Monitoring and alarming method and system for on-orbit abnormity of remote sensing satellite instrument

Abstract

The invention discloses a remote sensing satellite instrument in-orbit abnormity monitoring and warning method and system. The method comprises the steps that in-orbit operation data of a remote sensing satellite instrument are collected and obtained; presetting a parameter threshold value and a trend threshold value of the instrument telemetering parameter data; generating telemetering abnormity alarm information; presetting data integrity constraints and business process constraints of the instrument remote sensing business data; remote sensing abnormal alarm information is generated; performing multi-source alarm information matching on the telemetering abnormal alarm information and the remote sensing abnormal alarm information, and generating composite abnormal alarm information based on a matching result; and performing feedback optimization according to the composite abnormal alarm information. According to the invention, the technical problems of incomplete alarm information, easy missing or false alarm, and low accuracy and reliability of anomaly monitoring in the existing anomaly monitoring of the remote sensing satellite instrument are solved, and the technical effects of improving the information coverage of the anomaly alarm of the instrument, reducing the missing and false alarm of the alarm information, and improving the accuracy and reliability of the anomaly monitoring are achieved.

Description

Technical Field

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

[0002] With the continuous development of satellite technology, remote sensing satellites are increasingly widely used in various fields, and the stability of their operating states is directly related to the effects and benefits of related applications. Therefore, it is particularly important to promptly detect and alarm on-orbit anomalies of remote sensing satellite instruments. The existing technology mainly relies on preset key parameter thresholds 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 judgment will cause some potential problems to be ignored, especially when the abnormal data shows a gradual change or intermittency. Moreover, the existing technology usually processes telemetry and remote sensing service data separately, ignoring the internal connection between them, often resulting in the omission or false alarm of alarm information.

[0003] In the current related technologies, there are technical problems in the abnormal monitoring of remote sensing satellite instruments, such as incomplete alarm information, easy omission or false alarm, and low accuracy and reliability of abnormal monitoring. Summary of the Invention

[0004] The embodiments of the present invention provide a method and system for monitoring and alarming on-orbit anomalies of remote sensing satellite instruments. By collecting and obtaining the on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing service data, and performing multi-source alarm information matching to generate composite anomaly alarm information and other technical means, the technical effects of improving the information coverage of anomaly alarms, reducing the omission and false alarm of alarm information, and improving the accuracy and reliability of anomaly monitoring are achieved.

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

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

[0007] Preset the parameter threshold and trend threshold of the instrument telemetry parameter data;

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

[0009] Preset the data integrity constraint and service process constraint of the instrument remote sensing service data;

[0010] If the instrument remote sensing service data does not meet the data integrity constraint or the service process constraint, generate remote sensing anomaly alarm information;

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

[0012] Perform feedback optimization according to the composite anomaly alarm information.

[0013] In an embodiment of the present invention, the step of using the timestamp, satellite number, and instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry anomaly alarm information and the remote sensing anomaly alarm information, and generating composite anomaly alarm information based on the matching results includes:

[0014] Perform temporal alignment on the telemetry anomaly alarm information and the remote sensing anomaly alarm information to obtain telemetry and remote sensing synchronous anomaly alarm information;

[0015] Based on the timestamp, preset a time window, and use the satellite number and instrument number as matching constraints. Slide the time window along the telemetry and remote sensing synchronous anomaly alarm information to extract potential alarm information matching items within the preset time window;

[0016] According to the potential alarm information matching items, perform abnormal time correlation analysis and abnormal type correlation analysis, and generate an abnormal correlation coefficient according to the correlation analysis results;

[0017] If the abnormal correlation coefficient is greater than the preset abnormal correlation coefficient threshold, generate the composite anomaly alarm information.

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

[0019] Based on historical data, determine alarm index intervals, including a first-level alarm index interval, a second-level alarm index interval, and a third-level alarm index interval;

[0020] Match the abnormal correlation coefficient with the alarm index intervals, and correspondingly generate a first-level composite anomaly alarm information, a second-level composite anomaly alarm information, and a third-level composite anomaly alarm information;

[0021] Add the first-level composite anomaly alarm information, the second-level composite anomaly alarm information, and the third-level composite anomaly alarm information into the composite anomaly alarm information.

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

[0023] For each instrument telemetry parameter, initially set the corresponding telemetry parameter threshold 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 the time interval according to 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 mean value of the parameter data and the initially set parameter threshold value. If the distance is less than a preset range, correct the parameter threshold value to a parameter characteristic value; otherwise, the parameter threshold value continues to use the initial parameter threshold value.

[0028] According to the time interval setting, the above process is repeated 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 chronologically ordered historical telemetry parameter data set;

[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 purification historical telemetry parameter data set along the time series, calculate the average value of the data in the sliding window, and obtain a smoothed historical telemetry parameter data set;

[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 telemetry parameter time variation trend model.

[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] Based on the file name constraint, file size constraint, remote sensing data field constraint and remote sensing data quality code constraint, a remote sensing business data verification mechanism is set;

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

[0039] Second aspect, the present invention provides a monitoring and warning system for on-orbit anomalies of remote sensing satellite instruments, including: a collection 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 collection module is used to collect and obtain the on-orbit operation data of the remote sensing satellite instruments, including instrument telemetry parameter data and instrument remote sensing service data. The first preset module is used to preset the parameter threshold and trend threshold of the instrument telemetry parameter data. The first generation module is used to generate a telemetry anomaly warning message if the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold. The second preset module is used to preset the data integrity constraint of the instrument remote sensing service data. The second generation module is used to generate a remote sensing anomaly warning message if the instrument remote sensing service data does not meet the data integrity constraint. The third generation module is used to use the timestamp, satellite number, and instrument number as matching identifiers to match the telemetry anomaly warning message and the remote sensing anomaly warning message, and generate a composite anomaly warning message based on the matching result. The feedback optimization module is used to perform feedback optimization according to the composite anomaly warning message.

[0040] Third aspect, the present invention provides an electronic device, including:

[0041] At least one processor; and

[0042] A memory communicatively connected to the at least one processor;

[0043] Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments as described above.

[0044] Fourth aspect, the present invention provides a computer-readable storage medium, including a computer program and instructions, and when the computer program or the instructions are run on a computer, the computer is enabled to execute the method for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments as described above.

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

[0046] Figure 1 is a schematic flowchart of a method for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument in Embodiment 1 of the present invention;

[0047] Figure 2 is a schematic structural diagram of a system for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument in Embodiment 2 of the present invention;

[0048] Figure 3 is a schematic structural diagram of an electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following further describes the embodiments of the present invention in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, rather than limiting the embodiments of the present invention. Additionally, it should be noted that for the sake of description, only parts related to the embodiments of the present invention are shown in the drawings, rather than all the structures.

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

[0051] In the context of the rapid development of remote sensing satellite technology, the application fields of remote sensing satellites are becoming increasingly extensive, including but not limited to environmental monitoring, resource exploration, weather forecasting, disaster warning, etc. These applications pose extremely high requirements for the stable operation of remote sensing satellites because any anomaly of the instruments may directly affect the accuracy and timeliness of the data, and thus affect the effects and benefits of related applications.

[0052] The prior art mainly relies on preset fixed thresholds to determine whether data is abnormal. However, the setting of thresholds is often based on experience or historical data, which has certain subjectivity and uncertainty. When abnormal data shows a gradual change or intermittency, a single threshold judgment may not be able to capture these subtle changes in time, resulting in the omission of potential problems.

[0053] The prior art usually processes instrument telemetry parameter data and instrument remote sensing service data separately, ignoring the internal connection between them. In fact, many instrument anomalies are not only reflected in the telemetry parameters but also manifested in the instrument remote sensing service data. Due to the fragmentation of data processing, the prior art often fails to comprehensively capture abnormal information, resulting in the omission or false alarm of warning information.

[0054] Due to the lack of integration in the processing of different data sources, warning information often exists in an isolated form, lacking comprehensive analysis and judgment. For the above reasons, the warning information of the prior art has deficiencies in accuracy and reliability and is difficult to meet the requirements of high-precision monitoring and warning.

[0055] For example, when a remote sensing satellite is performing an environmental monitoring task, the temperature sensor of the satellite shows an abnormal phenomenon of slow increase. In the prior art, if only relying on the preset fixed threshold for judgment, this gradual change anomaly may not be captured in time. At the same time, due to the separate processing of telemetry data and instrument remote sensing service data, even if the remote sensing data has started to show abnormal characteristics (such as a decrease in quality), it may be ignored due to the lack of comprehensive analysis. In this case, not only will the monitoring task fail, but also the best opportunity to take remedial measures may be missed due to the failure to give a warning in time.

[0056] By discovering the above-mentioned defects of the prior art, the inventor provides a method and system for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments, which can comprehensively monitor on-orbit anomalies of remote sensing satellite instruments, reduce the omission and false alarm of warning information, and improve the accuracy and reliability of anomaly monitoring. This method and system can comprehensively consider instrument telemetry parameter data and instrument remote sensing service data, and generate more comprehensive and accurate composite anomaly warning information through multi-source warning information matching and comprehensive analysis. At the same time, it can also flexibly respond to gradual or intermittent anomalies, improving the sensitivity and response speed of the warning system.

[0057] Embodiment 1

[0058] Figure 1 is a schematic flowchart of a method for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments in Embodiment 1 of the present invention. As Figure 1 shown, Embodiment 1 provides a method for monitoring and warning of on-orbit anomalies of remote sensing satellite instruments, including:

[0059] Step S100, collect and obtain the on-orbit operation data of the remote sensing satellite instrument, 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 comprehensively collecting and obtaining the key data during the satellite's on-orbit operation. This process not only covers the instrument telemetry parameter data, that is, the technical indicators directly reflecting the operation status of the satellite and its carried instruments, such as temperature, voltage, current, etc., but also includes the instrument remote sensing service data, that is, the information of the observed targets (such as the Earth, the Sun) obtained and processed by the satellite instrument. By collecting these multi-dimensional and 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 fields such as scientific research, environmental monitoring, and disaster warning.

[0061] Step S200, preset the parameter threshold and trend threshold of the instrument telemetry parameter data;

[0062] Specifically, according to factors such as the design, specifications, performance requirements, engineering safety margins, and environmental conditions of the remote sensing satellite instrument, preset various parameter thresholds for the instrument telemetry parameter data. The parameter threshold is the value range of each parameter when the instrument is working normally. Exceeding the parameter threshold may mean that there is an anomaly in the instrument. According to the characteristics of the on-orbit observation data, adaptively revise the parameter threshold. By analyzing historical data, combining expert experience and judgment, etc., preset the trend threshold of the instrument telemetry parameter data. The trend threshold is the limit on the parameter change rate. For example, if a parameter rises or falls rapidly within a short period of time, even if the parameter value is still within the normal range, it may also be an abnormal manifestation.

[0063] Step S300, if the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold, generate a telemetry anomaly alarm message;

[0064] Specifically, compare the real-time received instrument telemetry parameter data with the previously preset parameter threshold and trend threshold. If the instrument telemetry parameter data exceeds the range of the parameter threshold, that is, the parameter value is too high or too low, it is considered an abnormal situation. Similarly, if the change trend of the parameter does not conform to the preset trend threshold, such as the change speed is too fast or too slow, it will also be judged as abnormal. Once an abnormal situation is detected, immediately generate a telemetry anomaly alarm message, and the telemetry anomaly alarm message includes information such as the name of the abnormal parameter, the abnormal value, and the abnormal time.

[0065] Step S400, preset the data integrity constraint of the instrument remote sensing service data;

[0066] Specifically, for the instrument remote sensing service data, according to the characteristics and assessment requirements of the remote sensing service, combined with the actual situation, data integrity constraints are formulated. Among them, the data integrity constraints are used to ensure the quality and accuracy of the instrument remote sensing service data, and to ensure that the acquired data is complete and intact. The data integrity constraints define a set of rules, such as file names, file sizes, remote sensing data fields, remote sensing data quality code constraints, etc., and set the data integrity verification standards for remote sensing service data. The data integrity constraints cover the continuity of files, the continuity of scan line data within files, the missing data within a day, the statistics and proportion of data with different quality levels, and abnormal data, aiming to identify and mark any situations of data discontinuity or abnormality.

[0067] Step S500, if the instrument remote sensing service data does not meet the data integrity constraints, generate a remote sensing anomaly warning message.

[0068] Specifically, during the processing of remote sensing service data, the remote sensing service data is checked according to the preset data integrity constraints. When it is detected that the remote sensing service data does not meet the data integrity constraints, it indicates that there may be problems such as missing data, data errors, and quality changes, which will affect the accuracy and usability of the data. Once an abnormal situation is detected, a remote sensing anomaly warning message is immediately generated. The remote sensing anomaly warning message records information such as the type of anomaly, the occurrence time, the location, and the data involved, and is used to quickly locate and determine the nature of the problem.

[0069] Step S600, use the timestamp, satellite number, and instrument number as matching identifiers to perform multi-source warning message matching on the telemetry anomaly warning message and the remote sensing anomaly warning message, and generate a composite anomaly warning message based on the matching result.

[0070] Specifically, the timestamp, satellite number, and instrument number are selected as matching identifiers. The matching identifiers are unique and deterministic, and are used to ensure the accurate identification and matching of alarm information from different sources. By comparing the matching identifiers, it is determined which telemetry anomaly alarm information is associated with which remote sensing anomaly alarm information. If it is found that telemetry and remote sensing anomalies occur simultaneously at the same time and on the same instrument, a composite anomaly alarm information is generated. The composite anomaly alarm information includes the content of alarm information from different sources, such as the anomaly type, occurrence time, and involved data. At the same time, it can further include the correlation and mutual influence between alarm information, as well as the analysis of anomaly causes and suggestions for solutions. By generating the composite anomaly alarm information, the originally scattered and isolated telemetry anomaly alarm information and remote sensing anomaly alarm information are integrated and associated, forming a more complete and accurate description of the anomaly situation, thereby helping the operation and maintenance personnel to more comprehensively understand the anomaly situation, more quickly locate the cause of the problem, and take effective measures to solve it. At the same time, the generation of the composite anomaly alarm information helps to improve the processing efficiency and accuracy of alarm information, providing a strong guarantee for the normal operation of satellites and remote sensing instruments.

[0071] Step S700, perform feedback optimization according to the composite anomaly alarm information;

[0072] Specifically, the feedback optimization includes notifying the satellite instrument operator to conduct fault troubleshooting, adjusting the working parameters of the satellite instrument, optimizing the data processing scheme, etc., so as to eliminate or reduce the abnormal state of the satellite instrument and ensure the normal progress of the observation task and the quality of data. Further, statistical analysis is performed on the composite anomaly alarm information to find the patterns and problems therein, and the causes of the alarms are deeply explored. Through analysis, the deficiencies in the alarm system are found, and the direction for improvement is identified. For example, if it is found that a certain type of composite anomaly alarm information appears frequently and the response and handling effect is not good, then special treatment needs to be considered for this type of alarm, such as adjusting the alarm threshold, optimizing the alarm generation mechanism, or improving the alarm response and handling process. At the same time, machine learning and other methods can be introduced to predict and analyze the alarm data to detect potential problems in advance and reduce the probability of faults. At the same time, as the system environment and business requirements change, the alarm management system needs to be continuously adjusted and optimized to adapt to the new environment and requirements. Therefore, the feedback optimization needs to be carried out regularly to achieve the continuous improvement and optimization of the alarm management system. Through technical means such as collecting and obtaining the on-orbit operation data of remote sensing satellite instruments, including instrument telemetry parameter data and remote sensing service data, and performing multi-source alarm information matching to generate composite anomaly alarm information, the embodiments of the present application achieve the technical effects of improving the information coverage of instrument anomaly alarms, reducing the omission and false alarms of alarm information, and improving the accuracy and reliability of anomaly monitoring.

[0073] In this embodiment, step S600 includes:

[0074] Step S601: Perform time series alignment on the telemetry anomaly warning information and the remote sensing anomaly warning information to obtain telemetry and remote sensing synchronous anomaly warning information;

[0075] Specifically, due to possible time differences in data collection, processing, and analysis of telemetry anomaly warning information and remote sensing anomaly warning information, time series alignment operations are required to ensure their consistency in the time dimension. Methods such as timestamp standardization, timestamp calibration, and dynamic time warping are used for alignment operations to obtain a sequence of telemetry and remote sensing synchronous anomaly warning information (telemetry and remote sensing synchronous anomaly warning information) synchronized on a timeline, where each anomaly warning information corresponds to a unified time point.

[0076] Step S602: Based on timestamps, preset a time window, and slide the time window along the telemetry and remote sensing synchronous anomaly warning information with the satellite number and instrument number as matching constraints to extract potential warning information matching items within the preset time window;

[0077] Specifically, after time series alignment, a preset time window is set. The time window is set according to factors such as the possible duration of warning information, the response time of the system, and the specifications and real-time requirements of data processing. The size of the time window can cover the complete time range of relevant warning information. After determining the size of the time window, based on timestamps, slide this time window along the telemetry and remote sensing synchronous anomaly warning information. The sliding method can be to move by each time unit (such as seconds, minutes, etc.) or to move in a jump manner according to a certain fixed step size. At the same time, use the satellite number and instrument number as matching constraints to find telemetry anomaly warning information and remote sensing anomaly warning information with the same satellite number and instrument number, filter out warning information that is close in time but from different sources, and thus extract potential warning information matching items that meet the constraint conditions within the time window. The potential warning information matching items include multiple telemetry anomaly warning information and multiple remote sensing anomaly warning information that appear within the same time window, and they may point to the same problem or the same cause.

[0078] Step S603: According to the potential warning information matching items, perform abnormal time correlation analysis and abnormal type correlation analysis, and generate an abnormal correlation coefficient according to the correlation analysis results;

[0079] Specifically, for each potential alarm information match item, by comparing the timestamps of the two, calculate the time interval between the telemetry anomaly alarm information and the remote sensing anomaly alarm information. Then, count the distribution of the time intervals among all potential alarm information match items to determine the relative position and distribution of the two types of anomalies in time. Based on the statistical results of the time intervals, judge whether there is a significant time correlation between the telemetry anomaly alarm information and the remote sensing anomaly alarm information. For example, if the time intervals of most potential alarm information match items are very short, it indicates that the two types of anomalies are closely related in time. At the same time, classify the telemetry anomaly alarm information and the remote sensing anomaly alarm information to clarify their respective anomaly types, compare the types of the telemetry anomaly alarm information and the remote sensing anomaly alarm information in the potential alarm information match items, and find out whether there are the same or related anomaly types. Based on the matching results of the anomaly types, evaluate the correlation in type between the two types of anomaly alarms. For example, certain specific telemetry anomalies are always accompanied by specific remote sensing anomalies, which indicates a strong type correlation between them. According to the analysis results of the time correlation and the type correlation, and according to the actual situation and needs, assign corresponding weights to them to reflect the importance of different factors in judging the anomaly correlation. Perform a comprehensive calculation on the analysis results of the time correlation and the type correlation to obtain a comprehensive anomaly correlation coefficient. The anomaly correlation coefficient can be a value between 0 and 1, which is used to quantify the degree of correlation between the telemetry anomaly alarm information and the remote sensing anomaly alarm information.

[0080] Step S604, if the anomaly correlation coefficient is greater than the preset anomaly correlation coefficient threshold, generate the composite anomaly alarm information; among them, the anomaly correlation coefficient can be combined with the telemetry and remote sensing anomaly information to generate an alarm index.

[0081] Specifically, an abnormal correlation coefficient threshold is set according to factors such as the actual application scenario, the importance of the alarm information, the stability requirements of the system, the false alarm rate of the alarm information, and the processing ability of the operation and maintenance personnel. The abnormal correlation coefficient threshold represents the lowest standard for sufficient correlation between two abnormal alarms. Compare the abnormal correlation coefficient calculated in step S630 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 integrates the alarm information from two different sources, telemetry and remote sensing, and provides a more comprehensive abnormal description and cause analysis, including timestamp, satellite number, instrument number, abnormal type, abnormal description, cause analysis, etc. This implementation ensures that a composite abnormal alarm information is generated only when there is a significant correlation between the telemetry abnormal alarm information and the remote sensing abnormal alarm information, reduces unnecessary interference and false alarms, achieves the technical effects of improving the accuracy and effectiveness of the alarm information, and provides more valuable information support for the operation and maintenance personnel. Combine the abnormal correlation coefficient with the telemetry and remote sensing abnormal information, and assign scores and weights based on the severity of the abnormal impact to generate an alarm index.

[0082] In this embodiment, step S604 includes:

[0083] Step S641, based on historical data, determine the alarm index intervals, 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 information such as past telemetry abnormal alarm information, remote sensing abnormal alarm information, their abnormal correlation coefficients, and corresponding actual processing situations. By analyzing the historical data, the corresponding relationship between different alarm indexes and the severity of the actual problem is obtained, so as to divide different levels of alarm index intervals. Among them, the first-level alarm index interval corresponds to a relatively high abnormal correlation coefficient and serious data integrity problems, indicating a serious problem; the second-level alarm coefficient interval corresponds to a medium abnormal correlation coefficient and data integrity problems, which requires further attention; the third-level alarm coefficient interval corresponds to a relatively low abnormal correlation coefficient, indicating a weak correlation between the two abnormal alarms, but still worthy of attention.

[0085] Step S642, match the alarm index with the alarm index intervals, and correspondingly generate a first-level composite abnormal alarm information, a second-level composite abnormal alarm information, and a third-level composite abnormal alarm information;

[0086] Specifically, match the calculated alarm index with the alarm index range determined in step S641. By comparing the magnitudes of the alarm indices, determine which alarm index range the alarm index falls into, so as to determine which level of composite anomaly alarm information should be generated.

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

[0088] Specifically, once the level of the composite anomaly alarm information is determined, add it into the final composite anomaly alarm information. In this way, when the operation and maintenance personnel receive the composite anomaly alarm information, they can not only understand the correlation between the two anomaly alarms, but also judge the severity of the problem according to the level of the anomaly alarm information, so as to take corresponding handling measures. Through this implementation method, different levels of composite anomaly alarm information are generated more accurately, thus achieving the technical effects of improving the pertinence and effectiveness of the alarm information and helping the operation and maintenance personnel better respond to and handle various abnormal situations.

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

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

[0091] Based on the instrument on-orbit observation data, perform adaptive correction on the initially set parameter threshold:

[0092] For each telemetry parameter, set a time interval according to the change characteristics of the historical data;

[0093] For the historical data within each time interval, 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 initially set parameter threshold. If the distance is less than the preset range (e.g., 3 times the standard deviation), then correct the parameter threshold to the parameter characteristic value; otherwise, the parameter threshold continues to use the initial parameter threshold;

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

[0096] Specifically, according to the instrument design and ground simulation results, the corresponding telemetry parameter thresholds are initially set. During the satellite and instrument design phases, the normal operating ranges of some key parameters can be predicted through simulation analysis. These ranges are determined based on factors such as the physical characteristics of the instrument, the working environment, and design requirements, and serve as the basis for the initial setting of the parameter thresholds. Based on the on-orbit observation data of the instrument, the initially set parameter thresholds are adaptively corrected to improve the accuracy and adaptability of the thresholds. The specific steps include: for each telemetry parameter, a reasonable time interval is set according to the change characteristics of the historical data. This helps to capture the dynamic change characteristics of the parameter at different time periods. Within each time interval, the mean and standard deviation of the historical data are calculated, and then the parameter characteristic value of this time interval (such as mean ± 3 times the standard deviation) is determined. This characteristic value reflects the normal fluctuation range of the parameter within this time period. Calculate the distance between the mean of the parameter data and the initially set parameter threshold. If this distance is less than the preset range (such as 3 times the standard deviation), it is considered that the initially set threshold is not accurate enough within this time interval, and it 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 intervals 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 in line with the actual situation of the satellite and instrument during actual operation, thereby improving the accuracy of anomaly detection. Different time intervals may correspond to different environmental conditions and operating states, and the adaptive correction can ensure that the parameter thresholds are still effective under different conditions. More accurate parameter thresholds help to reduce false alarms and missed alarms caused by improper threshold setting and improve the reliability of the monitoring system. The refinement process of parameter threshold setting in step S200 reflects the innovative idea and technical implementation path 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, and dynamically adjusting the 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, obtaining N historical instrument telemetry parameters, and traversing the N historical instrument telemetry parameters to obtain the first historical instrument telemetry parameter;

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

[0100] Step S220: Calculate the distances 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 distances 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 have been standardized to eliminate the influence of dimensions. Subsequently, select or define an appropriate distance metric formula according to the specific nature of the parameters. If the parameters are continuous numerical and uniformly distributed, the Euclidean distance or Manhattan distance can be used; if the parameters have special physical meanings or non - linear relationships, a dedicated distance function needs to be designed. By traversing the remaining N - 1 parameters and calculating the distance values from the first parameter one by one, a set containing N - 1 parameter distances is finally obtained. This process aims to accurately quantify the similarity and difference between historical parameters, laying a foundation for subsequent analysis.

[0102] Step S230: Sort the N - 1 parameter distances to obtain the 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 the N - 1 parameter distances to find the k parameter distances closest to the first historical instrument telemetry parameter. By only focusing on the nearest neighbors, it is used to focus on the set of parameters most similar to the first historical instrument telemetry parameter.

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

[0105] Specifically, calculate the average value of these k nearest - neighbor parameter distances and take this average value 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 parameters. If the first anomaly score is low, it indicates that the distance between this historical instrument telemetry parameter and its nearest - neighbor parameters is close, that is, the performance of this historical instrument telemetry parameter in historical data is relatively normal and does not deviate significantly from other similar parameters. On the contrary, if the first anomaly score is high, it means that the distance between this historical instrument telemetry parameter and its nearest - neighbor parameters is far, indicating that this historical instrument telemetry parameter has anomalies or deviates from the normal state in historical data. Then, use the same method to traverse the remaining N - 1 historical instrument telemetry parameters, calculate the corresponding anomaly scores respectively, and finally obtain the N anomaly scores of the N historical instrument telemetry parameters.

[0106] Step S250: Determine the parameter threshold according to the distribution of the N anomaly scores.

[0107] Specifically, the distribution of the N abnormal scores is analyzed, and a fixed threshold of the abnormal score is set, and a statistical method (such as mean plus or minus standard deviation) or a machine learning algorithm (such as clustering, abnormality detection algorithm, etc.) is used to determine a suitable parameter threshold, which can accurately distinguish normal parameter values ​​from potential abnormal values. This implementation method sets the parameter threshold by an analysis method based on historical data, which is scientific and objective, thereby achieving the technical effect of improving the accuracy and reliability of abnormality detection.

[0108] In this embodiment, the 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 removed to obtain a purified historical telemetry parameter data set;

[0110] Specifically, a historical telemetry parameter data set arranged in chronological order is obtained, and the historical telemetry parameter data set contains parameter values ​​of the satellite instrument during its operation over a period of time in the past. 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 purification historical telemetry parameter data set 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 data set (average value and standard deviation);

[0112] Specifically, a sliding window is preset according to actual needs, and 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 data set. 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 data set. The smoothed historical telemetry parameter data set 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, set the trend threshold based on the telemetry parameter time variation trend model;

[0116] Specifically, set the trend threshold based on the obtained telemetry parameter time variation trend model. The trend threshold is determined according to the parameter value range predicted by the telemetry parameter time variation trend model and a certain safety margin. When the change trend of the actual parameter value exceeds the trend threshold, it is considered that an abnormal situation has occurred and corresponding processing or alarms are required. This implementation method sets the trend threshold through the analysis and modeling method based on historical data, which is scientific and objective, thus achieving the technical effect of improving the accuracy and reliability of anomaly detection.

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

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

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

[0120] Specifically, based on the remote sensing service data file name constraints, judge whether the agreed file is not generated; based on the remote sensing service data file size constraints, judge whether the agreed file is incomplete; based on the remote sensing data field constraints, judge whether the agreed field data is missing or incomplete, whether the numerical range is reasonable, and whether the logical relationship between fields is reasonable (such as the time relationship between longitude and latitude before and after should be reasonably matched); based on the remote sensing data quality code constraints, judge whether the agreed quality code data does not meet the expectations. Check data integrity, including file continuity, scan line data continuity within the file, missing data within a day, different quality level data, statistics and proportion of abnormal data, etc., to ensure that remote sensing service data meets the requirements of observation data integrity 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, seamlessly integrate the remote sensing data verification mechanism into an efficient stream processing framework to ensure real-time execution of data integrity verification during the dynamic transmission of data. The verification mechanism strictly monitors the scan line continuity of observation data, detection of missing time periods, evaluation of different quality level data and abnormal data ratios according to the integrity standard. This integration not only improves the data processing efficiency, but also fundamentally guarantees the integrity and accuracy of remote sensing data streams in a high-speed processing environment.

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

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

[0125] Specifically, "setting constraints for remote sensing service data fields according to the service requirements of remote sensing service data" means: according to the requirements for data accuracy, frequency, and integrity in specific remote sensing applications (such as environmental monitoring, resource exploration), setting constraints such as the mandatory nature of each field of remote sensing service data, data type (such as integer, floating point, date and time), value range (such as temperature data should be between -100°C and 100°C), and logical relationships between fields (such as longitude and latitude data should be reasonably matched), to ensure that the collected remote sensing service data meets the service requirement standards.

[0126] Step S420, setting a remote sensing service data verification mechanism based on the constraints of the remote sensing service data fields;

[0127] Specifically, based on the set constraints of the remote sensing service data fields, a data verification mechanism is constructed, focusing on checking data integrity, including the continuity of the scan lines of observation data, the identification of missing time data, the proportion of abnormal data, etc., to ensure that the remote sensing service data meets the requirements of observation data integrity, reference data integrity, and simulation data integrity. At the same time, verify the data type, value range, and logical relationships between fields to comprehensively guarantee data quality.

[0128] Step S430, integrating the remote sensing service data verification mechanism into the stream processing framework to complete the setting of the data integrity constraints;

[0129] Specifically, seamlessly integrate the remote sensing data verification mechanism into an efficient stream processing framework to ensure real-time execution of data integrity verification during the dynamic transmission of data. The verification mechanism strictly monitors the continuity of the scan lines of observation data, the detection of missing time periods, and the evaluation of the proportion of data with different quality levels and abnormal data according to the integrity standard. This integration not only improves the data processing efficiency, but also fundamentally guarantees the integrity and accuracy of the remote sensing data stream in a high-speed processing environment.

[0130] Embodiment 2

[0131] Figure 2 is a schematic structural diagram of a monitoring and warning system for on-orbit anomalies of a remote sensing satellite instrument, as Figure 2As shown in the figure, Embodiment 2 provides a monitoring and warning system for on-orbit anomalies of a remote sensing satellite instrument, including: a collection 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 collection module 10 is used to collect the on-orbit operation data of the remote sensing satellite instrument, including instrument telemetry parameter data and remote sensing service data. The first preset module 20 is used to preset the parameter threshold and trend threshold of the instrument telemetry parameter data. The first generation module 30 is used to generate a telemetry anomaly warning message if the instrument telemetry parameter data does not meet the parameter threshold or the trend threshold. The second preset module 40 is used to preset the data integrity constraint of the remote sensing service data. The second generation module 50 is used to generate a remote sensing anomaly warning message if the remote sensing service data does not meet the data integrity constraint. The third generation module 60 is used to use the timestamp, satellite number, and instrument number as matching identifiers to perform multi-source warning message matching on the telemetry anomaly warning message and the remote sensing anomaly warning message, and generate a composite anomaly warning message based on the matching result. The feedback optimization module 70 is used to perform feedback optimization according to the composite anomaly warning message.

[0132] In this embodiment, the third generation module 60 includes: an obtaining unit, an extracting unit, a first generating unit, and a second generating unit. The obtaining unit is used to perform time series alignment on the telemetry anomaly warning message and the remote sensing anomaly warning message to obtain a telemetry and remote sensing synchronous anomaly warning message. The extracting unit is used to preset a time window based on the timestamp, and use the satellite number and instrument number as matching constraints to slide the time window along the telemetry and remote sensing synchronous anomaly warning message to extract potential warning message matching items within the preset time window. The first generating unit is used to perform anomaly time correlation analysis and anomaly type correlation analysis according to the potential warning message matching items, and generate an anomaly correlation coefficient according to the correlation analysis result. The second generating unit is used to generate the composite anomaly warning message if the anomaly correlation coefficient is greater than a preset anomaly correlation coefficient threshold. Among them, the anomaly correlation coefficient can be combined with the telemetry and remote sensing anomaly information to generate a warning index.

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

[0134] a determination subunit, configured to determine a warning index interval based on historical data, including a first-level warning index interval, a second-level warning index interval, and a third-level warning index interval;

[0135] a generation subunit, configured to match the anomaly correlation coefficient with the warning index interval, and correspondingly generate a first-level composite anomaly warning message, a second-level composite anomaly warning message, and a third-level composite anomaly warning message;

[0136] The adding subunit is used to add the first-level composite abnormal alarm information, the second-level composite abnormal alarm information, and the third-level composite abnormal alarm information into the composite abnormal 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 the time interval according to 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 mean value of the parameter data and the initially set parameter threshold value. If the distance is less than a preset range, correct the parameter threshold value to a parameter characteristic value; otherwise, the parameter threshold value continues to use the initial parameter threshold value.

[0143] According to the time interval setting, the above process is repeated to achieve adaptive correction of parameter thresholds.

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

[0145] For each instrument telemetry parameter, obtain a chronologically ordered historical telemetry parameter data set;

[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 purification historical telemetry parameter data set along the time series, calculate the average value of the data in the sliding window, and obtain a smoothed historical telemetry parameter data set;

[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 telemetry parameter time variation trend model.

[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 service 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 service data;

[0152] Based on the file name constraints, file size constraints, remote sensing data field constraints, and remote sensing data quality code constraints, set up a remote sensing service data verification mechanism;

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

[0154] The various change modes and specific examples of the method for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument provided in the first embodiment are equally applicable to the system for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument provided in this embodiment. Through the foregoing detailed description of a method for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument, those skilled in the art can clearly know the implementation manner of the system for monitoring and alarming the on-orbit anomalies of a remote sensing satellite instrument in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated herein.

[0155] Embodiment Three

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

[0157] The memory 302 is used to store programs; the memory 302 may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static random-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory 302 is used to store computer programs (such as application programs and functional modules for implementing the above methods), computer instructions, etc. The above computer programs, computer instructions, etc. may be stored in one or more memories 302 in a partitioned manner. And the above computer programs, computer instructions, data, etc. may be called by the processor 301.

[0158] The above-mentioned computer programs, computer instructions, etc. can be stored in partitions in one or more memories 302. And the above-mentioned computer programs, computer instructions, etc. can be called by the processor 301.

[0159] The processor 301 is configured to execute the computer program stored in the memory 302 to implement each step in the method involved in the above embodiments.

[0160] Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0161] The processor 301 and the memory 302 can be of independent structures or integrated structures integrated together. When the processor 301 and the memory 302 are of independent structures, the memory 302 and the processor 301 can be coupled and connected through a bus 303.

[0162] The electronic device in this embodiment can execute the technical solutions in the above method. The specific implementation process and technical principle are the same and will not be elaborated here.

[0163] Embodiment 4

[0164] Embodiment 4 further provides a computer-readable storage medium, including a computer program and instructions. When the computer program or instructions run on a computer, the computer is enabled to execute the method for monitoring and alarming the on-orbit anomalies of remote sensing satellite instruments according to any embodiment of the present invention.

[0165] The computer-readable storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, ROMs, RAMs, magnetic disks, or optical discs.

[0166] This embodiment also provides a computer program product. The computer program product includes a computer program. The computer program is stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program to enable the electronic device to execute the solution provided in any of the above embodiments.

[0167] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recorded in the disclosure of the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. No limitations are imposed herein.

[0168] In summary, for the method and system for monitoring and alarming the on-orbit anomalies of remote sensing satellite instruments according to the present invention, first, the on-orbit operation data of the remote sensing satellite instruments are collected, including instrument telemetry parameter data and remote sensing service data. Then, the parameter thresholds and trend thresholds of the instrument telemetry parameter data are preset. If the instrument telemetry parameter data do not meet the parameter thresholds or trend thresholds, a telemetry anomaly alarm message is generated. Next, the data integrity constraints of the remote sensing service data are preset. If the remote sensing service data do not meet the data integrity constraints, a remote sensing anomaly alarm message is generated. Then, the timestamp, satellite number, and instrument number are used as matching identifiers to match the telemetry anomaly alarm message and the remote sensing anomaly alarm message for multi-source alarm message matching. Based on the matching results, a composite anomaly alarm message is generated. Finally, feedback optimization is performed according to the composite anomaly alarm message, achieving the technical effects of improving the information coverage of instrument anomaly alarms, reducing the omission and false alarms of alarm messages, and improving the accuracy and reliability of anomaly monitoring.

[0169] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope 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 only. Without departing from the inventive concept, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A remote sensing satellite instrument on-orbit abnormal monitoring and alarm method, characterized in that: include: Collect and obtain the 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 telemetry parameter data of the instrument; 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 satisfy the data integrity constraint, remote sensing abnormality alarm information is generated; Using the timestamp, satellite number, and instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry abnormal alarm information and the remote sensing abnormal alarm information, and generating composite abnormal 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 remote sensing satellite instruments on orbit as claimed in claim 1, characterized in that: The method of using the timestamp, the satellite number, and the instrument number as matching identifiers, performing multi-source alarm information matching on the telemetry abnormal alarm information and the remote sensing abnormal alarm information, and generating composite abnormal 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 the instrument number are used as matching constraints, and the time window is slid along the telemetry and telesensing synchronization abnormal alarm information to extract potential alarm information matching items within the preset time window; According to the potential alarm information matching items, abnormal time correlation analysis and abnormal type correlation analysis are performed, and an abnormal correlation coefficient is generated according to the correlation analysis results; If the abnormal correlation coefficient is greater than a preset abnormal correlation coefficient threshold, the composite abnormal alarm information is generated.

3. The method for monitoring and warning of anomalies of remote sensing satellite instruments on orbit as claimed in claim 2, characterized in that: If the abnormal correlation coefficient is greater than the preset abnormal correlation coefficient threshold, generating the composite abnormal 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 remote sensing satellite instruments on orbit as claimed in claim 1, characterized in that: The parameter thresholds of the preset 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 the time interval according to 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 mean value of the parameter data and the initially set parameter threshold value. If the distance is less than a preset range, correct the parameter threshold value to a parameter characteristic value; otherwise, the parameter threshold value continues to use the initial parameter threshold value. According to the time interval setting, the above process is repeated to achieve adaptive correction of parameter thresholds.

5. The method for monitoring and warning of anomalies of remote sensing satellite instruments on orbit as claimed in claim 1, characterized in that: The trend thresholds of the preset instrument telemetry parameter data include: For each instrument telemetry parameter, obtain a chronologically ordered historical telemetry parameter data set; 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 purification historical telemetry parameter data set 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 data set; 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 telemetry parameter time variation trend model.

6. The method for monitoring and warning of anomalies of remote sensing satellite instruments 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; Based on the file name constraint, file size constraint, remote sensing data field constraint and remote sensing data quality code constraint, a remote sensing business data verification mechanism is set; 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 warning system, characterized in that: 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, used to preset parameter thresholds and trend thresholds of the instrument telemetry parameter data; A first generating module, 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, used for presetting data integrity constraints of the remote sensing service data of the instrument; A second generating module is used to generate remote sensing abnormal alarm information if the instrument remote sensing service data does not meet the data integrity constraint; A third generating module is used to use the timestamp, the satellite number, and the instrument number as matching identifiers, perform multi-source alarm information matching on the telemetry abnormal alarm information and the remote sensing abnormal alarm information, and generate composite abnormal alarm information based on the matching result; A feedback optimization module is used to perform feedback optimization according to 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 as described in 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 executed on a computer, the computer is enabled to execute the monitoring and alarm method for in-orbit abnormality of a remote sensing satellite instrument as described in any one of claims 1 to 6.

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