multiple interference sources, the generation process does not fully restore the physical characteristics and dynamic constraints of these real scenes, resulting in the simulated energy distribution failing to match the actual energy propagation law of the real scene, and the energy distribution similarity coefficient being lower than the preset threshold. By increasing the conditional constraint weights of electromagnetic environment samples, the generation process is forced to conform to the physical propagation characteristics and dynamic interference law of the real scene, reducing the energy distribution difference between the simulated scene and the actual scene, and further improving the realism of complex electromagnetic environment generation.

[0017] Furthermore, the method of the present invention adjusts the field uniformity correction coefficient generated by the electromagnetic environment by setting a preset first overlap degree and a preset second overlap degree. Since the terrain is irregular and the data acquisition path has slight deviation in real electromagnetic scenes, by increasing the field uniformity correction coefficient, the spatial distribution adaptability of the sample field strength can be optimized in a targeted manner, the deviation from the actual electromagnetic contour lines can be reduced, and the realism of the complex electromagnetic environment generation can be further improved.

[0018] Furthermore, the method described in this invention adjusts the frequency hopping rate of multi-source electromagnetic data transmission by setting a preset signal-to-noise ratio. Due to interference sources such as electromagnetic radiation from equipment start-up and shutdown and the superposition of multiple signal frequency bands in complex industrial environments, the signal-to-noise ratio of the data transmission link is lower than the qualified threshold for anti-interference, and the signal is prone to distortion and packet loss, affecting transmission stability. By increasing the transmission frequency hopping rate, the dwell time of the signal in a single frequency band can be shortened, the probability of being continuously affected by interference sources can be reduced, and the signal can be quickly switched to a non-interference or low-interference frequency band, thereby improving the anti-interference capability of data transmission and further enhancing the realism of the complex electromagnetic environment generated. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of the method for generating complex electromagnetic environments based on multi-source data fusion according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of determining the conditional constraint weights of electromagnetic environment samples using a complex electromagnetic environment generation method based on multi-source data fusion, as described in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the process of determining the field uniformity correction coefficient of the generated electromagnetic environment using a multi-source data fusion-based complex electromagnetic environment generation method according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the process of determining the frequency hopping rate of multi-source electromagnetic data transmission using a complex electromagnetic environment generation method based on multi-source data fusion, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, it is an overall flowchart of the method for generating complex electromagnetic environments based on multi-source data fusion in an embodiment of the present invention.

[0023] This invention provides a method for generating complex electromagnetic environments based on multi-source data fusion, comprising: Step S1: Preprocess the multi-source electromagnetic data collected by the UAV to output a fusion feature set that can reflect the electromagnetic situation. Step S2: Input the fused feature set into the initial model for training to obtain the electromagnetic environment generation model; input the preset scene conditions into the electromagnetic environment generation model to generate electromagnetic environment samples; and optimize the electromagnetic environment generation model based on the electromagnetic environment samples and the actual scene conditions. Step S3: Obtain the spatial power distribution similarity rate between the electromagnetic environment sample and the actual scene, and determine whether the authenticity of the generated complex electromagnetic environment meets the requirements based on the spatial power distribution similarity rate between the electromagnetic environment sample and the actual scene conditions. Step S4: If the realism of the generated complex electromagnetic environment does not meet the requirements, determine whether it is necessary to increase the condition constraint weight of the electromagnetic environment sample. Step S5: If it is not necessary to increase the conditional constraint weight of the electromagnetic environment sample, then obtain the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines to determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements. Step S6: If the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements, determine whether it is necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment. Step S7: If it is not necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment, then determine the frequency hopping rate of multi-source electromagnetic data transmission based on the signal-to-noise ratio of multi-source electromagnetic data.

[0024] Specifically, the method for generating complex electromagnetic environments based on multi-source data fusion can be used for complex electromagnetic environment construction, targeted radar evaluation, and radar research and testing.

[0025] Specifically, multi-source electromagnetic data includes base station signal strength data, radar pulse data, and navigation and positioning data.

[0026] Specifically, multi-source electromagnetic data can originate from communication base station signal sources, radar signal sources, and BeiDou navigation signal sources.

[0027] Specifically, electromagnetic environment characteristics include base station signal strength distribution characteristics, radar pulse current density, and positioning interference signal strength.

[0028] Specifically, the process of inputting the fused feature set into the initial model for training to obtain the electromagnetic environment generation model involves dividing the fused feature set into a training set, a validation set, and a test set. The initial model is trained using the training set to learn the intrinsic distribution of the electromagnetic environment. The parameters are optimized and adjusted using the validation set. Finally, the quality of the generated model is evaluated using the test set to obtain the electromagnetic environment generation model.

[0029] Specifically, the preset scenario conditions include electromagnetic monitoring scenarios in dense urban areas, radar monitoring scenarios in open areas, and electromagnetic interference scenarios in industrial plant areas.

[0030] Specifically, the electromagnetic environment sample is a digital dataset created by the electromagnetic environment generation model based on preset scenario conditions, reflecting the frequency domain and energy distribution of electromagnetic signals under the preset scenario.

[0031] Specifically, the process of optimizing the electromagnetic environment generation model based on the electromagnetic environment samples and actual scene conditions involves comparing the differences between the generated electromagnetic environment samples and the real data corresponding to the actual scene using a multi-dimensional loss function, calculating the gradient using the backpropagation algorithm, and iteratively updating the model parameters.

[0032] Specifically, the constraint weights of electromagnetic environment samples are coefficients that measure the relative importance of the constraints on electromagnetic environment samples during the optimization of the electromagnetic environment generation model.

[0033] Specifically, the constraints include physical law constraints, signal quality constraints, and spatial distribution constraints.

[0034] Specifically, the field uniformity correction coefficient triggered by electromagnetic data resampling is the maximum allowable difference when the logarithmic measurement values ​​of electromagnetic data from different sources at the same time exceed a preset value, thus triggering electromagnetic data resampling.

[0035] Specifically, the frequency hopping rate of multi-source electromagnetic data transmission is the number of times the transmitting end and the receiving end synchronously switch between multiple transmission frequency points during the multi-source electromagnetic data transmission process per unit time.

[0036] In implementation, the method of this invention adjusts the conditional constraint weights of the electromagnetic environment samples based on the similarity rate of spatial power distribution between the electromagnetic environment samples and actual scene conditions. Because real electromagnetic scenes exhibit dynamic interference source fluctuations, propagation losses due to terrain occlusion, and the energy superposition effect of multiple interference sources, the generation process does not fully reproduce these physical characteristics and dynamic constraints of the real scene. This results in the simulated energy distribution failing to match the actual energy propagation laws of the real scene, and the energy distribution similarity coefficient falling below a preset threshold. By increasing the conditional constraint weights of the electromagnetic environment samples, the generation process is forced to conform to the physical propagation characteristics and dynamic interference laws of the real scene, reducing the energy distribution difference between the simulated and actual scenes. Furthermore, the field uniformity correction coefficient of the generated electromagnetic environment is adjusted based on the overlap between the electromagnetic environment samples and actual electromagnetic contour lines. Adjustments are made to optimize the spatial distribution of the sample field strength by increasing the field uniformity correction coefficient, which can be used to adapt to the irregular terrain and slight offsets in the data acquisition path in real electromagnetic scenarios. This reduces the deviation from the actual electromagnetic contour lines. The frequency hopping rate of multi-source electromagnetic data is adjusted based on the signal-to-noise ratio (SNR) of the multi-source electromagnetic data. In complex industrial environments, interference sources such as electromagnetic radiation from equipment startup and shutdown and the superposition of multiple signal frequency bands can cause the SNR of the data transmission link to fall below the acceptable threshold for anti-interference, leading to signal distortion, packet loss, and affecting transmission stability. Increasing the frequency hopping rate shortens the dwell time of the signal in a single frequency band, reduces the probability of continuous interference, and allows for rapid switching to interference-free or low-interference frequency bands, improving the anti-interference capability of data transmission and enhancing the realism of the generated complex electromagnetic environment.

[0037] Please continue reading. Figure 2 As shown, it is a logical flowchart of the process of determining the conditional constraint weights of electromagnetic environment samples by the complex electromagnetic environment generation method based on multi-source data fusion according to an embodiment of the present invention.

[0038] Specifically, determining whether the authenticity of the generated complex electromagnetic environment meets the requirements based on the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene includes: The similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is compared with a preset second similarity rate; If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset second similarity rate, then the authenticity of the generated complex electromagnetic environment is determined to meet the requirements. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset second similarity rate, it is determined that the authenticity of the generated complex electromagnetic environment does not meet the requirements.

[0039] The reasons why the generated complex electromagnetic environment may not be realistic enough could be that the spatial consistency between the electromagnetic environment sample and the actual scene is not up to standard, or that the constraint weights of the electromagnetic environment sample are not up to standard. The next step is to determine which specific cause it is, which is essentially the process of determining whether to increase the constraint weights of the electromagnetic environment sample.

[0040] Specifically, determining whether the constraint weights of the electromagnetic environment samples need to be increased includes: The similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is compared with the preset first similarity rate and the preset second similarity rate, respectively. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset first similarity rate, then it is determined that the conditional constraint weight of the electromagnetic environment sample needs to be increased. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset first similarity rate and less than or equal to the preset second similarity rate, then it is determined that there is no need to increase the condition constraint weight of the electromagnetic environment sample.

[0041] Specifically, if the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than a preset first similarity rate, it is determined that the reason for the unrealistic generation of the complex electromagnetic environment is that the conditional constraint weight of the electromagnetic environment sample does not meet the requirements. Therefore, it is necessary to increase the conditional constraint weight of the electromagnetic environment sample. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than a preset first similarity rate and less than or equal to a preset second similarity rate, it can be preliminarily determined that the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements. Next, it is necessary to make a final determination on whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements based on the overlap degree between the electromagnetic environment sample and the actual electromagnetic contour lines. That is, to determine whether the reason for the unrealistic generation of the complex electromagnetic environment is that the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements.

[0042] It is understandable that the preset first similarity rate is less than the preset second similarity rate, and the three intervals divided by the preset first similarity rate and the preset second similarity rate correspond to three different situations: The first interval is when the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset first similarity rate. The corresponding situation is: due to the dynamic interference source fluctuations, propagation loss caused by terrain occlusion and the superposition effect of multiple interference sources in the real electromagnetic scene, the generation process does not fully restore the physical characteristics and dynamic constraints of these real scenes, resulting in the simulated energy distribution being unable to match the actual energy propagation law of the real scene. The energy distribution similarity coefficient is lower than the preset threshold. At this time, it is necessary to adjust the condition constraint weight of the electromagnetic environment sample. The second interval is when the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset first similarity rate and less than or equal to the preset second similarity rate. The corresponding situation is: due to the irregular terrain undulation and slight offset of the data acquisition path in the real electromagnetic scene, it is necessary to further determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements. The third interval is when the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset second similarity rate. The corresponding situation is that the authenticity of the generated complex electromagnetic environment meets the requirements, and no adjustment is needed.

[0043] Understandably, in the process of generating complex electromagnetic environments, the core logic of using preset first similarity rates and preset second similarity rates to characterize the authenticity of the generated complex electromagnetic environment is to transform the authenticity of the generated complex electromagnetic environment into a quantifiable spatial power distribution similarity rate range judgment. The preset first similarity rate is the boundary distinguishing between the redundancy information attenuation coefficient that needs to be adjusted and the spatial consistency between the electromagnetic environment sample and the actual scene that needs to be judged. The preset second similarity rate is the critical point for judging whether the authenticity meets the standard, providing a basis for targeted optimization. The preset first similarity rate and preset second similarity rate can be set according to the actual working conditions. The setting of the preset first similarity rate and preset second similarity rate aims to ensure the authenticity and practicality of the generated complex electromagnetic environment. Optionally, the preset first similarity rate and preset second similarity rate are determined through a limited number of experiments by evaluating the generation effect of different generation similarity rates on the complex electromagnetic environment. The determined preset first similarity rate and preset second similarity rate should meet the requirement that they are neither too small nor cause excessive interference to the generation process of complex electromagnetic environments. For example, the preset first similarity rate is generally selected in the range of [79%, 81%], and the preset second similarity rate is generally selected in the range of [89%, 91%].

[0044] Preferably, the first similarity rate is 0.8 in a preferred embodiment, and the second similarity rate is 0.9 in a preferred embodiment.

[0045] Specifically, the similarity rate of spatial power distribution between electromagnetic environment samples and actual scenarios is a dimensionless parameter that measures the degree of consistency between the generated electromagnetic environment samples and the real scenarios in terms of energy distribution.

[0046] Specifically, the increase in the conditional constraint weights of the electromagnetic environment sample is determined by the difference between the preset first similarity rate and the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene.

[0047] Specifically, when the difference between the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene and the preset first similarity rate is within 0.05, the condition constraint weight of the electromagnetic environment sample is increased to 1.1 times the original value. When the difference between the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene and the preset first similarity rate exceeds 0.05, in addition to increasing to 1.1 times the original value, the condition constraint weight of the electromagnetic environment sample increases by 0.03 for every 0.02 increase. For example, when the difference between the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene and the preset first similarity rate is 0.09, the current condition constraint weight of the electromagnetic environment sample is 0.5, and the increased condition constraint weight of the electromagnetic environment sample is 0.5×1.1+0.03×2=0.61.

[0048] In practice, the method of this invention adjusts the constraint weights of electromagnetic environment samples by setting a preset first similarity rate and a preset second similarity rate. Because real electromagnetic scenes have dynamic interference source fluctuations, propagation losses caused by terrain occlusion, and the superposition effect of multiple interference sources, the generation process does not fully restore the physical characteristics and dynamic constraints of these real scenes, resulting in the simulated energy distribution failing to match the actual energy propagation law of the real scene, and the energy distribution similarity coefficient being lower than the preset threshold. By increasing the constraint weights of electromagnetic environment samples, the generation process is forced to conform to the physical propagation characteristics and dynamic interference law of the real scene, reducing the energy distribution difference between the simulated scene and the actual scene, and further improving the realism of the generation of complex electromagnetic environments.

[0049] Please continue reading. Figure 3 The diagram shown is a logical flowchart illustrating the process of determining the field uniformity correction coefficients for generating an electromagnetic environment based on a multi-source data fusion method according to an embodiment of the present invention. Specifically, the spatial consistency between electromagnetic environment samples and actual scenes is determined based on the overlap between electromagnetic environment samples and actual electromagnetic contour lines, including: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset second overlap. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset second overlap, then it is determined that the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements, and it is determined whether the condition constraint weights of the electromagnetic environment sample meet the requirements. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset second overlap, then the spatial consistency between the electromagnetic environment sample and the actual scene is determined to be non-compliant.

[0050] Specifically, when the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset second overlap, it is determined that the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements. However, if the authenticity of the complex electromagnetic environment generation has been determined to be unacceptable, it is necessary to further determine whether the conditional constraint weights of the electromagnetic environment sample meet the requirements.

[0051] In practice, the condition constraint weights of the actual electromagnetic environment sample are compared with the predetermined constraint weight threshold to determine whether the condition constraint weights of the electromagnetic environment sample meet the requirements. If the condition constraint weights of the actual electromagnetic environment sample are less than the predetermined constraint weight threshold, the condition constraint weights of the electromagnetic environment sample are determined to be non-compliant. The predetermined constraint weight threshold is the average value of the condition constraint weights of the electromagnetic environment samples monitored in the previous three months of the historical period.

[0052] If the constraint weights of the electromagnetic environment samples do not meet the requirements, the constraint weights of the electromagnetic environment samples are increased; if the constraint weights of the electromagnetic environment samples meet the requirements, the spatial power distribution similarity rate between the electromagnetic environment samples and the actual scene is re-collected, and the authenticity of the generated complex electromagnetic environment is re-evaluated.

[0053] When the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to a preset second overlap, it can be determined that the reason for the unrealistic nature of the generated complex electromagnetic environment is a lack of spatial consistency between the electromagnetic environment sample and the actual scene. This lack of spatial consistency could be due to either an inadequate field uniformity correction coefficient or inadequate data transmission anti-interference capabilities. The next step is to determine which specific cause it is, which involves deciding whether to increase the field uniformity correction coefficient for the generated electromagnetic environment.

[0054] Specifically, determining whether it is necessary to increase the field uniformity correction factor generated by the electromagnetic environment includes: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset first overlap and a preset second overlap, respectively. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset first overlap and less than or equal to the preset second overlap, then it is determined that the field uniformity correction coefficient generated by the electromagnetic environment needs to be increased, and the field uniformity correction coefficient generated by the electromagnetic environment needs to be increased. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset first overlap, then it is determined that there is no need to increase the field uniformity correction coefficient generated by the electromagnetic environment.

[0055] Specifically, when the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than a preset first overlap but less than or equal to a preset second overlap, it is determined that the reason for the data transmission interference immunity failure is that the field uniformity correction coefficient generated by the electromagnetic environment does not meet the requirements. Therefore, it is necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment. When the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset first overlap, it can be preliminarily determined that the data transmission interference immunity failure is not met. Next, it is necessary to make a final determination on whether the data transmission interference immunity meets the requirements based on the signal-to-noise ratio of the multi-source electromagnetic data, that is, to determine whether the reason for the data transmission interference immunity failure is the data transmission interference immunity failure.

[0056] It is understandable that the preset first overlap degree is less than the preset second overlap degree, and the three intervals divided by the preset first overlap degree and the preset second overlap degree correspond to three different situations: The first interval is when the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset first overlap. The corresponding situation is: due to the presence of interference sources such as electromagnetic radiation from equipment start-up and shutdown and the superposition of multiple signal frequency bands in complex industrial environments, the signal-to-noise ratio of the data transmission link is lower than the anti-interference qualification threshold, and the signal is prone to distortion and packet loss, affecting the transmission stability. At this time, it is necessary to further determine whether the anti-interference of data transmission meets the requirements. The second interval is when the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset first overlap and less than or equal to the preset second overlap. The corresponding situation is: due to the irregular terrain undulations and slight offsets in the data acquisition path in the real electromagnetic scene, it is necessary to adjust the field uniformity correction coefficient generated by the electromagnetic environment. The third interval is when the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset second overlap. The corresponding situation is: in order to determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements, it is necessary to further determine whether the condition constraint weights of the electromagnetic environment sample meet the requirements.

[0057] Understandably, in the process of generating complex electromagnetic environments, the introduction of preset first and second coincidence degrees characterizes the spatial consistency between the electromagnetic environment sample and the actual scene. The core logic is to transform the spatial consistency between the electromagnetic environment sample and the actual scene into a quantifiable electromagnetic contour line coincidence interval judgment. The preset first coincidence degree serves as the boundary between judging transmission anti-interference and adjusting the uniformity correction coefficient of the resampling field. The preset second coincidence degree serves as the critical point between judging the threshold to be adjusted and the conditional constraint weight of the electromagnetic environment sample to be confirmed, providing a quantitative basis for targeted optimization. The preset first and second coincidence degrees can be set according to actual working conditions. The setting of the preset first and second coincidence degrees aims to ensure the authenticity and practicality of the generated complex electromagnetic environment. Optionally, the preset first and second coincidence degrees are determined through a limited number of experiments by evaluating the effect of different contour line coincidence degrees on the generation of complex electromagnetic environments. The determined preset first and second coincidence degrees should satisfy the condition that they are neither too small nor cause excessive interference to the generation process of complex electromagnetic environments. For example, the preset first overlap degree is generally selected in the range of [0.74, 0.76], and the preset second overlap degree is generally selected in the range of [0.84, 0.86].

[0058] Preferably, the first degree of overlap is 0.75 in a preferred embodiment, and the second degree of overlap is 0.85 in a preferred embodiment.

[0059] Specifically, the degree of overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is an important parameter for measuring the spatial overlap between the electromagnetic field strength distribution of the generated sample and the contour lines of the actual scene.

[0060] Specifically, the increase in the field uniformity correction coefficient generated by the electromagnetic environment is determined by the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap.

[0061] Specifically, when the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap is within 0.05, the field uniformity correction coefficient generated by the electromagnetic environment is increased to 1.1 times the original value. When the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap exceeds 0.05, in addition to increasing to 1.1 times the original value, for every 0.02 exceeding the original value, the field uniformity correction coefficient generated by the electromagnetic environment increases by 0.03. For example, when the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap is 0.07, the current field uniformity correction coefficient generated by the electromagnetic environment is 0.6, and the increased field uniformity correction coefficient generated by the electromagnetic environment is 0.6×1.1+0.03×1=0.69.

[0062] In practice, the method of the present invention adjusts the field uniformity correction coefficient generated by the electromagnetic environment by setting a preset first overlap degree and a preset second overlap degree. Since the terrain is irregular and the data acquisition path has slight deviation in real electromagnetic scenes, by increasing the field uniformity correction coefficient, the spatial distribution adaptability of the sample field strength can be optimized in a targeted manner, the deviation from the actual electromagnetic contour lines can be reduced, and the realism of the complex electromagnetic environment generation can be further improved.

[0063] Please continue reading. Figure 4 As shown, it is a logical flowchart of the process of determining the frequency hopping rate of multi-source electromagnetic data transmission using a complex electromagnetic environment generation method based on multi-source data fusion according to an embodiment of the present invention.

[0064] Specifically, determining the frequency hopping rate of multi-source electromagnetic data transmission based on the signal-to-noise ratio of multi-source electromagnetic data includes: The signal-to-noise ratio of the multi-source electromagnetic data is compared with a preset signal-to-noise ratio; If the signal-to-noise ratio of the multi-source electromagnetic data is greater than the preset signal-to-noise ratio, then it is determined that the anti-interference performance of the multi-source electromagnetic data transmission meets the requirements, and it is not necessary to increase the frequency hopping rate of the multi-source electromagnetic data transmission. It is also determined whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements. If the signal-to-noise ratio of the multi-source electromagnetic data is less than or equal to the preset signal-to-noise ratio, it is determined that the anti-interference performance of the multi-source electromagnetic data transmission does not meet the requirements, and the frequency hopping rate of the multi-source electromagnetic data transmission needs to be increased.

[0065] When the signal-to-noise ratio of multi-source electromagnetic data is greater than the preset signal-to-noise ratio, it is determined that the data transmission anti-interference capability meets the requirements. However, if it has been previously determined that the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements, then it is necessary to further determine whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements.

[0066] In practice, the field uniformity correction coefficient generated by the actual electromagnetic environment is compared with the predetermined field uniformity correction coefficient to determine whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements. If the field uniformity correction coefficient generated by the actual electromagnetic environment is less than the predetermined field uniformity correction coefficient, it is determined that the field uniformity correction coefficient generated by the electromagnetic environment does not meet the requirements. The predetermined field uniformity correction coefficient is the average value of the field uniformity correction coefficients generated by the electromagnetic environment monitored in the previous three months of the historical period.

[0067] If the field uniformity correction coefficient generated by the actual electromagnetic environment does not meet the requirements, the field uniformity correction coefficient generated by the actual electromagnetic environment is increased; if the field uniformity correction coefficient generated by the actual electromagnetic environment meets the requirements, the overlap between the electromagnetic environment sample and the actual electromagnetic contour line is re-acquired, and the spatial consistency between the electromagnetic environment sample and the actual scene is re-evaluated.

[0068] When the signal-to-noise ratio of multi-source electromagnetic data is less than or equal to the preset signal-to-noise ratio, it can be determined that the reason why the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements is that the anti-interference capability of data transmission does not meet the requirements. Therefore, it is necessary to increase the frequency hopping rate of multi-source electromagnetic data transmission.

[0069] It is understandable that the two intervals of the preset signal-to-noise ratio correspond to two different situations: The first interval is when the signal-to-noise ratio of multi-source electromagnetic data is greater than the preset signal-to-noise ratio. The corresponding situation is that the data transmission anti-interference capability meets the requirements. At this time, it is necessary to further determine whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements. The second interval is when the signal-to-noise ratio of multi-source electromagnetic data is less than or equal to the preset signal-to-noise ratio. The corresponding situation is: due to the presence of interference sources such as electromagnetic radiation from equipment start-up and shutdown and the superposition of multiple signal frequency bands in complex industrial environments, the signal-to-noise ratio of the data transmission link is lower than the anti-interference qualification threshold, and the signal is prone to distortion and packet loss, affecting the transmission stability. At this time, it is necessary to adjust the frequency hopping rate of multi-source electromagnetic data transmission.

[0070] Understandably, in the process of generating complex electromagnetic environments, using a preset signal-to-noise ratio (SNR) to characterize the anti-interference capability of data transmission is based on the core logic of transforming the anti-interference capability of data transmission into a quantifiable judgment of the SNR range of multi-source electromagnetic data. The preset SNR serves as a critical point to distinguish between the need to confirm the uniformity correction coefficient of the resampling field and the need to adjust the transmission frequency hopping rate, clarifying the targeted processing direction under different SNR scenarios, and providing a quantitative basis for ensuring the stability of data transmission. The preset SNR can be set according to actual working conditions. The setting of the preset SNR aims to ensure the authenticity and practicality of generating complex electromagnetic environments. Optionally, the preset SNR is determined through a limited number of experiments by evaluating the effect of different SNRs on the generation of complex electromagnetic environments. The determined preset SNR should satisfy the condition that it is neither too small nor will it cause excessive interference to the generation process of complex electromagnetic environments. For example, the preset SNR is generally selected in the range of [9dB, 11dB].

[0071] Preferably, the preset signal-to-noise ratio is 10dB in the preferred embodiment.

[0072] Specifically, the signal-to-noise ratio of the multi-source electromagnetic data is the ratio of the signal power of the available data to the overall noise power under multi-source data acquisition conditions.

[0073] Specifically, the reduction in the frequency hopping rate of the multi-source electromagnetic data transmission is determined by the difference between the preset signal-to-noise ratio and the signal-to-noise ratio of the multi-source electromagnetic data.

[0074] Specifically, when the difference between the multi-source electromagnetic data signal-to-noise ratio and the preset signal-to-noise ratio is within 2dB, the multi-source electromagnetic data transmission frequency hopping rate increases to 1.05 times the original value. When the difference between the multi-source electromagnetic data signal-to-noise ratio and the preset signal-to-noise ratio exceeds 2dB, in addition to increasing to 1.05 times the original value, the multi-source electromagnetic data transmission frequency hopping rate increases by 120 hops / second for every 1dB increase. For example, when the difference between the multi-source electromagnetic data signal-to-noise ratio and the preset signal-to-noise ratio is 4dB, and the current multi-source electromagnetic data transmission frequency hopping rate is 3000 hops / second, the increased multi-source electromagnetic data transmission frequency hopping rate is 3000×1.1+120×2=3540 hops / second.

[0075] In practice, the method described in this invention adjusts the frequency hopping rate of multi-source electromagnetic data transmission by setting a preset signal-to-noise ratio. Due to interference sources such as electromagnetic radiation from equipment start-up and shutdown and the superposition of multiple signal frequency bands in complex industrial environments, the signal-to-noise ratio of the data transmission link is lower than the qualified threshold for anti-interference, and the signal is prone to distortion and packet loss, affecting transmission stability. By increasing the transmission frequency hopping rate, the dwell time of the signal in a single frequency band can be shortened, the probability of being continuously affected by interference sources can be reduced, and the signal can be quickly switched to a non-interference or low-interference frequency band, thereby improving the anti-interference capability of data transmission and further enhancing the realism of the complex electromagnetic environment generated.

[0076] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for generating complex electromagnetic environments based on multi-source data fusion, characterized in that, include: The multi-source electromagnetic data collected by the UAV is preprocessed to output a fusion feature set that reflects the electromagnetic situation. The fused feature set is input into the initial model for training to obtain the electromagnetic environment generation model. Preset scene conditions are input into the electromagnetic environment generation model to generate electromagnetic environment samples. The electromagnetic environment generation model is optimized based on the electromagnetic environment samples and the actual scene conditions. Obtain the spatial power distribution similarity rate between electromagnetic environment samples and actual scenes, and determine whether the authenticity of the generated complex electromagnetic environment meets the requirements based on the spatial power distribution similarity rate between the electromagnetic environment samples and actual scene conditions. If the realism of the generated complex electromagnetic environment does not meet the requirements, then it is determined whether the conditional constraint weights of the electromagnetic environment samples need to be increased. If it is not necessary to increase the conditional constraint weight of the electromagnetic environment sample, then obtain the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines to determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements. If the spatial consistency between the electromagnetic environment sample and the actual scene does not meet the requirements, then determine whether it is necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment. If it is not necessary to increase the field uniformity correction coefficient generated by the electromagnetic environment, the frequency hopping rate of multi-source electromagnetic data transmission is determined based on the signal-to-noise ratio of multi-source electromagnetic data. Determine whether the constraint weights for the electromagnetic environment samples need to be increased, including: The similarity rates of the spatial power distribution between the electromagnetic environment sample and the actual scene are compared with the preset first similarity rate and the preset second similarity rate, respectively. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset first similarity rate, then it is determined that the conditional constraint weight of the electromagnetic environment sample needs to be increased. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset first similarity rate and less than or equal to the preset second similarity rate, then it is determined that there is no need to increase the condition constraint weight of the electromagnetic environment sample. Determine whether it is necessary to increase the field uniformity correction factor generated by the electromagnetic environment, including: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset first overlap and a preset second overlap, respectively. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset first overlap and less than or equal to the preset second overlap, then it is determined that the field uniformity correction coefficient generated by the electromagnetic environment needs to be increased. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset first overlap, then it is determined that there is no need to increase the field uniformity correction coefficient generated by the electromagnetic environment. Determining the frequency hopping rate of multi-source electromagnetic data transmission based on the signal-to-noise ratio of multi-source electromagnetic data includes: The signal-to-noise ratio of the multi-source electromagnetic data is compared with a preset signal-to-noise ratio; If the signal-to-noise ratio of the multi-source electromagnetic data is greater than the preset signal-to-noise ratio, then it is determined that the anti-interference performance of the multi-source electromagnetic data transmission meets the requirements, and it is not necessary to increase the frequency hopping rate of the multi-source electromagnetic data transmission. It is also determined whether the field uniformity correction coefficient generated by the electromagnetic environment meets the requirements. If the signal-to-noise ratio of the multi-source electromagnetic data is less than or equal to the preset signal-to-noise ratio, it is determined that the anti-interference performance of the multi-source electromagnetic data transmission does not meet the requirements, and the frequency hopping rate of the multi-source electromagnetic data transmission needs to be increased.

2. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 1, characterized in that, Determining whether the realism of the generated complex electromagnetic environment meets the requirements based on the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene includes: The similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is compared with a preset second similarity rate; If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is greater than the preset second similarity rate, then the authenticity of the generated complex electromagnetic environment is determined to meet the requirements. If the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene is less than or equal to the preset second similarity rate, it is determined that the authenticity of the generated complex electromagnetic environment does not meet the requirements.

3. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 2, characterized in that, The increase in the conditional constraint weights of the electromagnetic environment sample is determined by the difference between the preset first similarity rate and the similarity rate of the spatial power distribution between the electromagnetic environment sample and the actual scene.

4. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 3, characterized in that, Determine whether the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements based on the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines, including: The overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is compared with a preset second overlap. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is greater than the preset second overlap, then it is determined that the spatial consistency between the electromagnetic environment sample and the actual scene meets the requirements, and it is determined whether the condition constraint weights of the electromagnetic environment sample meet the requirements. If the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines is less than or equal to the preset second overlap, then the spatial consistency between the electromagnetic environment sample and the actual scene is determined to be non-compliant.

5. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 4, characterized in that, The increase in the field uniformity correction coefficient generated by the electromagnetic environment is determined by the difference between the overlap between the electromagnetic environment sample and the actual electromagnetic contour lines and the preset first overlap.

6. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 5, characterized in that, The signal-to-noise ratio of the multi-source electromagnetic data is the ratio of the total power of the effective electromagnetic signal to the total power of the environmental noise in the acquired multi-source electromagnetic data.

7. The method for generating complex electromagnetic environments based on multi-source data fusion according to claim 6, characterized in that, The reduction in the frequency hopping rate of the multi-source electromagnetic data transmission is determined by the difference between the preset signal-to-noise ratio and the signal-to-noise ratio of the multi-source electromagnetic data.

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