Scene-adaptive satellite phased-array antenna anti-interference method and electronic equipment

Abstract

The invention provides a scene-adaptive satellite phased-array antenna anti-interference method and electronic equipment, relates to the technical field of satellite communication, and aims to obtain environment sensing data, perform interference characteristic analysis and perform interference characteristic fluctuation analysis based on interference characteristic distribution by using a sensor system of a satellite phased-array antenna under a predetermined monitoring node. The method comprises the following steps: acquiring satellite working parameters to perform satellite state fluctuation analysis, calling a strategy optimization analysis plug-in based on an interference fluctuation scale and a state fluctuation scale, performing anti-interference analysis, outputting an optimal anti-interference strategy, and executing operation control of a satellite phased-array antenna in a future time zone according to the optimal anti-interference strategy. The technical problems that in the prior art, a satellite phased-array antenna is insufficient in anti-interference adaptability and limited in suppression capacity are solved. The technical effects of improving the anti-interference adaptability of the satellite phased-array antenna, enhancing the interference suppression capability and ensuring that the antenna can effectively avoid or suppress interference are achieved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a scene-adaptive satellite phased array antenna anti-interference method and electronic equipment. Background Art

[0002] Satellite phased array antennas have been widely used in modern communications, navigation and remote sensing due to their flexible and controllable beams and strong multi-target coverage capabilities. However, with the complexity of the application environment, electromagnetic interference or other interference problems have become increasingly prominent. Traditional anti-interference methods mainly rely on fixed-parameter beamforming technology or simple frequency domain filtering technology, which has limited anti-interference suppression capabilities, resulting in poor anti-interference effects and unable to meet the requirements of real-time and high efficiency in complex scenarios.

[0003] In the prior art, satellite phased array antennas have technical problems such as insufficient anti-interference adaptability and limited suppression capability. Summary of the invention

[0004] The present application provides a scene-adaptive satellite phased array antenna anti-interference method and electronic equipment, which are used to solve the technical problems in the prior art that satellite phased array antennas have insufficient anti-interference adaptability and limited suppression capability.

[0005] In view of the above problems, the present application provides a scene-adaptive satellite phased array antenna anti-interference method and electronic equipment.

[0006] The first aspect of the present application provides a scene-adaptive satellite phased array antenna anti-interference method, the method comprising: at a predetermined monitoring node, using a sensor system of a satellite phased array antenna to perform environmental detection, obtain environmental perception data, and perform interference feature analysis based on the environmental perception data to determine the interference feature distribution; perform interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale; obtain satellite operating parameters of the predetermined monitoring node, perform satellite state fluctuation analysis based on the satellite operating parameters, and determine the state fluctuation scale; based on the interference fluctuation scale and the state fluctuation scale, call a strategy optimization analysis plug-in, perform anti-interference analysis based on the interference feature distribution and the satellite operating parameters, output an array element parameter matrix, and set it as an optimal anti-interference strategy; and execute operation control of the satellite phased array antenna in the future time zone according to the optimal anti-interference strategy.

[0007] The second aspect of the present application provides an electronic device, characterized in that the electronic device includes: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a scene-adaptive satellite phased array antenna anti-interference method provided by the present application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in the embodiment of the present application uses the sensor system of the satellite phased array antenna to detect the environment at a predetermined monitoring node, obtains environmental perception data, and performs interference feature analysis based on the environmental perception data to determine the interference feature distribution, and performs interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale. The satellite operating parameters of the predetermined monitoring node are obtained, and satellite state fluctuation analysis is performed based on the satellite operating parameters to determine the state fluctuation scale. Based on the interference fluctuation scale and the state fluctuation scale, a strategy optimization analysis plug-in is called, and anti-interference analysis is performed based on the interference feature distribution and the satellite operating parameters. The array element parameter matrix is ​​output and set as the optimal anti-interference strategy. The operation control of the satellite phased array antenna in the future time zone is executed according to the optimal anti-interference strategy. The technical effect of improving the anti-interference adaptability of the satellite phased array antenna, enhancing the interference suppression capability, and ensuring that the antenna can effectively avoid or suppress interference is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a scenario-adaptive satellite phased array antenna anti-interference method provided in this application; Figure 2 A schematic diagram of the structure of an electronic device provided in this application.

[0011] Description of the reference numerals: processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0012] The present application provides a scenario-adaptive satellite phased array antenna anti-interference method and electronic equipment, which are used to solve the technical problems of insufficient anti-interference adaptability and limited suppression capability of satellite phased array antennas in the prior art. The technical effect of improving the anti-interference adaptability of satellite phased array antennas, enhancing interference suppression capability, and ensuring that the antenna can effectively avoid or suppress interference is achieved.

[0013] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a scenario-adaptive satellite phased array antenna anti-interference method, the method comprising: At a predetermined monitoring node, a sensor system of a satellite phased array antenna is used to detect the environment, obtain environmental perception data, and perform interference feature analysis based on the environmental perception data to determine the interference feature distribution.

[0015] Specifically, the predetermined monitoring node refers to a location or area that is pre-set based on task requirements and environmental characteristics for performing environmental detection and interference analysis. Under the predetermined monitoring node, the sensor system of the satellite phased array antenna is used to scan and collect electromagnetic environment information within the coverage area. The sensor system can capture multi-dimensional data including frequency, signal strength, and signal direction. According to the information collected by the sensor system, environmental perception data is obtained, and the environmental perception data includes electromagnetic interference sources, obstacles, and other interference factors in the current environment. The satellite phased array antenna is an antenna system that uses electronic control of beam direction and characteristics, and has the characteristics of electronic beam control, versatility, high sensitivity, and resolution. Then, the interference characteristics of the spectrum, time domain, and spatial distribution of the environmental perception data are comprehensively analyzed to determine the distribution of interference characteristics. The distribution of interference characteristics reflects multi-dimensional data such as the type, strength, and location of the interference source, which is the key basis for formulating effective anti-interference methods. For example, through analysis, it can be determined which directions have the strongest interference, which frequency bands are most susceptible to interference, and how obstacles affect the transmission path of the signal. Through the coordinated work of the predetermined monitoring node and the satellite phased array antenna, the comprehensiveness and real-time nature of environmental detection and interference analysis are ensured.

[0016] Furthermore, interference feature analysis is performed according to the environmental perception data to determine the interference feature distribution, including: configuring an interference analysis strategy, wherein the interference analysis strategy includes spectrum analysis, time domain analysis, and spatial distribution analysis; based on the interference analysis strategy, interference feature analysis is performed according to the environmental perception data to determine a number of interference features, wherein the interference features include at least the type of interference source, the location of the interference source, and the interference intensity; and the several interference features are mapped and arranged according to the location of the interference source to generate the interference feature distribution.

[0017] Specifically, the interference analysis strategy is configured according to the analysis target and environmental factors. The interference analysis strategy refers to a systematic method for feature analysis of environmental perception data, including spectrum analysis, time domain analysis and spatial distribution analysis. Spectrum analysis refers to the decomposition and evaluation of the frequency components of electromagnetic signals to identify different types of interference sources, such as fixed frequency interference, broadband interference, etc. Time domain analysis refers to the characteristic changes of signals over time, such as analyzing whether the interference is intermittent or periodic to determine the behavior pattern of the interference source. Spatial distribution analysis is to accurately determine the geographical location and orientation of the interference source through the beam pointing characteristics of the satellite phased array antenna. Then, according to the configured interference analysis strategy, the collected environmental perception data is analyzed for interference characteristics, and the spectrum analysis technology is used to identify interference signals at different frequencies. The time evolution law of the interference signal is tracked by the time domain analysis technology, and the spatial distribution analysis technology is used to determine the possible location of the interference source. Through analysis, several interference features are obtained, and the interference features at least include the type of interference source, the location of the interference source and the interference intensity. The type of interference source is radar interference, communication interference, etc. The location of the interference source is the geographical coordinates or angle range obtained by spatial distribution analysis, and the interference intensity represents the power or amplitude of the interference signal.

[0018] Next, all identified interference features are mapped and arranged according to the location of the interference source, that is, the type and intensity data of each interference source are located in the geographic space or logical space according to the location of the interference source, forming an intuitive interference feature distribution. Interference feature distribution is a comprehensive data expression method that not only clearly reveals the spatial layout of the interference source, but also distinguishes the type and intensity of the interference source through markers of different colors, sizes or shapes, thereby providing users with a comprehensive and intuitive view of the interference environment. Through the multi-dimensional analysis of interference features, the type and distribution of interference can be comprehensively and intuitively reflected, providing basic data for subsequent anti-interference decisions.

[0019] An interference feature fluctuation analysis is performed based on the interference feature distribution to determine the interference fluctuation scale.

[0020] Specifically, using the interference feature distribution map as the basis for analysis, interference feature fluctuation analysis is performed on each interference feature to quantify the volatility of the interference feature and determine the scale of interference fluctuation.

[0021] Furthermore, interference feature fluctuation analysis is performed based on the interference feature distribution to determine the interference fluctuation scale, including: obtaining multiple historical interference feature distributions of multiple monitoring nodes within a historical time window, and selecting adjacent interference feature distributions of adjacent nodes of the predetermined monitoring node; performing similarity traversal comparison on the adjacent interference feature distribution and the interference feature distribution to determine the adjacent distribution similarity, and subtracting the adjacent distribution similarity from 1 to obtain a first interference fluctuation coefficient; performing interference fluctuation trend analysis on the multiple historical interference feature distributions to determine a second interference fluctuation coefficient; and obtaining the interference fluctuation scale by weighted calculation based on the first interference fluctuation coefficient and the second interference fluctuation coefficient.

[0022] Specifically, multiple historical interference feature distributions of multiple monitoring nodes within a historical time window are obtained, where the historical time window refers to the interference feature distribution data collected within a certain period of time in the past, reflecting the dynamic change process of the interference environment, and the monitoring node refers to the specific location or area set up for monitoring interference features. At the same time, the adjacent interference feature distributions of the adjacent nodes of the predetermined monitoring node are selected. An adjacent node refers to a node that is spatially or logically adjacent to a predetermined monitoring node. By selecting the interference feature distribution of adjacent nodes, it is helpful to evaluate the consistency or difference of interference within the area, and then more comprehensively consider the local impact of interference.

[0023] The interference feature distribution of the predetermined monitoring node is compared with the adjacent interference feature distribution of the adjacent node by similarity traversal, and the feature parameters in the two groups of interference feature distributions, such as the type, location, and intensity of the interference source, are compared item by item. The similarity calculation algorithms such as cosine similarity and Pearson correlation coefficient are used to calculate the similarity between the two groups of distributions to obtain the adjacent distribution similarity. Similarity is an indicator used to quantify the consistency between two distributions. The higher the value, the closer the distribution is and the smaller the fluctuation is. Subsequently, the first interference fluctuation coefficient is calculated by the formula "1 minus the adjacent distribution similarity". The first interference fluctuation coefficient represents the degree of interference difference between the adjacent node and the predetermined node. The larger the adjacent deviation, the greater the overall environmental fluctuation. At the same time, a time series analysis is performed on multiple historical interference feature distributions within the historical time window, and the interference fluctuation change trend is evaluated according to the time order of the distribution, including rising, falling or stable. By calculating the similarity change of adjacent distributions in the historical feature distribution sequence, the mean of multiple historical interference fluctuation coefficients is gradually accumulated to generate a second interference fluctuation coefficient. The second interference fluctuation coefficient reflects the degree of fluctuation of the interference feature within the overall time window. Finally, a weighted calculation is performed based on the first interference fluctuation coefficient and the second interference fluctuation coefficient to obtain the interference fluctuation scale. Optionally, based on expert experience or historical data, the first interference fluctuation coefficient A and the second interference fluctuation coefficient B are weighted to obtain weighted coefficients 𝑤1 and 𝑤2, and then, the interference fluctuation scale is obtained according to the weighted calculation formula: 𝑆=𝑤1⋅A+𝑤2⋅B. The interference fluctuation scale is an indicator used to quantify the complexity and dynamics of the interference environment. The larger the value, the more significant the environmental fluctuation. In order to ensure the accuracy of the analysis, the more branches are called. When the fluctuation scale is large, more branch units need to be called to ensure the adaptability and reliability of the analysis strategy. By calculating the interference fluctuation scale, objective quantitative data of the dynamic characteristics of the interference environment are provided. According to the fluctuation scale, the number of branches of the analysis plug-in can be reasonably allocated to improve the calculation efficiency and the accuracy of the anti-interference strategy.

[0024] Furthermore, an interference fluctuation trend analysis is performed on the multiple historical interference feature distributions to determine a second interference fluctuation coefficient, including: arranging the multiple historical interference feature distributions in order of monitoring nodes to generate a historical interference feature distribution sequence, and selecting a first feature distribution and a second feature distribution, wherein the first feature distribution is the first feature distribution of the historical interference feature distribution sequence, and the second feature distribution is an adjacent feature distribution of the first feature distribution; performing a similarity traversal comparison on the first feature distribution and the second feature distribution to determine a first similarity, and obtaining a first historical interference fluctuation coefficient by subtracting the first similarity from 1; continuing to perform interference fluctuation analysis on adjacent feature distributions of the historical interference feature distribution sequence to obtain multiple historical interference fluctuation coefficients, and obtaining the second interference fluctuation coefficient by average calculation.

[0025] Specifically, multiple historical interference feature distributions are arranged in the order of monitoring nodes to generate a historical interference feature distribution sequence. Historical interference feature distribution refers to the interference environment characteristics recorded by each monitoring node within a specific time window, including information such as the location, intensity and type of the interference source. In the historical interference feature distribution sequence, the first feature distribution and its adjacent second feature distribution are selected for analysis. The first feature distribution is the first distribution of the sequence, indicating the earliest recorded interference feature distribution. The second feature distribution is the distribution immediately after the first feature distribution, reflecting the interference feature data of the subsequent period of time. By selecting the first feature distribution and the adjacent second feature distribution, the change of the interference feature in a short period of time can be captured. Then, the first feature distribution and the second feature distribution are similarly traversed and compared, and the interference feature data of the two are compared item by item. The similarity calculation algorithms such as cosine similarity and Pearson correlation coefficient are used to calculate the similarity of each data point in the two feature distributions to obtain a quantitative similarity value, namely the first similarity. The first similarity is a value between 0 and 1. The closer to 1, the more similar the two distributions are. By subtracting the first similarity from Formula 1, the first historical interference fluctuation coefficient is calculated to quantify the difference between the first feature distribution and the second feature distribution. The higher the first historical interference fluctuation coefficient, the more drastic the environmental change. Repeat the above steps to continue to perform historical interference fluctuation analysis on the remaining adjacent feature distributions in the historical interference feature distribution sequence, calculate the historical interference fluctuation coefficients between adjacent distributions one by one, and obtain multiple historical interference fluctuation coefficients. The multiple historical interference fluctuation coefficients respectively reflect the fluctuation of interference features in different time periods. Finally, the multiple historical interference fluctuation systems are averaged to obtain the second interference fluctuation coefficient. The second interference fluctuation coefficient is an average of all historical interference fluctuation coefficients, which can reflect the fluctuation trend of the overall interference environment in the historical time window as a whole. By combining the detailed analysis of adjacent distributions with the comprehensive evaluation of the overall trend, the comprehensiveness and accuracy of the interference analysis results are improved.

[0026] The satellite operating parameters of the predetermined monitoring node are obtained, and satellite state fluctuation analysis is performed according to the satellite operating parameters to determine the state fluctuation scale.

[0027] Specifically, the satellite operating parameters of the predetermined monitoring node are obtained, including various performance indicators and data generated by the satellite during operation, such as beam direction, frequency, signal mode, etc. Then, the satellite state fluctuation analysis is performed according to the satellite operating parameters to determine the state fluctuation scale. The satellite state fluctuation analysis process is similar to the interference fluctuation analysis steps. The state fluctuation scale is a quantitative indicator used to measure the degree and range of satellite state fluctuations.

[0028] Furthermore, the satellite operating parameters of the predetermined monitoring node are obtained, and satellite state fluctuation analysis is performed based on the satellite operating parameters to determine the state fluctuation scale, including: obtaining the satellite operating parameters of the predetermined monitoring node, wherein the satellite operating parameters include at least beam direction, frequency and signal mode; obtaining multiple historical satellite operating parameters of multiple monitoring nodes within the historical time window, and selecting adjacent working parameters of adjacent nodes of the predetermined monitoring node; obtaining a first state fluctuation coefficient based on the satellite operating parameters and adjacent working parameters analysis, and determining a second state fluctuation coefficient by performing state fluctuation trend analysis based on the multiple historical satellite operating parameters; and generating the state fluctuation scale based on the first state fluctuation coefficient and the second state fluctuation coefficient.

[0029] Specifically, the satellite operating parameters of the predetermined monitoring node are obtained. The satellite operating parameters are used to describe the current operating state of the satellite, including but not limited to beam direction, frequency and signal mode. The beam direction refers to the beam pointing of the satellite phased array antenna, which is used to determine the geographical area covered by the signal. The frequency is used to transmit the electromagnetic wave frequency of the signal, which directly affects the communication quality and interference resistance. The signal mode represents the modulation mode, bandwidth and other characteristics of the signal, which determines the reliability and stability of the communication performance. Similar to the interference fluctuation analysis to determine the interference fluctuation scale step, the working parameters of the adjacent nodes adjacent to the predetermined monitoring node are extracted from the multiple monitoring nodes in the historical time window. By comparing the adjacent working parameters with the current satellite working parameters, the differences between them are analyzed through similarity calculation, the satellite state similarity is obtained, and the state similarity is subtracted from formula 1 to obtain the first state fluctuation coefficient. The first state fluctuation coefficient reflects the state consistency between the satellite working parameter state of the predetermined monitoring node and its adjacent nodes. The larger the value, the more significant the state difference. The historical satellite working parameters of multiple monitoring nodes are extracted from the historical time window and arranged into a state distribution sequence in chronological order. By traversing and comparing adjacent feature distributions, the fluctuation coefficients between historical state distributions are calculated, and the second state fluctuation coefficient is generated by calculating the mean of these fluctuation coefficients. The second state fluctuation coefficient represents the overall fluctuation trend of the satellite state in the historical time dimension. Finally, the state fluctuation scale is calculated by the weighted calculation formula, state fluctuation scale = 𝑤3⋅first state fluctuation coefficient + 𝑤4⋅second state fluctuation coefficient, where 𝑤3 and 𝑤4 are weight parameters, which are obtained by assigning weights according to the importance of the actual environment. The state fluctuation scale quantifies the dynamic change level of the current satellite state. By analyzing the state fluctuation scale, we can fully grasp the working state of the satellite and its fluctuation characteristics, and then combine the interference fluctuation analysis results to more comprehensively evaluate the interaction between the satellite and the external environment, and improve the comprehensiveness and accuracy of anti-interference decision-making.

[0030] Based on the interference fluctuation scale and the state fluctuation scale, a strategy optimization analysis plug-in is called, anti-interference analysis is performed according to the interference feature distribution and satellite working parameters, and an array element parameter matrix is ​​output and set as the optimal anti-interference strategy.

[0031] Specifically, based on the obtained interference fluctuation scale and state fluctuation scale, the strategy optimization analysis plug-in is called. The strategy optimization analysis plug-in is a modular intelligent analysis system that contains multiple independent model branches, each of which is designed for specific interference characteristics and state fluctuation conditions. Optionally, the interference fluctuation scale and the state fluctuation scale are combined to calculate the comprehensive fluctuation scale by weighted calculation to quantify the overall dynamic changes of the current environment and satellite status. Then, according to the comprehensive fluctuation scale, the number of branch calls of the strategy optimization analysis plug-in is dynamically adjusted. When the comprehensive fluctuation scale is small, it indicates that the environment is relatively stable, and the number of model branch calls can be reduced, thereby saving computing resources. When the comprehensive fluctuation scale is large, the environment fluctuates violently, and more model branches need to be called to improve the analysis accuracy. The number of branch calls is calculated by rounding the ratio of the comprehensive fluctuation scale to the historical maximum fluctuation scale to ensure that the number of branches called matches the current environmental requirements. In the strategy optimization analysis plug-in, a selected number of model branches are called and the interference feature distribution and satellite operating parameters are used as input. Each model branch analyzes the interference features and satellite state parameters through an algorithm to generate a corresponding array element parameter matrix. The array element parameter matrix is ​​the core output of the anti-interference strategy and contains the key parameters of each antenna array element, such as phase, amplitude, frequency, gain and delay. The array element parameter matrix with the highest anti-interference effect is selected from the analysis results of all model branches and set as the optimal anti-interference strategy. The optimal anti-interference strategy optimizes and adjusts the satellite phased array antenna to improve the anti-interference adaptability of the satellite phased array antenna, ensures that the antenna can effectively avoid or suppress interference, and thus effectively improves the anti-interference capability of the satellite phased array antenna in complex environments.

[0032] Before entering the future time zone, the array element parameter matrix of the optimal anti-interference strategy is loaded into the control module of the satellite phased array antenna. According to the generated optimal anti-interference strategy, the satellite phased array antenna will be operated and controlled according to the strategy in the future time zone to ensure the optimal performance in a dynamic and complex environment. The optimal anti-interference strategy includes dynamically adjusting the antenna's beam direction and shape, adjusting the signal frequency, changing the antenna's working mode, etc., which is used to adjust the antenna's beam characteristics and interference suppression capabilities in real time to ensure that the antenna can effectively avoid or suppress interference. During the operation in the future time zone, the dynamic changes of the environment are continuously monitored, and the strategy is adaptively adjusted based on the new perception data. In the above way, the satellite phased array antenna can make full use of the optimal anti-interference strategy and operate efficiently in the future time zone, ensuring that the antenna can effectively avoid or suppress interference and improve the reliability and stability of the communication signal in the future time zone.

[0033] Furthermore, based on the interference fluctuation scale and the state fluctuation scale, a strategy optimization analysis plug-in is called, anti-interference analysis is performed according to the interference feature distribution and satellite operating parameters, and an array element parameter matrix is ​​output, including: determining a comprehensive fluctuation scale based on the interference fluctuation scale and the state fluctuation scale analysis, and determining the number of branch calls according to the comprehensive fluctuation scale, wherein the number of branch calls is obtained by multiplying the ratio of the comprehensive fluctuation scale to the historical maximum comprehensive fluctuation scale by N and rounding it off; performing random calls in N optimization analysis units of the strategy optimization analysis plug-in according to the number of branch calls to generate an adaptation analysis plug-in; inputting the interference feature distribution and satellite operating parameters into the adaptation analysis plug-in to perform anti-interference analysis, and outputting the array element parameter matrix.

[0034] Specifically, in order to achieve efficient anti-interference analysis, the comprehensive fluctuation scale is obtained by weighted calculation of the interference fluctuation scale and the state fluctuation scale. The comprehensive fluctuation scale is a quantitative indicator of environmental complexity and volatility, which is used to guide the allocation of analysis resources. The calculation formula is: comprehensive fluctuation scale = 𝑤5⋅interference fluctuation scale + 𝑤6⋅state fluctuation scale, where 𝑤5 and 𝑤6 are weight parameters adjusted according to specific scenarios. Then, according to the ratio of the comprehensive fluctuation scale to the historical maximum comprehensive fluctuation scale, the number of branches to be called is calculated. The number of branches is determined by rounding, and the formula is: number of branch calls = (historical maximum comprehensive fluctuation scale / comprehensive fluctuation scale) × N, and rounded, where N is the total number of optimization analysis units available in the plug-in, which is an integer greater than 10. For example, if the comprehensive fluctuation scale accounts for 40% of the historical maximum scale and N=10, the number of branch calls is 4. The unit corresponding to the number of branch calls is randomly selected from the N optimization analysis units of the plug-in to form an adaptation analysis plug-in. For example, if the number of branch calls is 4, 4 are randomly selected from 10 optimization units. By calculating the branch call data, computing resources can be flexibly allocated according to the actual environment requirements, while avoiding the waste of computing power caused by excessive calls. The interference feature distribution and satellite operating parameters are used as input and input into the adaptation analysis plug-in for anti-interference analysis. The interference feature distribution provides the type, location and strength information of the interference source. The satellite operating parameters, such as beam direction, frequency, and signal mode, provide necessary operating information for optimizing the antenna array element configuration. Each analysis unit in the adaptation analysis plug-in runs independently and calculates the output array element parameter matrix based on the input data. The array element parameter matrix contains parameters such as phase, amplitude, frequency, gain and delay of each antenna array element, which is used to dynamically adjust the beam characteristics of the antenna. By intelligently calling the strategy optimization analysis plug-in, the analysis accuracy and computing power resources are balanced to provide efficient and reliable parameter configuration for the anti-interference operation of the satellite antenna.

[0035] Furthermore, a strategy optimization analysis plug-in is constructed, including: obtaining layout characteristics of multiple antenna array elements of the satellite phased array antenna, and constructing an element distribution topology; taking the element distribution topology as a constraint, collecting a sample interference feature distribution set and a sample satellite working parameter set, and randomly selecting a first sample interference feature distribution and a first sample satellite working parameter; performing anti-interference optimization analysis according to the first sample interference feature distribution and the first sample satellite working parameter, and outputting a first sample array element parameter matrix; constructing a first training data based on the first sample interference feature distribution, the first sample satellite working parameter and the first sample array element parameter matrix, and analyzing them in turn to obtain a training data set; dividing the training data set into N equal parts, performing supervised learning on a random decision forest through the N training sets until convergence, harvesting N optimization analysis units, and integrating them to construct the strategy optimization analysis plug-in, wherein N is an integer greater than 10.

[0036] Specifically, the layout features of multiple antenna elements of the satellite phased array antenna are obtained, and the layout features of the antenna elements refer to parameters such as the position, arrangement and distance between each antenna element in the satellite phased array antenna. These features determine the antenna's directional pattern and beam control capability. According to the antenna element layout features, the element distribution topology is constructed. Topology is a structured data. The element distribution topology is a geometric structure diagram of the antenna array formed on the basis of the layout features, which is used to constrain the interaction and physical restrictions between the elements in the optimization process. Taking the element distribution topology as a constraint, the sample interference feature distribution set under different scenarios and the sample satellite working parameter set under the corresponding scenario are collected from the historical operation data. The sample interference feature distribution set includes information such as the location, type and intensity of the interference source, reflecting the characteristics of different interference environments. The sample satellite working parameter set includes satellite working parameters, such as operating state parameters such as beam direction, frequency, signal mode, etc., which are input conditions for optimization analysis. Randomly select one of the sample interference feature distribution set and the sample satellite working parameter set as the initial input for anti-interference optimization. According to the first sample interference feature distribution and the first sample satellite working parameters, the first sample array element parameter matrix is ​​calculated and generated using anti-interference algorithms such as beamforming and zero point control. The first sample array element parameter matrix contains key configurations such as phase, amplitude, frequency, gain and delay of each antenna array element, which is used to optimize the anti-interference performance. The first sample interference feature distribution, the first sample satellite working parameters and the first sample array element parameter matrix are combined to construct the first training data. Repeat the above process, select other sample data in the sample interference feature distribution set and the sample satellite working parameter set, and generate multiple training data in sequence to form a complete training data set to provide comprehensive support for model learning. Then, the training data set is divided into N parts, where N is an integer greater than 10, and each data set is used to train an independent decision unit of the random decision forest. For each data set, supervised learning is performed using the random decision forest algorithm. Random decision forest is an integrated learning method that builds multiple decision tree models and comprehensively predicts the results to improve classification and regression performance. During the model training process, the parameters are continuously adjusted until the model converges to ensure that each optimization unit can efficiently analyze and predict anti-interference performance. The N independent optimization analysis units that have been trained are integrated to construct the final strategy optimization analysis plug-in. For example, the training data set contains 1100 sample data, each of which contains input features and output labels. The input features are the interference feature distribution and satellite operating parameters, and the output labels are the array element parameter matrix. The data set is randomly shuffled and divided into 11 equal parts, each containing 100 training data, and each data set is used to train a decision tree unit.For example, a decision tree is trained for the first data: randomly select features in the training data, build a decision tree, select split points through information gain or Gini coefficient, generate nodes, and continue splitting until the preset depth is reached or the leaf node cannot be further split. Repeat the above steps, use the other 10 data to train independent decision tree units, and get a total of 11 trees. The 11 decision trees are integrated into a random decision forest. Each tree makes predictions independently, and finally generates the final prediction result through a voting mechanism or weighted average. Each independent decision tree is an optimization analysis unit, which can input data for interference distribution and satellite working parameters and output the optimized array element parameter matrix. By fusion, a strategy optimization analysis plug-in is constructed. The strategy optimization analysis plug-in has a multi-branch structure and can quickly output the optimal anti-interference strategy based on the input interference feature distribution and satellite working parameters. By using diversified training data and random decision forests to construct a strategy optimization analysis plug-in, the optimization strategy can be flexibly adjusted according to environmental requirements to improve resource utilization efficiency and anti-interference ability.

[0037] Furthermore, an anti-interference optimization analysis is performed according to the first sample interference feature distribution and the first sample satellite operating parameters, and a first sample array element parameter matrix is ​​output, including: obtaining multiple array element parameter thresholds of multiple antenna array elements of the satellite phased array antenna, and randomly generating multiple initial array element parameter matrices based on the multiple array element parameter thresholds, wherein the array element parameters at least include phase, amplitude, frequency, gain and delay; based on a simulation test plug-in, according to the first sample interference feature distribution and the first sample satellite operating parameters, the multiple initial array element parameter matrices are respectively evaluated for anti-interference effects, and multiple anti-interference fitness degrees are determined, wherein the anti-interference fitness degree is positively correlated with the signal-to-noise ratio and the interference suppression ratio, and negatively correlated with the bit error rate; the initial array element parameter matrices are arranged from large to small according to the anti-interference fitness degree to generate an initial solution sequence, and the initial array element parameter matrix is ​​randomly generated based on the multiple array element parameter thresholds, wherein the array element parameters at least include phase, amplitude, frequency, gain and delay; based on the simulation test plug-in, according to the first sample interference feature distribution and the first sample satellite operating parameters, the multiple initial array element parameter matrices are respectively evaluated for anti-interference effects to determine multiple anti-interference fitness degrees, wherein the anti-interference fitness degree is positively correlated with the signal-to-noise ratio and the interference suppression ratio, and negatively correlated with the bit error rate; the initial array element parameter matrix is ​​arranged from large to small according to the anti-interference fitness degree to generate an initial solution sequence, and the initial array element parameter matrix is ​​randomly generated based on the multiple array element parameter thresholds, wherein the array element parameters at least include phase, amplitude, frequency, gain and delay; ... The first K solutions of the initial solution sequence are set as optimal solutions, and the last S solutions are set as inferior solutions, where S is an integer multiple of K; based on the K optimal solutions, the S inferior solutions are randomly clustered to determine K solution sets, and within the K solution sets, the inferior solutions are adjusted according to a predetermined step size with the optimal solution as the direction to obtain K updated solution sets, wherein if the adjusted inferior solution does not meet the multiple array element parameter thresholds, no adjustment is performed; the K updated solution sets are identified, and if within the same updated solution set, the fitness of the inferior solution is greater than or equal to the fitness of the optimal solution, the superior solution is replaced by the inferior solution; the iterative optimization is continued until a predetermined number of convergences is reached, K current updated solution sets are output, and the overall fitness of the K current updated solution sets are respectively calculated, and the current updated solution set with the maximum overall fitness is set as the optimal solution set, and the superior solution in the optimal solution set is output as the first sample array element parameter matrix.

[0038] Specifically, multiple element parameter thresholds of multiple antenna elements of the satellite phased array antenna are obtained. The element parameters refer to the key control parameters of each element in the antenna array, including at least phase, amplitude, frequency, gain and delay. According to hardware limitations and design requirements, the parameter threshold of each element parameter is obtained, that is, the upper and lower limits of the parameters, such as: phase range: 0°~360°, amplitude range: 0~1. Based on the above thresholds, multiple initial element parameter matrices are randomly generated. Each matrix corresponds to a possible antenna configuration. The element parameter threshold ensures that the antenna element can be kept within a reasonable parameter range when working to avoid performance abnormalities. Using the simulation test plug-in, combined with the given first sample interference feature distribution and the first sample satellite working parameters, the anti-interference effect of each initial element parameter matrix is ​​evaluated to obtain multiple anti-interference fitness. The simulation test simulates the actual operating environment according to the interference feature distribution of the first sample and the satellite working parameters, and tests the anti-interference effect of each matrix. For each initial element parameter matrix, its anti-interference fitness is calculated, that is, the interference suppression capability of the satellite phased array antenna under the element configuration is evaluated. Anti-interference fitness can be calculated in the following ways: Signal-to-noise ratio improvement: The signal-to-noise ratio measures the change in the signal-to-noise ratio of the system under different array element configurations. The higher the signal-to-noise ratio, the better the anti-interference effect; Bit error rate reduction: Under different array element configurations, the anti-interference capability is evaluated by comparing the change in the bit error rate; Interference suppression ratio: Calculate the power ratio of the interference to the target signal. The larger the interference suppression ratio, the stronger the anti-interference capability. That is, anti-interference fitness is a key indicator for measuring the performance of the array element parameter matrix. When calculated, it is positively correlated with the signal-to-noise ratio and the interference suppression ratio, and negatively correlated with the bit error rate. According to the anti-interference fitness, all initial array element parameter matrices are sorted to generate an initial solution sequence, and the first K solutions of the initial solution sequence are set as optimal solutions, and the last S solutions are set as inferior solutions, where S and K are both positive integers, and S is an integer multiple of K. Then, the S inferior solutions are randomly clustered based on the K optimal solutions to form K solution sets. In each solution set, the inferior solution is adjusted according to the predetermined step size with the optimal solution as the direction to seek a better parameter configuration. If the adjusted inferior solution still meets the array parameter threshold, the adjustment result is retained. When the adjusted inferior solution exceeds the originally set array parameter threshold, the adjustment is not performed to ensure that all solutions are within a reasonable parameter range. After each adjustment, K updated solution sets are identified. If the fitness of the inferior solution is greater than or equal to the fitness of the superior solution in the same updated solution set, the inferior solution is used to replace the superior solution. Otherwise, the superior solution remains unchanged. Through identification and comparison, the self-update and optimization of the solution is achieved. Continue to adjust and replace until the predetermined number of iterative convergences is reached, output K current updated solution sets, and calculate the overall fitness of each solution set respectively. The overall fitness refers to the sum of multiple fitnesses in the solution set. Among all solution sets, the updated solution set with the largest overall fitness is regarded as the optimal solution set, and the optimal solution matrix in the optimal solution set is taken as the final result and output as the first sample array parameter matrix.The accuracy of training data is improved through optimization, thereby improving the accuracy of model training and the reliability of anti-interference strategies, ensuring that antennas can effectively avoid or suppress interference.

[0039] Embodiment 2, based on the same inventive concept as the scenario-adaptive satellite phased array antenna anti-interference method in the above embodiment, the present application provides an electronic device, Figure 2 The schematic structural diagram of the electronic device provided in the second embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 2 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 2 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 2 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0040] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0041] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A scene-adaptive satellite phased array antenna anti-interference method, characterized in that: Methods include: At a predetermined monitoring node, a sensor system of a satellite phased array antenna is used to detect the environment, obtain environmental perception data, and perform interference feature analysis based on the environmental perception data to determine the distribution of interference features; Perform interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale; Acquiring the satellite operating parameters of the predetermined monitoring node, performing satellite state fluctuation analysis according to the satellite operating parameters, and determining the state fluctuation scale; Based on the interference fluctuation scale and the state fluctuation scale, the strategy optimization analysis plug-in is called, anti-interference analysis is performed according to the interference feature distribution and satellite working parameters, and an array element parameter matrix is ​​outputted to be set as the optimal anti-interference strategy; The operation control of the satellite phased array antenna in the future time zone is performed according to the optimal anti-interference strategy.

2. The scene-adaptive satellite phased array antenna anti-interference method according to claim 1, characterized in that: Performing interference feature analysis according to the environmental perception data to determine interference feature distribution includes: Configuring an interference analysis strategy, wherein the interference analysis strategy includes spectrum analysis, time domain analysis, and spatial distribution analysis; Based on the interference analysis strategy, interference feature analysis is performed according to the environmental perception data to determine a plurality of interference features, wherein the interference features at least include interference source type, interference source location and interference intensity; According to the location of the interference source, the plurality of interference features are mapped and arranged to generate the interference feature distribution.

3. The scene-adaptive satellite phased array antenna anti-interference method according to claim 2, characterized in that: Performing interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale includes: Acquire multiple historical interference feature distributions of multiple monitoring nodes within a historical time window, and select adjacent interference feature distributions of adjacent nodes of the predetermined monitoring node; Performing a similarity traversal comparison on the adjacent interference feature distribution and the interference feature distribution to determine the adjacent distribution similarity, and subtracting the adjacent distribution similarity from 1 to obtain a first interference fluctuation coefficient; Performing interference fluctuation trend analysis on the plurality of historical interference feature distributions to determine a second interference fluctuation coefficient; The interference fluctuation scale is obtained by weighted calculation according to the first interference fluctuation coefficient and the second interference fluctuation coefficient.

4. The scene-adaptive satellite phased array antenna anti-interference method according to claim 3, characterized in that: Performing interference fluctuation trend analysis on the plurality of historical interference feature distributions to determine a second interference fluctuation coefficient includes: Arrange the multiple historical interference feature distributions according to the order of monitoring nodes to generate a historical interference feature distribution sequence, and select a first feature distribution and a second feature distribution, wherein the first feature distribution is the first feature distribution of the historical interference feature distribution sequence, and the second feature distribution is an adjacent feature distribution of the first feature distribution; Performing a similarity traversal comparison on the first characteristic distribution and the second characteristic distribution to determine a first similarity, and subtracting the first similarity from 1 to obtain a first historical interference fluctuation coefficient; Continue to perform interference fluctuation analysis on adjacent feature distributions of the historical interference feature distribution sequence to obtain multiple historical interference fluctuation coefficients, and calculate the average to obtain the second interference fluctuation coefficient.

5. The scene-adaptive satellite phased array antenna anti-interference method according to claim 4, characterized in that: Acquiring the satellite operating parameters of the predetermined monitoring node, performing satellite state fluctuation analysis according to the satellite operating parameters, and determining the state fluctuation scale, including: Acquire the satellite operating parameters of the predetermined monitoring node, wherein the satellite operating parameters at least include beam direction, frequency and signal mode; Acquire multiple historical satellite operating parameters of multiple monitoring nodes within the historical time window, and select adjacent operating parameters of adjacent nodes of the predetermined monitoring node; Obtain a first state fluctuation coefficient according to the satellite operating parameters and adjacent operating parameters, and perform state fluctuation trend analysis according to the plurality of historical satellite operating parameters to determine a second state fluctuation coefficient; The state fluctuation scale is generated based on the first state fluctuation coefficient and the second state fluctuation coefficient.

6. The scene-adaptive satellite phased array antenna anti-interference method according to claim 1, characterized in that: Based on the interference fluctuation scale and the state fluctuation scale, the strategy optimization analysis plug-in is called, anti-interference analysis is performed according to the interference feature distribution and satellite working parameters, and an array element parameter matrix is ​​output, including: Determine the comprehensive fluctuation scale based on the interference fluctuation scale and the state fluctuation scale, and determine the number of branch calls according to the comprehensive fluctuation scale, wherein the number of branch calls is obtained by multiplying the ratio of the comprehensive fluctuation scale to the historical maximum comprehensive fluctuation scale by N and rounding; Randomly call the N optimization analysis units of the strategy optimization analysis plug-in according to the number of branch calls to generate an adaptation analysis plug-in; The interference feature distribution and satellite operating parameters are input into the adaptation analysis plug-in for anti-interference analysis, and the array element parameter matrix is ​​output.

7. The scene-adaptive satellite phased array antenna anti-interference method according to claim 6, characterized in that: Build strategy optimization analysis plug-in, including: Acquire layout features of multiple antenna array elements of the satellite phased array antenna, and construct an array element distribution topology; Taking the array element distribution topology as a constraint, collecting a sample interference feature distribution set and a sample satellite operating parameter set, and randomly selecting a first sample interference feature distribution and a first sample satellite operating parameter; Performing anti-interference optimization analysis according to the first sample interference feature distribution and the first sample satellite operating parameters, and outputting a first sample array element parameter matrix; Constructing first training data based on the first sample interference feature distribution, the first sample satellite operating parameters and the first sample array element parameter matrix, and analyzing them in sequence to obtain a training data set; The training data set is divided into N equal parts, and the random decision forest is supervised by learning through the N training sets until convergence, and N optimization analysis units are obtained, which are integrated to construct the strategy optimization analysis plug-in, where N is an integer greater than 10.

8. The scene-adaptive satellite phased array antenna anti-interference method according to claim 7, characterized in that: An anti-interference optimization analysis is performed according to the first sample interference feature distribution and the first sample satellite operating parameters, and a first sample array element parameter matrix is ​​output, including: Acquire multiple array element parameter thresholds of multiple antenna array elements of the satellite phased array antenna, and randomly generate multiple initial array element parameter matrices based on the multiple array element parameter thresholds, wherein the array element parameters include at least phase, amplitude, frequency, gain and delay; Based on the simulation test plug-in, according to the first sample interference feature distribution and the first sample satellite operating parameters, the multiple initial array element parameter matrices are respectively evaluated for anti-interference effects, and multiple anti-interference fitnesses are determined, wherein the anti-interference fitness is positively correlated with the signal-to-noise ratio and the interference suppression ratio, and negatively correlated with the bit error rate; Arrange the initial array element parameter matrix according to the anti-interference fitness from large to small to generate an initial solution sequence, and set the first K solutions of the initial solution sequence as optimal solutions, and the last S solutions as inferior solutions, where S is an integer multiple of K; Based on the K optimal solutions, the S inferior solutions are randomly clustered to determine K solution sets, and within the K solution sets, the inferior solutions are adjusted according to a predetermined step length with the optimal solutions as the direction to obtain K updated solution sets, wherein if the adjusted inferior solutions do not meet the multiple array element parameter thresholds, this adjustment is not performed; Identify the K update solution sets, and if the fitness of the inferior solution is greater than or equal to the fitness of the superior solution in the same update solution set, replace the superior solution with the inferior solution; Continue to iterate and optimize until a predetermined number of convergences is reached, output K current update solution sets, calculate the overall fitness of the K current update solution sets respectively, set the current update solution set with the maximum overall fitness as the optimal solution set, and output the optimal solution in the optimal solution set as the first sample array element parameter matrix.

9. An electronic device, characterized in that: The electronic device includes: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a scene-adaptive satellite phased array antenna anti-interference method as described in any one of claims 1 to 8.

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