Ground satellite health management system and method implemented in stages

Abstract

The invention discloses a ground satellite health management method implemented in stages, which comprises the following steps: S1, in the first stage, training an intelligent algorithm by using health data and a rule-based result, and when the intelligent algorithm reaches a certain accuracy rate, performing automatic judgment by a system or manual judgment by a system until entering a second stage; s2, in the second stage, the health data in the S2 and the final result in the S2 are utilized to train the intelligent algorithm trained in the S1, and when the intelligent algorithm reaches a certain accuracy rate, the system performs automatic judgment or manual judgment until a third stage is entered; and S3, in the third stage, the health data in the S3 and the final result in the S3 are utilized to train the intelligent algorithm trained in the S2. The safety and reliability of satellite operation are improved, the service life of the satellite is prolonged, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of spacecraft health management, and in particular relates to a ground satellite health management system and method implemented in stages. Background Art

[0002] With the rapid development of space technology, satellites play an increasingly important role in communications, navigation, remote sensing, etc. However, satellites face challenges in extreme environments and complex working conditions in space, and effective health management strategies are needed to ensure their normal operation.

[0003] Traditional satellite health management mainly relies on monitoring and analysis by ground personnel. This approach has the following limitations:

[0004] Slow response time: Ground control personnel need time to analyze data and make decisions, which may lead to delayed response in emergency situations.

[0005] Human Error: Reliance on manual analysis increases the likelihood of error, especially in complex and high-pressure work environments.

[0006] High resource consumption: A large number of ground personnel and equipment are required to support satellite monitoring and maintenance, which increases costs.

[0007] Insufficient data utilization: A large amount of satellite operation data is not fully utilized, and there is a lack of effective data analysis and processing methods.

[0008] The development of artificial intelligence technology, especially machine learning and deep learning technology, provides new solutions for satellite health management. Artificial intelligence algorithms are able to process and analyze large amounts of data, identify patterns and trends, predict failures, and provide decision support. This enables satellite health management to achieve the following advantages:

[0009] Automation: Reduce reliance on manual intervention and improve response speed and processing efficiency.

[0010] Accuracy: Improve the accuracy of fault diagnosis and prediction through algorithm optimization.

[0011] Predictive: Leverages historical data and pattern recognition to predict potential failures and problems.

[0012] Cost-effectiveness: Reduced reliance on ground personnel, lower operating costs.

[0013] Traditional satellite health management is mainly based on rules, and satellite fault diagnosis is performed through expert systems. This method has a high diagnostic accuracy for known problems, but it is difficult to detect gradual and trending problems, and it can only be diagnosed based on existing rules, and new rules cannot be automatically generated. Health management based on intelligent algorithms is trained through a large amount of data, and the accuracy of intelligent algorithms is greatly dependent on training data. In order to achieve a relatively high accuracy, it is necessary to train with a large amount of data for a period of time. Therefore, traditional health management has problems such as rule limitations, insufficient data utilization, and limited processing capabilities, while health management based on intelligent algorithms requires long-term data training to achieve a certain accuracy. Both have their advantages and disadvantages.

[0014] In order to address these problems, the present invention proposes a satellite health management system implemented in stages. Summary of the invention

[0015] The purpose of the present invention is to provide a ground satellite health management system and method that is implemented in stages. The satellite health management system of the present invention can gradually realize health management from manual dependence to full automation, improve the safety and reliability of satellite operation, extend the service life of the satellite, and reduce operating costs.

[0016] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0017] The ground satellite health management system implemented in stages includes data acquisition module, knowledge management module, intelligent algorithm management module, data management module and business module, among which:

[0018] The data collection module is deployed on the ground to collect health data sent by satellite;

[0019] The knowledge management module is deployed on the ground to build a detailed knowledge base and provide rules for the business module;

[0020] The intelligent algorithm management module is deployed on the ground to provide intelligent algorithms for the business module, call the health data and results of the data management module, and conduct intelligent algorithm training;

[0021] The business module is deployed on the ground, and extracts business features based on the health data from the data acquisition module. According to different feature values, the corresponding rules in the knowledge management module are called in the first stage to obtain rule-based results. In the second and third stages, the corresponding rules in the knowledge management module are called to obtain rule-based results. The intelligent algorithm in the intelligent algorithm management module is called to obtain results based on the intelligent algorithm. After judging the rule-based results and the intelligent algorithm-based results, the final results are saved in the data management module.

[0022] The data management module is deployed on the ground to manage the satellite's health data and results;

[0023] The ground integrated management module is deployed on the ground to provide ground control personnel with more comprehensive satellite status analysis and prediction, and to perform manual judgment on scenarios where manual judgment is required by the business module.

[0024] The phased approach to ground satellite health management includes the following steps:

[0025] S1, in the first stage, the business module manages the health of the satellite based on rules and obtains rule-based results. The intelligent algorithm management module uses health data and rule-based results to train the intelligent algorithm. When the intelligent algorithm reaches a certain accuracy rate, the system automatically or manually determines until entering the second stage;

[0026] S2, the second stage business module manages the health of the satellite based on the rules and the intelligent algorithm trained in S1, obtains the rule-based results and the intelligent algorithm-based results, and judges the rule-based results and the intelligent algorithm-based results in S2 to obtain the final result. The intelligent algorithm management module uses the health data in S2 and the final result in S2 to train the intelligent algorithm trained in S1. When the intelligent algorithm reaches a certain accuracy rate, the system automatically judges or manually judges until entering the third stage;

[0027] S3, the third-stage business module manages the health of the satellite based on the rules and the intelligent algorithm trained in S2, obtains the rule-based results and the intelligent algorithm-based results, and judges the rule-based results and the intelligent algorithm-based results in S3 to obtain the final result. The intelligent algorithm management module uses the health data in S3 and the final result in S3 to train the intelligent algorithm trained in S2.

[0028] Preferably, in S1, the intelligent algorithm management module uses health data and rule-based results to train the intelligent algorithm. When the intelligent algorithm reaches a certain accuracy rate, the system automatically judges or manually judges until entering the second stage. The specific steps are:

[0029] S10301, the intelligent algorithm management module obtains health data and rule-based results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training;

[0030] S10302, the intelligent algorithm management module determines whether it can enter the second stage through automatic judgment based on the intelligent algorithm training results, and if so, determines whether the accuracy of the intelligent algorithm is better than the preset value; if not, performs manual judgment;

[0031] S10303, the intelligent algorithm management module determines whether the accuracy of the intelligent algorithm is better than the preset value. If yes, it enters the second stage and the process ends. If no, it repeats S10301 to S10303;

[0032] The user manually determines whether to enter the second stage based on the results of the intelligent algorithm training; if yes, then enter the second stage and the process ends; if not, repeat S10301 to S10303.

[0033] Preferably, the specific steps of S2 are:

[0034] S201, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results;

[0035] S202, judging the rule-based results and the intelligent algorithm-based results, and saving the final results;

[0036] S203, the intelligent algorithm management module obtains health data and final results, and conducts intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the system makes autonomous decisions or manual judgments until entering the third stage.

[0037] Preferably, the specific steps of S202 are: determining whether the result based on the rule is consistent with the result based on the intelligent algorithm, and if they are consistent, storing the final result in the database; if they are inconsistent, determining whether manual judgment is required;

[0038] Determine whether human judgment is required, and if so, make a human judgment; if not, determine whether to default to the rule-based result;

[0039] Determine whether the rule-based results are used by default. If so, the rule-based results are used. If not, enter each business module to make a decision.

[0040] Preferably, the specific steps of S203 are:

[0041] S20301, the intelligent algorithm obtains health data and final results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training;

[0042] S20302, the intelligent algorithm management module determines whether the third stage can be entered through automatic judgment based on the intelligent algorithm training results, and if so, determines whether the accuracy rate obtained by the intelligent algorithm is better than the preset value; if not, performs manual judgment;

[0043] S20303, determining whether the accuracy rate obtained by the intelligent algorithm is better than the preset value, if yes, entering the third stage and ending the process, if no, repeating S20301 to S20303;

[0044] It is manually determined whether to enter the third stage; if yes, the third stage is entered and the process ends; if not, S20301 to S20303 are repeated.

[0045] Preferably, the specific steps of S3 are:

[0046] S301, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results;

[0047] S302, judging the rule-based results and the intelligent algorithm-based results, and saving the final results;

[0048] S303, the intelligent algorithm management module obtains health data and final results, and performs intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the process ends.

[0049] Preferably, the specific steps of S302 are:

[0050] Determine whether the rule-based results and the intelligent algorithm-based results are consistent. If they are consistent, save the final results in the database; if they are inconsistent, determine whether manual judgment is required;

[0051] Determine whether human judgment is required, if so, make a human judgment, if not, determine whether to default to the result based on the intelligent algorithm;

[0052] Determine whether to use the result based on the intelligent algorithm as the default. If so, use the result based on the intelligent algorithm as the default. If not, enter each business module to make a decision.

[0053] The beneficial effects of the present invention are that the present invention can gradually realize health management from manual dependence to full automation, improve the safety and reliability of satellite operation, extend the service life of satellites, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Flowchart of the phased implementation of the ground autonomous health management approach. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention.

[0056] The ground satellite health management system implemented in stages includes data acquisition module, knowledge management module, intelligent algorithm management module, data management module and business module, among which:

[0057] The data collection module is deployed on the ground to collect health data sent by satellite.

[0058] The knowledge management module is deployed on the ground to build a detailed knowledge base and provide rules for the business module.

[0059] The intelligent algorithm management module is deployed on the ground to provide intelligent algorithms for the business module, call the health data and results of the data management module, and perform intelligent algorithm training.

[0060] The business module is deployed on the ground, and extracts business features based on the health data from the data acquisition module. According to different feature values, the corresponding rules in the knowledge management module are called in the first stage to obtain rule-based results. In the second and third stages, the corresponding rules in the knowledge management module are called to obtain rule-based results. The intelligent algorithm in the intelligent algorithm management module is called to obtain results based on the intelligent algorithm. After judging the rule-based results and the intelligent algorithm-based results, the final results are saved in the data management module.

[0061] The data management module is deployed on the ground to manage the satellite's health data and results.

[0062] The ground integrated management module is deployed on the ground to provide ground control personnel with more comprehensive satellite status analysis and prediction, and to perform manual judgment on scenarios where manual judgment is required by the business module.

[0063] 1. The data acquisition module is responsible for collecting real-time data from satellite components, extensions, and subsystems, and obtaining satellite mission plans, remote control instructions, etc. All of this information serves as the basis for health management.

[0064] 2. The knowledge management module builds a detailed knowledge base (mainly rules) based on satellite design parameters and historical operation data, defining the thresholds and conditions of normal and abnormal states.

[0065] 3. The intelligent algorithm management module manages the intelligent algorithms used in the health management system and provides algorithm version management and intelligent algorithm operation and maintenance functions. It has various algorithm training management functions and completes algorithm training tasks according to existing samples, simulated samples, and real satellite operation data, so that the algorithm has practical application capabilities. And it upgrades the intelligent algorithm according to the algorithm upgrade rules and model algorithm training results.

[0066] Artificial intelligence algorithm development: Develop machine learning algorithms suitable for satellite health management, such as decision trees, random forests, support vector machines, neural networks, etc.

[0067] Data training and model optimization: Use existing samples (relevant design documents, test data of components, single machines, subsystems, desktop joint debugging data, previous operation data of satellites on the same platform, etc.), simulation samples (simulated data combining design documents, test data, previous experience, etc.) and the operation data of the first phase of satellites to train and optimize the artificial intelligence algorithm to improve its ability to identify and predict changes in satellite status.

[0068] Data training and model optimization: Utilizing the large amount of data accumulated in the first phase and the satellite operation data in the second phase, the artificial intelligence algorithm is trained and optimized to improve its ability to identify and predict changes in satellite status.

[0069] Continuous learning and optimization mechanism (deployed on the ground): Establish a continuous learning and optimization mechanism so that the system can continuously improve the health management strategy based on new data and experience and adapt to changes in the satellite operating environment.

[0070] 4. Business modules include fault diagnosis module, operation prediction module, health assessment module and performance analysis module.

[0071] The fault diagnosis module can match and process the data collected by the data acquisition module with the rule base and intelligent algorithm of the knowledge management module in real time, and quickly evaluate whether there are faults in the satellite components, single machines, subsystems, and the entire satellite.

[0072] (2) The operation prediction module can match and process the data collected by the data acquisition module with the rule base and intelligent algorithm of the knowledge management module in real time, and perform life prediction and fault prediction on the key equipment of the satellite platform and the key equipment of the payload.

[0073] (3) The health assessment module can match and process the data collected by the data acquisition module with the rule base and intelligent algorithm of the knowledge management module in real time, and quickly assess the health status of each component, single machine, subsystem, and the entire satellite.

[0074] (4) The performance analysis module can match and process the data collected by the data acquisition module with the rule base and intelligent algorithm of the knowledge management module in real time, and quickly evaluate the performance of each component, single machine, subsystem, and the entire satellite.

[0075] 5. The data management module is responsible for the storage and management of all key data in the health management system, such as telemetry data, satellite mission plans, diagnostic results, knowledge bases, intelligent algorithms, etc.

[0076] 6. The ground integrated management module provides result comparison and analysis functions and auxiliary decision support functions.

[0077] Result comparison and analysis: Establish a result comparison and analysis system to compare the evaluation results of the artificial intelligence algorithm with the results of the knowledge base, analyze the consistency and difference between the two, and provide a basis for algorithm optimization.

[0078] Auxiliary decision support: Using artificial intelligence algorithms as auxiliary tools to provide ground control personnel with more comprehensive satellite status analysis and predictions, helping them make more accurate decisions.

[0079] The satellite autonomous health management system, which is implemented in stages, uses intelligent algorithms, knowledge bases, and automated health management processes to achieve real-time monitoring of satellite status, fault diagnosis, predictive maintenance, and decision support to improve the operating efficiency and lifespan of satellites.

[0080] Intelligent algorithm-led: Based on the second phase, further optimize and improve the artificial intelligence algorithm to make it the main tool for satellite health management.

[0081] Knowledge base assistance: The knowledge base is used as an auxiliary tool, and the generated results are used for reference. In addition, it can also be used to handle special situations or boundary conditions that are difficult for intelligent algorithms to identify, ensuring the robustness of the system.

[0082] Automated health management process: fully automate satellite health management, including data collection, status assessment, fault diagnosis, predictive maintenance, etc., to reduce dependence on ground control personnel.

[0083] like Figure 1 As shown, the phased implementation of the ground satellite health management method includes the following steps:

[0084] S1, the first stage is to manage the health of the satellite based on rules, obtain rule-based results, and use the health data and rule-based results to train the intelligent algorithm. When the intelligent algorithm reaches a certain accuracy rate, the system automatically or manually judges until entering the second stage.

[0085] S101, perform intelligent algorithm training based on existing samples and simulated samples.

[0086] Users input existing samples (relevant design documents, test data of components, single machines, subsystems, desktop joint debugging data, previous operation data of satellites on the same platform, etc.) and simulation samples (simulated data combining design documents, test data, past experience, etc.) into the data management module of the satellite health management system. The intelligent algorithm management module can train the intelligent algorithm based on these sample data before obtaining the actual on-orbit operation data of the satellite.

[0087] Users can modify, add, delete, etc. the knowledge and intelligent algorithms in the health management system, and the knowledge management module and intelligent algorithm management module can update the knowledge and intelligent algorithms.

[0088] S102, the business module extracts business features based on the satellite health data, calls the corresponding rules of the knowledge management module, and obtains corresponding results.

[0089] (1) After the satellite is in orbit, it will periodically send telemetry data to the measurement and control system and receive remote control commands from the measurement and control system.

[0090] (2) The data acquisition module collects the health data of the satellite.

[0091] The data acquisition module collects telemetry data, external measurement data, space environment data, satellite mission plans, remote control instructions, etc. transmitted by satellites, and sends these health data to other business modules as a basis for health management judgment, and saves them to the database through the data management module.

[0092] (3) The business module receives health data, extracts business features, and calls the corresponding rules in the knowledge management module based on different feature values ​​to obtain corresponding results.

[0093] The business modules include fault diagnosis module, operation prediction module, health assessment module and performance analysis module.

[0094] The fault diagnosis module receives the health data, extracts the fault features, calls the corresponding rules in the knowledge management module according to different feature values, obtains the fault diagnosis results, and saves the results to the database through the data management module.

[0095] The running prediction module receives the health data, extracts fault features and life features, calls the corresponding rules in the knowledge management module according to different feature values, obtains fault prediction results and life prediction results, and saves the results to the database through the data management module.

[0096] The health assessment module receives health data, extracts health features, calls corresponding rules in the knowledge management module according to different feature values, obtains health assessment results, and saves the results to the database through the data management module.

[0097] The performance analysis module receives health data, extracts performance features, calls corresponding rules in the knowledge management module according to different feature values, obtains performance analysis results, and saves the results to the database through the data management module.

[0098] The fault diagnosis module can also perform corresponding fault handling according to the preset fault handling method. If the diagnosed fault matches the corresponding handling method, the fault diagnosis result and fault handling method can also be pushed to the operator for manual decision-making.

[0099] The operation prediction module can also be based on the preset fault prediction and processing method. If the predicted fault has reached the fault processing conditions, the predicted fault processing can be carried out to avoid the occurrence of the fault in advance; of course, the fault prediction results and fault processing suggestions can also be pushed to the operator, and the next step of decision-making can be made manually; at the same time, pre-decommissioning suggestions can be made based on the life prediction results, and the pre-decommissioning suggestions can be pushed to the operator, who will make the next step of decision-making manually.

[0100] The health assessment module assesses that the satellite is in an unhealthy state. If the corresponding maintenance suggestions can be matched in the preset maintenance suggestions based on the current condition of the satellite, the maintenance suggestions can be prompted to the operator.

[0101] The performance analysis module can provide optimization suggestions to the operator based on the current satellite status if it can match corresponding optimization suggestions in the preset optimization suggestions.

[0102] S103, the intelligent algorithm management module obtains health data and results, and continues to train the intelligent algorithm trained in S101. When the intelligent algorithm reaches a certain accuracy rate, the system automatically judges or manually judges until entering the second stage.

[0103] S10301, the intelligent algorithm management module obtains health data and rule-based results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training.

[0104] When the intelligent algorithm is trained, the accuracy is continuously improved.

[0105] S10302, the intelligent algorithm management module determines whether it can enter the second stage through automatic judgment based on the intelligent algorithm training results. If so, it determines whether the accuracy of the intelligent algorithm is better than the preset value; if not, it makes a manual judgment.

[0106] S10303, the intelligent algorithm management module determines whether the accuracy of the intelligent algorithm is better than the preset value. If so, it enters the second stage and the process ends. If not, repeat S10301 to S10303.

[0107] The user manually determines whether to enter the second stage based on the results of the intelligent algorithm training; if yes, then enter the second stage and the process ends; if not, repeat S10301 to S10303.

[0108] S2, the second stage manages the health of the satellite based on the rules and the intelligent algorithm trained in S1, obtains the rule-based results and the intelligent algorithm-based results, and judges the rule-based results and the intelligent algorithm-based results in S2 to get the final result. The intelligent algorithm trained in S1 is trained using the health data in S2 and the final result in S2. When the intelligent algorithm reaches a certain accuracy rate, the system automatically judges or manually judges until entering the third stage.

[0109] S201, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results.

[0110] Users can modify, add, delete, etc. the knowledge and intelligent algorithms in the health management system, and the knowledge management module and intelligent algorithm management module can update the knowledge and intelligent algorithms.

[0111] (1) After the satellite is in orbit, it will periodically send telemetry data to the measurement and control system and receive remote control commands sent by the measurement and control system.

[0112] (2) The data acquisition module collects the health data of the satellite.

[0113] The data acquisition module collects telemetry data, external measurement data, space environment data, satellite mission plans, remote control instructions, etc. transmitted by satellites, and sends these health data to other business modules as a basis for health management judgment, and saves them to the database through the data management module.

[0114] (3) The business module receives health data, extracts business features, and calls corresponding rules and intelligent algorithms in the knowledge management module based on different feature values ​​to obtain rule-based results and intelligent algorithm-based results.

[0115] The business modules include fault diagnosis module, operation prediction module, health assessment module and performance analysis module.

[0116] The fault diagnosis module receives the health data, extracts the fault features, calls the corresponding rules in the knowledge management module according to different feature values, obtains the rule-based fault diagnosis results, and saves the results to the database through the data management module.

[0117] The running prediction module receives the health data, extracts fault features and life features, and calls the corresponding rules in the knowledge management module according to different feature values ​​to obtain rule-based fault prediction results and life prediction results, and saves the results to the database through the data management module.

[0118] The health assessment module receives health data, extracts health features, calls corresponding rules in the knowledge management module according to different feature values, obtains rule-based health assessment results, and saves the results to the database through the data management module.

[0119] The performance analysis module receives health data, extracts performance features, calls corresponding rules in the knowledge management module according to different feature values, obtains rule-based performance analysis results, and saves the results to the database through the data management module.

[0120] S202, judging the rule-based results and the intelligent algorithm-based results, and saving the final results.

[0121] Determine whether the rule-based results and the intelligent algorithm-based results are consistent. If they are consistent, the final results will be stored in the database; if they are inconsistent, determine whether manual judgment is required.

[0122] Determine whether manual judgment is required. If so, manual judgment is performed, and the result under which method is determined manually and the final result is stored in the database; otherwise, determine whether the rule-based result is used by default.

[0123] Determine whether the rule-based result is the default. If so, the rule-based result is the default and the final result is stored in the database. If not, enter each business module to make a decision through conflict resolution, voting mechanism and other methods, and store the final result in the database, and proceed to the next step (refer to the processing of the first stage).

[0124] S203, the intelligent algorithm management module obtains health data and final results, and conducts intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the system makes autonomous decisions or manual judgments until entering the third stage.

[0125] S20301, the intelligent algorithm obtains health data and final results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training.

[0126] Intelligent algorithm training continuously improves accuracy.

[0127] S20302, the intelligent algorithm management module determines whether it can enter the third stage through automatic judgment based on the intelligent algorithm training results. If so, it determines whether the accuracy obtained by the intelligent algorithm is better than the preset value; if not, manual judgment is performed.

[0128] S20303, determine whether the accuracy obtained by the intelligent algorithm is better than the preset value. If so, enter the third stage and the process ends. If not, repeat S20301 to S20303.

[0129] It is manually determined whether to enter the third stage; if yes, the third stage is entered and the process ends; if not, S20301 to S20303 are repeated.

[0130] S3, the third stage manages the health of the satellite based on the rules and the intelligent algorithm trained in S2, obtains the rule-based results and the intelligent algorithm-based results, judges the rule-based results and the intelligent algorithm-based results in S3 to get the final result, and uses the health data in S3 and the final result in S3 to train the intelligent algorithm trained in S2.

[0131] S301, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results.

[0132] Users can modify, add, delete, etc. the knowledge and intelligent algorithms in the health management system, and the knowledge management module and intelligent algorithm management module can update the knowledge and intelligent algorithms.

[0133] (1) After the satellite is in orbit, it will periodically send telemetry data to the measurement and control system and receive remote control commands sent by the measurement and control system.

[0134] (2) The data acquisition module collects the health data of the satellite.

[0135] The data acquisition module collects telemetry data, external measurement data, space environment data, satellite mission plans, remote control instructions, etc. transmitted by satellites, and sends these health data to other business modules as a basis for health management judgment, and saves them to the database through the data management module.

[0136] (3) The business module receives health data, extracts business features, and calls corresponding rules and intelligent algorithms in the knowledge management module based on different feature values ​​to obtain rule-based results and intelligent algorithm-based results.

[0137] The business modules include fault diagnosis module, operation prediction module, health assessment module and performance analysis module.

[0138] The fault diagnosis module receives the health data, extracts the fault features, calls the corresponding rules in the knowledge management module according to different feature values, obtains the rule-based fault diagnosis results, and saves the results to the database through the data management module.

[0139] The running prediction module receives the health data, extracts fault features and life features, and calls the corresponding rules in the knowledge management module according to different feature values ​​to obtain rule-based fault prediction results and life prediction results, and saves the results to the database through the data management module.

[0140] The health assessment module receives health data, extracts health features, calls corresponding rules in the knowledge management module according to different feature values, obtains rule-based health assessment results, and saves the results to the database through the data management module.

[0141] The performance analysis module receives health data, extracts performance features, calls corresponding rules in the knowledge management module according to different feature values, obtains rule-based performance analysis results, and saves the results to the database through the data management module.

[0142] S302, judging the rule-based results and the intelligent algorithm-based results, and saving the final results.

[0143] Determine whether the rule-based results and the intelligent algorithm-based results are consistent. If they are consistent, the final results will be stored in the database; if they are inconsistent, determine whether manual judgment is required.

[0144] Determine whether human judgment is required. If so, human judgment will be performed, and the human will decide which method will be the standard result, and save the final result in the database; otherwise, determine whether to default to the result based on the intelligent algorithm.

[0145] Determine whether the result based on the intelligent algorithm is the default. If so, the result based on the intelligent algorithm is the default, and the final result is stored in the database; if not, enter each business module to make a decision through conflict resolution, voting mechanism and other methods, and store the final result in the database, and proceed to the next step (refer to the processing of the first stage).

[0146] S303, the intelligent algorithm management module obtains health data and final results, and performs intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the process ends.

Claims

1. The ground satellite health management system implemented in stages includes data acquisition module, knowledge management module, intelligent algorithm management module, data management module and business module, among which: The data collection module is deployed on the ground to collect health data sent by satellite; The knowledge management module is deployed on the ground to build a detailed knowledge base and provide rules for the business module; The intelligent algorithm management module is deployed on the ground to provide intelligent algorithms for the business module, call the health data and results of the data management module, and conduct intelligent algorithm training; The business module is deployed on the ground, and extracts business features based on the health data from the data acquisition module. According to different feature values, the corresponding rules in the knowledge management module are called in the first stage to obtain rule-based results. In the second and third stages, the corresponding rules in the knowledge management module are called to obtain rule-based results. The intelligent algorithm in the intelligent algorithm management module is called to obtain results based on the intelligent algorithm. After judging the rule-based results and the intelligent algorithm-based results, the final results are saved in the data management module. The data management module is deployed on the ground to manage the satellite's health data and results; The ground integrated management module is deployed on the ground to provide ground control personnel with more comprehensive satellite status analysis and prediction, and to perform manual judgment on scenarios where manual judgment is required by the business module.

2. A phased implementation method for ground satellite health management, characterized in that: The phased implementation of the ground satellite health management system according to claim 1 comprises the following steps: S1, in the first stage, the business module manages the health of the satellite based on rules and obtains rule-based results. The intelligent algorithm management module uses health data and rule-based results to train the intelligent algorithm. When the intelligent algorithm reaches a certain accuracy rate, the system automatically or manually determines until entering the second stage; S2, the second stage business module manages the health of the satellite based on the rules and the intelligent algorithm trained in S1, obtains the rule-based results and the intelligent algorithm-based results, and judges the rule-based results and the intelligent algorithm-based results in S2 to obtain the final result. The intelligent algorithm management module uses the health data in S2 and the final result in S2 to train the intelligent algorithm trained in S1. When the intelligent algorithm reaches a certain accuracy rate, the system automatically judges or manually judges until entering the third stage; S3, the third-stage business module manages the health of the satellite based on the rules and the intelligent algorithm trained in S2, obtains the rule-based results and the intelligent algorithm-based results, and judges the rule-based results and the intelligent algorithm-based results in S3 to obtain the final result. The intelligent algorithm management module uses the health data in S3 and the final result in S3 to train the intelligent algorithm trained in S2.

3. The method for ground satellite health management implemented in stages according to claim 2, characterized in that: In S1, the intelligent algorithm management module uses health data and rule-based results to train the intelligent algorithm. When the intelligent algorithm reaches a certain accuracy, the system automatically judges or manually judges until entering the second stage. The specific steps are: S10301, the intelligent algorithm management module obtains health data and rule-based results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training; S10302, the intelligent algorithm management module determines whether it can enter the second stage through automatic judgment based on the intelligent algorithm training results, and if so, determines whether the accuracy of the intelligent algorithm is better than the preset value; if not, performs manual judgment; S10303, the intelligent algorithm management module determines whether the accuracy of the intelligent algorithm is better than the preset value. If yes, it enters the second stage and the process ends. If no, it repeats S10301 to S10303; The user manually determines whether to enter the second stage based on the results of intelligent algorithm training; If yes, enter the second stage and the process ends. If no, repeat S10301 to S10303.

4. The method for ground satellite health management implemented in stages according to claim 2, characterized in that: The specific steps of S2 are: S201, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results; S202, judging the rule-based results and the intelligent algorithm-based results, and saving the final results; S203, the intelligent algorithm management module obtains health data and final results, and conducts intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the system makes autonomous decisions or manual judgments until entering the third stage.

5. The method for ground satellite health management implemented in stages according to claim 4, characterized in that: The specific steps of S202 are: determining whether the result based on the rule is consistent with the result based on the intelligent algorithm, if they are consistent, storing the final result in the database; if they are inconsistent, determining whether manual judgment is required; Determine whether human judgment is required, and if so, make a human judgment; if not, determine whether to default to the rule-based result; Determine whether the rule-based results are used by default. If so, the rule-based results are used. If not, enter each business module to make a decision.

6. The method for ground satellite health management implemented in stages according to claim 4, characterized in that: The specific steps of S203 are: S20301, the intelligent algorithm obtains health data and final results from the data management module in a periodic or data-triggered manner to perform intelligent algorithm training; S20302, the intelligent algorithm management module determines whether the third stage can be entered through automatic judgment based on the intelligent algorithm training results, and if so, determines whether the accuracy rate obtained by the intelligent algorithm is better than the preset value; if not, performs manual judgment; S20303, determining whether the accuracy rate obtained by the intelligent algorithm is better than the preset value, if yes, entering the third stage and ending the process, if no, repeating S20301 to S20303; Humans decide whether to enter the third stage; If yes, enter the third stage and the process ends. If no, repeat S20301 to S20303.

7. The phased implementation method for ground satellite health management according to claim 2, characterized in that: The specific steps of S3 are: S301, the business module extracts business features based on health data, and calls the rules corresponding to the knowledge management module and the intelligent algorithm corresponding to the intelligent algorithm management module respectively to obtain rule-based results and intelligent algorithm-based results; S302, judging the rule-based results and the intelligent algorithm-based results, and saving the final results; S303, the intelligent algorithm management module obtains health data and final results, and performs intelligent algorithm training. When the intelligent algorithm reaches a certain accuracy rate, the process ends.

8. The method for ground satellite health management implemented in stages according to claim 7, characterized in that: The specific steps of S302 are: Determine whether the rule-based results and the intelligent algorithm-based results are consistent. If they are consistent, save the final results in the database; if they are inconsistent, determine whether manual judgment is required; Determine whether human judgment is required, if so, make a human judgment, if not, determine whether to default to the result based on the intelligent algorithm; Determine whether to use the result based on the intelligent algorithm as the default. If so, use the result based on the intelligent algorithm as the default. If not, enter each business module to make a decision.

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