A satellite remote measurement, operation and control system

Abstract

The present invention discloses a satellite remote measurement, operation and control system, which relates to the field of measurement, operation and control technology. The present invention comprises the following steps: adopting a measurement and control link combination of a low-orbit satellite, a geosynchronous orbit satellite and a high-altitude platform, dynamically selecting an optimal measurement and control path through reinforcement learning, optimizing measurement and control scheduling based on link status, task priority and resource competition, and improving measurement and control coverage and resource utilization; during the execution of a task, integrating an AI model to monitor the satellite task status in real time, obtain task status, equipment load and communication link delay data, and combining a Bayesian probability model and an LSTM neural network to predict abnormal situations, thereby avoiding potential risks in advance and improving the stability of the measurement and control task; and autonomously adjusting the measurement and control mode after abnormality detection, adopting a predictive control model to dynamically correct orbit parameters, or executing ground command adjustments after the measurement and control link is restored, thereby improving orbit maintenance capability.

Description

Technical Field

[0001] The present invention relates to the field of measurement, operation and control technology, and in particular to a satellite remote measurement, operation and control system. Background Art

[0002] The satellite remote measurement, operation and control system is responsible for tracking and measuring spacecraft, collecting telemetry data, issuing remote control commands and scheduling tasks. For a long time, the system has mainly relied on ground measurement and control stations to establish measurement and control links, and to conduct data exchange and command transmission when the satellite passes by.

[0003] However, the distribution of ground tracking and control stations is constrained by geographical factors. Satellites can only establish tracking and control links within a brief visible window. For high-orbit satellites, this window can be as long as tens of minutes, while low-orbit satellites often have only a few minutes of tracking and control time. Mission scheduling must be adjusted around these limited windows, resulting in limited flexibility and real-time performance of mission execution. In the event of an unexpected failure, if the satellite fails to enter the tracking and control range in a timely manner, the anomaly may further deteriorate and even affect normal operation. In deep space exploration missions, the execution efficiency of tracking and control instructions is further affected by communication delays. For example, the round-trip data time of the Mars rover can reach tens of minutes, seriously reducing the ability to respond to emergencies. Secondly, insufficient tracking and control coverage is also a key factor affecting mission stability. The layout of traditional ground-based tracking and control stations is mainly concentrated in mid- and low-latitude regions, resulting in polar-orbiting satellites in the tracking and control blind spots in the Arctic and Antarctic regions for long periods of time. In addition, global missions such as ocean observation and remote exploration require all-weather data feedback, but the limited coverage of ground tracking and control stations cannot support continuous tracking and control.

[0004] Some solutions use space-based tracking and control relay satellites, such as TDRSS and Tianlian enhanced coverage. However, due to their fixed orbital altitude, it is difficult to provide full-time tracking and control support for all orbiting satellites. For low-orbit satellite constellations, the competition for link resources is high. In addition, high-security missions such as deep space exploration and military aerospace require extremely high anti-interference capabilities of the tracking and control system, but traditional tracking and control systems are prone to signal loss in strong interference environments, affecting mission continuity. With the increase in the number of low-orbit satellites and the increase in mission complexity, how to build a more flexible, efficient and autonomous tracking and control system has become an urgent problem to be solved. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a satellite remote measurement, operation and control system to solve the problem that ground measurement and control is limited by coverage and time window, and relay satellites are constrained by orbits, making it difficult to meet the continuous measurement and control requirements of low-orbit and deep space missions.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a satellite remote measurement, operation and control system, which includes:

[0009] Track measurement and control module, used to collect initial track status data and plan the measurement and control link;

[0010] Track monitoring and prediction module, which performs track monitoring and correction based on track status data and predicts mission execution parameters;

[0011] The measurement and control link optimization module optimizes the measurement and control link according to the mission execution parameters and performs orbit adjustments;

[0012] The optimization of the measurement and control link includes dynamically selecting the link based on reinforcement learning;

[0013] The measurement and control link includes ground measurement and control stations, relay satellites, high-altitude platforms and intersatellite links;

[0014] The orbit adjustment includes attitude correction, orbit maintenance and calculation anomaly warning;

[0015] The mission monitoring module is used to monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control situations;

[0016] The task execution data includes task status, device workload and communication link delay;

[0017] The abnormal measurement and control prediction includes signal loss, link interruption and mission failure probability;

[0018] The measurement and control adjustment module adjusts the measurement and control mode based on the abnormal prediction results of the mission monitoring module, optimizes the data return path, and performs orbit adjustment; the adjustment of the measurement and control mode includes autonomous execution of the measurement and control strategy.

[0019] As an optimal solution of the satellite remote measurement, operation and control system described in the present invention, the orbit measurement and control module adopts a measurement and control link combination of low-orbit satellite LEO, geosynchronous orbit satellite GEO and high-altitude platform HAPS to provide full time domain measurement and control coverage.

[0020] As a preferred solution of the satellite remote measurement, operation and control system described in the present invention, the measurement and control link optimization module adopts a reinforcement learning algorithm to dynamically adjust the measurement and control path based on the real-time measurement and control link status.

[0021] As a preferred solution of the satellite remote measurement, operation and control system described in the present invention, the mission monitoring module integrates an AI model for abnormal state prediction and fault mode identification, so as to avoid risks that may affect mission execution in advance.

[0022] As a preferred solution of the satellite remote measurement, operation and control system of the present invention, the measurement, operation and control method of the system is:

[0023] Step S1: Collect initial track state data and plan the measurement and control link;

[0024] The orbital status data includes satellite orbit parameters, attitude angle and measurement and control signal quality;

[0025] The TT&C link planning includes TT&C availability assessment of ground TT&C stations, relay satellites and intersatellite links;

[0026] Step S2: perform orbit monitoring and correction based on orbit status data and predict mission execution parameters;

[0027] Said track monitoring includes collecting telemetry data and signal integrity analysis;

[0028] The mission execution parameters include orbit drift trend, attitude change range and measurement and control signal attenuation degree;

[0029] Step S3, optimizing the measurement and control link of step S1 according to the mission execution parameters, adjusting the measurement and control link, and performing orbit adjustment;

[0030] The measurement and control link optimization includes dynamically selecting links based on reinforcement learning;

[0031] The measurement and control link includes ground measurement and control stations, relay satellites, high-altitude platforms and intersatellite links;

[0032] The orbit adjustment includes attitude correction, orbit maintenance and calculation anomaly warning;

[0033] Step S4: monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control conditions;

[0034] The task execution data includes task status, device workload and communication link delay;

[0035] The abnormal measurement and control prediction includes signal loss, link interruption and mission failure probability;

[0036] Step S5: adjusting the measurement and control mode based on the abnormal prediction result of S4, optimizing the data return path, and performing orbit adjustment;

[0037] The measurement and control mode adjustment includes autonomously executing the measurement and control strategy;

[0038] The orbit adjustment includes autonomous orbit correction or ground command adjustment after the measurement and control link is restored.

[0039] As a preferred solution of the satellite remote measurement, operation and control system of the present invention, in step S3, the step of dynamically selecting a link based on reinforcement learning is:

[0040] Modeling state space, defining the state vector of the measurement and control link to describe the current state of the link, expressed as:

[0041] s t =(b t , d t ,q t , r t ),

[0042] Among them, s t represents the state of the measurement and control link at time t, b t represents the bandwidth of the measurement and control link at time t, d t represents the delay of the measurement and control link at time t, q t represents the quality of the measurement and control signal at time t, r t represents the measurement and control resource occupancy rate at time t;

[0043] Define the action space, expressed as:

[0044] a t ∈{L1,L2,…,L N},

[0045] where a t represents the measurement and control link selected at time t, L i represents the i-th TT&C link, i ranges from i∈{1, 2, …, N}, and N is the total number of optional TT&C links, including ground TT&C stations, relay satellites, high-altitude platforms, and intersatellite links;

[0046] Set the reward function R t The formula for calculating the quality of the measurement and control path is:

[0047] R t =α1f1(b t )+α2f2(d t )+α3f3(q t )+α4f4(r t ),

[0048] Among them, R t is the reward value, f1(b t ) represents the bandwidth gain function, which is expressed as a normalized linear function:

[0049] f1(b t )=b t / b max , where b max is the maximum bandwidth of the measurement and control link,

[0050] f2(d t ) represents the delay loss function, which is expressed as a negative exponential decay function:

[0051] where λ d is the delay weight factor,

[0052] f3(q t ) represents the signal quality function, which is represented by a logarithmic mapping:

[0053] f3(q t )=log(1+q t ),

[0054] f4(r t ) represents the resource occupancy penalty function, which is expressed using a linear penalty model:

[0055] f4(r t )=-r t / r max , α1, α2, α3, α4 are the weight factors of the reward function, satisfying the normalization condition: α1+α2+α3+α4=1,

[0056] Among them, α1 weighs the importance of bandwidth, α2 weighs the impact of latency on the link, α3 weighs the impact of signal quality on the measurement and control link, and α4 weighs the impact of resource contention.

[0057] As a preferred solution of the satellite remote measurement, operation and control system of the present invention, the step of dynamically selecting a link based on reinforcement learning further includes:

[0058] Define the task priority function, the function formula is:

[0059] P t =β1T d +β2W c +β3C o ,

[0060] Among them, P t is the task priority at time t, T d is the task deadline, W c Calculate the load for the task, C o is the occupancy rate of the measurement and control link, β1, β2, β3 are priority weight factors, and the normalization condition is satisfied:

[0061] β1+β2+β3=1,

[0062] Among them, β1 weighs the importance of task deadline, β2 weighs the impact of computational load on priority, and β3 weighs the impact of measurement and control link occupancy;

[0063] When measurement and control link resources are limited, the allocation strategy is adjusted. The adjustment formula is:

[0064] U t =γ1Pt +γ2R t ,

[0065] Among them, U t is the comprehensive score, γ1 and γ2 are adjustment factors, satisfying: γ1+γ2=1, P t is the task priority, R t is the reward value of the measurement and control link;

[0066] Deep Q learning is used for link optimization to update the Q value. The update formula is:

[0067] in,

[0068] In state s t+1 Select the Q value of the best action,

[0069] Q(s t , a t ) means in state s t Select action a t Q value, η is the learning rate, which determines the Q value update step size, R t is the current reward value, and λ is the discount factor.

[0070] As a preferred solution of the satellite remote measurement, operation and control system described in the present invention, in step S4, the method of monitoring the satellite status in real time during the mission execution, obtaining mission execution data, and predicting abnormal measurement and control conditions is as follows:

[0071] Real-time monitoring of satellite mission status, defining the state vector as S' t :

[0072] S′ t =(X t , Y t , Z t ,Θ t , P′ t , L′ t ),

[0073] Among them, S' ′t is the satellite status monitored at time t, X t , Y t , Z t is the three-dimensional position of the satellite in the orbital coordinate system,

[0074] Θ t is the satellite attitude angle, P' t is the task status, L' t is the link communication delay;

[0075] The Bayesian method is used for anomaly detection to calculate the probability of anomaly occurrence. The calculation formula is:

[0076]

[0077] Among them, P(A|S' t ) is a given state S' t The abnormal probability under t |A) is the probability of the observed state under abnormal conditions, P(A) is the prior probability of abnormality, P(S' t ) is the total probability of the current state.

[0078] As a preferred solution of the satellite remote measurement, operation and control system of the present invention, wherein: in the process of predicting abnormal measurement and control conditions in step S4, an error judgment control mechanism is introduced,

[0079] The chi-square test method is used for abnormality identification, and the formula is:

[0080]

[0081] Among them, X 2 is the statistic for anomaly detection, O i is the observed value, E i is the expected value,

[0082] Setting thresholds like It is judged as abnormal;

[0083] The LSTM neural network is used to predict abnormal status, and the status update is as follows:

[0084] h' t =f(W' h h' t-1 +W' x S' t +b'),

[0085] Among them, h' t is the hidden state predicted by LSTM, h' t-1 Represents the hidden state of the previous time step, W' h is the hidden state weight matrix, W' x is the input weight matrix, b' is the bias term, and f(·) is the activation function.

[0086] As a preferred solution of the satellite remote measurement, operation and control system of the present invention, in step S5, the step of autonomously executing the measurement and control strategy is:

[0087] After predicting an anomaly, the system automatically adjusts the measurement and control strategy:

[0088] Optimize the data return path to achieve the lowest cost and fastest response time. The optimization formula is:

[0089]

[0090] Among them, R opt is the optimal data return path, C(L i ) is the link L i The transmission cost, T(L i ) is the link L i The transmission time, μ is the adjustment factor,

[0091] Hybrid encryption is used for encryption. The encryption process is as follows:

[0092]

[0093] Among them, C' is the encrypted data, E k (D') represents the encrypted data D' with key k, Represents the XOR operation, which is used to combine the encrypted data and the hash value, H(S' t ) is the data integrity hash value;

[0094] Adjust the measurement and control mode according to the abnormal situation and define the adjustment function, which is expressed as:

[0095] M′ t =δ1A′ t +δ2R′ t ,

[0096] Among them, M' t Adjust the parameters for the measurement and control strategy, A' t is the abnormal level, R' t is the current measurement and control link status, δ1 and δ2 are adjustment factors;

[0097] Track adjustment is performed based on the predictive control model, and the adjustment formula is:

[0098] X′ t+1 =A′X′ t +B′U′ t ,

[0099] Among them, X' t+1 is the adjusted orbital state, A' is the orbital dynamics matrix, B' is the control input matrix, U' t Adjust strategy for the track.

[0100] The beneficial effects of the present invention are as follows: the present invention adopts a measurement and control link combination of low-orbit satellites, geosynchronous orbit satellites and high-altitude platforms, dynamically selects the optimal measurement and control path through reinforcement learning, optimizes measurement and control scheduling based on link status, task priority and resource competition, and improves measurement and control coverage and resource utilization; during the task execution process, the integrated AI model monitors the satellite task status in real time, obtains task status, equipment load and communication link delay data, and combines the Bayesian probability model and LSTM neural network to predict abnormal situations, thereby avoiding potential risks in advance and improving the stability of the measurement and control task.

[0101] The present invention can autonomously adjust the measurement and control mode after an anomaly detection, dynamically correct orbit parameters using a predictive control model, or execute ground command adjustments after the measurement and control link is restored, thereby improving orbit maintenance capabilities. In addition, to improve the security and reliability of measurement and control data transmission, the present invention optimizes the data return path based on the optimal path selection algorithm, reducing data transmission delay while lowering communication costs, and adopts a hybrid encryption method to ensure data security and prevent link attacks and data tampering.

[0102] The present invention adopts a task priority scheduling mechanism, calculates the importance of tasks according to task deadlines, computing load, and link occupancy, and intelligently allocates measurement and control resources through the DQN algorithm, so that high-priority tasks are given priority under resource-constrained conditions, thereby improving the measurement and control stability of critical tasks.

[0103] In summary, the present invention improves the stability of the measurement and control link, the reliability of task execution, and the security of data transmission, while reducing communication latency and the response time of orbit adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0105] Figure 1 It is a schematic diagram of the framework of the satellite remote measurement, operation and control system of the present invention. DETAILED DESCRIPTION

[0106] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0107] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0108] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0109] Example 1, with reference to Figure 1 This embodiment provides a satellite remote measurement, operation and control system, including:

[0110] Track measurement and control module, used to collect initial track status data and plan the measurement and control link;

[0111] The orbit tracking and control module uses a combination of tracking and control links of low-orbit satellites (LEO), geosynchronous orbit satellites (GEO), and high-altitude platforms (HAPS) to provide full-time-domain tracking and control coverage.

[0112] Track monitoring and prediction module, which performs track monitoring and correction based on track status data and predicts mission execution parameters;

[0113] The measurement and control link optimization module optimizes the measurement and control link according to the mission execution parameters and performs orbit adjustments;

[0114] The optimization of the measurement and control link includes dynamic link selection based on reinforcement learning;

[0115] The tracking and control links include ground tracking and control stations, relay satellites, high-altitude platforms and inter-satellite links;

[0116] Orbital adjustments include attitude correction, orbit maintenance, and calculation anomaly warnings;

[0117] The measurement and control link optimization module uses a reinforcement learning algorithm to dynamically adjust the measurement and control path based on the real-time measurement and control link status;

[0118] The mission monitoring module is used to monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control situations;

[0119] Task execution data includes task status, device workload, and communication link latency;

[0120] Abnormal measurement and control prediction includes signal loss, link interruption and mission failure probability;

[0121] The mission monitoring module integrates AI models to predict abnormal conditions and identify fault patterns, thus proactively avoiding risks that may affect mission execution.

[0122] The measurement and control adjustment module adjusts the measurement and control mode based on the abnormal prediction results of the mission monitoring module, optimizes the data return path, and performs orbit adjustments; the adjustment of the measurement and control mode includes the autonomous execution of measurement and control strategies.

[0123] This embodiment further provides a measurement, operation and control method for the satellite remote measurement, operation and control system, including:

[0124] Step S1: Collect initial track state data and plan the measurement and control link;

[0125] Orbital status data includes satellite orbit parameters, attitude angle, and measurement and control signal quality;

[0126] TT&C link planning includes TT&C availability assessment of ground TT&C stations, relay satellites, and intersatellite links;

[0127] Step S2: perform orbit monitoring and correction based on orbit status data and predict mission execution parameters;

[0128] Track monitoring includes collecting telemetry data and signal integrity analysis;

[0129] Mission execution parameters include orbit drift trend, attitude change range, and measurement and control signal attenuation degree;

[0130] Step S3, optimizing the measurement and control link of step S1 according to the mission execution parameters, adjusting the measurement and control link, and performing orbit adjustment;

[0131] Measurement and control link optimization includes dynamic link selection based on reinforcement learning;

[0132] The tracking and control links include ground tracking and control stations, relay satellites, high-altitude platforms and inter-satellite links;

[0133] Orbital adjustments include attitude correction, orbit maintenance, and calculation anomaly warnings;

[0134] In step S3, the steps of dynamically selecting links based on reinforcement learning are:

[0135] Modeling state space, defining the state vector of the measurement and control link to describe the current state of the link, expressed as:

[0136] s t =(b t , d t ,q t , r t ),

[0137] Among them, s t represents the state of the measurement and control link at time t, bt represents the bandwidth of the measurement and control link at time t, d t represents the delay of the measurement and control link at time t, q t represents the quality of the measurement and control signal at time t, r t represents the measurement and control resource occupancy rate at time t;

[0138] Define the action space, expressed as:

[0139] a t ∈{L1,L2,…,L N},

[0140] where a t represents the measurement and control link selected at time t, L i represents the i-th TT&C link, i ranges from i∈{1, 2, …, N}, and N is the total number of optional TT&C links, including ground TT&C stations, relay satellites, high-altitude platforms, and intersatellite links;

[0141] Set the reward function R t The formula for calculating the quality of the measurement and control path is:

[0142] R t =α1f1(b t )+α2f2(d t )+α3f3(q t )+α4f4(r t ),

[0143] Among them, R t is the reward value, f1(b t ) represents the bandwidth gain function, which is expressed as a normalized linear function:

[0144] f1(b t )=b t / b max , where b max is the maximum bandwidth of the measurement and control link,

[0145] f2(d t ) represents the delay loss function, which is expressed as a negative exponential decay function:

[0146] where λ d is the delay weight factor,

[0147] f3(q t ) represents the signal quality function, which is represented by a logarithmic mapping:

[0148] f3(q t )=log(1+q t ),

[0149] f4(r t ) represents the resource occupancy penalty function, which is expressed using a linear penalty model:

[0150] f4(r t )=-r t / r max , α1, α2, α3, α4 are the weight factors of the reward function, satisfying the normalization condition: α1+α2+α3+α4=1,

[0151] Among them, α1 weighs the importance of bandwidth, α2 weighs the impact of latency on the link, α3 weighs the impact of signal quality on the measurement and control link, and α4 weighs the impact of resource contention;

[0152] The steps of dynamically selecting links based on reinforcement learning also include:

[0153] Define the task priority function, the function formula is:

[0154] P t =β1T d +β2W c +β3C o ,

[0155] Among them, P t is the task priority at time t, T d is the task deadline, W c Calculate the load for the task, C o is the occupancy rate of the measurement and control link, β1, β2, β3 are priority weight factors, and the normalization condition is satisfied:

[0156] β1+β2+β3=1,

[0157] Among them, β1 weighs the importance of task deadline, β2 weighs the impact of computational load on priority, and β3 weighs the impact of measurement and control link occupancy;

[0158] When measurement and control link resources are limited, the allocation strategy is adjusted. The adjustment formula is:

[0159] U t =γ1P t +γ2R t ,

[0160] Among them, U t is the comprehensive score, γ1 and γ2 are adjustment factors, satisfying: γ1+γ2=1, P t is the task priority, R t is the reward value of the measurement and control link;

[0161] Deep Q learning is used for link optimization to update the Q value. The update formula is:

[0162] in,

[0163] In state s t+1 Select the Q value of the best action,

[0164] Q(s t , a t ) means in state s t Select action a t Q value, η is the learning rate, which determines the Q value update step size, R t is the current reward value, λ is the discount factor;

[0165] Specifically, reinforcement learning is used here to optimize the measurement and control link selection. The deep Q-learning method is adopted to dynamically adjust the decision according to the measurement and control link status. Through state-space modeling, the bandwidth, delay, signal quality and resource occupancy of the measurement and control link are included in the optimization range, and a reward function based on bandwidth gain, delay loss, signal quality and resource competition is designed to ensure the optimality of link selection. In addition, a task priority scheduling mechanism is introduced to calculate the importance of tasks according to task deadlines, computing load and link occupancy. High-priority tasks receive priority measurement and control resources. In terms of resource competition management, measurement and control resources are reasonably allocated through a comprehensive scoring function to ensure the stability of the measurement and control link for critical tasks. The DQN algorithm is used to train the Q value, making the dynamic adjustment of the measurement and control link more intelligent, effectively reducing communication delays and improving the reliability of measurement and control data transmission.

[0166] Step S4: monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control conditions;

[0167] Task execution data includes task status, device workload, and communication link latency;

[0168] Abnormal measurement and control prediction includes signal loss, link interruption and mission failure probability;

[0169] In step S4, during the mission execution process, the satellite status is monitored in real time, mission execution data is acquired, and abnormal measurement and control conditions are predicted in the following manner:

[0170] Real-time monitoring of satellite mission status, defining the state vector as S' t :

[0171] S′ t =(X t , Y t , Z T ,Θ t , P′ t , L′ t ),

[0172] Among them, S't is the satellite status monitored at time t, X t , Y t , Z t is the three-dimensional position of the satellite in the orbital coordinate system,

[0173] Θ t is the satellite attitude angle, P' t is the task status, L' t is the link communication delay;

[0174] The Bayesian method is used for anomaly detection to calculate the probability of anomaly occurrence. The calculation formula is:

[0175]

[0176] Among them, P(A|S' t ) is a given state S' t The abnormal probability under t |A) is the probability of the observed state under abnormal conditions, P(A) is the prior probability of abnormality, P(S' t ) is the total probability of the current state;

[0177] In step S4, a misjudgment control mechanism is introduced during the process of predicting abnormal measurement and control conditions.

[0178] The chi-square test method is used for abnormality identification, and the formula is:

[0179]

[0180] Among them, X 2 is the statistic for anomaly detection, O i is the observed value, E i is the expected value,

[0181] Setting thresholds like It is judged as abnormal;

[0182] The LSTM neural network is used to predict abnormal status, and the status update is as follows:

[0183] h' t =f(W' h h' t-1 +W' x S' t +b'),

[0184] Among them, h' t is the hidden state predicted by LSTM, h' t-1 Represents the hidden state of the previous time step, W' h is the hidden state weight matrix, W' xis the input weight matrix, b' is the bias term, and f(·) is the activation function;

[0185] Specifically, this paper focuses on satellite status monitoring and anomaly prediction during mission execution, and adopts a multi-level approach to ensure the accuracy and real-time performance of measurement and control data. Specifically:

[0186] Real-time monitoring of satellite orbital coordinates, attitude angles, mission status, and link delays constructs a state space. A Bayesian probability model is used to calculate the probability of anomalies, and a chi-squared detection method is employed to reduce the risk of misjudgment and ensure the accuracy of anomaly detection. An LSTM long short-term memory neural network is introduced to train a model based on historical monitoring data. This predicts possible future anomalies such as link interruptions and signal loss, enabling early detection of anomalies in satellite tracking and control links and improving the reliability of tracking and control missions.

[0187] Step S5: adjusting the measurement and control mode based on the abnormal prediction result of S4, optimizing the data return path, and performing orbit adjustment;

[0188] Measurement and control mode adjustment includes autonomous execution of measurement and control strategies;

[0189] Orbital adjustment includes autonomous orbit correction or ground command adjustment after the measurement and control link is restored;

[0190] In step S5, the steps of autonomously executing the measurement and control strategy are:

[0191] After predicting an anomaly, the system automatically adjusts the measurement and control strategy:

[0192] Optimize the data return path to achieve the lowest cost and fastest response time. The optimization formula is:

[0193] Among them, R opt is the optimal data return path, C(L i ) is the link L i The transmission cost, T(L i ) is the link L i The transmission time, μ is the adjustment factor,

[0194] Hybrid encryption is used for encryption. The encryption process is as follows:

[0195]

[0196] Among them, C' is the encrypted data, E k (D') represents the encrypted data D' with key k, Represents the exclusive OR operation, which is used to combine the encrypted data and the hash value, H(S′ t ) is the data integrity hash value;

[0197] Adjust the measurement and control mode according to the abnormal situation and define the adjustment function, which is expressed as:

[0198] M′ t =δ1A′ t +δ2R′ t ,

[0199] Among them, M' t Adjust the parameters for the measurement and control strategy, A' t is the abnormal level, R' t is the current measurement and control link status, δ1 and δ2 are adjustment factors;

[0200] Track adjustment is performed based on the predictive control model, and the adjustment formula is:

[0201] X′ t+1 =A′X′ t +B′U′ t ,

[0202] Among them, X' t+1 is the adjusted orbital state, A' is the orbital dynamics matrix, B' is the control input matrix, U' t Adjust strategy for track;

[0203] Specifically, the optimal path selection algorithm is used here to dynamically optimize the return path of measurement and control data by comprehensively considering transmission cost and delay, ensuring the efficiency of data transmission. Secondly, a hybrid encryption method is adopted, including symmetric encryption and hash integrity verification, to improve the security of measurement and control data and prevent link attacks and data tampering. In terms of measurement and control mode adjustment, the measurement and control strategy is autonomously adjusted according to the abnormality level and measurement and control link status, so that the satellite has autonomous measurement and control capabilities and reduces dependence on ground stations.

[0204] In addition, the predictive control model is used here to achieve orbit adjustment, including orbit correction, orbit maintenance and anomaly warning, to improve the satellite's orbit stability.

[0205] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A satellite remote measurement, operation and control system, characterized by: include, Track measurement and control module, used to collect initial track status data and plan the measurement and control link; Track monitoring and prediction module, which performs track monitoring and correction based on track status data and predicts mission execution parameters; The measurement and control link optimization module optimizes the measurement and control link according to the mission execution parameters and performs orbit adjustments; The optimization of the measurement and control link includes dynamically selecting the link based on reinforcement learning; The measurement and control link includes ground measurement and control stations, relay satellites, high-altitude platforms and intersatellite links; The orbit adjustment includes attitude correction, orbit maintenance and calculation anomaly warning; The mission monitoring module is used to monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control situations; The task execution data includes task status, device workload and communication link delay; The abnormal measurement and control conditions include signal loss, link interruption and mission failure probability; A measurement and control adjustment module adjusts the measurement and control mode based on the abnormal prediction results of the mission monitoring module, optimizes the data return path, and performs orbit adjustment; the adjustment of the measurement and control mode includes autonomous execution of the measurement and control strategy; The task monitoring module integrates AI models for abnormal state prediction and fault pattern identification, avoiding risks that may affect task execution in advance; The satellite remote measurement, operation and control system's measurement, operation and control methods are as follows: Step S1: Collect initial track state data and plan the measurement and control link; The orbital status data includes satellite orbit parameters, attitude angle and measurement and control signal quality; The TT&C link planning includes TT&C availability assessment of ground TT&C stations, relay satellites and intersatellite links; Step S2: perform orbit monitoring and correction based on orbit status data and predict mission execution parameters; Said track monitoring includes collecting telemetry data and signal integrity analysis; The mission execution parameters include orbit drift trend, attitude change range and measurement and control signal attenuation degree; Step S3, optimizing the measurement and control link of step S1 according to the mission execution parameters, adjusting the measurement and control link, and performing orbit adjustment; The measurement and control link optimization includes dynamically selecting links based on reinforcement learning; The measurement and control link includes ground measurement and control stations, relay satellites, high-altitude platforms and intersatellite links; The orbit adjustment includes attitude correction, orbit maintenance and calculation anomaly warning; Step S4: monitor the satellite status in real time during the mission execution, obtain mission execution data, and predict abnormal measurement and control conditions; The task execution data includes task status, device workload and communication link delay; The abnormal measurement and control conditions include signal loss, link interruption and mission failure probability; Step S5: adjusting the measurement and control mode based on the abnormal prediction result of S4, optimizing the data return path, and performing orbit adjustment; The measurement and control mode adjustment includes autonomously executing the measurement and control strategy; The orbit adjustment includes autonomous orbit correction or ground command adjustment after the measurement and control link is restored.

2. A satellite remote measurement, operation and control system according to claim 1, characterized in that: The orbit measurement and control module adopts the measurement and control link combination of low-orbit satellite LEO, geosynchronous orbit satellite GEO and high-altitude platform HAPS to provide full time domain measurement and control coverage.

3. A satellite remote measurement, operation and control system according to claim 2, characterized in that: The measurement and control link optimization module adopts a reinforcement learning algorithm to dynamically adjust the measurement and control path based on the real-time measurement and control link status.

4. A satellite remote measurement, operation and control system according to claim 3, characterized in that: In step S3, the step of dynamically selecting a link based on reinforcement learning is: Modeling state space, defining the state vector of the measurement and control link to describe the current state of the link, expressed as: s t =(b t ,d t ,q t ,r t ), Among them, s t represents the state of the measurement and control link at time t, b t represents the bandwidth of the measurement and control link at time t, d t represents the delay of the measurement and control link at time t, q t represents the quality of the measurement and control signal at time t, r t represents the measurement and control resource occupancy rate at time t; Define the action space, expressed as: to t ∈{L1,L2,...,L N }, where a t represents the measurement and control link selected at time t, L i represents the i-th TT&C link, i ranges from i∈{1, 2, ..., N}, and N is the total number of optional TT&C links, including ground TT&C stations, relay satellites, high-altitude platforms, and intersatellite links; Set the reward function R t The formula for calculating the quality of the measurement and control path is: R t =α1f1(b t )+α2f2(d t )+α3f3(q t )+α4f4(r t ), Among them, R t is the reward value, f1(b t ) represents the bandwidth gain function, which is expressed as a normalized linear function: f1(b t )=b t / b max , where b max is the maximum bandwidth of the measurement and control link, f2(d t ) represents the delay loss function, which is expressed as a negative exponential decay function: where λ d is the delay weight factor, f3(q t ) represents the signal quality function, which is represented by a logarithmic mapping: f3(q t )=log(1+q t ), f4(r t ) represents the resource occupancy penalty function, which is expressed using a linear penalty model: f4(r t )=-r t / r max , α1, α2, α3, α4 are the weight factors of the reward function, satisfying the normalization condition: α1+α2+α3+α4=1, Among them, α1 weighs the importance of bandwidth, α2 weighs the impact of latency on the link, α3 weighs the impact of signal quality on the measurement and control link, and α4 weighs the impact of resource contention.

5. A satellite remote measurement, operation and control system according to claim 4, characterized in that: The step of dynamically selecting a link based on reinforcement learning also includes: Define the task priority function, the function formula is: P t =β1T d +β2W c +β3C o , Among them, P t is the task priority at time t, T d is the task deadline, W c Calculate the load for the task, C o is the occupancy rate of the measurement and control link, β1, β2, β3 are priority weight factors, and the normalization condition is satisfied: β1+β2+β3＝1， Among them, β1 weighs the importance of task deadline, β2 weighs the impact of computational load on priority, and β3 weighs the impact of measurement and control link occupancy; When measurement and control link resources are limited, the allocation strategy is adjusted. The adjustment formula is: U t =γ1P t +γ2R t , Among them, U t is the comprehensive score, γ1 and γ2 are adjustment factors, satisfying: γ1+γ2=1, P t is the task priority, R t is the reward value of the measurement and control link; Deep Q learning is used for link optimization to update the Q value. The update formula is: in, In state s t+1 Select the Q value of the best action, Q(s t , a t ) means in state s t Select action a t Q value, η is the learning rate, which determines the Q value update step size, R t is the current reward value, and λ is the discount factor.

6. A satellite remote measurement, operation and control system according to claim 5, characterized in that: In step S4, during the mission execution process, the satellite status is monitored in real time, mission execution data is acquired, and abnormal measurement and control conditions are predicted in the following manner: Real-time monitoring of satellite mission status, defining the state vector as S' t : S′ t (X t ,Y t ,z t ,Θ t ,P t ,L t ), Among them, S' t is the satellite status monitored at time t, X t , Y t , Z t is the three-dimensional position of the satellite in the orbital coordinate system, Θ t is the satellite attitude angle, P' t is the task status, L' t is the link communication delay; The Bayesian method is used for anomaly detection to calculate the probability of anomaly occurrence. The calculation formula is: Among them, P(A|S' t ) is a given state S' t The abnormal probability under t |A) is the probability of the observed state under abnormal conditions, P(A) is the prior probability of abnormality, P(S' t ) is the total probability of the current state.

7. A satellite remote measurement, operation and control system according to claim 6, characterized in that: In step S4, a misjudgment control mechanism is introduced during the process of predicting abnormal measurement and control conditions. The chi-square test method is used for abnormality identification, and the formula is: Among them, X 2 is the statistic for anomaly detection, O i is the observed value, E i is the expected value, Setting thresholds like It is judged as abnormal; The LSTM neural network is used to predict abnormal status, and the status update is as follows: h' t =f(W' h h' t-1 +W' x S' t +b'), Among them, h' t is the hidden state predicted by LSTM, h' t-1 Represents the hidden state of the previous time step, W' h is the hidden state weight matrix, W' x is the input weight matrix, b' is the bias term, and f(·) is the activation function.

8. A satellite remote measurement, operation and control system according to claim 7, characterized in that: In step S5, the steps of autonomously executing the measurement and control strategy are: After predicting an anomaly, the system automatically adjusts the measurement and control strategy: Optimize the data return path to achieve the lowest cost and fastest response time. The optimization formula is: Among them, R opt is the optimal data return path, C(L i ) is the link L i The transmission cost, T(L i ) is the link L i The transmission time, μ is the adjustment factor, Hybrid encryption is used for encryption. The encryption process is as follows: Among them, C' is the encrypted data, E k (D') represents the encrypted data D' with key k, Represents the XOR operation, which is used to combine the encrypted data and the hash value, H(S' t ) is the data integrity hash value; Adjust the measurement and control mode according to the abnormal situation and define the adjustment function, which is expressed as: M’ t =δ1A’ t +δ2R’ t , Among them, M' t Adjust the parameters for the measurement and control strategy, A' t is the abnormal level, R' t is the current measurement and control link status, δ1 and δ2 are adjustment factors; Track adjustment is performed based on the predictive control model, and the adjustment formula is: X’ t+1 =A’X’ t +B’U’ t , Among them, X' t+1 is the adjusted orbital state, A' is the orbital dynamics matrix, B' is the control input matrix, U' t Adjust strategy for the track.

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