Visual monitoring and alarming method and device for single event

Abstract

The invention discloses a visual monitoring and alarming method and device for a single event. The method comprises the following steps: acquiring observation data of a target satellite; performing data cleaning based on the observation data to generate standard observation data; acquiring telemetering data and data quality inspection result data, and generating instrument alarm information in combination with a preset alarm threshold value; performing data matching based on the standard observation data and the instrument alarm information to generate an associated data set; based on the historical single-particle event data, constructing a single-particle event model; carrying out confidence level marking on the associated data set based on the single-particle event model, and carrying out risk assessment on a confidence level marking result to generate an assessment result; and drawing a visual distribution map based on the standard observation data and the evaluation result, and performing display and alarm. Therefore, the monitoring and early warning capabilities of the satellite on the single-particle event during the in-orbit operation period are improved, so that the safe and stable operation of the satellite is guaranteed.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of event monitoring, and in particular to a visual monitoring and alarm method and device for single particle events. Background Art

[0002] With the rapid development of aerospace technology, the risk of satellite instruments encountering single particle events during operation is increasing. A single particle event refers to a single high-energy proton or heavy ion bombarding a microelectronic device, causing the device state on the satellite to change, thereby causing anomalies and failures. In particular, the incidence of single particle events increases significantly in the polar regions, the South Atlantic Anomaly, and during solar proton events. Therefore, the development of a platform that can monitor, warn, and visualize single particle events in real time is of great significance to ensure the normal operation of satellite instruments.

[0003] At present, the existing monitoring method is to monitor the instrument data at regular intervals, which cannot predict the occurrence of particle events and the impact of the corresponding particle events on the satellite.

[0004] In summary, existing technologies have limitations in real-time monitoring and early warning of particle events, making it difficult to predict the occurrence of events in a timely manner and to accurately assess the severity of events. Summary of the invention

[0005] The embodiments of the present invention provide a visual monitoring and alarm method and device for single particle events, which are used to solve the limitations of the prior art in real-time monitoring and early warning of single particle events, making it difficult to timely predict the occurrence of events and to accurately assess the severity of events.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a visual monitoring and alarm method for a single particle event, comprising: Step S100, obtaining observation data of the target satellite; Step S200, performing data cleaning based on the observation data to generate standard observation data; Step S300, obtaining telemetry data and data quality inspection result data, and generating instrument alarm information in combination with a preset alarm threshold; Step S400, performing data matching based on the standard observation data and the instrument alarm information to generate a related data set; Step S500, constructing a single particle event model based on historical single particle event data; Step S600, marking the associated data set with confidence levels based on the single particle event model, and performing risk assessment on the confidence level marking results to generate an assessment result; Step S700, drawing a visual distribution diagram based on the standard observation data and the evaluation results, and performing display and alarm.

[0007] In one embodiment of the present invention, step S100 includes: Step S101, acquiring high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor; Step S102, obtaining time information and position latitude information corresponding to the high-energy proton and electron channel data; Step S103, synthesizing the high-energy proton and electron channel data within a preset time to generate the observation data.

[0008] In one embodiment of the present invention, step S200 includes: Step S201, performing data cleaning based on the observation data to generate cleaned observation data; Step S202: performing standardization processing based on the cleaned observation data to generate the standard observation data.

[0009] In one embodiment of the present invention, step S300 includes: Step S301, obtaining telemetry data, data quality inspection result data and preset alarm thresholds of each instrument; Step S302, performing data matching and comparison based on the telemetry data of each instrument and the data quality inspection result data and the preset alarm threshold, and generating a matching and comparison result; Step S303, determining whether the matching comparison result triggers an alarm mechanism; Step S304, if the alarm mechanism is triggered, the matching comparison result is evaluated to generate an evaluation result; Step S305: Generate the instrument alarm information based on the evaluation result.

[0010] In one embodiment of the present invention, step S400 includes: Step S401, extracting an observation-related feature set from the standard observation data, including observation timestamp, particle event record, energy level, and geographic location information; Step S402, extracting an alarm-related feature set from the instrument alarm information, including an alarm timestamp, an alarm type, an alarm level, an instrument identifier, and a geographic location information; Step S403: performing data matching according to the observation timestamp and the alarm timestamp to generate the associated data set.

[0011] In one embodiment of the present invention, step S403 includes: Step S4031, sorting the observation-related feature set and the alarm-related feature set according to the timestamp; Step S4032, traversing the alarm-related feature set, and searching for the particle event record within a preset time window; Step S4033: if the particle event is found, the associated data set is generated.

[0012] In one embodiment of the present invention, step S500 includes: Step S501, obtaining historical single particle event data; Step S502: generating the single particle event model based on the historical single particle event data.

[0013] In one embodiment of the present invention, step S600 includes: Step S601, marking the associated data set with a confidence level based on the single particle model to generate a confidence level marking result; Step S602: Perform risk assessment based on the confidence level marking result to generate an assessment result.

[0014] In a second aspect, the present invention provides a visual monitoring and alarm device for single particle events, comprising: an acquisition module, a first generation module, a second generation module, a third generation module, a construction module, a fourth generation module, and a display and alarm module. The acquisition module is used to acquire the observation data of the target satellite. The first generation module is used to perform data cleaning based on the observation data to generate standard observation data. The second generation module is used to acquire telemetry data and data quality inspection result data, and generate instrument alarm information in combination with a preset alarm threshold. The third generation module is used to perform data matching based on the standard observation data and the instrument alarm information to generate an associated data set. The construction module is used to construct a single particle event model based on historical single particle event data. The fourth generation module is used to mark the associated data set with a confidence level based on the single particle event model, and to perform risk assessment on the confidence level marking result to generate an assessment result. The display and alarm module is used to draw a visual distribution map based on the standard observation data and the assessment result, and to display and alarm.

[0015] In one embodiment of the present invention, the acquisition module includes: a first acquisition unit, a second acquisition unit and a first generation unit. The first acquisition unit is used to acquire high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor. The second acquisition unit is used to acquire time information and position latitude information corresponding to the high-energy proton and electron channel data. And the first generation unit is used to synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

[0016] Compared with the prior art, the visual monitoring and alarm method and device for single particle events according to the present invention have the following beneficial effects: It effectively solves the limitations of existing technologies in real-time monitoring and early warning of single-particle events, making it difficult to predict the occurrence of events in a timely manner and accurately assess the severity of events. It uses data processing, model analysis and visualization technologies to improve the satellite's monitoring and early warning capabilities for single-particle events during its in-orbit operation, thereby ensuring the satellite's safe and stable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a visual monitoring and alarm method for a single particle event in the first embodiment of the present invention; Figure 2 It is a structural schematic diagram of a visual monitoring and alarm device for single particle events in the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, rather than to limit the embodiments of the present invention. It is also necessary to explain that, for ease of description, only parts related to the embodiments of the present invention are shown in the accompanying drawings, rather than all structures.

[0019] Embodiment 1

[0020] Figure 1 FIG. 1 is a flow chart of a visual monitoring and alarm method for a single particle event in Embodiment 1 of the present invention. Figure 1 As shown, embodiment 1 provides a visual monitoring and alarm method for a single particle event, including: Step S100, obtaining observation data of the target satellite; Specifically, the target satellite can be Fengyun-3E, or other Fengyun series polar-orbiting satellites and geostationary satellites, and the high-energy proton P5X and high-energy electron E3X channel data can be obtained from the data storage system or data distribution center of the space environment monitor. These data are usually stored in a specific format, such as binary, HDF5, NetCDF or CSV, etc. After obtaining the data, they are parsed according to the format of the data, and the data of the high-energy proton P3X channel with an energy range of 40MeV to 100MeV and the high-energy electron E3X channel with an energy range of 0.65MeV to 1.2MeV are screened from the parsed data, including particle flux, energy spectrum, timestamp, etc.

[0021] Step S200, performing data cleaning based on the observation data to generate standard observation data; Specifically, the first observation data is cleaned, including missing value processing and outlier processing. For example, if data is missing due to instrument failure or data transmission error, missing value filling is performed according to the nature of the data, such as using mean, median, interpolation method, etc. Data conversion, for example, for timestamp data, it will be parsed into the correct date and time format, etc., to obtain standard observation data.

[0022] Step S300, obtaining telemetry data and data quality inspection result data, and generating instrument alarm information in combination with a preset alarm threshold; Specifically, by comprehensively acquiring the telemetry data of each instrument (for example, including key parameters such as working mode, status, current, voltage, etc.) and data quality inspection results (for example, including radiation deviation, spectral deviation, etc.), combined with the alarm thresholds preset by experts, the instrument status is monitored and analyzed in real time. When the instrument parameters exceed the set threshold or the data quality does not meet the standards, the system will automatically trigger the alarm mechanism and generate instrument alarm information containing key information such as alarm indicator parameters, alarm values, occurrence time, spatial location and alarm level. This process ensures the accuracy and real-time nature of the instrument alarm information, provides a strong guarantee for the safe and stable operation of satellite instruments, and also provides an important basis for subsequent troubleshooting and maintenance work.

[0023] Step S400, performing data matching based on the standard observation data and the instrument alarm information to generate a related data set; Specifically, standard observation data is matched with instrument alarm data in time and space. For example, it can be determined whether a specific instrument has issued an alarm signal within a certain time window. The matching process is automated through algorithms, such as using timestamps to align different data streams, or using geographic location information to associate specific events with instruments.

[0024] Step S500, constructing a single particle event model based on historical single particle event data; Specifically, historical single-particle event data is obtained and divided according to preset weights to generate training sets and test sets. Subsequently, machine learning techniques such as neural networks are used to train the model based on the training set, and the accuracy of the model is verified by the test set. This process not only utilizes the power of big data, but also combines expert knowledge and experience, so that the model can accurately identify and predict the occurrence of single-particle events. The constructed single-particle event model will provide strong technical support for subsequent confidence level marking, risk assessment, and visualization, thereby effectively improving the monitoring and early warning capabilities of satellite instruments for single-particle events.

[0025] Step S600, marking the associated data set with confidence levels based on the single particle event model, and performing risk assessment on the confidence level marking results to generate an assessment result; Specifically, the associated data set is analyzed by applying the constructed single particle event model, the identified single particle events are marked with confidence levels, and a comprehensive risk assessment is performed based on the model output and risk assessment tools. This process not only considers the confidence level of the event, but also combines multiple dimensions such as the possibility of the event, the severity of the impact, and the potential loss, so as to quantitatively assess the risk level of the single particle event. The results of the confidence level marking and risk assessment provide decision makers with clear risk information, helping them understand the risk status of the current system and formulate corresponding risk management strategies to ensure the safe and stable operation of satellite instruments.

[0026] Step S700, drawing a visual distribution map based on the standard observation data and the evaluation result, and performing display and alarming; Specifically, for example, a standard observation data set can be used for mapping analysis to provide intuitive and comprehensive single particle event monitoring information. First, a spatial distribution map of a particle environment is generated using a standard observation data set, which is usually a global distribution map that shows the intensity and distribution of particle activities in different regions. The hot spots and changing trends of particle activities can be clearly displayed through color, size or other visual elements. Next, according to the instrument alarm information generated in step S300, the spatial position of the alarm event is marked on the particle environment distribution map. In this way, business personnel can see at a glance the specific location of the alarm event and its relationship with the distribution of particle activity. By comparing the location of the alarm event and the hot spots of particle activity, business personnel can preliminarily determine whether the alarm event is related to particle activity, as well as the possible scope and extent of impact. In addition to the basic particle environment distribution map and alarm event annotation, more visualization elements and functions can be added according to actual needs. For example, a timeline can be added to display the distribution of particle activity and alarm event conditions at different time points to help business personnel understand the development trend and changing laws of events. In addition, interactive functions can also be introduced to allow business personnel to view detailed information of different regions or time periods by clicking, zooming, and other operations.

[0027] In terms of alarm, once the system detects an alarm event that meets the preset conditions, the alarm mechanism will be triggered immediately. The alarm information will contain key information such as alarm indicator parameters, alarm value, alarm occurrence time, alarm occurrence spatial location, and alarm level. This information will be displayed to business personnel in a striking manner through a visual interface, and can also be notified to designated relevant personnel through emails, text messages, etc. Business personnel can quickly locate the problem based on the alarm information and take corresponding maintenance and processing measures to ensure the safe and stable operation of satellite instruments.

[0028] In summary, step S700 provides intuitive and comprehensive single particle event monitoring information to business personnel by comprehensively using standard observation data sets and alarm information, drawing analysis and alarm mechanism. This not only helps business personnel quickly understand the current system status and risk situation, but also provides strong support for subsequent troubleshooting and maintenance work.

[0029] In this embodiment, step S100 includes: Step S101, acquiring high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor; Step S102, obtaining time information and position latitude information corresponding to the high-energy proton and electron channel data; Step S103, synthesizing the high-energy proton and electron channel data within a preset time to generate the observation data; Specifically, obtain the data output format and interface of the space environment monitor, which may be through satellite downlink, ground receiving station or other data transmission methods. Use data processing software or programming languages ​​such as Python, MATLAB, etc. to write scripts or programs to extract data from P5X and E3X channels. Verify the extracted data to ensure data integrity and accuracy. Time information will be automatically recorded as part of the data. Ensure that the timestamp corresponds accurately to the P5X and E3X channel data. If the space environment monitor is equipped with GPS or other positioning systems, longitude and latitude information can be obtained directly. Otherwise, it may be necessary to calculate the position based on the orbital parameters of the monitor. Ensure that the time information and position information are synchronized with the P5X and E3X channel data in time. Define a preset time range such as the last hour, day, etc. Filter P5X and E3X channel data, time information and position information according to the preset time range. Synthesize the filtered data in chronological order. Each data point should include the P5X and E3X channel reading, timestamp and location longitude and latitude. Convert the data into a format that is easy to analyze and visualize, such as CSV, NetCDF, HDF5, etc. The synthesized first observation data is stored in a safe and reliable storage medium and backed up to prevent data loss.

[0030] In this embodiment, step S200 includes: Step S201, performing data cleaning based on the observation data to generate cleaned observation data; Step S202, performing standardization processing based on the cleaned observation data to generate the standard observation data; Specifically, in the visual monitoring and alarm process of single particle events, data cleaning and standardization are key steps to ensure data quality. First, data cleaning is performed based on the observation data obtained from the target satellite, which is a meticulous and necessary process. The cleaning work mainly includes missing value processing and outlier processing. Missing value processing is to solve the problem of incomplete data caused by instrument failure, data transmission errors, etc. For missing values, a variety of strategies can be adopted, such as using the mean, median or interpolation method to fill in to ensure the integrity of the data. Outlier processing is for extreme values ​​in the data that are significantly deviated from the normal range. These values ​​may be caused by measurement errors, instrument abnormalities or other unknown factors. By identifying and processing these outliers, the accuracy and reliability of the data can be further improved. After completing data cleaning, the next step is to standardize the cleaned data. Standardization is a process of converting data to a unified scale or range, with the aim of eliminating the dimensional differences between different indicators and making the data comparable. Through standardization, the original observation data can be converted into standard observation data, which have the same scale, which is convenient for subsequent data analysis and model application. Data cleaning and standardization play a vital role in the entire monitoring and alarm process. They can not only ensure the accuracy and reliability of data and improve data quality, but also provide a good foundation for subsequent data analysis and model training. Through the processing of these two steps, high-quality standard observation data can be obtained, which will provide strong data support for building single particle event models, conducting confidence level marking and risk assessment, and realizing visual display and alarm.

[0031] In this embodiment, step S300 includes: Step S301, obtaining telemetry data, data quality inspection result data and preset alarm thresholds of each instrument; Step S302, performing data matching and comparison based on the telemetry data of each instrument and the data quality inspection result data and the preset alarm threshold, and generating a matching and comparison result; Step S303, determining whether the matching comparison result triggers an alarm mechanism; Step S304, if the alarm mechanism is triggered, the matching comparison result is evaluated to generate an evaluation result; Step S305, generating the instrument alarm information based on the evaluation result; Specifically, the telemetry data of each instrument is first obtained, including key parameters such as working mode, working status, working current, and data quality inspection result data, such as radiation deviation, spectral deviation, etc. These data provide comprehensive information for evaluating the status of the instrument. Subsequently, based on expert knowledge and historical experience, the system will set thresholds for various indicators to determine whether the instrument status is normal. When an indicator continues to exceed the preset threshold, the system will further consider the duration of the abnormality of the indicator and the impact of the instrument status change on the L1 data product. Based on this information, the system will generate alarm information for the corresponding time and divide the alarms into level one, level two, and level three. The first level alarm indicates that the instrument status has changed significantly abnormally, resulting in the unusable L1 data product; the second level alarm indicates that the instrument status is abnormal, although the L1 data product is still available, the quality has declined; the third level alarm means that the instrument status has changed abnormally, but the L1 data product still has a certain degree of availability, but there is a potential quality risk. The generated alarm information will be recorded in detail in the alarm information data file, including key information such as alarm indicator parameters, alarm value, alarm occurrence time, alarm occurrence spatial location and alarm level, providing comprehensive and accurate data support for subsequent analysis and processing. Once the alarm is triggered, the system will display the alarm information on the visual interface through the monitoring platform, and promptly notify the designated relevant personnel, such as the instrument manager, chief designer and user, through DingTalk, email, etc., so that they can respond quickly and take corresponding maintenance and processing measures to ensure the safe and stable operation of satellite instruments.

[0032] In this embodiment, step S400 includes: Step S401, extracting an observation-related feature set from the standard observation data, including observation timestamp, particle event record, energy level, and geographic location information; Step S402, extracting an alarm-related feature set from the instrument alarm information, including an alarm timestamp, an alarm type, an alarm level, an instrument identifier, and a geographic location information; Step S403, performing data matching according to the observation timestamp and the alarm timestamp to generate the associated data set; Specifically, extract the timestamp information of each observation record from the standard observation data, which is the key field for data matching. Extract records related to high-energy particle events, which may include event types, particle types, etc. Extract the energy level information of each particle event. Extract geographic location information such as longitude and latitude contained in standard observation data. Extract the timestamp information of each alarm record from the instrument alarm data for matching with the observation data. Extract the specific type of alarm, such as excessive high-energy particles, excessive radiation dose, etc. Extract the level information of the alarm, which is usually divided into different severity levels such as high, medium, and low. Extract the unique identification information of the instrument and equipment that generates the alarm. Extract the geographic location information contained in the instrument alarm data. Use the observation timestamp and the alarm timestamp as matching criteria to match the observation data and the alarm data. The match can be exact timestamps that are exactly the same or approximate timestamps within a certain time window. Associate the observation data and alarm data that are successfully matched to form a new data set. This data set should contain all relevant features of the observation and the alarm. Delete the observation data that failed to match the alarm or the alarm data that failed to match the observation. Store the resulting linked dataset in an appropriate format such as CSV, database table, etc.

[0033] In this embodiment, step S403 includes: Step S4031, sorting the observation-related feature set and the alarm-related feature set according to the timestamp; Step S4032, traversing the alarm-related feature set, and searching for the particle event record within a preset time window; Step S4033, if the particle event is found, generating the associated data set; Specifically, sort the observation-related feature set according to the observation timestamp to ensure that the data is arranged in increasing or decreasing time order. Perform the same sorting operation on the alarm-related feature set according to the alarm timestamp. Initialize an empty associated data set. Traverse the sorted alarm-related feature set, and for each alarm record: define a preset time window, for example, 5 minutes before and after the alarm timestamp. Search the sorted observation-related feature set for particle event records that fall within this time window. If the corresponding particle event records are found, associate these records with the current alarm record. For each successfully matched alarm record and particle event record pair, merge their related features such as timestamp, particle event details, alarm type and level into a new data entry. Add this new data entry to the associated data set. Continue to traverse the alarm-related feature set and repeat the above process until all alarm records have been processed.

[0034] In this embodiment, step S500 includes: Step S501, obtaining historical single particle event data; Step S502, generating the single particle event model based on the historical single particle event data; Specifically, it is necessary to first obtain rich historical single-particle event data, which covers single-particle events under different conditions and provides a solid foundation for model training. Subsequently, the historical data is divided into training sets and test sets according to the preset weight ratio to ensure the comprehensiveness of model learning and the accuracy of evaluation. The model is trained on the training set based on the neural network, and the network parameters and weights are adjusted to enable the model to accurately identify and predict single-particle events. At the same time, the validation set is used to monitor the model performance in real time to prevent overfitting. After the training is completed, an independent test set is used to evaluate the generalization ability of the model to ensure its effectiveness in practical applications. After a series of training and testing, a high-performance single-particle event model was successfully constructed. This model can not only accurately identify single-particle events, but also give corresponding alarm information according to the severity and scope of the event. The integration of this model will greatly enhance the real-time and accuracy of the single-particle event monitoring system, and provide a solid guarantee for the safe and stable operation of satellite instruments.

[0035] In this embodiment, step S600 includes: Step S601, marking the associated data set with a confidence level based on the single particle model to generate a confidence level marking result; Step S602, performing risk assessment based on the confidence level marking result to generate an assessment result; Specifically, confidence level labeling and risk probability level assessment are key steps to ensure the accuracy and effectiveness of warning information. Once the single particle event model is constructed, the associated data set can be deeply analyzed and processed based on this model. The associated data set is generated by matching the standard data of satellite observations with the instrument warning information. It contains rich information related to single particle events, such as the type of event, the time of occurrence, the location, and related observation data. This information provides a solid foundation for confidence level labeling and risk probability level assessment. First, confidence level labeling of the associated data set based on the single particle model is a multi-level judgment process. The confidence level reflects the assessment of the credibility of the identified single particle event. The model will calculate the probability of each potential single particle event in the associated data set based on the historical data features it has learned. These probability values ​​not only take into account the direct observation data of the event, but also combine the model's past experience and expert knowledge of similar events. By setting different confidence thresholds, events can be divided into different confidence levels, such as "high", "medium", and "low", so that users can quickly understand the reliability of events based on the confidence level.

[0036] After the confidence level is marked, the risk probability level is evaluated. This step aims to quantify the potential impact of single particle events on satellite instrument operation. The evaluation process takes into account multiple factors, including the confidence level of the event, the type of event, the location of occurrence, the scope of impact, etc. For events with high confidence levels, their potential risks will be analyzed in more depth, including possible consequences such as data loss, instrument failure, and satellite performance degradation. By using risk assessment methods, such as failure mode and effect analysis (FMEA) or risk matrix method, a risk probability level can be assigned to each event, which directly reflects the risk level of the event to the operation of satellite instruments.

[0037] During the assessment process, historical data will also be referenced to ensure the accuracy and reliability of the assessment results. Historical data provides information on the past impacts and consequences of similar events, which helps to better understand the potential risks of current events. In addition, the risk probability level assessment also needs to consider the mutual influence and superposition effects between different events. In a complex space environment, single particle events often do not occur in isolation, and there may be mutual influence and superposition effects between them. Therefore, during the assessment process, it is necessary to comprehensively consider the impact of multiple events to avoid underestimating or overestimating the risk.

[0038] The final evaluation results will be an important part of the warning information and will be communicated to users through visual display and alarm mechanism. Users can quickly understand the risk status of the current satellite instruments based on the evaluation results and take corresponding countermeasures. At the same time, the evaluation results can also serve as a basis for subsequent system improvement and optimization, and better understand and respond to the impact of single particle events on satellite instruments.

[0039] In a specific embodiment, the visual monitoring and alarm method for single particle events further includes: Build satellite data communication channels; Acquiring a single particle event monitoring signal through the satellite data communication channel; A visual early warning is performed according to the single particle event monitoring signal.

[0040] Specifically, determine the data transmission rate and frequency between the target satellite and the ground station, and select ground communication facilities, including antennas, receivers and transmitters, to establish a stable communication link with the satellite. Test the communication link, including signal reception quality, data transmission rate, etc. Adjust equipment parameters, optimize communication effects, receive single particle event monitoring signals transmitted by satellites in real time, decode and process the received signals, and extract effective monitoring data. Perform real-time analysis on the received single particle event monitoring data to identify abnormal or high-risk events. Collect monitoring data through monitoring signals, and pre-process the received monitoring data, such as denoising, filtering, and standardization, to improve data quality and analysis accuracy. According to the output results of the single particle event model and anomaly alarm level annotation, obtain a multi-level alarm data set, and then generate alarm information according to the preset value, and perform visual warning based on the alarm information and high-energy particle flux distribution map.

[0041] In short, through this process, the reliability and risk level of single-particle events can be accurately identified and evaluated, providing strong guarantees for the safe and stable operation of satellite instruments.

[0042] In a specific embodiment, the method for performing data cleaning based on the observation data to generate standard observation data may also be: According to the observation data, obtaining a first observation feature data set; Decentralizing the first observation feature data set to obtain a second observation feature data set; Obtaining a first observation feature covariance matrix according to the second observation feature data set; Obtaining a first observed eigenvalue and a first observed eigenvector according to the first observed feature covariance matrix; The standard observation data is obtained according to the first observation eigenvalue and the first observation eigenvector.

[0043] Specifically, from the first observation data, select features that can represent the core characteristics of the data, such as the energy, flux, direction, etc. of the particles. Extract these features and construct a first observation feature data set. Preprocess the data set, such as scaling or encoding the features. Calculate the average value of each feature in the first observation feature data set. Subtract the average value of the corresponding feature from each data point in the data set to obtain a decentralized second observation feature data set. For each pair of features in the second observation feature data set, calculate the covariance between them. Use these covariance values ​​to construct a covariance matrix, namely the first observation feature covariance matrix. Perform eigenvalue decomposition on the first observation feature covariance matrix to obtain a series of eigenvalues ​​and corresponding eigenvectors. Sort these eigenvalues ​​by size, and select larger eigenvalues ​​and their corresponding eigenvectors, which represent the main change direction of the data. Use the selected main eigenvectors, namely the principal components, to transform the second observation feature data set into a new coordinate system. It can be a projection or transformation of the data, and the projected data is the standard observation data. These standard observation data express the main features of the original data in the new coordinate system.

[0044] Embodiment 2

[0045] Figure 2 FIG. 1 is a schematic diagram of a visual monitoring and alarm device for a single particle event in Embodiment 2 of the present invention. Figure 2 As shown, embodiment 2 provides a visual monitoring and alarm device for single particle events, including: an acquisition module, a first generation module, a second generation module, a third generation module, a construction module, a fourth generation module and a display alarm module. The acquisition module is used to acquire the observation data of the target satellite. The first generation module is used to perform data cleaning based on the observation data to generate standard observation data. The second generation module is used to acquire telemetry data and data quality inspection result data, and generate instrument alarm information in combination with a preset alarm threshold. The third generation module is used to perform data matching based on the standard observation data and the instrument alarm information to generate an associated data set. The construction module is used to construct a single particle event model based on historical single particle event data. The fourth generation module is used to mark the associated data set with a confidence level based on the single particle event model, and to perform risk assessment on the confidence level marking result to generate an assessment result. The display alarm module is used to draw a visual distribution map based on the standard observation data and the assessment result, and to display and alarm.

[0046] In this embodiment, the acquisition module includes: a first acquisition unit, a second acquisition unit and a first generation unit. The first acquisition unit is used to acquire high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor. The second acquisition unit is used to acquire time information and position latitude information corresponding to the high-energy proton and electron channel data. And the first generation unit is used to synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

[0047] In this embodiment, the first generation module includes: a second generation unit and a third generation unit. The second generation unit is used to perform data cleaning based on the observation data to generate cleaned observation data. The third generation unit is used to perform standardization processing based on the cleaned observation data to generate the standard observation data.

[0048] In this embodiment, the second generation module includes: a third acquisition unit, a fourth generation unit, a judgment unit, a fifth generation unit and a sixth generation unit. The third acquisition unit is used to acquire the telemetry data, data quality inspection result data and preset alarm threshold of each instrument. The fourth generation unit is used to perform data matching and comparison based on the telemetry data of each instrument and the data quality inspection result data and the preset alarm threshold, and generate a matching and comparison result. The judgment unit is used to determine whether the matching and comparison result triggers an alarm mechanism. The fifth generation unit is used to evaluate the matching and comparison result if the alarm mechanism is triggered, and generate an evaluation result. The sixth generation unit is used to generate the instrument alarm information based on the evaluation result.

[0049] In this embodiment, the third generation module includes: a first extraction unit, a second extraction unit and a seventh generation unit. The first extraction unit is used to extract an observation-related feature set from the standard observation data, including an observation timestamp, a particle event record, an energy level, and geographic location information. The second extraction unit is used to extract an alarm-related feature set from the instrument alarm information, including an alarm timestamp, an alarm type, an alarm level, an instrument identifier, and geographic location information. The seventh generation unit is used to perform data matching according to the observation timestamp and the alarm timestamp to generate the associated data set.

[0050] In this embodiment, the seventh generation unit includes: a sorting subunit, a searching subunit and a generating subunit. The sorting subunit is used to sort the observation-related feature set and the alarm-related feature set according to the timestamp. The searching subunit is used to traverse the alarm-related feature set and search for the particle event record within a preset time window. The generating subunit is used to generate the associated data set if the particle event is found.

[0051] In this embodiment, the construction module includes: a fourth acquisition unit and an eighth generation unit. The fourth acquisition unit is used to acquire historical single particle event data. The eighth generation unit is used to generate the single particle event model based on the historical single particle event data.

[0052] In this embodiment, the fourth generation module includes: a ninth generation unit and a tenth generation unit. The ninth generation unit is used to mark the associated data set with a confidence level based on the single particle model and generate a confidence level marking result. The tenth generation unit is used to perform risk assessment based on the confidence level marking result and generate an assessment result.

[0053] The various variations and specific examples of the visual monitoring and alarm method for single-particle events provided in Example 1 are also applicable to the visual monitoring and alarm device for single-particle events provided in this example. Through the above detailed description of a visual monitoring and alarm method for single-particle events, those skilled in the art can clearly understand the implementation method of a visual monitoring and alarm device for single-particle events in this example. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0054] In summary, the visual monitoring and alarm device for single-particle events of the present invention effectively solves the limitations of the existing technology in real-time monitoring and early warning of single-particle events, making it difficult to predict the occurrence of events in a timely manner and accurately assess the severity of events. It uses data processing, model analysis and visualization technologies to improve the monitoring and early warning capabilities of single-particle events during the satellite's in-orbit operation, thereby ensuring the safety and stable operation of the satellite.

[0055] Note that the above are only preferred embodiments of the present invention and the technical principles used. 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 more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A visual monitoring and alarm method for single particle events, characterized in that: include: Step S100, obtaining observation data of the target satellite; Step S200, performing data cleaning based on the observation data to generate standard observation data; Step S300, obtaining telemetry data and data quality inspection result data, and generating instrument alarm information in combination with a preset alarm threshold; Step S400, performing data matching based on the standard observation data and the instrument alarm information to generate a related data set; Step S500, constructing a single particle event model based on historical single particle event data; Step S600, marking the associated data set with confidence levels based on the single particle event model, and performing risk assessment on the confidence level marking results to generate an assessment result; Step S700, drawing a visual distribution diagram based on the standard observation data and the evaluation results, and performing display and alarm.

2. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S100 includes: Step S101, acquiring high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor; Step S102, obtaining time information and position latitude information corresponding to the high-energy proton and electron channel data; Step S103, synthesizing the high-energy proton and electron channel data within a preset time to generate the observation data.

3. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S200 includes: Step S201, performing data cleaning based on the observation data to generate cleaned observation data; Step S202: performing standardization processing based on the cleaned observation data to generate the standard observation data.

4. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S300 includes: Step S301, obtaining telemetry data, data quality inspection result data and preset alarm thresholds of each instrument; Step S302, performing data matching and comparison based on the telemetry data of each instrument and the data quality inspection result data and the preset alarm threshold, and generating a matching and comparison result; Step S303, determining whether the matching comparison result triggers an alarm mechanism; Step S304, if the alarm mechanism is triggered, the matching comparison result is evaluated to generate an evaluation result; Step S305: Generate the instrument alarm information based on the evaluation result.

5. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S400 includes: Step S401, extracting an observation-related feature set from the standard observation data, including observation timestamp, particle event record, energy level, and geographic location information; Step S402, extracting an alarm-related feature set from the instrument alarm information, including an alarm timestamp, an alarm type, an alarm level, an instrument identifier, and a geographic location information; Step S403: performing data matching according to the observation timestamp and the alarm timestamp to generate the associated data set.

6. The visual monitoring and alarm method for single particle events according to claim 5, characterized in that: The step S403 includes: Step S4031, sorting the observation-related feature set and the alarm-related feature set according to the timestamp; Step S4032, traversing the alarm-related feature set, and searching for the particle event record within a preset time window; Step S4033: if the particle event is found, the associated data set is generated.

7. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S500 includes: Step S501, obtaining historical single particle event data; Step S502: generating the single particle event model based on the historical single particle event data.

8. The visual monitoring and alarm method for single particle events according to claim 1, characterized in that: The step S600 includes: Step S601, marking the associated data set with a confidence level based on the single particle model to generate a confidence level marking result; Step S602: Perform risk assessment based on the confidence level marking result to generate an assessment result.

9. A visual monitoring and alarm device for single particle events, characterized in that: include: An acquisition module is used to acquire observation data of a target satellite; A first generating module, used for performing data cleaning based on the observation data to generate standard observation data; The second generation module is used to obtain telemetry data and data quality inspection result data, and generate instrument alarm information in combination with preset alarm thresholds; A third generating module is used to perform data matching based on the standard observation data and the instrument alarm information to generate a related data set; A construction module for constructing a single particle event model based on historical single particle event data; A fourth generating module is used to mark the associated data set with a confidence level based on the single particle event model, and to perform risk assessment on the confidence level marking result to generate an assessment result; as well as The display and alarm module is used to draw a visual distribution diagram based on the standard observation data and the evaluation results, and to display and alarm.

10. The visual monitoring and alarm device for single particle events according to claim 9, characterized in that: The acquisition module comprises: A first acquisition unit is used to acquire high-energy proton and electron channel data of a high-energy particle detector of a space environment monitor; A second acquisition unit is used to acquire the time information and position latitude information corresponding to the high-energy proton and electron channel data; and The first generating unit is used to synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

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