Satellite-based base station fault processing method and system
By using spaceborne multi-agent and digital twin systems, early diagnosis and autonomous repair of spaceborne base station faults are achieved, solving the problems of delay and dependence in fault handling in existing technologies and improving the reliability and autonomous operation capability of spaceborne base stations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING XINGYI LIANXIN TECH DEV CO LTD
- Filing Date
- 2026-05-26
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies cannot effectively achieve early diagnosis and autonomous repair of satellite-borne base station faults, resulting in communication interruptions and service degradation. Furthermore, ground intervention is subject to significant delays and high costs, and lacks system-level collaborative analysis capabilities.
The system employs a spaceborne multi-agent and spaceborne digital twin system, including a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent. Through autonomous mechanisms of perception, prediction, diagnosis, decision-making, and execution, it achieves early fault identification and repair.
It enables early fault diagnosis and autonomous repair of satellite-borne base stations, improving reliability and availability, reducing reliance on ground intervention, and enhancing on-orbit autonomous operation capabilities.
Smart Images
Figure CN122437590A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of communication technology, and in particular to a method and system for handling faults in a spaceborne base station. Background Technology
[0002] With the rapid development of low-Earth orbit satellite internet constellations, satellite-borne base stations, as the core nodes of satellite communication networks, undertake the tasks of massive user access, data relay, and routing forwarding.
[0003] Due to the complex operating environment of satellites, a failure of an onboard base station can lead to communication interruptions, service degradation, or even satellite failure. Furthermore, ground intervention is time-consuming and costly, rendering the traditional "post-event alarm + manual intervention" operation and maintenance model inadequate for high availability requirements. Existing fault detection technologies for onboard equipment largely rely on threshold comparisons, rule engines, or single-model predictions. These solutions involve independent monitoring of each subsystem (power supply, communication, attitude control, and thermal control), lacking system-level collaborative analysis capabilities. Moreover, they rely solely on simple analysis of telemetry data, making it difficult to identify early, minor faults. Additionally, fault handling depends on ground commands, resulting in long response cycles and an inability to achieve immediate self-healing.
[0004] Therefore, to address the above shortcomings, a method and system for handling faults in spaceborne base stations are needed. Summary of the Invention
[0005] The technical problem to be solved by this invention is how to achieve early diagnosis, decision-making and repair of faults in spaceborne base stations, improve the reliability, availability and on-orbit autonomous operation capability of spaceborne base stations, and provide a method and system for handling faults in spaceborne base stations in response to the deficiencies in the prior art.
[0006] To address the aforementioned technical problems, this invention provides a method for handling faults in a spaceborne base station, executed by a spaceborne base station fault handling system. The spaceborne base station fault handling system includes a spaceborne multi-agent system and a spaceborne digital twin. The spaceborne multi-agent system includes a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent, comprising: S1: The sensing agent preprocesses the acquired satellite base station operation data to obtain the satellite base station operation status vector and sends it to the satellite digital twin. S2: The spaceborne digital twin determines the spaceborne base station degradation state parameters based on the spaceborne base station's operating state vector using a preset spaceborne base station degradation prediction model, and sends them to the prediction agent. The spaceborne base station degradation state parameters are data related to the spaceborne base station degradation state. S3: The predictive agent predicts the module failure probability distribution data of each module of the satellite-borne base station based on the degradation state parameters of the satellite-borne base station and the historical degradation trajectory of the satellite-borne base station, and sends it to the diagnostic agent; S4: If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent analyzes the failure propagation path based on the satellite base station operation status vector, obtains a failure diagnosis report, and sends it to the decision agent. S5: The decision-making agent determines the fault repair strategy based on the fault diagnosis report and sends it to the execution agent; S6: The executing agent performs fault repair on the faulty module based on the fault repair strategy.
[0007] This invention also provides a spaceborne base station fault handling system for executing the spaceborne base station fault handling method described above, comprising a spaceborne multi-agent system and a spaceborne digital twin, wherein the spaceborne multi-agent system includes a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent, wherein: The sensing agent is used to preprocess the acquired satellite base station operation data to obtain the satellite base station operation status vector, and send it to the satellite digital twin. The spaceborne digital twin is used to determine the spaceborne base station degradation state parameters based on the spaceborne base station's operating state vector using a preset spaceborne base station degradation prediction model, and send them to the prediction agent. The spaceborne base station degradation state parameters are data related to the spaceborne base station degradation state. The predictive agent is used to predict the module failure probability distribution data of each module of the satellite base station based on the degradation state parameters and the historical degradation trajectory of the satellite base station, and then send it to the diagnostic agent. If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent is used to analyze the failure propagation path based on the satellite base station operation status vector, obtain a failure diagnosis report, and send it to the decision agent. The decision-making agent is used to determine the fault repair strategy based on the fault diagnosis report and send it to the execution agent; The execution agent is used to repair the faulty module based on the fault repair strategy.
[0008] The system for handling faults in a spaceborne base station according to the present invention has the following beneficial effects: By deploying onboard multi-agent systems and onboard digital twins, these agents share information, breaking down subsystem barriers and enabling the system to possess distributed perception, collaborative decision-making, and autonomous execution capabilities. Simultaneously, it allows for the mapping of the physical system's state in a virtual mirror. Specifically, the perception agent enables real-time multi-dimensional state perception; the onboard digital twin, predictive agent, and diagnostic agent enable early fault prediction and root cause analysis; and the decision-making agent and execution agent autonomously generate and execute repair strategies. This forms a self-diagnostic and self-healing mechanism of "perception-prediction-diagnosis-decision-execution," realizing early fault diagnosis, decision-making, and repair functions for onboard base stations. This reduces reliance on ground-based systems and improves the reliability, availability, and on-orbit autonomous operation capabilities of onboard base stations. Attached Figure Description
[0009] Figure 1 This is a flowchart illustrating a method for handling faults in a satellite-borne base station according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating another method for handling faults in a satellite-borne base station provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a satellite-borne base station fault handling system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of another satellite-borne base station fault handling system provided in an embodiment of the present invention. Detailed Implementation
[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0011] Figure 1 This is a flowchart illustrating a satellite-borne base station fault handling method provided in an embodiment of the present invention. The method is executed by a satellite-borne base station fault handling system, which includes a satellite-borne multi-agent system and a satellite-borne digital twin. The satellite-borne multi-agent system includes a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent. Figure 1 As shown, it includes: S1: The sensing agent preprocesses the acquired satellite base station operation data to obtain the satellite base station operation status vector, and sends it to the satellite digital twin.
[0012] In this embodiment, the spaceborne base station fault handling system includes a spaceborne base station body, spaceborne multi-agents, and a spaceborne digital twin. The spaceborne base station refers to a wireless communication device deployed on a satellite. Preferably, the spaceborne base station body includes at least a communication module, a power module, an attitude control module, a thermal control module, a computing module, and a sensor network. Both the spaceborne multi-agents and the spaceborne digital twin are deployed on a spaceborne computing platform. The spaceborne multi-agents communicate based on the FIPA (Foundation for Intelligent Physical Agents) standard, employing a publish / subscribe and request / response hybrid communication mode, and also introducing a lightweight consensus mechanism (such as a Raft variant) to ensure consistency in key decisions. The spaceborne digital twin is a lightweight digital twin model deployed on the spaceborne computing platform, including a physical structure model, a behavioral dynamics model, a fault evolution model, and a repair strategy simulation engine.
[0013] Specifically, the operational data of the spaceborne base station refers to the operational data corresponding to each module of the spaceborne base station itself, which may include at least voltage, current, temperature, vibration, power, bit error rate, CPU load, memory usage, attitude angle, and orbital parameters. Preferably, the sensing agent can collect the operational data of the spaceborne base station through the spaceborne sensor network.
[0014] In a preferred embodiment of this example, the sensing agent in step S1 preprocesses the acquired spaceborne base station operation data to obtain a spaceborne base station operation state vector, including: The sensing agent uses a spatiotemporal alignment algorithm to synchronize and register the operational data of the satellite base station in time and space to obtain the operational alignment data of the satellite base station. The operational data of the satellite base station includes at least voltage, current, temperature, vibration, power, bit error rate, CPU load, memory usage, attitude angle and orbital parameters. Adaptive filtering algorithm and outlier removal algorithm are respectively used to perform data denoising and data cleaning on the spaceborne base station operation alignment data to obtain spaceborne base station operation optimization data. The operational state vector of the satellite base station is constructed based on the operational optimization data of the satellite base station.
[0015] S2: The spaceborne digital twin determines the spaceborne base station degradation state parameters based on the spaceborne base station's operating state vector using a preset spaceborne base station degradation prediction model, and sends them to the prediction agent. The spaceborne base station degradation state parameters are data related to the spaceborne base station's degradation state.
[0016] In this embodiment, after receiving the operating state vector of the spaceborne base station, the spaceborne digital twin dynamically updates its internal state based on the state vector. Preferably, the preset spaceborne base station degradation model is constructed by fusing a physical structure model and a behavioral dynamics model. The physical structure model may include heat conduction equations and power circuit models, while the behavioral dynamics model may include LSTM (Long Short-Term Memory) and GCN (Graph Convolutional Network), etc.
[0017] In a preferred embodiment of this example, the degradation status parameters of the satellite-borne base station include the degradation status vector, current health index, and current remaining service life of the satellite-borne base station. Correspondingly, in step S2, the satellite-borne digital twin determines the degradation status parameters of the satellite-borne base station based on the satellite-borne base station operating status vector using a preset satellite-borne base station degradation prediction model, including: The spaceborne digital twin, based on the operational state vector of the spaceborne base station, uses a preset spaceborne base station degradation prediction model to predict the degradation trend of the spaceborne base station within a preset time period, including the current health index and the current remaining service life. The preset spaceborne base station degradation prediction model is constructed by fusing a physical structure model and a behavioral dynamics model; the degradation state vector is constructed based on the degradation trend. This technical solution combines digital twins with deep learning models to achieve early fault prediction, enabling the prediction window to be 6-24 hours earlier.
[0018] The degradation state vector is a mathematical representation of the degradation state trend, specifically manifested in multiple dimensions such as reduced accuracy, decreased efficiency, increased failure rate, increased energy consumption, and prominent safety hazards. For example, the degradation state trend within a preset time period can be the degradation state trend for the next 72 hours.
[0019] S3: The predictive agent predicts the module failure probability distribution data of each module of the satellite base station based on the degradation state parameters of the satellite base station and the historical degradation trajectory of the satellite base station, and sends it to the diagnostic agent.
[0020] Among them, the module failure probability distribution data refers to the probability distribution of each module failing over time.
[0021] In a preferred embodiment of this example, the predictive agent in step S3 predicts the module failure probability distribution data of each module of the satellite-borne base station based on the degradation state parameters and historical degradation trajectory of the satellite-borne base station, and sends it to the diagnostic agent, including: The predictive agent, based on the degradation state parameters of the satellite base station and the historical degradation trajectory of the satellite base station, uses a deep survival model or a prediction network based on the Transformer architecture to predict the module failure probability and failure time window of each module of the satellite base station. Based on the module failure probability and failure time window of each module of the satellite base station, a module failure probability heat map is generated and sent to the diagnostic agent.
[0022] Preferably, after generating the module failure probability heatmap, high-risk modules can be marked on the heatmap so that subsequent diagnostic processes can diagnose the faulty modules.
[0023] S4: If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent analyzes the failure propagation path based on the satellite base station operation state vector, obtains a failure diagnosis report, and sends it to the decision agent.
[0024] For example, the preset failure probability can be 0.8.
[0025] In a preferred embodiment of this example, the onboard multi-agent further includes a communication coordination agent. Correspondingly, the diagnostic agent in step S4 analyzes the fault propagation path based on the onboard base station's operating state vector, obtains a fault diagnosis report, and sends it to the decision-making agent, including: The diagnostic agent, based on the operational state vector of the satellite-borne base station and combined with the input data of the satellite-borne multi-agent, uses a hybrid diagnostic model based on Bayesian networks and causal reasoning to analyze the fault propagation path and obtain a fault diagnosis report. The input data of the satellite-borne multi-agent includes the range of abnormal satellite-borne base station operation data input by the perception agent, the historical strategy feedback data input by the decision-making agent, and the inter-satellite correlation impact data input by the communication coordination agent. The fault diagnosis report includes at least the following parameters: fault type, confidence level, impact range, and urgency level.
[0026] Among them, the communication coordination agent is mainly used for intra-satellite agent message routing, inter-satellite state synchronization (through inter-satellite links), satellite-to-ground command relay, and communication resource scheduling and congestion control.
[0027] In this embodiment, the operational data of the onboard base station acquired by the sensing agent preferably includes not only the current values of each operational data point but also the abnormal data range of each operational data point, i.e., the abnormal data range of the onboard base station operation in this embodiment. Historical strategy feedback data may include information such as whether historical strategies were correct in relation to historical prediction module failures. Inter-satellite impact correlation data may be shared fault judgment experience data between satellites. The above embodiment combines input data from multiple onboard agents, such as the sensing agent, decision-making agent, and communication coordination agent, to diagnose module faults, thereby improving diagnostic accuracy.
[0028] Furthermore, it can also include: If all module failure probabilities in the module failure probability distribution data are less than or equal to the preset failure probability, then steps S1-S3 are repeated until the module failure probability in the module failure probability distribution data is greater than the preset failure probability. This embodiment, by repeating steps S1-S3, enables timely early prediction of module failures, improving the on-orbit autonomous operation capability of the spaceborne base station.
[0029] S5: The decision-making agent determines the fault repair strategy based on the fault diagnosis report and sends it to the execution agent.
[0030] In this embodiment, the decision-making agent is pre-configured with a strategy knowledge base. Preferably, the fault repair strategy can be determined directly from the strategy knowledge base, or it can be determined after being selected from the strategy knowledge base and optimized. The strategy knowledge base stores fault repair strategies corresponding to each module. For example, it may include: fault repair strategies such as frequency switching, beam redirection, and modulation order reduction for the communication module; fault repair strategies such as switching to backup power and reducing power for the power supply module; fault repair strategies such as switching gyroscopes and activating magnetometers for the attitude control module; fault repair strategies such as starting heaters and adjusting heat pipe flow for the thermal control module; and fault repair strategies such as entering safe mode and shutting down non-critical loads.
[0031] S6: The executing agent performs fault repair on the faulty module based on the fault repair strategy.
[0032] The execution agent responds to the fault repair strategy sent by the decision-making agent, issues fault repair control commands through the onboard bus interface, and monitors the fault repair execution process in real time. All process logs are stored in encrypted form.
[0033] This invention proposes a method for handling faults in a spaceborne base station, executed by a spaceborne base station fault handling system. The system includes a spaceborne multi-agent and a spaceborne digital twin. The spaceborne multi-agent includes a sensing agent, a predictive agent, a diagnostic agent, a decision-making agent, and an execution agent. The method includes: S1: The sensing agent preprocesses the acquired spaceborne base station operating data to obtain a spaceborne base station operating state vector and sends it to the spaceborne digital twin; S2: Based on the spaceborne base station operating state vector, the spaceborne digital twin determines the spaceborne base station degradation state parameters using a preset spaceborne base station degradation prediction model and sends them to the predictive agent. The spaceborne base station degradation state... The parameters are data related to the degradation state of the satellite-borne base station; S3: The predictive agent predicts the module failure probability distribution data of each module of the satellite-borne base station based on the degradation state parameters and the historical degradation trajectory of the satellite-borne base station, and sends it to the diagnostic agent; S4: If there is a module failure probability in the module failure probability distribution data that is greater than the preset failure probability, the diagnostic agent analyzes the failure propagation path based on the satellite-borne base station operating state vector, obtains a failure diagnosis report, and sends it to the decision agent; S5: The decision agent determines the failure repair strategy based on the failure diagnosis report and sends it to the execution agent; S6: The execution agent repairs the faulty module based on the failure repair strategy. The above-mentioned system, by setting up satellite-borne multi-agents and satellite-borne digital twins, allows multiple agents to share information, breaking through subsystem barriers and enabling the system to possess distributed perception, collaborative decision-making, and autonomous execution capabilities. It also enables the state mapping of the physical system in a virtual mirror. Specifically, the sensing agent can achieve real-time perception of multi-dimensional states, the spaceborne digital twin, predictive agent, and diagnostic agent can achieve early fault prediction and root cause analysis, and the decision-making agent and execution agent can autonomously generate and execute repair strategies, forming a self-diagnosis and self-healing mechanism of "perception-prediction-diagnosis-decision-execution". This realizes the early fault diagnosis, decision-making, and repair functions of the spaceborne base station, reduces ground dependence, and improves the reliability, availability, and on-orbit autonomous operation capability of the spaceborne base station.
[0034] Based on the above embodiments, the spaceborne base station fault handling system further includes a ground-based digital twin center. Correspondingly, the decision-making agent in step S5 determines a fault repair strategy based on the fault diagnosis report, including: The decision-making agent matches the fault repair strategy to be verified from the preset strategy knowledge base based on the fault diagnosis report. If the fault repair strategy to be verified is matched, the fault repair strategy to be verified is sent to the on-board digital twin. The onboard digital twin performs simulation verification on the fault repair strategy to be verified. If the simulation is successful, the decision-making agent adopts the fault repair strategy to be verified as the fault repair strategy. If the simulation fails, the decision-making agent sends a request for collaborative optimization of fault repair strategy to the ground digital twin center. The request for collaborative optimization of fault repair strategy includes the fault repair strategy to be verified. The ground digital twin center optimizes the fault repair strategy to be verified and sends the optimized fault repair strategy to the decision-making agent, which then uses the optimized fault repair strategy as the fault repair strategy. If the fault repair strategy to be verified is not matched, the decision-making agent sends a fault repair strategy acquisition request to the ground digital twin center. The ground-based digital twin center generates the fault repair strategy and sends it to the decision-making intelligent agent; The decision-making agent receives the fault repair strategy.
[0035] The ground-based digital twin center is a digital twin system deployed at ground stations. It receives data and event reports from the satellite; trains better prediction and diagnostic models using global data; periodically pushes model update packages and policy library increments to the satellite; and supports multi-satellite collaborative learning to construct a constellation-level fault knowledge graph. The ground-based digital twin center communicates with the onboard multi-agent system and the onboard digital twin system via a satellite-ground collaborative communication link. This link is an S / Ka band or laser communication link, supporting satellite-ground model synchronization and policy distribution.
[0036] In this embodiment, there may be a situation where the fault repair strategy to be verified cannot be matched from the preset strategy knowledge base. In this case, it is preferable to send a fault repair strategy acquisition request to the ground digital twin center to obtain the fault repair strategy.
[0037] The onboard digital twin performs simulation verification of the matched fault repair strategy to be verified. This includes: the onboard digital twin simulates the execution of fault repair actions. At the same time, it can also evaluate factors such as system stability, resource consumption, and communication interruption duration to determine whether there are risks in the fault repair process. If the simulated fault repair actions are completed and the risk value is less than the preset risk value, the simulation is considered successful; otherwise, the simulation fails.
[0038] In this embodiment, after matching a fault repair strategy from a preset strategy knowledge base, it does not directly use this strategy as the final strategy for repairing module faults. Instead, it utilizes the simulation verification function of the onboard digital twin to simulate and verify the fault repair strategy. This technical solution can autonomously generate and verify repair strategies in orbit, reducing ground dependence and shortening response time from hours to minutes. It also avoids secondary faults caused by misoperation. Furthermore, if simulation verification fails, it can request the ground digital twin center for collaborative optimization. Through space-ground collaborative optimization, the fault repair strategy is continuously optimized, improving the system's self-improvement capability.
[0039] Based on the above embodiments, further, after the executing agent in step S6 repairs the faulty module based on the fault repair strategy, the method further includes: The sensing agent acquires the repaired operational data of the spaceborne base station and preprocesses it to obtain the repaired operational state vector of the spaceborne base station. The diagnostic agent compares the preset verification satellite base station operation status vector with the repaired satellite base station operation status vector to determine the fault repair confidence level. Determine whether a fault has been repaired based on the fault repair confidence level; If so, the executing agent updates the health status of the satellite base station and sends the fault repair experience to the ground digital twin center. The ground digital twin center stores the fault repair experience in a preset database and calibrates the preset satellite base station degradation prediction model in the satellite digital twin. If not, the decision-making agent re-executes step S5 to determine the fault repair strategy.
[0040] Among them, the preset verification satellite base station operation status vector is a pre-stored status vector used to verify whether the satellite base station is operating normally.
[0041] In this embodiment, the backup fault repair strategy can be determined directly from the strategy knowledge base, or it can be determined after strategy optimization processing following selection from the strategy knowledge base, or it can be determined directly by the ground digital twin center. After repairing the faulty module, this embodiment verifies and provides feedback on the success of the fault repair, forming a complete closed loop of "perception—prediction—diagnosis—decision—execution—feedback," enabling automatic repair of module faults.
[0042] Figure 2 This is a flowchart illustrating another method for handling faults in a satellite-borne base station according to an embodiment of the present invention. The flowchart specifically describes the process of the above-mentioned method for handling faults in a satellite-borne base station using a preferred embodiment. (See attached diagram.) Figure 2S21. The perceptual agent performs time synchronization, spatial registration, data denoising, and data cleaning on the acquired spaceborne base station operation data to construct a spaceborne base station operation state vector. S22. Based on the spaceborne base station operation state vector, the spaceborne digital twin uses a spaceborne base station degradation prediction model constructed by fusing a physical structure model and a behavioral dynamics model to predict the degradation trend, current health index, and current remaining lifespan of the spaceborne base station over the next 72 hours. S23. Based on the output of the spaceborne digital twin and the historical degradation trajectory of the spaceborne base station, the predictive agent uses a deep survival model or a prediction network based on the Transformer architecture to predict the module failure probability and failure time window of each module of the spaceborne base station, generating a module failure probability heatmap. If the module failure probability is greater than the preset failure probability, then in step S24, the diagnostic agent analyzes the fault propagation path based on the onboard base station's operating state vector and the onboard multi-agent input data, using a hybrid diagnostic model based on Bayesian networks and causal reasoning, to obtain a fault diagnosis report. If not, then steps S21-S23 are repeated until a module failure probability greater than the preset failure probability is found in the module failure probability distribution data. In step S25, the decision agent matches a fault repair strategy to be verified from the preset strategy knowledge base based on the fault diagnosis report. If a fault repair strategy is matched, it is sent to the onboard digital twin; the onboard digital twin then performs simulation verification of the fault repair strategy. If the simulation verification is successful, the decision-making agent sends the fault repair strategy to be verified as the fault repair strategy to the execution agent. If not, the decision-making agent sends a request for collaborative optimization of the fault repair strategy to the ground digital twin center. The ground digital twin center optimizes the fault repair strategy to be verified and sends the optimized fault repair strategy to the decision-making agent. The decision-making agent then sends the optimized fault repair strategy as the fault repair strategy to the execution agent. If no fault repair strategy to be verified is matched from the preset strategy knowledge base, the decision-making agent sends a fault repair strategy acquisition request to the ground digital twin center. The ground digital twin center generates a fault repair strategy and sends it to the decision-making agent. The decision-making agent then sends the fault repair strategy to the execution agent. S26. The execution agent repairs the fault module based on the fault repair strategy. S27. The perception agent acquires the repaired satellite base station operation data and preprocesses it to obtain the repaired satellite base station operation state vector. The diagnostic agent compares the preset verification satellite base station operation state vector with the repaired satellite base station operation state vector to determine the fault repair confidence.Based on the fault repair confidence level, determine whether the fault has been repaired. If so, in step S28, the agent updates the health status of the satellite-borne base station and sends the fault repair experience to the ground digital twin center. The ground digital twin center stores the fault repair experience in a preset database and calibrates the preset satellite-borne base station degradation prediction model in the satellite-borne digital twin. If not, the decision agent re-executes step S25 to determine the fault repair strategy.
[0043] The following is a detailed description of the above-mentioned satellite-borne base station fault handling method using a specific embodiment. In this embodiment, the satellite-borne base station fault handling system is a self-healing system for a low-Earth orbit communication satellite. The satellite platform is a certain type of low-Earth orbit communication satellite with an orbital altitude of 550km and an inclination of 53°. It is equipped with a satellite-borne base station and supports Ka-band multi-beam communication. The hardware configuration includes: a radiation-hardened multi-core processor (1.2GHz, 4 cores), 4GB DDR4 ECC memory, 64GB solid-state storage, and 16 temperature sensors, 8 voltage sensors, 8 current sensors, 3 accelerometers, and 2 other sensors. The satellite-borne multi-agent deployment involves each agent running in a containerized manner on a satellite-borne real-time operating system (such as VxWorks), and the agents communicate with each other through the DDS (Data Distribution Service) distributed real-time communication middleware. The satellite-borne digital twin is modeled using Unity3D and MATLAB, exported as a lightweight GLTF+JSON format, and runs the prediction model using an embedded Python interpreter (PyTorch Lite).
[0044] The sensing agent perceives the operational data of each module of the spaceborne base station, detecting voltage fluctuations (±8%) and a temperature rise to 65℃ in the power module. The spaceborne digital twin updates its status based on the module's operational data, predicting a remaining lifespan of 14 days. The predictive agent, based on the output of the spaceborne digital twin and historical degradation trajectories, predicts that the spaceborne base station may shut down due to overvoltage protection within 7 days, with a failure probability of 0.9. The diagnostic agent diagnoses aging of the DC-DC converter (an electrical device that converts DC power from one voltage level to another), with a root cause confidence level of 92%. The decision-making agent retrieves policies from the policy knowledge base: "Switch to backup power channel + Reduce communication module power to 70% + "Initiate heat pipe to enhance heat dissipation"; Spaceborne digital twin simulation shows that the system is stable, communication rate decreases by 20%, and there are no secondary faults; the agent executes the strategy and completes the switchover within 30 seconds; after fault repair, the sensing agent and diagnostic agent verify that the voltage has returned to stability and the temperature has dropped to 52℃; the system resumes normal service, the executing agent records events to the log and sends them to the ground digital twin center; the ground digital twin center receives the report and pushes a new aging compensation model to the spaceborne digital twin two weeks later.
[0045] Figure 3 This is a schematic diagram of a satellite-borne base station fault handling system provided in an embodiment of the present invention, used to execute the satellite-borne base station fault handling methods in the above embodiments, such as... Figure 3 As shown, the spaceborne base station fault handling system includes a spaceborne multi-agent 31 and a spaceborne digital twin 32. The spaceborne multi-agent includes a perception agent 311, a prediction agent 312, a diagnosis agent 313, a decision-making agent 314, and an execution agent 315, wherein: The sensing agent 311 is used to preprocess the acquired satellite base station operation data to obtain the satellite base station operation status vector, and send it to the satellite digital twin 32. The spaceborne digital twin 32 is used to determine the spaceborne base station degradation state parameters based on the spaceborne base station operation state vector using a preset spaceborne base station degradation prediction model, and send them to the prediction agent 312, wherein the spaceborne base station degradation state parameters are data related to the spaceborne base station degradation state. The predictive agent 312 is used to predict the module failure probability distribution data of each module of the satellite base station based on the degradation state parameters of the satellite base station and the historical degradation trajectory of the satellite base station, and send it to the diagnostic agent 313. If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent 313 is used to analyze the failure propagation path based on the satellite base station operation state vector, obtain a failure diagnosis report, and send it to the decision agent 314. The decision-making agent 314 is used to determine the fault repair strategy based on the fault diagnosis report and send it to the execution agent 315; The execution agent 315 is used to perform fault repair on the faulty module based on the fault repair strategy.
[0046] This invention proposes a spaceborne base station fault handling system, which is used for spaceborne base station fault handling methods. It includes a spaceborne multi-agent system and a spaceborne digital twin. The spaceborne multi-agent system includes a perception agent, a prediction agent, a diagnosis agent, a decision agent, and an execution agent. The perception agent preprocesses the acquired spaceborne base station operating data to obtain a spaceborne base station operating state vector and sends it to the spaceborne digital twin. The spaceborne digital twin determines spaceborne base station degradation state parameters based on the spaceborne base station operating state vector using a preset spaceborne base station degradation prediction model and sends them to the prediction agent. The spaceborne base station degradation state parameters are related to the satellite... The system includes data related to the degradation state of the onboard base station. The predictive agent predicts the module failure probability distribution data for each module of the onboard base station based on the degradation state parameters and historical degradation trajectories, and sends this data to the diagnostic agent. If a module failure probability in the distribution data is greater than a preset failure probability, the diagnostic agent analyzes the fault propagation path based on the onboard base station's operating state vector, obtains a fault diagnosis report, and sends it to the decision agent. The decision agent determines a fault repair strategy based on the fault diagnosis report and sends it to the execution agent. The execution agent repairs the faulty module based on the fault repair strategy. By setting up onboard multi-agent systems and onboard digital twins in the system, multiple agents share information, breaking down subsystem barriers and enabling the system to possess distributed perception, collaborative decision-making, and autonomous execution capabilities. It also allows for the mapping of the physical system's state in a virtual mirror. Specifically, the sensing agent can achieve real-time perception of multi-dimensional states, the spaceborne digital twin, predictive agent, and diagnostic agent can achieve early fault prediction and root cause analysis, and the decision-making agent and execution agent can autonomously generate and execute repair strategies, forming a self-diagnosis and self-healing mechanism of "perception-prediction-diagnosis-decision-execution". This realizes the early fault diagnosis, decision-making, and repair functions of the spaceborne base station, reduces ground dependence, and improves the reliability, availability, and on-orbit autonomous operation capability of the spaceborne base station.
[0047] Based on the above technical solutions, the satellite-borne base station fault handling system further includes a ground-based digital twin center, wherein: The decision-making agent 314 is also used to match the fault repair strategy to be verified from the preset strategy knowledge base based on the fault diagnosis report. If the fault repair strategy to be verified is matched, the fault repair strategy to be verified is sent to the on-board digital twin 32. The onboard digital twin 32 is also used to simulate and verify the fault repair strategy to be verified. If the simulation is successful, the decision-making intelligent agent 314 is also used to use the fault repair strategy to be verified as the fault repair strategy. If the simulation fails, the decision-making agent 314 is also used to send a collaborative optimization fault repair strategy request to the ground digital twin center, the collaborative optimization fault repair strategy request including the fault repair strategy to be verified. The ground digital twin center is used to optimize the fault repair strategy to be verified, and send the optimized fault repair strategy to the decision-making agent 314. The decision-making agent 314 is also used to use the optimized fault repair strategy as the fault repair strategy. If the fault repair strategy to be verified is not matched, the decision-making agent 314 sends a fault repair strategy acquisition request to the ground digital twin center. The ground-based digital twin center generates the fault repair strategy and sends it to the decision-making intelligent agent 314; The decision-making agent 314 receives the fault repair strategy.
[0048] Based on the above technical solutions, further, after the execution agent 315 repairs the fault module based on the fault repair strategy, the perception agent 311 is also used to acquire the repaired satellite base station operation data and perform preprocessing to obtain the repaired satellite base station operation state vector. The diagnostic agent 313 is also used to compare the preset verification satellite base station operating state vector with the repaired satellite base station operating state vector to determine the fault repair confidence level. Determine whether a fault has been repaired based on the fault repair confidence level; If so, the executing agent 315 updates the health status of the satellite base station and sends the fault repair experience to the ground digital twin center. The ground digital twin center stores the fault repair experience in a preset database and calibrates the preset satellite base station degradation prediction model in the satellite digital twin 32. If not, the decision-making agent 314 will re-execute the operation of "determining the fault repair strategy based on the fault diagnosis report" to determine the fault repair strategy.
[0049] Based on the above technical solutions, the sensing agent 311 can further be used for: A spatiotemporal alignment algorithm is used to perform time synchronization and spatial registration on the operational data of the satellite base station to obtain operational alignment data of the satellite base station. The operational data of the satellite base station includes at least voltage, current, temperature, vibration, power, bit error rate, CPU load, memory usage, attitude angle and orbital parameters. Adaptive filtering algorithm and outlier removal algorithm are respectively used to perform data denoising and data cleaning on the spaceborne base station operation alignment data to obtain spaceborne base station operation optimization data. The operational state vector of the satellite base station is constructed based on the operational optimization data of the satellite base station.
[0050] Based on the above technical solutions, the degradation state parameters of the spaceborne base station further include the degradation state vector of the spaceborne base station, the current health index, and the current remaining service life. Correspondingly, the spaceborne digital twin 32 can also be used for: Based on the operational state vector of the satellite base station, the degradation trend of the satellite base station within a preset time period is predicted using a preset satellite base station degradation prediction model, including the current health index and the current remaining service life. The preset satellite base station degradation prediction model is constructed by fusing a physical structure model and a behavioral dynamics model. The degradation state vector is constructed based on the degradation state trend.
[0051] Based on the above technical solutions, the predictive agent 312 can also be further used for: Based on the degradation state parameters and historical degradation trajectory of the satellite base station, the module failure probability and failure time window of each module of the satellite base station are predicted using a deep survival model or a prediction network based on the Transformer architecture. Based on the module failure probability and failure time window of each module of the satellite base station, a module failure probability heat map is generated and sent to the diagnostic agent.
[0052] Based on the above technical solutions, the spaceborne base station fault handling system can also be used for: If all module failure probabilities in the module failure probability distribution data are less than or equal to the preset failure probability, then the operations S1-S3 in the satellite base station failure handling method are re-executed until there is a module failure probability in the module failure probability distribution data that is greater than the preset failure probability.
[0053] Based on the above technical solutions, the spaceborne multi-agent 31 may further include a communication coordination agent, and correspondingly, the diagnostic agent 313 may also be used for: Based on the operational state vector of the satellite-borne base station and combined with the input data of the satellite-borne multi-agent system, a hybrid diagnostic model based on Bayesian networks and causal reasoning is used to analyze the fault propagation path and obtain a fault diagnosis report. The input data of the satellite-borne multi-agent system includes the range of abnormal satellite-borne base station operation data input by the sensing agent, the historical strategy feedback data input by the decision-making agent, and the inter-satellite correlation impact data input by the communication coordination agent. The fault diagnosis report includes at least the following parameters: fault type, confidence level, impact range, and urgency level.
[0054] Figure 4This is a schematic diagram of another spaceborne base station fault handling system provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the spaceborne base station fault handling system includes a spaceborne terminal and a ground terminal. The spaceborne terminal includes the spaceborne base station itself, spaceborne multi-agents, and a spaceborne digital twin. The ground terminal includes a ground-based digital twin center and a space-ground collaborative communication link. The spaceborne base station itself includes a communication module, a power module, an attitude control module, a thermal control module, a computing module, and a sensor network. The spaceborne multi-agents include a perception agent, a prediction agent, a diagnostic agent, a decision-making agent, an execution agent, and a communication coordination agent. The spaceborne digital twin includes a physical structure model, a behavioral dynamics model, a fault evolution model, and a repair strategy simulation engine. The ground-based digital twin center communicates with the spaceborne terminal via the space-ground collaborative communication link.
[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for handling faults in a spaceborne base station, characterized in that, This is executed by the spaceborne base station fault handling system, which includes a spaceborne multi-agent system and a spaceborne digital twin. The spaceborne multi-agent system includes a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent. S1: The sensing agent preprocesses the acquired satellite base station operation data to obtain the satellite base station operation status vector and sends it to the satellite digital twin. S2: The spaceborne digital twin determines the spaceborne base station degradation state parameters based on the spaceborne base station's operating state vector using a preset spaceborne base station degradation prediction model, and sends them to the prediction agent. The spaceborne base station degradation state parameters are data related to the spaceborne base station degradation state. S3: The predictive agent predicts the module failure probability distribution data of each module of the satellite-borne base station based on the degradation state parameters of the satellite-borne base station and the historical degradation trajectory of the satellite-borne base station, and sends it to the diagnostic agent; S4: If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent analyzes the failure propagation path based on the satellite base station operation status vector, obtains a failure diagnosis report, and sends it to the decision agent. S5: The decision-making agent determines the fault repair strategy based on the fault diagnosis report and sends it to the execution agent; S6: The executing agent performs fault repair on the faulty module based on the fault repair strategy.
2. The method according to claim 1, characterized in that, The satellite-borne base station fault handling system also includes a ground-based digital twin center. Correspondingly, the decision-making agent in step S5 determines a fault repair strategy based on the fault diagnosis report, including: The decision-making agent matches the fault repair strategy to be verified from the preset strategy knowledge base based on the fault diagnosis report. If the fault repair strategy to be verified is matched, the fault repair strategy to be verified is sent to the on-board digital twin. The onboard digital twin performs simulation verification on the fault repair strategy to be verified. If the simulation is successful, the decision-making agent adopts the fault repair strategy to be verified as the fault repair strategy. If the simulation fails, the decision-making agent sends a request for collaborative optimization of fault repair strategy to the ground digital twin center. The request for collaborative optimization of fault repair strategy includes the fault repair strategy to be verified. The ground digital twin center optimizes the fault repair strategy to be verified and sends the optimized fault repair strategy to the decision-making agent, which then uses the optimized fault repair strategy as the fault repair strategy. If the fault repair strategy to be verified is not matched, the decision-making agent sends a fault repair strategy acquisition request to the ground digital twin center. The ground-based digital twin center generates the fault repair strategy and sends it to the decision-making intelligent agent; The decision-making agent receives the fault repair strategy.
3. The method according to claim 2, characterized in that, After the executing agent in step S6 repairs the faulty module based on the fault repair strategy, the process further includes: The sensing agent acquires the repaired operational data of the spaceborne base station and preprocesses it to obtain the repaired operational state vector of the spaceborne base station. The diagnostic agent compares the preset verification satellite base station operation status vector with the repaired satellite base station operation status vector to determine the fault repair confidence level. Determine whether a fault has been repaired based on the fault repair confidence level; If so, the executing agent updates the health status of the satellite base station and sends the fault repair experience to the ground digital twin center. The ground digital twin center stores the fault repair experience in a preset database and calibrates the preset satellite base station degradation prediction model in the satellite digital twin. If not, the decision-making agent re-executes step S5 to determine the fault repair strategy.
4. The method according to any one of claims 1-3, characterized in that, In step S1, the sensing agent preprocesses the acquired satellite-borne base station operation data to obtain the satellite-borne base station operation state vector, including: The sensing agent uses a spatiotemporal alignment algorithm to synchronize and register the operational data of the satellite base station in time and space to obtain the operational alignment data of the satellite base station. The operational data of the satellite base station includes at least voltage, current, temperature, vibration, power, bit error rate, CPU load, memory usage, attitude angle and orbital parameters. Adaptive filtering algorithm and outlier removal algorithm are respectively used to perform data denoising and data cleaning on the spaceborne base station operation alignment data to obtain spaceborne base station operation optimization data. The operational state vector of the satellite base station is constructed based on the operational optimization data of the satellite base station.
5. The method according to any one of claims 1-3, characterized in that, The degradation status parameters of the satellite-borne base station include the degradation status vector, current health index, and current remaining service life of the satellite-borne base station. Correspondingly, in step S2, the satellite-borne digital twin determines the degradation status parameters of the satellite-borne base station based on the satellite-borne base station operating status vector using a preset satellite-borne base station degradation prediction model, including: The spaceborne digital twin, based on the operating state vector of the spaceborne base station, uses a preset spaceborne base station degradation prediction model to predict the degradation trend of the spaceborne base station within a preset time period, the current health index, and the current remaining service life. The preset spaceborne base station degradation prediction model is constructed by fusing a physical structure model and a behavioral dynamics model. The degradation state vector is constructed based on the degradation state trend.
6. The method according to any one of claims 1-3, characterized in that, The predictive agent in step S3 predicts the module failure probability distribution data of each module of the satellite-borne base station based on the degradation state parameters and historical degradation trajectory of the satellite-borne base station, and sends it to the diagnostic agent, including: The predictive agent, based on the degradation state parameters of the satellite base station and the historical degradation trajectory of the satellite base station, uses a deep survival model or a prediction network based on the Transformer architecture to predict the module failure probability and failure time window of each module of the satellite base station. Based on the module failure probability and failure time window of each module of the satellite base station, a module failure probability heat map is generated and sent to the diagnostic agent.
7. The method according to any one of claims 1-3, characterized in that, Also includes: If all module failure probabilities in the module failure probability distribution data are less than or equal to the preset failure probability, then repeat steps S1-S3 until there is a module failure probability in the module failure probability distribution data that is greater than the preset failure probability.
8. The method according to any one of claims 1-3, characterized in that, The onboard multi-agent system also includes a communication coordination agent. Correspondingly, the diagnostic agent in step S4 analyzes the fault propagation path based on the onboard base station's operating state vector, obtains a fault diagnosis report, and sends it to the decision-making agent, including: The diagnostic agent, based on the operational state vector of the satellite-borne base station and combined with the input data of the satellite-borne multi-agent, uses a hybrid diagnostic model based on Bayesian networks and causal reasoning to analyze the fault propagation path and obtain a fault diagnosis report. The input data of the satellite-borne multi-agent includes the range of abnormal satellite-borne base station operation data input by the perception agent, the historical strategy feedback data input by the decision-making agent, and the inter-satellite correlation impact data input by the communication coordination agent. The fault diagnosis report includes at least the following parameters: fault type, confidence level, impact range, and urgency level.
9. A spaceborne base station fault handling system, characterized in that, The method for handling faults in a satellite-based base station according to any one of claims 1-8 includes a satellite-based multi-agent and a satellite-based digital twin, wherein the satellite-based multi-agent includes a perception agent, a prediction agent, a diagnosis agent, a decision-making agent, and an execution agent, wherein: The sensing agent is used to preprocess the acquired satellite base station operation data to obtain the satellite base station operation status vector, and send it to the satellite digital twin. The spaceborne digital twin is used to determine the spaceborne base station degradation state parameters based on the spaceborne base station's operating state vector using a preset spaceborne base station degradation prediction model, and send them to the prediction agent. The spaceborne base station degradation state parameters are data related to the spaceborne base station degradation state. The predictive agent is used to predict the module failure probability distribution data of each module of the satellite base station based on the degradation state parameters and the historical degradation trajectory of the satellite base station, and then send it to the diagnostic agent. If there is a module failure probability greater than the preset failure probability in the module failure probability distribution data, the diagnostic agent is used to analyze the failure propagation path based on the satellite base station operation status vector, obtain a failure diagnosis report, and send it to the decision agent. The decision-making agent is used to determine the fault repair strategy based on the fault diagnosis report and send it to the execution agent; The execution agent is used to repair the faulty module based on the fault repair strategy.
10. The system according to claim 9, characterized in that, It also includes ground-based digital twin centers, among which: The decision-making agent is also used to match a fault repair strategy to be verified from a preset strategy knowledge base based on the fault diagnosis report and send it to the onboard digital twin. The onboard digital twin is also used to simulate and verify the fault repair strategy to be verified. If the simulation is successful, the decision-making agent is also used to use the fault repair strategy to be verified as the fault repair strategy. If the simulation fails, the decision-making agent is also used to send a collaborative optimization fault repair strategy request to the ground digital twin center, the collaborative optimization fault repair strategy request including the fault repair strategy to be verified. The ground-based digital twin center is used to optimize the fault repair strategy to be verified, and send the optimized fault repair strategy to the decision-making agent. The decision-making agent is also used to adopt the optimized fault repair strategy as the fault repair strategy.