An anti-interference method for LEO satellite systems

Abstract

The present invention relates to the field of satellite communication technology, and in particular to an anti-interference method for LEO satellite systems, comprising: using a preset prediction model to perform spectrum prediction on multiple LEO satellite systems within the current spectrum management blockchain network, and based on the prediction results, using the spectrum with the highest spectrum efficiency as the spectrum used by the LEO satellite system, and performing a signal-to-noise ratio evaluation to determine the target LEO satellite system whose signal-to-noise ratio is less than a preset threshold. After reducing the influence coefficient of the signal-to-noise ratio, the step of performing spectrum prediction on the LEO satellite system using the preset prediction model is re-executed until the signal-to-noise ratio of the LEO satellite system is greater than or equal to the preset threshold. Thus, the problem of serious mutual interference between satellite systems and insufficient anti-interference capability in emergency situations in the background technology is solved. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and anti-interference capability improvement are achieved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to an anti-interference method for a LEO satellite system. Background Art

[0002] With the rapid development of mobile internet, the Internet of Things (IoT), and 5G networks, the growing demand for communications has driven the development of low-Earth orbit (LEO) satellite communication systems. LEO satellite systems, due to their low orbit, low latency, and high coverage, have become a hot topic in the current satellite communications field, and countries and commercial companies have joined the wave of LEO satellite construction. However, with the continuous increase in the number of LEO satellites, spectrum and orbital resources are becoming increasingly scarce. The rational and efficient use of spectrum resources is crucial to promoting the sustainable construction and development of LEO satellite systems. To achieve efficient use of spectrum and orbital resources, effectively mitigating interference between LEO satellite systems is becoming increasingly important.

[0003] Blockchain technology, as a distributed ledger, records all transactions and data in a decentralized manner, ensuring transparency and immutability. In spectrum management, blockchain technology can be used to record spectrum usage, ensuring transparency and security. Furthermore, smart contracts, automated programs deployed on the blockchain, can automatically execute contracts based on predefined rules and conditions, enabling dynamic allocation and automated management of spectrum resources, ensuring efficient utilization among different users.

[0004] As the number of LEO satellites in space grows, spectrum resources have become critical for supporting wireless communications. However, when a cognitive LEO system detects a GEO (Geostationary Earth Orbit) system, it may be affected by unexpected situations (such as ground-based microwave interference, propagation fade and sun outage, ionosphere scintillation and aerosphere scintillation, unauthorized use and malicious interference, and many other environmental factors). This can lead to further interference from other LEO satellite systems, severely affecting the spectrum and causing it to change.

[0005] Although existing LEO satellite systems have made significant progress in spectrum management and anti-environmental interference, there are still deficiencies in the LEO satellite system's anti-mutual interference system under sudden conditions and how to resist attacks from other attacking satellites without affecting information transmission. As a result, the robustness of LEO giant satellite constellations consisting of multiple satellite orbits and hundreds or thousands of small satellites is low, and the satellite-to-ground link transmission rate (bit rate), bit error rate (BER) and spectrum efficiency (spectral efficiency) need to be improved. Summary of the Invention

[0006] The present invention provides an anti-interference method for LEO satellite systems to solve the problems of severe mutual interference and insufficient anti-interference capabilities between satellite systems in emergency situations in the background technology. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and improved anti-interference capabilities are achieved.

[0007] A first embodiment of the present invention provides an anti-interference method for a LEO satellite system, comprising the following steps:

[0008] Acquire multiple LEO satellite systems of the current spectrum management blockchain network;

[0009] performing spectrum prediction for the multiple LEO satellite systems using a preset prediction model, screening a spectrum with the highest spectrum efficiency based on the prediction result, using the spectrum with the highest spectrum efficiency as a spectrum for use by the multiple LEO satellite systems, and performing a signal-to-noise ratio evaluation during signal transmission by the multiple LEO satellite systems to obtain an evaluation result;

[0010] Based on the evaluation result, a target LEO satellite system having a signal-to-noise ratio less than a preset threshold is determined, and after reducing an influence coefficient of the signal-to-noise ratio of the target LEO satellite system, the step of performing spectrum prediction on the multiple LEO satellite systems using a preset prediction model is re-executed until the signal-to-noise ratios of the multiple LEO satellite systems are all greater than or equal to the preset threshold.

[0011] According to one embodiment of the present invention, before using the preset prediction model to perform spectrum prediction on the multiple LEO satellite systems, the method further includes:

[0012] obtaining historical spectrum usage data of the plurality of LEO satellite systems;

[0013] The historical spectrum usage data is used to train a deep learning-based prediction model to obtain the preset prediction model, and the preset prediction model is deployed to the current spectrum management blockchain network.

[0014] According to one embodiment of the present invention, the anti-interference method for a LEO satellite system further includes:

[0015] determining whether any of the plurality of LEO satellite systems is in a full spectrum suppression environment;

[0016] If any of the LEO satellite systems is in the full spectrum suppression environment, the suppressed signal is restored based on a pre-trained neural network model, and the restored signal is output.

[0017] According to one embodiment of the present invention, after outputting the restored signal, the method further includes:

[0018] Determine whether the current malicious interference is co-orbit interference;

[0019] If the current malicious interference is not co-orbit interference, the orbit of any LEO satellite system or the attitude of the LEO satellite system is adjusted based on the satellite attitude control system to achieve anti-interference.

[0020] According to an embodiment of the present invention, an anti-interference method for LEO satellite systems uses a preset prediction model to predict the spectrum of multiple LEO satellite systems within the current spectrum management blockchain network. Based on the prediction results, the spectrum with the highest spectrum efficiency is used as the spectrum used by the LEO satellite system. A signal-to-noise ratio evaluation is performed to determine the target LEO satellite system whose signal-to-noise ratio is less than a preset threshold. After reducing the influence coefficient of the signal-to-noise ratio, the step of using the preset prediction model to predict the spectrum of the LEO satellite system is re-executed until the signal-to-noise ratio of the LEO satellite system is greater than or equal to the preset threshold. This solves the problem of severe mutual interference between satellite systems and insufficient anti-interference capabilities in the background art in emergency situations. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and improved anti-interference capabilities are achieved.

[0021] A second embodiment of the present invention provides an anti-interference device for a LEO satellite system, comprising:

[0022] An acquisition module, used to acquire multiple LEO satellite systems of the current spectrum management blockchain network;

[0023] a spectrum optimization and evaluation module, configured to perform spectrum prediction for the multiple LEO satellite systems using a preset prediction model, screen out a spectrum with the highest spectrum efficiency based on the prediction results, use the spectrum with the highest spectrum efficiency as a spectrum for use by the multiple LEO satellite systems, and perform signal-to-noise ratio evaluation during signal transmission by the multiple LEO satellite systems to obtain an evaluation result;

[0024] an adaptive adjustment module configured to determine, based on the evaluation result, a target LEO satellite system having a signal-to-noise ratio (SNR) less than a preset threshold, and, after reducing an influence coefficient of the SNR of the target LEO satellite system, re-execute the step of performing spectrum prediction on the multiple LEO satellite systems using a preset prediction model until the SNRs of the multiple LEO satellite systems are all greater than or equal to the preset threshold.

[0025] According to one embodiment of the present invention, before performing spectrum prediction on the multiple LEO satellite systems using the preset prediction model, the spectrum optimization and evaluation module is further configured to:

[0026] obtaining historical spectrum usage data of the plurality of LEO satellite systems;

[0027] The historical spectrum usage data is used to train a deep learning-based prediction model to obtain the preset prediction model, and the preset prediction model is deployed to the current spectrum management blockchain network.

[0028] According to one embodiment of the present invention, the anti-interference device for the LEO satellite system is further used to:

[0029] determining whether any of the plurality of LEO satellite systems is in a full spectrum suppression environment;

[0030] If any of the LEO satellite systems is in the full spectrum suppression environment, the suppressed signal is restored based on a pre-trained neural network model, and the restored signal is output.

[0031] According to one embodiment of the present invention, after outputting the recovered signal, the anti-interference device for the LEO satellite system is further used to:

[0032] Determine whether the current malicious interference is co-orbit interference;

[0033] If the current malicious interference is not co-orbit interference, the orbit of any LEO satellite system or the attitude of the LEO satellite system is adjusted based on the satellite attitude control system to achieve anti-interference.

[0034] According to an embodiment of the present invention, the anti-interference device for LEO satellite systems uses a preset prediction model to predict the spectrum of multiple LEO satellite systems within the current spectrum management blockchain network. Based on the prediction results, the spectrum with the highest spectrum efficiency is used as the spectrum used by the LEO satellite system. A signal-to-noise ratio evaluation is performed to determine the target LEO satellite system whose signal-to-noise ratio is less than a preset threshold. After reducing the influence coefficient of the signal-to-noise ratio, the step of using the preset prediction model to predict the spectrum of the LEO satellite system is re-executed until the signal-to-noise ratio of the LEO satellite system is greater than or equal to the preset threshold. This solves the problem of severe mutual interference between satellite systems and insufficient anti-interference capabilities in the background technology under sudden situations. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and improved anti-interference capabilities are achieved.

[0035] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the anti-interference method for LEO satellite systems as described in the above embodiment.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the anti-interference method for a LEO satellite system as described in the above embodiment.

[0037] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A flowchart of an anti-interference method for a LEO satellite system provided according to an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a flow chart of an anti-interference method for a LEO satellite system according to an embodiment of the present invention;

[0041] Figure 3 2 is a block diagram of an anti-interference device for a LEO satellite system according to an embodiment of the present invention;

[0042] Figure 4 FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but are not to be construed as limiting the present invention.

[0044] The following describes an anti-interference method for a LEO satellite system according to an embodiment of the present invention with reference to the accompanying drawings. In view of the problem that most of the existing technical methods mentioned in the above background technology only consider the transmission loss model of the satellite-to-ground link, atmospheric absorption, cloud attenuation and the influence of antenna angle, and do not take the mutual interference between LEO systems into consideration, the present invention provides an anti-interference method for a LEO satellite system, which effectively suppresses the mutual interference between LEO satellite systems in emergency situations, solves the problem of serious mutual interference between satellite systems and insufficient anti-interference capability in emergency situations in the background technology, and realizes efficient and intelligent spectrum management and improved anti-interference capability by combining blockchain and deep learning technology.

[0045] Specifically, Figure 1 A flowchart of an anti-interference method for a LEO satellite system provided by an embodiment of the present invention.

[0046] like Figure 1 As shown, the anti-interference method for the LEO satellite system includes the following steps:

[0047] In step S101, multiple LEO satellite systems of the current spectrum management blockchain network are obtained.

[0048] Specifically, in the initial stage, the embodiment of the present invention connects all LEO satellite systems that agree to the blockchain network protocol (i.e., comply with non-interference and are willing to provide their own historical data) to the current spectrum management blockchain network through the blockchain client, obtains initial network configuration information, and receives necessary blockchain network access information.

[0049] Furthermore, LEO satellite communication equipment establishes a connection with the blockchain network access node via a satellite link, ensuring that all communication devices can participate in the blockchain network and download smart contract logic and other necessary spectrum management configuration data.

[0050] In step S102, spectrum prediction is performed on multiple LEO satellite systems using a preset prediction model, and the spectrum with the highest spectrum efficiency is screened out based on the prediction results. The spectrum with the highest spectrum efficiency is used as the spectrum used by the multiple LEO satellite systems, and a signal-to-noise ratio evaluation is performed during signal transmission by the multiple LEO satellite systems to obtain an evaluation result.

[0051] Among them, in some embodiments, before using a preset prediction model to perform spectrum prediction on multiple LEO satellite systems, it also includes: obtaining historical spectrum usage data of multiple LEO satellite systems; using the historical spectrum usage data to train a deep learning-based prediction model to obtain a preset prediction model, and deploying the preset prediction model to the current spectrum management blockchain network.

[0052] Each LEO satellite system participating in the blockchain network runs a lightweight blockchain client on its device to collect and record satellite data. When each satellite uses the spectrum, its respective spectrum usage information (such as frequency band, time, location, interference situation and user ID, etc.) is recorded in the blockchain network. At the same time, the spectrum usage information is pre-processed (including data cleaning, denoising and normalization, etc.) to ensure data consistency and quality.

[0053] For example, the specific recorded spectrum usage information can be represented by the following vector:

[0054]

[0055] in, For spectrum usage information, is the frequency band, For time, For location, For interference situations, is the user ID.

[0056] Therefore, various LEO satellite systems are connected through blockchain technology, and the ledgers are maintained collaboratively, ensuring the transparency and non-tamperability of historical data, thereby providing a reliable data foundation. At the same time, it can also be called in real time to ensure the timeliness of the data.

[0057] Furthermore, when a cognitive LEO satellite system in the blockchain network is affected by an unexpected situation (such as ground microwave interference, abnormal radio wave fading and solar eclipse interference, ionospheric scintillation and atmospheric scintillation, unauthorized use and malicious interference) when detecting the GEO system, this unstable satellite is regarded as an interfering LEO satellite system, and the signal-to-noise ratio (SNR) of the interfering LEO satellite system to the normal LEO satellite system is recorded. The signal-to-noise ratio is calculated as follows:

[0058]

[0059] in, is the signal-to-noise ratio, is the signal power, is the noise power.

[0060] Furthermore, based on the new signal-to-noise ratio (the signal-to-noise ratio of the LEO satellite system after the sudden interference) and the original signal-to-noise ratio (the signal-to-noise ratio of the LEO satellite system before the sudden interference, that is, the signal-to-noise ratio under normal conditions without interference), the influence coefficient of the interfering LEO satellite system on the normal LEO satellite system signal-to-noise ratio can be obtained. ( ):

[0061]

[0062] in, is the influence coefficient of the interference LEO satellite system on the signal-to-noise ratio of the normal LEO satellite system, is the new signal-to-noise ratio, is the original signal-to-noise ratio.

[0063] Furthermore, the obtained influence coefficient of the interfering LEO satellite system on the signal-to-noise ratio of the normal LEO satellite system is recorded in the blockchain network as historical data.

[0064] Furthermore, after experiencing “enough” “emergency events,” that is, after collecting enough historical spectrum usage data from LEO satellite systems, the historical spectrum usage data is extracted from the blockchain network and used to train a deep learning-based prediction model. Spectrum usage prediction is then performed based on the trained preset prediction model. The specific steps are as follows:

[0065] The first step is to select and train a deep learning-based prediction model. For example, a suitable time series prediction model is chosen, such as a bidirectional long short-term memory network (Bi-LSTM) model, to capture the temporal characteristics of spectrum usage. The Bi-LSTM model is then trained using preprocessed historical spectrum usage data.

[0066] Among them, the structure of the internal unit of the Bi-LSTM model includes the input gate gating unit , forget gate gating unit , output gate gating unit , the cell state at the current time step , the cell state at the previous time step , the hidden state at the current time step , the hidden state of the previous time step , the input of the current time step , candidate memory state at the current time step , activation function , activation function φ and activation function ћ.

[0067] Furthermore, the update formula of the Bi-LSTM model is as follows:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] in, is the Hadamard product operator, is the input weight of the input gate, is the hidden state weight of the input gate, is the input weight of the forget gate, is the hidden state weight of the forget gate, is the input weight of the output gate, is the hidden state weight of the output gate, is the input weight of the cell state, is the hidden state weight of the cell state, is the bias term, and .

[0075] Secondly, the trained model is verified and deployed. Specifically, the model is validated using selected data from historical spectrum usage data, and model parameters are adjusted and optimized to improve prediction accuracy, resulting in a pre-set prediction model. Finally, the trained pre-set prediction model is deployed on the current spectrum management blockchain network, enabling smart contracts to use it for spectrum prediction. By calculating the spectrum efficiency of the remaining available spectrum, the system selects the spectrum that minimizes signal transmission interference with the LEO satellite system, thereby mitigating interference from other satellites.

[0076] Therefore, in addition to basic smart contract functions (such as registration of satellite and spectrum information, querying spectrum usage, updating spectrum information, and deleting unusable spectrum), the embodiment of the present invention adds a function to avoid the spectrum information predicted by the preset prediction model (such as the above-mentioned Bi-LSTM model) of other interference LEO satellite systems, and simultaneously performs spectrum efficiency screening. The calculation formula of spectrum efficiency is:

[0077]

[0078] in, is the spectrum efficiency, R is the data transmission rate in bps; B is the signal bandwidth in Hz.

[0079] Furthermore, the spectrum with the highest spectral efficiency is selected within the scope of the smart contract and used as the spectrum for multiple LEO satellite systems to communicate with the GEO system. Furthermore, for multiple LEO satellite systems after the full set of smart contracts is activated, the signal-to-noise ratio is evaluated during signal transmission to obtain evaluation results.

[0080] In step S103, based on the evaluation result, a target LEO satellite system having a signal-to-noise ratio less than a preset threshold is determined, and after reducing the influence coefficient of the signal-to-noise ratio of the target LEO satellite system, the step of performing spectrum prediction on multiple LEO satellite systems using the preset prediction model is re-executed until the signal-to-noise ratios of the multiple LEO satellite systems are all greater than or equal to the preset threshold.

[0081] The preset threshold may be a threshold preset by those skilled in the art, such as 30 db, and is not specifically limited here.

[0082] Specifically, the embodiment of the present invention is to mark the LEO satellite system with a signal-to-noise ratio greater than or equal to a preset threshold as excellent, and for the target LEO satellite system with a signal-to-noise ratio less than the preset threshold, by reducing the influence coefficient of the signal-to-noise ratio After the value is obtained, the influence coefficient of the reduced signal-to-noise ratio is input into the database of the preset prediction model for self-loop adjustment until the signal-to-noise ratios of multiple LEO satellite systems are greater than or equal to the preset threshold.

[0083] Therefore, this invention utilizes AI-based deep learning models (such as Bi-LSTM and CNN) combined with machine learning algorithms to predict spectrum usage, improving prediction accuracy. Max pooling or average pooling layers are used to reduce feature dimensionality, retaining important information. Fully connected layers are then used to map the extracted features to the output signal. Neural network model training utilizes supervised learning using the original signal and its compressed version, optimizing CNN parameters to recover an output close to the original signal from the compressed signal. Furthermore, the neural network model evaluation phase utilizes group signal-to-noise ratio assessment to ensure its effectiveness in practical applications. The application of this deep learning model not only improves the LEO satellite system's anti-interference capabilities in emergency situations but also provides new insights for technological advancement in satellite communications.

[0084] Furthermore, in some embodiments, the anti-interference method for LEO satellite systems also includes: determining whether any LEO satellite system among multiple LEO satellite systems is in a full-spectrum suppression environment; if any LEO satellite system is in a full-spectrum suppression environment, recovering the suppressed signal based on a pre-trained neural network model, and outputting the recovered signal.

[0085] Specifically, when the LEO satellite system within the blockchain network is subject to malicious interference from satellite systems outside the blockchain network (such as spy satellites from other countries, unilateral breaking of satellite agreements during extreme periods such as war), in order to suppress the interference and ensure the normal connection between the LEO satellite system and other satellites, the malicious interference sources can be divided into uplink satellites (such as MEO (Medium Earth Orbit), GEO), satellites in the same orbit (LEO) and ground satellite signal jammers.

[0086] In malicious interference, a common method is "full spectrum suppression", and how to effectively recover signals and defend against them in a full spectrum suppression environment is particularly important. This is the prerequisite for ensuring the coordinated operation of the system.

[0087] Based on this, the present invention proposes a method for applying convolutional neural networks (CNNs) and synchronous smart contract updates in a full-spectrum suppression environment to enhance signal recovery and anti-malicious interference capabilities. The method specifically includes the following steps:

[0088] First, a large amount of original signal and corresponding suppressed signal data is collected, wherein the data can be generated by simulation tools or obtained from actual environments.

[0089] Secondly, the collected data is preprocessed. Because raw sequence data exhibits significant inconsistencies for deep neural network learning, preprocessing is necessary to better reflect the impact of certain characteristics of the space target on the changing trends of the photometric sequence data. This process removes discontinuities and measurement biases in the raw data. This preprocessing process can include normalization, distance correction, phase angle correction, smoothing filtering (noise reduction), and framing, which are not specifically defined here.

[0090] Next, the neural network model is established. The preprocessed data is input into the input layer of the neural network model. The main information in the input layer, that is, the baseband waveform of the signal can be expressed as:

[0091]

[0092] in, is the baseband waveform of the signal, is the time variable, is the symbol sequence sent by the transmitter, is the index of the symbol sequence, is additive white Gaussian noise, It is an equivalent filter, including shaping filter, channel filter and matched filter.

[0093] Furthermore, multiple convolutional layers are used to extract local features, such as different frequencies and amplitude changes:

[0094]

[0095] in, is the output feature map, Represents the value of position (i, j) in the output feature map, X is the input signal (such as time series, baseband waveform, etc.), K is the convolution kernel, is the row offset of the convolution kernel K, is the column offset of the convolution kernel K.

[0096] Furthermore, activation functions such as ReLU (Rectified Linear Unit, linear rectification function) are added ( ) introduces nonlinear features and imports pooling layers, so that the neural network model reduces the feature dimension through maximum pooling or average pooling layers, thereby retaining important information. Among them, the maximum pooling operation is defined as:

[0097]

[0098] The above formula represents the maximum value selected in each 2 x 2 area.

[0099] Furthermore, after adding activation functions such as ReLU to introduce nonlinear features and importing the pooling layer, the extracted features are mapped to the output signal through the fully connected layer, thereby realizing the establishment of the neural network model.

[0100] The established neural network model is then trained. Supervised learning is performed using the original signal and its compressed version. A loss function, such as mean squared error (MSE), is used to optimize the CNN parameters so that the neural network model can recover an output close to the original signal from the compressed signal. The mean squared error in this embodiment of the present invention can be expressed as:

[0101]

[0102] in, is the mean square error, is the original signal, is the restored signal output by the neural network model, and N is the total number of samples.

[0103] Finally, the trained neural network model is evaluated. The embodiment of the present invention can perform a group signal-to-noise ratio evaluation on the trained neural network model, namely:

[0104]

[0105] Therefore, based on the evaluation results of the neural network model after training, the signals that meet the preset conditions (such as the group signal-to-noise ratio is higher than a certain threshold) are retained, and the above data collection, preprocessing, model establishment, model training and model evaluation operations are repeated for the signals that do not meet the preset conditions.

[0106] After completing the above operations, the CNN signal recovery system is integrated into the original smart contract to realize a new collaborative system, thereby improving the anti-interference capability of the LEO satellite system.

[0107] Furthermore, in some embodiments, after outputting the recovered signal, it also includes: determining whether the current malicious interference is co-orbit interference; if the current malicious interference is not co-orbit interference, adjusting the orbit of any LEO satellite system or the attitude of the LEO satellite system based on the satellite attitude control system to achieve anti-interference.

[0108] Specifically, for long-range signal directional interference such as uplink satellite and ground satellite signal jammers, since this type of malicious interference is fixed and difficult to steer, further methods can be taken to resist interference independently of the same orbiting satellite (LEO), that is, using the satellite attitude control system to adjust the thrusters or reaction wheels to change the orbit or attitude, thereby achieving physical anti-interference.

[0109] Specifically, the embodiments of the present invention can avoid interfering with the radar signal of the satellite by pre-setting the target attitude and orbit; at the same time, use multiple sensors to fuse data to improve the accuracy of state estimation; and the degree of anti-interference should be monitored, such as setting a threshold. When the attitude or orbit deviation exceeds the threshold, the anti-interference mechanism is triggered to select appropriate thrusters and reaction wheels for adjustment, and the adjusted state is continuously monitored, thereby optimizing the control strategy and improving the anti-interference capability at the physical location.

[0110] It should be noted that when using the satellite attitude control system to adjust the thrusters or reaction wheels to change the orbit or attitude, attention should be paid to power consumption management, that is, balancing the adjustment frequency with fuel consumption, and managing the heat and vibration caused by the thruster and reaction wheel operations to avoid component damage.

[0111] Thus, the embodiments of the present invention effectively enhance the anti-interference capabilities of LEO satellite systems by setting attitude and orbit deviation thresholds, triggering anti-interference mechanisms when these thresholds are exceeded, and utilizing thrusters and reaction wheels for adjustments. By continuously monitoring and optimizing control strategies, the system can respond to external interference in real time, minimizing the impact on communication quality. Furthermore, this method considers power consumption management, balancing adjustment frequency with fuel consumption. It also manages the heat and vibration generated by thruster and reaction wheel operations to avoid component damage and ensure stable system operation.

[0112] In order to facilitate those skilled in the art to more clearly and intuitively understand the anti-interference method for LEO satellite system proposed in the present invention, the following is a Figure 2 Provide detailed explanation.

[0113] like Figure 2 As shown, the anti-interference method for the LEO satellite system includes the following steps:

[0114] S201, connect to the blockchain network.

[0115] S202, collect historical data and perform preprocessing.

[0116] S203, establish a deep learning model (Bi-LSTM model).

[0117] S204: Associate the smart contract with the Bi-LSTM model.

[0118] S205 , perform signal-to-noise ratio (SNR) evaluation. If the SNR is less than 30 db, execute S206 ; if the SNR is greater than or equal to 30 db, execute S207 .

[0119] S206, reducing the impact coefficient by modification and updating the Bi-LSTM model system in real time.

[0120] S207, further introduce an anti-malicious interference collaborative system.

[0121] S208, applying convolutional neural network to establish and train a neural network model.

[0122] S209 , determining whether the malicious interference is co-track interference, if yes, executing S211 , otherwise executing S210 .

[0123] S210, using a satellite attitude control system to change the orbit or attitude.

[0124] S211, add the neural network model to the original smart contract.

[0125] S212, end.

[0126] Therefore, the anti-interference method for LEO satellite systems proposed in the present invention solves the problem of other LEO satellite systems occupying spectrum resources and interfering with transmission results when LEO satellite systems transmit signals to GEO systems, thereby improving the bit rate, bit error rate and spectrum efficiency of the satellite-to-ground link.

[0127] According to an embodiment of the present invention, an anti-interference method for LEO satellite systems uses a preset prediction model to predict the spectrum of multiple LEO satellite systems within the current spectrum management blockchain network. Based on the prediction results, the spectrum with the highest spectrum efficiency is used as the spectrum used by the LEO satellite system. A signal-to-noise ratio evaluation is performed to determine the target LEO satellite system whose signal-to-noise ratio is less than a preset threshold. After reducing the influence coefficient of the signal-to-noise ratio, the step of using the preset prediction model to predict the spectrum of the LEO satellite system is re-executed until the signal-to-noise ratio of the LEO satellite system is greater than or equal to the preset threshold. This solves the problem of severe mutual interference between satellite systems and insufficient anti-interference capabilities in the background art in emergency situations. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and improved anti-interference capabilities are achieved.

[0128] Next, an anti-interference device for a LEO satellite system according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0129] Figure 3 It is a block diagram of an anti-interference device for a LEO satellite system according to an embodiment of the present invention.

[0130] like Figure 3 As shown, the anti-interference device 10 for the LEO satellite system includes: an acquisition module 100 , a spectrum optimization and evaluation module 200 and an adaptive adjustment module 300 .

[0131] Among them, the acquisition module 100 is used to obtain multiple LEO satellite systems of the current spectrum management blockchain network; the spectrum optimization and evaluation module 200 is used to use a preset prediction model to perform spectrum prediction on multiple LEO satellite systems, and screen out the spectrum with the highest spectrum efficiency based on the prediction results, and use the spectrum with the highest spectrum efficiency as the use spectrum of multiple LEO satellite systems, and perform signal-to-noise ratio evaluation during signal transmission of multiple LEO satellite systems to obtain evaluation results; the adaptive adjustment module 300 is used to determine, based on the evaluation results, a target LEO satellite system with a signal-to-noise ratio less than a preset threshold, and after reducing the influence coefficient of the signal-to-noise ratio of the target LEO satellite system, re-execute the step of performing spectrum prediction on multiple LEO satellite systems using the preset prediction model until the signal-to-noise ratios of multiple LEO satellite systems are greater than or equal to the preset threshold.

[0132] Furthermore, in some embodiments, before using a preset prediction model to perform spectrum prediction for multiple LEO satellite systems, the spectrum optimization and evaluation module 200 is also used to: obtain historical spectrum usage data of multiple LEO satellite systems; use the historical spectrum usage data to train a deep learning-based prediction model to obtain a preset prediction model, and deploy the preset prediction model to the current spectrum management blockchain network.

[0133] Furthermore, in some embodiments, the anti-interference device 10 for the LEO satellite system is also used to: determine whether any LEO satellite system among multiple LEO satellite systems is in a full-spectrum suppression environment; if any LEO satellite system is in a full-spectrum suppression environment, restore the suppressed signal based on a pre-trained neural network model, and output the restored signal.

[0134] Furthermore, in some embodiments, after outputting the recovered signal, the anti-interference device 10 for the LEO satellite system is also used to: determine whether the current malicious interference is co-orbit interference; if the current malicious interference is not co-orbit interference, adjust the orbit of any LEO satellite system or the attitude of the LEO satellite system based on the satellite attitude control system to achieve anti-interference.

[0135] It should be noted that the aforementioned explanation of the embodiment of the anti-interference method for the LEO satellite system is also applicable to the anti-interference device for the LEO satellite system of this embodiment, and will not be repeated here.

[0136] According to an embodiment of the present invention, the anti-interference device for LEO satellite systems uses a preset prediction model to predict the spectrum of multiple LEO satellite systems within the current spectrum management blockchain network. Based on the prediction results, the spectrum with the highest spectrum efficiency is used as the spectrum used by the LEO satellite system. A signal-to-noise ratio evaluation is performed to determine the target LEO satellite system whose signal-to-noise ratio is less than a preset threshold. After reducing the influence coefficient of the signal-to-noise ratio, the step of using the preset prediction model to predict the spectrum of the LEO satellite system is re-executed until the signal-to-noise ratio of the LEO satellite system is greater than or equal to the preset threshold. This solves the problem of severe mutual interference between satellite systems and insufficient anti-interference capabilities in the background technology under sudden situations. By combining blockchain and deep learning technology, efficient and intelligent spectrum management and improved anti-interference capabilities are achieved.

[0137] Figure 4 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0138] Memory 401 , processor 402 , and computer programs stored in the memory 401 and executable on the processor 402 .

[0139] When the processor 402 executes the program, the anti-interference method for the LEO satellite system provided in the above embodiment is implemented.

[0140] Furthermore, the electronic device further includes:

[0141] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0142] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0143] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0144] If the memory 401, processor 402, and communication interface 403 are implemented independently, the communication interface 403, memory 401, and processor 402 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0145] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0146] The processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0147] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the anti-interference method for a LEO satellite system as described above is implemented.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0149] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0150] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An anti-interference method for a LEO satellite system, characterized in that: The following steps are involved: Acquire multiple LEO satellite systems of the current spectrum management blockchain network; performing spectrum prediction for the multiple LEO satellite systems using a preset prediction model, screening a spectrum with the highest spectrum efficiency based on the prediction result, using the spectrum with the highest spectrum efficiency as a spectrum for use by the multiple LEO satellite systems, and performing a signal-to-noise ratio evaluation during signal transmission by the multiple LEO satellite systems to obtain an evaluation result; Based on the evaluation result, determining a target LEO satellite system having a signal-to-noise ratio less than a preset threshold, reducing an influence coefficient of the signal-to-noise ratio of the target LEO satellite system, and re-inputting the reduced influence coefficient of the signal-to-noise ratio into a preset prediction model for self-loop adjustment until the signal-to-noise ratios of the plurality of LEO satellite systems are all greater than or equal to the preset threshold; The calculation formula of the influence coefficient of the signal-to-noise ratio is: ； in, is the influence coefficient of the signal-to-noise ratio, is the signal-to-noise ratio of the LEO satellite system after being disturbed by an unexpected event, is the signal-to-noise ratio of the LEO satellite system before it is interfered with by the sudden situation.

2. The anti-interference method for LEO satellite system according to claim 1, characterized in that: Before re-inputting the influence coefficient of the reduced signal-to-noise ratio into the preset prediction model for self-loop adjustment, the following steps are also included: obtaining historical spectrum usage data of the plurality of LEO satellite systems; The historical spectrum usage data is used to train a deep learning-based prediction model to obtain the preset prediction model, and the preset prediction model is deployed to the current spectrum management blockchain network.

3. The anti-interference method for a LEO satellite system according to claim 1, wherein: Also includes: determining whether any of the plurality of LEO satellite systems is in a full spectrum suppression environment; If any of the LEO satellite systems is in the full spectrum suppression environment, the suppressed signal is restored based on a pre-trained neural network model, and the restored signal is output.

4. The anti-interference method for LEO satellite systems according to claim 3, characterized in that: After outputting the restored signal, it also includes: Determine whether the current malicious interference is co-orbit interference; If the current malicious interference is not co-orbit interference, the orbit of any LEO satellite system or the attitude of the LEO satellite system is adjusted based on the satellite attitude control system to achieve anti-interference.

5. An anti-interference device for a LEO satellite system, characterized in that: include: An acquisition module, used to acquire multiple LEO satellite systems of the current spectrum management blockchain network; a spectrum optimization and evaluation module, configured to perform spectrum prediction for the multiple LEO satellite systems using a preset prediction model, screen out a spectrum with the highest spectrum efficiency based on the prediction results, use the spectrum with the highest spectrum efficiency as a spectrum for use by the multiple LEO satellite systems, and perform signal-to-noise ratio evaluation during signal transmission by the multiple LEO satellite systems to obtain an evaluation result; an adaptive adjustment module configured to determine, based on the evaluation result, a target LEO satellite system having a signal-to-noise ratio less than a preset threshold, reduce an influence coefficient of the signal-to-noise ratio of the target LEO satellite system, and then re-input the reduced influence coefficient of the signal-to-noise ratio into a preset prediction model for self-loop adjustment until the signal-to-noise ratios of the plurality of LEO satellite systems are all greater than or equal to the preset threshold; The calculation formula of the influence coefficient of the signal-to-noise ratio is: ； in, is the influence coefficient of the signal-to-noise ratio, is the signal-to-noise ratio of the LEO satellite system after being disturbed by an unexpected event, is the signal-to-noise ratio of the LEO satellite system before it is interfered with by the sudden situation.

6. The anti-interference device for LEO satellite system according to claim 5, characterized in that: Before re-inputting the reduced signal-to-noise ratio influence coefficient into the preset prediction model for self-loop adjustment, the spectrum optimization and evaluation module is further configured to: obtaining historical spectrum usage data of the plurality of LEO satellite systems; The historical spectrum usage data is used to train a deep learning-based prediction model to obtain the preset prediction model, and the preset prediction model is deployed to the current spectrum management blockchain network.

7. The anti-interference device for LEO satellite system according to claim 5, characterized in that: Also used for: determining whether any of the plurality of LEO satellite systems is in a full spectrum suppression environment; If any of the LEO satellite systems is in the full spectrum suppression environment, the suppressed signal is restored based on a pre-trained neural network model, and the restored signal is output.

8. The anti-interference device for LEO satellite system according to claim 7, characterized in that: After outputting the recovered signal, the anti-interference device for the LEO satellite system is further used to: Determine whether the current malicious interference is co-orbit interference; If the current malicious interference is not co-orbit interference, the orbit of any LEO satellite system or the attitude of the LEO satellite system is adjusted based on the satellite attitude control system to achieve anti-interference.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the anti-interference method for a LEO satellite system according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the anti-interference method for a LEO satellite system according to any one of claims 1 to 4.

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