Single event detection method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of spaceflight measurement and control, and particularly relates to a single event detection method and device, electronic equipment and a medium. Determining a target abnormal point when the abnormity starts, and obtaining time information when the target abnormal point appears and position information of the target abnormal point; based on the time information and the position information, obtaining high-energy particle flow characteristic information at the same time and space as the target abnormal point; based on the position information of the target abnormal point, determining that the spatial density distribution condition between the target abnormal point and other abnormal points monitored at other time is discrete or aggregated; and according to the abnormal conditions of the telemetry data and the observation data, the high-energy particle flow characteristic information and the spatial density distribution condition, determining the confidence coefficient of the single-particle event of the target abnormal point. According to the technical scheme, the single-particle events with substantial influence on various satellites can be identified at low cost.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of spaceflight TT&C (Tracking, Telemetry, Command), in particular to a single event detection method and device, electronic equipment and medium. BACKGROUND

[0002] Single event (Single Event Effect, SEE) refers to a phenomenon that when a high-energy particle (such as a proton or neutron in cosmic rays, or a secondary particle such as a muon produced by cosmic rays in the earth's atmosphere) hits a semiconductor device, due to the interaction of these particles with atomic nuclei or electrons in the semiconductor material, additional charge carriers (electron-hole pairs) are generated, thereby causing transient or permanent changes in the internal state of the device. This phenomenon is particularly important in spacecraft electronic systems, avionics equipment, and some radiation-sensitive applications on the ground.

[0003] At present, the main idea of identifying single events is to increase the hardware single event identification module on the satellite based on the principle of single event, to identify single events. For example, the reverse bias detection method is a relatively direct hardware identification method. Since the reverse current of some devices will increase transiently when a single event occurs, a corresponding hardware circuit can be added to the satellite to make the corresponding device in the reverse bias condition, and by applying a corresponding reverse bias voltage to it, the single event can be identified by monitoring the fluctuation of the reverse current of the device.

[0004] The existing hardware identification scheme for single events has the following shortcomings and deficiencies:

[0005] 1. An additional single event identification module is needed to monitor single events, which increases hardware costs and energy consumption;

[0006] 2. For different satellites and remote sensing payloads, the corresponding single event identification module needs to be designed according to the device characteristics, and there is a lack of universal method, and the generalization ability is poor;

[0007] 3. The main purpose of single event identification is to analyze the impact of single events on the current remote sensing satellite operation, and then determine the impact on the downlink data and products. However, in many cases, a slight single event will not affect the operation of the satellite, and some payloads can even directly repair the impact of single events through existing design. That is, most of the single events identified by the existing method will not cause substantial impact on business and data. After identifying single events using the existing method, professional personnel such as business maintenance personnel and researchers need to analyze and judge whether each single event affects the current business, and then perform corresponding event processing, thereby causing a large amount of analysis work. SUMMARY

[0008] To solve the problems in the related art, the embodiments of the present disclosure provide a single particle event detection method and device, electronic equipment and medium.

[0009] In a first aspect, the embodiments of the present disclosure provide a single particle event detection method, comprising:

[0010] Monitoring telemetry data and observation data of a satellite;

[0011] When the telemetry data is monitored to have a first anomaly and / or the observation data is monitored to have a second anomaly, determining a target anomaly point at the beginning of the anomaly and obtaining time information when the target anomaly point occurs and position information of the target anomaly point, the position information of the target anomaly point being space information of the satellite at the occurrence of the target anomaly point;

[0012] Based on the time information and the position information of the target anomaly point, obtaining high-energy particle flux feature information at the same space-time as the target anomaly point;

[0013] Based on the position information of the target anomaly point, determining the spatial density distribution between the target anomaly point and other anomaly points monitored at other times, the spatial density distribution including discrete or aggregated spatial density distribution;

[0014] According to whether the telemetry data has a first anomaly, whether the observation data has a second anomaly, the high-energy particle flux feature information at the same space-time as the target anomaly point, and the spatial density distribution between the target anomaly point and other anomaly points monitored at other times, determining the confidence of the target anomaly point having a single particle event.

[0015] In a possible implementation, the determining the target anomaly point at the beginning of the anomaly when the telemetry data is monitored to have a first anomaly and / or the observation data is monitored to have a second anomaly comprises:

[0016] If a preset telemetry parameter is monitored to have an abnormal increase and the duration is equal to or greater than a first preset duration, it is monitored that the telemetry data has a first anomaly, and a target anomaly point at which the preset telemetry parameter starts to have an abnormal increase is determined;

[0017] If the observation data is monitored to have a loss and the duration is equal to or greater than a second preset duration, it is monitored that the observation data has a second anomaly, and a target anomaly point at which the observation data starts to have a loss is determined.

[0018] In a possible implementation, the high-energy particle flux feature information includes: high-energy electron differential flux and high-energy proton differential flux within a predetermined distance range of the target abnormal point on the day of the time information, whether a jump occurs in the high-energy particle differential flux, and whether the target abnormal point is located in a preset high-energy particle multiple occurrence area; the high-energy particle flux feature information at the same space-time as the target abnormal point is obtained based on the time information and the location information of the target abnormal point, and includes:

[0019] high-energy electron differential flux and high-energy proton differential flux within a predetermined distance range of the target abnormal point on the day of the time information and the previous day are obtained;

[0020] whether a jump occurs in the high-energy particle differential flux is determined based on the high-energy electron differential flux and the high-energy proton differential flux on the day of the time information and the previous day;

[0021] whether the target abnormal point is located in a preset high-energy particle multiple occurrence area is determined based on the location information of the target abnormal point.

[0022] In a possible implementation, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is determined based on the location information of the target abnormal point and other abnormal points monitored at other times, and includes:

[0023] the target abnormal point and other target abnormal points are distance clustered based on the location information of the target abnormal point and other abnormal points monitored at other times, if the target abnormal point and part of the other abnormal points are clustered into one cluster, the target abnormal point is successfully clustered, otherwise the target abnormal point is unsuccessfully clustered;

[0024] if the target abnormal point is successfully clustered, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is gathered, and if the target abnormal point is unsuccessfully clustered, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

[0025] In a possible implementation, the target abnormal point and other abnormal points are distance clustered based on the location information of the target abnormal point and other abnormal points monitored at other times, and includes:

[0026] the location information of the target abnormal point and other abnormal points when the target abnormal point and other abnormal points appear is obtained, the target abnormal point and other abnormal points are abnormal points appearing in N days, and N is an integer greater than 1;

[0027] abnormal location information in the location information of the target abnormal point and other abnormal points is removed to obtain remaining abnormal points;

[0028] distance clustering is performed on the remaining abnormal points based on the position information of the remaining abnormal points.

[0029] In a possible implementation, the confidence of the target abnormal point in the single event is determined according to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flux feature information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, and the confidence of the target abnormal point in the single event is determined according to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flux feature information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

[0030] According to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flux feature information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, the following five evaluation elements are obtained:

[0031] The first evaluation element is that the telemetry data is abnormal, the second evaluation element is that the observation data is abnormal, the third evaluation element is that the satellite is located in a preset high-energy particle multiple occurrence region when the target abnormal point occurs, the fourth evaluation element is that the high-energy electron differential flux or the high-energy proton differential flux at the same space-time as the target abnormal point exceeds a preset safety threshold, or the fourth evaluation element is that the high-energy particle differential flux at the same space-time as the target abnormal point jumps, and the fifth evaluation element is that the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

[0032] The confidence of the target abnormal point in the single event is determined based on the five evaluation elements.

[0033] In a possible implementation, the confidence of the target abnormal point in the single event is determined based on the five evaluation elements, and the confidence of the target abnormal point in the single event is determined according to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flux feature information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

[0034] When the above five evaluation elements are met, the confidence of the target abnormal point in the single event is determined to be a first confidence.

[0035] When all the evaluation elements meet except that the first evaluation element or the second evaluation element does not meet, the confidence of the target abnormal point in the single event is determined to be a second confidence.

[0036] When one of the first evaluation element and the second evaluation element is met, one of the third evaluation element and the fourth evaluation element is met, and the fifth evaluation element is met, the confidence of the target abnormal point in the single event is a third confidence.

[0037] When the target abnormal point meets one of the first evaluation element and the second evaluation element, the third evaluation element and the fourth evaluation element, but meets the fifth evaluation element, the confidence of the target abnormal point in the single event is the fourth confidence;

[0038] The first confidence, the second confidence, the third confidence and the fourth confidence are in descending order.

[0039] In a second aspect, the embodiments of the present disclosure provide a single event detection device, comprising:

[0040] A data monitoring module configured to monitor telemetry data and observation data of a satellite;

[0041] A target abnormal point information acquisition module configured to determine a target abnormal point at the beginning of an anomaly and acquire time information and position information of the target abnormal point when the target abnormal point occurs when the telemetry data has a first anomaly and / or the observation data has a second anomaly, the position information of the target abnormal point being space information of the satellite when the target abnormal point occurs;

[0042] A high-energy particle information acquisition module configured to acquire high-energy particle flow characteristic information at the same space-time as the target abnormal point based on the time information and the position information of the target abnormal point;

[0043] A space distribution determination module configured to determine a spatial density distribution between the target abnormal point and other abnormal points monitored at other times based on the position information of the target abnormal point, the spatial density distribution including discrete or aggregated spatial density distribution;

[0044] A confidence evaluation module configured to determine a confidence of the target abnormal point in the single event according to whether the telemetry data has the first anomaly, whether the observation data has the second anomaly, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

[0045] In a third aspect, the embodiments of the present disclosure provide an electronic device, comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any one of the first aspect.

[0046] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium having computer instructions stored thereon, the computer instructions being executed by a processor to implement the method of any one of the first aspect.

[0047] According to the technical scheme provided by the embodiment of the present disclosure, the confidence of the single event of the target abnormal point can be comprehensively judged from the high-energy particle flow feature information of the same space-time of the target abnormal point in the observation data and / or telemetry data and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times. Compared with the existing hardware recognition scheme for single particle events, the corresponding hardware does not need to be added on the satellite, which greatly reduces the design cost, launch cost, circuit manufacturing cost, etc. The method does not need to design different schemes for different satellites, but is suitable for various satellites and has high generalization. Moreover, the method starts from the observation data and telemetry data itself, focuses on the single particle events that have substantial impact on business and data, and no longer focuses on the single particle events that have no impact on satellite business and data. The method is efficient and saves a lot of analysis cost of professional personnel, provides key and efficient decision support for data repair and business recovery, etc.

[0048] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0049] Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings:

[0050] Figure 1 A flow chart of a single particle event detection method provided by an embodiment of the present disclosure is shown.

[0051] Figure 2 A structural block diagram of a single particle event detection device provided by an embodiment of the present disclosure is shown.

[0052] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0053] Figure 4 A structural schematic diagram of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0054] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily implement them. In addition, parts irrelevant to the description of the exemplary embodiments are omitted in the drawings for the sake of clarity.

[0055] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate that there exist the features, numbers, steps, actions, components, parts or combinations thereof disclosed in the specification, and do not exclude the possibility that one or more other features, numbers, steps, actions, components, parts or combinations thereof exist or are added.

[0056] It should also be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] Figure 1 A flowchart of a single particle event detection method provided by an embodiment of the present disclosure is shown. As shown in the figure, the single particle event detection method comprises the following steps S101-S105: Figure 1

[0058] In step S101, telemetry data and observation data of a satellite are monitored.

[0059] In step S102, when the telemetry data is monitored to have a first anomaly and / or the observation data is monitored to have a second anomaly, a target anomaly point at the start of the anomaly is determined, and time information when the target anomaly point occurs and position information of the target anomaly point are obtained.

[0060] In step S103, based on the time information and the position information of the target anomaly point, high-energy particle flux feature information at the same space-time as the target anomaly point is obtained.

[0061] In step S104, based on the position information of the target anomaly point, a spatial density distribution between the target anomaly point and other anomaly points monitored at other times is determined, and the spatial density distribution includes discrete or aggregated spatial density distribution.

[0062] In step S105, according to whether the telemetry data has a first anomaly, whether the observation data has a second anomaly, the high-energy particle flux feature information at the same space-time as the target anomaly point, and the spatial density distribution between the target anomaly point and other anomaly points monitored at other times, a confidence level of the target anomaly point having a single particle event is determined.

[0063] In a possible implementation, the single particle event detection method can be applied to electronic devices such as computers, servers, computer clusters and the like capable of performing single particle event detection.

[0064] ​In a possible implementation, the telemetry data refers to data continuously sent back to the ground station by the satellite during operation, mainly used for monitoring the health of the satellite to ensure normal operation and make adjustments or repairs if necessary. The telemetry data includes the device status of the satellite payload, such as the voltage, current, temperature, and other parameters of components. The observation data refers to information of the target celestial body such as the Earth or other celestial bodies observed by various sensors carried on the satellite. The observation data varies according to the satellite mission and can include optical data of the target celestial body observed by a multispectral or hyperspectral imager, radar data of the target celestial body observed by a radar, and the like. The telemetry data and the observation data can be transmitted to the ground station through the satellite downlink and are existing data that can be collected without adding new hardware devices on the satellite.

[0065] In a possible implementation, when a single particle event occurs on the satellite, the most common abnormal situation caused is the abnormality of the telemetry parameters and the observation data of the satellite. For the telemetry parameters, a single particle event often causes abnormal rise of key parameters of components such as voltage and temperature, and cannot be restored to normal level in a short time. For the observation data, the most common phenomenon is loss of corresponding observation data. Therefore, when the first abnormality of certain telemetry data, that is, abnormal rise for a long time, or the second abnormality of the observation data, that is, loss for a long time, or the abnormal rise of certain telemetry data for a long time and the loss of the observation data for a long time are monitored, the target abnormal point at the beginning of the abnormality (for example, at the beginning of abnormal rise of the telemetry data or at the beginning of loss of the observation data) is determined. The occurrence of the target abnormal point indicates that a single particle event may occur at this time and at this place, and the confidence of the single particle event at the target abnormal point can be calculated at this time.

[0066] In a possible implementation, the time information when the target abnormal point occurs and the position information of the target abnormal point can be acquired, the position information of the target abnormal point being space information of the satellite when the abnormality occurs, which can be latitude and longitude information of the satellite, that is, a ground track point at which the satellite is projected onto the earth's surface at a certain moment, for example, the satellite is located above the South Pole of the earth at a certain orbit speed, and the latitude and longitude information of the satellite at this moment is the latitude and longitude information of the satellite projected onto the South Pole of the earth (80 degrees south latitude and 120 degrees east longitude). The latitude and longitude of the satellite is not a fixed value, and the spatial position of the satellite changes constantly with the movement of the satellite. Usually, the satellite regularly publishes a TLE (Two-Line Element Set) file, and the accurate position (including longitude and latitude) of the satellite can be calculated based on the relevant position parameters recorded in the TLE file.

[0067] In a possible implementation, since a single particle event refers to abnormal behavior or failure caused by a single high-energy particle hitting an electronic device, the time information and the position information of the target abnormal point can be used to acquire high-energy particle flow feature information at the same space-time as the target abnormal point. The same space-time as the target abnormal point refers to the same time period as the time information when the target abnormal point occurs and the same space as the position information of the target abnormal point. The high-energy particle flow feature information can indicate feature information related to the high-energy particle flow at the same space-time as the target abnormal point. If the high-energy particle flow at the same space-time as the target abnormal point is relatively large, it is highly likely that the target abnormal point has a single particle event; if the high-energy particle flow at the same space-time as the target abnormal point is relatively small, the confidence of the target abnormal point having a single particle event is relatively low.

[0068] In a possible implementation, since a single particle event is a random event, and the spatial density distribution caused by space is discrete, if the target abnormal points are clustered, the confidence of the target abnormal points having a single particle event will be reduced. Therefore, based on the position information of the target abnormal point, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times can be determined to be discrete or clustered. If the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete, the confidence of the target abnormal point having a single particle event is relatively high, and if the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is clustered, the confidence of the target abnormal point having a single particle event is relatively low.

[0069] In a possible implementation, the confidence of the single particle event causing the anomaly can be determined according to whether the telemetry data and / or the observation data is monitored to be abnormal, the high-energy particle characteristic information at the same space-time as the target abnormal point, and the spatial density distribution of the abnormal occurrence. For example, if the telemetry data is monitored to be abnormal and the observation data is monitored to be abnormal, the high-energy particle flow characteristic information at the same space-time as the target abnormal point indicates that the anomaly occurs in a region with more high-energy particles, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete, it can be determined that the confidence of the single particle event causing the anomaly is the highest confidence; if one of the telemetry data and the observation data is monitored to be abnormal, the high-energy particle flow characteristic information at the same space-time as the target abnormal point indicates that the anomaly occurs in a region with more high-energy particles, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete, it can be determined that the confidence of the single particle event causing the anomaly is higher, and so on.

[0070] In a possible implementation, after determining the confidence of the single particle event causing the anomaly, the single particle event can be identified according to the confidence. A very high confidence indicates that the anomaly can be basically considered as a single particle event, and a low confidence basically indicates that the anomaly is not a single particle event. Subsequent professionals can determine whether the anomaly is a single particle event according to the confidence. These single particle events cause data anomalies and have substantial impacts, and do not need to be analyzed by subsequent professionals, thereby saving a large amount of analysis cost of subsequent professionals. The confidence of the single particle event can also assist professionals in analyzing the reason for the telemetry data or the observation data anomaly, and provide key and efficient decision support for repairing data and resuming business.

[0071] In addition, in other possible implementations, the type of the abnormal telemetry data and the lost observation data can also be obtained, so that subsequent professionals can repair data and resume business according to the type.

[0072] The embodiment can start from the observation data and telemetry data that can be collected from the satellite, combine the high-energy particle flow characteristic information of the target abnormal point at the same space-time as the target abnormal point and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, and comprehensively identify the confidence of the single event of the target abnormal point. Compared with the existing hardware recognition scheme for single particle events, no corresponding hardware needs to be added on the satellite, which greatly reduces the design cost, launch cost, circuit manufacturing cost, etc. The method does not need to design different schemes for different satellites, but is suitable for various satellites and has high generalization. Moreover, the method starts from the observation data and telemetry data itself, focuses on the single particle events that have substantial impact on business and data, and no longer focuses on the single particle events that have no impact on the satellite business and data. The method is efficient and saves a large amount of analysis cost of subsequent professionals, provides key and efficient decision support for data repair and business recovery, etc.

[0073] In a possible implementation, the target abnormal point at the beginning of the abnormality is determined when the first abnormality of the telemetry data and / or the second abnormality of the observation data is monitored, including:

[0074] If it is monitored that the preset telemetry parameter abnormally increases and the duration is equal to or greater than a first preset duration, the first abnormality of the telemetry data is monitored, and the target abnormal point at the beginning of the abnormal increase of the preset telemetry parameter is determined.

[0075] If it is monitored that the observation data is lost and the duration is equal to or greater than a second preset duration, the second abnormality of the observation data is monitored, and the target abnormal point at the beginning of the loss of the observation data is determined.

[0076] In this embodiment, the first anomaly can be that the preset telemetry parameter abnormally increases and lasts for a time length equal to or greater than a first preset time length. The preset telemetry parameter can be a key parameter of a component affected by a single event, such as a voltage, a current, a temperature, or the like. The preset telemetry parameter is detected by a corresponding sensor at a certain time frequency, and one acquisition of the telemetry parameter can be defined as one frame. The preset telemetry parameter abnormally increasing can mean that the preset telemetry parameter acquired in a current frame exceeds a preset parameter threshold compared with a last frame, and the preset parameter threshold can be a preset fixed value or a predetermined proportion of the preset telemetry parameter acquired in the last frame, for example, 50% of the preset telemetry parameter acquired in the last frame. The first preset time length can be a fixed value, for example, 30 minutes, or the first preset time length is set according to the acquisition frequency of the preset telemetry parameter. When it is monitored that the preset telemetry parameter abnormally increases and lasts for a time length equal to or greater than the first preset time length, it is indicated that the telemetry data is monitored to have the first anomaly, at this time, time information of a starting frame when the abnormality of the preset telemetry parameter starts and spatial information of the satellite at this time can be acquired. For example, if it is monitored that the preset telemetry parameter acquired in the ath frame exceeds 50% of the preset telemetry parameter acquired in the last frame and remains in this increasing state for 30 minutes, the time information of the ath frame and the spatial information of the satellite at this time are acquired.

[0077] In this embodiment, the second anomaly can be that the observation data is lost and lasts for a time length equal to or greater than a second preset time length. When the observation data is lost, the observation data can not be acquired or the acquired observation data is in the form of a filling value. The second preset time length can be a fixed value, for example, 30 minutes, or the second preset time length is set according to the acquisition frequency of the observation parameter. When it is monitored that the observation data is lost and lasts for a time length equal to or greater than the second preset time length, it is indicated that the observation data is monitored to have the second anomaly, at this time, time information of a starting frame when the observation data starts to be lost and spatial information of the satellite at this time can be acquired. For example, if it is monitored that the observation data is not acquired or the acquired observation data is in the form of a filling value for 30 minutes from the bth frame, the time information of the bth frame and the spatial information of the satellite at this time are acquired.

[0078] In this embodiment, the first anomaly is that the preset telemetry parameter abnormally increases and lasts for a time length equal to or greater than the first preset time length, and the second anomaly is that the observation data is lost and lasts for a time length equal to or greater than the second preset time length, so that the target anomaly point affected by the business and data can be accurately monitored, and the single event affecting the business and data can be detected.

[0079] In a possible implementation, based on the time information and the location information of the target anomaly point, the high-energy particle flow characteristic information at the same space-time as the target anomaly point is acquired, including:

[0080] acquire the high-energy electron differential flux and the high-energy proton differential flux within a predetermined distance range of the location information of the target abnormal point on the day and the previous day of the time information;

[0081] compare the high-energy electron differential flux and the high-energy proton differential flux on the day and the previous day of the time information to determine whether the high-energy particles have a jump;

[0082] determine whether the target abnormal point is located in a preset high-energy particle multiple occurrence area based on the location information of the target abnormal point.

[0083] In this embodiment, the high-energy particles include high-energy electrons and high-energy protons, and the differential flux of the high-energy electrons refers to the number of electrons per unit energy interval that reach or pass through a specific location per unit area, unit solid angle and unit time. The differential flux of the high-energy protons refers to the number of protons per unit energy interval that reach or pass through a specific location per unit area, unit solid angle and unit time.

[0084] In this embodiment, due to the influence of the earth's magnetic field and the anisotropy caused by solar activity, the spatial distribution of high-energy protons or high-energy electrons is often not uniform. The energy and incident angle of the incident particles (i.e. electrons / protons) can be measured by a space environment monitoring load or other high-energy particle detection load carried on a remote sensing satellite, and the high-energy proton differential flux and the high-energy electron differential flux in each direction can be calculated accordingly, and then the high-energy proton differential flux in each direction is added to obtain the high-energy proton differential flux in the corresponding range, and the high-energy electron differential flux in each direction is added to obtain the high-energy electron differential flux in the corresponding range. Alternatively, the high-energy proton differential flux and the high-energy electron differential flux encountered by the satellite at different times and different positions can also be predicted in combination with an orbit model and a geomagnetic field model. Those skilled in the art are aware of the above-mentioned acquisition schemes of the high-energy proton differential flux and the high-energy electron differential flux. In general, satellite observation can obtain the high-energy particle differential flux of a low-orbit satellite at about 800-850 kilometers, and model prediction can infer the high-energy particle differential flux of any satellite at a corresponding orbit position. Therefore, the present embodiment can theoretically be applied to single particle event detection of various orbit satellites.

[0085] In this embodiment, the high-energy electron differential flux and the high-energy proton differential flux within a predetermined distance range of the location information of the target abnormal point on the day and the previous day of the time information can be acquired by satellite observation or model prediction. The predetermined distance range mentioned here can be a range within 1° of longitude and latitude distance from the location information of the target abnormal point.

[0086] In this embodiment, the energy mutation may occur due to cosmic rays, solar proton events and other factors, causing the sudden jump of the high-energy particle differential flux at a certain spatial position. In order to determine whether the high-energy particle differential flux jumps, the high-energy electron differential flux and the high-energy proton differential flux of the day where the time information is located and the previous day can be compared. If the high-energy particle differential flux of the previous day is less than a preset low threshold, and the high-energy particle differential flux of the day where the time information is located is greater than a preset high threshold, it is determined that the high-energy particle differential flux jumps. For example, if the high-energy electron differential flux of the previous day is less than 5000, and the high-energy electron differential flux of the day where the time information is located is greater than 10000, it is determined that the high-energy particle differential flux jumps. If the high-energy proton differential flux of the previous day is less than 5000, and the high-energy proton differential flux of the day where the time information is located is greater than 10000, it is determined that the high-energy particle differential flux jumps.

[0087] In this embodiment, the preset high-energy particle multiple occurrence area is a conventional high differential flux area, i.e. the South Atlantic anomaly area, the North and South Pole area and other areas. Due to factors such as the Earth's magnetic field, the high-energy particle differential flux in these areas is in a relatively high state all year round. Whether the position information of the target anomaly point is located in the preset high-energy particle multiple occurrence area can be determined.

[0088] In this way, the high-energy particle flux characteristic information at the same space-time as the target anomaly point can be obtained, including the high-energy electron differential flux and the high-energy proton differential flux within a predetermined distance range of the position information of the target anomaly point on the day where the time information is located, whether the high-energy particle differential flux jumps, and whether the target anomaly point is located in the preset high-energy particle multiple occurrence area.

[0089] The embodiment can obtain the high-energy electron differential flux and the high-energy proton differential flux within a predetermined distance range of the position information of the target anomaly point on the day where the time information is located, whether the high-energy particle differential flux jumps, and whether the target anomaly point is located in the preset high-energy particle multiple occurrence area as the high-energy particle flux characteristic information. The characteristics of the high-energy particle flux at the same space-time as the target anomaly point can be accurately described, which can be used as the basis for evaluating single particle events. Based on the causal relationship between single particle events and high-energy particles, the confidence of single particle events can be more accurately evaluated.

[0090] In a possible implementation, the determination of the spatial density distribution between the target anomaly point and other anomaly points monitored at other times based on the position information of the target anomaly point comprises:

[0091] based on the position information of the target abnormal point and other abnormal points monitored at other times, the target abnormal point is distance clustered with other target abnormal points, if the target abnormal point and part of the other abnormal points are clustered into one cluster, the target abnormal point clustering is successful, otherwise the target abnormal point clustering fails;

[0092] If the target abnormal point clustering is successful, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is aggregated, and if the target abnormal point clustering fails, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

[0093] In this embodiment, any distance clustering algorithm can be used to distance cluster the target abnormal point with other target abnormal points monitored at other times. The distance between any two abnormal points can be calculated based on the position information of the target abnormal point and other abnormal points, and distance clustering can be performed based on the distance between any two abnormal points. If the target abnormal point and part of the other abnormal points are clustered into one cluster, even if some of the other abnormal points are not clustered into any cluster, it means that the target abnormal point clustering is successful; if the target abnormal point is not clustered into any cluster, even if some of the other abnormal points are clustered into a cluster, it means that the target abnormal point clustering fails. If the target abnormal point clustering is successful, i.e. the target abnormal point is clustered into a certain cluster, it means that the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is aggregated, indicating that the occurrence of the target abnormal point is frequent and not random, and the confidence of the target abnormal point in single particle event is low; if the target abnormal point clustering fails, i.e. the target abnormal point is not clustered into any cluster, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete, indicating that the occurrence of the target abnormal point is random, and the confidence of the target abnormal point in single particle event is high.

[0094] The embodiment determines the spatial density distribution between the target abnormal point and other abnormal points monitored at other times by distance clustering the target abnormal point with other abnormal points monitored at other times, and the determination method is simple and easy to implement.

[0095] In one possible embodiment, the distance clustering of the target abnormal point with other abnormal points based on the position information of the target abnormal point and other abnormal points monitored at other times comprises:

[0096] The position information of the target abnormal point and other abnormal points is obtained, the target abnormal point and other abnormal points are abnormal points occurring in N days, and N is an integer greater than 1.

[0097] Discard the abnormal position information in the position information of the target abnormal point and other target abnormal points to obtain residual abnormal points;

[0098] Based on the position information of the residual abnormal points, distance clustering is performed on the residual abnormal points.

[0099] In this embodiment, the position information of the target abnormal point appearing in N (for example, N = 30) days can be summarized, and the target abnormal point is the target abnormal point monitored on a certain day in N days. For example, the date of appearance of the target abnormal point can be determined first, and then the position information (i.e., the latitude and longitude information of the satellite at the time of appearance of the abnormal point) of other abnormal points appearing in the total N days including the date of appearance can be determined. For example, when N = 30, the position information of the abnormal points appearing in the 15 days before the date of appearance of the target abnormal point, the date of appearance of the target abnormal point, and the 14 days after the date of appearance of the target abnormal point, a total of 30 days, can be summarized; or the position information of the abnormal points appearing in the 29 days before the date of appearance of the target abnormal point and the date of appearance of the target abnormal point, a total of 30 days, can be summarized.

[0100] In this embodiment, the IQR (Interquartile Range, interquartile range) algorithm is an effective method for identifying potential outliers in a data set, and the IQR algorithm can be used to identify abnormal position information in the position information of the target abnormal point and other abnormal points. For example, based on the position information of the target abnormal point and other abnormal points, the upper and lower limits of normal position information are calculated using the IQR algorithm, and if the position information of a certain other abnormal point exceeds the upper and lower limits of the normal position information, the position information of the other abnormal point is identified as abnormal position information. The identified abnormal information can be discarded to obtain residual abnormal points with normal position information; of course, other outlier identification algorithms can also be used to identify and discard abnormal position information, which will not be listed here.

[0101] In this embodiment, for the residual abnormal points, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density-based spatial clustering of applications with noise) algorithm can be used for clustering. DBSCAN is a density-based clustering algorithm that can discover clusters of arbitrary shape and can identify noise points, so it performs well in processing complex data sets. Of course, other distance clustering algorithms can also be used for distance clustering, which is not limited here, and the following will be described by taking DBSCAN as an example.

[0102] In this embodiment, the main idea of DBSCAN is to define clusters and noise based on the density of data points, i.e., the number of points in a local region. When clustering using the DBSCAN algorithm, a neighborhood radius and a minimum number of neighborhood points need to be defined. For any given data point, all points within the neighborhood are points that are not more than the neighborhood radius away from the data point, and there are at least the minimum number of neighborhood points (including the data point) in the neighborhood of the data point. The data point can be defined as a core point. The points in the neighborhood of a core point can be referred to as boundary points. Points that are neither core points nor boundary points are referred to as noise points or outliers.

[0103] For example, the clustering process of the DBSCAN algorithm in this embodiment can be as follows:

[0104] The neighborhood radius of the latitude and longitude is set to a distance difference of 1° (latitude and longitude), which can convert the neighborhood radius of the latitude and longitude into a distance range for distance measurement in clustering analysis, ensuring the accuracy and effectiveness of the analysis. The minimum number of neighborhood points in a cluster is set to 3. Starting with an unvisited remaining outlier point, check whether the number of outlier points in its neighborhood is greater than or equal to the minimum number of neighborhood points. If yes, create a new cluster and continue to explore other points in the neighborhood; if no, mark the point as noise (which may be reclassified as a boundary point later); for each newly found core point, recursively add all points in its neighborhood to the current cluster until no new points can be added. This process ensures that the clustering cluster is a continuous, density-based region. In this way, all points are visited. Finally, each point belongs to a certain clustering cluster or is marked as noise. If the DBSCAN algorithm is used, after clustering the position information of the remaining outlier points, if the target outlier point is in a clustering cluster, i.e., the target outlier point is a core point or a boundary point, it means that the clustering of the target outlier point is successful, and the spatial density distribution between the target outlier point and other outlier points monitored at other times is aggregated, reducing the confidence of the target outlier point in a single particle event. If the target outlier point is not in any clustering cluster, i.e., the target outlier point is a noise point, it means that the clustering of the target outlier point fails, and the spatial density distribution between the target outlier point and other outlier points monitored at other times is discrete, increasing the confidence of the target outlier point in a single particle event.

[0105] This embodiment summarizes the position information of the outlier points in the N days containing the target outlier point, eliminates abnormal position information, and clusters the remaining outlier points, which can accurately determine whether the spatial distribution of the target outlier point is discrete or aggregated, and further accurately evaluate the confidence of the target outlier point in a single particle event.

[0106] In a possible implementation, the confidence level of the target abnormal point in the single event is determined according to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

[0107] According to whether the telemetry data is abnormal, whether the observation data is abnormal, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, the following five evaluation elements are obtained:

[0108] The first evaluation element is that the telemetry data is abnormal, the second evaluation element is that the observation data is abnormal, the third evaluation element is that the satellite is located in a preset high-energy particle multiple occurrence area when the target abnormal point occurs, the fourth evaluation element is that the high-energy electron differential flux or the high-energy proton differential flux at the same space-time as the target abnormal point exceeds a preset safety threshold, or the fourth evaluation element is that the high-energy particle differential flux at the same space-time as the target abnormal point jumps, and the fifth evaluation element is that the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

[0109] Based on the five evaluation elements, the confidence level of the target abnormal point in the single event is determined.

[0110] In this implementation, the high-energy electron differential flux or the high-energy proton differential flux at the same space-time as the target abnormal point can be the high-energy electron differential flux and the high-energy proton differential flux within a predetermined distance range of the target abnormal point position information on the day of the time information. The preset safety threshold can be adjusted according to the specific design index of the satellite payload. For example, the preset safety threshold can be 10000.

[0111] In the embodiment, based on the target abnormal point position information, it is determined whether the target abnormal point is located in a preset high-energy particle multiple occurrence region; based on the high-energy electron differential flux and the high-energy proton differential flux of the day and the previous day of the time information, it is determined whether the high-energy particle differential flux at the same space-time as the target abnormal point jumps, and if the high-energy particle differential flux of the previous day is less than a preset low threshold and the high-energy particle differential flux of the day of the time information is greater than a preset high threshold, it is determined that the high-energy particle differential flux jumps. For example, if the high-energy electron differential flux of the previous day is less than 5000 and the high-energy electron differential flux of the day of the time information is greater than 10000, it is determined that the high-energy particle differential flux jumps; if the high-energy proton differential flux of the previous day is less than 5000 and the high-energy proton differential flux of the day of the time information is greater than 10000, it is determined that the high-energy particle differential flux jumps.

[0112] The embodiment can obtain five evaluation elements, and based on the five evaluation elements, the confidence of the target abnormal point in occurrence of a single particle event is comprehensively evaluated, and the evaluation is accurate.

[0113] In a possible embodiment, the determination of the confidence of the target abnormal point in occurrence of a single particle event based on the five evaluation elements comprises:

[0114] When the above five evaluation elements are met, the confidence of the target abnormal point in occurrence of a single particle event is determined as a first confidence;

[0115] When all the evaluation elements meet except that the first evaluation element or the second evaluation element does not meet, the confidence of the target abnormal point in occurrence of a single particle event is determined as a second confidence;

[0116] When one of the first evaluation element and the second evaluation element is met, one of the third evaluation element and the fourth evaluation element is met, and the fifth evaluation element is met, the confidence of the target abnormal point in occurrence of a single particle event is a third confidence;

[0117] When one of the first evaluation element and the second evaluation element is met, the third evaluation element and the fourth evaluation element are not met, and the fifth evaluation element is met, the confidence of the target abnormal point in occurrence of a single particle event is a fourth confidence;

[0118] The confidence of the first confidence, the second confidence, the third confidence, and the fourth confidence decreases in turn.

[0119] In this embodiment, the determination of the first confidence, the second confidence, the third confidence and the fourth confidence described herein is only one of the confidence evaluation methods. In other possible embodiments, other confidence evaluation methods can also be used, for example, different scores are set for five evaluation elements, the total score of the five evaluation elements is 100, and the target abnormal point meets one evaluation element to obtain the score corresponding to the evaluation element. In this way, the final comprehensive score can be calculated, and the comprehensive score is the confidence value of the single event of the target abnormal point. In this way, the confidence of the single event of the target abnormal point is determined.

[0120] The embodiment limits the specific confidence evaluation rule, so that the confidence evaluation of the single event is simpler and more accurate.

[0121] The present disclosure also provides a single event detection device, Figure 2 A structural block diagram of a single event detection device provided by an embodiment of the present disclosure is shown. The device can be realized as part or all of an electronic device by software, hardware or a combination of the two. As shown in Figure 2 The single event detection device includes:

[0122] The data monitoring module 201 is configured to monitor telemetry data and observation data of a satellite;

[0123] The target abnormal point information acquisition module 202 is configured to determine a target abnormal point at the beginning of an anomaly and acquire time information and position information of the target abnormal point when the target abnormal point occurs when the telemetry data has a first anomaly and / or the observation data has a second anomaly. The position information of the target abnormal point is the space information of the satellite when the target abnormal point occurs.

[0124] The high-energy particle information acquisition module 203 is configured to acquire high-energy particle flow characteristic information at the same space-time as the target abnormal point based on the time information and the position information of the target abnormal point.

[0125] The spatial distribution determination module 204 is configured to determine the spatial density distribution between the target abnormal point and other abnormal points monitored at other times based on the position information of the target abnormal point. The spatial density distribution includes discrete or aggregated spatial density distribution.

[0126] The confidence evaluation module 205 is configured to determine the confidence of the single event of the target abnormal point according to whether the telemetry data has a first anomaly, whether the observation data has a second anomaly, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

[0127] In a possible implementation, the target abnormal point information acquisition module, when monitoring that the telemetry data has the first abnormality and / or the observation data has the second abnormality, is configured to:

[0128] If it is monitored that the preset telemetry parameter has an abnormal increase and the duration is equal to or greater than the first preset duration, it is monitored that the telemetry data has the first abnormality, and a target abnormal point when the preset telemetry parameter starts to have an abnormal increase is determined.

[0129] If it is monitored that the observation data has a loss and the duration is equal to or greater than the second preset duration, it is monitored that the observation data has the second abnormality, and a target abnormal point when the observation data starts to have a loss is determined.

[0130] In a possible implementation, the high-energy particle flux feature information includes high-energy electron differential flux and high-energy proton differential flux within a predetermined distance range of the location information of the target abnormal point on the day of the time information, whether the high-energy particle differential flux jumps, and whether the target abnormal point is located in a preset high-energy particle multiple occurrence area, and the high-energy particle information acquisition module is configured to:

[0131] Obtain the high-energy electron differential flux and the high-energy proton differential flux within the predetermined distance range of the location information of the target abnormal point on the day of the time information and the previous day.

[0132] Based on the high-energy electron differential flux and the high-energy proton differential flux on the day of the time information and the previous day, it is determined whether the high-energy particle differential flux jumps.

[0133] Based on the location information of the target abnormal point, it is determined whether the target abnormal point is located in a preset high-energy particle multiple occurrence area.

[0134] In a possible implementation, the spatial distribution determination module is configured to:

[0135] Based on the location information of the target abnormal point and other abnormal points monitored at other times, the target abnormal point and other target abnormal points are clustered by distance, if the target abnormal point and part of the other abnormal points are clustered into one cluster, the target abnormal point clustering is successful, otherwise the target abnormal point clustering fails.

[0136] If the target abnormal point clustering is successful, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is concentrated, and if the target abnormal point clustering fails, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

[0137] In a possible implementation, the distance clustering of the target abnormal point and other abnormal points monitored at other times in the spatial distribution determination module is configured to:

[0138] acquire position information of the target abnormal point and other abnormal points, the target abnormal point and other abnormal points being abnormal points occurring in N days, N being an integer greater than 1;

[0139] eliminate abnormal position information in the position information of the target abnormal point and other abnormal points to obtain residual abnormal points;

[0140] distance cluster the residual abnormal points based on the position information of the residual abnormal points.

[0141] In a possible implementation, the confidence assessment module is configured to:

[0142] acquire the following five evaluation elements according to whether the telemetry data appears the first abnormality, whether the observation data appears the second abnormality, the high-energy particle flux feature information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times:

[0143] the first evaluation element is that the telemetry data appears the first abnormality, the second evaluation element is that the observation data appears the second abnormality, the third evaluation element is that the satellite is located in a preset high-energy particle multiple occurrence area when the target abnormal point occurs, the fourth evaluation element is that the high-energy electron differential flux or the high-energy proton differential flux at the same space-time as the target abnormal point exceeds a preset safety threshold, or the fourth evaluation element is that the high-energy particle differential flux at the same space-time as the target abnormal point jumps, and the fifth evaluation element is that the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete;

[0144] determine the confidence of the target abnormal point in the single event based on the five evaluation elements.

[0145] In a possible implementation, the part of the confidence assessment module that determines the confidence of the target abnormal point in the single event based on the five evaluation elements is configured to:

[0146] when the above five evaluation elements are met, determine that the confidence of the target abnormal point in the single event is a first confidence;

[0147] when all the evaluation elements except the first evaluation element or the second evaluation element are met, determine that the confidence of the target abnormal point in the single event is a second confidence;

[0148] When the target abnormal point meets one of the first evaluation element and the second evaluation element, meets one of the third evaluation element and the fourth evaluation element, and meets the fifth evaluation element, the confidence that the target abnormal point has a single event is a third confidence;

[0149] When the target abnormal point meets one of the first evaluation element and the second evaluation element, meets one of the third evaluation element and the fourth evaluation element, and meets the fifth evaluation element, the confidence that the target abnormal point has a single event is a third confidence;

[0150] The first confidence, the second confidence, the third confidence, and the fourth confidence are in descending order.

[0151] The technical terms and technical features mentioned in the device embodiments are the same as or similar to those mentioned in the above method embodiments. For the explanation and description of the technical terms and technical features involved in the device, reference can be made to the explanation and description of the above method embodiments, which will not be repeated here.

[0152] The present disclosure also discloses an electronic device, Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0153] As Figure 3 shown, the electronic device 300 includes a memory 301 and a processor 302, wherein the memory 301 is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor 302 to implement the method according to the embodiment of the present disclosure.

[0154] Figure 4 A structural schematic diagram of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown.

[0155] As Figure 4 shown, the computer system 400 includes a processing unit 401, which can perform various processes in the above embodiments according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage portion 408 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the computer system 400 are also stored. The processing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0156] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as necessary. A removable media 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 410 as necessary, so that a computer program read out therefrom is installed in the storage section 408 as necessary. Among them, the processing unit 401 can be implemented as a CPU, a GPU, a TPU, a FPGA, a NPU, etc.

[0157] In particular, according to embodiments of the present disclosure, the method described above can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising computer instructions which, when executed by a processor, implement the method steps described above. In such embodiments, the computer program product can be downloaded and installed from a network by the communication section 409, and / or installed from the removable media 411.

[0158] The flow diagrams and the block diagrams in the drawings are illustrations of possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0159] The units or modules described in the embodiments of the present disclosure can be implemented by means of software, or by means of programmable hardware. The described units or modules can also be provided in a processor, and the names of these units or modules do not constitute a limitation on the units or modules themselves in some cases.

[0160] As another aspect, the disclosure also provides a computer readable storage medium, which can be the computer readable storage medium contained in the electronic device or the computer system in the above embodiments; or can be a computer readable storage medium existing separately and not assembled into a device. The computer readable storage medium stores one or more programs used by one or more processors to execute the method described in the disclosure.

[0161] The above description is merely the preferred embodiments of the disclosure and the explanation of the principles of the applied technology. It should be understood by those skilled in the art that the inventive scope of the disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by the combinations of the above technical features or equivalent features without departing from the inventive concept. For example, the technical solutions formed by the mutual replacement of the above features and the technical features disclosed in the disclosure (but not limited to) having similar functions.

Claims

1. A single event detection method, characterized by, The method comprises the following steps: monitoring telemetry data and observation data of a satellite; when a first anomaly in the telemetry data and / or a second anomaly in the observation data is detected, determining a target anomaly point at the start of the anomaly and obtaining time information and position information of the target anomaly point, the position information of the target anomaly point being spatial information of the satellite at the occurrence of the target anomaly point; based on the time information and the position information of the target anomaly point, obtaining high-energy particle flux feature information at the same space-time as the target anomaly point; based on the position information of the target anomaly point, determining the spatial density distribution between the target anomaly point and other anomaly points monitored at other times, the spatial density distribution including discrete or clustered spatial density distribution; based on whether the telemetry data has the first anomaly, whether the observation data has the second anomaly, the high-energy particle flux feature information at the same space-time as the target anomaly point, and the spatial density distribution between the target anomaly point and other anomaly points monitored at other times, determining the confidence level of the target anomaly point in a single event.

2. The method of claim 1, wherein, The method comprises the following steps: if a preset telemetry parameter is detected to have an abnormal increase and the duration is equal to or greater than a first preset duration, it is determined that the telemetry data has the first anomaly, and a target anomaly point at the start of the abnormal increase of the preset telemetry parameter is determined; if the observation data is detected to have a loss and the duration is equal to or greater than a second preset duration, it is determined that the observation data has the second anomaly, and a target anomaly point at the start of the loss of the observation data is determined.

3. The method of claim 1, wherein, The high-energy particle flux feature information includes high-energy electron differential flux and high-energy proton differential flux within a predetermined distance range of the position information of the target anomaly point on the day of the time information, whether the high-energy particle differential flux jumps, and whether the target anomaly point is located in a preset high-energy particle multiple occurrence area; the method comprises the following steps: obtaining high-energy electron differential flux and high-energy proton differential flux within a predetermined distance range of the position information of the target anomaly point on the day of the time information and the previous day; based on the high-energy electron differential flux and the high-energy proton differential flux on the day of the time information and the previous day, determining whether the high-energy particle differential flux jumps; based on the position information of the target anomaly point, determining whether the target anomaly point is located in a preset high-energy particle multiple occurrence area.

4. The method of claim 1, wherein, The method comprises the following steps: The target abnormal point is clustered with other abnormal points based on position information of the target abnormal point and other abnormal points monitored at other times, and if the target abnormal point is clustered with part of the other abnormal points into one cluster, the target abnormal point clustering is successful, otherwise, the target abnormal point clustering fails; If the target abnormal point clustering is successful, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is aggregated, and if the target abnormal point clustering fails, the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete.

5. The method of claim 4, wherein, The target abnormal point is clustered with other abnormal points based on position information of the target abnormal point and other abnormal points monitored at other times, and if the target abnormal point is clustered with part of the other abnormal points into one cluster, the target abnormal point clustering is successful, otherwise, the target abnormal point clustering fails; Obtain the position information of the target abnormal point and other abnormal points, the target abnormal point and other abnormal points are abnormal points appearing in N days, N is an integer greater than 1; Eliminate abnormal position information in the position information of the target abnormal point and other abnormal points to obtain remaining abnormal points; The remaining abnormal points are clustered based on the position information of the remaining abnormal points.

6. The method of claim 1, wherein, The confidence of the target abnormal point in the single particle event is determined according to whether the telemetry data appears the first abnormality, whether the observation data appears the second abnormality, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, including: According to whether the telemetry data appears the first abnormality, whether the observation data appears the second abnormality, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times, the following five evaluation elements are obtained: The first evaluation element is that the telemetry data appears the first abnormality, the second evaluation element is that the observation data appears the second abnormality, the third evaluation element is that the satellite is located in a preset high-energy particle multiple area when the target abnormal point appears, the fourth evaluation element is that the high-energy electron differential flux or the high-energy proton differential flux at the same space-time as the target abnormal point exceeds a preset safety threshold, or the fourth evaluation element is that the high-energy particle differential flux at the same space-time as the target abnormal point jumps, and the fifth evaluation element is that the spatial density distribution between the target abnormal point and other abnormal points monitored at other times is discrete; The confidence of the target abnormal point in the single particle event is determined based on the five evaluation elements.

7. The method of claim 6, wherein, The confidence of the target abnormal point in the single particle event is determined based on the five evaluation elements, including: When the above five evaluation elements are met, the confidence of the target abnormal point in the single particle event is determined to be a first confidence; When all the evaluation elements except the first evaluation element or the second evaluation element are met, the confidence of the target abnormal point in the single particle event is determined to be a second confidence; When one of the first evaluation element and the second evaluation element is met, one of the third evaluation element and the fourth evaluation element is met, and the fifth evaluation element is met, the confidence level of the target abnormal point being a single particle event is a third confidence level; When one of the first evaluation element and the second evaluation element is met, one of the third evaluation element and the fourth evaluation element is met, and the fifth evaluation element is met, the confidence level of the target abnormal point being a single particle event is a fourth confidence level; The first confidence level, the second confidence level, the third confidence level, and the fourth confidence level are in descending order.

8. A single event detection device, comprising: The method comprises: a data monitoring module configured to monitor telemetry data and observation data of a satellite; a target abnormal point information acquisition module configured to determine a target abnormal point at the beginning of an anomaly and acquire time information and position information of the target abnormal point when the target abnormal point occurs when the telemetry data exhibits a first anomaly and / or the observation data exhibits a second anomaly, the position information of the target abnormal point being space information of the satellite when the target abnormal point occurs; a high-energy particle information acquisition module configured to acquire high-energy particle flow characteristic information at the same space-time as the target abnormal point based on the time information and the position information of the target abnormal point; a spatial distribution determination module configured to determine a spatial density distribution between the target abnormal point and other abnormal points monitored at other times based on the position information of the target abnormal point, the spatial density distribution including discrete or aggregated spatial density distribution; a confidence level evaluation module configured to determine a confidence level of the target abnormal point being a single particle event based on whether the telemetry data exhibits a first anomaly, whether the observation data exhibits a second anomaly, the high-energy particle flow characteristic information at the same space-time as the target abnormal point, and the spatial density distribution between the target abnormal point and other abnormal points monitored at other times.

9. An electronic device, comprising: A memory and a processor, the memory being configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any one of claims 1 to 7.

10. A readable storage medium, characterized by, A computer instruction is stored thereon, and the computer instruction is executed by a processor to implement the method of any one of claims 1 to 7.

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