A method for anti-interference of a satellite phased array antenna adaptable to scenarios and an electronic device

Abstract

The present invention provides a method for anti-interference of a satellite phased array antenna adaptable to scenarios and an electronic device, which relates to the field of satellite communication technology. Under a predetermined monitoring node, environmental perception data is obtained by using a sensor system of the satellite phased array antenna, and interference feature analysis is carried out. Based on the interference feature distribution, interference feature fluctuation analysis is carried out. Satellite operating parameters are obtained for satellite state fluctuation analysis. Based on the interference fluctuation scale and the state fluctuation scale, a strategy optimization analysis plug-in is called to carry out anti-interference analysis, and an optimal anti-interference strategy is output. The operation control of the satellite phased array antenna in a future time zone is executed according to the optimal anti-interference strategy. The technical problems of insufficient anti-interference adaptability and limited suppression ability existing in the satellite phased array antenna in the prior art are solved. The technical effects of improving the anti-interference adaptability of the satellite phased array antenna, enhancing the interference suppression ability, and ensuring that the antenna can effectively avoid or suppress interference are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite communication, and particularly relates to a method for anti-interference of a scene-adaptive satellite phased array antenna and an electronic device. Background Art

[0002] Satellite phased array antennas have been widely used in modern communication, navigation, and remote sensing fields due to their advantages of flexible beam control 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 beamforming techniques with fixed parameters or simple frequency-domain filtering techniques, which have limited anti-interference suppression capabilities, resulting in poor anti-interference effects and unable to meet the requirements for real-time performance and efficiency in complex scenarios.

[0003] In the prior art, there are technical problems of insufficient anti-interference adaptability and limited suppression capabilities in satellite phased array antennas. Summary of the Invention

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

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

[0006] In the first aspect of the present application, a method for anti-interference of a scene-adaptive satellite phased array antenna is provided. The method includes: under a predetermined monitoring node, using the sensor system of the satellite phased array antenna to perform environmental detection, obtaining environmental perception data, and performing interference feature analysis based on the environmental perception data to determine the interference feature distribution; performing interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale; obtaining the satellite operating parameters of the predetermined monitoring node, performing satellite state fluctuation analysis based on the satellite operating parameters to determine the state fluctuation scale; calling a strategy optimization analysis plugin based on the interference fluctuation scale and the state fluctuation scale, performing anti-interference analysis based on the interference feature distribution and the satellite operating parameters, outputting an array element parameter matrix, which is set as the optimal anti-interference strategy; and performing operation control of the satellite phased array antenna in the future time zone according to the optimal anti-interference strategy.

[0007] In the second aspect of the present application, an electronic device is provided, which is 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 method for anti-interference of a scene-adaptive satellite phased array antenna provided by the present application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The method provided in the embodiment of this application performs environmental detection by using the sensor system of the satellite phased array antenna under a predetermined monitoring node, obtains environmental perception data, analyzes the interference characteristics based on the environmental perception data to determine the interference characteristic distribution, performs interference characteristic fluctuation analysis based on the interference characteristic distribution to determine the interference fluctuation scale. Obtain the satellite operating parameters of the predetermined monitoring node, perform satellite state fluctuation analysis based on the satellite operating parameters to determine the state fluctuation scale, call the strategy optimization analysis plug-in based on the interference fluctuation scale and state fluctuation scale, perform anti-interference analysis based on the interference characteristic distribution and satellite operating parameters, output the array element parameter matrix, set it as the optimal anti-interference strategy, and execute the operation control of the satellite phased array antenna in the future time zone according to the optimal anti-interference strategy. It achieves the technical effects of improving the anti-interference adaptability of the satellite phased array antenna, enhancing the interference suppression ability, and ensuring that the antenna can effectively avoid or suppress interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic flowchart of a method for anti-interference of a scene-adaptive satellite phased array antenna provided in this application;

[0012] Figure 2 It is a schematic structural diagram of an electronic device provided in this application.

[0013] Description of reference numerals: Processor 21, Memory 22, Input device 23, Output device 24. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] This application provides a method and an electronic device for anti-interference of a scene-adaptive satellite phased array antenna, which are used to solve the technical problems of insufficient anti-interference adaptability and limited suppression ability of satellite phased array antennas in the prior art. It achieves the technical effects of improving the anti-interference adaptability of the satellite phased array antenna, enhancing the interference suppression ability, and ensuring that the antenna can effectively avoid or suppress interference.

[0015] Next, 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 a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all of them.

[0016] Embodiment 1, as Figure 1 shown, the present application provides a method for anti-interference of a satellite phased array antenna with scene adaptability, and the method includes:

[0017] Under a predetermined monitoring node, use the sensor system of the 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.

[0018] Specifically, the predetermined monitoring node refers to a position or area preset based on task requirements and environmental characteristics for performing environmental detection and interference analysis. Under the predetermined monitoring node, use the sensor system of the satellite phased array antenna to scan and collect the electromagnetic environment information within the coverage range. The sensor system can capture multi-dimensional data including frequency, signal strength, and signal direction. According to the information collected by the sensor system, obtain the environmental perception data, 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 the beam direction and characteristics, and has the characteristics of electronic beam control, versatility, high sensitivity, and high resolution. Furthermore, perform comprehensive analysis of the interference characteristics in the spectral, time-domain, and spatial distributions on the environmental perception data to determine the interference feature distribution. The interference feature distribution reflects multi-dimensional data such as the type, intensity, and position of the interference source, and is the key basis for formulating an effective anti-interference method. For example, through analysis, it can be determined which directions have the strongest interference, which frequency bands are most vulnerable to interference, and how obstacles affect the signal transmission path, etc. Through the collaborative work of the predetermined monitoring node and the satellite phased array antenna, ensure the comprehensiveness and real-time nature of environmental detection and interference analysis.

[0019] Furthermore, perform interference feature analysis based on the environmental perception data to determine the interference feature distribution, including: configuring an interference analysis strategy, where the interference analysis strategy includes spectrum analysis, time-domain analysis, and spatial distribution analysis; based on the interference analysis strategy, perform interference feature analysis according to the environmental perception data to determine a number of interference features, where the interference features at least include the type of interference source, the location of the interference source, and the interference intensity; arrange the number of interference features in a mapped manner according to the location of the interference source to generate the interference feature distribution.

[0020] Specifically, configure the interference analysis strategy according to the analysis target and environmental factors. The interference analysis strategy refers to a systematic method for performing feature analysis on environmental perception data, including spectrum analysis, time-domain analysis, and spatial distribution analysis. Spectrum analysis refers to decomposing and evaluating the frequency components of electromagnetic signals for identifying 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 judge the behavior pattern of the interference source. Spatial distribution analysis is to accurately determine the geographical location and azimuth of the interference source through the beam pointing characteristics of the satellite phased array antenna. Then, based on the configured interference analysis strategy, perform interference feature analysis on the collected environmental perception data, use spectrum analysis technology to identify interference signals at different frequencies, track the time evolution law of the interference signals through time-domain analysis technology, and use spatial distribution analysis technology to determine the possible location of the interference source. Through analysis, a number of interference features are obtained. 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 such as radar interference, communication interference, etc., the location of the interference source is the geographical coordinate or angular range obtained through spatial distribution analysis, and the interference intensity represents the power or amplitude of the interference signal.

[0021] Next, arrange all the identified interference features in a mapped manner according to the location of the interference source, that is, position the type and intensity data of each interference source in the geographical space or logical space according to the location of the interference source to form an intuitive interference feature distribution. The interference feature distribution is a comprehensive data expression method, which not only clearly reveals the spatial layout of the interference source, but also can distinguish the type and intensity of the interference source through marks of different colors, sizes, or shapes, thus providing a comprehensive and intuitive view of the interference environment for users. Through the multi-dimensional analysis of the interference features, the type and distribution of the interference can be comprehensively and intuitively reflected, providing basic data for subsequent anti-interference decisions.

[0022] Perform interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale.

[0023] Specifically, using the interference feature distribution map as the basis for analysis, perform interference feature fluctuation analysis on each interference feature, quantify the volatility of the interference feature, and determine the interference fluctuation scale.

[0024] Furthermore, based on the interference feature distribution, perform interference feature fluctuation analysis to determine the interference fluctuation scale, including: obtaining multiple historical interference feature distributions of multiple monitoring nodes within a historical time window, and selecting the adjacent interference feature distributions of the 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 obtaining the first interference fluctuation coefficient by subtracting the adjacent distribution similarity from 1; performing interference fluctuation trend analysis on the multiple historical interference feature distributions to determine the second interference fluctuation coefficient; and calculating the interference fluctuation scale by weighted calculation based on the first interference fluctuation coefficient and the second interference fluctuation coefficient.

[0025] Specifically, obtain multiple historical interference feature distributions of multiple monitoring nodes within a historical time window. The historical time window refers to the interference feature distribution data collected within a certain past time range, which reflects the dynamic change process of the interference environment. The monitoring node refers to the specific location or area set for monitoring interference features. At the same time, select the adjacent interference feature distributions of the adjacent nodes of the predetermined monitoring node. The adjacent node refers to the node that is spatially or logically adjacent to the predetermined monitoring node. By selecting the interference feature distributions of the adjacent nodes, it helps to evaluate the interference consistency or difference within the region, and thus more comprehensively consider the local impact of interference.

[0026] The interference feature distribution of a predetermined monitoring node is compared with the adjacent interference feature distributions of adjacent nodes through similarity traversal. The feature parameters in the two sets of interference feature distributions, such as the type, location, and intensity of the interference source, are compared item by item. Similarity calculation algorithms such as cosine similarity and Pearson correlation coefficient are used to calculate the similarity between the two distributions, and the adjacent distribution similarity is obtained. Similarity is an index used to quantify the consistency between two distributions. The higher the value, the closer the distributions are, and the smaller the fluctuations. Subsequently, the first interference fluctuation coefficient is calculated through 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 greater the adjacent deviation, the greater the overall environmental fluctuation. At the same time, time series analysis is performed on multiple historical interference feature distributions within the historical time window. According to the time order of the distributions, the trend of interference fluctuation changes is evaluated, including rising, falling, or stable, etc. By calculating the similarity changes between adjacent distributions in the historical feature distribution sequence, the mean value of multiple historical interference fluctuation coefficients is gradually accumulated to generate the second interference fluctuation coefficient. The second interference fluctuation coefficient reflects the degree of fluctuation of the interference features within the overall time window. Finally, weighted calculation is performed based on the first interference fluctuation coefficient and the second interference fluctuation coefficient to obtain the interference fluctuation scale. Optionally, according to expert experience or historical data, weight assignments are given to the first interference fluctuation coefficient A and the second interference fluctuation coefficient B respectively to obtain the weighted coefficients 𝑤1 and 𝑤2. Then, according to the weighted calculation formula: 𝑆 = 𝑤1⋅A + 𝑤2⋅B, the interference fluctuation scale is obtained. The interference fluctuation scale is an index used to quantify the complexity and dynamics of the interference environment. The larger its value, the more significant the environmental fluctuation. To ensure the analysis accuracy, the more branch units need to be 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 on the dynamic characteristics of the interference environment is provided. Based on the size of the fluctuation scale, the number of branches of the analysis plugin can be reasonably allocated, improving the calculation efficiency and the accuracy of the anti-interference strategy.

[0027] Further, performing interference fluctuation trend analysis on the multiple historical interference feature distributions to determine the second interference fluctuation coefficient includes: arranging the multiple historical interference feature distributions in the order of the monitoring nodes to generate a historical interference feature distribution sequence, and selecting a first feature distribution and a second feature distribution, where the first feature distribution is the first feature distribution in the historical interference feature distribution sequence, and the second feature distribution is the adjacent feature distribution of the first feature distribution; performing similarity traversal comparison on the first feature distribution and the second feature distribution to determine the first similarity, and using 1 minus the first similarity to obtain the first historical interference fluctuation coefficient; continuing to perform interference fluctuation analysis on the adjacent feature distributions of the historical interference feature distribution sequence to obtain multiple historical interference fluctuation coefficients, and calculating the mean value to obtain the second interference fluctuation coefficient.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Further, obtain the satellite working parameters of the predetermined monitoring node, and perform satellite state fluctuation analysis based on the satellite working parameters to determine the state fluctuation scale, including: obtaining the satellite working parameters of the predetermined monitoring node, where the satellite working parameters at least include beam direction, frequency, and signal mode; obtaining the multiple historical satellite working parameters of multiple monitoring nodes within the historical time window, and selecting the adjacent working parameters of the adjacent nodes of the predetermined monitoring node; analyzing the first state fluctuation coefficient based on the satellite working parameters and the adjacent working parameters, and performing state fluctuation trend analysis based on the multiple historical satellite working parameters to determine the second state fluctuation coefficient; generating the state fluctuation scale based on the first state fluctuation coefficient and the second state fluctuation coefficient.

[0032] Specifically, obtain the satellite working parameters of the predetermined monitoring node. The satellite working 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 and is used to determine the geographical area covered by the signal. The frequency is the electromagnetic wave frequency used to transmit the signal and directly affects the communication quality and interference resistance. The signal mode represents characteristics such as the modulation method and bandwidth of the signal and determines the reliability and stability of the communication performance. Similar to the steps of determining the interference fluctuation scale for interference fluctuation analysis, extract the working parameters of the adjacent nodes adjacent to the predetermined monitoring node from multiple monitoring nodes within the historical time window. By comparing the adjacent working parameters with the current satellite working parameters and calculating the similarity, analyze the differences between them to obtain the satellite state similarity, and use formula 1 minus the state similarity 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. Extract the historical satellite working parameters of multiple monitoring nodes from the historical time window and arrange them in a state distribution sequence in chronological order. Use the traversal comparison of adjacent feature distributions to calculate the fluctuation coefficients between historical state distributions, and generate the second state fluctuation coefficient through the mean calculation of these fluctuation coefficients. The second state fluctuation coefficient represents the overall fluctuation trend of the satellite state in the historical time dimension. Finally, through the weighted calculation formula, state fluctuation scale = 𝑤3 ⋅ first state fluctuation coefficient + 𝑤4 ⋅ second state fluctuation coefficient, calculate to obtain the state fluctuation scale, where 𝑤3 and 𝑤4 are weight parameters 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 and obtaining the state fluctuation scale, the working state and its fluctuation characteristics of the satellite can be comprehensively grasped. Furthermore, combined with the interference fluctuation analysis results, the interaction between the satellite and the external environment can be more comprehensively evaluated, improving the comprehensiveness and accuracy of anti-interference decision-making.

[0033] Call the policy optimization analysis plugin based on the interference fluctuation scale and the state fluctuation scale, perform anti-interference analysis according to the interference feature distribution and satellite operating parameters, and output the array element parameter matrix, which is set as the optimal anti-interference policy.

[0034] Specifically, based on the obtained interference fluctuation scale and state fluctuation scale, call the policy optimization analysis plugin. The policy optimization analysis plugin is a modular intelligent analysis system, which contains multiple independent model branches, and each branch is designed for specific interference features and state fluctuation situations. Optionally, combine the interference fluctuation scale and the state fluctuation scale, and calculate the comprehensive fluctuation scale through weighted calculation to quantify the overall dynamic changes of the current environment and satellite state. Then, according to the comprehensive fluctuation scale, dynamically adjust the number of model branch calls of the policy optimization analysis plugin. 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 to save 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 called branches matches the current environmental requirements. In the policy optimization analysis plugin, call the selected number of model branches and use the interference feature distribution and satellite operating parameters as inputs. Each model branch analyzes the interference features and satellite state parameters through algorithms and generates the corresponding array element parameter matrix. The array element parameter matrix is the core output of the anti-interference policy and contains the key parameters of each antenna array element, such as phase, amplitude, frequency, gain, and time delay. Select the array element parameter matrix with the highest anti-interference effect from the analysis results of all model branches and set it as the optimal anti-interference policy. The optimal anti-interference policy optimizes and adjusts the satellite phased array antenna to enhance the anti-interference adaptability of the satellite phased array antenna, ensuring that the antenna can effectively avoid or suppress interference, and thus effectively improving the anti-interference ability of the satellite phased array antenna in a complex environment.

[0035] Before entering the future time zone, load the array element parameter matrix of the optimal anti-interference policy into the control module of the satellite phased array antenna. According to the generated optimal anti-interference policy, the satellite phased array antenna will perform operation control according to this policy in the future time zone to ensure optimal performance in a dynamic complex environment. The optimal anti-interference policy includes dynamically adjusting the beam direction and shape of the antenna, adjusting the signal frequency, changing the operating mode of the antenna, etc., to adjust the beam characteristics and interference suppression ability of the antenna in real time to ensure that the antenna can effectively avoid or suppress interference. During the operation in the future time zone, continuously monitor the dynamic changes of the environment and adaptively adjust the policy based on the new perception data. In this way, the satellite phased array antenna can make full use of the optimal anti-interference policy to operate efficiently in the future time zone, ensuring that the antenna can effectively avoid or suppress interference and improving the reliability and stability of communication signals in the future time zone.

[0036] Furthermore, a policy optimization analysis plugin is invoked based on the interference fluctuation scale and the state fluctuation scale, and anti-interference analysis is performed according to the interference feature distribution and satellite working parameters, and an array element parameter matrix is output, including: determining a comprehensive fluctuation scale based on the analysis of the interference fluctuation scale and the state fluctuation scale, and determining the number of branch calls according to the comprehensive fluctuation scale, where the number of branch calls is obtained by rounding the ratio of the comprehensive fluctuation scale to the historical maximum comprehensive fluctuation scale multiplied by N; randomly calling according to the number of branch calls among N optimization analysis units of the policy optimization analysis plugin to generate an adaptation analysis plugin; inputting the interference feature distribution and satellite working parameters into the adaptation analysis plugin for anti-interference analysis, and outputting the array element parameter matrix.

[0037] Specifically, to achieve efficient anti-interference analysis, a comprehensive fluctuation scale is obtained by performing weighted calculation on the interference fluctuation scale and the state fluctuation scale. The comprehensive fluctuation scale is a quantitative index of environmental complexity and volatility, and 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 a rounding operation, and the formula is: number of branch calls = (historical maximum comprehensive fluctuation scale / comprehensive fluctuation scale) × N, and rounding is taken, where N is the total number of available optimization analysis units in the plugin and is an integer greater than 10. For example, if the comprehensive fluctuation scale accounts for 40% of the historical maximum scale and N = 10, then the number of branch calls is 4. Randomly select the units corresponding to the number of branch calls from the N optimization analysis units of the plugin to form an adaptation analysis plugin. For example, if the number of branch calls is 4, then randomly select 4 from 10 optimization units. By calculating the branch call data, the computing resources can be flexibly allocated according to the actual environmental requirements, and at the same time, the waste of computing power caused by excessive calls can be avoided. Input the interference feature distribution and satellite working parameters as inputs into the adaptation analysis plugin for anti-interference analysis. The interference feature distribution provides information on the type, location, and intensity of interference sources, and the satellite working 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 plugin operates independently, and calculates and outputs an array element parameter matrix according to the input data. The array element parameter matrix contains parameters such as the phase, amplitude, frequency, gain, and time delay of each antenna array element, and is used to dynamically adjust the beam characteristics of the antenna. By intelligently invoking the policy optimization analysis plugin, the analysis accuracy and computing power resources are balanced, providing an efficient and reliable parameter configuration for the anti-interference operation of the satellite antenna.

[0038] Further, a strategy optimization analysis plugin is constructed, including: obtaining the layout characteristics of multiple antenna elements of the satellite phased array antenna to construct an element distribution topology; taking the 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 parameter, and outputting a first sample element parameter matrix; constructing first training data based on the first sample interference feature distribution, the first sample satellite operating parameter and the first sample element parameter matrix, and sequentially analyzing to obtain a training data set; equally dividing the training data set into N parts, performing supervised learning on a random decision forest through the N training sets until convergence, harvesting N optimization analysis units, and fusing to construct the strategy optimization analysis plugin, where N is an integer greater than 10.

[0039] Specifically, obtain the layout characteristics of multiple antenna elements of the satellite phased array antenna. The layout characteristics of the antenna elements refer to parameters such as the position, arrangement method, and distance between each antenna element in the satellite phased array antenna. These characteristics determine the antenna pattern and beam control ability. According to the layout characteristics of the antenna elements, construct an element distribution topology. Topology is a type of structured data. The element distribution topology is a geometric structure diagram of the antenna array formed based on the layout characteristics, and is used to constrain the interaction and physical limitations between elements during the optimization process. Using the element distribution topology as a constraint, collect the sample interference feature distribution sets in different scenarios and the sample satellite operating parameter sets corresponding to the scenarios from the historical operation data. The sample interference feature distribution set includes information such as the position, type, and intensity of the interference source, reflecting the characteristics of different interference environments. The sample satellite operating parameter set includes satellite operating parameters, such as operating state parameters like beam direction, frequency, signal mode, etc., which are the input conditions for optimization analysis. Randomly select one sample interference feature distribution and one sample satellite operating parameter from the sample interference feature distribution set and the sample satellite operating parameter set as the initial input for anti-interference optimization. According to the first sample interference feature distribution and the first sample satellite operating parameter, use anti-interference algorithms, such as beamforming, null control, etc., to calculate and generate the first sample element parameter matrix. The first sample element parameter matrix contains key configurations such as the phase, amplitude, frequency, gain, and time delay of each antenna element, and is used to optimize the anti-interference performance. Combine the first sample interference feature distribution, the first sample satellite operating parameter, and the first sample element parameter matrix 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 operating parameter set, and sequentially generate multiple training data to form a complete training data set, providing comprehensive support for model learning. Then, divide the training data set into N equal 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, use the random decision forest algorithm for supervised learning. The random decision forest is an ensemble learning method that improves classification and regression performance by constructing multiple decision tree models. During the model training process, continuously adjust the parameters until the model converges to ensure that each optimization unit can efficiently analyze and predict the anti-interference performance. Integrate the N trained independent optimization analysis units to construct the final strategy optimization analysis plug-in. For example, the training data set contains 1100 sample data, and each data contains input features and output labels. Among them, the input features are the interference feature distribution and satellite operating parameters, and the output labels are the element parameter matrix. Randomly shuffle the data set and divide it into 11 equal parts, each containing 100 training data, and use each data set to train a decision tree unit respectively.For example, train a decision tree for the first piece of data: randomly select features in the training data, construct a decision tree, select split points through information gain or Gini coefficient to generate nodes, and continue splitting until the preset depth is reached or the leaf nodes cannot be further split. Repeat the above steps to train independent decision tree units using the other 10 pieces of data respectively, obtaining a total of 11 trees. Integrate the 11 decision trees into a random decision forest, with each tree making predictions independently. Finally, generate the final prediction result through a voting mechanism or weighted average. Each independent decision tree is an optimization analysis unit that can output an optimized array element parameter matrix for the input data of the interference distribution and satellite working parameters. By fusing the construction strategy optimization analysis plugin, the strategy optimization analysis plugin has a multi-branch structure and can quickly output the optimal anti-interference strategy according to the input interference feature distribution and satellite working parameters. By using diverse training data and the random decision forest construction strategy optimization analysis plugin, the optimization strategy can be flexibly adjusted according to environmental requirements, improving resource utilization efficiency and anti-interference ability.

[0040] Further, perform anti-interference optimization analysis based on the first sample interference feature distribution and the first sample satellite working parameters, and output the first sample array element parameter matrix, including: obtaining multiple array element parameter thresholds of multiple antenna array elements of the satellite phased array antenna, randomly generating multiple initial array element parameter matrices based on the multiple array element parameter thresholds, where the array element parameters at least include phase, amplitude, frequency, gain, and time delay; based on the simulation test plugin, evaluate the anti-interference effects of the multiple initial array element parameter matrices respectively according to the first sample interference feature distribution and the first sample satellite working parameters to determine multiple anti-interference fitness values, where 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 matrices in descending order of anti-interference fitness to generate an initial solution sequence, and set the first K solutions of the initial solution sequence as excellent solutions and the last S solutions as inferior solutions, where S is an integer multiple of K; randomly cluster the S inferior solutions based on the K excellent solutions to determine K solution sets, and within the K solution sets, adjust the inferior solutions in the direction of the excellent solutions according to a predetermined step size to obtain K updated solution sets, where if the adjusted inferior solutions do not meet the multiple array element parameter thresholds, this adjustment is not performed; identify the K updated solution sets, and if within the same updated solution set, the fitness of the inferior solution is greater than or equal to the fitness of the excellent solution, use the inferior solution to replace the excellent solution; continue iterative optimization until the predetermined convergence number of times is reached, output the K current updated solution sets, calculate the overall fitness of the K current updated solution sets respectively, and set the current updated solution set with the maximum overall fitness as the optimal solution set, and output the excellent solutions within the optimal solution set as the first sample array element parameter matrix.

[0041] Specifically, obtain the threshold values of multiple element parameters for multiple antenna elements of the satellite phased array antenna. The element parameters refer to the key control parameters of each element in the antenna array, including at least phase, amplitude, frequency, gain, and time delay. According to the hardware limitations and design requirements, obtain the parameter threshold values for each element parameter, that is, the upper and lower limits of the parameters. For example, the phase range is 0° to 360°, and the amplitude range is 0 to 1. Based on the above thresholds, randomly generate multiple initial element parameter matrices, with each matrix corresponding to a possible antenna configuration. The element parameter thresholds ensure that the antenna elements can maintain within a reasonable parameter range during operation, avoiding abnormal performance. Use the simulation test plug-in to evaluate the anti-interference effect of each initial element parameter matrix in combination with the given first sample interference feature distribution and the first sample satellite operating parameters, and obtain multiple anti-interference fitness values. The simulation test simulates the actual operating environment according to the interference feature distribution and satellite operating parameters of the first sample, and tests the anti-interference effect of each matrix. For each initial element parameter matrix, calculate its anti-interference fitness, that is, evaluate the interference suppression ability of the satellite phased array antenna under this element configuration. The 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 element configurations. The higher the signal-to-noise ratio, the better the anti-interference effect; bit error rate reduction: Under different element configurations, evaluate the anti-interference ability by comparing the change in the bit error rate; interference suppression ratio: Calculate the power ratio of interference to the target signal. The larger the interference suppression ratio, the stronger the anti-interference ability. That is, the anti-interference fitness is a key indicator for measuring the performance of the element parameter matrix. When calculating, it is positively correlated with the signal-to-noise ratio and interference suppression ratio, and negatively correlated with the bit error rate. According to the anti-interference fitness, sort all the initial element parameter matrices to generate an initial solution sequence, and set the first K solutions in the initial solution sequence as the optimal solutions and the last S solutions as the inferior solutions, where S and K are both positive integers, and S is an integer multiple of K. Then, randomly cluster the S inferior solutions based on the K optimal solutions to form K solution sets. Within each solution set, adjust the inferior solutions in the direction of the optimal solution according to a predetermined step size to seek a better parameter configuration. If the adjusted inferior solution still satisfies the element parameter threshold, retain the adjustment result. When the adjusted inferior solution exceeds the originally set element parameter threshold, do not perform this adjustment to ensure that all solutions are within a reasonable parameter range. After each adjustment, identify the K updated solution sets. 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, use the inferior solution to replace the optimal solution; otherwise, the optimal solution remains unchanged. Through identification and comparison, realize the self-update and optimization of the solutions. Continuously perform adjustments and replacements until the predetermined iteration convergence times are reached, output the K current updated solution sets, and calculate the overall fitness of each solution set respectively. The overall fitness refers to the sum of multiple fitness values in the solution set. Among all the solution sets, regard the updated solution set with the largest overall fitness as the optimal solution set, and use the optimal solution matrix in the optimal solution set as the final result, which is output as the first sample element parameter matrix.The accuracy of the training data is improved through optimization, thereby improving the training accuracy of the model and the reliability of the anti-interference strategy, and ensuring that the antenna can effectively avoid or suppress interference.

[0042] Embodiment 2. Based on the same inventive concept as the satellite phased array antenna anti-interference method adaptable to a scenario in the foregoing embodiment, the present application provides an electronic device. Figure 2 It is a schematic structural diagram of the electronic device provided in Embodiment 2 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2 The displayed electronic device is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention. As Figure 2 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 one processor 21 as an example, the processor 21, the memory 22, the input device 23, and the output device 24 in the electronic device can be connected through a bus or other means. Figure 2 Taking the connection through a bus as an example.

[0043] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0044] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the scope of the present application. Thus, if these modifications and deformations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations.

Claims

1. A method for anti-interference of a satellite phased array antenna adaptable to scenarios, characterized in that The method includes: Under a predetermined monitoring node, using the sensor system of a satellite phased array antenna to conduct environmental detection, obtaining environmental perception data, and performing interference feature analysis based on the environmental perception data to determine the interference feature distribution, where the environmental perception data are environmental interference factors, at least including electromagnetic interference sources and obstacles in the current environment; Performing interference feature fluctuation analysis based on the interference feature distribution to determine the interference fluctuation scale; Obtaining the satellite operating parameters of the predetermined monitoring node, and performing satellite state fluctuation analysis based on the satellite operating parameters to determine the state fluctuation scale; Invoking a strategy optimization analysis plugin based on the interference fluctuation scale and the state fluctuation scale, and performing anti-interference analysis based on the interference feature distribution and the satellite operating parameters to output an array element parameter matrix, which is set as the optimal anti-interference strategy; Executing the operation control of the satellite phased array antenna in the future time zone according to the optimal anti-interference strategy; Performing interference feature analysis based on the environmental perception data to determine the interference feature distribution, including: Configuring an interference analysis strategy, where the interference analysis strategy includes spectrum analysis, time-domain analysis, and spatial distribution analysis; Based on the interference analysis strategy, performing interference feature analysis according to the environmental perception data to determine a number of interference features, where the interference features at least include interference source type, interference source location, and interference intensity; Mapping and arranging the number of interference features according to the interference source location to generate the interference feature distribution; Performing interference feature fluctuation analysis 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 the adjacent interference feature distributions of the adjacent nodes of the predetermined monitoring node; Performing a similarity traversal comparison between the adjacent interference feature distributions and the interference feature distribution to determine the adjacent distribution similarity, and using 1 minus the adjacent distribution similarity 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; Calculating the interference fluctuation scale by weighted calculation based on the first interference fluctuation coefficient and the second interference fluctuation coefficient; Obtaining the satellite operating parameters of the predetermined monitoring node, and performing satellite state fluctuation analysis based on the satellite operating parameters to determine the state fluctuation scale, including: Obtaining the satellite operating parameters of the predetermined monitoring node, where the satellite operating parameters at least include beam direction, frequency, and signal mode; Obtaining the multiple historical satellite operating parameters of multiple monitoring nodes within the historical time window, and selecting the adjacent operating parameters of the adjacent nodes of the predetermined monitoring node; Analyzing to obtain a first state fluctuation coefficient based on the satellite operating parameters and the adjacent operating parameters, and performing state fluctuation trend analysis on the multiple historical satellite operating parameters to determine a second state fluctuation coefficient; Generating the state fluctuation scale based on the first state fluctuation coefficient and the second state fluctuation coefficient.

2. A method for anti-interference of a satellite phased array antenna with scene adaptability according to claim 1, characterized in that, Performing interference fluctuation trend analysis on the multiple historical interference feature distributions to determine the second interference fluctuation coefficient, including: Arrange the multiple historical interference feature distributions in the order of the monitoring nodes to generate a historical interference feature distribution sequence, and select a first feature distribution and a second feature distribution, where the first feature distribution is the first feature distribution in the historical interference feature distribution sequence, and the second feature distribution is the adjacent feature distribution of the first feature distribution; Perform a similarity traversal comparison on the first feature distribution and the second feature distribution to determine a first similarity, and use 1 minus the first similarity to obtain a first historical interference fluctuation coefficient; Continue to perform interference fluctuation analysis on the adjacent feature distributions of the historical interference feature distribution sequence to obtain multiple historical interference fluctuation coefficients, and calculate the second interference fluctuation coefficient by averaging; 3. A method for anti-interference of a satellite phased array antenna adaptable to scenarios according to claim 1, characterized in that, Based on the interference fluctuation scale and the state fluctuation scale, call a strategy optimization analysis plugin, and perform anti-interference analysis according to the interference feature distribution and the satellite working parameters, and output an array element parameter matrix, including: Analyze and determine a 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, where the number of branch calls is obtained by rounding up the ratio of the comprehensive fluctuation scale to the historical maximum comprehensive fluctuation scale multiplied by N, and N is an integer greater than 10; Perform random calls among the N optimization analysis units of the strategy optimization analysis plugin according to the number of branch calls to generate an adaptation analysis plugin; Input the interference feature distribution and the satellite working parameters into the adaptation analysis plugin for anti-interference analysis, and output the array element parameter matrix.

4. A method for anti-interference of a scene-adaptive satellite phased array antenna according to claim 1, characterized in that, Before calling the strategy optimization analysis plugin based on the interference fluctuation scale and the state fluctuation scale, it also includes: Obtain the layout features of multiple antenna elements of the satellite phased array antenna and construct an array element distribution topology; Taking the array element distribution topology as a constraint, collect a sample interference feature distribution set and a sample satellite working parameter set, and randomly select a first sample interference feature distribution and a first sample satellite working parameter; Perform anti-interference optimization analysis according to the first sample interference feature distribution and the first sample satellite working parameter, and output a first sample array element parameter matrix; Construct 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 sequentially analyze to obtain a training data set; Divide the training data set into N equal parts, perform supervised learning on a random decision forest through the N training sets until convergence, harvest N optimization analysis units, and fuse and construct the strategy optimization analysis plugin.

5. The anti-interference method for a scene-adaptive satellite phased array antenna according to claim 4, wherein, Perform anti-interference optimization analysis according to the first sample interference feature distribution and the first sample satellite working parameter, and output a first sample array element parameter matrix, including: Obtain multiple array element parameter thresholds of multiple antenna elements of the satellite phased array antenna, and randomly generate multiple initial array element parameter matrices based on the multiple array element parameter thresholds, where 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, evaluate the anti-interference effects of the multiple initial array element parameter matrices respectively, and determine multiple anti-interference fitness values, where 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 matrices in descending order of anti-interference fitness 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, perform random clustering on the S inferior solutions to determine K solution sets, and within the K solution sets, adjust the inferior solutions in the direction of the optimal solutions according to a predetermined step size to obtain K updated solution sets, where if the adjusted inferior solutions do not meet the multiple array element parameter thresholds, this adjustment is not performed; Identify the K updated solution sets. 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, then use the inferior solution to replace the optimal solution; Continue iterative optimization until a predetermined number of convergence times is reached, output the K current updated solution sets, calculate the overall fitness of the K current updated solution sets respectively, and set the current updated solution set with the maximum overall fitness as the optimal solution set, and extract the optimal solution from the optimal solution set as the first sample array element parameter matrix.

6. 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 configured to execute the instructions to implement a scenario-adaptive satellite phased array antenna anti-interference method according to any one of claims 1 to 5.

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