Visual Monitoring and Alarm Method and Device for Single-Particle Events

Abstract

The present invention discloses a method and device for visual monitoring and alarming of single particle events. The method includes: obtaining observation data of a target satellite; performing data cleaning on the observation data to generate standard observation data; obtaining telemetry data and data quality inspection result data, and combining with a preset alarm threshold to generate instrument alarm information; performing data matching based on the standard observation data and the instrument alarm information to generate an associated data set; constructing a single particle event model based on historical single particle event data; performing confidence level marking on the associated data set based on the single particle event model, and performing risk assessment on the confidence level marking result to generate an assessment result; drawing a visual distribution map based on the standard observation data and the assessment result, and performing display and alarm. Thereby, the present invention improves the monitoring and early warning capabilities of single particle events during the on-orbit operation of the satellite, thus ensuring the safe and stable operation of the satellite.

Description

Technical Field

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

[0002] With the rapid development of space technology, the risk of satellite instruments encountering single particle events during operation is increasing day by day. A single particle event refers to the bombardment of microelectronic devices by a single high-energy proton or heavy ion, etc., resulting in a change in the state of the devices on the satellite, thereby causing anomalies and failures. Especially in the polar regions, the South Atlantic Anomaly region, and during solar proton events, the incidence of single particle events increases significantly. Therefore, developing a platform that can monitor, warn, and visually monitor single particle events in real time is of great significance for ensuring the normal operation of satellite instruments.

[0003] Currently, the existing monitoring method is to monitor the instrument data at regular intervals, and it is impossible to predict the occurrence of particle events and the degree of impact on the satellite according to the corresponding particle events.

[0004] In summary, the existing technology has limitations in real-time monitoring and warning of particle events, it is difficult to predict the occurrence of events in a timely manner, and it is impossible to accurately evaluate the severity of events. Summary of the Invention

[0005] The embodiments of the present invention provide a method and device for visual monitoring and alarming of single particle events to solve the limitations of the existing technology in real-time monitoring and warning of single particle events, which are difficult to predict the occurrence of events in a timely manner and impossible to accurately evaluate the severity of events.

[0006] To achieve the above object, in the first aspect, the present invention provides a method for visual monitoring and alarming of single particle events, including:

[0007] Step S100, obtaining the observation data of the target satellite;

[0008] Step S200, performing data cleaning based on the observation data to generate standard observation data;

[0009] Step S300, obtaining telemetry data and data quality inspection result data, and generating instrument alarm information in combination with a preset alarm threshold;

[0010] Step S400, performing data matching based on the standard observation data and the instrument alarm information to generate an associated data set;

[0011] Step S500, constructing a single particle event model based on historical single particle event data;

[0012] Step S600: Based on the single-particle event model, perform confidence level marking on the associated dataset, and conduct risk assessment on the confidence level marking results to generate an assessment result;

[0013] Step S700: Based on the standard observation data and the assessment result, draw a visualization distribution map, and conduct display and alarm.

[0014] In an embodiment of the present invention, the step S100 includes:

[0015] Step S101: Obtain the high-energy proton and electron channel data of the high-energy particle detector of the space environment monitor;

[0016] Step S102: Obtain the time information and position latitude information corresponding to the high-energy proton and electron channel data;

[0017] Step S103: Synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

[0018] In an embodiment of the present invention, the step S200 includes:

[0019] Step S201: Based on the observation data, perform data cleaning to generate the cleaned observation data;

[0020] Step S202: Based on the cleaned observation data, perform standardization processing to generate the standard observation data.

[0021] In an embodiment of the present invention, the step S300 includes:

[0022] Step S301: Obtain the telemetry data, data quality inspection result data, and preset alarm threshold of each instrument;

[0023] Step S302: Based on the telemetry data of each instrument, the data quality inspection result data, and the preset alarm threshold, perform data matching and comparison to generate a matching and comparison result;

[0024] Step S303: Determine whether the matching and comparison result triggers an alarm mechanism;

[0025] Step S304: If the alarm mechanism is triggered, evaluate the matching and comparison result to generate an evaluation result;

[0026] Step S305: Based on the evaluation result, generate the instrument alarm information.

[0027] In an embodiment of the present invention, the step S400 includes:

[0028] Step S401: Extract the observation-related feature set from the standard observation data, including the observation timestamp, particle event record, energy level, and geographical location information;

[0029] Step S402: Extract the alarm-related feature set from the instrument alarm information, including the alarm timestamp, alarm type, alarm level, instrument identifier, and geographical location information;

[0030] Step S403: Perform data matching based on the observation timestamp and the alarm timestamp to generate the associated data set.

[0031] In an embodiment of the present invention, step S403 includes:

[0032] Step S4031: Sort the observation-related feature set and the alarm-related feature set according to the timestamp;

[0033] Step S4032: Traverse the alarm-related feature set to find the particle event record within a preset time window;

[0034] Step S4033: If the particle event is found, generate the associated data set.

[0035] In an embodiment of the present invention, step S500 includes:

[0036] Step S501: Obtain historical single-particle event data;

[0037] Step S502: Generate the single-particle event model based on the historical single-particle event data.

[0038] In an embodiment of the present invention, step S600 includes:

[0039] Step S601: Perform confidence level marking on the associated data set based on the single-particle model to generate a confidence level marking result;

[0040] Step S602: Perform risk assessment based on the confidence level marking result to generate an assessment result.

[0041] In a second aspect, the present invention provides a visualization 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 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 combine with a preset alarm threshold to generate instrument alarm information. The third generation module is used to perform data matching 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 perform confidence level marking on the associated data set based on the single particle event model, and perform risk assessment on the confidence level marking result to generate an assessment result. The display and alarm module is used to draw a visualization distribution map based on the standard observation data and the assessment result, and perform display and alarm.

[0042] In an 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 the high-energy proton and electron channel data of the high-energy particle detector of the space environment monitor. The 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 generation unit is used to synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

[0043] Compared with the prior art, the visualization monitoring and alarm method and device for single particle events according to the present invention have the following beneficial effects:

[0044] It effectively solves the limitations of the prior art in real-time monitoring and early warning of single particle events, is difficult to predict the occurrence of events in a timely manner, and cannot accurately evaluate 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 on-orbit operation of satellites, thereby ensuring the safe and stable operation of satellites. Description of the Drawings

[0045] Figure 1 is a schematic flow chart of a visualization monitoring and alarm method for single particle events in Embodiment 1 of the present invention;

[0046] Figure 2 is a schematic structural diagram of a visualization monitoring and alarm device for single particle events in Embodiment 2 of the present invention. Detailed Embodiments

[0047] The following further elaborates on the embodiments of the present invention in conjunction with the accompanying drawings and examples. It can be understood that the specific embodiments described herein are merely for explaining the embodiments of the present invention and not for limiting the embodiments of the present invention. Additionally, it should be noted that for ease of description, only parts related to the embodiments of the present invention are shown in the drawings rather than all the structures.

[0048] Embodiment 1

[0049] Figure 1 is a schematic flowchart of a visualization monitoring and alarm method for a single-particle event in Embodiment 1 of the present invention. As Figure 1 shown, Embodiment 1 provides a visualization monitoring and alarm method for a single-particle event, including:

[0050] Step S100, obtaining the observation data of the target satellite;

[0051] Specifically, the target satellite can be Fengyun-3 E satellite, or other Fengyun series polar-orbiting satellites and geostationary satellites. Obtain the data of high-energy proton P5X and high-energy electron E3X channels from the data storage system or data distribution center of the space environment monitor. These data are usually stored in specific formats, such as binary, HDF5, NetCDF, or CSV, etc. After obtaining the data, parse it according to the data format, and screen out the data of the high-energy proton P3X channel with an energy range between 40 MeV and 100 MeV and the high-energy electron E3X channel with an energy range between 0.65 MeV and 1.2 MeV from the parsed data, including particle flux, energy spectrum, timestamp, etc.

[0052] Step S200, performing data cleaning on the observation data to generate standard observation data;

[0053] Specifically, perform data cleaning on the first observation data, including missing value processing and outlier processing. For example, due to instrument failure or data transmission error, data is missing. According to the nature of the data, fill in the missing values, such as using the mean, median, interpolation method, etc., and data conversion. For example, for timestamp data, parse it into the correct date and time format, etc., to obtain standard observation data.

[0054] Step S300, obtaining the telemetry data and the data quality inspection result data, and generating instrument alarm information in combination with the preset alarm threshold;

[0055] Specifically, by comprehensively obtaining the telemetry data of each instrument (such as key parameters including working mode, status, current, voltage, etc.) and the data quality inspection results (such as radiation deviation, spectral deviation, etc.), and combining with the alarm thresholds preset by experts, the instrument status is monitored and analyzed in real time. When the instrument parameters exceed the set thresholds 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 index parameters, alarm values, occurrence time, spatial location, and alarm level. This process ensures the accuracy and timeliness 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 fault troubleshooting and maintenance work.

[0056] Step S400, perform data matching based on the standard observation data and the instrument alarm information to generate an associated dataset;

[0057] Specifically, match the standard observation data with the instrument alarm data in terms of time and space. For example, it can be determined whether a certain instrument has sent an alarm signal within a certain time window. The automatic matching process is implemented through algorithms, such as aligning different data streams using timestamps, or associating specific events with instruments using geographical location information.

[0058] Step S500, construct a single-event upset model based on the historical single-event upset data;

[0059] Specifically, by obtaining the historical single-event upset data and dividing the data with preset weights, a training set and a test set are generated. 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 through the test set. This process not only utilizes the power of big data but also combines expert knowledge and experience, enabling the model to accurately identify and predict the occurrence of single-event upsets. The constructed single-event upset model will provide strong technical support for subsequent confidence level marking, risk assessment, and visualization display, thus effectively enhancing the monitoring and early warning capabilities of satellite instruments for single-event upsets.

[0060] Step S600, perform confidence level marking on the associated dataset based on the single-event upset model, and conduct a risk assessment on the confidence level marking results to generate an assessment result;

[0061] Specifically, by applying the constructed single-particle event model to analyze the associated data set, confidence level marking is performed on the identified single-particle events, and a comprehensive risk assessment is carried out based on the model output and the risk assessment tool. This process not only considers the confidence level of the event, but also combines multiple dimensions such as the likelihood of the event occurring, the severity of the impact, and the potential losses, so as to quantitatively evaluate the risk level of the single-particle event. The results of the confidence level marking and the risk assessment provide clear risk information for decision-makers, helping them understand the current risk situation faced by the system and formulate corresponding risk management strategies to ensure the safe and stable operation of the satellite instrument.

[0062] Step S700, draw a visualization distribution map based on the standard observation data and the evaluation result, and perform display and alarm;

[0063] Specifically, for example, the standard observation data set can be used for drawing analysis to provide intuitive and comprehensive single-particle event monitoring information. First, a spatial distribution map of the particle environment is generated using the standard observation data set. This is usually a global distribution map that shows the intensity and distribution of particle activities in different regions. Through colors, sizes, or other visual elements, the hot spots and changing trends of particle activities can be clearly displayed. Next, according to the instrument alarm information generated in step S300, the spatial positions of the alarm events are marked on the particle environment distribution map. In this way, the business personnel can clearly see the specific positions where the alarm events occur and their relationships with the particle activity distributions. By comparing the positions of the alarm events and the hot spots of particle activities, the business personnel can initially judge whether the alarm events are related to particle activities and the possible scope and degree of influence. In addition to the basic particle environment distribution map and the marking of alarm events, more visualization elements and functions can be added according to actual needs. For example, a time axis can be added to show the particle activity distributions and alarm event situations at different time points, helping the business personnel understand the development trends and changing rules of the events. In addition, interactive functions can be introduced to allow the business personnel to view detailed information in different regions or time periods through operations such as clicking and zooming.

[0064] In terms of alarm, once the system detects an alarm event that meets the preset conditions, the alarm mechanism will be immediately triggered. The alarm information will include key information such as alarm index parameters, alarm values, the time when the alarm occurs, the spatial position where the alarm occurs, and the alarm level. This information will be displayed to the business personnel in a prominent manner through the visualization interface, and can also be notified to the designated relevant personnel by means of emails, text messages, etc. The business personnel can quickly locate the problem based on the alarm information and take corresponding maintenance and handling measures to ensure the safe and stable operation of the satellite instrument.

[0065] In summary, step S700 provides intuitive and comprehensive single-particle event monitoring information for business personnel by comprehensively using standard observation datasets and alarm information, and leveraging plotting analysis and alarm mechanisms. This not only helps business personnel quickly understand the current system status and risk situation but also provides strong support for subsequent fault troubleshooting and maintenance work.

[0066] In this embodiment, step S100 includes:

[0067] Step S101, obtaining high-energy proton and electron channel data of the high-energy particle detector of the space environment monitor;

[0068] Step S102, obtaining the time information and position latitude information corresponding to the high-energy proton and electron channel data;

[0069] Step S103, synthesizing the high-energy proton and electron channel data within a preset time to generate the observation data;

[0070] 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. The time information will be automatically recorded as part of the data. Ensure that the timestamps accurately correspond to the P5X and E3X channel data. If the space environment monitor is equipped with a GPS or other positioning system, the latitude and longitude information can be directly obtained. Otherwise, the position may need to be calculated based on the monitor's orbital parameters. 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, one day, etc. Filter the 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 P5X and E3X channel readings, timestamp, and position latitude and longitude. Convert the data into a format convenient for analysis and visualization, such as CSV, NetCDF, HDF5, etc. Store the synthesized first observation data in a secure storage medium and make a backup to prevent data loss.

[0071] In this embodiment, step S200 includes:

[0072] Step S201, performing data cleaning on the observation data to generate cleaned observation data;

[0073] Step S202, performing standardization processing on the cleaned observation data to generate the standard observation data;

[0074] Specifically, in the visualization monitoring and alarming process of single-particle events, data cleaning and standardization are crucial steps to ensure data quality. First, based on the observation data obtained from the target satellite, data cleaning is carried out, which is a meticulous and necessary process. The cleaning work mainly includes missing value handling and outlier handling. Missing value handling is to solve the problem of incomplete data caused by instrument failures, data transmission errors, etc. For missing values, various strategies can be adopted, such as using the mean, median or interpolation methods to fill them to ensure data integrity. Outlier handling is aimed at the extreme values in the data that significantly deviate from the normal range, which may be caused by measurement errors, instrument abnormalities or other unknown factors. By identifying and handling 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, aiming to eliminate the dimensional differences between different indicators and make the data comparable. Through standardization, the original observation data can be converted into standard observation data with the same scale, which is convenient for subsequent data analysis and model application. Data cleaning and standardization play a crucial role in the entire monitoring and alarming process. They can not only ensure the accuracy and reliability of the data, 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 a single-particle event model, performing confidence level marking and risk assessment, and realizing visual display and alarming.

[0075] In this embodiment, step S300 includes:

[0076] Step S301, obtaining the telemetry data, data quality inspection result data and preset alarm thresholds of each instrument;

[0077] Step S302, based on the telemetry data of each instrument and the data quality inspection result data, comparing with the preset alarm thresholds for data matching to generate a matching comparison result;

[0078] Step S303, determining whether the matching comparison result triggers an alarm mechanism;

[0079] Step S304, if the alarm mechanism is triggered, evaluating the matching comparison result to generate an evaluation result;

[0080] Step S305, based on the evaluation result, generating the instrument alarm information;

[0081] Specifically, first, telemetry data of each instrument is obtained, including key parameters such as working mode, working status, working current, and data quality inspection result data such as radiation deviation and spectral deviation. These data provide comprehensive information for evaluating the instrument status. Subsequently, based on expert knowledge and historical experience, the system sets thresholds for each indicator to determine whether the instrument status is normal. When a certain indicator continuously exceeds the preset threshold, the system further considers the duration of the abnormal persistence of this indicator and the impact degree of the instrument status change on the L1-level data product. According to this information, the system generates alarm information corresponding to the time and classifies the alarms into level 1, level 2, and level 3. A level 1 alarm indicates that a significant abnormal change has occurred in the instrument status, resulting in the inability to use the L1-level data product; a level 2 alarm indicates that the instrument status is abnormal, although the L1-level data product is still usable but its quality has decreased; a level 3 alarm means that an abnormal change has occurred in the instrument status, but the L1-level data product still has a certain usability, but there are potential quality risks. The generated alarm information is detailedly recorded in the alarm information data file, including key information such as alarm indicator parameters, alarm values, 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 displays the alarm information on the visualization interface through the monitoring platform and timely notifies the designated relevant personnel, such as instrument responsible persons, chief designers, and users, via DingTalk, email, etc., so that they can quickly respond and take corresponding maintenance and processing measures to ensure the safe and stable operation of the satellite instrument.

[0082] In this embodiment, step S400 includes:

[0083] Step S401, extracting an observation-related feature set from the standard observation data, including an observation timestamp, a particle event record, an energy level, and geographical location information;

[0084] 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 geographical location information;

[0085] Step S403, performing data matching according to the observation timestamp and the alarm timestamp to generate the associated data set;

[0086] Specifically, extract the timestamp information of each observation record from the standard observation data, which is the key field for data matching. Extract the records related to high-energy particle events, which may include event types, particle species, etc. Extract the energy level information of each particle event. Extract the geographical location information such as longitude and latitude contained in the 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 types of alarms, such as high-energy particle overrun, excessive radiation dose, etc. Extract the level information of the alarms, 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 alarms. Extract the geographical location information contained in the instrument alarm data. Use the observation timestamp and the alarm timestamp as the matching criteria to match the observation data and the alarm data. The matching can be that the exact timestamps are the same or the approximate timestamps are within a certain time window. Associate the successfully matched observation data and alarm data 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 fails to match an alarm or the alarm data that fails to match an observation. Store the generated associated data set in an appropriate format such as CSV, database table, etc.

[0087] In this embodiment, step S403 includes:

[0088] Step S4031, sort the observation-related feature set and the alarm-related feature set according to the timestamp;

[0089] Step S4032, traverse the alarm-related feature set and search for the particle event records within a preset time window;

[0090] Step S4033, if the particle event is found, generate the associated data set;

[0091] Specifically, sort the observation-related feature set according to the observation timestamp to ensure that the data is arranged in ascending or descending order of time. 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. For each alarm record: Define a preset time window, for example, 5 minutes before and after the alarm timestamp. Search for the particle event records that fall within this time window in the sorted observation-related feature set. If the corresponding particle event records are found, associate these records with the current alarm record. For each successfully matched pair of alarm records and particle event records, merge their relevant features such as timestamps, particle event details, alarm types, and levels 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.

[0092] In this embodiment, step S500 includes:

[0093] Step S501, obtaining historical single-particle event data;

[0094] Step S502, generating the single-particle event model based on the historical single-particle event data;

[0095] Specifically, it is first necessary to obtain rich historical single-particle event data, which cover single-particle events under different conditions and provide a solid foundation for model training. Subsequently, according to the preset weight ratio, the historical data is divided into a training set and a test set to ensure the comprehensiveness of model learning and the accuracy of evaluation. Based on a neural network, the training set is used for model training. By adjusting the network parameters and weights, the model can accurately identify and predict single-particle events. At the same time, a validation set is used to monitor the model performance in real time to prevent overfitting. After training is completed, an independent test set is used to evaluate the generalization ability of the model to ensure its effectiveness in practical applications. Through a series of training and testing, a high-performance single-particle event model is successfully constructed. This model can not only accurately identify single-particle events but also give corresponding warning information according to the severity and impact range of the events. The integration of this model will greatly enhance the real-time performance and accuracy of the single-particle event monitoring system and provide a solid guarantee for the safe and stable operation of satellite instruments.

[0096] In this embodiment, step S600 includes:

[0097] Step S601, performing a confidence level marking on the associated data set based on the single-particle model to generate a confidence level marking result;

[0098] Step S602, performing a risk assessment based on the confidence level marking result to generate an assessment result;

[0099] Specifically, confidence level marking and risk probability level assessment are key steps to ensure the accuracy and effectiveness of alarm information. Once the single particle event model is constructed, in-depth analysis and processing of the associated dataset can be carried out based on this model. The associated dataset is generated by matching the standard data observed by the satellite with the instrument alarm information. It contains rich information related to single particle events, such as the type of event, the time of occurrence, the location, and the relevant observation data. These information provide a solid foundation for confidence level marking and risk probability level assessment. First, confidence level marking of the associated dataset 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 for each potential single particle event in the associated dataset according to the characteristics of the historical data it has learned. These probability values not only consider 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 the event based on the confidence level.

[0100] After the confidence level marking is completed, the risk probability level is then evaluated. This step aims to quantitatively evaluate the potential impact degree of single particle events on the operation of satellite instruments. The evaluation process comprehensively considers multiple factors, including the confidence level of the event, the type of event, the occurrence location, the influence range, etc. For events with a high confidence level, a more in-depth analysis of their potential risks will be carried out, including possible consequences such as data loss, instrument failure, and satellite performance degradation. By applying risk assessment methods, such as Failure Mode and Effects Analysis (FMEA) or risk matrix method, a risk probability level can be assigned to each event, which directly reflects the risk degree of the event to the operation of satellite instruments.

[0101] During the evaluation process, historical data will also be referred to to ensure the accuracy and reliability of the evaluation results. Historical data provides information on the past impacts and consequences of similar events, which helps to better understand the potential risks of the current event. In addition, risk probability level assessment also needs to consider the mutual influence and superposition effect between different events. In the complex space environment, single particle events often do not occur in isolation, and there may be mutual influence and superposition effect between them. Therefore, during the evaluation process, the impacts of multiple events need to be comprehensively considered to avoid underestimating or overestimating the risks.

[0102] The finally generated evaluation results will serve as an important part of the alarm information and be conveyed to users through visualization display and alarm mechanisms. Users can quickly understand the risk status faced by the current satellite instrument based on the evaluation results and take corresponding countermeasures. At the same time, the evaluation results can also be used as a basis for subsequent system improvement and optimization to better understand and address the impact of single-event upsets on satellite instruments.

[0103] In a specific embodiment, the visualization monitoring and alarm method for single-event upsets further includes:

[0104] Construct a satellite data communication channel;

[0105] Through the satellite data communication channel, obtain single-event upset monitoring signals;

[0106] Perform visualization early warning according to the single-event upset monitoring signals.

[0107] Specifically, determine the data transmission rate, frequency, etc. between the target satellite and the ground station, select ground communication facilities, including antennas, receivers, and transmitters, etc., to establish a stable communication link with the satellite. Conduct tests on the communication link, including signal reception quality, data transmission rate, etc. Adjust device parameters to optimize the communication effect, receive single-event upset monitoring signals transmitted by the satellite in real time, decode and process the received signals, and extract effective monitoring data. Conduct real-time analysis on the received single-event upset monitoring data to identify abnormal or high-risk events. Collect monitoring data through monitoring signals, preprocess the received monitoring data, such as denoising, filtering, standardization, etc., to improve data quality and analysis accuracy. Mark the abnormal alarm level according to the output result of the single-event upset model to obtain a multi-level alarm data set, then generate alarm information according to the preset value, and perform visualization early warning according to the alarm information and the high-energy particle flux distribution map.

[0108] In summary, through this process, the reliability and risk level of single-event upsets can be accurately identified and evaluated, providing a strong guarantee for the safe and stable operation of satellite instruments.

[0109] In a specific embodiment, the method for generating standard observation data by performing data cleaning on the observation data can also be:

[0110] According to the observation data, obtain a first observation feature data set;

[0111] Perform a decentralization process on the first observation feature data set to obtain a second observation feature data set;

[0112] According to the second observation feature data set, obtain a first observation feature covariance matrix;

[0113] Obtain a first observation eigenvalue and a first observation eigenvector according to the first observation feature covariance matrix;

[0114] Obtain the standard observation data according to the first observation eigenvalue and the first observation eigenvector.

[0115] 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 particles. Extract these features to construct a first observation feature data set. Preprocess the data set, such as scaling or encoding the features. For each feature in the first observation feature data set, calculate its average value. Subtract the average value of its corresponding feature from each data point in the data set to obtain a second observation feature data set after decentralization. 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, that is, 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 the larger eigenvalues and their corresponding eigenvectors, which represent the main change directions of the data. Use the selected main eigenvectors, that is, the principal components, to transform the second observation feature data set into a new coordinate system. It can be 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.

[0116] Embodiment 2

[0117] Figure 2 is a schematic structural diagram of a visualization monitoring and alarm device for a single-particle event in Embodiment 2 of the present invention, as Figure 2 shown, Embodiment 2 provides a visualization monitoring and alarm device for a single-particle event, 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 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 on the basis of 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 combine preset alarm thresholds to generate instrument alarm information. The third generation module is used to perform data matching on the basis of 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 perform confidence level marking on the associated data set based on the single-particle event model, and perform risk assessment on the confidence level marking result to generate an assessment result. The display and alarm module is used to draw a visualization distribution map based on the standard observation data and the assessment result, and perform display and alarm.

[0118] In this embodiment, the obtaining module includes: a first obtaining unit, a second obtaining unit, and a first generating unit. The first obtaining unit is configured to obtain the high-energy proton and electron channel data of the high-energy particle detector of the space environment monitor. The second obtaining unit is configured to obtain the time information and position latitude information corresponding to the high-energy proton and electron channel data. And the first generating unit is configured to synthesize the high-energy proton and electron channel data within a preset time to generate the observation data.

[0119] In this embodiment, the first generating module includes: a second generating unit and a third generating unit. The second generating unit is configured to perform data cleaning based on the observation data to generate the cleaned observation data. The third generating unit is configured to perform normalization processing based on the cleaned observation data to generate the standard observation data.

[0120] In this embodiment, the second generating module includes: a third obtaining unit, a fourth generating unit, a judging unit, a fifth generating unit, and a sixth generating unit. The third obtaining unit is configured to obtain the telemetry data of each instrument, the data quality inspection result data, and a preset alarm threshold. The fourth generating unit is configured to perform data matching and comparison on the telemetry data of each instrument, the data quality inspection result data, and the preset alarm threshold to generate a matching and comparison result. The judging unit is configured to judge whether the matching and comparison result triggers an alarm mechanism. The fifth generating unit is configured to evaluate the matching and comparison result if the alarm mechanism is triggered to generate an evaluation result. The sixth generating unit is configured to generate the instrument alarm information based on the evaluation result.

[0121] In this embodiment, the third generating module includes: a first extracting unit, a second extracting unit, and a seventh generating unit. The first extracting unit is configured to extract an observation-related feature set from the standard observation data, including an observation timestamp, a particle event record, an energy level, and geographical location information. The second extracting unit is configured 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 geographical location information. The seventh generating unit is configured to perform data matching based on the observation timestamp and the alarm timestamp to generate the associated data set.

[0122] In this embodiment, the seventh generating unit includes: a sorting subunit, a searching subunit, and a generating subunit. The sorting subunit is configured to sort the observation-related feature set and the alarm-related feature set according to the timestamp. The searching subunit is configured to traverse the alarm-related feature set to search for the particle event record within a preset time window. The generating subunit is configured to generate the associated data set if the particle event is found.

[0123] In this embodiment, the construction module includes: a fourth acquisition unit and an eighth generation unit. The fourth acquisition unit is configured to acquire historical single-particle event data. The eighth generation unit is configured to generate the single-particle event model based on the historical single-particle event data.

[0124] In this embodiment, the fourth generation module includes: a ninth generation unit and a tenth generation unit. The ninth generation unit is configured to perform a confidence level marking on the correlation data set based on the single-particle model to generate a confidence level marking result. The tenth generation unit is configured to perform a risk assessment based on the confidence level marking result to generate an assessment result.

[0125] The various change modes and specific examples of the visualization monitoring and alarm method for single-particle events provided in the first embodiment are equally applicable to the visualization monitoring and alarm device for single-particle events provided in this embodiment. Through the foregoing detailed description of a visualization monitoring and alarm method for single-particle events, those skilled in the art can clearly know the implementation manner of the visualization monitoring and alarm device for single-particle events in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0126] In summary, the visualization monitoring and alarm for single-particle events and the device of the present invention effectively solve the limitations of the prior art 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 unable to accurately evaluate the severity of events. By applying data processing, model analysis, and visualization technologies, the monitoring and early warning capabilities of single-particle events during the on-orbit operation of satellites are improved, thereby ensuring the safe and stable operation of satellites.

[0127] Note that the above is only the preferred embodiment of the present invention and the applied technical principle. 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. Without departing from the concept of the present invention, 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 visualization monitoring and alarm method for single-particle events, characterized in that, Including: Step S100, obtaining the 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 combining with a preset alarm threshold to generate instrument alarm information; Step S400, performing data matching based on the standard observation data and the instrument alarm information to generate an associated data set; Step S500, constructing a single particle event model based on historical single particle event data; Step S600, performing confidence level marking on the associated data set based on the single particle event model, and performing risk assessment on the confidence level marking result to generate an assessment result; Step S700, drawing a visualization distribution map based on the standard observation data and the assessment result, and performing display and alarm; The observation data is to obtain high-energy proton P5X and high-energy electron E3X channel data from the data storage system or data distribution center of the space environment monitor, parse according to the data format, and filter out data of the high-energy proton P3X channel with an energy range between 40 MeV and 100 MeV and the high-energy electron E3X channel between 0.65 MeV and 1.2 MeV from the parsed data, including particle flux, energy spectrum, and timestamp.

2. The visualization monitoring and alarming method for single particle events according to claim 1, wherein The step S100 includes: Step S101, obtaining high-energy proton and electron channel data of the high-energy particle detector of the space environment monitor; Step S102, obtaining the 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 visualization monitoring and alarming 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 visualization monitoring and alarming method for single-particle events according to claim 1, wherein The step S300 includes: Step S301, obtaining telemetry data, data quality inspection result data, and a preset alarm threshold 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 with the preset alarm threshold to generate a matching and comparison result; Step S303, determining whether the matching and comparison result triggers an alarm mechanism; Step S304, if the alarm mechanism is triggered, evaluating the matching and comparison result to generate an evaluation result; Step S305, generating the instrument alarm information based on the evaluation result.

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

6. The visualization monitoring and alarm method for single particle events according to claim 5, characterized in that The step S403 includes: Step S4031: Sort the observation-related feature set and the alarm-related feature set according to the time stamp; Step S4032: Traverse the alarm-related feature set to find the particle event record within a preset time window; Step S4033: If the particle event is found, generate the associated data set.

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

8. The visualization monitoring and alarming method for single particle events according to claim 1, characterized in that, The step S600 includes: Step S601: Mark the confidence level of the associated data set 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 visualization monitoring and alarming device for single particle events, characterized in that, The visualization monitoring and alarm device for a single-particle event is used to implement the visualization monitoring and alarm method for a single-particle event according to any one of claims 1 to 8. The visualization monitoring and alarm device for a single-particle event includes: An acquisition module, configured to acquire the observation data of a target satellite; A first generation module, configured to perform data cleaning on the observation data to generate standard observation data; A second generation module, configured to acquire telemetry data and data quality inspection result data, and combine with a preset alarm threshold to generate instrument alarm information; A third generation module, configured to perform data matching on the standard observation data and the instrument alarm information to generate an associated data set; A construction module, configured to construct a single-particle event model based on historical single-particle event data; A fourth generation module, configured to mark the confidence level of the associated data set based on the single-particle event model, and perform risk assessment on the confidence level marking result to generate an assessment result; and A display and alarm module, configured to draw a visualization distribution map based on the standard observation data and the assessment result, and perform display and alarm.

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

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