Remote sensing satellite instrument on-orbit abnormality monitoring and alarm method and system

Abstract

The present invention discloses a method and system for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments, wherein the method comprises: collecting and acquiring on-orbit operation data of remote sensing satellite instruments; presetting parameter thresholds and trend thresholds of instrument telemetry parameter data; generating telemetry anomaly alarm information; presetting data integrity constraints and business process constraints of instrument remote sensing business data; generating remote sensing anomaly alarm information; performing multi-source alarm information matching on telemetry anomaly alarm information and remote sensing anomaly alarm information, generating composite anomaly alarm information based on the matching results; and performing feedback optimization based on the composite anomaly alarm information. The method solves the technical problems of incomplete alarm information, easy omission or false alarm, and low accuracy and reliability of anomaly monitoring in existing remote sensing satellite instrument anomaly monitoring, thereby achieving the technical effects of improving the information coverage of instrument anomaly alarms, reducing omissions and false alarms of alarm information, and improving the accuracy and reliability of anomaly monitoring.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of remote sensing technology, and in particular to a monitoring and alarm method and system for on-orbit anomalies of remote sensing satellite instruments. Background Art

[0002] With the continuous development of satellite technology, remote sensing satellites are increasingly being used in various fields. The stability of their operating status is directly related to the effectiveness and benefits of related applications. Therefore, timely detection and warning of on-orbit anomalies of remote sensing satellite instruments are particularly important. Existing technologies mainly rely on preset thresholds of key parameters to judge data. Once the data exceeds or does not meet the preset thresholds, corresponding anomaly alarm information is generated. However, relying solely on threshold judgments can lead to the neglect of some potential problems, especially when the abnormal data exhibits gradual or intermittent changes. Existing technologies usually process telemetry and remote sensing service data separately, ignoring the inherent connection between them, which often leads to the omission of alarm information or false alarms.

[0003] At present, relevant technologies in remote sensing satellite instrument abnormal monitoring have technical problems such as incomplete alarm information, easy omissions or false alarms, and low accuracy and reliability of abnormal monitoring. Summary of the Invention

[0004] An embodiment of the present invention provides a monitoring and alarm method and system for on-orbit anomalies of remote sensing satellite instruments. The method and system achieve the technical effects of improving the information coverage of abnormal alarms, reducing omissions and false alarms of alarm information, and improving the accuracy and reliability of abnormal monitoring by collecting on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing business data, and matching multi-source alarm information to generate composite abnormal alarm information.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments, comprising:

[0006] Collect and obtain on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing business data;

[0007] Presetting parameter thresholds and trend thresholds for the instrument telemetry parameter data;

[0008] If the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold, generating telemetry abnormality alarm information;

[0009] Presetting data integrity constraints and business process constraints for the instrument remote sensing business data;

[0010] If the instrument remote sensing business data does not meet the data integrity constraint or the business process constraint, generating remote sensing abnormality alarm information;

[0011] Using the timestamp, satellite number, and instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry abnormality alarm information and the remote sensing abnormality alarm information, and generating composite abnormality alarm information based on the matching results;

[0012] Feedback optimization is performed based on the composite abnormality alarm information.

[0013] In one embodiment of the present invention, the timestamp, satellite number, and instrument number are used as matching identifiers to perform multi-source alarm information matching on the telemetry abnormality alarm information and the remote sensing abnormality alarm information, and generating composite abnormality alarm information based on the matching results includes:

[0014] Performing time alignment on the telemetry abnormal alarm information and the remote sensing abnormal alarm information to obtain telemetry and remote sensing synchronous abnormal alarm information;

[0015] Based on the timestamp, a time window is preset, and the satellite number and instrument number are used as matching constraints, the time window is slid along the telemetry and telesensing synchronization abnormality alarm information, and potential alarm information matching items within the preset time window are extracted;

[0016] Performing abnormal time correlation analysis and abnormal type correlation analysis based on the potential alarm information matching items, and generating an abnormal correlation coefficient based on the correlation analysis results;

[0017] If the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, the composite abnormality alarm information is generated.

[0018] In one embodiment of the present invention, if the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, generating the composite abnormality alarm information includes:

[0019] Based on historical data, determine the alarm index range, including the first-level alarm index range, the second-level alarm index range, and the third-level alarm index range;

[0020] Matching the abnormal correlation coefficient with the alarm index interval, and correspondingly generating first-level composite abnormal alarm information, second-level composite abnormal alarm information, and third-level composite abnormal alarm information;

[0021] The first-level composite abnormality alarm information, the second-level composite abnormality alarm information, and the third-level composite abnormality alarm information are added to the composite abnormality alarm information.

[0022] In one embodiment of the present invention, the parameter thresholds of the preset instrument telemetry parameter data include:

[0023] For each instrument telemetry parameter, the corresponding telemetry parameter threshold is initially set according to the instrument design and ground simulation results;

[0024] Based on the on-orbit observation data of the instrument, the initial parameter thresholds are adaptively corrected:

[0025] For each telemetry parameter, set a time interval based on the changing characteristics of historical data;

[0026] For the historical data in each time interval, calculate the mean and standard deviation of the data and determine the corresponding parameter characteristic value;

[0027] Calculate the distance between the parameter data mean and the initially set parameter threshold; if the distance is less than a preset range, correct the parameter threshold to the parameter characteristic value; otherwise, keep the parameter threshold as the initial threshold;

[0028] Repeat the above process according to the time interval setting to achieve adaptive correction of parameter thresholds.

[0029] In one embodiment of the present invention, the trend threshold of the preset instrument telemetry parameter data includes:

[0030] For each instrument telemetry parameter, obtain a historical telemetry parameter data set arranged in chronological order;

[0031] Based on the parameter threshold, data outside the threshold range is eliminated to obtain a purified historical telemetry parameter data set;

[0032] Preset a sliding window, slide the preset sliding window on the purified historical telemetry parameter dataset along a time series, calculate the average value of the data in the sliding window, and obtain a smoothed historical telemetry parameter dataset;

[0033] Fitting the smoothed historical telemetry parameter data set to obtain a telemetry parameter time variation trend model;

[0034] The trend threshold is set based on the time variation trend model of the telemetry parameter.

[0035] In one embodiment of the present invention, the data integrity constraints of the preset instrument remote sensing service data include:

[0036] According to the characteristics and assessment requirements of remote sensing business data, set file name constraints, file size constraints, remote sensing data field constraints and remote sensing data quality code constraints;

[0037] Setting a remote sensing service data verification mechanism based on the file name constraint, file size constraint, remote sensing data field constraint, and remote sensing data quality code constraint;

[0038] The remote sensing data verification mechanism is integrated into the stream processing framework to complete the setting of the data integrity constraints.

[0039] In a second aspect, the present invention provides a remote sensing satellite instrument on-orbit anomaly monitoring and alarm system, comprising: an acquisition module, a first preset module, a first generation module, a second preset module, a second generation module, a third generation module, and a feedback optimization module. The acquisition module is configured to acquire on-orbit operational data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing service data. The first preset module is configured to preset parameter thresholds and trend thresholds for the instrument telemetry parameter data. The first generation module is configured to generate a telemetry anomaly alarm message if the instrument telemetry parameter data does not meet the parameter threshold or trend threshold. The second preset module is configured to preset data integrity constraints for the instrument remote sensing service data. The second generation module is configured to generate a remote sensing anomaly alarm message if the instrument remote sensing service data does not meet the data integrity constraints. The third generation module is configured to perform multi-source alarm information matching on the telemetry anomaly alarm message and the remote sensing anomaly alarm message, using a timestamp, satellite number, and instrument number as matching identifiers, and to generate a composite anomaly alarm message based on the matching results. The feedback optimization module is configured to perform feedback optimization based on the composite anomaly alarm message.

[0040] In a third aspect, the present invention provides an electronic device, comprising:

[0041] at least one processor; and

[0042] a memory communicatively coupled to the at least one processor;

[0043] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned monitoring and alarm method for in-orbit anomalies of remote sensing satellite instruments.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium comprising a computer program and instructions. When the computer program or the instructions are executed on a computer, the computer executes the above-described method for monitoring and alarming in-orbit anomalies of remote sensing satellite instruments.

[0045] Compared with the prior art, the monitoring and alarm method and system for on-orbit anomalies of remote sensing satellite instruments according to the present invention first collect and obtain on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing business data, and then preset parameter thresholds and trend thresholds of the instrument telemetry parameter data. If the instrument telemetry parameter data does not meet the parameter threshold or trend threshold, telemetry anomaly alarm information is generated. Then, data integrity constraints and business process constraints of the instrument remote sensing business data are preset. If the instrument remote sensing business data does not meet the data integrity constraints or business process constraints, remote sensing anomaly alarm information is generated. Then, the timestamp, satellite number, and instrument number are used as matching identifiers to perform multi-source alarm information matching on the telemetry anomaly alarm information and the remote sensing anomaly alarm information. Composite anomaly alarm information is generated based on the matching results. Finally, feedback optimization is performed based on the composite anomaly alarm information, thereby achieving the technical effects of improving the information coverage of instrument anomaly alarms, reducing omissions and false alarms of alarm information, and improving the accuracy and reliability of anomaly monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a method for monitoring and alarming anomalies of a remote sensing satellite instrument on-orbit in the first embodiment of the present invention;

[0047] Figure 2 This is a schematic structural diagram of a monitoring and alarm system for an on-orbit anomaly of a remote sensing satellite instrument in a second embodiment of the present invention;

[0048] Figure 3 It is a structural diagram of an electronic device in embodiment 3 of the present invention. DETAILED DESCRIPTION

[0049] The following is a further detailed description of the embodiments of the present invention in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the embodiments of the present invention, rather than all structures.

[0050] To facilitate understanding, the main implementation concepts of the embodiments of the present invention are first briefly described.

[0051] With the rapid development of remote sensing satellite technology, the application areas of remote sensing satellites are becoming increasingly broad, including but not limited to environmental monitoring, resource exploration, weather forecasting, and disaster warning. These applications place extremely high demands on the stable operation of remote sensing satellites, as any instrument anomaly can directly affect the accuracy and timeliness of data, and thus the effectiveness and benefits of related applications.

[0052] Existing technologies primarily rely on preset fixed thresholds to determine whether data is abnormal. However, threshold settings are often based on experience or historical data, which is subjective and uncertain. When abnormal data exhibits gradual or intermittent changes, a single threshold may not capture these subtle changes in a timely manner, leading to the omission of potential issues.

[0053] Existing technologies typically process instrument telemetry parameter data and instrument remote sensing service data separately, ignoring the inherent connection between them. In reality, many instrument anomalies are reflected not only in telemetry parameters but also in instrument remote sensing service data. Due to this disconnected data processing, existing technologies often fail to fully capture anomaly information, leading to missed alarms or false alarms.

[0054] Due to the lack of integration between different data sources, alarm information often exists in isolated forms, lacking comprehensive analysis and judgment. Due to the above reasons, the alarm information of existing technologies has deficiencies in accuracy and reliability, making it difficult to meet the needs of high-precision monitoring and alarming.

[0055] For example, while a remote sensing satellite is performing an environmental monitoring mission, its temperature sensor may exhibit an abnormal, slowly rising temperature. Under existing technology, relying solely on a preset fixed threshold for judgment may not capture this gradual anomaly in a timely manner. Furthermore, because telemetry data is processed separately from instrument remote sensing data, even if the remote sensing data begins to exhibit abnormal characteristics (such as quality degradation), it may be overlooked due to a lack of comprehensive analysis. This situation not only leads to the failure of the monitoring mission, but also may miss the optimal opportunity to take remedial measures due to the lack of timely warnings.

[0056] By identifying the aforementioned deficiencies in the prior art, the inventors have developed a method and system for monitoring and warning remote sensing satellite instrument anomalies on-orbit. This method and system comprehensively monitors anomalies in remote sensing satellite instruments, reduces missed warnings and false alarms, and improves the accuracy and reliability of anomaly monitoring. This method and system comprehensively considers instrument telemetry parameter data and instrument remote sensing service data, and through multi-source alarm information matching and comprehensive analysis, generates more comprehensive and accurate composite anomaly alarm information. Furthermore, it can flexibly respond to gradual or intermittent anomalies, improving the sensitivity and response speed of the alarm system.

[0057] Example 1

[0058] Figure 1 This is a flow chart of a method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit in the first embodiment of the present invention. Figure 1 As shown, embodiment 1 provides a method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit, including:

[0059] Step S100, collecting and acquiring on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing service data;

[0060] Specifically, in the remote sensing satellite monitoring mission, step S100 is a crucial link, which involves the comprehensive collection and acquisition of key data during the satellite's on-orbit operation. This process not only covers instrument telemetry parameter data, that is, technical indicators that directly reflect the operating status of the satellite and its onboard instruments, such as temperature, voltage, current, etc., but also includes instrument remote sensing service data, that is, information about the observation target (such as the Earth and the Sun) acquired and processed by the satellite instrument. By collecting these multi-dimensional, high-precision data in real time and continuously, step S100 provides a solid data foundation for subsequent data analysis, anomaly detection and alarm generation, ensuring that the remote sensing satellite system can efficiently and accurately perform its monitoring mission, and provide strong support for scientific research, environmental monitoring, disaster warning and other fields.

[0061] Step S200, presetting parameter thresholds and trend thresholds of the instrument telemetry parameter data;

[0062] Specifically, parameter thresholds for telemetry parameter data of various instruments are pre-set based on factors such as the design, specifications, performance requirements, engineering safety margins, and environmental conditions of remote sensing satellite instruments. These thresholds represent the range of values ​​for each parameter when the instrument is operating normally. Exceeding the thresholds may indicate an instrument anomaly. Parameter thresholds are adaptively corrected based on the characteristics of on-orbit observation data. Trend thresholds for instrument telemetry parameter data are pre-set by analyzing historical data and combining expert experience and judgment. These trend thresholds limit the rate of change of parameters. For example, a rapid rise or fall in a parameter within a short period of time may indicate an anomaly, even if the parameter value is still within the normal range.

[0063] Step S300: If the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold, a telemetry abnormality alarm message is generated;

[0064] Specifically, real-time instrument telemetry parameter data is compared with pre-set parameter and trend thresholds. If the instrument telemetry parameter data exceeds the parameter threshold range (i.e., the parameter value is too high or too low), it is considered an anomaly. Similarly, if the parameter change trend does not meet the pre-set trend threshold, such as the rate of change is too fast or too slow, it is also considered an anomaly. Once an anomaly is detected, a telemetry anomaly alarm is immediately generated. This alarm includes information such as the abnormal parameter name, abnormal value, and abnormal time.

[0065] Step S400, presetting data integrity constraints for the instrument remote sensing service data;

[0066] Specifically, for instrument remote sensing service data, data integrity constraints are formulated based on the characteristics and assessment requirements of remote sensing services, combined with actual conditions. These constraints are used to ensure the quality and accuracy of instrument remote sensing service data and the integrity of acquired data. These constraints define a set of rules, such as file name, file size, remote sensing data fields, and remote sensing data quality code constraints, setting standards for remote sensing service data integrity verification. These constraints cover file continuity, the continuity of scanned line data within a file, missing data within a day, data of different quality levels, and the statistics and proportion of abnormal data, aiming to identify and mark any data discontinuities or anomalies.

[0067] Step S500: if the instrument remote sensing service data does not meet the data integrity constraint, remote sensing abnormality alarm information is generated;

[0068] Specifically, during remote sensing data processing, the data is checked against pre-set data integrity constraints. If the data does not meet these constraints, it indicates potential data loss, errors, or quality issues, which can impact data accuracy and usability. Once an anomaly is detected, a remote sensing anomaly alert is immediately generated. This alert records the anomaly type, time of occurrence, location, and affected data, allowing for rapid identification and determination of the problem.

[0069] Step S600, using the timestamp, satellite number, and instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry abnormality alarm information and the remote sensing abnormality alarm information, and generating composite abnormality alarm information based on the matching results;

[0070] Specifically, timestamps, satellite numbers, and instrument numbers are selected as matching identifiers. These unique and deterministic matching identifiers ensure accurate identification and matching of alarm messages from different sources. By comparing matching identifiers, it is determined which telemetry anomaly alarm messages are associated with which remote sensing anomaly alarm messages. If both telemetry and remote sensing anomalies occur simultaneously on the same instrument during the same period, a composite anomaly alarm message is generated. This composite anomaly alarm message contains the content of the alarm messages from different sources, such as the anomaly type, occurrence time, and involved data. It can also further include the correlation and mutual impact between the alarm messages, as well as an analysis of the anomaly cause and recommended solutions. By generating a composite anomaly alarm message, the previously scattered and isolated telemetry and remote sensing anomaly alarm messages are integrated and linked, forming a more complete and accurate description of the anomaly situation. This helps operation and maintenance personnel more comprehensively understand the anomaly situation, more quickly locate the cause of the problem, and take effective measures to resolve it. Furthermore, the generation of composite anomaly alarm messages helps improve the efficiency and accuracy of alarm processing, providing a strong guarantee for the normal operation of satellites and remote sensing instruments.

[0071] Step S700, performing feedback optimization according to the composite abnormality alarm information;

[0072] Specifically, feedback optimization involves notifying satellite instrument operators to troubleshoot, adjust satellite instrument operating parameters, and optimize data processing schemes, thereby eliminating or mitigating abnormal satellite instrument conditions, ensuring the smooth progress of observation missions and data quality. Furthermore, statistical analysis of compound abnormality alarm information is conducted to identify patterns and problems, and the causes of the alarms are thoroughly investigated. Through this analysis, deficiencies in the alarm system are identified and directions for improvement are identified. For example, if a certain type of compound abnormality alarm is found to occur frequently and the response and handling are ineffective, special handling of this type of alarm may be considered, such as adjusting the alarm threshold, optimizing the alarm generation mechanism, or improving the alarm response and handling process. Furthermore, methods such as machine learning can be introduced to predict and analyze alarm data, identifying potential problems in advance and reducing the probability of failure. Furthermore, as the system environment and business needs change, the alarm management system needs to be continuously adjusted and optimized to adapt to the new environment and needs. Therefore, feedback optimization needs to be performed regularly to achieve continuous improvement and optimization of the alarm management system. The embodiment of the present application achieves the technical effect of improving the information coverage of instrument abnormal alarms, reducing omissions and false alarms, and improving the accuracy and reliability of abnormal monitoring by collecting and obtaining on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and remote sensing business data, and performing multi-source alarm information matching to generate composite abnormal alarm information.

[0073] In this embodiment, step S600 includes:

[0074] Step S601, performing time alignment on the telemetry abnormal alarm information and the remote sensing abnormal alarm information to obtain telemetry and remote sensing synchronous abnormal alarm information;

[0075] Specifically, because there may be time differences in the data collection, processing, and analysis of telemetry and remote sensing anomaly alarm information, time series alignment is required to ensure that the two remain consistent in time. This alignment is performed using methods such as timestamp standardization, timestamp calibration, and dynamic time warping. This results in a timeline-synchronized sequence of telemetry and remote sensing anomaly alarm information (telemetry and remote sensing synchronized anomaly alarm information), where each anomaly alarm information corresponds to a unified time point.

[0076] Step S602: Based on the timestamp and a preset time window, the satellite number and the instrument number are used as matching constraints, and the time window is slid along the telemetry and telesensing synchronization abnormality alarm information to extract potential alarm information matching items within the preset time window;

[0077] Specifically, after time series alignment, a preset time window is set. This time window is determined based on factors such as the potential duration of the alarm information, the system's response time, and the specifications and real-time requirements of data processing. The size of the time window should cover the entire time range of the relevant alarm information. After determining the size of the time window, this time window is slid along the telemetry and remote sensing synchronization anomaly alarm information based on the timestamp. The sliding method can be moving one time unit (such as seconds, minutes, etc.) at a time, or it can be jumping according to a fixed step size. At the same time, using the satellite number and instrument number as matching constraints, the telemetry anomaly alarm information and remote sensing anomaly alarm information with the same satellite number and instrument number are searched. Alarm information that is close in time but has different sources is filtered out, thereby extracting potential alarm information matches that meet the constraints and appear within the time window. The potential alarm information matches include multiple telemetry anomaly alarm information and multiple remote sensing anomaly alarm information that appear in the same time window, which may point to the same problem or the same cause.

[0078] Step S603: performing abnormal time correlation analysis and abnormal type correlation analysis based on the potential alarm information matching items, and generating an abnormal correlation coefficient based on the correlation analysis results;

[0079] Specifically, for each potential alarm information match, the time interval between the telemetry anomaly alarm information and the remote sensing anomaly alarm information is calculated by comparing the timestamps of the two. Next, the distribution of time intervals across all potential alarm information matches is statistically analyzed to determine the relative temporal position and distribution of the two anomaly alarms. Based on the time interval statistics, it is determined whether there is a significant temporal correlation between the telemetry anomaly alarm information and the remote sensing anomaly alarm information. For example, if the time intervals for most potential alarm information matches are very short, it indicates that the two anomalies are closely related in time. At the same time, the telemetry anomaly alarm information and the remote sensing anomaly alarm information are classified to determine their respective anomaly types. The types of the telemetry anomaly alarm information and the remote sensing anomaly alarm information in the potential alarm information matches are compared to determine whether there are the same or related anomaly types. Based on the anomaly type matching results, the type correlation between the two anomaly alarms is evaluated. For example, certain specific telemetry anomalies are always accompanied by specific remote sensing anomalies, indicating a strong type correlation between them. According to the analysis results of time correlation and type correlation, corresponding weights are assigned to them according to actual conditions and needs to reflect the importance of different factors in judging abnormal correlation. The analysis results of time correlation and type correlation are comprehensively calculated to obtain a comprehensive abnormal correlation coefficient. The abnormal correlation coefficient can be a value between 0 and 1, which is used to quantify the degree of correlation between telemetry abnormal alarm information and remote sensing abnormal alarm information.

[0080] Step S604: If the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, the composite abnormal alarm information is generated; wherein the abnormal correlation coefficient can be combined with the telemetry and remote sensing abnormal information to generate an alarm index.

[0081] Specifically, an abnormal correlation coefficient threshold is set based on factors such as the actual application scenario and the importance of the alarm information, as well as system stability requirements, the false alarm rate of the alarm information, and the processing capabilities of the operation and maintenance personnel. The abnormal correlation coefficient threshold represents the minimum standard for sufficient correlation between the two abnormal alarms. The abnormal correlation coefficient calculated in step S630 is compared with the abnormal correlation coefficient threshold. If the abnormal correlation coefficient is greater than the abnormal correlation coefficient threshold, it is considered that there is a significant correlation between the telemetry abnormal alarm information and the remote sensing abnormal alarm information, and the two may point to the same problem or the same cause. In this case, a composite abnormal alarm information is generated. The composite abnormal alarm information combines the alarm information from two different sources, telemetry and remote sensing, and provides a more comprehensive abnormality description and cause analysis, including timestamp, satellite number, instrument number, abnormality type, abnormality description, cause analysis, etc. This implementation ensures that the composite abnormal alarm information is only generated when there is a significant correlation between the telemetry abnormal alarm information and the remote sensing abnormal alarm information, reducing unnecessary interference and false alarms, achieving the technical effect of improving the accuracy and effectiveness of the alarm information and providing more valuable information support for operation and maintenance personnel. Combine the anomaly correlation coefficient with the telemetry and remote sensing anomaly information, assign scores and weights based on the severity of the anomaly impact, and generate an alarm index.

[0082] In this embodiment, step S604 includes:

[0083] Step S641: determining alarm index intervals based on historical data, including a first-level alarm index interval, a second-level alarm index interval, and a third-level alarm index interval;

[0084] Specifically, the historical data includes past telemetry anomaly alarm information, remote sensing anomaly alarm information, their anomaly correlation coefficients, and corresponding actual handling situations. By analyzing historical data, the corresponding relationship between different alarm indices and the actual severity of the problem is obtained, thereby dividing the alarm index range into different levels. Among them, the first-level alarm index range corresponds to a high anomaly correlation coefficient and severe data integrity issues, indicating a serious problem; the second-level alarm coefficient range corresponds to a medium anomaly correlation coefficient and data integrity issues, requiring further attention; the third-level alarm coefficient range corresponds to a low anomaly correlation coefficient, indicating that the correlation between the two anomaly alarms is weak, but still worthy of attention.

[0085] Step S642: Match the alarm index with the alarm index range, and generate level one compound abnormal alarm information, level two compound abnormal alarm information, and level three compound abnormal alarm information accordingly;

[0086] Specifically, the calculated alarm index is matched with the alarm index interval determined in step S641, and by comparing the alarm indexes, it is determined in which alarm index interval the alarm index falls, thereby determining which level of composite abnormality alarm information should be generated.

[0087] Step S643, adding the first-level composite abnormality alarm information, the second-level composite abnormality alarm information, and the third-level composite abnormality alarm information into the composite abnormality alarm information;

[0088] Specifically, once the level of a composite abnormality alarm is determined, it is added to the final composite abnormality alarm. This allows operations personnel to not only understand the correlation between the two abnormality alarms but also determine the severity of the problem based on the level of the abnormality alarm, allowing them to take appropriate action. This approach allows for more accurate generation of composite abnormality alarms at different levels, thereby improving the pertinence and effectiveness of alarm information and helping operations personnel better respond to and handle various abnormal situations.

[0089] In this embodiment, step S200 further includes:

[0090] For each instrument telemetry parameter, the corresponding telemetry parameter threshold is initially set according to the instrument design and ground simulation results;

[0091] Based on the on-orbit observation data of the instrument, the initial parameter thresholds are adaptively corrected:

[0092] For each telemetry parameter, set a time interval based on the changing characteristics of historical data;

[0093] For each time interval of historical data, calculate the mean and standard deviation of the data and determine the corresponding parameter characteristic value (e.g., mean ± 3 times the standard deviation);

[0094] Calculate the distance between the mean of the parameter data and the initial parameter threshold. If the distance is less than a preset range (e.g., 3 times the standard deviation), correct the parameter threshold to the parameter characteristic value; otherwise, the parameter threshold remains the initial parameter threshold.

[0095] Repeat the above process according to the time interval setting to achieve adaptive correction of parameter thresholds.

[0096] Specifically, corresponding telemetry parameter thresholds are initially set based on instrument design and ground simulation results. During the satellite and instrument design phase, simulation analysis can predict the normal operating ranges of some key parameters. These ranges are determined based on factors such as the instrument's physical properties, operating environment, and design requirements, and serve as the initial basis for setting parameter thresholds. Based on the instrument's on-orbit observation data, these initially set parameter thresholds are adaptively corrected to improve their accuracy and adaptability. The specific steps include: For each telemetry parameter, a reasonable time interval is set based on the variation characteristics of historical data. This helps capture the dynamic characteristics of the parameter over different time periods. Within each time interval, the mean and standard deviation of the historical data are calculated to determine the parameter characteristic value for that time interval (e.g., mean ± 3 standard deviations). This characteristic value reflects the normal fluctuation range of the parameter within that time period. The distance between the parameter data mean and the initially set parameter threshold is calculated. If this distance is less than a preset range (e.g., 3 standard deviations), the initially set threshold is considered inaccurate for that time interval and should be corrected to the calculated parameter characteristic value. If the distance is greater than the preset range, the initially set parameter threshold is retained. Repeat the above process according to the set time interval until the parameter thresholds of all time intervals are adaptively corrected. By combining historical data and on-orbit observation data, the parameter thresholds are dynamically adjusted to make them more consistent with the actual conditions of the satellite and instruments in actual operation, thereby improving the accuracy of anomaly detection. Different time intervals may correspond to different environmental conditions and operating states, and adaptive correction can ensure that the parameter thresholds are still valid under different conditions. More accurate parameter thresholds help reduce false alarms and missed alarms caused by improper threshold settings, and improve the reliability of the monitoring system. The refinement process of parameter threshold setting in step S200 reflects the innovative ideas and technical implementation paths of the present invention in improving the accuracy and adaptability of parameter thresholds. By combining instrument design, ground simulation results and on-orbit observation data to dynamically adjust parameter thresholds, the present invention effectively improves the accuracy and reliability of on-orbit anomaly monitoring of remote sensing satellite instruments.

[0097] In another embodiment, step S200 may further include:

[0098] Step S210: Obtain N historical instrument telemetry parameters, and traverse the N historical instrument telemetry parameters to obtain a first historical instrument telemetry parameter;

[0099] Specifically, N historical instrument telemetry parameter data are retrieved from a database or storage medium, where the N historical instrument telemetry parameters record the instrument parameter values ​​of the remote sensing satellite at various time points in its past operation. Then, the N historical instrument telemetry parameters are traversed, and one historical instrument telemetry parameter is selected as a reference point, namely, the first historical instrument telemetry parameter.

[0100] Step S220, calculating the distance between the first historical instrument telemetry parameter and the remaining N-1 historical instrument telemetry parameters to obtain N-1 parameter distances;

[0101] Specifically, when calculating the distance between the first historical instrument telemetry parameter and the remaining N-1 historical instrument telemetry parameters, it is first necessary to ensure that all parameters are normalized to eliminate dimensionality effects. Subsequently, an appropriate distance metric formula is selected or defined based on the specific properties of the parameters. If the parameters are continuous and uniformly distributed, Euclidean distance or Manhattan distance can be used. If the parameters have special physical meaning or nonlinear relationships, a dedicated distance function must be designed. By traversing the remaining N-1 parameters, the distance value to the first parameter is calculated one by one, ultimately obtaining a set containing the distances of the N-1 parameters. This process aims to accurately quantify the similarities and differences between each historical parameter, laying the foundation for subsequent analysis.

[0102] Step S230, sorting the N-1 parameter distances to obtain k parameter distances closest to the first historical instrument telemetry parameter;

[0103] Specifically, k is a preset integer used to determine the number of nearest neighbors to be considered, sort these N-1 parameter distances, find the k parameter distances closest to the first historical instrument telemetry parameter, and focus on the parameter set that is most similar to the first historical instrument telemetry parameter by only paying attention to the nearest neighbors.

[0104] Step S240: Taking the average value of the k parameter distances as the first anomaly score of the first historical instrument telemetry parameter, and calculating N anomaly scores of the N historical instrument telemetry parameters based on the same method;

[0105] Specifically, the average of the k nearest neighbor parameter distances is calculated and used as the first anomaly score of the first historical instrument telemetry parameter. The first anomaly score reflects the degree of deviation of the first historical instrument telemetry parameter from its nearest neighbor parameter. If the first anomaly score is low, it indicates that the distance between the historical instrument telemetry parameter and its nearest neighbor parameter is close, that is, the performance of the historical instrument telemetry parameter in the historical data is relatively normal and does not significantly deviate from other similar parameters. On the contrary, if the first anomaly score is high, it indicates that the distance between the historical instrument telemetry parameter and its nearest neighbor parameter is far, indicating that the historical instrument telemetry parameter is abnormal or deviates from the normal state in the historical data. Then, the same method is used to traverse the remaining N-1 historical instrument telemetry parameters, and the corresponding anomaly scores are calculated respectively, ultimately obtaining N anomaly scores for the N historical instrument telemetry parameters.

[0106] Step S250, determining the parameter threshold according to the distribution of the N abnormality scores;

[0107] Specifically, the distribution of the N anomaly scores is analyzed. By setting a fixed threshold for the anomaly scores and using statistical methods (such as the mean plus or minus the standard deviation) or machine learning algorithms (such as clustering or anomaly detection algorithms), an appropriate parameter threshold is determined. This parameter threshold can accurately distinguish between normal parameter values ​​and potential anomalies. This implementation method, which sets parameter thresholds based on an analysis method based on historical data, is scientific and objective, thereby achieving the technical effect of improving the accuracy and reliability of anomaly detection.

[0108] In this embodiment, step S200 further includes:

[0109] Step S260: for each instrument telemetry parameter, a historical telemetry parameter data set arranged in chronological order is obtained, and based on the parameter threshold, data outside the threshold range is eliminated to obtain a purified historical telemetry parameter data set;

[0110] Specifically, a historical telemetry parameter data set arranged in chronological order is obtained, wherein the historical telemetry parameter data set contains parameter values ​​of the satellite instrument during its operation over the past period of time. The parameter threshold set in step S250 is used to eliminate data points that are beyond the normal range, that is, to remove extreme values ​​that may be caused by abnormal events or erroneous records, thereby obtaining the purified historical telemetry parameter data set.

[0111] Step S270: Preset a sliding window, slide the preset sliding window on the purified historical telemetry parameter dataset along the time series, calculate the average value and standard deviation of the data in the sliding window, and obtain a smoothed historical telemetry parameter dataset (average value and standard deviation);

[0112] Specifically, a sliding window is preset according to actual needs. The sliding window determines the number of data points used to calculate the mean and standard deviation. The sliding window is slid along the time series on the purified historical telemetry parameter dataset. During each sliding process, the mean and standard deviation of all data points in the sliding window are calculated, and then the mean and standard deviation are added as new data points to the smoothed historical telemetry parameter dataset. The smoothed historical telemetry parameter dataset describes the changes in parameter values ​​over time.

[0113] Step S280, fitting the smoothed historical telemetry parameter data set to obtain a telemetry parameter time variation trend model;

[0114] Specifically, linear regression, polynomial fitting and other methods are used to fit the smoothed historical telemetry parameter data set to obtain a telemetry parameter time change trend model, which describes the law of parameter value change over time and can be used to predict parameter values ​​in the future.

[0115] Step S290, setting the trend threshold based on the telemetry parameter time change trend model;

[0116] Specifically, a trend threshold is set based on the obtained telemetry parameter temporal trend model. This threshold is determined based on the parameter value range predicted by the telemetry parameter temporal trend model and a certain safety margin. When the actual parameter value trend exceeds the trend threshold, an abnormality is considered to have occurred, requiring appropriate processing or an alarm. This implementation method uses historical data analysis and modeling to set trend thresholds, which is scientific and objective, thereby achieving the technical effect of improving the accuracy and reliability of anomaly detection.

[0117] In this embodiment, step S400 further includes:

[0118] According to the characteristics and assessment requirements of remote sensing business data, set file name constraints, file size constraints, remote sensing data field constraints and remote sensing data quality code constraints to verify the data integrity of remote sensing business data;

[0119] Setting a remote sensing service data verification mechanism based on the file name constraint, file size constraint, remote sensing data field constraint, and remote sensing data quality code constraint;

[0120] Specifically, based on the remote sensing business data file name constraint, determine whether the agreed file has not been generated; based on the remote sensing business data file size constraint, determine whether the agreed file is incomplete; based on the remote sensing data field constraint, determine whether the agreed field data is missing or incomplete, whether the value range is reasonable, and whether the logical relationship between fields is reasonable (such as the time relationship before and after the longitude and latitude should be reasonably matched); based on the remote sensing data quality code constraint, determine whether the agreed quality code data does not meet expectations. Check data integrity, including file continuity, continuity of scan line data in the file, missing data within a day, data of different quality levels, statistics and proportion of abnormal data, etc., to ensure that remote sensing business data meets the observation data integrity requirements and quality requirements.

[0121] Integrate the remote sensing data verification mechanism into the stream processing framework to complete the setting of the data integrity constraints;

[0122] Specifically, a remote sensing data validation mechanism is seamlessly integrated into an efficient stream processing framework, ensuring real-time data integrity verification during dynamic data flow. Based on integrity standards, the validation mechanism closely monitors the continuity of observed data scan lines, detects missing time periods, and assesses the proportion of data of varying quality levels and abnormal data. This integration not only improves data processing efficiency but also fundamentally ensures the integrity and accuracy of remote sensing data streams in high-speed processing environments.

[0123] In another embodiment, the step S400 may further include:

[0124] Step S410, setting remote sensing service data field constraints according to the service requirements of the remote sensing service data;

[0125] Specifically, based on the business needs of remote sensing business data, "remote sensing business data field constraints" are set as follows: based on the accuracy, frequency and integrity requirements of specific remote sensing applications (such as environmental monitoring and resource exploration), set constraints such as the mandatory nature of each field of remote sensing business data, data type (such as integer, floating point number, date and time), value range (such as temperature data should be between -100℃ and 100℃), and logical relationship between fields (such as latitude and longitude data should be reasonably matched) to ensure that the collected remote sensing business data meets the business demand standards.

[0126] Step S420: setting a remote sensing service data verification mechanism based on the remote sensing service data field constraint;

[0127] Specifically, based on the constraints set for remote sensing data fields, a data validation mechanism was established, focusing on checking data integrity. This included checking the continuity of observation data scan lines, identifying missing time data, and the proportion of abnormal data. This ensured that remote sensing data met the requirements for observational, reference, and simulation data integrity. Furthermore, data types, value ranges, and logical relationships between fields were verified to comprehensively ensure data quality.

[0128] Step S430: Integrate the remote sensing service data verification mechanism into the stream processing framework to complete the setting of the data integrity constraint;

[0129] Specifically, a remote sensing data validation mechanism is seamlessly integrated into an efficient stream processing framework, ensuring real-time data integrity verification during dynamic data flow. Based on integrity standards, the validation mechanism closely monitors the continuity of observed data scan lines, detects missing time periods, and assesses the proportion of data of varying quality levels and abnormal data. This integration not only improves data processing efficiency but also fundamentally ensures the integrity and accuracy of remote sensing data streams in high-speed processing environments.

[0130] Example 2

[0131] Figure 2 FIG. 1 is a schematic diagram of a monitoring and warning system for an abnormality of a remote sensing satellite instrument on orbit in a second embodiment of the present invention. Figure 2As shown, Embodiment 2 provides a remote sensing satellite instrument on-orbit anomaly monitoring and alarm system, comprising: an acquisition module 10, a first preset module 20, a first generation module 30, a second preset module 40, a second generation module 50, a third generation module 60, and a feedback optimization module 70. The acquisition module 10 is configured to acquire on-orbit operational data of remote sensing satellite instruments, including instrument telemetry parameter data and remote sensing service data. The first preset module 20 is configured to preset parameter thresholds and trend thresholds for the instrument telemetry parameter data. The first generation module 30 is configured to generate a telemetry anomaly alarm message if the instrument telemetry parameter data does not meet the parameter threshold or trend threshold. The second preset module 40 is configured to preset data integrity constraints for the remote sensing service data. The second generation module 50 is configured to generate a remote sensing anomaly alarm message if the remote sensing service data does not meet the data integrity constraints. The third generation module 60 is configured to perform multi-source alarm information matching on the telemetry anomaly alarm message and the remote sensing anomaly alarm message, using the timestamp, satellite number, and instrument number as matching identifiers, and to generate a composite anomaly alarm message based on the matching results. The feedback optimization module 70 is used to perform feedback optimization according to the composite abnormality alarm information.

[0132] In this embodiment, the third generation module 60 includes: an acquisition unit, an extraction unit, a first generation unit, and a second generation unit. The acquisition unit is used to perform time alignment on the telemetry anomaly alarm information and the remote sensing anomaly alarm information to obtain telemetry and remote sensing synchronization anomaly alarm information. The extraction unit is used to slide the time window along the telemetry and remote sensing synchronization anomaly alarm information based on the timestamp and the preset time window, with the satellite number and the instrument number as matching constraints, to extract potential alarm information matching items within the preset time window. The first generation unit is used to perform anomaly time correlation analysis and anomaly type correlation analysis based on the potential alarm information matching items, and generate an anomaly correlation coefficient based on the correlation analysis results. The second generation unit is used to generate the composite anomaly alarm information if the anomaly correlation coefficient is greater than a preset anomaly correlation coefficient threshold. The anomaly correlation coefficient can be combined with the telemetry and remote sensing anomaly information to generate an alarm index.

[0133] In this embodiment, the second generating unit includes:

[0134] A determination subunit is used to determine the alarm index interval based on historical data, including the first-level alarm index interval, the second-level alarm index interval, and the third-level alarm index interval;

[0135] A generating subunit, configured to match the abnormal correlation coefficient with the alarm index interval, and correspondingly generate first-level composite abnormal alarm information, second-level composite abnormal alarm information, and third-level composite abnormal alarm information;

[0136] The adding subunit is used to add the first-level composite abnormality alarm information, the second-level composite abnormality alarm information, and the third-level composite abnormality alarm information into the composite abnormality alarm information.

[0137] In this embodiment, the parameter thresholds of the preset instrument telemetry parameter data include:

[0138] For each instrument telemetry parameter, the corresponding telemetry parameter threshold is initially set according to the instrument design and ground simulation results;

[0139] Based on the on-orbit observation data of the instrument, the initial parameter thresholds are adaptively corrected:

[0140] For each telemetry parameter, set a time interval based on the changing characteristics of historical data;

[0141] For the historical data in each time interval, calculate the mean and standard deviation of the data and determine the corresponding parameter characteristic value;

[0142] Calculate the distance between the parameter data mean and the initially set parameter threshold; if the distance is less than a preset range, correct the parameter threshold to the parameter characteristic value; otherwise, keep the parameter threshold as the initial threshold;

[0143] Repeat the above process according to the time interval setting to achieve adaptive correction of parameter thresholds.

[0144] In this embodiment, the trend thresholds of the preset instrument telemetry parameter data include:

[0145] For each instrument telemetry parameter, obtain a historical telemetry parameter data set arranged in chronological order;

[0146] Based on the parameter threshold, data outside the threshold range is eliminated to obtain a purified historical telemetry parameter data set;

[0147] Preset a sliding window, slide the preset sliding window on the purified historical telemetry parameter dataset along a time series, calculate the average value of the data in the sliding window, and obtain a smoothed historical telemetry parameter dataset;

[0148] Fitting the smoothed historical telemetry parameter data set to obtain a telemetry parameter time variation trend model;

[0149] The trend threshold is set based on the time variation trend model of the telemetry parameter.

[0150] In this embodiment, the data integrity constraints of the preset instrument remote sensing service data include:

[0151] According to the characteristics and assessment requirements of remote sensing business data, set file name constraints, file size constraints, remote sensing data field constraints and remote sensing data quality code constraints to verify the data integrity of remote sensing business data;

[0152] Setting a remote sensing service data verification mechanism based on the file name constraint, file size constraint, remote sensing data field constraint, and remote sensing data quality code constraint;

[0153] The remote sensing data verification mechanism is integrated into the stream processing framework to complete the setting of the data integrity constraints.

[0154] The various variations and specific examples of the method for monitoring and alarming anomalies of remote sensing satellite instruments on-orbit provided in Example 1 are also applicable to the system for monitoring and alarming anomalies of remote sensing satellite instruments on-orbit provided in this embodiment. Through the above detailed description of the method for monitoring and alarming anomalies of remote sensing satellite instruments on-orbit, those skilled in the art can clearly understand the implementation method of the system for monitoring and alarming anomalies of remote sensing satellite instruments on-orbit in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0155] Example 3

[0156] Figure 3 This is a schematic diagram of the structure of an electronic device in the third embodiment of the present invention. Figure 3 As shown, the third embodiment further provides an electronic device 300 , which may include: a processor 301 and a memory 302 .

[0157] Memory 302 is used to store programs. Memory 302 may include volatile memory (volatile memory), such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc. Memory may also include non-volatile memory (non-volatile memory), such as flash memory. Memory 302 is used to store computer programs (such as applications and functional modules that implement the above-mentioned methods), computer instructions, etc. The above-mentioned computer programs and computer instructions may be partitioned and stored in one or more memories 302. Furthermore, the above-mentioned computer programs, computer instructions, data, etc. may be called by processor 301.

[0158] The aforementioned computer programs, computer instructions, etc. may be stored in partitions in one or more memories 302 , and the aforementioned computer programs, computer instructions, etc. may be called by the processor 301 .

[0159] The processor 301 is configured to execute the computer program stored in the memory 302 to implement the various steps in the method involved in the above embodiment.

[0160] For details, please refer to the relevant description in the previous method embodiment.

[0161] The processor 301 and the memory 302 may be independent structures or integrated structures. When the processor 301 and the memory 302 are independent structures, the memory 302 and the processor 301 may be coupled via a bus 303 .

[0162] The electronic device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0163] Example 4

[0164] Embodiment 4 also provides a computer-readable storage medium, including a computer program and instructions. When the computer program or instructions are run on a computer, the computer executes the method for monitoring and alarming on-orbit anomalies of remote sensing satellite instruments in any embodiment of the present invention.

[0165] Computer-readable storage media include: USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks, and other media that can store program codes.

[0166] This embodiment also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0167] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved. This is not limited herein.

[0168] In summary, the monitoring and alarm method and system for on-orbit anomalies of remote sensing satellite instruments of the present invention first collect and obtain on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and remote sensing business data, and then preset parameter thresholds and trend thresholds for the instrument telemetry parameter data. If the instrument telemetry parameter data does not meet the parameter threshold or trend threshold, telemetry anomaly alarm information is generated. Then, data integrity constraints for remote sensing business data are preset. If the remote sensing business data does not meet the data integrity constraints, remote sensing anomaly alarm information is generated. Then, the timestamp, satellite number, and instrument number are used as matching identifiers to perform multi-source alarm information matching on the telemetry anomaly alarm information and the remote sensing anomaly alarm information. Composite anomaly alarm information is generated based on the matching results. Finally, feedback optimization is performed based on the composite anomaly alarm information, thereby achieving the technical effects of improving the information coverage of instrument anomaly alarms, reducing omissions and false alarms of alarm information, and improving the accuracy and reliability of anomaly monitoring.

[0169] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for monitoring and warning of on-orbit abnormalities of remote sensing satellite instruments, characterized in that: include: Collect and obtain on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing business data; Presetting parameter thresholds and trend thresholds for the instrument telemetry parameter data; If the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold, generating telemetry abnormality alarm information; Presetting data integrity constraints for the remote sensing service data of the instrument; If the instrument remote sensing service data does not meet the data integrity constraint, generating remote sensing abnormality alarm information; Using the timestamp, satellite number, and instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry abnormality alarm information and the remote sensing abnormality alarm information, and generating composite abnormality alarm information based on the matching results; Feedback optimization is performed based on the composite abnormality alarm information.

2. The method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit according to claim 1, characterized in that: The multi-source alarm information matching of the telemetry abnormality alarm information and the remote sensing abnormality alarm information is performed using the timestamp, satellite number, and instrument number as matching identifiers, and generating composite abnormality alarm information based on the matching results includes: Performing time alignment on the telemetry abnormal alarm information and the remote sensing abnormal alarm information to obtain telemetry and remote sensing synchronous abnormal alarm information; Based on the timestamp, a time window is preset, and the satellite number and instrument number are used as matching constraints, the time window is slid along the telemetry and telesensing synchronization abnormality alarm information, and potential alarm information matching items within the preset time window are extracted; Performing abnormal time correlation analysis and abnormal type correlation analysis based on the potential alarm information matching items, and generating an abnormal correlation coefficient based on the correlation analysis results; If the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, the composite abnormality alarm information is generated.

3. The method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit as claimed in claim 2, characterized in that: If the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, generating the composite abnormality alarm information includes: Based on historical data, determine the alarm index range, including the first-level alarm index range, the second-level alarm index range, and the third-level alarm index range; Matching the abnormal correlation coefficient with the alarm index interval, and correspondingly generating first-level composite abnormal alarm information, second-level composite abnormal alarm information, and third-level composite abnormal alarm information; The first-level composite abnormality alarm information, the second-level composite abnormality alarm information, and the third-level composite abnormality alarm information are added to the composite abnormality alarm information.

4. The method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit as claimed in claim 1, characterized in that: The preset parameter thresholds of the instrument telemetry parameter data include: For each instrument telemetry parameter, the corresponding telemetry parameter threshold is initially set according to the instrument design and ground simulation results; Based on the on-orbit observation data of the instrument, the initial parameter thresholds are adaptively corrected: For each telemetry parameter, set a time interval based on the changing characteristics of historical data; For the historical data in each time interval, calculate the mean and standard deviation of the data and determine the corresponding parameter characteristic value; Calculate the distance between the parameter data mean and the initially set parameter threshold; if the distance is less than a preset range, correct the parameter threshold to the parameter characteristic value; otherwise, keep the parameter threshold as the initial threshold; Repeat the above process according to the time interval setting to achieve adaptive correction of parameter thresholds.

5. The method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit as claimed in claim 1, characterized in that: The preset trend thresholds of the instrument telemetry parameter data include: For each instrument telemetry parameter, obtain a historical telemetry parameter data set arranged in chronological order; Based on the parameter threshold, data outside the threshold range is eliminated to obtain a purified historical telemetry parameter data set; Preset a sliding window, slide the preset sliding window on the purified historical telemetry parameter dataset along the time series, calculate the average value and standard deviation of the data in the sliding window, and obtain a smoothed historical telemetry parameter dataset; Fitting the smoothed historical telemetry parameter data set to obtain a telemetry parameter time variation trend model; The trend threshold is set based on the time variation trend model of the telemetry parameter.

6. The method for monitoring and warning of anomalies of a remote sensing satellite instrument on orbit as claimed in claim 1, characterized in that: The data integrity constraints of the preset instrument remote sensing service data include: According to the characteristics and assessment requirements of remote sensing business data, set file name constraints, file size constraints, remote sensing data field constraints and remote sensing data quality code constraints; Setting a remote sensing service data verification mechanism based on the file name constraint, file size constraint, remote sensing data field constraint, and remote sensing data quality code constraint; The remote sensing data verification mechanism is integrated into the stream processing framework to complete the setting of the data integrity constraints.

7. A remote sensing satellite instrument on-orbit abnormality monitoring and alarm system, characterized by: include: The acquisition module is used to acquire the on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing business data; A first preset module is used to preset parameter thresholds and trend thresholds of the instrument telemetry parameter data; A first generating module is configured to generate telemetry abnormality alarm information if the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold; A second preset module is used to preset data integrity constraints for the instrument remote sensing service data; A second generating module is configured to generate remote sensing abnormality alarm information if the instrument remote sensing service data does not satisfy the data integrity constraint; a third generating module, configured to perform multi-source alarm information matching on the telemetry abnormality alarm information and the remote sensing abnormality alarm information using the timestamp, satellite number, and instrument number as matching identifiers, and generate composite abnormality alarm information based on the matching results; A feedback optimization module is used to perform feedback optimization based on the composite abnormality alarm information.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the monitoring and alarm method for in-orbit anomalies of remote sensing satellite instruments according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The method comprises a computer program and instructions. When the computer program or the instructions are run on a computer, the computer is caused to execute the method for monitoring and alarming on-orbit anomalies of a remote sensing satellite instrument according to any one of claims 1 to 6.

Images