Satellite instruction automatic generation method based on self-attention mechanism

Abstract

The invention discloses a satellite instruction automatic generation method based on a self-attention mechanism, and belongs to the technical field of satellite data processing. The method comprises the following steps: acquiring satellite instruction interference data, and processing the data to obtain an instruction interference evaluation value; acquiring a satellite instruction sequence, converting the satellite instruction sequence into an instruction representation matrix, encoding the position of the instruction sequence, and adding the position code into the instruction representation matrix to generate an instruction sequence representation matrix; fusing the instruction sequence representation matrix with global information by using a zoom dot product attention component to obtain an updated instruction sequence representation matrix, outputting an interval time sequence through a full-connection neural network according to the updated instruction sequence representation matrix, and correcting the interval time sequence according to an instruction interference evaluation value to obtain an instruction interference evaluation value; the problem that a traditional method is low in efficiency and insufficient in accuracy when facing a complex task instruction sequence is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite data processing, and in particular to a method for automatically generating satellite instructions based on a self-attention mechanism. Background Art

[0002] In modern aerospace engineering, the instruction sequence interval specifies the time interval between two instructions. A reasonable and effective time interval can improve the quality of instructions and shorten the total time cost of the task. Therefore, automatic generation of instruction sequence interval time is one of the core technologies in the field of spacecraft mission planning.

[0003] Existing satellite command automatic generation methods are mainly implemented through meta-task decomposition and structural variables, basic instruction codes and verification, metadata models and databases, and satellite autonomous mission planning technology to improve the efficiency and accuracy of command generation.

[0004] For example, the patent application with publication number: CN117632430A discloses a method for autonomously generating agile satellite imaging instruction sequences based on mapping tables, including: matching the task system mapping table, the business instruction mapping table and the time constraint mapping table in sequence according to the basic task information, business information and time information of the current task; obtaining the constraint conditions for executing the current task; generating a satellite imaging task instruction sequence according to the basic task information, system, execution content and constraint conditions, wherein the satellite imaging task instruction sequence is used to instruct the system to execute the current task according to the execution content under the constraints of the constraints.

[0005] For example, the patent application with publication number: CN118276941A discloses a method, apparatus, device and storage medium for generating an instruction sequence, comprising: receiving an instruction generation operation command; wherein the instruction generation operation command includes an instruction category; calling an instruction generation architecture according to the instruction generation operation command; calling an instruction set corresponding to the instruction category from the instruction generation architecture; and generating an instruction sequence according to the selected order of instructions in the instruction set.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, under the resource-constrained satellite software environment, data processing capabilities are limited, resulting in low efficiency and insufficient accuracy when faced with complex mission instruction sequences. Summary of the invention

[0008] The present invention provides a method for automatically generating satellite commands based on a self-attention mechanism, thereby solving the problem in the prior art that, in a resource-constrained satellite software environment, data processing capabilities are limited, resulting in low efficiency and insufficient accuracy when faced with complex task command sequences, thereby achieving significant optimization of timeliness, availability and time overhead.

[0009] The present invention provides a satellite command automatic generation method based on a self-attention mechanism, comprising the following steps: collecting satellite command interference data, and obtaining a command interference evaluation value through processing; obtaining a satellite command sequence, converting the satellite command sequence into a command representation matrix, encoding the command sequence position, and adding the position code to the command representation matrix to generate the command sequence representation matrix; using a scaled dot product attention component to fuse the command sequence representation matrix with global information to obtain an updated command sequence representation matrix, outputting an interval time series through a fully connected neural network according to the updated command sequence representation matrix, and correcting the interval time series according to the command interference evaluation value.

[0010] Furthermore, the steps of collecting satellite command interference data and obtaining a command interference evaluation value through processing include: the satellite command interference data includes satellite equipment data, satellite attitude evaluation value and command interference data; obtaining a satellite equipment evaluation value based on a comprehensive analysis of the satellite equipment data, and performing adjustment feedback based on the satellite equipment evaluation value; obtaining a satellite attitude evaluation value based on a comprehensive analysis of the satellite attitude data; and obtaining a command interference evaluation value based on a comprehensive analysis of the command interference data, the satellite attitude evaluation value and the satellite equipment evaluation value.

[0011] Furthermore, the step of obtaining a satellite equipment evaluation value based on a comprehensive analysis of satellite equipment data includes: the satellite equipment data includes transmission power, receiving sensitivity and bit error rate; obtaining critical transmission power, critical receiving sensitivity and critical bit error rate from an instruction generation database; and obtaining a satellite equipment evaluation value through comprehensive analysis.

[0012] Furthermore, the step of obtaining the instruction interference evaluation value through comprehensive analysis based on the instruction interference data, satellite attitude evaluation value and satellite equipment evaluation value includes: the instruction interference data includes electromagnetic interference intensity and the number of adjacent signals; obtaining the critical electromagnetic interference intensity, the critical number of adjacent signals, the critical satellite attitude evaluation value and the critical satellite equipment evaluation value from the instruction generation database; and obtaining the instruction interference evaluation value through comprehensive analysis.

[0013] Furthermore, the instruction interference evaluation value is obtained as follows:

[0014]

[0015] In the formula, ξI represents the command interference evaluation value, α4 represents the command interference evaluation influence factor corresponding to the electromagnetic interference intensity, α5 represents the command interference evaluation influence factor corresponding to the number of adjacent signals, α6 represents the command interference evaluation influence factor corresponding to the satellite equipment evaluation value, α7 represents the command interference evaluation influence factor corresponding to the satellite attitude evaluation value, E1 represents the electromagnetic interference intensity, E0 represents the critical electromagnetic interference intensity, C1 represents the number of adjacent signals, C0 represents the critical number of adjacent signals, ξE1 represents the satellite equipment evaluation value, ξE0 represents the critical satellite equipment evaluation value, ξA1 represents the satellite attitude evaluation value, ξA0 represents the critical satellite attitude evaluation value, and e is a natural constant.

[0016] Furthermore, the step of converting the satellite instruction sequence into an instruction representation matrix includes: inputting the satellite instruction sequence and marking each satellite instruction with a digital number; using one-hot encoding to generate a corresponding feature representation for each satellite instruction, marked as each high-dimensional feature vector; and mapping each high-dimensional feature vector to the required low-dimensional space to obtain an embedding matrix, which is the instruction representation matrix.

[0017] Furthermore, the instruction sequence representation matrix is ​​fused with global information by using the scaled dot product attention component to obtain the updated instruction sequence representation matrix. The steps include: obtaining a query matrix, a key matrix, and a value matrix through three linear transformations of the instruction sequence representation matrix, which are marked as q, k, and v respectively; obtaining three weight matrices to be learned, which are marked as W respectively. q , W k and W v ; Use the weight matrix to map the input to the query matrix, key matrix and value matrix, and obtain the corresponding query embedding matrix, key embedding matrix and value embedding matrix by performing matrix multiplication with the instruction sequence representation matrix, which are marked as Q, K and V respectively; According to the query embedding matrix, key embedding matrix and value embedding matrix, the updated instruction sequence representation matrix is ​​obtained.

[0018] Furthermore, the step of obtaining an updated instruction sequence representation matrix according to the query embedding matrix, the key embedding matrix and the value embedding matrix includes: calculating the dot product of q and k to obtain an attention score matrix; scaling the attention score matrix and then normalizing it to obtain a normalized attention weight matrix, labeled A; multiplying the attention weight matrix A with the value embedding matrix V, and the output matrix obtained is the updated instruction sequence representation matrix, labeled Z.

[0019] Furthermore, according to the matrix represented by the updated instruction sequence, the step of outputting the interval time series through the fully connected neural network includes: passing the input matrix Z through the first intermediate layer of the fully connected neural network, performing a linear transformation and applying an activation function to obtain a matrix Z1; passing Z1 through the second intermediate layer of the fully connected neural network, also performing a linear transformation and applying an activation function to obtain a matrix Z2; and linearly transforming Z2 to obtain an output vector of dimension n-1, which is the interval time series.

[0020] Furthermore, the step of correcting the interval time series according to the instruction interference evaluation value includes: matching the instruction interference evaluation value with the interval time series correction vector corresponding to each instruction interference evaluation value interval preset in the instruction generation database to obtain the interval time series correction vector; summing the interval time series and the interval time series correction vector to obtain a corrected interval time series.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The present invention provides a satellite command automatic generation method based on a self-attention mechanism, so that satellite command interference data can be efficiently collected and processed, accurate command interference evaluation values ​​can be obtained, and command interval time series correction is performed based on the command interference evaluation values, thereby achieving improved accuracy and efficiency of satellite command generation.

[0023] 2. The present invention utilizes one-hot encoding and multi-layer perception mechanisms, thereby being able to effectively reduce dimensionality and overcome sparsity processing while retaining important features of the original instruction sequence, thereby achieving efficient conversion of feature representation.

[0024] 3. The present invention adds the position code and adds the instruction representation matrix to the position code embedding matrix, so that the generated instruction sequence representation matrix contains the characteristics of each instruction and the positional relationship between the instructions in the instruction sequence, thereby improving the accuracy of the generated result. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for automatically generating satellite instructions based on a self-attention mechanism provided in an embodiment of the present application.

[0026] Figure 2 A diagram of changes in satellite equipment evaluation values ​​based on a method for automatically generating satellite instructions provided in an embodiment of the present application.

[0027] Figure 3 A structural diagram of a method for automatically generating satellite instructions based on a self-attention mechanism provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiment of the present application provides a method for automatically generating satellite instructions based on a self-attention mechanism, which solves the problem in the prior art that, under resource-constrained satellite software environment conditions, data processing capabilities are limited, resulting in low efficiency and insufficient accuracy when facing complex task instruction sequences. Satellite instruction interference data is collected and processed to obtain instruction interference evaluation values; a satellite instruction sequence is obtained, the satellite instruction sequence is converted into an instruction representation matrix, the instruction sequence position is encoded, and the position code is added to the instruction representation matrix to generate an instruction sequence representation matrix; the instruction sequence representation matrix is ​​fused with global information using a scaled dot product attention component to obtain an updated instruction sequence representation matrix, an interval time series is output through a fully connected neural network according to the updated instruction sequence representation matrix, and the interval time series is corrected according to the instruction interference evaluation value, thereby achieving significant optimization of timeliness, availability and time overhead.

[0029] The technical solution in the embodiment of the present application is to solve the problem that the data processing capability is limited under the resource-constrained satellite software environment, resulting in low efficiency and insufficient accuracy when facing complex task instruction sequences. The overall idea is as follows:

[0030] By collecting satellite command interference data and processing it, the command interference evaluation value is obtained; the satellite command sequence is obtained, the satellite command sequence is converted into a command representation matrix, the command sequence position is encoded, and the position code is added to the command representation matrix to generate the command sequence representation matrix; the command sequence representation matrix is ​​fused with global information using the scaled dot product attention component to obtain an updated command sequence representation matrix, and according to the updated command sequence representation matrix, the interval time series is output through a fully connected neural network, and the interval time series is corrected according to the command interference evaluation value, thereby achieving accurate prediction of the interval time series.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] like Figure 1 , Figure 3 As shown, a satellite command automatic generation method based on a self-attention mechanism is provided in an embodiment of the present application, and the method includes the following steps: collecting satellite command interference data, and obtaining a command interference evaluation value after processing; obtaining a satellite command sequence, converting the satellite command sequence into a command representation matrix, encoding the command sequence position, and adding the position code to the command representation matrix to generate an instruction sequence representation matrix; using a scaled dot product attention component to fuse the instruction sequence representation matrix with global information to obtain an updated instruction sequence representation matrix, outputting an interval time series through a fully connected neural network according to the updated instruction sequence representation matrix, and correcting the interval time series according to the command interference evaluation value.

[0033] In this embodiment, the present invention generates an optimized interval time sequence for a given instruction sequence through a series of processing steps based on instruction interval constraints. First, the instruction sequence is encoded and embedded to ensure that the position and context information of each instruction in the sequence are retained and transmitted. Then, through the scaled dot product attention mechanism, the system can effectively evaluate the relationship between instructions and accurately calculate the most appropriate time interval between each pair of instructions. Finally, the system performs interval time prediction. This step ultimately generates the optimal interval time sequence that maximizes the utilization of time under the premise of determining that all constraints are met. This sequence not only ensures that all instructions can be executed normally as expected, but also has the shortest time interval between every two instructions, thereby maximizing time utilization. The present invention not only improves the efficiency of instruction execution, but also effectively reduces the waste of resources caused by too long instruction intervals.

[0034] In addition, the instruction generation database is used to store data related to the automatic generation method of satellite instructions based on the self-attention mechanism, including: critical transmission power, critical receiving sensitivity, critical bit error rate, satellite equipment evaluation influencing factors corresponding to receiving sensitivity, and interval time series correction vectors corresponding to the interference evaluation value intervals of each instruction. The data in the instruction generation database can be obtained through satellite databases or websites such as NASA's satellite database, the European Space Agency's database, etc., and can also be obtained through professional space segment monitoring equipment or systems, such as satellite orbit monitoring stations, space debris monitoring networks, etc.

[0035] Furthermore, the steps of collecting satellite command interference data and obtaining a command interference evaluation value through processing include: the satellite command interference data includes satellite equipment data, satellite attitude evaluation value and command interference data; obtaining a satellite equipment evaluation value based on a comprehensive analysis of the satellite equipment data, and performing adjustment feedback based on the satellite equipment evaluation value; obtaining a satellite attitude evaluation value based on a comprehensive analysis of the satellite attitude data; and obtaining a command interference evaluation value based on a comprehensive analysis of the command interference data, the satellite attitude evaluation value and the satellite equipment evaluation value.

[0036] In this embodiment, the satellite attitude data describes the attitude of the satellite relative to the earth, including attitude angular velocity (the speed at which the satellite attitude angle changes over time), pitch angle (the angle at which the satellite rotates around the earth's lateral axis (perpendicular to the orbital plane)) and yaw angle (the angle at which the satellite rotates around the earth's longitudinal axis (orbital direction)). The pitch angle, yaw angle and attitude angular velocity can all be monitored by an attitude measuring instrument (such as an infrared attitude measuring instrument, a star camera, a gyroscope, etc.). The command interference data may cause changes in the satellite attitude, thereby affecting the accuracy of the attitude evaluation value. The command interference data may also reflect the performance and status of the satellite equipment. The degradation of equipment performance or failure may also cause instability or deviation of the satellite attitude, thereby affecting the accuracy of the attitude evaluation value. Therefore, there is a mutual influence relationship between the three, and a comprehensive analysis is required to accurately evaluate the health status of the satellite equipment. By comprehensively analyzing satellite equipment data, satellite attitude data and command interference data, the performance of satellite equipment and the impact of command interference can be comprehensively considered from multiple dimensions, and the data can be mutually verified to ensure the reliability and accuracy of the evaluation results. In addition, when adjusting feedback based on the satellite equipment evaluation value, the satellite equipment evaluation threshold can be obtained from the instruction generation database, and the satellite equipment evaluation value can be compared with the satellite equipment evaluation threshold. When the satellite equipment evaluation value is less than the satellite equipment evaluation threshold, it means that the current satellite equipment performance status is poor, and it is necessary to immediately check the hardware and software configuration of the satellite equipment. If there is a problem with the hardware, consider replacing the damaged parts or upgrading the hardware to improve performance. When there is a problem with the software configuration, adjust the software configuration of the device according to the recommendations in the evaluation report, such as updating the firmware, optimizing parameter settings, etc. During the adjustment process, continuously monitor the performance of the equipment to ensure the effectiveness of the adjustment measures; when the satellite equipment evaluation value is greater than or equal to the satellite equipment evaluation threshold, no additional processing is performed.

[0037] When the satellite attitude evaluation value is obtained based on the comprehensive analysis of the satellite attitude data, it is necessary to first obtain the reference attitude angular velocity, the allowable deviation attitude angular velocity, the reference pitch angle, the allowable deviation pitch angle, the reference yaw angle and the allowable deviation yaw angle from the instruction generation database, wherein the satellite attitude evaluation value is obtained in the following manner:

[0038]

[0039] In the formula, ξA1 represents the satellite attitude evaluation value, β1 represents the satellite attitude evaluation influence factor corresponding to the attitude angular velocity, β2 represents the satellite attitude evaluation influence factor corresponding to the pitch angle, β3 represents the satellite attitude evaluation influence factor corresponding to the yaw angle, A1 represents the attitude angular velocity, A0 represents the reference attitude angular velocity, A2 represents the allowable deviation attitude angular velocity, PA1 represents the pitch angle, PA0 represents the reference pitch angle, PA2 represents the allowable deviation pitch angle, Y1 represents the yaw angle, Y0 represents the reference yaw angle, and Y2 represents the allowable deviation yaw angle.

[0040] β1, β2 and β3 respectively represent the numerical values ​​of the influence of the attitude angular velocity, pitch angle and yaw angle on the satellite attitude evaluation value, and can be directly obtained from the instruction generation database when used. For example, the attitude angular velocity and the satellite attitude evaluation influence factor corresponding to the attitude angular velocity preset in the instruction generation database form a mapping set, and the satellite attitude evaluation influence factor corresponding to the attitude angular velocity is obtained according to the attitude angular velocity input mapping set; the pitch angle and the satellite attitude evaluation influence factor corresponding to the pitch angle preset in the instruction generation database form a mapping set, and the satellite attitude evaluation influence factor corresponding to the pitch angle is obtained according to the pitch angle input mapping set; the yaw angle and the satellite attitude evaluation influence factor corresponding to the yaw angle preset in the instruction generation database form a mapping set, and the satellite attitude evaluation influence factor corresponding to the yaw angle is obtained according to the yaw angle input mapping set, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0041] Furthermore, the step of obtaining a satellite equipment evaluation value based on a comprehensive analysis of satellite equipment data includes: the satellite equipment data includes transmission power, receiving sensitivity and bit error rate; obtaining critical transmission power, critical receiving sensitivity and critical bit error rate from an instruction generation database; and obtaining a satellite equipment evaluation value through comprehensive analysis.

[0042] The satellite equipment evaluation value is obtained as follows:

[0043]

[0044] In the formula, ξE1 represents the satellite equipment evaluation value, α1 represents the satellite equipment evaluation influence factor corresponding to the transmit power, α2 represents the satellite equipment evaluation influence factor corresponding to the receive sensitivity, α3 represents the satellite equipment evaluation influence factor corresponding to the bit error rate, P1 represents the transmit power, P0 represents the critical transmit power, S1 represents the receive sensitivity, S0 represents the critical receive sensitivity, B1 represents the bit error rate. In practical applications, due to the complexity and uncertainty of various factors, the bit error rate cannot be 0, and B0 represents the critical bit error rate.

[0045] α1, α2 and α3 are satellite equipment evaluation impact factors corresponding to the transmission power, receiving sensitivity and bit error rate preset in the instruction generation database, respectively, which respectively represent the numerical values ​​of the degree of influence of the transmission power, receiving sensitivity and bit error rate on the satellite equipment evaluation value, and can be directly obtained from the instruction generation database when used. For example, the transmission power and the satellite equipment evaluation impact factors corresponding to the transmission power preset in the instruction generation database form a mapping set, and the satellite equipment evaluation impact factor corresponding to the transmission power is obtained according to the transmission power input mapping set; the receiving sensitivity and the satellite equipment evaluation impact factors corresponding to the receiving sensitivity preset in the instruction generation database form a mapping set, and the satellite equipment evaluation impact factor corresponding to the receiving sensitivity is obtained according to the receiving sensitivity input mapping set; the bit error rate and the satellite equipment evaluation impact factors corresponding to the bit error rate preset in the instruction generation database form a mapping set, and the satellite equipment evaluation impact factor corresponding to the bit error rate is obtained according to the bit error rate input mapping set, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the impact factor in this embodiment is between 0 and 1.

[0046] In this embodiment, the transmission power refers to the power of electromagnetic waves emitted by the satellite antenna to the surrounding space, and its value can be directly measured by using a radio frequency power meter; the receiving sensitivity can be obtained by using a signal generator to generate a signal with known power and frequency, which is then received by the satellite's receiving antenna. A spectrum analyzer or a network analyzer is used at the receiving end to measure the received signal power. The power of the signal generator is adjusted until the signal can be just detected at the receiving end. The power at this time is the receiving sensitivity of the satellite. Generally speaking, the smaller the receiving sensitivity, the lower the minimum signal power that the receiver can correctly receive, so the greater the satellite equipment evaluation value. In the satellite field, since the receiving sensitivity is a negative number, the greater the absolute value of the receiving sensitivity, the greater the satellite equipment evaluation value; the bit error rate can be established by establishing a signal transmission link between the measurement and control subsystem of the satellite under test and the bit error rate test system, setting the satellite downlink telemetry mode to the bit error rate test mode, outputting a downlink telemetry signal with a fixed data telemetry frame data field, using the telemetry demodulation module to demodulate the downlink telemetry signal, and outputting the telemetry frame to the bit error test module. The bit error test module starts the automatic acquisition of the comparison benchmark data function, and compares the received telemetry frame data with the benchmark frame data for consistency. The ratio of the number of bit errors to the total number of bits transmitted is the bit error rate. Under the condition that other conditions remain unchanged, an increase in the transmission power usually leads to an increase in the signal strength at the receiving end, thereby reducing the bit error rate; similarly, an increase in the receiving sensitivity will also reduce the bit error rate, because it allows the receiving device to detect weaker signals, thereby reducing the bit errors caused by insufficient signal strength. Transmitting power, receiving sensitivity and bit error rate are three parameters that can fully reflect the communication performance of satellite equipment. By comprehensively analyzing these three parameters, the transmission capacity, receiving capacity and communication quality of satellite equipment can be evaluated.

[0047] The satellite equipment evaluation impact factor corresponding to the transmit power is set to 0.2, the satellite equipment evaluation impact factor corresponding to the receive sensitivity is set to 0.4, the satellite equipment evaluation impact factor corresponding to the bit error rate is set to 0.4, the transmit power is set to 2500W, the critical transmit power is set to 2000W, the receive sensitivity is set to -100dBm, the critical receive sensitivity is set to -120dBm, and the critical bit error rate is set to 1.0*e -6 , when the bit error rate continues to increase, the satellite equipment evaluation value is calculated. As shown in Table 1, the satellite equipment evaluation value data table based on the satellite command automatic generation method.

[0048] Table 1 Satellite equipment evaluation value data table based on satellite command automatic generation method

[0049] serial number <![CDATA[B1]]> <![CDATA[ξE1]]> 1 <![CDATA[1.0*e -8 ]]> 14.381 2 <![CDATA[1.0*e -7 ]]> 2.110 3 <![CDATA[1.0*e -6 ]]> 0.894 4 <![CDATA[1.0*e -5 ]]> 0.580 5 <![CDATA[1.0*e -4 ]]> 0.475

[0050] like Figure 2As shown in Table 1 and Figure 2 It can be seen that when the satellite equipment evaluation influence factor corresponding to the transmit power, the satellite equipment evaluation influence factor corresponding to the receiving sensitivity, the satellite equipment evaluation influence factor corresponding to the bit error rate, the transmit power, the critical transmit power, the receiving sensitivity, the critical receiving sensitivity and the critical bit error rate remain unchanged, and the bit error rate continues to increase, the satellite equipment evaluation value continues to decrease.

[0051] Furthermore, the step of obtaining the instruction interference evaluation value through comprehensive analysis based on the instruction interference data, satellite attitude evaluation value and satellite equipment evaluation value includes: the instruction interference data includes electromagnetic interference intensity and the number of adjacent signals; obtaining the critical electromagnetic interference intensity, the critical number of adjacent signals, the critical satellite attitude evaluation value and the critical satellite equipment evaluation value from the instruction generation database; and obtaining the instruction interference evaluation value through comprehensive analysis.

[0052] In this embodiment, the electromagnetic interference intensity can be obtained by directly measuring the satellite command receiving point through an interference monitoring receiver or an oscilloscope and other equipment. The greater the electromagnetic interference intensity, the stronger the destructive power of the interference signal on the satellite communication signal, which may lead to problems such as decreased communication quality and increased bit error rate; the number of adjacent signals refers to the number of signals in a certain frequency band or communication system that are adjacent to a specific signal, and these adjacent signals may come from different communication sources or devices, and may have the possibility of spectrum overlap or interference with the target signal. The number of adjacent signals can be obtained by monitoring the current satellite command frequency band using a radio spectrum monitoring device. The more adjacent signals there are, the more serious the adjacent frequency interference or co-frequency interference that may be generated, and these interferences will further affect the stability and reliability of the satellite communication signal; the higher the satellite equipment evaluation value, the better the performance of the satellite equipment, the stronger the anti-interference ability, and the better it can cope with electromagnetic interference and interference from adjacent signals. By comprehensively considering the electromagnetic interference intensity, the number of adjacent signals and the satellite equipment evaluation value, the interference situation of the satellite communication system in a specific environment can be comprehensively evaluated, and at the same time, the performance of the satellite communication system in actual operation can be more accurately reflected, providing strong support for optimizing system configuration and improving communication quality.

[0053] Furthermore, the instruction interference evaluation value is obtained as follows:

[0054]

[0055] In the formula, ξI represents the command interference evaluation value, α4 represents the command interference evaluation influence factor corresponding to the electromagnetic interference intensity, α5 represents the command interference evaluation influence factor corresponding to the number of adjacent signals, α6 represents the command interference evaluation influence factor corresponding to the satellite equipment evaluation value, α7 represents the command interference evaluation influence factor corresponding to the satellite attitude evaluation value, E1 represents the electromagnetic interference intensity, E0 represents the critical electromagnetic interference intensity, C1 represents the number of adjacent signals, C0 represents the critical number of adjacent signals, ξE1 represents the satellite equipment evaluation value, ξE0 represents the critical satellite equipment evaluation value, ξA1 represents the satellite attitude evaluation value, ξA0 represents the critical satellite attitude evaluation value, and e is a natural constant.

[0056] In this embodiment, α4, α5, α6 and α7 are the command interference assessment influencing factors corresponding to the electromagnetic interference intensity, number of adjacent signals, satellite equipment assessment value and satellite attitude assessment value preset in the instruction generation database, respectively indicating the numerical values ​​of the influence of the electromagnetic interference intensity, number of adjacent signals, satellite equipment assessment value and satellite attitude assessment value on the command interference assessment value, and can be directly obtained from the instruction generation database when used. For example, the electromagnetic interference intensity and the instruction interference assessment influence factor corresponding to the electromagnetic interference intensity preset in the instruction generation database form a mapping set, and the instruction interference assessment influence factor corresponding to the electromagnetic interference intensity is obtained by inputting the mapping set according to the electromagnetic interference intensity; the number of adjacent signals and the instruction interference assessment influence factor corresponding to the number of adjacent signals preset in the instruction generation database form a mapping set, and the instruction interference assessment influence factor corresponding to the number of adjacent signals is obtained by inputting the mapping set according to the number of adjacent signals; the satellite equipment evaluation value and the instruction interference assessment influence factor corresponding to the satellite equipment evaluation value preset in the instruction generation database form a mapping set, and the instruction interference assessment influence factor corresponding to the satellite equipment evaluation value is obtained by inputting the mapping set according to the satellite equipment evaluation value; the satellite attitude evaluation value and the instruction interference assessment influence factor corresponding to the satellite attitude evaluation value preset in the instruction generation database form a mapping set, and the instruction interference assessment influence factor corresponding to the satellite attitude evaluation value is obtained by inputting the mapping set according to the satellite attitude evaluation value, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0057] Furthermore, the step of converting the satellite instruction sequence into an instruction representation matrix includes: inputting the satellite instruction sequence and marking each satellite instruction with a digital number; using one-hot encoding to generate a corresponding feature representation for each satellite instruction, marked as each high-dimensional feature vector; and mapping each high-dimensional feature vector to the required low-dimensional space to obtain an embedding matrix, which is the instruction representation matrix.

[0058] In this embodiment, digital numbering is to mark each satellite instruction with a number from 1 to n, which is convenient for instruction representation; unique hot encoding is a technique for converting categorical variables into binary matrices, and each instruction is represented as a binary vector of length n, in which only one position is 1 and the rest are 0; and when each high-dimensional feature vector is mapped to the required low-dimensional space to obtain an embedding matrix, a multi-layer perceptron can be used for feature learning. A multi-layer perceptron is a feedforward artificial neural network that can capture and extract complex feature relationships through a series of weighted inputs, activation functions, and output layers. Here, the multi-layer perceptron maps the high-dimensional feature vectors generated by unique hot encoding to the required low-dimensional space. Through the processing of the multi-layer perceptron, an embedding matrix is ​​obtained, which contains the basic features of each instruction. The dimension of the embedding matrix is ​​usually lower than that of the original unique hot encoding, but it can effectively represent the unique features of each instruction. It not only effectively reduces the dimension of the data, but also retains the important features of the original instruction sequence. The generation of a dense representation matrix significantly improves the efficiency of storage and calculation, and provides high-quality input data for subsequent deep learning processing.

[0059] For example, if task M contains N instructions, the one-hot encoding process generates a feature vector of length N for each instruction. One-hot encoding embedding can effectively avoid the numerical relationship between instructions and provide effective data for subsequent deep learning algorithms. After one-hot encoding, the entire instruction sequence is converted into an instruction representation matrix of size n×N. Although the instruction representation matrix can effectively represent the instructions, the vector generated by one-hot encoding embedding is sparse, which may lead to low storage and computational efficiency. In order to overcome the determination of sparsity, a multi-layer perceptron is introduced to further learn and process the embedding results. The multi-layer perceptron maps the high-dimensional sparse one-hot encoding vector to a low-dimensional dense space through a series of linear transformations and nonlinear activation functions. Specifically, the multi-layer perceptron transforms the n×N sparse one-hot encoding embedding matrix into a dense representation matrix of size n×d, where d is the length of the feature vector after dimensionality reduction.

[0060] In order to ensure that the subsequent self-attention mechanism can accurately capture the position information in the sequence, position encoding is introduced after generating the instruction representation matrix. In order to accurately represent the position of the instruction in the sequence, sine and cosine functions are used to encode the information of each position. The method of introducing position encoding is as follows:

[0061]

[0062] Among them, PE i represents the position encoding of the vector of instruction i, T represents the linear transformation matrix, ω d Represents the encoding frequency of dimension d, where the value range of d is model represents the latitude of the model embedding vector.

[0063] In this way, a positional encoding embedding matrix of size n×d is generated, in which each position has a unique encoding, ensuring the uniform distribution of position information in different dimensions. The generated positional encoding embedding matrix has the same dimension n×d as the previously obtained instruction representation matrix. The introduction of the positional encoding matrix allows each instruction to contain not only its feature information but also its position information in the sequence. The generated positional encoding embedding matrix is ​​added to the instruction representation matrix to obtain the final instruction sequence representation matrix. Since each input vector calculates the inner product with other vectors in the subsequent self-attention mechanism, the order information of the original input sequence will be lost. By adding position information to each instruction in this step, the instruction sequence representation matrix can distinguish the task features in the instruction embedding, and the model's perception of the sequence order is enhanced, so that the relationship and dependency between instructions can be captured more accurately when processing instruction sequences.

[0064] Furthermore, the instruction sequence representation matrix is ​​fused with global information by using the scaled dot product attention component to obtain the updated instruction sequence representation matrix. The steps include: obtaining a query matrix, a key matrix, and a value matrix through three linear transformations of the instruction sequence representation matrix, which are marked as q, k, and v respectively; obtaining three weight matrices to be learned, which are marked as W respectively. q , W k and W v ; Use the weight matrix to map the input to the query matrix, key matrix and value matrix, and obtain the corresponding query embedding matrix, key embedding matrix and value embedding matrix by performing matrix multiplication with the instruction sequence representation matrix, which are marked as Q, K and V respectively; According to the query embedding matrix, key embedding matrix and value embedding matrix, the updated instruction sequence representation matrix is ​​obtained.

[0065] Among them, the query matrix q represents the vector of relevant information that needs to be obtained from other positions in the sequence, which is used to calculate the attention weight of the current instruction on other instructions. The key matrix k represents the feature vector of each instruction in the sequence, which is used to perform dot product with the query vector, that is, to determine the degree of attention of each instruction to other instructions, and then calculate the attention weight. The value matrix v also represents the feature vector of each instruction in the sequence, which is used for weighted summation to generate the final feature representation. Under the action of the attention weight, the features of the corresponding instruction are emphasized or suppressed. W q , W k and W v is the weight matrix to be learned, with sizes of d q ×d,d k ×d,d v ×d. These weight matrices are used to map the input to a q The query q and dimension dk The key is k and the dimension is d v The value v, where d q =d k By performing matrix multiplication with the input instruction sequence representation, we obtain the corresponding query, key, and value embedding matrices Q, K, and V.

[0066] In this embodiment, the self-attention mechanism allows attention to instructions at all other positions in the input sequence when computing the representation of a certain instruction. This feature can capture the dependencies between distant elements in the input sequence, rather than just relying on local information. At the same time, by calculating the attention weights, the weight of each instruction can be dynamically assigned, so that more attention is paid to instructions with higher correlation during calculation. Its key idea is to calculate the attention weight of each position in the sequence to other positions through the query, key and value matrices, and then perform weighted summation. Therefore, in the second step of generating the interval time of the instruction sequence, a scaled dot product attention component is introduced to associate different positions of the sequence to update the representation of the sequence.

[0067] Furthermore, the step of obtaining an updated instruction sequence representation matrix according to the query embedding matrix, the key embedding matrix and the value embedding matrix includes: calculating the dot product of q and k to obtain an attention score matrix; scaling the attention score matrix and then normalizing it to obtain a normalized attention weight matrix, labeled A; multiplying the attention weight matrix A with the value embedding matrix V, and the output matrix obtained is the updated instruction sequence representation matrix, labeled Z.

[0068] In this embodiment, the updated instruction sequence representation matrix Z not only contains the characteristics and location information of each instruction, but also reflects the global dependencies. By introducing the scaled dot product attention mechanism, the system can more effectively capture the long-distance dependencies in the instruction sequence, thus laying a solid foundation for generating the most optimized interval time series. The whole process formula of the above steps is as follows:

[0069]

[0070] Where Z is the updated instruction sequence representation matrix, q is the query matrix, k is the key matrix, T is the scaling factor of the attention score matrix, and d is k is the normalized latitude, V is the value embedding matrix, and softmax() is the softmax function. The dimension of the output matrix Z is n×d v , where each query vector performs a weighted sum operation on the value vector according to its corresponding attention weight, and the resulting output vector contains the global information of the instruction sequence.

[0071] Furthermore, according to the matrix represented by the updated instruction sequence, the step of outputting the interval time series through the fully connected neural network includes: passing the input matrix Z through the first intermediate layer of the fully connected neural network, performing a linear transformation and applying an activation function to obtain a matrix Z1; passing Z1 through the second intermediate layer of the fully connected neural network, also performing a linear transformation and applying an activation function to obtain a matrix Z2; and linearly transforming Z2 to obtain an output vector of dimension n-1, which is the interval time series.

[0072] In this embodiment, the ReLU activation function is an activation function commonly used in artificial neural networks, which defines a simple nonlinear transformation to map input values ​​to output values. The instruction sequence interval prediction module receives the instruction sequence representation matrix that integrates global information, passes through a three-layer fully connected neural network, and finally completes the prediction of the time interval sequence. The fully connected neural network includes two intermediate layers and one output layer, which converts the input n×d v The matrix of size is gradually converted into the final prediction output. In this process, the weights and biases of each layer are optimized through training to minimize the prediction error. The ReLU activation function of each layer introduces nonlinearity, allowing the prediction part to capture more complex feature relationships. The specific conversion formula of the input matrix Z through the first and second intermediate layers of the fully connected neural network is as follows:

[0073] Z1=ReLU(Z*W1+b1);

[0074] Z2 = ReLU(Z1*W2+b2);

[0075] Among them, W1 and W2 are weight matrices, b1 and b2 are bias vectors, all of which are obtained through the training process. The size of matrix Z1 is n×d1, and the size of matrix Z2 is n×d2. The intermediate feature representation is obtained by transforming the two intermediate layers and applying the activation function.

[0076] The mathematical expression of the interval time series is as follows:

[0077] Output = Z2*W3+b3;

[0078] Among them, Output is the interval time series, Z2 is the output matrix of the second intermediate layer, W3 is the weight matrix of size d2×(n-1), and b3 is the bias vector. The output layer converts the intermediate features into the final prediction results, and obtains the final prediction output through linear transformation. By combining mechanisms such as one-hot encoding embedding, multi-layer perceptron, and scaled dot product attention, the instruction sequence time interval generation method ensures the rationality of the final generated time interval. The whole process fully considers the inherent characteristics of each instruction in the instruction sequence and the order information of the sequence. Through instruction representation learning and self-attention mechanism, the system can reasonably arrange the instruction sequence time interval to ensure the smooth and efficient completion of the task.

[0079] Furthermore, the step of correcting the interval time series according to the instruction interference evaluation value includes: matching the instruction interference evaluation value with the interval time series correction vector corresponding to each instruction interference evaluation value interval preset in the instruction generation database to obtain the interval time series correction vector; summing the interval time series and the interval time series correction vector to obtain a corrected interval time series.

[0080] In this embodiment, the correction vector is a set of values ​​determined based on a preset instruction interference evaluation value interval and a corresponding correction strategy. The instruction interference evaluation value reflects the degree of interference that may occur during the execution of the instruction. By matching the evaluation value with the preset interval and applying the corresponding correction vector, the impact of interference on the interval time series can be more accurately reflected, thereby improving the accuracy of the corrected interval time series; at the same time, the impact of these uncertain factors on the interval time series can be reduced, making the system more stable and reliable.

[0081] In summary, the embodiments of the present application collect satellite command interference data and obtain a command interference evaluation value after processing; obtain a satellite command sequence, convert the satellite command sequence into a command representation matrix, encode the command sequence position, and add the position code to the command representation matrix to generate an instruction sequence representation matrix; use the scaled dot product attention component to fuse the instruction sequence representation matrix with global information to obtain an updated instruction sequence representation matrix, output an interval time series through a fully connected neural network according to the updated instruction sequence representation matrix, and correct the interval time series according to the command interference evaluation value, thereby achieving significant optimization of timeliness, availability, and time overhead.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0087] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for automatically generating satellite instructions based on a self-attention mechanism, characterized in that: The following steps are involved: Collect satellite command interference data and obtain command interference evaluation value after processing; Obtaining a satellite command sequence, converting the satellite command sequence into a command representation matrix, encoding the command sequence position, and adding the position code to the command representation matrix to generate the command sequence representation matrix; The scaled dot product attention component is used to fuse the instruction sequence representation matrix with the global information to obtain the updated instruction sequence representation matrix. According to the updated instruction sequence representation matrix, the interval time series is output through a fully connected neural network, and the interval time series is corrected according to the instruction interference evaluation value.

2. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 1, characterized in that: The step of collecting satellite command interference data and obtaining a command interference evaluation value through processing comprises: The satellite command interference data includes satellite equipment data, satellite attitude data and command interference data; Obtain satellite equipment evaluation values ​​based on comprehensive analysis of satellite equipment data, and make adjustments and feedback based on the satellite equipment evaluation values; The satellite attitude evaluation value is obtained based on comprehensive analysis of satellite attitude data; The command interference evaluation value is obtained by comprehensive analysis of the command interference data, satellite attitude evaluation value and satellite equipment evaluation value.

3. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 2, characterized in that: The step of obtaining the satellite equipment evaluation value based on the comprehensive analysis of the satellite equipment data comprises: The satellite equipment data includes transmission power, receiving sensitivity and bit error rate; Obtain critical transmission power, critical receiving sensitivity and critical bit error rate from a command generation database; Comprehensive analysis is performed to obtain the satellite equipment evaluation value.

4. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 2, characterized in that: The step of obtaining the instruction interference evaluation value by comprehensive analysis of the instruction interference data, the satellite attitude evaluation value and the satellite equipment evaluation value comprises: The instruction interference data includes electromagnetic interference intensity and the number of adjacent signals; Obtaining a critical electromagnetic interference intensity, a critical number of adjacent signals, a critical satellite attitude evaluation value, and a critical satellite equipment evaluation value from a command generation database; The instruction interference evaluation value is obtained through comprehensive analysis.

5. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 4, characterized in that: The instruction interference evaluation value is obtained in the following manner: In the formula, ξI represents the command interference evaluation value, α4 represents the command interference evaluation influence factor corresponding to the electromagnetic interference intensity, α5 represents the command interference evaluation influence factor corresponding to the number of adjacent signals, α6 represents the command interference evaluation influence factor corresponding to the satellite equipment evaluation value, α7 represents the command interference evaluation influence factor corresponding to the satellite attitude evaluation value, E1 represents the electromagnetic interference intensity, E0 represents the critical electromagnetic interference intensity, C1 represents the number of adjacent signals, C0 represents the critical number of adjacent signals, ξE1 represents the satellite equipment evaluation value, ξE0 represents the critical satellite equipment evaluation value, ξA1 represents the satellite attitude evaluation value, ξA0 represents the critical satellite attitude evaluation value, and e is a natural constant.

6. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 1, characterized in that: The step of converting the satellite command sequence into a command representation matrix comprises: Input satellite command sequence and mark each satellite command with a number; Use one-hot encoding to generate corresponding feature representations for each satellite command, marked as high-dimensional feature vectors; The embedding matrix obtained by mapping each high-dimensional feature vector to the required low-dimensional space is the instruction representation matrix.

7. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 1, characterized in that: The step of fusing the instruction sequence representation matrix with global information using the scaled dot product attention component to obtain an updated instruction sequence representation matrix comprises: The instruction sequence representation matrix is ​​transformed through three linear transformations to obtain the query matrix, key matrix and value matrix, which are marked as q, k and v respectively; Get three weight matrices that need to be learned, marked as W q , W k and W v ; The weight matrix is ​​used to map the input to the query matrix, key matrix and value matrix. The corresponding query embedding matrix, key embedding matrix and value embedding matrix are obtained by matrix multiplication with the instruction sequence representation matrix, which are marked as Q, K and V respectively. The updated instruction sequence representation matrix is ​​obtained according to the query embedding matrix, the key embedding matrix and the value embedding matrix.

8. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 7, characterized in that: The step of obtaining an updated instruction sequence representation matrix according to the query embedding matrix, the key embedding matrix and the value embedding matrix comprises: Calculate the dot product of q and k to get the attention score matrix; After scaling the attention score matrix, it is normalized to obtain the normalized attention weight matrix, marked as A; Multiply the attention weight matrix A by the value embedding matrix V, and the output matrix obtained is the updated instruction sequence representation matrix, marked as Z.

9. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 1, characterized in that: The step of representing the matrix according to the updated instruction sequence and outputting the interval time series through a fully connected neural network comprises: Pass the input matrix Z through the first intermediate layer of the fully connected neural network, perform linear transformation and apply the activation function to obtain the matrix Z1; Pass Z1 through the second middle layer of the fully connected neural network, perform the same linear transformation and apply the activation function to obtain the matrix Z2; Perform a linear transformation on Z2 to obtain an output vector of dimension n-1, which is an interval time series.

10. The method for automatically generating satellite instructions based on the self-attention mechanism as claimed in claim 1, characterized in that: The step of correcting the interval time sequence according to the instruction interference evaluation value comprises: Matching the instruction interference evaluation value with the interval time series correction vector corresponding to each instruction interference evaluation value interval preset in the instruction generation database to obtain the interval time series correction vector; The interval time series is summed with the interval time series correction vector to obtain the corrected interval time series.

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