Method and system for avoiding frequency interference by using AI and digital twinning combination

Abstract

The invention discloses a method and system for avoiding frequency interference through AI and digital twinning combination, and relates to the technical field of satellite communication, and the method comprises the steps: building a big data satellite frequency interference avoiding system, and collecting the current orbit information and frequency information of a communication satellite; simulation is performed through a preset interference scene prediction model, a satellite adjustment attitude is determined, the preset interference scene prediction model is obtained by training a normal scene and an interference scene of a satellite system based on an AI algorithm and a digital twin model, and the digital twin model comprises an orbit prediction module and an attitude adjustment prediction module which are connected in sequence. The orbit prediction module is used for outputting an orbit prediction value according to orbit information and frequency information, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; therefore, the interference scene of the communication satellite can be predicted in advance through the preset interference scene prediction model so as to adjust the beam direction, and automatic early warning and processing of the interference of the communication satellite are realized.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a method and system for avoiding frequency interference by combining AI with digital twins. Background Art

[0002] Due to the limitations of the satellite system's orbital position and communication frequency resources, different communication satellites generally need to share communication frequencies, so the probability of harmful interference between systems is extremely high. To avoid frequency interference with geostationary orbit satellites, when low-orbit communication satellites operate near the equator, the payload beam of low-orbit communication satellites near the equator will be turned off, and the payload beam will be turned on again after leaving the shutdown area.

[0003] However, when in the shutdown area, the normal business of the communication satellite will be affected. At the same time, the shutdown of the communication satellite will also affect the tracking accuracy and stability of the communication satellite. Summary of the invention

[0004] The main purpose of this application is to provide a method and system for avoiding frequency interference by combining AI with digital twins, aiming to solve the technical problem that when a communication satellite is shut down in a shutdown area to avoid frequency interference, it will affect the normal business of the communication satellite. At the same time, the shutdown of the communication satellite will also affect the tracking accuracy and stability of the communication satellite.

[0005] To achieve the above objectives, the present application proposes a method for avoiding frequency interference by combining AI with digital twins, the method comprising:

[0006] Establish a big data satellite frequency interference avoidance system, and collect the current orbit and frequency information of communication satellites through the system;

[0007] The orbital information and the frequency information are simulated by a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, wherein the preset interference scenario prediction model is obtained by training the normal scenario and the interference scenario of the satellite system based on the AI ​​algorithm and the digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, wherein the orbit prediction module is used to output an orbit prediction value according to the orbital information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value;

[0008] The beam pointing of the communication satellite is adjusted according to the satellite adjustment attitude.

[0009] In one embodiment, before the step of establishing a big data satellite frequency interference avoidance system and collecting the current orbit information and frequency information of the communication satellite through the system, the step further includes:

[0010] Simulate and build a digital twin model corresponding to the satellite system;

[0011] Acquiring communication link data of the satellite system in a normal scenario and an interference scenario;

[0012] Based on the communication link data, the digital twin model is trained through an AI algorithm to obtain a preset interference scenario prediction model.

[0013] In one embodiment, the specific training process of the preset interference scenario prediction model includes:

[0014] Step 1: Standardize the communication link data of the satellite system in the normal scenario and interference scenario recorded in the history to obtain a standardized data set;

[0015] Step 2: The digital twin model construction includes a trajectory prediction module and an attitude adjustment prediction module connected in sequence;

[0016] Step 3: Train the track prediction module according to the normal scene data set and calculate the loss function of the track prediction module until the loss function converges to the preset value;

[0017] Step 4: Train the posture adjustment prediction module according to the interference scene data set until the target accuracy is reached, then stop the training process and obtain the preset interference scene prediction model.

[0018] In one embodiment, the process of the standardization includes:

[0019] For the original data x in the communication link data, the mean μ and standard deviation σ of the communication link data are calculated; wherein the original data include the eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate of each communication satellite in the satellite system; the mean is the average value of the same type of data in each communication satellite, and the mean includes the mean eccentricity, the mean orbital inclination, the mean ascending right ascension, the mean perigee argument, the mean true anomaly, the mean signal interference intensity, the mean signal interference frequency and the mean bit error rate; the standard deviation is a statistical value to measure the degree of dispersion of the same type of data, indicating the degree of deviation of each data from the mean, including the standard deviation of eccentricity, the standard deviation of orbital inclination, the standard deviation of ascending right ascension, the standard deviation of perigee argument, the standard deviation of true anomaly, the standard deviation of signal interference intensity, the standard deviation of signal interference frequency and the standard deviation of bit error rate;

[0020] Subtract the mean μ from the original data x, and then divide it by the standard deviation σ to get the standardized data point z, that is, the calculation formula is as follows:

[0021]

[0022] Among them, x is the original data in the communication link data, μ is the mean of the communication link data, and σ is the standard deviation of the communication link data;

[0023] The standardized dataset includes a normal scene dataset and an interference scene dataset, and both the normal scene dataset and the interference scene dataset are divided into a training set and a test set in a ratio of 2:1.

[0024] In one embodiment, the training process of the trajectory prediction module includes:

[0025] Step 3.1: Train the track prediction module according to the training set of the normal scene data set, and calculate the loss function of the track prediction module;

[0026] Step 3.2: Test and train the trained model according to the test set of the normal scene data set until the loss function converges to the preset value;

[0027] Among them, the loss function is:

[0028] θ1=θ SE +θ PE +θ C ;

[0029] Among them, θ1 is the loss function value, θ SE is the root mean square error between the predicted value and the actual value, θ PE is the average relative percentage error between the predicted value and the actual value; θ C The coefficient of determination is a measure of the degree of correlation between the predicted value and the actual value. The larger the value, the higher the degree of correlation and the better the prediction effect.

[0030] In one embodiment, θ SE ,θ PE and θ C The specific formula is as follows:

[0031]

[0032] Where a represents the number of samples in the training set; z' i is the orbit prediction value, x i is the actual value in the training set, μ i is the average of the actual values.

[0033] In one embodiment, the training process of the posture adjustment prediction module includes:

[0034] Step 4.1: Using T re represents the training time of the posture adjustment prediction module, F represents the total number of training rounds of the posture adjustment prediction module, V represents the total number of target data aggregated by the posture adjustment prediction module, Indicates every The wheel posture adjustment prediction module aggregates the target data once, and then:

[0035] Step 4.2: Select the optimal aggregation frequency through K-Means clustering algorithm;

[0036] Step 4.3: During training time T re The posture adjustment prediction module is based on the optimal aggregation frequency Towards target data training aggregation, if the training time T is reached re , then proceed to step 4.4, otherwise go to step 4.2;

[0037] Step 4.4: Aggregate the model weights through the FedProx algorithm. If the target accuracy is achieved, stop the training process and obtain the preset interference scenario prediction model. Otherwise, go to step 4.2.

[0038] In one embodiment, the optimal aggregation frequency The steps include:

[0039] Step 4.2.1: Establish the time consumption constraint of the posture adjustment prediction module. The expression of the time consumption constraint is:

[0040]

[0041] in, It represents the time taken by the posture adjustment prediction module to train the interference scene dataset in the nth round. represents the time it takes for the posture adjustment prediction module to achieve aggregation in the nth round, T re Indicates the training time,

[0042] Step 4.2.2: Use the K-Means clustering algorithm to find the connection between the posture adjustment prediction module and the time consumption constraint and determine the optimal aggregation frequency

[0043] In addition, to achieve the above objectives, the present application also proposes a system for avoiding frequency interference by combining AI with digital twins, the system comprising:

[0044] Information collection module, used to establish a big data satellite frequency interference avoidance system, and collect the current orbit information and frequency information of the communication satellite through the system;

[0045] A model prediction module, used to simulate the orbit information and the frequency information through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, wherein the preset interference scenario prediction model is obtained by training the normal scenario and interference scenario of the satellite system based on an AI algorithm and a digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, wherein the orbit prediction module is used to output an orbit prediction value according to the orbit information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value;

[0046] The beam adjustment module is used to adjust the beam pointing of the communication satellite according to the satellite adjustment attitude.

[0047] One or more technical solutions proposed in the present application have at least the following technical effects: the present application first establishes a big data satellite frequency interference avoidance system, and collects the current orbital information and frequency information of the communication satellite through the system; then, the orbital information and the frequency information are simulated through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, the preset interference scenario prediction model is obtained by training the normal scenario and interference scenario of the satellite system based on the AI ​​algorithm and the digital twin model, the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, the orbit prediction module is used to output an orbit prediction value according to the orbital information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs the satellite adjustment attitude according to the orbit prediction value; finally, the beam pointing of the communication satellite is adjusted according to the satellite adjustment attitude. Since the present application introduces a preset interference scenario prediction model to predict in advance the current orbital prediction value of the communication satellite and the satellite adjustment attitude, the interference scenario of the communication satellite is predicted in advance, and the beam pointing of the communication satellite is adjusted. By identifying potential frequency interference situations, it can avoid the problem that traditional communication satellites are shut down in the shutdown area to avoid frequency interference, which affects the tracking accuracy and stability of the communication satellite, thereby realizing automatic early warning and processing of frequency interference, adaptively reducing frequency interference, and ensuring the normal business of the communication satellite. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A flowchart diagram of a method for avoiding frequency interference by combining AI and digital twins is provided in Example 1 of the present application;

[0051] Figure 2 A flow chart of a method for avoiding frequency interference by combining AI and digital twins provided in Embodiment 2 of the present application;

[0052] Figure 3 This is a schematic diagram of the module structure of a system that utilizes a combination of AI and digital twins to avoid frequency interference according to an embodiment of the present application.

[0053] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] It should be noted that the execution subject of this embodiment can be a communication satellite, ground measurement and control equipment, etc., or an electronic device that can realize the above functions, a device that uses AI and digital twins to avoid frequency interference (referred to as anti-interference equipment), etc., and this embodiment does not limit this. The following takes the anti-interference equipment as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the embodiment of the present application provides a method for avoiding frequency interference by combining AI with digital twins. Figure 1 , Figure 1 A flow chart of a method for avoiding frequency interference by combining AI and digital twins is provided in Example 1 of the present application.

[0058] In this embodiment, the method of avoiding frequency interference by combining AI with digital twins includes steps S10 to S30:

[0059] Step S10: Establish a big data satellite frequency interference avoidance system, and collect the current orbit information and frequency information of the communication satellite through the system.

[0060] It should be noted that the big data satellite frequency interference avoidance system is a system that uses big data technology to deal with satellite frequency interference problems.

[0061] Specifically, we can collect information such as frequency usage, signal characteristics, and orbital parameters of different satellites. We can use this data for comprehensive analysis and modeling to build a big data satellite frequency interference avoidance system. Through real-time monitoring and identification of potential frequency interference situations, we can issue early warnings and take corresponding measures to avoid or mitigate the impact of interference. For example, we can adjust the frequency allocation of satellites, optimize communication strategies, etc., so as to ensure the normal operation and service quality of satellite communication systems and reduce the adverse consequences of interference.

[0062] It should be noted that a communication satellite is an artificial satellite in space, and its function is to relay communications. It can receive signals from earth stations (communication sites on the ground, at sea or in the air), amplify and convert the signals, and then forward them to other earth stations, thus achieving long-distance communications.

[0063] It should be noted that orbital information refers to the specific orbital parameters of a satellite in space or in low-Earth orbit, including orbital altitude, orbital shape (such as circular, elliptical, etc.), orbital inclination, etc. Orbital information describes the satellite's path and position relative to the Earth.

[0064] The orbit information may be determined based on data such as the semi-major axis, eccentricity, orbit inclination, ascending focal right ascension, etc. of the satellite, and this embodiment does not impose any limitation on this.

[0065] It should be noted that frequency information refers to the range of radio frequencies used by communication satellites to transmit signals. Different communication services generally use specific frequency bands to avoid mutual interference. However, there may be situations where it is necessary to share communication frequencies, and the probability of harmful interference is extremely high. Therefore, it is necessary to determine whether there is interference based on frequency information.

[0066] In one embodiment, the anti-interference device can collect information such as frequency usage, signal characteristics, orbital parameters, etc. of different satellites in advance to establish a big data satellite frequency interference avoidance system. The system can collect the current orbital information of the communication satellite, such as orbital altitude, orbital shape (such as circular, elliptical, etc.), orbital inclination, etc., as well as frequency information. The frequency information can be used to determine whether there is interference. If so, the orbital information can be used for path planning to avoid mutual interference.

[0067] Step S20: The orbital information and the frequency information are simulated through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite. The preset interference scenario prediction model is obtained by training the normal scenarios and interference scenarios of the satellite system based on the AI ​​algorithm and the digital twin model. The digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence. The orbit prediction module is used to output an orbit prediction value according to the orbital information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs the satellite adjustment attitude according to the orbit prediction value.

[0068] It should be noted that the preset interference scenario prediction model is constructed by first digitally simulating the satellite system to form a virtual digital twin model corresponding to the real satellite system, and then using the AI ​​algorithm to learn and train this digital twin model to obtain the model.

[0069] It should be noted that in the process of learning and training the digital twin model, the communication link data of the satellite system in the interference scenario can be used as the core for training and learning, so that the interference situation of the communication satellite can be quickly determined through the preset interference scenario prediction model.

[0070] It is understandable that the preset interference scenario prediction model can analyze the frequency information of the communication satellite based on the knowledge and patterns obtained through training to determine whether there is a potential interference scenario; if there is a potential interference situation, the latitude and angle of the communication satellite in the future period of time is predicted through orbital information to quickly determine whether the communication satellite is obviously in an interference area in the future period of time.

[0071] It should be noted that the interference zone is an area within the coverage of the beam transmitted by the communication satellite where signal transmission is adversely affected due to various reasons (such as co-channel interference, interference from other signal sources, etc.), resulting in reduced communication quality or failure of normal communication.

[0072] The interference area can be determined by interference information from other satellite signals, ground radio equipment, electromagnetic radiation, etc. Signal attenuation, distortion, increased bit error rate, etc. may occur in this area, affecting the normal operation and services of the communication satellite system.

[0073] It should be noted that satellite attitude adjustment is the attitude of the satellite changing its position, direction or rotation in space. When a satellite is operating in space, if it encounters interference, it needs to adjust its attitude in order to better maintain stable communication signals and accurately point to the target. For example, the control of rotation, pitch, yaw and other angles can be used to adjust and maintain the attitude.

[0074] In one embodiment, the anti-interference device can pre-digitally simulate the satellite system to form a virtual digital twin model corresponding to the real satellite system, and then use the AI ​​algorithm to learn and train the digital twin model with the communication link data of the satellite system in the interference scenario as the core to obtain a preset interference scenario prediction model. After obtaining the orbital information and frequency information, the frequency information of the communication satellite can be simulated and analyzed through the preset interference scenario prediction model to determine whether there is a potential interference scenario; if there is a potential interference situation, the satellite adjustment attitude is output.

[0075] Step S30: adjusting the beam pointing of the communication satellite according to the satellite adjustment attitude.

[0076] Exemplarily, the satellite attitude adjustment may include the specific angle of satellite attitude adjustment, the value of direction change, orbit fine-tuning data, etc. This enables the communication satellite to move or adjust along a specific attitude change path, prevent interference from sources such as beam interference areas, and ensure the normal and stable communication of the communication satellite.

[0077] It should be noted that beam pointing is the direction in which the signal beam transmitted by the communication satellite is focused.

[0078] In one embodiment, when a communication satellite is in an interference area, the anti-interference device can adjust the beam pointing of the communication satellite by adjusting the satellite's attitude, and plan the attitude angle of the communication satellite with the goal of a more stable attitude maneuver, so that the beam pointing of the communication satellite avoids the beam interference area and accurately points to the target receiving area, ensuring that the signal can be effectively delivered to the location that needs to be covered, thereby ensuring the communication service of the communication satellite.

[0079] In this embodiment, the anti-interference device can collect information such as frequency usage, signal characteristics, orbital parameters, etc. of different satellites in advance to establish a big data satellite to avoid frequency interference system. Then, the satellite system is digitally simulated and constructed in advance to form a virtual digital twin model corresponding to the real satellite system. Using the AI ​​algorithm, the digital twin model is studied and trained with the communication link data of the satellite system in the interference scenario as the core to obtain a preset interference scenario prediction model. After that, the system can collect the current orbital information of the communication satellite, such as orbital altitude, orbital shape (such as circular, elliptical, etc.), orbital inclination, etc., as well as collect frequency information. After obtaining the orbital information and frequency information, the frequency information of the communication satellite can be simulated and analyzed through the preset interference scenario prediction model to determine whether there is a potential interference scenario; if there is a potential interference situation, the satellite adjustment attitude is output. When the communication satellite is in the interference area, the anti-interference equipment can adjust the beam pointing of the communication satellite by adjusting the satellite's attitude. With the goal of more stable attitude maneuvers, the attitude angle of the communication satellite is planned to make the beam pointing of the communication satellite avoid the beam interference area and accurately point to the target receiving area, ensuring that the signal can be effectively delivered to the location that needs to be covered, thereby guaranteeing the communication service of the communication satellite.

[0080] In a feasible implementation manner, step S10 of this embodiment may include the steps of: establishing a big data satellite frequency interference avoidance system; collecting the current semi-major axis, eccentricity, orbital inclination, ascending focus right ascension, perigee argument and true anomaly of the communication satellite through the system to determine the current orbital information of the communication satellite; collecting the current antenna pointing and receiving power of the communication satellite through the system to determine the current frequency information of the communication satellite.

[0081] It should be noted that the semi-major axis is half of the major axis of the elliptical orbit and determines the size of the communication satellite orbit.

[0082] It should be noted that the eccentricity describes the degree of oblateness of the elliptical orbit of the communication satellite. The greater the eccentricity, the flatter the orbit.

[0083] It should be noted that the orbital inclination is the angle between the satellite orbit plane and the Earth's equatorial plane, which determines the degree of inclination of the satellite orbit relative to the Earth.

[0084] It should be noted that the ascending right ascension is the angle measured eastward along the equator from the vernal equinox to the ascending node, which is used to determine the position of the satellite orbit on the equatorial plane.

[0085] It should be noted that the argument of perigee is the angle measured from the ascending node to the perigee along the direction of satellite motion, which describes the shape and orientation of the orbit.

[0086] It should be noted that the true anomaly angle is the angle measured from the perigee along the direction of satellite motion to the satellite's position, and is used to specifically determine the satellite's position in orbit.

[0087] Specifically, the semi-major axis determines the size and range of the orbit; the eccentricity reflects the shape of the orbit; the orbital inclination indicates the inclination angle of the satellite orbit relative to the Earth's equatorial plane; the ascending focal right ascension determines the orientation of the orbit on the equatorial plane; the perigee argument further refines the orientation of the orbit; and the true anomaly can accurately indicate the specific position of the satellite on the orbit. Therefore, the above parameters can accurately describe the current orbital state and position information of the communication satellite.

[0088] It should be noted that antenna pointing refers to the direction or target location that the satellite antenna is pointed at. Antenna pointing can be used to understand the relative position relationship between the satellite and the ground receiving station.

[0089] It should be noted that the received power refers to the power of the signal from the communication satellite received by the ground receiving end. The transmission effect of the received power can be used to determine whether there is signal attenuation.

[0090] In this embodiment, the system can collect the current semi-major axis, eccentricity, orbital inclination, ascending focus right ascension, perigee argument, true anomaly, antenna pointing and receiving power of the communication satellite, so that the current orbital information and frequency information of the communication satellite can be accurately understood through the above parameters.

[0091] This embodiment provides a method for avoiding frequency interference by combining AI with digital twins. The anti-interference device can collect information such as frequency usage, signal characteristics, orbital parameters, etc. of different satellites in advance to establish a big data satellite frequency interference avoidance system. Then, the satellite system is digitally simulated and constructed in advance to form a virtual digital twin model corresponding to the real satellite system. Using the AI ​​algorithm, the digital twin model is studied and trained with the communication link data of the satellite system in the interference scenario as the core to obtain a preset interference scenario prediction model. After that, the system can collect the current orbital information of the communication satellite, such as orbital altitude, orbital shape (such as circular, elliptical, etc.), orbital inclination, etc., as well as collect frequency information. After obtaining the orbital information and frequency information, the frequency information of the communication satellite can be simulated and analyzed by the preset interference scenario prediction model to determine whether there is a potential interference scenario; if there is a potential interference situation, the satellite adjustment attitude is output. When the communication satellite is in the interference area, the anti-interference device can adjust the beam pointing of the communication satellite by adjusting the attitude of the satellite, and plan the attitude angle of the communication satellite with the goal of more stable attitude maneuvering, so that the beam pointing of the communication satellite avoids the beam interference area and accurately points to the target receiving area, ensuring that the signal can be effectively delivered to the location that needs to be covered, and guaranteeing the communication service of the communication satellite. Since the present application introduces a preset interference scenario prediction model to predict the current orbit prediction value of the communication satellite and the satellite adjustment attitude in advance, the interference scenario of the communication satellite is predicted in advance, and the beam pointing of the communication satellite is adjusted. By identifying potential frequency interference situations, it can avoid the problem that traditional communication satellites are shut down in the shutdown area to avoid frequency interference, affecting the tracking accuracy and stability of the communication satellite, thereby realizing automatic early warning and processing of frequency interference, adaptively reducing frequency interference, and ensuring the normal business of the communication satellite.

[0092] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 1 and Figure 2 , Figure 2 A flow chart of a method for avoiding frequency interference by combining AI with digital twins is provided in Example 2 of the present application.

[0093] Before step S10, this example also includes steps S01 to S03:

[0094] Step S01: Simulate and build a digital twin model corresponding to the satellite system.

[0095] It should be noted that the digital twin model is a virtual model corresponding to the real satellite system formed by digital simulation of the satellite system. The digital twin model can reflect the state and behavior of the physical entity of the simulated satellite system in real time.

[0096] Step S02: Acquire communication link data of the satellite system in a normal scenario and an interference scenario.

[0097] It should be noted that the communication link data under normal scenarios are various data generated during the communication between the satellite and the ground station or other satellites when there is no abnormal interference or influence from the outside world, such as signal strength, transmission rate, bit error rate, delay and other data.

[0098] Communication link data in interference scenarios are presented when there are situations such as electromagnetic interference, human interference, and communication interference between satellites. These data are different from the data in normal scenarios, and may show changes such as signal weakening, decreased transmission rate, increased bit error rate, and increased latency. By analyzing the communication link data in interference scenarios, we can understand the specific extent and method of the impact of interference on satellite communications.

[0099] Step S03: Based on the communication link data, the digital twin model is trained through an AI algorithm to obtain a preset interference scenario prediction model.

[0100] It should be noted that AI algorithm, or artificial intelligence algorithm, enables the digital twin model to intelligently simulate and learn communication link data.

[0101] Exemplarily, the AI ​​algorithm may include a machine learning algorithm, a federated proximal algorithm, a natural language processing algorithm, etc., which is not limited in this embodiment.

[0102] In this implementation, the satellite system can be digitally simulated and constructed first to form a virtual digital twin model corresponding to the real satellite system. Then, the communication link data of the satellite system in normal scenarios and interference scenarios are obtained; finally, based on the communication link data, the digital twin model is trained and learned through the AI ​​algorithm to obtain the preset interference scenario prediction model. Therefore, the preset interference scenario prediction model can be used to quickly simulate and predict the latitude and angle of the communication satellite in the interference area in the future.

[0103] The specific training process of the preset interference scenario prediction model includes:

[0104] Step 1: Standardize the communication link data of the satellite system in normal scenarios and interference scenarios recorded in history to obtain a standardized data set to unify the data scale and convert data of different scales to the same scale.

[0105] Since the communication link data is continuous, Z-score standardization can be used to convert each data in the communication link data into a multiple of the standard deviation of its mean to eliminate the scale differences between different data.

[0106] Step 1.1: The standardization process includes:

[0107] For the original data x in the communication link data, first calculate the mean μ and standard deviation σ of the communication link data; the original data includes the eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate of each communication satellite in the satellite system; the mean is the average value of the same data in each communication satellite, including the mean eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate; the standard deviation is a statistical value to measure the degree of dispersion of the same data, indicating the degree of deviation of each data from the mean, including the standard deviation of eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate. Through the above mean and standard deviation, the scale difference of the same data in the original data between each communication satellite can be eliminated.

[0108] Then subtract the mean μ from the original data x and divide it by the standard deviation σ to get the standardized data point z. The calculation formula is as follows:

[0109]

[0110] Among them, x is the original data in the communication link data, μ is the mean of the communication link data, and σ is the standard deviation of the communication link data.

[0111] Through such an operation, the original data in the communication link data is converted into standard normal distribution data with a mean of 0 and a standard deviation of 1, thereby achieving data standardization.

[0112] Step 1.2: The standardized data set includes a normal scene data set and an interference scene data set. The normal scene data set and the interference scene data set are divided into a training set and a test set in a ratio of 2:1.

[0113] Among them, the normal scene data set is a variety of data generated during the communication between the satellite and the ground station or other satellites when there is no abnormal interference from the outside world, such as signal strength, transmission rate, orbit, satellite attitude and other data.

[0114] The interference scenario dataset is the corresponding data generated when satellite communications are interfered with by various interference sources. These interferences may cause signal attenuation and reduced communication quality. At this time, the collected data will reflect the specific impact of the interference on the communication link, including the degree of signal strength reduction, bit error rate increase, adjusted orbit and satellite attitude data.

[0115] Step 2: The digital twin model construction includes a trajectory prediction module and an attitude adjustment prediction module which are connected in sequence.

[0116] The orbit prediction module includes a CNN layer, multiple LSTM layers and multiple fully connected layers connected in sequence, and the CNN layer includes multiple convolutional layers and pooling layers connected in sequence. The previous orbit information and frequency information of the communication satellite are input into the orbit prediction module, and the orbit prediction module outputs the orbit prediction value (i.e., predicting the satellite orbit of the communication satellite for a period of time in the future to determine whether it is in an interference scenario).

[0117] The attitude adjustment module includes a regularization layer, multiple residual units ResNet, a bidirectional LSTM unit and a fully connected unit connected in sequence. The residual unit ResNet includes two convolutional layers connected in sequence. The residual unit is used to extract features of the current orbit information and frequency information of the communication satellite, and make predictions in the bidirectional LSTM unit and the fully connected unit to obtain the final prediction value (i.e., predict the satellite adjustment attitude of the communication satellite for a period of time in the future).

[0118] Step 3: Train the track prediction module according to the normal scene data set and calculate the loss function of the track prediction module until the loss function converges to the preset value.

[0119] The training process of the orbit prediction module includes:

[0120] Step 3.1: Train the track prediction module according to the training set of the normal scene data set, and calculate the loss function of the track prediction module;

[0121] Step 3.2: Then test the trained model according to the test set of the normal scene data set until the loss function converges to the preset value.

[0122] Among them, the loss function is:

[0123] θ1=θ SE +θ PE +θ C ;

[0124] Among them, θ1 is the loss function value, θ SE is the root mean square error between the predicted value and the actual value, θ PE is the average relative percentage error between the predicted value and the actual value. C The coefficient of determination is a measure of the degree of correlation between the predicted value and the actual value. The larger the value, the higher the degree of correlation and the better the prediction effect.

[0125] Among them, θ SE ,θ PE and θ CThe specific formula is as follows:

[0126]

[0127] Where a represents the number of samples in the training set; z' i is the orbit prediction value, x i is the actual value in the training set, μ i is the average of the actual values.

[0128] Step 4: Train the posture adjustment prediction module according to the interference scene data set until the target accuracy is reached, then stop the training process and obtain the preset interference scene prediction model.

[0129] The training process of the posture adjustment prediction module includes:

[0130] Step 4.1: First use T re represents the training time of the attitude adjustment prediction module, F represents the total number of training rounds of the attitude adjustment prediction module, V represents the total number of target data aggregated by the attitude adjustment prediction module (i.e., the data of the orbit or satellite attitude adjusted by the model in the interference scene dataset), Indicates every The wheel posture adjustment prediction module aggregates the target data once, and then:

[0131] Step 4.2: Select the optimal aggregation frequency through K-Means Clustering Algorithm

[0132] Among them, the optimal aggregation frequency The steps include:

[0133] Step 4.2.1: First, a time consumption constraint condition of the posture adjustment prediction module can be established. The expression of the time consumption constraint condition is:

[0134]

[0135] in, It represents the time taken by the posture adjustment prediction module to train the interference scene dataset in the nth round. represents the time it takes for the posture adjustment prediction module to achieve aggregation in the nth round, T re Indicates the training time,

[0136] Step 4.2.2: Then use the K-Means clustering algorithm to find the connection between the posture adjustment prediction module and the time consumption constraint and determine the optimal aggregation frequency.

[0137] Among them, the K-Means clustering aggregation algorithm is an unsupervised learning algorithm, which aims to continuously adjust the attribution of data points and the location of cluster centers so that data points in the same cluster have higher similarity, while data points between different clusters have greater differences.

[0138] Step 4.3: During training time T re The posture adjustment prediction module is based on the optimal aggregation frequency Towards target data training aggregation, if the training time T is reached re , then proceed to step 4.4, otherwise go to step 4.2.

[0139] Step 4.4: Aggregate the model weights through the FedProx algorithm (Federated Proximal Algorithm). If the target accuracy is achieved, stop the training process and obtain the preset interference scenario prediction model. Otherwise, go to step 4.2.

[0140] Among them, the federated proximal algorithm is an optimization algorithm used in federated learning scenarios. Since the communication link data is distributed on multiple different nodes or devices and the data cannot be concentrated together, in federated learning, this algorithm can solve the problem of how to effectively train and optimize the model in such a distributed environment to update and optimize the model parameters and achieve better learning results.

[0141] By continuously aggregating and learning the data of the adjusted orbit or satellite attitude in the interference scenario data set, the attitude adjustment prediction module can determine whether it is an interference scenario based on the predicted orbit output by the orbit prediction module, and then predict the satellite attitude adjustment of the communication satellite.

[0142] In this embodiment, the satellite system can be digitally simulated and constructed to form a virtual digital twin model corresponding to the real satellite system. Then, the communication link data of the satellite system in normal scenarios and interference scenarios are obtained; finally, based on the communication link data, the digital twin model is trained and learned through the AI ​​algorithm to obtain the preset interference scenario prediction model. Therefore, the preset interference scenario prediction model can be used to quickly simulate and predict the orbit of the communication satellite in the future, so that the preset interference scenario prediction model finally trained can quickly determine the potential interference situation of the communication satellite.

[0143] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the present application's method of avoiding frequency interference by combining AI with digital twins. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0144] This application also provides a system that uses AI and digital twins to avoid frequency interference. Please refer to Figure 3 , Figure 3 This is a schematic diagram of the module structure of a system for avoiding frequency interference by combining AI with digital twins according to an embodiment of the present application; the system for avoiding frequency interference by combining AI with digital twins includes:

[0145] Information collection module 401, used to establish a big data satellite frequency interference avoidance system, and collect the current orbit information and frequency information of the communication satellite through the system;

[0146] A model prediction module 402 is used to simulate the orbit information and the frequency information through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, wherein the preset interference scenario prediction model is obtained by training the normal scenario and the interference scenario of the satellite system based on the AI ​​algorithm and the digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, wherein the orbit prediction module is used to output an orbit prediction value according to the orbit information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value;

[0147] The beam adjustment module 403 is used to adjust the beam pointing of the communication satellite according to the satellite adjustment attitude.

[0148] Furthermore, the system for avoiding frequency interference by combining AI with digital twins also includes a model construction module 404, which is used to simulate the construction of a digital twin model corresponding to the satellite system; obtain the communication link data of the satellite system in normal scenarios and interference scenarios; based on the communication link data, the digital twin model is trained by an AI algorithm to obtain a preset interference scenario prediction model.

[0149] Furthermore, in the model building module 404, the specific training process of the preset interference scenario prediction model includes:

[0150] Step 1: Standardize the communication link data of the satellite system in the normal scenario and interference scenario recorded in the history to obtain a standardized data set;

[0151] Step 2: The digital twin model construction includes a trajectory prediction module and an attitude adjustment prediction module connected in sequence;

[0152] Step 3: Train the track prediction module according to the normal scene data set and calculate the loss function of the track prediction module until the loss function converges to the preset value;

[0153] Step 4: Train the posture adjustment prediction module according to the interference scene data set until the target accuracy is reached, then stop the training process and obtain the preset interference scene prediction model.

[0154] Furthermore, in the model building module 404, the standardization process includes:

[0155] For the original data x in the communication link data, the mean μ and standard deviation σ of the communication link data are calculated; wherein the original data include the eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate of each communication satellite in the satellite system; the mean is the average value of the same type of data in each communication satellite, and the mean includes the mean eccentricity, the mean orbital inclination, the mean ascending right ascension, the mean perigee argument, the mean true anomaly, the mean signal interference intensity, the mean signal interference frequency and the mean bit error rate; the standard deviation is a statistical value to measure the degree of dispersion of the same type of data, indicating the degree of deviation of each data from the mean, including the standard deviation of eccentricity, the standard deviation of orbital inclination, the standard deviation of ascending right ascension, the standard deviation of perigee argument, the standard deviation of true anomaly, the standard deviation of signal interference intensity, the standard deviation of signal interference frequency and the standard deviation of bit error rate;

[0156] Subtract the mean μ from the original data x, and then divide it by the standard deviation σ to get the standardized data point z, that is, the calculation formula is as follows:

[0157]

[0158] Among them, x is the original data in the communication link data, μ is the mean of the communication link data, and σ is the standard deviation of the communication link data;

[0159] The standardized dataset includes a normal scene dataset and an interference scene dataset, and both the normal scene dataset and the interference scene dataset are divided into a training set and a test set in a ratio of 2:1.

[0160] Furthermore, in the model building module 404, the training process of the track prediction module includes:

[0161] Step 3.1: Train the track prediction module according to the training set of the normal scene data set, and calculate the loss function of the track prediction module;

[0162] Step 3.2: Test and train the trained model according to the test set of the normal scene data set until the loss function converges to the preset value;

[0163] Among them, the loss function is:

[0164] θ1=θ SE +θ PE +θ C ;

[0165] Among them, θ1 is the loss function value, θ SE is the root mean square error between the predicted value and the actual value, θ PE is the average relative percentage error between the predicted value and the actual value; θ C The coefficient of determination is a measure of the degree of correlation between the predicted value and the actual value. The larger the value, the higher the degree of correlation and the better the prediction effect.

[0166] Furthermore, in the model building module 404, θ SE ,θ PE and θ C The specific formula is as follows:

[0167]

[0168] Where a represents the number of samples in the training set; z' i is the orbit prediction value, x i is the actual value in the training set, μ i is the average of the actual values.

[0169] Furthermore, in the model building module 404, the training process of the posture adjustment prediction module includes:

[0170] Step 4.1: Using T re represents the training time of the posture adjustment prediction module, F represents the total number of training rounds of the posture adjustment prediction module, V represents the total number of target data aggregated by the posture adjustment prediction module, Indicates every The wheel posture adjustment prediction module aggregates the target data once, and then:

[0171] Step 4.2: Select the optimal aggregation frequency through K-Means clustering algorithm;

[0172] Step 4.3: During training time T re The posture adjustment prediction module is based on the optimal aggregation frequency Towards target data training aggregation, if the training time T is reached re , then proceed to step 4.4, otherwise go to step 4.2;

[0173] Step 4.4: Aggregate the model weights through the FedProx algorithm. If the target accuracy is achieved, stop the training process and obtain the preset interference scenario prediction model. Otherwise, go to step 4.2.

[0174] Furthermore, in the model building module 404, the optimal aggregation frequency The steps include:

[0175] Step 4.2.1: Establish the time consumption constraint of the posture adjustment prediction module. The expression of the time consumption constraint is:

[0176]

[0177] in, It represents the time taken by the posture adjustment prediction module to train the interference scene dataset in the nth round. represents the time it takes for the posture adjustment prediction module to achieve aggregation in the nth round, T re Indicates the training time,

[0178] Step 4.2.2: Use the K-Means clustering algorithm to find the connection between the posture adjustment prediction module and the time consumption constraint and determine the optimal aggregation frequency

[0179] Other embodiments or specific implementation methods of the system of the present application that utilizes a combination of AI and digital twins to avoid frequency interference can refer to the above-mentioned method embodiments and will not be repeated here.

[0180] The present application provides a system that utilizes a combination of AI and digital twins to avoid frequency interference. The method of utilizing a combination of AI and digital twins to avoid frequency interference in the above-mentioned embodiment is used to solve the technical problem that when a communication satellite is shut down in a shutdown area to avoid frequency interference, it will affect the normal business of the communication satellite. At the same time, the shutdown of the communication satellite will also affect the tracking accuracy and stability of the communication satellite. Compared with the prior art, the beneficial effects of the system that utilizes a combination of AI and digital twins to avoid frequency interference provided by the present application are the same as the beneficial effects of the method of utilizing a combination of AI and digital twins to avoid frequency interference provided by the above-mentioned embodiment, and other technical features of the system that utilizes a combination of AI and digital twins to avoid frequency interference are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0181] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute a method of avoiding frequency interference by combining AI with digital twins in the above-mentioned embodiment.

[0182] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0183] The above-mentioned computer-readable storage medium may be included in a device that utilizes a combination of AI and digital twins to avoid frequency interference; or it may exist independently without being assembled into a device that utilizes a combination of AI and digital twins to avoid frequency interference.

[0184] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a device that combines AI with digital twins to avoid frequency interference, the device that combines AI with digital twins to avoid frequency interference: establishes a big data satellite frequency interference avoidance system, and collects the current orbit information and frequency information of the communication satellite through the system; simulates the orbit information and the frequency information through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, the preset interference scenario prediction model is obtained by training the normal scenario and interference scenario of the satellite system based on the AI ​​algorithm and the digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, the orbit prediction module is used to output an orbit prediction value according to the orbit information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; adjusts the beam pointing of the communication satellite according to the satellite adjustment attitude.

[0185] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0186] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0187] The modules involved in the embodiments described in the present application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0188] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method of using AI and digital twins to avoid frequency interference, which can solve the technical problem that when a communication satellite is shut down in a shutdown area to avoid frequency interference, it will affect the normal business of the communication satellite, and at the same time, the shutdown of the communication satellite will also affect the tracking accuracy and stability of the communication satellite. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method of using AI and digital twins to avoid frequency interference provided in the above-mentioned embodiment, which will not be repeated here.

[0189] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a method for avoiding frequency interference by combining AI with digital twins as described above.

[0190] The computer program product provided by the present application can solve the technical problem that when a communication satellite is shut down in a shutdown area to avoid frequency interference, the normal business of the communication satellite will be affected, and the tracking accuracy and stability of the communication satellite will also be affected due to the shutdown of the communication satellite. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of a method for avoiding frequency interference by combining AI with digital twins provided in the above embodiment, which will not be repeated here.

[0191] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for avoiding frequency interference by combining AI with digital twins, characterized in that: The method includes: Establish a big data satellite frequency interference avoidance system, and collect the current orbit and frequency information of communication satellites through the system; The orbital information and the frequency information are simulated by a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, wherein the preset interference scenario prediction model is obtained by training the normal scenario and the interference scenario of the satellite system based on the AI ​​algorithm and the digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, wherein the orbit prediction module is used to output an orbit prediction value according to the orbital information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; The beam pointing of the communication satellite is adjusted according to the satellite adjustment attitude.

2. The method according to claim 1, characterized in that Before the step of establishing a big data satellite frequency interference avoidance system and collecting the current orbit information and frequency information of the communication satellite through the system, the method further includes: Simulate and build a digital twin model corresponding to the satellite system; Acquiring communication link data of the satellite system in a normal scenario and an interference scenario; Based on the communication link data, the digital twin model is trained through an AI algorithm to obtain a preset interference scenario prediction model.

3. The method according to claim 2, characterized in that The specific training process of the preset interference scenario prediction model includes: Step 1: Standardize the communication link data of the satellite system in the normal scenario and interference scenario recorded in the history to obtain a standardized data set; Step 2: The digital twin model construction includes a trajectory prediction module and an attitude adjustment prediction module connected in sequence; Step 3: Train the track prediction module according to the normal scene data set and calculate the loss function of the track prediction module until the loss function converges to the preset value; Step 4: Train the posture adjustment prediction module according to the interference scene data set until the target accuracy is reached, then stop the training process and obtain the preset interference scene prediction model.

4. The method according to claim 3, characterized in that The process of the standardization process includes: For the original data x in the communication link data, the mean μ and standard deviation σ of the communication link data are calculated; wherein the original data include the eccentricity, orbital inclination, ascending right ascension, perigee argument, true anomaly, signal interference intensity, signal interference frequency and bit error rate of each communication satellite in the satellite system; the mean is the average value of the same type of data in each communication satellite, and the mean includes the mean eccentricity, the mean orbital inclination, the mean ascending right ascension, the mean perigee argument, the mean true anomaly, the mean signal interference intensity, the mean signal interference frequency and the mean bit error rate; the standard deviation is a statistical value to measure the degree of dispersion of the same type of data, indicating the degree of deviation of each data from the mean, including the standard deviation of eccentricity, the standard deviation of orbital inclination, the standard deviation of ascending right ascension, the standard deviation of perigee argument, the standard deviation of true anomaly, the standard deviation of signal interference intensity, the standard deviation of signal interference frequency and the standard deviation of bit error rate; Subtract the mean μ from the original data x, and then divide it by the standard deviation σ to get the standardized data point z, that is, the calculation formula is as follows: Among them, x is the original data in the communication link data, μ is the mean of the communication link data, and σ is the standard deviation of the communication link data; The standardized dataset includes a normal scene dataset and an interference scene dataset, and both the normal scene dataset and the interference scene dataset are divided into a training set and a test set in a ratio of 2:

1.

5. The method according to claim 4, characterized in that The training process of the orbit prediction module includes: Step 3.1: Train the track prediction module according to the training set of the normal scene data set, and calculate the loss function of the track prediction module; Step 3.2: Test and train the trained model according to the test set of the normal scene data set until the loss function converges to the preset value; Among them, the loss function is: θ1=θ SE +θ PE +θ C ; Among them, θ1 is the loss function value, θ SE is the root mean square error between the predicted value and the actual value, θ PE is the average relative percentage error between the predicted value and the actual value; θ C The coefficient of determination is a measure of the degree of correlation between the predicted value and the actual value. The larger the value, the higher the degree of correlation and the better the prediction effect.

6. The method according to claim 5, characterized in that θ SE ,θ PE and θ C The specific formula is as follows: Where a represents the number of samples in the training set; z' i is the orbit prediction value, x i is the actual value in the training set, μ i is the average of the actual values.

7. The method according to claim 6, characterized in that The training process of the posture adjustment prediction module includes: Step 4.1: Using T re represents the training time of the posture adjustment prediction module, F represents the total number of training rounds of the posture adjustment prediction module, V represents the total number of target data aggregated by the posture adjustment prediction module, Indicates every The wheel posture adjustment prediction module aggregates the target data once, and then: Step 4.2: Select the optimal aggregation frequency through K-Means clustering algorithm; Step 4.3: During training time T re The posture adjustment prediction module is based on the optimal aggregation frequency Towards target data training aggregation, if the training time T is reached re , then proceed to step 4.4, otherwise go to step 4.2; Step 4.4: Aggregate the model weights through the FedProx algorithm. If the target accuracy is achieved, stop the training process and obtain the preset interference scenario prediction model. Otherwise, go to step 4.

2.

8. The method according to claim 7, characterized in that The optimal aggregation frequency The steps include: Step 4.2.1: Establish the time consumption constraint of the posture adjustment prediction module. The expression of the time consumption constraint is: in, It represents the time taken by the posture adjustment prediction module to train the interference scene dataset in the nth round. represents the time it takes for the posture adjustment prediction module to achieve aggregation in the nth round, T re Indicates the training time, Step 4.2.2: Use the K-Means clustering algorithm to find the connection between the posture adjustment prediction module and the time consumption constraint and determine the optimal aggregation frequency 9. A system that uses AI and digital twins to avoid frequency interference, characterized in that: The system comprises: Information collection module, used to establish a big data satellite frequency interference avoidance system, and collect the current orbit information and frequency information of the communication satellite through the system; A model prediction module, used to simulate the orbit information and the frequency information through a preset interference scenario prediction model to determine the satellite adjustment attitude of the communication satellite, wherein the preset interference scenario prediction model is obtained by training the normal scenario and interference scenario of the satellite system based on an AI algorithm and a digital twin model, and the digital twin model construction includes an orbit prediction module and an attitude adjustment prediction module connected in sequence, wherein the orbit prediction module is used to output an orbit prediction value according to the orbit information and frequency information of the communication satellite, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; The beam adjustment module is used to adjust the beam pointing of the communication satellite according to the satellite adjustment attitude.

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