Space weather real-time monitoring system and method based on multi-satellite cooperation

Abstract

The invention discloses a space weather real-time monitoring system and method based on multi-satellite cooperation, and relates to the technical field of space weather monitoring, and the system comprises a multi-satellite networking module, a data cooperation processing module, a bidirectional early warning analysis module and a scheduling control module. According to the invention, by constructing the multi-satellite collaborative space weather real-time monitoring system and method, a two-way early warning mechanism of space weather and satellite operation is established, so that accurate pre-judgment of the influence of space weather on satellites is realized, and whether a satellite anomaly is caused by space weather influence or equipment failure is distinguished through multi-satellite data cross validation; the problems of one-way monitoring and easy false early warning in the prior art are solved, the comprehensiveness and the data utilization rate of space weather monitoring are improved through multi-satellite networking cooperative data acquisition and establishment of a correlation analysis mechanism, meanwhile, the accuracy of satellite anomaly judgment is improved, interference of false early warning on a space task is effectively avoided, and the safety of space weather monitoring is improved. And reliable technical support is provided for safe and stable development of aerospace tasks.

Description

Technical Field

[0001] This invention relates to the field of space weather monitoring technology, specifically to a real-time space weather monitoring system and method based on multi-satellite collaboration. Background Technology

[0002] Space weather refers to various short-term scale changes occurring in the space environment, including solar activity, interplanetary space, magnetosphere, ionosphere, and upper atmosphere. Its core driving factors include solar flares, coronal mass ejections, magnetospheric disturbances, and fluctuations in ionospheric electron density. Space weather is closely related to human space activities, radio communications, and power system operation. Severe space weather events can lead to serious consequences such as satellite orbital deviations, payload failures, and communication interruptions. Therefore, conducting space weather monitoring is of significant practical importance for ensuring the safety of space missions and maintaining the stability of infrastructure.

[0003] Real-time space weather monitoring refers to the continuous collection, analysis, and interpretation of space environment parameters through various observation equipment, timely capturing dynamic changes in space weather, and predicting its impact. With the development of aerospace technology, multi-satellite network monitoring has become the mainstream method for real-time space weather monitoring. Through the collaborative observation of multiple satellites, it is possible to collect space environment data over a wider range and in more dimensions, significantly improving monitoring coverage and data richness compared to single-satellite monitoring.

[0004] However, existing space weather monitoring technologies based on multi-satellite collaboration still have significant shortcomings. Current technologies primarily focus on unidirectional monitoring of the impact of space weather on satellite operations, neglecting the feedback effect of the satellite's own operational status on the monitoring data. They cannot distinguish whether satellite operational anomalies are caused by space weather or equipment malfunctions through multi-satellite collaborative data. In existing technologies, multi-satellite data is only used for space weather parameter acquisition and fusion, without establishing a correlation analysis mechanism between space weather parameters and satellite operational status parameters. Furthermore, there is a lack of dedicated algorithms for data correlation and anomaly cause judgment, leading to false alarms, insufficient functional completeness and data utilization, and difficulty in meeting the requirements for high-precision and high-reliability space weather monitoring. Therefore, developing a real-time space weather monitoring system and method based on multi-satellite collaboration is of great significance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a real-time space weather monitoring system and method based on multi-satellite collaboration. By constructing a real-time space weather monitoring system and method based on multi-satellite collaboration, it can establish a two-way early warning mechanism for space weather and satellite operation. This enables accurate prediction of the impact of space weather on satellites and distinguishes whether the cause of satellite anomalies is space weather or equipment failure through cross-verification of multi-satellite data. By collecting data collaboratively through multi-satellite networking and establishing a correlation analysis mechanism, it improves the comprehensiveness of space weather monitoring and data utilization, while also increasing the accuracy of satellite anomaly judgment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a real-time space weather monitoring system based on multi-satellite collaboration, the system comprising: a multi-satellite networking module, a data collaborative processing module, a two-way early warning analysis module, and a scheduling and control module;

[0007] The multi-satellite networking module consists of heterogeneous orbital satellites, each of which carries a space weather parameter detection payload and a satellite operation status monitoring payload, used to collaboratively collect space weather data and satellite operation status data.

[0008] The data collaborative processing module is communicatively connected to the multi-satellite networking module and is used to receive data collected by multiple satellites, perform noise reduction, synchronization alignment and correlation processing on the data, and establish the correspondence between space weather parameters and satellite operating status parameters.

[0009] The bidirectional early warning analysis module is connected to the data collaborative processing module. It is used to analyze the potential impact of space weather on satellite operation based on the correlated data and generate the first early warning information. At the same time, it receives abnormal satellite operation signals, calls up multi-satellite synchronous space weather data and operation status data for cross-verification, determines the cause of the abnormality and generates the second early warning information.

[0010] The scheduling and control module is connected to the multi-satellite networking module and the two-way early warning analysis module, respectively. It is used to schedule the multi-satellite networking module to adjust the observation parameters according to the first early warning information and the second early warning information, and to push the early warning information to the ground terminal.

[0011] Furthermore, the data collaborative processing module processes the data by including the following steps:

[0012] It receives space weather data and satellite operation status data transmitted by a multi-satellite networking module, processes the received data using an adaptive filtering algorithm, and dynamically adjusts the filtering parameters according to the different noise characteristics of the space weather data and satellite operation status data.

[0013] Using the satellite's high-precision timestamp as a benchmark, and combining it with the satellite's orbital coordinate information, data collected by different satellites in the same time period and the same observation area are matched and aligned.

[0014] A data association algorithm is used to calculate the correlation degree between space weather parameters and satellite operational status parameters. The correlation degree is calculated using the following formula: ,in, For the first Space weather parameters and the first The correlation of satellite-like operational status parameters For the first The first of space weather parameters One collected value, For the first The average value of space-like weather parameters collected. For the first The first satellite operational status parameter One collected value, For the first The average value of collected satellite-like operational status parameters. The number of times data is collected within the same time period. For the first Weighting coefficients for space-like weather parameters, Based on the frequency and degree of the impact of this type of parameter on satellite operation in historical space weather events, it is determined through statistical regression analysis and updated every six months based on newly added historical data;

[0015] Based on the correlation results, space weather data and satellite operational status data are grouped according to observation time and observation area. A mapping table of space weather parameters and satellite operational status parameters within each group is constructed, forming a correlated dataset and storing it in the module's built-in database.

[0016] Furthermore, the two-way early warning analysis module includes the following steps when performing analysis and verification operations:

[0017] Retrieve the associated dataset from the data collaboration module and extract the spatial weather parameter variation trends from the associated dataset;

[0018] Combining satellite orbital characteristic parameters and payload tolerance threshold parameters, a trend prediction algorithm is used to calculate the potential satellite orbital offset and payload malfunction probability caused by changes in space weather parameters. The formula for calculating the payload malfunction probability is: ,in, This represents the probability of abnormal load operation. This is the probability correction coefficient. For the first Current collected values ​​of space-like weather parameters. For the first Normal thresholds for space-like weather parameters For the types of space weather parameters involved in the calculation, Based on the experimental data of space environment tolerance of different types of loads, the parameters are determined and calibrated separately for each type of load, and then stored in the parameter database of the two-way early warning analysis module.

[0019] Based on the calculation results of the load operation anomaly probability and track offset, different levels of first warning information are generated, including minor warning, moderate warning and severe warning.

[0020] After receiving a signal indicating abnormal satellite operation, the abnormal parameters of the abnormal satellite's operational status are extracted. Space weather data and operational status data collected by other satellites during the same period are retrieved. The abnormal parameters of the abnormal satellite are compared with the parameters of other satellites during the same period to determine the cause of the abnormality and generate a second early warning message.

[0021] Furthermore, when the two-way early warning analysis module determines the cause of the anomaly, if the space weather parameters of other satellites are abnormal and the abnormal trend is consistent with the abnormal parameter trend of the abnormal satellite, the anomaly is determined to be caused by space weather. If the space weather parameters of other satellites are normal and there is no similar abnormal operating status, the anomaly is determined to be caused by equipment failure.

[0022] Furthermore, when the scheduling and control module schedules the multi-satellite networking module to adjust the observation parameters, it includes the following steps:

[0023] Receive the first and second early warning messages sent by the two-way early warning analysis module;

[0024] The warning level of the first warning information is identified. When it is a minor warning, the original observation frequency of the multi-satellite networking module is maintained. When it is a moderate warning, the observation frequency is increased to 1.5 times the original frequency. When it is a severe warning, the observation frequency is increased to 2 times the original frequency.

[0025] The system identifies the abnormal causes of the second early warning information, schedules surrounding satellites to adjust their observation angles to cover the observation blind spots of abnormal satellites when space weather affects the area, and schedules backup satellites to take over the observation tasks of abnormal satellites when equipment fails. The system then pushes the first and second early warning information to the ground terminal.

[0026] Furthermore, the heterogeneous orbital satellites in the multi-satellite networking module include geostationary satellites, polar-orbiting satellites, and inclined orbit satellites. Geostationary satellites are used for routine space weather data and their own operational status data collection in the equatorial and mid-latitude regions. Polar-orbiting satellites are used for periodic data collection in polar regions. Inclined orbit satellites are used to adjust their orbital attitude according to the instructions of the scheduling and control module when sudden space weather events are detected, and to perform encrypted data collection in the event outbreak area.

[0027] Furthermore, the space weather parameter detection payload includes a magnetic field strength detector, a particle flux detector, and an ionospheric electron density detector. The magnetic field strength detector is used to collect magnetic field strength data in the space environment, the particle flux detector is used to collect flux data of high-energy particles, and the ionospheric electron density detector is used to collect electron density distribution data in the ionospheric region.

[0028] Furthermore, the satellite operation status monitoring payload includes an orbit measurement unit, a payload operation status monitoring unit, and a signal transmission monitoring unit. The orbit measurement unit is used to collect the satellite's orbital parameters, the payload operation status monitoring unit is used to collect the operating current and voltage data of each probe payload, and the signal transmission monitoring unit is used to collect the bit error rate data of satellite data transmission.

[0029] A method for real-time space weather monitoring based on multi-satellite collaboration, applicable to the aforementioned real-time space weather monitoring system based on multi-satellite collaboration, includes the following steps:

[0030] S1. The scheduling and control module sends observation commands to the multi-satellite networking module to coordinate each satellite to synchronously collect space weather data and its own operational status data according to a preset time sequence.

[0031] S2. The data collaborative processing module receives data transmitted from each satellite, performs noise reduction and synchronization alignment on the data in sequence, and then establishes the correspondence between space weather parameters and satellite operating status parameters to form an associated dataset.

[0032] S3, the two-way early warning analysis module analyzes the associated dataset, predicts the potential impact of space weather on satellite operation, generates the first early warning information and sends it to the scheduling and control module;

[0033] S4. When a satellite experiences an operational anomaly, it sends an anomaly signal to the data collaborative processing module. The data collaborative processing module then extracts the satellite's anomaly status parameters and other space weather and operational status data collected concurrently by other satellites.

[0034] S5. The two-way early warning analysis module determines the cause of the anomaly through cross-validation, generates a second early warning message, and sends it to the scheduling control module.

[0035] S6. The scheduling and control module schedules the multi-satellite networking module to adjust the observation parameters based on the first and second early warning information, and pushes the early warning information to the ground terminal.

[0036] Furthermore, in step S2, when the data collaborative processing module establishes the correspondence between space weather parameters and satellite operational status parameters, it first divides the synchronized and aligned data into multiple time windows according to the observation time, with each time window lasting no more than thirty minutes. Then, within each time window, based on the satellite's observation area coordinates, it associates and matches the space weather data with the operational status data of the satellite in the corresponding area. Finally, it generates an associated data subset for each time window, and the associated data subsets of all time windows together constitute an associated dataset.

[0037] Compared with existing technologies, this real-time space weather monitoring system and method based on multi-satellite collaboration has the following advantages:

[0038] This invention constructs a multi-satellite collaborative space weather real-time monitoring system and method, establishing a two-way early warning mechanism for space weather and satellite operation. This enables accurate prediction of the impact of space weather on satellites and distinguishes between space weather influences and equipment malfunctions through cross-verification of multi-satellite data. It solves the problems of one-way monitoring and erroneous early warning in existing technologies. By collecting data collaboratively through multi-satellite networking and establishing a correlation analysis mechanism, it improves the comprehensiveness of space weather monitoring and data utilization, while also enhancing the accuracy of satellite anomaly judgment. This effectively avoids the interference of false early warnings on space missions and provides reliable technical support for the safe and stable conduct of space missions.

[0039] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0041] Figure 1 This is a schematic diagram of the structure of a space weather real-time monitoring system based on multi-satellite collaboration;

[0042] Figure 2 This is a flowchart of a method for real-time space weather monitoring based on multi-satellite collaboration;

[0043] Figure 3 This is a flowchart of a real-time space weather monitoring method based on multi-satellite collaboration. Detailed Implementation

[0044] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0045] This invention provides a real-time space weather monitoring system and method based on multi-satellite collaboration, see [link to relevant documentation]. Figure 1 , Figure 2 and Figure 3 The core technical solution is as follows:

[0046] The system comprises four core modules: a multi-satellite networking module consisting of geostationary satellites, polar-orbiting satellites, and inclined orbit satellites forming a heterogeneous orbital network; each satellite carrying space weather parameter detection payloads such as magnetic field strength detectors and operational status monitoring payloads such as orbit measurement units, enabling collaborative acquisition of multi-dimensional data across the entire domain; a data collaborative processing module receiving satellite data, performing adaptive filtering and noise reduction, synchronizing time and orbital coordinates, and establishing a correspondence between space weather parameters and satellite operational status parameters through an association algorithm, forming an associated dataset; a two-way early warning analysis module predicting the potential impact of space weather on satellites based on the associated data, generating first-level early warning information in the categories of slight, moderate, and severe; simultaneously receiving abnormal satellite signals, and through cross-validation of synchronous data from multiple satellites, distinguishing between two types of abnormal causes—space weather impact and equipment failure—and generating second-level early warning information; and a scheduling and control module adjusting the observation frequency according to the early warning level, scheduling satellites to fill gaps or take over tasks based on the cause of the abnormality, and pushing the early warning to the ground terminal.

[0047] The monitoring process is as follows: the scheduling and control module coordinates multiple satellites to collect data synchronously; the data collaborative processing module completes data noise reduction, alignment and correlation matching, and divides time windows to generate correlated datasets; the two-way early warning analysis module generates the first early warning information, and if satellite anomalies are detected, it extracts relevant data for cross-validation and generates the second early warning information; the scheduling and control module adjusts satellite observation parameters according to the two types of early warning information and pushes the early warning to the ground terminal.

[0048] This solution addresses the problems of unidirectional monitoring and erroneous early warning in existing technologies through multi-satellite collaboration and a two-way early warning mechanism, thereby improving the comprehensiveness of monitoring and the accuracy of anomaly judgment and providing reliable support for space missions.

[0049] Example 1

[0050] This embodiment applies to a space weather monitoring scenario during a deep space exploration mission conducted collaboratively by a low-Earth orbit satellite constellation and a high-Earth orbit early warning satellite. This mission needs to cover the routine monitoring area in the mid-equatorial latitudes, specific observation areas in the polar regions, and dynamic areas potentially affected by sudden solar storms. It requires real-time capture of changes in the space magnetic field, high-energy particle flux, and ionospheric electron density, as well as accurate determination of the causes of satellite orbital deviations and payload malfunctions. This is to avoid false warnings that could lead to mission interruptions or wasted equipment maintenance resources, ensuring the continuity and reliability of deep space exploration data acquisition. (See also...) Figure 1 , Figure 2 and Figure 3 The specific content of this embodiment is as follows:

[0051] In this embodiment, the multi-satellite networking module includes two geostationary satellites, three polar-orbiting satellites, and two inclined orbit satellites. The geostationary satellites continuously collect space weather data and their own operational status data in the equatorial and mid-latitude regions. The polar-orbiting satellites orbit the polar orbit according to a preset period to complete data collection in the polar regions. The inclined orbit satellites are initially in a standby state. Each satellite carries three types of space weather parameter detection payloads: a magnetic field strength detector, a particle flux detector, and an ionospheric electron density detector; as well as three types of satellite operational status monitoring payloads: an orbit measurement unit, a payload operational status monitoring unit, and a signal transmission monitoring unit.

[0052] After the monitoring mission is initiated, the scheduling and control module sends observation commands to the multi-satellite networking module, coordinating all satellites to synchronously collect data according to the preset time sequence. The geostationary satellite collects data on magnetic field strength, high-energy particle flux, and ionospheric electron density every ten minutes, while simultaneously recording orbital parameters, payload operating current and voltage, and data transmission error rate. The polar-orbiting satellite completes polar region data acquisition every fifteen minutes, with data types consistent with the geostationary satellite. The inclined orbit satellites are currently operating at a frequency of collecting data for regular regions every thirty minutes.

[0053] After receiving data transmitted from various satellites, the data collaborative processing module first employs an adaptive filtering algorithm for noise reduction. Based on the different noise characteristics of space weather data and satellite operational status data, the filtering parameters are dynamically adjusted to reduce interference. Subsequently, using the satellite's high-precision timestamp as a benchmark, and combining it with the orbital coordinate information of each satellite, data collected by different satellites in the same observation area during the same time period are matched and aligned to ensure consistency in both temporal and spatial dimensions.

[0054] Next, a data association algorithm is used to calculate the correlation degree between space weather parameters and satellite operational status parameters. The correlation degree calculation formula is as follows: ,in For the first Space weather parameters and the first The correlation of satellite-like operational status parameters For the first The first of space weather parameters One collected value, For the first The average value of space-like weather parameters collected. For the first The first satellite operational status parameter One collected value, For the first The average value of collected satellite-like operational status parameters. The number of times data is collected within the same time period. For the first Weighting coefficients for spatial weather parameters. Finally, based on the correlation results, the data are grouped by observation time and observation area, a mapping table is constructed to form a correlated dataset, and the dataset is stored.

[0055] The two-way early warning analysis module retrieves the associated dataset from the data collaborative processing module, extracts the changing trends of space weather parameters, and combines them with satellite orbital characteristic parameters and payload tolerance threshold parameters. It then calculates the orbital offset and the probability of payload malfunction using a trend prediction algorithm. The formula for calculating the probability of payload malfunction is as follows: ,in This represents the probability of abnormal load operation. This is the probability correction coefficient. For the first Current collected values ​​of space-like weather parameters. For the first Normal thresholds for space-like weather parameters The types of space weather parameters used in the calculation are as follows: Based on the calculation results, if the probability of payload malfunction is low and the orbital offset is small, a minor warning is generated; if the probability and offset are at a moderate level, a moderate warning is generated; if the probability is high and the offset is large, a severe warning is generated, and the first warning information is sent to the scheduling and control module.

[0056] When a polar-orbiting satellite experiences a sudden increase in its signal transmission error rate, it sends an anomaly signal to the data collaborative processing module. The module extracts the satellite's error rate data and concurrent space weather and operational status data from other satellites. A two-way early warning analysis module compares this data and finds an abnormal increase in high-energy particle flux in the concurrent space weather parameters of other satellites, with the anomaly trend consistent with the increasing error rate trend of this polar-orbiting satellite. The module determines that the anomaly is caused by space weather and generates a second early warning message, which is then sent to the scheduling and control module.

[0057] The scheduling and control module receives two types of early warning information. If the first warning is a moderate warning, the observation frequency of all satellites is increased to 1.5 times the original frequency; if it is a severe warning, it is increased to 2 times the original frequency; and for minor warnings, the original frequency is maintained. Regarding the space weather impact determined by the second warning, surrounding satellites are scheduled to adjust their observation angles to cover the observation blind spots of the anomalous polar-orbiting satellites, and both types of warning information are pushed to the ground terminal. If the anomaly is caused by equipment failure, standby inclined orbit satellites are scheduled to take over the observation tasks of the faulty satellites.

[0058] This embodiment achieves full-domain monitoring of space weather through multi-satellite heterogeneous networking and collaborative data processing. Utilizing correlation algorithms and a two-way early warning mechanism, it accurately predicts the impact of space weather on satellites and precisely distinguishes the causes of anomalies, avoiding false early warning interference. The scheduling and control module flexibly adjusts observation strategies based on early warnings, ensuring the stability and continuity of data acquisition during deep space exploration missions. This provides effective technical support for the safe conduct of space missions. Compared to existing one-way monitoring technologies, it significantly improves the comprehensiveness of monitoring and the accuracy of anomaly identification, while also greatly enhancing data utilization.

[0059] Example 2

[0060] This embodiment is applied to a scenario where a low-Earth orbit communication satellite constellation and a geostationary orbit space weather monitoring satellite work together to perform a global communication support mission. This mission needs to focus on covering densely populated communication areas in the mid- and low-latitude regions. It is necessary to monitor space weather phenomena such as magnetospheric disturbances and ionospheric electron density anomalies caused by solar activity in real time to prevent them from interfering with satellite communication signal transmission. It is also necessary to quickly identify whether the interruption of communication satellite signals is due to space weather or equipment hardware failure to avoid errors in emergency dispatch of the communication network due to misjudgment, and to ensure the stability and continuity of communication services for global users.

[0061] See Figure 1 , Figure 2 and Figure 3 The system foundation of this embodiment includes the multi-satellite networking module, data collaborative processing module, two-way early warning analysis module, and scheduling control module provided in the aforementioned embodiments. The multi-satellite networking module is adjusted to consist of four geostationary satellites and two low-Earth orbit (LEO) communication satellites. The geostationary satellites focus on densely populated communication areas in the mid- and low-latitudes for routine data acquisition, while the LEO communication satellites operate in near-Earth orbit, simultaneously handling data acquisition and communication signal relay in mid- and high-latitude regions. In addition to the magnetic field strength detector, particle flux detector, and ionospheric electron density detector, the space weather parameter detection payloads on each satellite also include a solar flare monitor to capture solar flare burst signals. The satellite operation status monitoring payloads also include a communication signal strength monitoring unit to collect power and signal-to-noise ratio data of the satellite's relayed communication signals.

[0062] During the mission initiation phase, the scheduling and control module sends collaborative observation commands to the multi-satellite networking module. Geostationary satellites collect space weather data and their own operational status data every eight minutes, while also recording communication signal strength and signal-to-noise ratio. Low-Earth orbit communication satellites complete data collection of their orbital coverage area every twelve minutes, with data types consistent with those of the geostationary satellites. When passing through areas with peak communication traffic, the space weather data collection interval is temporarily halved to ensure data density in critical areas.

[0063] After receiving data transmitted from various satellites, the data collaborative processing module first employs an adaptive filtering algorithm for noise reduction. For newly added solar flare monitoring data and communication signal strength data, the filtering parameters are adjusted individually based on their noise characteristics. Then, using high-precision satellite timestamps combined with orbital coordinates, data collected by different satellites within the same communication coverage area at the same time are synchronized and aligned. Subsequently, a data association algorithm is used to calculate the correlation between space weather parameters and satellite operational status parameters (including communication signal parameters). The calculation formula is as follows: Finally, the data are grouped according to observation time and communication coverage area, a mapping table is constructed to form an associated dataset and stored.

[0064] After retrieving the associated dataset, the two-way early warning analysis module focuses on extracting the changing trends of solar flare intensity and ionospheric electron density. Combined with the signal tolerance threshold and orbit maintenance parameters of the satellite communication payload, it calculates the probability of communication signal interruption and the need for satellite orbit fine-tuning using a trend prediction algorithm. The calculation of the communication signal interruption probability employs the payload malfunction probability calculation formula. Based on the calculation results, if the probability of communication signal interruption is lower than the preset low threshold and the track deviation does not require adjustment, a minor warning is generated; if the probability is between the low and high thresholds and a small track adjustment is required, a moderate warning is generated; if the probability is higher than the high threshold and an emergency track adjustment is required, a severe warning is generated, and the first warning information is sent to the scheduling control module.

[0065] When a low-Earth orbit communication satellite experiences a sudden drop in communication signal strength and an abnormal signal-to-noise ratio, it sends an anomaly signal to the data collaborative processing module. The data collaborative processing module extracts the satellite's communication status parameters and space weather and operational status data collected concurrently by other satellites. The two-way early warning analysis module compares these data and finds that the space weather parameters of other satellites are all within normal ranges and there are no similar communication signal anomalies. It determines that the anomaly is caused by equipment failure and generates a second early warning message, which is then sent to the scheduling and control module.

[0066] After receiving the early warning information, the scheduling and control module will adjust the frequency of all satellite observations to twice the original frequency if the first warning is a severe warning; to 1.5 times the original frequency if it is a moderate warning; and maintain the original frequency if it is a minor warning. For equipment failures identified by the second warning, a standby geostationary satellite will be scheduled to temporarily take over the communication signal relay task of the faulty low-Earth orbit satellite. Simultaneously, the orbital attitudes of surrounding low-Earth orbit satellites will be adjusted to expand communication coverage and compensate for the signal gaps caused by the faulty satellite. Both types of early warning information will then be pushed to the ground communication command terminal.

[0067] In summary, this embodiment, based on the aforementioned embodiments, specifically adapts to the needs of global communication support missions by adding detection payloads and operational status monitoring parameters, achieving collaborative analysis of space weather monitoring and communication signal status. Utilizing a two-way early warning mechanism and flexible scheduling strategies, it not only accurately predicts the impact of space weather on communication satellites but also quickly locates equipment faults and completes mission takeover, effectively avoiding the risk of communication interruptions. Compared to a single-orbit satellite monitoring scheme, this embodiment significantly improves the accuracy of space weather monitoring and emergency response efficiency in densely populated communication areas, providing more reliable technical support for the stable operation of global communication networks.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A real-time space weather monitoring system based on multi-satellite collaboration, characterized in that, The system includes: a multi-satellite networking module, a data collaborative processing module, a two-way early warning analysis module, and a scheduling and control module; The multi-satellite networking module consists of heterogeneous orbital satellites, each of which carries a space weather parameter detection payload and a satellite operation status monitoring payload, used to collaboratively collect space weather data and satellite operation status data. The data collaborative processing module is communicatively connected to the multi-satellite networking module and is used to receive data collected by multiple satellites, perform noise reduction, synchronization alignment and correlation processing on the data, and establish the correspondence between space weather parameters and satellite operating status parameters. The bidirectional early warning analysis module is connected to the data collaborative processing module. It is used to analyze the potential impact of space weather on satellite operation based on the correlated data and generate the first early warning information. At the same time, it receives abnormal satellite operation signals, calls up multi-satellite synchronous space weather data and operation status data for cross-verification, determines the cause of the abnormality and generates the second early warning information. The scheduling and control module is connected to the multi-satellite networking module and the two-way early warning analysis module, respectively. It is used to schedule the multi-satellite networking module to adjust the observation parameters according to the first early warning information and the second early warning information, and to push the early warning information to the ground terminal.

2. The space weather real-time monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, The data collaborative processing module processes data by including the following steps: It receives space weather data and satellite operation status data transmitted by a multi-satellite networking module, processes the received data using an adaptive filtering algorithm, and dynamically adjusts the filtering parameters according to the different noise characteristics of the space weather data and satellite operation status data. Using the satellite's high-precision timestamp as a benchmark, and combining it with the satellite's orbital coordinate information, data collected by different satellites in the same time period and the same observation area are matched and aligned. A data association algorithm is used to calculate the correlation degree between space weather parameters and satellite operational status parameters. The correlation degree is calculated using the following formula: ,in, For the first Space weather parameters and the first The correlation of satellite-like operational status parameters For the first The first type of space weather parameter One collected value, For the first The average value of space-like weather parameters collected. For the first The first satellite operational status parameter One collected value, For the first The average value of collected satellite-like operational status parameters. The number of times data is collected within the same time period. For the first Weighting coefficients for space-like weather parameters; Based on the correlation results, space weather data and satellite operational status data are grouped according to observation time and observation area. A mapping table of space weather parameters and satellite operational status parameters within each group is constructed, forming a correlated dataset and storing it in the module's built-in database.

3. The space weather real-time monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, The bidirectional early warning analysis module includes the following steps when performing analysis and verification operations: Retrieve the associated dataset from the data collaboration module and extract the spatial weather parameter variation trends from the associated dataset; Combining satellite orbital characteristic parameters and payload tolerance threshold parameters, a trend prediction algorithm is used to calculate the potential satellite orbital offset and payload malfunction probability caused by changes in space weather parameters. The formula for calculating the payload malfunction probability is: ,in, This represents the probability of abnormal load operation. This is the probability correction coefficient. For the first Current collected values ​​of space-like weather parameters. For the first Normal thresholds for space-like weather parameters The types of space weather parameters used in the calculation; Based on the calculation results of the load operation anomaly probability and track offset, different levels of first warning information are generated, including minor warning, moderate warning and severe warning. After receiving a signal indicating abnormal satellite operation, the abnormal parameters of the abnormal satellite's operational status are extracted. Space weather data and operational status data collected by other satellites during the same period are retrieved. The abnormal parameters of the abnormal satellite are compared with the parameters of other satellites during the same period to determine the cause of the abnormality and generate a second early warning message.

4. A real-time space weather monitoring system based on multi-satellite collaboration according to claim 3, characterized in that, When the two-way early warning analysis module determines the cause of the anomaly, if the space weather parameters of other satellites are abnormal and the abnormal trend is consistent with the abnormal parameter trend of the abnormal satellite, the anomaly is determined to be caused by space weather. If the space weather parameters of other satellites are normal and there is no similar abnormal operating status, the anomaly is determined to be caused by equipment failure.

5. A real-time space weather monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, When the scheduling control module schedules the multi-satellite networking module to adjust the observation parameters, it includes the following steps: Receive the first and second early warning messages sent by the two-way early warning analysis module; The warning level of the first warning information is identified. When it is a minor warning, the original observation frequency of the multi-satellite networking module is maintained. When it is a moderate warning, the observation frequency is increased to 1.5 times the original frequency. When it is a severe warning, the observation frequency is increased to 2 times the original frequency. The system identifies the abnormal causes of the second early warning information, schedules surrounding satellites to adjust their observation angles to cover the observation blind spots of abnormal satellites when space weather affects the area, and schedules backup satellites to take over the observation tasks of abnormal satellites when equipment fails. The system then pushes the first and second early warning information to the ground terminal.

6. A real-time space weather monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, The heterogeneous orbital satellites in the multi-satellite networking module include geostationary satellites, polar-orbiting satellites, and inclined orbit satellites. Geostationary satellites are used for routine space weather data and their own operational status data collection in the equatorial and mid-latitude regions. Polar-orbiting satellites are used for periodic data collection in polar regions. Inclined orbit satellites are used to adjust their orbital attitude according to the instructions of the scheduling and control module when sudden space weather events are detected, and to perform encrypted data collection in the event outbreak area.

7. A real-time space weather monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, The space weather parameter detection payload includes a magnetic field strength detector, a particle flux detector, and an ionospheric electron density detector. The magnetic field strength detector is used to collect magnetic field strength data in the space environment, the particle flux detector is used to collect flux data of high-energy particles, and the ionospheric electron density detector is used to collect electron density distribution data in the ionospheric region.

8. A real-time space weather monitoring system based on multi-satellite collaboration according to claim 1, characterized in that, The satellite operation status monitoring payload includes an orbit measurement unit, a payload operation status monitoring unit, and a signal transmission monitoring unit. The orbit measurement unit is used to collect the satellite's orbital parameters, the payload operation status monitoring unit is used to collect the operating current and voltage data of each probe payload, and the signal transmission monitoring unit is used to collect the bit error rate data of satellite data transmission.

9. A method for real-time space weather monitoring based on multi-satellite collaboration, applicable to the real-time space weather monitoring system based on multi-satellite collaboration as described in any one of claims 1-8, characterized in that, The method includes the following steps: S1. The scheduling and control module sends observation commands to the multi-satellite networking module to coordinate each satellite to synchronously collect space weather data and its own operational status data according to a preset time sequence. S2. The data collaborative processing module receives data transmitted from each satellite, performs noise reduction and synchronization alignment on the data in sequence, and then establishes the correspondence between space weather parameters and satellite operating status parameters to form an associated dataset. S3, the two-way early warning analysis module analyzes the associated dataset, predicts the potential impact of space weather on satellite operation, generates the first early warning information and sends it to the scheduling and control module; S4. When a satellite experiences an operational anomaly, it sends an anomaly signal to the data collaborative processing module. The data collaborative processing module then extracts the satellite's anomaly status parameters and other space weather and operational status data collected concurrently by other satellites. S5. The two-way early warning analysis module determines the cause of the anomaly through cross-validation, generates a second early warning message, and sends it to the scheduling control module. S6. The scheduling and control module schedules the multi-satellite networking module to adjust the observation parameters based on the first and second early warning information, and pushes the early warning information to the ground terminal.

10. A method for real-time space weather monitoring based on multi-satellite collaboration according to claim 9, characterized in that, In step S2, when the data collaborative processing module establishes the correspondence between space weather parameters and satellite operational status parameters, it first divides the synchronized and aligned data into multiple time windows according to the observation time. The duration of each time window does not exceed thirty minutes. Then, within each time window, the space weather data is associated and matched with the operational status data of the satellite in the corresponding area based on the satellite's observation area coordinates. Finally, an associated data subset is generated for each time window, and the associated data subsets of all time windows together constitute the associated dataset.

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