Method and system for identifying and handling abnormal power-on and power-off of TT&C equipment in satellite mission system

Abstract

The present invention provides a method and system for identifying and processing abnormal power-on and power-off of a TT&C device in a satellite service system, including: S1: Denoising the telemetry information received by the preprocessed TT&C transponder using a filtering model; S2: Dynamically adjusting the weight ratios of signal strength, bit error rate, and response time to evaluate the on-orbit performance of the TT&C transponder; S3: If the TT&C transponder is in a normal working state and receives a power-off instruction from the main or standby transponder host, first judge the overall satellite state, and then judge the block telemetry information. If there are no problems, then judge this instruction as an abnormal behavior, and the security software will block it. The present invention uses Kalman filtering to continuously update the state estimate, can respond to new measurement values in real time, and ensure the adaptability of the system to dynamic changes; judges the system characteristics through linear fitting and calculates the Kalman gain, making the weighting between the predicted value and the measured value more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite security protection, and specifically, to a method and system for identifying and processing abnormal power-on and power-off of satellite mission control equipment. Background Art

[0002] In modern satellite measurement and control subsystems, for the identification and processing of abnormal power-on and power-off of transponders, traditional methods often first judge the on-orbit performance of the measurement and control transponder, then manually check the telemetry information and the working conditions of the entire satellite, and manually send a shutdown command to the measurement and control transponder through telemetry commands. Then, this kind of measurement and control transponder usually relies on simple signal processing and fixed-weight evaluation techniques to monitor and evaluate on-orbit performance. Although these methods are effective in some cases, they tend to be inadequate when dealing with complex dynamic environments.

[0003] First of all, traditional signal processing techniques, such as low-pass filtering and threshold-based noise suppression, are mainly applicable to static or simple dynamic systems. When environmental noise and system uncertainty increase, these methods are prone to cause errors in signal estimation, thereby affecting the identification of abnormal states. For example, during the operation of a satellite, if the received signal is interfered by noise, traditional methods may not be able to accurately judge the actual state of the transponder, resulting in misjudgment or missed judgment.

[0004] Secondly, the performance evaluation method with fixed weights cannot meet the requirements of different mission scenarios. During the on-orbit operation of a satellite, different tasks (such as real-time control, data transmission, status monitoring, etc.) have different focuses on performance indicators. Fixed weight settings may not be able to reflect these changes, resulting in the system being unable to respond quickly and accurately when an abnormal state occurs, increasing potential risks.

[0005] The efficiency of manually checking the telemetry information of code groups is relatively low, especially when dealing with a large amount of telemetry data, which often requires a large amount of time and manpower. This low efficiency may cause the system to be unable to respond quickly at critical moments, thereby affecting the real-time performance of the overall operation. Therefore, a new method for identifying and processing abnormal power-on and power-off of satellite mission control equipment is needed.

[0006] Patent application document CN113114186A discloses a method for autonomous reset control of a measurement and control transponder. The satellite autonomously identifies the measurement and control arc segment and autonomously calculates a measurement and control reset time window that does not overlap with the normal measurement and control time, and performs a reset of the measurement and control transponder within the measurement and control reset time window to avoid interruption of normal satellite measurement and control due to autonomous reset. However, this patent cannot completely solve the existing technical problems and cannot meet the requirements of the present invention. Summary of the Invention

[0007] Aiming at the defects in the prior art, the purpose of the present invention is to provide a method and system for identifying and processing abnormal power-on and power-off of satellite mission measurement and control equipment.

[0008] The method for identifying and processing abnormal power-on and power-off of satellite mission measurement and control equipment provided by the present invention includes:

[0009] Step 1: When the main / backup transponder of the satellite measurement and control subsystem receives a signal, first perform sampling and digital processing, discretize the signal using a preset sampling frequency, then filter out high-frequency noise using a low-pass filter, transform the time-domain signal into a frequency-domain signal using the fast Fourier transform, identify the energy distribution within the relevant frequency band, and delete the peaks in the spectrum that do not meet the preset requirements;

[0010] Step 2: Further filter using a filtering model. When the satellite measurement and control subsystem receives a new signal, the filtering model enters the update stage. In this stage, the model compares the actual measurement value with the predicted value to correct the state estimation, and then dynamically adjusts the weight ratios of the signal strength, bit error rate, and response time to evaluate the on-orbit performance of the measurement and control transponder;

[0011] Step 3: If the measurement and control transponder is in a normal working state and receives a power-off instruction from the main or backup transponder host, first judge the overall satellite state, and then judge the telemetry information of the code group. If there are no problems, judge this instruction as an abnormal behavior, and the security software will block it.

[0012] Preferably, it is judged whether it is a linear system through linear fitting. If it is a linear system, then calculate the Kalman gain K, weight between the predicted value and the measured value, and perform state estimation; based on the previous state and control input, the model predicts the current state according to the state equation of the system; the estimation of the system state at time k is , and the predicted error covariance of the state is ; is the state transition matrix, describing the dynamic change of the system from time k-1 to k; is the error covariance at the previous moment; is the process noise covariance matrix, representing the uncertainty of the random noise introduced during the state transition; through the following formula, the model updates the error covariance each time it is tested;

[0013]

[0014] The Kalman gain is , where is adaptively updated using the maximum likelihood method; first, define the residual distribution of the actual value and the predicted value as a zero-mean Gaussian distribution, and then construct the likelihood function, which is the probability of the observed data appearing under the given parameters; if the residual is greater than the preset threshold, then increase the value, if the residual is less than the preset threshold, then reduce the value;

[0015] Determine whether it is a linear system through linear fitting. If it is a non-linear system, then use particle filtering. Initialize a set of particles, where each particle represents a possible value of the system state and corresponds to a weight, which represents the relative importance of the particle in the current state; As time goes by, the particles are predicted according to the dynamic model and control input of the system. This process uses the state transition equation to update the state of each particle, thereby generating a new set of particles at each time step; As the model receives new observation data, the particle filter calculates the matching degree between each particle and the actual observation value, thereby updating the weight of the particle. The likelihood function is used to evaluate the degree of agreement between the particle and the observation data. The higher the weight of the particle, the higher the authenticity, and the particles with smaller weights are gradually eliminated; Periodically perform a resampling step, which extracts particles from the current set of particles and generates a new set of particles based on their weights.

[0016] Preferably, use the state transition equation to update the state of each particle. For particle i, there is:

[0017]

[0018] where is the state of particle i at the current time, is the state of the particle at the previous time, is the control input at the previous time; is the state transition function, which describes that the current state is determined by the state and control input at the previous time; is the process noise of particle i;

[0019] Use the likelihood function to evaluate the degree of agreement between the particle and the observation data The specific formula is as follows:

[0020]

[0021] where R is the observation noise covariance matrix, which represents the influence of the observation noise; is the actual observation value of particle i at the current time, represents the model prediction value of particle i at the current time; Eliminate the particles with preset small weights in .

[0022] Preferably, the signal output by the model is demodulated after low-pass filtering to extract effective telemetry data; the on-orbit performance of the main / backup transponders of the satellite TT&C sub-system is comprehensively evaluated through the dynamic weight ratio allocation of signal strength, error rate, and response time. The signal strength is obtained through the amplitude information of the received signal, the error rate is represented by the bit error rate during the telemetry information verification process, and the response time is obtained by acquiring the timestamp in the telemetry information packet; when analyzing telemetry data for satellite status detection, the proportion of signal strength is increased to the highest, the proportion of error rate is the second, and the proportion of response time is the least; when analyzing telemetry data for real-time control system applications, the proportion of response time is increased to the highest, the proportion of error rate is the second, and the proportion of signal strength is the smallest; when analyzing telemetry data for data transmission and downlink, the proportion of error rate is increased to the highest, the proportion of response time is the second, and the proportion of signal strength is the smallest.

[0023] Preferably, when it is confirmed that the on-orbit performance of the transponder is normal and a shutdown instruction for the main or backup transponder is received, after response, the corresponding transponder channel will be in a closed state; first, judge the status of the entire satellite. If the entire satellite is in a state of attitude instability, power system failure, or communication link interruption and enters an emergency response state, then this received shutdown instruction for the main or backup transponder is an abnormal behavior, and this instruction is blocked by the security software; if the entire satellite is in a normal working state and the parameter indicators of each sub-system are within the predetermined range, then further judge the code group telemetry information;

[0024] First, check the integrity of the code group telemetry information by using whether the time of the telemetry information matches the current time and is continuous; secondly, use the cross-validation method for a single code group, and compare the code groups related to satellite operation within the same time window. This time window is divided according to the length of the task time executed by the sub-system, and includes the verification of the transponder communication link status, data transmission and commands, positioning information and orbit parameters, temperature, and equipment status; if there is no time loss and the cross-validation result is normal, then it is determined that this received shutdown instruction for the main or backup transponder is an abnormal behavior, and this instruction is blocked by the security software.

[0025] According to the on-orbit equipment abnormal power-on / off identification and processing system for the satellite service system provided by the present invention, it includes:

[0026] Module M1: When the main / backup transponders of the satellite TT&C sub-system receive signals, first perform sampling and digital processing, discretize the signals using a preset sampling frequency, then use a low-pass filter to filter out high-frequency noise, use the fast Fourier transform to transform the time-domain signal into a frequency-domain signal, identify the energy distribution within the relevant frequency band, and delete the peaks in the spectrum that do not meet the preset requirements;

[0027] Module M2: Further filter using a filtering model. When the satellite TT&C subsystem receives a new signal, the filtering model enters the update phase. In this phase, the model compares the actual measurement value with the predicted value to correct the state estimation, and then dynamically adjusts the weight ratios of the signal strength, bit error rate, and response time to evaluate the on-orbit performance of the TT&C transponder.

[0028] Module M3: If the TT&C transponder is in a normal operating state and receives a shutdown instruction from the transponder main unit or standby unit, first judge the overall satellite state, and then judge the code group telemetry information. If there are no problems, the instruction is judged as an abnormal behavior and the security software will block it.

[0029] Preferably, judge whether it is a linear system through linear fitting. If it is a linear system, calculate the Kalman gain K, perform weighting between the predicted value and the measured value, and perform state estimation; based on the previous state and control input, the model predicts the current state according to the state equation of the system; the estimation of the system state at time k is , and the predicted error covariance of the state is ; is the state transition matrix, which describes the dynamic change of the system from time k - 1 to k; is the error covariance at the previous moment; is the process noise covariance matrix, which represents the uncertainty of the random noise introduced during the state transition; through the following formula, the model updates the error covariance for each test;

[0030]

[0031] The Kalman gain is , where The value of is adaptively updated using the maximum likelihood method; first, define the residual distribution of the actual value and the predicted value as a zero-mean Gaussian distribution, and then construct the likelihood function, which is the probability of the observed data given the parameters; if the residual is greater than the preset threshold, increase the value of, if the residual is less than the preset threshold, decrease the value of;

[0032] Determine whether it is a linear system through linear fitting. If it is a non-linear system, use particle filtering. Initialize a set of particles, where each particle represents a possible value of the system state and corresponds to a weight, which represents the relative importance of the particle in the current state. As time goes by, the particles are predicted according to the dynamic model of the system and the control input. This process uses the state transition equation to update the state of each particle, thereby generating a new set of particles at each time step. As the model receives new observation data, particle filtering calculates the degree of matching between each particle and the actual observation value, thereby updating the weight of the particle. The likelihood function is used to evaluate the degree of agreement between the particle and the observation data. The higher the weight of the particle, the higher its authenticity, and the particles with smaller weights are gradually eliminated. Periodically perform the resampling step, which extracts particles from the current set of particles and generates a new set of particles based on their weights.

[0033] Preferably, use the state transition equation to update the state of each particle. For particle i, there is:

[0034]

[0035] Where, is the state of particle i at the current time, is the state of the particle at the previous time, is the control input at the previous time; is the state transition function, which describes that the current state is determined by the state and control input at the previous time; is the process noise of particle i;

[0036] Use the likelihood function to evaluate the degree of agreement between the particle and the observation data The specific formula is as follows:

[0037]

[0038] Where, R is the observation noise covariance matrix, which represents the influence of the observation noise; is the actual observation value of particle i at the current time, represents the model prediction value of particle i at the current time; Eliminate the particles with preset small weights in .

[0039] Preferably, the signal output by the model is low-pass filtered and then demodulated to extract effective telemetry data; the on-orbit performance of the main / backup transponders of the satellite TT&C subsystem is comprehensively evaluated through the dynamic weight ratio distribution of signal strength, error rate, and response time. The signal strength is obtained through the amplitude information of the received signal, the error rate is represented by the bit error rate during the telemetry information verification process, and the response time is obtained by acquiring the timestamp in the telemetry information packet; when analyzing telemetry data for satellite status detection, the proportion of signal strength is increased to the highest, the proportion of error rate is the second, and the proportion of response time is the least; when analyzing telemetry data for real-time control system applications, the proportion of response time is increased to the highest, the proportion of error rate is the second, and the proportion of signal strength is the smallest; when analyzing telemetry data for data transmission and downlink, the proportion of error rate is increased to the highest, the proportion of response time is the second, and the proportion of signal strength is the smallest.

[0040] Preferably, when it is confirmed that the on-orbit performance of the transponder is normal and a shutdown instruction for the main or backup transponder is received, after response, the corresponding transponder channel will be in a closed state; first, judge the overall satellite status. If the satellite is in a state of attitude instability, power system failure, or communication link interruption and enters an emergency response state, then this received shutdown instruction for the main or backup transponder is an abnormal behavior, and this instruction is blocked by the security software; if the satellite is in a normal working state and the parameter indicators of each subsystem are within the predetermined range, then further judge the block telemetry information;

[0041] First, check the integrity of the block telemetry information by using whether the time of the telemetry information matches the current time and is continuous; secondly, use the cross-validation method for a single block, compare the blocks related to satellite operation within the same time window, and this time window is divided according to the task time length of the subsystem execution, including the verification of the transponder communication link status, data transmission and commands, positioning information and orbit parameters, temperature, and equipment status; if there is no time missing and the cross-validation result is normal, then it is determined that this received shutdown instruction for the main or backup transponder is an abnormal behavior, and this instruction is blocked by the security software.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) Through continuous updating of the state estimate, Kalman filtering can respond to new measurement values in real time, ensuring the system's adaptability to dynamic changes; judge the system characteristics through linear fitting and calculate the Kalman gain, making the weighting between the predicted value and the measured value more accurate; for nonlinear systems, the introduction of particle filtering provides flexibility, effectively estimating the system state using a set of representative particles and being able to handle more complex dynamic behaviors;

[0044] (2)When the transponder receives a shutdown command, the system first evaluates the overall satellite status to ensure that the command is executed only under normal operating conditions. This dual-verification mechanism greatly enhances the security of command processing by checking the integrity of telemetry information and performing cross-verification, avoiding the risk of misexecuting commands in abnormal states. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0046] Figure 1 It is a flowchart of a method for identifying and handling abnormal power-on and power-off of satellite mission control equipment;

[0047] Figure 2 It is a flowchart for identifying and handling abnormal behaviors of the primary transponder. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0049] Embodiment

[0050] The present invention provides a method for identifying and handling abnormal power-on and power-off of satellite mission control equipment, including:

[0051] 1) When the main / backup transponders of the satellite TT&C sub-system receive signals, they first perform sampling and digital processing, discretize the signals using an appropriate sampling frequency, and then use a low-pass filter for preliminary high-frequency noise filtering. The time-domain signals are transformed into frequency-domain signals using the Fast Fourier Transform (FFT) to identify the energy distribution within the relevant frequency bands, and the obvious peaks in the spectrum are removed.

[0052] 2) Further filtering is performed using a filtering model. When the system receives a new signal, the filtering model enters an update phase. In this phase, the model compares the actual measurement values with the predicted values to correct the state estimation. Linear fitting is used to determine whether it is a linear system,

[0053] The following two methods, the Kalman gain and particle filtering, are respectively applied to the process of "comparing the actual measurement value with the predicted value to correct the state estimation" for linear and non-linear systems. Now, a method for "judging whether a model is a linear system through linear fitting" is added: The model conducts a spectral analysis. If only the original fundamental frequency component is retained in the spectrum, no obvious harmonic distortion occurs, and the amplitude and phase responses vary linearly, it is considered a linear system; if new frequency components (frequencies other than the input signal frequency) appear in the spectrum, or the amplitude response is not proportional to the input signal, it indicates a non-linear system.

[0054] If it is a linear system, by calculating the Kalman gain K, the model can determine how to weight between the predicted value and the measured value for state estimation. The model predicts the current state according to the state equation of the system, which is based on the combination of the previous state and the control input. The estimation of the system state at time k is , and the predicted error covariance of the state is , is the state transition matrix, which describes the dynamic change of the system from time k - 1 to k, is the error covariance at the previous moment, is the process noise covariance matrix, which represents the uncertainty of the random noise introduced during the state transition; T is the transpose of the matrix. Through the following formula, the model updates the error covariance for each test;

[0055]

[0056] The Kalman gain is , where The value of is adaptively updated using the maximum likelihood method. First, define the residual distribution of the actual value and the predicted value as a zero-mean Gaussian distribution, and then construct a likelihood function, which is the probability of the observed data appearing under the given parameters. If the residual is large, appropriately increase the value, and if the residual is small, appropriately reduce the value.

[0057] 3) If the model determines it to be a non-linear system, particle filtering is used. Initialize a set of particles, where each particle represents a possible value of the system state and corresponds to a weight, which indicates the relative importance of the particle in the current state. As time progresses, the particles are predicted according to the system's dynamic model and control input. This process typically uses the state transition equation to update the state of each particle, thereby generating a new set of particles at each time step. As the model receives new observation data, particle filtering calculates the degree of match between each particle and the actual observed value, thereby updating the weights of the particles. Usually, the likelihood function is used to evaluate the degree of agreement between the particles and the observed data. Particles with larger weights represent more likely states, while particles with smaller weights may be gradually eliminated. The resampling step is performed regularly. This process extracts particles from the current set of particles and generates a new set of particles based on their weights.

[0058] Update the state of each particle using the state transition equation:

[0059] For particle i:

[0060]

[0061] Where, is the state of particle i at the current time, is the state of the particle at the previous time, is the control input at the previous time, is the state transition function, which describes how the current state is determined by the state and control input at the previous time, is the process noise of particle i.

[0062] Calculating the degree of match between each particle and the actual observed value is to use the likelihood function to evaluate the degree of agreement between the particles and the observed data. The specific formula is as follows:

[0063]

[0064] Where, R is the observation noise covariance matrix, indicating the impact of the observation noise, is the actual observed value of particle i at the current time, represents the model prediction value of particle i at the current time.

[0065] Eliminate the particles with small weights in .

[0066] "More likely state" refers to the state represented by particles with larger weights. In particle filtering, the weight of a particle reflects its matching degree with the observed data. The larger the weight, the closer the state corresponding to the particle is to the true state of the system. To avoid particle weight degradation (most particle weights approaching zero), particle filtering performs resampling regularly. The process of resampling is to draw particles from the current particle set according to the weight distribution of the particles to generate a new particle set. Particles with large weights will be drawn multiple times, while particles with small weights may be eliminated. This mechanism ensures that particle filtering focuses near the more likely states, thereby improving the estimation accuracy of the state of a nonlinear system.

[0067] 4) After low-pass filtering the signal output by the model, demodulation is performed to extract the effective telemetry data. The on-orbit performance of the main / backup transponder of the satellite TT&C sub-system is comprehensively evaluated through the dynamic weight ratio distribution of signal strength, error rate, and response time. The signal strength is obtained through the amplitude information of the received signal, the error rate is represented by the bit error rate during the telemetry information verification process, and the response time is obtained by acquiring the timestamp in the telemetry information packet. When analyzing telemetry data for satellite state detection, the proportion of signal strength is raised to the highest, the proportion of error rate is the second, and the proportion of response time is the least; when analyzing telemetry data for real-time control system applications (such as emergency response, mission scheduling and coordination, etc.), the proportion of response time is raised to the highest, the proportion of error rate is the second, and the proportion of signal strength is the least; when analyzing telemetry data for data transmission and downlink, the proportion of error rate is raised to the highest, the proportion of response time is the second, and the proportion of signal strength is the least.

[0068] 5) When it is confirmed that the on-orbit performance of the transponder is normal and a shutdown instruction for the main or backup transponder of the transponder is received, the corresponding transponder channel will be in a closed state after response. First, the overall satellite state is judged. If the overall satellite is in a state that requires an emergency response, such as attitude instability, power system failure, or communication link interruption, then this received shutdown instruction for the main or backup transponder of the transponder is an abnormal behavior, and this instruction is blocked by the safety software. If the overall satellite is in a normal working state and the parameter indicators of each sub-system are within the predetermined range, then the code group telemetry information is further judged.

[0069] 6) First, check the integrity of the telemetry information of the code group by using whether the time of the telemetry information matches the current time and is continuous. Secondly, for a single code group, use the cross - verification method to compare the code groups related to satellite operation within the same time window, which is divided according to the task time length of each subsystem and mainly includes the verification of the transponder communication link status, data transmission and instructions, positioning information and orbit parameters, temperature, and equipment status. If there is no time missing and the cross - verification result is normal, then the received shutdown instruction for the main or standby transponder host is considered an abnormal behavior, and this instruction is blocked through the security software.

[0070] As Figure 1 , the present invention provides a method for identifying and processing abnormal power - on and power - off of satellite - borne system measurement and control equipment, which includes the following steps:

[0071] S1: Use a filtering model to denoise the telemetry information received by the pre - processed measurement and control transponder.

[0072] S2: Dynamically adjust the weight ratios of signal strength, bit error rate, and response time to evaluate the on - orbit performance of the measurement and control transponder.

[0073] S3: If the measurement and control transponder is in a normal working state and receives a shutdown instruction for the main or standby transponder host, first judge the overall satellite state, and then judge the code - group telemetry information. If there are no problems, then judge this instruction as an abnormal behavior, and the security software will block it.

[0074] As Figure 2 , the identification and processing process of abnormal behavior of the primary transponder is as follows:

[0075] 1) The primary transponder of the satellite receives the telemetry signal, first performs sampling and digitization processing, discretizes the signal using an appropriate sampling frequency, and then uses a low - pass filter for preliminary high - frequency noise filtering. Use the Fast Fourier Transform (FFT) to transform the time - domain signal into a frequency - domain signal, identify the energy distribution within the relevant frequency bands, and delete the obvious peaks in the spectrum.

[0076] 2) The signal passes through the filtering model. After fitting, the signal is a non - linear system, and particle filtering is performed using a periodically updated particle set.

[0077] 3) After low - pass filtering the signal output by the model by the primary measurement and control transponder, demodulation is performed to extract the telemetry data as the data for the state detection of the satellite data transmission system. Dynamically evaluate the on - orbit performance of the primary measurement and control transponder by setting the proportion of signal strength to the highest, the proportion of error rate to the second highest, and the proportion of response time to the lowest.

[0078] 4) The performance of the primary TT&C transponder is normal. After receiving the shutdown command for the primary transponder's main unit, it first judges the status of the entire satellite and finds that the status of the entire satellite is normal;

[0079] 5) Check the integrity of the code group telemetry information. It is found that the timestamps are continuous. Through cross-verification of the working status of the entire satellite and the status detection data of the satellite data transmission system, it is found that there is no abnormality in the code group telemetry information. The shutdown command for this primary transponder is regarded as an abnormal behavior of the satellite, and the software shields it.

[0080] Those skilled in the art know that in addition to implementing the systems, devices, and their respective modules provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the systems, devices, and their respective modules provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc., to achieve the same program. Therefore, the systems, devices, and their respective modules provided by the present invention can be regarded as a kind of hardware component, and the modules included therein for implementing various programs can also be regarded as the structures within the hardware component; the modules for implementing various functions can also be regarded as both software programs for implementing the method and the structures within the hardware component.

[0081] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for identifying and handling abnormal power-on and power-off of spacecraft mission measurement and control equipment, characterized in that, Including: Step 1: When the main / backup transponder of the satellite TT&C subsystem receives a signal, first perform sampling and digital processing, discretize the signal using a preset sampling frequency, then filter out high-frequency noise using a low-pass filter, transform the time-domain signal into a frequency-domain signal using the fast Fourier transform, identify the energy distribution within the relevant frequency band, and delete the peaks in the spectrum that do not meet the preset requirements; Step 2: Further filter using a filtering model. When the satellite TT&C subsystem receives a new signal, the filtering model enters the update stage. In this stage, the model compares the actual measurement value with the predicted value to correct the state estimation, and then dynamically adjusts the weight ratios of the signal strength, bit error rate, and response time to evaluate the on-orbit performance of the TT&C transponder; Step 3: If the TT&C transponder is in a normal working state and receives an instruction to shut down the main or backup transponder, first judge the overall satellite state, and then judge the telemetry information of the code group. If there are no problems, judge this instruction as an abnormal behavior, and the safety software will block it.

2. The method for identifying and handling abnormal power-on and power-off of the star mission system measurement and control equipment according to claim 1, characterized in that, Judge whether it is a linear system through linear fitting. If it is a linear system, calculate the Kalman gain K, perform weighting between the predicted value and the measured value, and perform state estimation; based on the previous state and control input, the model predicts the current state according to the state equation of the system; The estimate of the system state at time k is , and the predicted error covariance of the state is ; is the state transition matrix, which describes the dynamic change of the system from time k-1 to k; is the error covariance at the previous time; is the process noise covariance matrix, which represents the uncertainty of the random noise introduced in the state transition process; Through the following formula, the model updates the error covariance each time it is tested; The Kalman gain is , where The value of is adaptively updated using the maximum likelihood method; First, define the residual distribution of the actual value and the predicted value as a zero-mean Gaussian distribution, and then construct the likelihood function, which is the probability of the observed data appearing under the given parameters; If the residual is greater than the preset threshold, increase the value. If the residual is less than the preset threshold, decrease the value; Judge whether it is a linear system through linear fitting. If it is a non-linear system, use particle filtering. Initialize a set of particles, each particle representing a possible value of the system state and corresponding to a weight, which represents the relative importance of the particle in the current state; over time, the particles are predicted according to the dynamic model and control input of the system, and this process uses the state transition equation to update the state of each particle, thus generating a new set of particles at each time step; as the model receives new observation data, particle filtering calculates the matching degree between each particle and the actual observation value, thereby updating the weight of the particle, and uses the likelihood function to evaluate the degree of conformity between the particle and the observation data. The higher the weight of the particle, the higher its authenticity, and the particles with smaller weights are gradually eliminated; perform the resampling step regularly, and this process extracts particles from the current set of particles and generates a new set of particles based on their weights.

3. The method for identifying and handling abnormal power-on and power-off of the spacecraft mission system measurement and control equipment according to claim 2, wherein, Use the state transition equation to update the state of each particle. For particle i, there is: wherein, is the state of particle i at the current moment, is the state of the particle at the previous moment, is the control input at the previous moment; is the state transition function, describing that the current state is determined by the state and control input at the previous moment; is the process noise of particle i; Use the likelihood function to evaluate the degree of agreement between the particles and the observed data The specific formula is as follows: where, R is the observation noise covariance matrix, representing the influence of the observation noise; is the actual observation value of particle i at the current moment, represents the model prediction value of particle i at the current moment; Eliminate the particles with preset small weights in 4. The method for identifying and processing abnormal power-on and power-off of the TT&C equipment in the satellite service system according to claim 1, wherein Demodulate the signal output by the model after low-pass filtering to extract effective telemetry data; comprehensively evaluate the on-orbit performance of the main / backup transponder of the satellite TT&C subsystem through the dynamic weight ratio distribution of signal strength, error rate, and response time. The signal strength is obtained through the amplitude information of the received signal, the error rate is represented by the bit error rate during the telemetry information verification process, and the response time is obtained by acquiring the timestamp in the telemetry information packet; when analyzing the telemetry data as data for satellite state detection, the proportion of the signal strength is increased to the highest, the proportion of the error rate is the second, and the proportion of the response time is the least; When analyzing telemetry data for real-time control system applications, the proportion of response time is set to the highest, the proportion of error rate is the second highest, and the proportion of signal strength is the lowest; when analyzing telemetry data for data transmission and downlink, the proportion of error rate is set to the highest, the proportion of response time is the second highest, and the proportion of signal strength is the lowest.

5. The method for identifying and processing abnormal power-on and power-off of the star mission system measurement and control equipment according to claim 1, wherein When it is confirmed that the on-orbit performance of the transponder is normal and a shutdown instruction for the transponder host or backup is received, after response, the corresponding transponder channel will be in a closed state; first, the overall satellite state is judged. If the satellite is in a state of attitude instability, power system failure, or communication link interruption and enters an emergency response state, then this received shutdown instruction for the transponder host or backup is an abnormal behavior, and the instruction is blocked by the safety software; if the satellite is in a normal working state and the parameter indicators of each subsystem are within the predetermined range, then the telemetry information of the code group is further judged; First, check the integrity of the telemetry information of the code group by using whether the time of the telemetry information matches the current time and is continuous; secondly, use the cross-validation method for a single code group, and compare the code groups related to satellite operation within the same time window. This time window is divided according to the length of the tasks executed by the subsystems, and includes the verification of the transponder communication link status, data transmission and instructions, positioning information and orbit parameters, temperature, and equipment status; if there is no time loss and the cross-validation result is normal, then this received shutdown instruction for the transponder host or backup is judged as an abnormal behavior, and the instruction is blocked by the safety software.

6. A star mission system measurement and control equipment abnormal power-on and power-off identification and processing system, characterized in that, Including: Module M1: When the main / backup transponder in the satellite TT&C subsystem receives a signal, first perform sampling and digital processing, discretize the signal using a preset sampling frequency, then use a low-pass filter to filter out high-frequency noise, use the fast Fourier transform to convert the time-domain signal into a frequency-domain signal, identify the energy distribution within the relevant frequency band, and delete the peaks in the spectrum that do not meet the preset requirements; Module M2: Further filter using a filtering model. When the satellite TT&C subsystem receives a new signal, the filtering model enters the update stage. In this stage, the model compares the actual measurement value with the predicted value to correct the state estimate, and then dynamically adjusts the weight ratios of signal strength, bit error rate, and response time to evaluate the on-orbit performance of the TT&C transponder; Module M3: If the TT&C transponder is in a normal working state and a shutdown instruction for the transponder host or backup is received, first judge the overall satellite state, and then judge the telemetry information of the code group. If there are no problems, then judge this instruction as an abnormal behavior, and the safety software will block it.

7. The abnormal power-on and power-off identification and processing system for the spacecraft mission control and measurement equipment according to claim 6, characterized in that, Judge whether it is a linear system through linear fitting. If it is a linear system, then calculate the Kalman gain K, perform weighting between the predicted value and the measured value, and perform state estimation; based on the previous state and control input, the model predicts the current state according to the state equation of the system; The estimate of the system state at time k is , and the predicted error covariance of the state is ; is the state transition matrix, which describes the dynamic change of the system from time k-1 to k; is the error covariance at the previous time; is the process noise covariance matrix, which represents the uncertainty of the random noise introduced during the state transition; through the following formula, the model updates the error covariance every time it is tested; The Kalman gain is , where is adaptively updated using the maximum likelihood method; first, the residual distribution of the actual value and the predicted value is defined as a zero-mean Gaussian distribution, and then the likelihood function, the probability of the observed data given the parameters, is constructed. If the residual is greater than the preset threshold, increase the value. If the residual is less than the preset threshold, decrease the value; Determine whether it is a linear system through linear fitting. If it is a non-linear system, use particle filtering. Initialize a set of particles, where each particle represents a possible value of the system state and corresponds to a weight, which represents the relative importance of the particle in the current state. As time goes by, the particles are predicted according to the system's dynamic model and control input. This process uses the state transition equation to update the state of each particle, thereby generating a new set of particles at each time step. As the model receives new observation data, particle filtering calculates the matching degree between each particle and the actual observed value, thereby updating the weight of the particle. Use the likelihood function to evaluate the degree of conformity between the particle and the observed data. The higher the weight of the particle, the higher its authenticity, and the particles with smaller weights are gradually eliminated. Periodically perform the resampling step, which extracts particles from the current set of particles and generates a new set of particles based on their weights.

8. The abnormal power-on and power-off identification and processing system for the star mission system measurement and control equipment according to claim 7, wherein, Use the state transition equation to update the state of each particle. For particle i, there is: wherein, is the state of particle i at the current moment, is the state of the particle at the previous moment, is the control input at the previous moment; is the state transition function, describing that the current state is determined by the state and control input at the previous moment; is the process noise of particle i; Use the likelihood function to evaluate the degree of agreement between the particles and the observed data The specific formula is as follows: where R is the observation noise covariance matrix, representing the impact of the observation noise; is the actual observation value of particle i at the current moment, represents the model prediction value of particle i at the current moment; particles with a preset small weight in are eliminated.

9. The star mission system measurement and control equipment abnormal power-on and power-off identification and processing system according to claim 6, characterized in that, Demodulate the signal output by the model after low-pass filtering to extract effective telemetry data. Comprehensively evaluate the on-orbit performance of the main / backup transponders of the satellite TT&C subsystem through the dynamic weight ratio distribution of signal strength, error rate, and response time. The signal strength is obtained through the amplitude information of the received signal, the error rate is represented by the bit error rate during the telemetry information verification process, and the response time is obtained by acquiring the timestamp in the telemetry information packet. When analyzing telemetry data for satellite status detection, the proportion of signal strength is increased to the highest, the proportion of error rate is the second, and the proportion of signal strength is the least. When analyzing telemetry data for real-time control system applications, the proportion of response time is increased to the highest, the proportion of error rate is the second, and the proportion of response time is the least. When analyzing telemetry data for data transmission and downlink, the proportion of error rate is increased to the highest, the proportion of response time is the second, and the proportion of signal strength is the least.

10. The star mission system measurement and control equipment abnormal power-on and power-off identification and processing system according to claim 6, characterized in that, When it is confirmed that the on-orbit performance of the transponder is normal and a shutdown instruction for the main or backup transponder is received, after response, the corresponding transponder channel will be in a closed state. First, judge the overall satellite state. If the satellite is in a state of attitude instability, power system failure, or communication link interruption and starts an emergency response state, then this received shutdown instruction for the main or backup transponder is an abnormal behavior, and the instruction is blocked by the security software. If the satellite is in a normal working state and the parameter indicators of each subsystem are within the predetermined range, then further judge the code group telemetry information. First, check the integrity of the telemetry information of the code group by using whether the time of the telemetry information matches the current time and is continuous. Secondly, for a single code group, use the method of cross-verification to compare the code groups related to satellite operation within the same time window. This time window is divided according to the length of the task time executed by the subsystem, and includes the verification of the transponder communication link status, data transmission and instructions, positioning information and orbital parameters, temperature, and equipment status. If there is no time missing and the cross-verification result is normal, then determine that this received shutdown instruction for the transponder main unit or standby unit is an abnormal behavior, and block this instruction through the security software.

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