Method, system, and apparatus for quality monitoring of gnss-r constellations and media

Abstract

The embodiment of the application provides a kind of GNSS-R networking constellation quality monitoring method and system, wherein the method comprises: obtaining the original data of GNSS-R networking constellation;The original data is uniformly preprocessed to obtain the file name of networking constellation multidimensional information and uniform naming method;The networking constellation multidimensional information is time-sliced to obtain the networking constellation segment multidimensional information;The networking constellation segment multidimensional information is rendered to three-dimensional data and two-dimensional visual processing to obtain multidimensional visual interface;According to the multidimensional visual interface, the quality monitoring result of the GNSS-R networking constellation is obtained by quality monitoring.The embodiment of the application realizes the quick, accurate, convenient and intuitive quality monitoring of GNSS-R large-scale networking constellation compatible with multiple GNSS systems, improves the efficiency, accuracy and convenience of monitoring, and provides strong technical support for future satellite networking constellation management.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of Global Navigation Satellite System (GNSS) reflectometry technology, in particular to a quality monitoring method of a Global Navigation Satellite System Reflectometry (GNSS-R) networking constellation, a quality monitoring system of the GNSS-R networking constellation, an electronic device and a computer readable storage medium. BACKGROUND

[0002] In the early 1990s, research revealed that GNSS signals could be received by the ground surface reflection in addition to the traditional positioning, navigation and timing functions, and then used for remote sensing of physical parameters of the ground surface reflection, which led to the emergence of GNSS-R technology. Currently, GNSS-R technology has been widely used in remote sensing of various physical parameters, such as GNSS-R height measurement, sea surface wind field, soil moisture, sea ice coverage, total amount of ground surface organisms, and ground surface freeze-thaw.

[0003] Since GNSS-R technology uses existing navigation satellite signals for remote sensing without the need for signal transmitting devices, the receiver is light in weight, low in power consumption, and high in cost performance, and is very suitable for being carried on a small satellite platform to form a large-scale networking detection. In recent years, with the maturation of GNSS-R receiver technology and inversion algorithms, GNSS-R large-scale networking constellations based on micro-nano satellite platforms and compatible with multiple GNSS systems, and GNSS-R detection network technologies based on orbiting final stage clusters have made significant progress, and GNSS-R large-scale networking constellations compatible with multiple GNSS systems are being formed.

[0004] However, due to the large number of satellites and reflection channels of GNSS-R large-scale networking constellations compatible with multiple GNSS systems, and due to the design life of micro-nano satellite platforms and final stage clusters and GNSS-R receivers, the platforms and GNSS-R receivers are more likely to fail and have quality problems in orbit. Therefore, how to realize rapid, accurate, convenient and intuitive quality monitoring of GNSS-R large-scale networking constellations compatible with multiple GNSS systems in orbit has become a problem to be solved. SUMMARY

[0005] In view of the above problems, the embodiments of the present application are proposed to provide a quality monitoring method of a GNSS-R networking constellation, a quality monitoring system of the GNSS-R networking constellation, an electronic device and a computer readable storage medium, which overcome the above problems or at least partially solve the above problems.

[0006] To solve the above problems, the embodiment of the application discloses a quality monitoring method of a GNSS-R networking constellation, the method comprising: acquiring original data of the GNSS-R networking constellation; uniformly pre-processing the original data to obtain multi-dimensional information of the networking constellation and a file name in a uniform naming manner; performing time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of a networking constellation segment; performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation segment to obtain a multi-dimensional visualization interface; and performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0007] The embodiment of the application also discloses a quality monitoring system of a GNSS-R networking constellation, the system comprising: an original data acquisition module configured to acquire original data of the GNSS-R networking constellation; an original data uniform pre-processing module configured to uniformly pre-process the original data to obtain multi-dimensional information of the networking constellation and a file name in a uniform naming manner; a multi-dimensional information slicing processing module configured to perform time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of a networking constellation segment; a multi-dimensional information visualization processing module configured to perform three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation segment to obtain a multi-dimensional visualization interface; and a quality monitoring module configured to perform quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0008] The embodiment of the application also discloses an electronic device comprising: one or more processors; and one or more machine-readable media having stored thereon instructions that, when executed by the one or more processors, cause the electronic device to perform the quality monitoring method of the GNSS-R networking constellation.

[0009] The embodiment of the application also discloses a computer-readable storage medium storing a computer program that causes a processor to perform the quality monitoring method of the GNSS-R networking constellation.

[0010] The embodiment of the application comprises the following advantages:

[0011] The quality monitoring scheme of the GNSS-R networking constellation provided by the embodiment of the application can be applied to a monitoring system. The monitoring system acquires original data of a GNSS-R networking constellation; uniformly pre-processes the original data to obtain multi-dimensional information of the networking constellation and a file name in a uniform naming manner; performs time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of a networking constellation segment; performs three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation segment to obtain a multi-dimensional visualization interface; and performs quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0012] The embodiment of the present application can finely analyze the constellation state of different time periods by time slicing processing on the multi-dimensional information of the networked constellation after unified preprocessing, thereby improving the accuracy of quality monitoring. The time slicing processing enables the monitoring system to adapt to a GNSS-R networked constellation with larger scale and more reflection channel numbers, and can more carefully capture potential faults or quality problems, thereby avoiding omissions caused by system collapse or insufficient analysis due to excessive data volume. The three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networked constellation segment enable the data to be presented not only in the form of text or table, but also through two-dimensional and three-dimensional visual interfaces to present multi-dimensional information. Such visualization processing improves the intuitiveness of the data, facilitates users to more quickly understand and analyze the monitoring data, and thereby makes more timely decisions. The data in multiple dimensions, such as time dimension, space dimension, and various physical parameter dimensions, can be monitored simultaneously. The comprehensive monitoring of multiple dimensions can more comprehensively master the overall operation state of the constellation, and is helpful to quickly identify the specific fault source and influence range. Through the comprehensive application of automatic unified preprocessing, time slicing processing, and multi-dimensional visualization, the steps of manual intervention are reduced, and the quality monitoring process is more convenient. Users can quickly obtain the monitoring results through the intuitive multi-dimensional visualization interface, and simplify the processing and analysis process of a large amount of complex data.

[0013] In summary, compared with the prior art, the embodiment of the present application realizes rapid, accurate, convenient, and intuitive quality monitoring of a GNSS-R large-scale networked constellation compatible with multiple GNSS systems through data unified preprocessing, time slicing, multi-dimensional information rendering, and multi-dimensional visualization interface, greatly improves the efficiency, accuracy, and convenience of monitoring, and simultaneously provides strong technical support for future satellite networked constellation management. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a step flow chart of a quality monitoring method of a GNSS-R networked constellation according to an embodiment of the present application;

[0015] Figure 2 is a step flow chart of a multi-dimensional information fusion GNSS-R networked constellation visualization quality monitoring scheme according to an embodiment of the present application;

[0016] Figure 3 is a three-dimensional visualization effect diagram of GNSS-R networked constellation space geometric information according to an embodiment of the present application;

[0017] Figure 4 is a two-dimensional visualization effect diagram of GNSS-R waveform data information according to an embodiment of the present application;

[0018] Figure 5 is a two-dimensional visualization effect diagram of GNSS-R specular point auxiliary information according to an embodiment of the present application;

[0019] Figure 6 is a structural block diagram of a GNSS-R network constellation quality monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the above objectives, characteristics and advantages of the present application more apparent, comprehensible and easily understood, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] The GNSS-R network constellation quality monitoring scheme according to the embodiment of the present application extracts multi-dimensional information by uniformly preprocessing the original data of the GNSS-R network constellation, and further realizes three-dimensional rendering and two-dimensional visualization display of multi-dimensional data by segmenting processing through time slicing technology, so as to construct an intuitive multi-dimensional visualization interface. With the help of the interface, real-time and fine quality monitoring of large-scale network constellation can be realized, and potential faults or quality problems can be effectively identified and analyzed. Compared with the traditional method, the scheme improves the data processing efficiency, monitoring accuracy and convenience, and is particularly suitable for quality management of GNSS-R large-scale network constellation compatible with multiple GNSS systems.

[0022] REFERENCE Figure 1 Fig. 1 shows a step flowchart of a GNSS-R network constellation quality monitoring method according to an embodiment of the present application. The GNSS-R network constellation quality monitoring method can be applied to a monitoring system, referred to as a system. The GNSS-R network constellation quality monitoring method can specifically include the following steps:

[0023] Step 101, obtaining original data of a GNSS-R network constellation.

[0024] In the embodiment of the present application, the key to obtaining the original data of the GNSS-R network constellation lies in collecting complete and accurate original data, which can be directly obtained from the satellite receiver capturing the ground reflected signal. The principle of GNSS-R technology is to receive the reflection wave of global navigation satellite system signal on the ground. The reflected signal carries information about the physical parameters of the ground, such as sea surface height, wind field, soil moisture, etc.

[0025] In practical applications, the raw data acquisition of GNSS-R constellation can be divided into the following main links: First, the reflected signals on the earth are captured by satellite receivers, which are specially designed devices for receiving these reflected signals. These receivers are usually carried on micro-nano satellite platforms, and due to the volume and weight limitations of these platforms, the design of the receiver must balance high sensitivity and low power consumption. Second, the received signal data is transmitted to the ground station or other relay stations through the satellite, and the signal may be forwarded and processed several times during this process to ensure the integrity and accuracy of data transmission. In order to obtain high-quality raw data, it is necessary to ensure the normal working state of the satellite receiver during on-orbit operation, and avoid data loss or distortion caused by receiver failure, signal interference or other external factors.

[0026] In addition, the compatibility problem between different GNSS systems needs to be considered in the acquisition of raw data. At present, GNSS systems include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), Galileo system and Beidou satellite navigation system (BDS) and other systems. In order to realize the compatibility of multi-GNSS system, the receiver must be able to receive and process signals from different systems at the same time, which puts higher requirements on the stability and consistency of raw data acquisition.

[0027] The acquired raw data includes a large amount of signal information, which directly reflects the physical state of the earth's surface, and therefore lays the foundation for subsequent quality monitoring. High-quality raw data can ensure the accuracy of subsequent data processing and analysis, thereby improving the reliability and effectiveness of the entire GNSS-R constellation quality monitoring system.

[0028] Step 102, uniformly pre-processing the raw data to obtain multi-dimensional information of the constellation and file names with uniform naming methods.

[0029] In the embodiments of the present application, after obtaining the raw data of the GNSS-R constellation, the next step is to uniformly pre-process these raw data in order to extract valid multi-dimensional information. The purpose of uniform pre-processing is to remove noise and irrelevant information in the raw data, and at the same time, to structure the useful information so that it can be effectively utilized by subsequent analysis steps. Due to the large amount and complexity of GNSS-R raw data, the step of uniform pre-processing is crucial to improve data quality and processing efficiency.

[0030] In practical applications, the first step of unified preprocessing is signal denoising. GNSS-R signals may be affected by various factors during transmission, such as atmospheric interference, electromagnetic noise, etc. These noises can cause the quality of the original data to decline. Through denoising processing, these interference signals can be filtered out, thereby extracting purer reflected signal information. Common denoising methods include filter design, waveform analysis, and spectral analysis, etc. These methods can be selectively applied according to different interference types. Secondly, data calibration and normalization processing is also an important part of unified preprocessing. Due to the sensitivity of satellite receivers and changes in environmental conditions, the received signal strength may differ. Through calibration, these differences can be eliminated, ensuring the comparability between different data sets. Normalization processing maps data to a standardized range, usually between 0 and 1, which can avoid analysis errors caused by different data scales. Next, unified preprocessing also includes data resampling and interpolation processing. The sampling rate of the original data may not be consistent, and even in some time periods, data may be missing. Through resampling processing, data can be rearranged on a unified time axis, while interpolation processing can fill in missing data to ensure data continuity and integrity. Resampling and interpolation processing can effectively improve the accuracy of subsequent analysis, especially in time series analysis. Finally, unified preprocessing also extracts multi-dimensional information. This step is to further analyze the data that has been denoised, calibrated, and resampled to extract multi-dimensional information related to surface physical parameters, such as reflection intensity, delay time, phase shift, etc. These multi-dimensional information will provide a basis for subsequent time slicing processing and visualization analysis.

[0031] Through unified preprocessing, the original data is converted into more structured, clear and useful multi-dimensional information. This not only improves the quality of the data, but also lays the foundation for subsequent time slicing processing and visualization analysis, ultimately contributing to the efficient quality monitoring of GNSS-R constellation.

[0032] Step 103, performing time slicing processing on the multi-dimensional information of the constellation to obtain multi-dimensional information of the constellation segment.

[0033] In the embodiments of the present application, after the data unified preprocessing is completed and the multi-dimensional information is obtained, the next step is to perform time slicing processing on these multi-dimensional information to generate constellation segment multi-dimensional information with time series characteristics. Time slicing processing refers to dividing continuous multi-dimensional information into multiple segments according to the time dimension, so that each segment represents the state in a specific time period. This step is crucial for analyzing and monitoring the dynamic changes of the constellation.

[0034] In practical applications, time slicing processing requires determining the time interval of the slices. The selection of this time interval needs to consider various factors, such as the satellite's orbit, the frequency of receiving data, and the speed of change of the physical parameters to be monitored. Generally, the selection of the time interval should ensure that key change information of the network constellation can be captured, while avoiding excessive fragmentation of data that leads to computational burden. For rapidly changing parameters, a shorter time interval may be needed, while for slower changing parameters, the time interval can be appropriately extended. Secondly, after determining the time interval, the multi-dimensional information is divided into several segments in chronological order. Each segment represents the running state of the network constellation in that time period. In this way, complex multi-dimensional information can be broken down into smaller pieces of data that are easier to analyze, thereby better understanding the trend of data changes over time.

[0035] An important advantage of time slicing processing is that it can capture dynamic characteristics in the operation of the constellation. For example, by comparing the reflectivity signal strength in different time periods, the effects of satellite orbit changes or changes in the surface environment can be found. In addition, time slicing can also be used to identify and locate abnormal events in a specific time period, such as sudden loss of satellite signals or sharp decline in receiver performance.

[0036] After time slicing processing, the generated multi-dimensional information of the network constellation segments not only contains information in the spatial and physical parameter dimensions, but also introduces the time dimension, which makes subsequent analysis more three-dimensional and comprehensive. In particular, in practical applications, time slicing processing helps to form a dynamic quality monitoring system, which can track the state changes of the constellation in real time and timely discover and handle potential problems.

[0037] Overall, time slicing processing divides continuous multi-dimensional information by time dimension, so that data analysis can better focus on the process of change, rather than just static results. This processing method provides a time sequence perspective for subsequent three-dimensional rendering and visualization analysis, enabling the quality monitoring system to more accurately and comprehensively grasp the running state of the GNSS-R network constellation.

[0038] Step 104, three-dimensional data rendering and two-dimensional visualization processing of the multi-dimensional information of the network constellation segments to obtain a multi-dimensional visualization interface.

[0039] In the embodiments of the present application, after completing the time slicing processing and obtaining the multi-dimensional information of the network constellation segments, the next step is to perform three-dimensional data rendering and two-dimensional visualization processing on these information, thereby generating a multi-dimensional visualization interface. The core of this step is to use visualization technology to intuitively present complex multi-dimensional data to users, helping users quickly understand and analyze the state of the network constellation.

[0040] In practical applications, three-dimensional data rendering is the process of converting multi-dimensional information of a segment into a three-dimensional image. Multi-dimensional information usually includes spatial coordinates, time dimensions, and other physical parameters such as signal strength, delay, etc. Through three-dimensional rendering technology, these information can be mapped into a three-dimensional space, thereby generating a stereoscopic visualization effect. For example, the trajectory of a satellite can be rendered as a curve in a three-dimensional space, while the intensity of the reflected signal can be indicated by color or transparency, thereby intuitively displaying the signal changes at different times and locations. Secondly, two-dimensional visualization processing is the process of planar graphing of multi-dimensional information of a segment. Compared with three-dimensional rendering, two-dimensional visualization processing pays more attention to the simplification and readability of information, and usually uses charts, curves, heat maps, etc. to present data. For example, the change of signal strength over time can be displayed by a line chart, while the spatial distribution of surface reflectivity can be presented by a heat map. The advantage of two-dimensional visualization is that it is easy to understand and analyze, especially suitable for displaying data changes that require quick decision-making or monitoring.

[0041] In actual operation, three-dimensional rendering and two-dimensional visualization processing are often used in combination. Three-dimensional rendering provides a global view and stereoscopic effect of data, allowing users to observe data changes from different angles; while two-dimensional visualization helps users quickly capture key information through simple and clear charts and graphs. For example, in a quality monitoring system, the real-time status of the entire GNSS-R constellation can be displayed through three-dimensional rendering, while abnormal data in a specific time period can be monitored through two-dimensional charts.

[0042] In order to realize this multi-dimensional visualization interface, advanced data processing and graphics rendering technology needs to be used. Common three-dimensional rendering techniques include ray tracing, volume rendering, etc., while two-dimensional visualization usually relies on chart libraries and data visualization tools such as Matplotlib, D3.js, etc. These technologies can be seamlessly integrated with uniformly preprocessed data through programming interfaces, thereby generating dynamic and interactive visualization interfaces.

[0043] In addition, the multi-dimensional visualization interface can also be designed to be interactive, allowing users to freely adjust the viewing angle, select the time period, or zoom in on the data of a specific area through mouse clicks, drags, zooms, etc. This interactive design not only improves user experience, but also enhances the flexibility of data analysis, allowing users to conduct in-depth exploration according to their needs and discover hidden trends or abnormalities.

[0044] By rendering the multi-dimensional information of the constellation segment in three dimensions and visualizing it in two dimensions, the final multi-dimensional visualization interface makes complex data easy to understand and analyze. Users can intuitively observe the state changes of the constellation through this interface, thereby better performing real-time quality monitoring and problem diagnosis. This method not only improves the accuracy and efficiency of monitoring, but also provides users with a powerful tool to respond to and solve possible quality problems in the constellation.

[0045] In step 105, quality monitoring results are obtained by monitoring the GNSS-R constellation based on the multi-dimensional visualization interface.

[0046] In the embodiments of the present application, after obtaining the multi-dimensional visualization interface, the core task of quality monitoring is to perform real-time quality monitoring of the GNSS-R constellation through this interface, and finally generate quality monitoring results. The focus of this process is to quickly and accurately assess the running state of the constellation using the information provided by the visualization interface, and to discover and diagnose possible faults or quality problems.

[0047] In practical applications, the basis of quality monitoring is to observe and analyze the key data displayed on the multi-dimensional visualization interface in real time. The visualization interface provides rich data information, such as satellite orbits, signal strengths, reflection parameters, time series changes, etc., which can help users understand the running status of the constellation comprehensively. For example, if the reflection signal of a satellite suddenly weakens or disappears on the interface, it may indicate that the receiver of the satellite has failed or that the signal transmission has an abnormality. Through this real-time monitoring, users can quickly locate the source of the problem. Secondly, when performing quality monitoring, some key indicators and thresholds are usually set for automatic detection and alarm. When some key indicators exceed the pre-set safety range, the system can automatically generate an alarm and highlight the problem area on the visualization interface. For example, if the signal delay is abnormally increased in some time period, the system can mark the abnormal area with color and prompt the user to further check the relevant data. This automated monitoring mechanism can significantly improve the efficiency of problem discovery and reduce the need for manual intervention.

[0048] The generation of quality monitoring results also includes the analysis of historical data. By retrospectively analyzing past monitoring data, patterns and trends in the operation of the constellation can be summarized, thereby providing a basis for future fault prevention and performance optimization. For example, if the analysis result shows that some satellites frequently experience signal interruption in a certain period of time, it may indicate that there is persistent external interference in that area or that there is a design flaw in that batch of satellite equipment. Through such historical data analysis, users can take appropriate improvement measures, such as adjusting satellite configuration, optimizing receiver design, or enhancing anti-interference capability.

[0049] Finally, the quality monitoring results are usually generated in the form of reports, providing detailed analysis and recommendations. These reports can include abnormal events in real-time monitoring, historical trend analysis, potential risk assessment, and improvement suggestions. Through these results, users can comprehensively understand the running status of the GNSS-R constellation, take timely measures for maintenance and optimization, and ensure the long-term stable operation of the constellation.

[0050] In summary, through the multi-dimensional visualization interface of the quality monitoring method, not only can the state of the GNSS-R constellation be evaluated in real time and accurately, but also comprehensive monitoring results can be generated to provide valuable decision support for users. This method greatly improves the efficiency and reliability of quality monitoring, enabling users to have a good grasp in the complex constellation management, and ensuring the efficient and stable operation of the system.

[0051] The quality monitoring scheme of the GNSS-R constellation provided by the embodiments of the present application can be applied to a monitoring system. The monitoring system obtains original data of the GNSS-R constellation; uniformly preprocesses the original data to obtain constellation multi-dimensional information and a uniformly named file name; performs time slicing processing on the constellation multi-dimensional information to obtain constellation segment multi-dimensional information; performs three-dimensional data rendering and two-dimensional visualization processing on the constellation segment multi-dimensional information to obtain a multi-dimensional visualization interface; and performs quality monitoring on the GNSS-R constellation according to the multi-dimensional visualization interface to obtain quality monitoring results.

[0052] The embodiments of the present application can finely analyze the constellation state of different time periods by performing time slicing processing on the uniformly preprocessed constellation multi-dimensional information, thereby improving the accuracy of quality monitoring. Time slicing processing enables the monitoring system to adapt to larger-scale GNSS-R constellations with more reflection channels, and can more carefully capture potential faults or quality problems, avoiding omissions caused by system crashes or insufficient analysis due to excessive data volume. Three-dimensional data rendering and two-dimensional visualization processing on the constellation segment multi-dimensional information enable the data to be presented not only in the form of text or table, but also through two-dimensional and three-dimensional visualization interfaces. Such visualization processing improves the intuitiveness of the data, making it easier for users to quickly understand and analyze the monitoring data and make more timely decisions. Multiple dimensions of data, such as time dimension, space dimension, and various physical parameter dimensions, can be monitored simultaneously. Comprehensive monitoring of multiple dimensions enables a more comprehensive understanding of the overall running status of the constellation, which helps to quickly identify the specific fault source and impact range. Through the automatic unified preprocessing, time slicing processing, and comprehensive application of multi-dimensional visualization, the steps of manual intervention are reduced, making the quality monitoring process more convenient. Users can quickly obtain monitoring results through the intuitive multi-dimensional visualization interface, simplifying the processing and analysis process of a large amount of complex data.

[0053] In summary, compared with the background art, the embodiment of the application realizes rapid, accurate, convenient and intuitive quality monitoring of the GNSS-R large-scale networking constellation compatible with multiple GNSS systems through technical means such as data unified preprocessing, time slicing, multi-dimensional information rendering and multi-dimensional visualization interface, greatly improves the efficiency, accuracy and convenience of monitoring, and provides strong technical support for future satellite networking constellation management.

[0054] In an exemplary embodiment of the application, one implementation of the unified preprocessing of the original data to obtain the multi-dimensional information of the networking constellation and the file name of the unified naming method is to obtain independent multi-dimensional information files for different GNSS-R networking satellites through unified preprocessing of the original data; wherein the multi-dimensional information file contains the following networking constellation multi-dimensional information: spatial position information of the networking constellation satellite, spatial position information of the GNSS-R mirror reflection point, spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, PRN code information of the GNSS satellite, in-orbit observation waveform information of the GNSS-R, and auxiliary information of the mirror reflection point. The file name of the unified naming method of the multi-dimensional information file contains the following information: networking satellite number, observation start time and end time of the networking satellite, and reflection channel number of the GNSS-R receiver.

[0055] The main purpose of the unified preprocessing of the original data is to convert the original GNSS-R data into multi-dimensional information files that are easy to analyze and process. Each independent multi-dimensional information file is specially designed for different networking satellites and contains a series of key information for subsequent data processing, analysis and quality monitoring. In the implementation process, the data from multiple satellites is first preliminarily sorted and cleaned. The original data may include various signal strengths, noise levels, time stamps and other information, and the quality and integrity of these data directly affect the accuracy of subsequent processing. Therefore, the first step of unified preprocessing is usually data cleaning, removing or correcting abnormal values caused by noise or equipment errors. For missing data, interpolation or other methods can be used to complete it, ensuring the continuity and consistency of the data. Next, the core part of the unified preprocessing is to generate independent multi-dimensional information files for different networking satellites.

[0056] These files contain the following several key networking constellation multi-dimensional information:

[0057] 1. Spatial position information of the networking constellation satellite: This includes the satellite's orbital parameters and real-time position data, usually represented in three-dimensional coordinates. Orbital parameters may include semi-major axis, eccentricity, inclination, etc., used to accurately calculate the satellite's position at different time points. These information is crucial for subsequent signal path analysis and mirror reflection point positioning.

[0058] 2. Spatial position information of GNSS-R specular reflection points: Specular reflection points are the intersection points of the Earth's surface and the GNSS signal path, which are the core areas for remote sensing detection. By calculating the position of the reflection point, the physical parameters of the Earth's surface, such as soil moisture and sea surface height, can be inferred. These position information is usually stored in the form of three-dimensional coordinates.

[0059] 3. Spatial position information of GNSS satellites corresponding to GNSS-R specular reflection points: In order to accurately analyze the reflected signal, it is necessary to know the position of the signal source, i.e. the GNSS satellite. Through these information, the path of the signal in the transmission process can be calculated, including the reflection angle, propagation delay and other parameters, which is crucial for accurate decoding of the signal and inversion of physical parameters.

[0060] 4. Pseudorandom Noise (PRN) encoding information of GNSS satellites: PRN encoding is the unique identifier of each GNSS satellite's transmitted signal, used to distinguish the signals of different satellites. In data processing, PRN encoding is used to match the received signal with the corresponding satellite to ensure the correctness and integrity of the data.

[0061] 5. GNSS-R in-orbit observation waveform information: This is the core data of GNSS-R technology, containing the signal waveform after reflection on the Earth's surface. By analyzing these waveforms, the physical characteristics of the reflection surface, such as reflection intensity and delay, can be obtained, which are the basis for subsequent inversion algorithms.

[0062] 6. Auxiliary information of specular reflection points: Auxiliary information may include environmental features around the reflection point, such as terrain type, elevation, vegetation coverage, etc. These information helps to further refine the inversion and analysis of the Earth's physical parameters, and improves the accuracy of remote sensing detection.

[0063] The embodiment of the present application generates independent multi-dimensional information files for different networking satellites through unified preprocessing of the original data, which not only makes the management and processing of data more systematic, but also provides clear input data source for subsequent analysis. This structured multi-dimensional information file enables independent analysis of data from different satellites, facilitating distributed monitoring and diagnosis of the status of the entire networking constellation, effectively improving the maintainability and reliability of the system. The multi-dimensional file containing multiple key information provides rich and accurate input for subsequent inversion algorithms and data visualization, improving the accuracy and efficiency of the entire quality monitoring system.

[0064] In an exemplary embodiment of the present application, one implementation of time-slicing the networking constellation multi-dimensional information to obtain networking constellation segment multi-dimensional information is as follows: obtaining specified quality monitoring time range information; dividing the quality monitoring time range information into multiple time periods; and reading out the networking constellation segment multi-dimensional information from the multi-dimensional information files corresponding to each time period, respectively.

[0065] The time-slicing of the networking constellation multi-dimensional information aims to divide the continuously changing multi-dimensional information into multiple time segments for more fine-grained analysis and monitoring. Through time-slicing, high-precision quality monitoring of the state of the networking constellation can be performed within a specified time range, thereby improving the efficiency and accuracy of monitoring. In actual applications, a specified quality monitoring time range information needs to be obtained. This time range is set by the user or the system according to the monitoring requirements, and may involve a time span of several minutes, several hours, or even several days. The setting of this time range is very critical, as it determines the starting point and endpoint of the time-slicing process, and also lays the foundation for subsequent time period division. After the time range is determined, the next step is to divide the time range into multiple time periods. The division of time periods can be flexibly adjusted according to different monitoring requirements. For example, in the case of fine monitoring, the time periods can be divided into shorter ones, each of which may only have a few minutes; while in a more relaxed monitoring scenario, the time periods can be longer, even up to several hours. The division of each time period not only affects the amount of data processing, but also affects the accuracy and real-time performance of subsequent analysis. Therefore, the division of time periods needs to be balanced according to the specific application scenario. After the time period division is completed, the corresponding networking constellation segment multi-dimensional information is read out from the multi-dimensional information files corresponding to each time period, respectively. These multi-dimensional information files are usually generated through unified preprocessing and contain all the key data related to each time period, including satellite position, GNSS-R specular reflection point position, PRN code, observation waveform information, etc. By reading these information, a complete networking constellation segment view can be constructed within each time period, ensuring real-time tracking and monitoring of the constellation state.

[0066] The embodiments of the present application can provide a more detailed monitoring perspective in the time dimension. By slicing the data into smaller time periods, subtle changes that may occur in a short period of time can be captured. This is of great significance for identifying abnormal situations in a short period of time, such as signal loss, data fluctuations, etc. In addition, the time slicing process can also improve the data processing speed and efficiency of the entire system by processing the data of multiple time periods in parallel. Through this time slicing process, high time resolution monitoring reports can be generated. These reports can show the running status of the networking constellation in each time period in detail, which helps engineers quickly locate and solve potential quality problems. At the same time, it can also provide support for subsequent historical data analysis, helping engineers review and analyze the long-term trends and changes of the constellation operation, and provide data support for future constellation design and optimization.

[0067] In an exemplary embodiment of the present application, one implementation of reading out the networking constellation segment multi-dimensional information from the multi-dimensional information file corresponding to each time period is as follows: selecting the multi-dimensional information file corresponding to each time period according to the observation start time and end time in the file name of each multi-dimensional information file; reading the sampling time matrix of the multi-dimensional information file corresponding to each time period; calculating the matrix subscript of the sampling time matrix within each time period; reading the networking constellation segment multi-dimensional information according to the matrix subscript; wherein the networking constellation segment multi-dimensional information includes: spatial position information of the networking constellation satellite, spatial position information of the GNSS-R specular reflection point, spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, and PRN code information of the GNSS satellite.

[0068] In practical applications, the time information in the file name of the multi-dimensional information file is used to select the file corresponding to each time period. During data acquisition and storage, the file name usually carries key information such as time stamp or time interval. These time information is an important basis for file classification and management. When performing time slicing processing, the multi-dimensional information file with time information meeting the conditions in the file name is matched and selected according to the predefined time period. This step ensures the accuracy and pertinence of subsequent processing, because only the files with matching time range are selected and further processed. Next, the sampling time matrix of the multi-dimensional information file corresponding to each time period is read. The sampling time matrix refers to the time sequence during data acquisition, which is usually organized in matrix form and marks the specific time points of data acquisition. By reading these time matrices, the time axis of data acquisition can be accurately determined, and accurate time reference is provided for subsequent data extraction. After obtaining the sampling time matrix, the matrix index in each time period needs to be calculated. The matrix index refers to the position or index of data in a specific time period in the sampling time matrix. By calculating these indexes, it can be determined which data points belong to the specified time period. This step is very important, because it ensures that only the data matching the time period is extracted and analyzed, avoiding the interference of irrelevant data. Finally, according to the calculated matrix index, the corresponding networking constellation segment multi-dimensional information is read. These information includes the spatial position information of the networking constellation satellite, the spatial position information of the GNSS-R specular reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, and the PRN code information of the GNSS satellite. The spatial position information of the networking constellation satellite is used to determine the trajectory and position of the satellite in a specific time period; the spatial position information of the GNSS-R specular reflection point helps to determine the position of the ground reflection area; the spatial position information of the GNSS satellite corresponding to the specular reflection point is used to calculate the signal path and reflection angle; the PRN code information is used to identify and distinguish the signals of different satellites. These information together constitute the view of the networking constellation segment in a specific time period, providing detailed data support for further quality monitoring.

[0069] The embodiment of the present application can perform more detailed and accurate quality monitoring in the time dimension through accurate time period matching and data extraction. By using the sampling time matrix and matrix index, relevant data can be effectively filtered and extracted, improving the efficiency and accuracy of data processing. In addition, by reading and analyzing the networking constellation segment multi-dimensional information, the running state of the constellation can be tracked and monitored in real time, which helps to quickly identify and locate potential abnormal and fault problems.

[0070] In an exemplary embodiment of the present application, one implementation of the multi-dimensional visualization interface obtained by three-dimensional data rendering of the networking constellation segment multi-dimensional information is a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting ground object types as the background, which is obtained by three-dimensional data rendering according to the spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R specular reflection points, and the spatial position information of the GNSS satellites corresponding to the GNSS-R specular reflection points. Three-dimensional data rendering of the networking constellation segment multi-dimensional information is a key step to convert complex multi-dimensional information into an intuitive and visual interface. By three-dimensional data rendering of different types of spatial position information, a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting ground object types as the background is constructed, providing an intuitive quality monitoring tool for users.

[0071] In practical applications, various types of spatial position information of the networking constellation are processed, including the spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R specular reflection points, and the spatial position information of the GNSS satellites corresponding to the GNSS-R specular reflection points. The spatial position information of the networking constellation satellites reflects the orbit and position of the satellites within a specific time period, which is the key data of the entire rendering process, because the position of the satellites directly affects the position of the reflection points and the path of signal transmission. The spatial position information of the GNSS-R specular reflection points identifies the position of the surface reflected signals, and these reflection points are important reference points for surface physical parameter detection. The spatial position information of the GNSS satellites corresponding to the GNSS-R specular reflection points is also crucial, as it provides the position data of the signal source and is the basis for reflection path calculation.

[0072] After obtaining these spatial position information, three-dimensional data rendering is performed according to these information. Specifically, the positions of the networking constellation satellites, GNSS-R specular reflection points, and corresponding GNSS satellites are geometrically configured in three-dimensional space with a three-dimensional earth reflecting ground object types as the background. This rendering method enables users to intuitively observe the relative positions and geometric relationships between satellites, reflection points, and signal sources in the background of a three-dimensional earth reflecting ground object types. For example, users can view the position of a certain satellite on its orbit within a specific time period and its relative relationship with surface reflection points through the three-dimensional interface. This three-dimensional spatial geometric configuration not only demonstrates the spatial relationship between satellites and the ground, but also reflects the signal propagation path and reflection process.

[0073] The embodiment of the present application can provide a user with an intuitive and three-dimensional quality monitoring interface through three-dimensional data rendering. Compared with traditional two-dimensional graphics, the three-dimensional rendering interface provides more information dimensions, and the user can observe and analyze the spatial configuration of the constellation from different angles, thereby improving the accuracy and efficiency of monitoring. Secondly, the rendering mode with a three-dimensional earth reflecting the types of ground objects also enhances the operability and understandability of the system, and the user can freely explore and analyze the running state of the constellation through rotation, scaling and other operations. Three-dimensional data rendering can also improve the real-time monitoring capability of the system to a certain extent. Through the display of the three-dimensional space, the user can quickly identify and locate potential abnormal and fault problems, such as satellite position deviation, abnormal reflection points, etc. This not only provides strong support for quality monitoring, but also provides a basis for engineers to quickly respond and solve problems.

[0074] In an exemplary embodiment of the present application, one implementation of obtaining a multi-dimensional visualization interface by performing two-dimensional visualization processing on the multi-dimensional information of the constellation segment is as follows: obtaining playback synchronization time information of three-dimensional data rendering, and obtaining the running state of the constellation and the trajectory of the GNSS-R specular reflection point from the three-dimensional spatial geometric configuration interface; determining the reflection channel number of the GNSS-R receiver of the constellation satellite to be monitored according to the running state and the trajectory; reading out the GNSS-R in-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file; and performing two-dimensional visualization processing on the read-out GNSS-R in-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

[0075] In actual application, the playback synchronization time information of three-dimensional data rendering is obtained. The playback synchronization time information refers to the time point information recorded by the system during three-dimensional data rendering. These time points correspond to the spatial positions of the constellation satellite and its reflection point at a specific time, and are the key basis for subsequent two-dimensional visualization processing. Through these synchronization time information, the trajectory of the constellation satellite and the GNSS-R specular reflection point can be accurately located, thereby ensuring the consistency of the time axis of the two-dimensional visualization processing and the three-dimensional rendering. Next, the running state of the constellation and the trajectory of the GNSS-R specular reflection point are obtained from the three-dimensional spatial geometric configuration interface. The purpose of this step is to provide necessary geometric and physical background information for two-dimensional visualization processing. The running state reflects the motion state of the constellation satellite on the orbit, including speed, trajectory and attitude parameters, and the trajectory of the GNSS-R specular reflection point describes the movement path of the reflected signal on the ground. These information lay a foundation for subsequent reflection channel number determination and waveform information extraction.

[0076] According to the acquired operating conditions and trajectories, the number of reflection channels of the GNSS-R receiver of the constellation satellite to be monitored can be determined. The GNSS-R receiver receives reflected signals from the ground through different reflection channels, and the quality of these signals directly affects the accuracy of remote sensing detection. By determining the number of reflection channels, the signal quality of a specific channel can be focused on, and further analysis of its performance on a two-dimensional visualization interface can be performed.

[0077] After determining the number of reflection channels, the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the number of reflection channels is read from the multi-dimensional information file. The GNSS-R on-orbit observation waveform information is important data for recording and analyzing the received reflected signals, and it contains multi-dimensional information such as amplitude, frequency, phase, etc. of the reflected signals. In this step, the system extracts these waveform information, providing basic data for two-dimensional visualization processing.

[0078] Finally, the read GNSS-R on-orbit observation waveform information is processed for two-dimensional visualization, thereby obtaining an intuitive two-dimensional visualization projection interface. On this interface, the user can clearly see the waveform characteristics of the reflected signals, and through observation of these waveforms, the quality and stability of the signals can be judged. For example, the user can identify possible signal attenuation problems by observing the amplitude changes of the waveforms, or detect possible interference sources by observing the frequency offset.

[0079] Through two-dimensional visualization processing, the user can quickly and intuitively obtain key waveform information without the need to deeply understand complex three-dimensional data. This interface design enhances the user-friendliness of the system, making quality monitoring more concise and efficient. Through the application of playback synchronization time information, the two-dimensional visualization interface maintains good time consistency with the three-dimensional rendering interface, thereby ensuring the continuity and accuracy of data analysis. In addition, by focusing on a specific number of reflection channels, accurate analysis of specific signal sources can be performed, thereby improving the targeting and effectiveness of quality monitoring.

[0080] In an exemplary embodiment of the present application, one implementation of two-dimensional visualization of the multi-dimensional information of the constellation segment to obtain a multi-dimensional visualization interface is to read the auxiliary information of the specular reflection point corresponding to the playback synchronization time information and the number of reflection channels from the multi-dimensional information file; and project the read auxiliary information of the specular reflection point to a two-dimensional visualization projection interface.

[0081] In practical applications, it is necessary to obtain the auxiliary information of the mirror reflection point corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file. The auxiliary information of the mirror reflection point usually includes additional data about the reflection point, such as the physical characteristics of the reflection point, environmental conditions, and other factors that may affect the signal reflection. The playback synchronization time information is the synchronization data at a specific time point, which helps ensure that the information extracted from the multi-dimensional information file is consistent with the actual time. These data are crucial for understanding the state and behavior of the reflection point, as they provide background information on the quality of the reflected signal.

[0082] After obtaining these auxiliary information, they are projected to a two-dimensional visualization projection interface. The purpose of two-dimensional visualization processing is to convert complex multi-dimensional data into easy-to-understand plane graphics, so that users can view and analyze data in a plane view. This process involves mapping the auxiliary information of the reflection point to a two-dimensional space, usually including plotting the location of the reflection point, displaying its related characteristic data, and providing comparisons with other key data.

[0083] For example, the two-dimensional visualization interface can display the spatial distribution of the reflection point, the change of signal intensity, and the reflection pattern related to time. These graphics may include marking the location of the reflection point, plotting the intensity map of the reflection signal, or showing the changes of the reflection point at different time points. Through these visualization graphics, users can quickly identify the state changes of the reflection point, detect signal abnormalities, and evaluate the performance of the reflection point.

[0084] The embodiments of the present application project the auxiliary information of the mirror reflection point to a two-dimensional interface, which converts complex reflection data into simple and easy-to-understand graphics. This way enhances the visualization of data, making it easier for users to analyze the state and performance of the reflection point. Through the two-dimensional visualization interface, users can quickly identify and locate abnormalities or problems in the reflection point. Such an interface provides an efficient data analysis tool, allowing users to monitor the running state of the system in real time, quickly find and solve potential problems. The two-dimensional visualization interface provides a rich display of reflection point data and characteristics, helping users to make detailed analysis and decision. Users can make optimization and adjustment based on these graphical data to improve the performance and stability of the GNSS-R system. Converting complex multi-dimensional data into two-dimensional graphics reduces the complexity of data processing. Users do not need to analyze each complex data dimension in depth, but can effectively monitor and analyze through intuitive two-dimensional graphics.

[0085] In an exemplary embodiment of the present application, one implementation of obtaining the quality monitoring result according to the multi-dimensional visualization interface for quality monitoring of GNSS-R constellation is to determine the number and number of constellation satellites currently in orbit and working normally according to the number and number of satellites with continuously changing spatial positions in the stereoscopic spatial geometric configuration interface.

[0086] In practical applications, the stereoscopic spatial geometric configuration interface is a three-dimensional visualization interface that displays the spatial positions and their continuous changes of the constellation satellites. This interface provides a comprehensive perspective, enabling real-time display of the position, trajectory, and mutual relationship of each satellite. In this interface, the spatial position of the satellite is dynamically changing, which means that each satellite has its specific spatial position data at every moment on the trajectory. By analyzing these dynamic data, the real-time state and motion of each satellite can be obtained. The number and number of satellites need to be extracted from the stereoscopic spatial geometric configuration interface. This data includes information about all satellites in orbit, usually represented by the satellite's identifier and position data. The satellite number is a unique identifier used to distinguish different satellites, while the number of satellites reflects the total number of satellites in orbit in the current constellation. Next, these data are analyzed to determine the number and number of satellites currently in orbit and working normally. This step involves checking whether the spatial position of each satellite conforms to the expected trajectory of operation to determine whether the satellite is working normally on the predetermined orbit. This can be verified by comparing real-time data with the preset normal working state. If the position and number of the satellite are consistent with the expected ones, it means that the satellite is working normally in orbit; otherwise, there may be a fault or other problems.

[0087] For example, some satellites may not work normally due to technical failure, orbital deviation, or other reasons. By analyzing the data in the stereoscopic spatial geometric configuration interface, these abnormal situations can be discovered in time, and further measures can be taken to repair or adjust. This real-time monitoring can help operators maintain the stability and reliability of the system.

[0088] The embodiments of the present application can analyze satellite data in a three-dimensional spatial geometric configuration interface, allowing real-time understanding of the operational status of satellites in the constellation. This real-time nature ensures timely detection and handling of problems in the system, thereby improving the stability of the overall system. By monitoring the position changes of the satellites, the working status of each satellite can be accurately determined. This precise data analysis helps ensure the normal operation of all on-orbit satellites, avoiding system performance degradation due to satellite failures. Real-time analysis of satellite data can quickly identify potential problems such as satellite orbit deviation or equipment failure. This allows timely remedial measures to be taken to ensure the reliability and continuity of system operation. By monitoring the normal working status of on-orbit satellites, operators can obtain valuable data on system performance. These data can be used to optimize system configuration, improve operational processes, and thus improve the efficiency and performance of the overall system.

[0089] In an exemplary embodiment of the present application, one implementation of obtaining quality monitoring results according to multi-dimensional visualization interface for GNSS-R constellation quality monitoring is to determine the number of currently visible reflected GNSS satellites for the constellation satellites according to the number of updated GNSS-R specular reflection point trajectories in the three-dimensional spatial geometric configuration interface.

[0090] Specular reflection points are key observation objects in GNSS-R technology, which are reflection points generated after satellite signals are reflected on the Earth's surface. By tracking the trajectories of these reflection points, information about the reflection signal propagation path, ground physical parameters, and other information can be obtained. In the multi-dimensional visualization interface, the trajectories of the specular reflection points are dynamically updated over time, reflecting the current signal reflection situation in the system. In the three-dimensional interface, when the number of trajectories is updated, it reflects how many GNSS satellite signals are reflected on the ground and received by the GNSS-R receiver. By counting the number of updated trajectories, the number of reflected satellites of the GNSS signals received by each micro-nano satellite in the constellation can be determined. Changes in this number can show the coverage range of the satellite observation network and the current signal reception quality. Visible satellites are those whose signals can be reflected on the Earth's surface and effectively captured by the GNSS-R receiver within the current time period. By analyzing the number of GNSS-R specular reflection point trajectories, users can determine the visibility of these reflected satellites. This is of great significance for monitoring the health and working status of the satellite network.

[0091] The embodiment of the present application can quickly determine the number of currently visible reflected GNSS satellites through real-time updated specular reflection point trajectories. This real-time monitoring capability ensures timely understanding of satellite operating status, improves data accuracy, and provides a reliable basis for system maintenance. By analyzing the number of visible satellites, users can evaluate the signal coverage of the current GNSS-R networking constellation. If the number of visible satellites decreases, it may indicate that some satellites or reflection channels have problems, which provides a direct reference for monitoring and optimizing the signal coverage capability of the networking constellation. The dynamic display of reflection point trajectories in the three-dimensional spatial geometric configuration interface not only shows the normal working state of the satellite network, but also identifies abnormal working states. For example, when the number of trajectories abnormally decreases, the user can be prompted to check whether some satellites or receiving equipment have failed. This timely feedback mechanism helps improve the reliability and stability of the system. The visualization interface improves the intuitiveness of data analysis through clear and intuitive three-dimensional geometric configuration display. Users can directly determine the satellite status and signal reception from the interface without processing complex text data. This not only improves work efficiency, but also reduces the professional threshold requirements for technical personnel. By monitoring and analyzing the number of visible satellites, users can effectively master the running state of the satellites and the quality of signal transmission, and make corresponding adjustment and optimization decisions. For example, in areas with insufficient signal coverage, the performance of the network can be optimized by increasing satellites or adjusting satellite orbits to ensure normal operation of the system.

[0092] In an exemplary embodiment of the present application, one implementation of obtaining quality monitoring results according to multi-dimensional visualization interface for GNSS-R networking constellation quality monitoring is to determine the GNSS system type and satellite PRN number corresponding to the reflection event according to the color depth of the GNSS-R specular reflection point in the three-dimensional spatial geometric configuration interface and the navigation satellite number. Among them, the GPS satellite PRN number is equal to PRN+0, the BDS satellite PRN number is equal to PRN+100, and the GAL satellite PRN number is equal to PRN+200. The color depth of the GNSS-R specular reflection point is usually used to represent the intensity or reflection characteristics of the reflected signal. For example, the color depth can reflect the reflection intensity of the signal, and the deeper the color, the stronger the reflected signal. Color coding provides an intuitive way for quality monitoring, allowing users to quickly identify and evaluate the performance status of each reflection point. Each reflection point has a unique number, which is used to identify a specific reflection point and its changes over time. The trajectory number helps users track and analyze the behavior patterns of specific reflection points and match them with other data in the GNSS system.

[0093] According to the color depth of the reflection point and the navigation satellite number, the specific GNSS system type and satellite PRN number corresponding to each reflection event can be determined. This process typically includes the following steps:

[0094] Color depth analysis: First, analyze the color depth of the reflection point to assess the strength and quality of the reflected signal. Reflection points with stronger signal strength may indicate more reliable data sources.

[0095] Navigation satellite number matching: Based on the navigation satellite number, the motion trajectory of the reflection point can be matched with the specific GNSS system type and satellite PRN number. This process involves comparing the navigation satellite number with the GNSS system information stored in the preset database or system to confirm the specific system type and satellite PRN number.

[0096] Data integration: Integrate the color depth and navigation satellite number information to determine the GNSS system type and satellite PRN number corresponding to the reflection event. This integration process can provide comprehensive information about the reflection event, including the signal source and related GNSS system.

[0097] The embodiments of the present invention can accurately identify the GNSS system type and satellite PRN number corresponding to each reflection event by analyzing the color depth and navigation satellite number of GNSS-R specular reflection points. This accurate identification helps ensure the accuracy and effectiveness of the data. Real-time tracking of the color and trajectory number of the reflection point can provide immediate information about the GNSS system type and satellite status. This real-time nature allows monitoring personnel to quickly identify and address any abnormalities or problems in the system, thereby improving the stability and reliability of the system. The color depth reflects the strength and quality of the signal, which can help evaluate the data quality of the reflection event. Through this evaluation, the system can judge the reliability of the data, ensuring the accuracy of subsequent analysis and application. Through detailed analysis of the reflection points, faults in the system can be quickly identified and located. For example, if the signal strength of a certain satellite's reflection point is abnormal or the trajectory does not match, it may indicate a problem with that satellite. The system can perform fault diagnosis and repair based on this information.

[0098] In an exemplary embodiment of the present invention, one implementation of the quality monitoring result obtained by monitoring the quality of the GNSS-R constellation based on the multi-dimensional visualization interface is that, according to the GNSS-R in-orbit observation waveform information in the two-dimensional visualization projection interface, it is determined whether the reflection channel waveform generation function is stopped working. The two-dimensional visualization projection interface is an interface for displaying GNSS-R in-orbit observation waveform information. This interface converts complex three-dimensional data into two-dimensional graphics, visually displaying the waveform characteristics and dynamic changes of the reflected signal. In this way, users can more clearly observe the strength, frequency and other key parameters of the signal.

[0099] GNSS-R in-orbit observation waveform information refers to the real-time observation data of reflected signals in the satellite orbit. These waveform information contains the signal intensity variation, frequency characteristics and the dynamic situation of the reflection point. Through the analysis of these waveforms, detailed information about the working status of the reflection orbit can be obtained. In order to determine whether the reflection orbit stops working, the following steps can be taken:

[0100] Data collection: Obtain GNSS-R in-orbit observation waveform information from the two-dimensional visual projection interface. These information is usually displayed in graphical waveform data, including signal intensity and frequency variation.

[0101] Waveform analysis: Analyze the features in the waveform information. For example, if the reflection orbit is working normally, the waveform should show stable periodic changes and certain signal intensity. If the waveform shows obvious interruption, missing or irregular changes, it may indicate that the reflection orbit has problems.

[0102] Orbit state judgment: According to the analysis result of waveform information, determine the state of reflection channel waveform generation function. If the waveform information shows that the signal intensity is continuously zero or significantly decreased, and there is no periodic change, it can be inferred that the reflection channel waveform generation function may have stopped working. Further verification is needed to determine whether there is a hardware failure or data transmission problem.

[0103] The embodiment of the invention can monitor the working state of the reflection channel waveform generation function in real time by analyzing the waveform information of the two-dimensional visual projection interface, and find any abnormal or stop working condition in time. This timeliness helps to quickly locate the problem, reduce the downtime of the system and potential loss. The analysis of waveform information can provide detailed state data of the reflection channel waveform generation function, including the change of signal intensity and the abnormality of waveform. Through these information, the fault point of the reflection channel waveform generation function can be located accurately, helping technicians to repair and maintain quickly. Improve the overall system reliability of GNSS-R networking constellation. By continuously monitoring the state of the reflection channel waveform generation function, the system can prevent potential failures and ensure the stability of the satellite network and the accuracy of the data.

[0104] In an exemplary embodiment of the present application, one implementation of obtaining quality monitoring results according to multi-dimensional visualization interface for quality monitoring of GNSS-R constellation is as follows: according to the in-orbit observation waveform information of GNSS-R in the two-dimensional visualization projection interface, the auxiliary information of the specular reflection point, and the waveform peak signal-to-noise ratio corresponding to the specular reflection point, the in-orbit observation quality of the two-dimensional observation waveform of the reflection channel is determined. The waveform peak signal-to-noise ratio (SNR) is an important indicator for measuring signal quality. It represents the ratio of signal strength to noise, and a higher signal-to-noise ratio usually means a clearer signal and higher observation quality. In the two-dimensional visualization projection interface, the waveform peak signal-to-noise ratio data can help determine the quality of the signal. Determining the in-orbit observation quality of the two-dimensional observation waveform of the reflection channel can include the following steps:

[0105] Data collection: Obtain the in-orbit observation waveform information of GNSS-R, the auxiliary information of the specular reflection point, and the waveform peak signal-to-noise ratio from the two-dimensional visualization projection interface. In this way, comprehensive data about the reflection channel can be obtained.

[0106] Data analysis: Analyze the signal strength and frequency changes in the waveform information, and evaluate the quality of the reflection channel in combination with the auxiliary information of the specular reflection point. At the same time, calculate the peak signal-to-noise ratio of the waveform to judge the clarity and reliability of the signal.

[0107] Quality evaluation: According to the analysis results, the observation quality of the reflection channel is determined. If the signal-to-noise ratio is high, and the waveform characteristics are stable and consistent, it can be judged that the quality of the reflection channel is good. On the contrary, if the signal-to-noise ratio is low or the waveform characteristics are unstable, it indicates that there may be a problem that needs to be further investigated and repaired.

[0108] Through detailed analysis of the waveform information in the two-dimensional visualization projection interface, the embodiment of the present application can obtain high-quality observation data about the reflection channel. High signal-to-noise ratio and stable waveform characteristics can provide accurate observation results, thereby improving the data accuracy of the system. By determining the observation quality of each reflection channel, potential quality problems can be identified and repaired, thereby optimizing the performance of the entire GNSS-R constellation. This optimization can improve the overall efficiency and reliability of the system.

[0109] Based on the above related description of the embodiment of the method for monitoring the quality of a GNSS-R networking constellation, a multi-dimensional information fusion GNSS-R networking constellation visual quality monitoring scheme is introduced below. The multi-dimensional information fusion GNSS-R networking constellation visual quality monitoring scheme uses a GNSS-R networking constellation containing 10 satellites as an example, and these satellites are compatible with the BDS, GPS, GAL, and GLO four navigation systems. It is assumed that the on-orbit operation quality of the 10 GNSS-R networking constellations in one hour starting at 00:00:00 on February 1, 2024 is monitored and evaluated.

[0110] Referring to Figure 2 , a step flowchart of the multi-dimensional information fusion GNSS-R networking constellation visual quality monitoring scheme according to an embodiment of the application is shown.

[0111] (1) Data uniform preprocessing

[0112] All the original code stream data of the received GNSS-R networking constellation are processed into uniform preprocessing files with consistent formats, and the uniform preprocessing files should contain multi-dimensional information of the networking constellation, specifically including spatial position information of the networking constellation satellites, spatial position information of the GNSS-R mirror reflection points, spatial position information of the GNSS satellites corresponding to the GNSS-R mirror reflection points, PRN coding information of the GNSS satellites, GNSS-R on-orbit observation waveform information, auxiliary information of the mirror reflection points, and other data products with uniform data formats. Among them, all data variables are time-aligned and arranged in ascending order of time. In order to distinguish the types of different GNSS systems, the PRN number of the GPS satellite is uniformly processed as PRN+0, the PRN number of the BDS satellite is uniformly processed as PRN+100, and the PRN number of the GAL satellite is uniformly processed as PRN+200. In order to distinguish different satellites of the networking constellation, independent uniform preprocessing files are generated for different networking satellites, and the uniform preprocessing files of different networking satellites are identified by numbers such as 01, 02, 03,..., 10. In order to distinguish different observation periods of the same satellite, the file name of the uniform preprocessing file should also identify the observation start time and end time of the satellite, for example, the file name of the No. 1 satellite is designed as GNSSR_01_20240201_000000_20240201_010000_C3.NC, where GNSSR represents the type of the networking constellation, 01 represents the No. 1 networking satellite, 20240201_000000 represents the observation start time of February 1, 2024 00:00:00, 20240201_010000 represents the observation end time of February 1, 2024 01:00:00, C3 represents the reflection channel 3, and NC represents the file type netcdf format.

[0113] (2) Time slicing processing

[0114] The time length of 1 hour from 00:00:00 on February 1, 2024 to 01:00:00 on February 1, 2024 is divided into 6 small time periods at intervals of 10 minutes, and the initial time period is from 00:00:00 on February 1, 2024 to 00:10:00 on February 1, 2024. The observation time range in the file name of the unified preprocessing file generated in step (1) is read one by one, the unified preprocessing file with the time range in the quality monitoring period is selected, and then the sampling time matrix in the file is read. The matrix index of the time period in the quality monitoring time range is calculated, the spatial position of the constellation satellite, the spatial position of the GNSS-R specular reflection point, the spatial position of the GNSS satellite corresponding to the GNSS-R specular reflection point, and the PRN code variable information of the GNSS satellite are read according to the matrix index. After the information in each small time slice is read, the information is pushed to the three-dimensional visualization system, and then the information in the next time slice is read, until all the time slices are traversed.

[0115] (3) GNSS-R constellation spatial geometric information data rendering and three-dimensional visualization processing

[0116] The GNSS-R constellation information in a complete time slice pushed by step (2) is sequentially rendered according to the time sequence, according to the spatial position of the constellation satellite, the spatial position of the GNSS-R specular reflection point, and the spatial position of the GNSS satellite corresponding to the GNSS-R specular reflection point, to form a three-dimensional spatial geometric configuration with a three-dimensional earth reflecting ground object types as the background. The three-dimensional earth surface reflecting ground object types uses different depths of color to represent different landforms. The spatial position of the GNSS-R specular reflection point is retained on the three-dimensional earth surface reflecting ground object types. The positions of the constellation satellites and the GNSS satellites only retain the position points at the current time. The GNSS-R constellation information data rendering and three-dimensional visualization playback are simultaneously transmitted to the GNSS-R on-orbit observation waveform visualization module and the auxiliary data information visualization module with the playback synchronization time information.

[0117] The above data rendering and three-dimensional visualization operation is realized by using the open source Cesium based on JavaScript 3D map framework. Referring to Figure 3 , a GNSS-R constellation spatial geometric information three-dimensional visualization effect diagram of an embodiment of the application is shown.

[0118] After the constellation data in the initial time period 2024-02-01 00:00:00 to 2024-02-01 00:10:00 is rendered, the data from 2024-02-01 00:10:01 to 2024-02-01 00:20:00 is read and the rendering process is started, the sample data after rendering is put into the playback sequence, and the quality monitoring visual interface is continuously updated according to step (3). The same steps are repeated for the remaining 4 time slices until the playback task of the on-orbit operation of the 10 GNSS-R constellation stars in the specified 1 hour starting from 2024-02-01 00:00:00 is completed.

[0119] (4) Two-dimensional visualization processing of GNSS-R on-orbit observation waveform data information

[0120] While the space geometry information data of the constellation is rendered and three-dimensional visualization is performed for playback, according to the playback synchronization time information transmitted in step (3), in order to judge the on-orbit observation performance of the 01 star reflection channel 1, the on-orbit observation waveform corresponding to the time and channel is selected and read from the unified preprocessing file generated in step (1), and the waveform data is projected for two-dimensional visualization according to the size of the waveform data. Referring to Figure 4 , a two-dimensional visualization effect diagram of GNSS-R waveform data information according to an embodiment of the present application is shown. The playback is synchronized with step (3) of space geometry information, and the playback synchronization time information, the constellation satellite number 01 star to be monitored and the GNSS-R receiver reflection channel number 1 are transmitted to the GNSS-R mirror reflection point auxiliary data information two-dimensional visualization processing module.

[0121] (5) Two-dimensional visualization processing of GNSS-R mirror reflection point auxiliary data information

[0122] While the space geometry information data of the constellation is rendered and three-dimensional visualization is performed for playback, according to the playback synchronization time information transmitted in step (4), the on-orbit observation waveform corresponding to the time and channel is selected and read from the unified preprocessing file generated in step (1), and the waveform data is projected for two-dimensional visualization according to the size of the waveform data. Referring to Figure 5 , a two-dimensional visualization effect diagram of GNSS-R mirror reflection point auxiliary information according to an embodiment of the present application is shown.

[0123] (6) Quality judgment of the constellation

[0124] Comprehensive utilization of multi-dimensional information such as spatial geometric information of network constellation, on-orbit observation waveform data information, and mirror reflection point auxiliary data information, judges the on-orbit working state of the 01 channel of the 01 satellite of the GNSS-R large-scale network constellation:

[0125] 1) Since the three-dimensional earth space positions of the reflected ground object types of the 10 network satellites in step (3) are continuously changed, and the trajectories of the mirror reflection points are constantly updated, it can be judged that the network constellation of the 10 satellites with satellite numbers 01-10 is normal, and the satellite 01 has three reflection point observation trajectories at the current time, so the current 01 satellite has three reflection channels receiving reflected satellites.

[0126] 2) Since the observation waveform of the 01 satellite reflection channel 1 is refreshed over time, it can be judged that the waveform generation function of the 01 satellite reflection channel 1 is normal.

[0127] 3) Since the observation waveform of the 01 satellite reflection channel 1 is a typical "horse's hoof type" distribution, and the GNSS-R mirror reflection point auxiliary data information shows that the position of the mirror reflection point at this time is on the sea surface, the waveform has no obvious interference stripes, and the peak signal-to-noise ratio of the mirror reflection point waveform is 3dB, the GNSS-R reflection waveform of this channel meets the expectation and has good quality.

[0128] It should be noted that for the method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the embodiments of the present application are not limited by the order of the described actions, because according to the embodiments of the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of the present application.

[0129] Referring to Figure 6 , a structure block diagram of a quality monitoring system of a GNSS-R network constellation according to an embodiment of the present application is shown. The quality monitoring system of the GNSS-R network constellation can specifically include the following modules.

[0130] The original data acquisition module 61 is used to acquire the original data of the GNSS-R network constellation;

[0131] The original data unified preprocessing module 62 is used to uniformly preprocess the original data to obtain network constellation multi-dimensional information and uniformly named file names;

[0132] The multi-dimensional information slicing processing module 63 is used to perform time slicing processing on the network constellation multi-dimensional information to obtain network constellation segment multi-dimensional information;

[0133] The multi-dimensional information visualization processing module 64 is used for performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the constellation segment to obtain a multi-dimensional visualization interface.

[0134] The quality monitoring module 65 is used for performing quality monitoring on the GNSS-R constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0135] In an exemplary embodiment of the present application, the original data unified preprocessing module 61 is used for performing unified preprocessing on the original data to obtain independent multi-dimensional information files for different GNSS-R constellation satellites.

[0136] The multi-dimensional information file contains the following multi-dimensional information of the constellation:

[0137] The spatial position information of the constellation satellite, the spatial position information of the GNSS-R specular reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, the PRN code information of the GNSS satellite, the on-orbit observation waveform information of the GNSS-R, and the auxiliary information of the specular reflection point.

[0138] The file name of the unified naming mode of the multi-dimensional information file contains the following information:

[0139] The constellation satellite number, the observation start time and end time of the constellation satellite, and the reflection channel number of the GNSS-R receiver.

[0140] In an exemplary embodiment of the present application, the multi-dimensional information slicing processing module 63 comprises:

[0141] The monitoring time acquisition module is used for acquiring specified quality monitoring time range information.

[0142] The monitoring time division module is used for dividing the quality monitoring time range information into multiple time periods.

[0143] The segment multi-dimensional information reading module is used for reading out the constellation segment multi-dimensional information from the multi-dimensional information file corresponding to each time period.

[0144] In an exemplary embodiment of the present application, the segment multi-dimensional information reading module comprises:

[0145] The multi-dimensional information file selection module is used for selecting the multi-dimensional information file corresponding to each time period according to the observation start time and the end time in the file name of each multi-dimensional information file.

[0146] The sampling time matrix reading module is used for reading the sampling time matrix of the multi-dimensional information file corresponding to each time period.

[0147] a matrix subscript calculation module, configured to calculate a matrix subscript of the sampling time matrix in each time period;

[0148] a networking constellation segment multi-dimensional information reading module, configured to read the networking constellation segment multi-dimensional information according to the matrix subscript;

[0149] The networking constellation segment multi-dimensional information comprises:

[0150] spatial position information of the networking constellation satellite, spatial position information of the GNSS-R specular reflection point, spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, and PRN code information of the GNSS satellite.

[0151] In an exemplary embodiment of the present application, the multi-dimensional information visualization processing module 64 comprises:

[0152] a three-dimensional data rendering module, configured to perform three-dimensional data rendering according to the spatial position information of the networking constellation satellite, the spatial position information of the GNSS-R specular reflection point, and the spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, to obtain a stereoscopic spatial geometric configuration interface with a three-dimensional earth reflecting ground object types as a background.

[0153] In an exemplary embodiment of the present application, the multi-dimensional information visualization processing module 64 further comprises:

[0154] a playback time acquisition module, configured to acquire playback synchronization time information of the three-dimensional data rendering;

[0155] a running status and trajectory acquisition module, configured to acquire a running status of the networking constellation and a trajectory of the GNSS-R specular reflection point from the stereoscopic spatial geometric configuration interface;

[0156] a reflection channel number determination module, configured to determine a reflection channel number of a GNSS-R receiver of a networking constellation satellite to be monitored according to the running status and the trajectory;

[0157] a waveform information reading module, configured to read out, from the multi-dimensional information file, GNSS-R in-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number;

[0158] a two-dimensional visualization processing module, configured to perform two-dimensional visualization processing on the read-out GNSS-R in-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

[0159] In an exemplary embodiment of the present application, the multi-dimensional information visualization processing module 64 further comprises:

[0160] An auxiliary information reading module is configured to read auxiliary information of the specular reflection point corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file.

[0161] An auxiliary information projection module is configured to project the read auxiliary information of the specular reflection point to the two-dimensional visualization projection interface.

[0162] In an exemplary embodiment of the present application, the quality monitoring module 65 comprises:

[0163] A normal working satellite quality determination module is configured to determine the number and the number of the currently on-orbit normal working constellation satellite according to the number and the number of the satellite with continuously changed spatial position in the three-dimensional spatial geometric configuration interface.

[0164] In an exemplary embodiment of the present application, the quality monitoring module 65 comprises:

[0165] A reflected GNSS satellite number determination module is configured to determine the number of the reflected GNSS satellite currently visible to the constellation satellite according to the number of the trajectory of the updated GNSS-R specular reflection point in the three-dimensional spatial geometric configuration interface.

[0166] In an exemplary embodiment of the present application, the quality monitoring module 65 comprises:

[0167] A reflected event quality determination module is configured to determine the GNSS system type and the satellite PRN number corresponding to the reflected event according to the color depth of the GNSS-R specular reflection point and the navigation satellite number in the three-dimensional spatial geometric configuration interface.

[0168] Wherein, the GPS satellite PRN number is equal to PRN+0, the BDS satellite PRN number is equal to PRN+100, and the GAL satellite PRN number is equal to PRN+200.

[0169] In an exemplary embodiment of the present application, the quality monitoring module 65 comprises:

[0170] A reflected channel quality determination module is configured to determine whether the reflected channel waveform generation function stops working according to the on-orbit observation waveform information of the GNSS-R in the two-dimensional visualization projection interface.

[0171] In an exemplary embodiment of the present application, the quality monitoring module 65 comprises:

[0172] The in-orbit observation quality determination module is configured to determine in-orbit observation quality of the two-dimensional observation waveform of the reflection channel according to the in-orbit observation waveform information of the GNSS-R in the two-dimensional visualization projection interface, the auxiliary information of the specular reflection point, and the waveform peak signal-to-noise ratio corresponding to the specular reflection point.

[0173] For the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts are described in the part of the method embodiment.

[0174] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts between the embodiments can be referred to each other.

[0175] Those skilled in the art should understand that the embodiments of the embodiments of the present application can be provided as a method, device, or computer program product. Therefore, the embodiments of the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0176] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, terminal device (system), and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of the flows and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device for implementing the functions specified in the flowchart and / or block diagram. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0177] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowchart and / or block diagram. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0178] These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operational steps are performed on the computer or other programmable terminal device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one block or multiple blocks.

[0179] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to cover all changes and modifications falling within the scope of the embodiments of the present application.

[0180] Finally, it should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or terminal device. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of other identical elements in the process, method, article or terminal device including the element.

[0181] The above describes in detail the quality monitoring method of a GNSS-R networking constellation and the quality monitoring system of a GNSS-R networking constellation provided by the present application, and the principles and implementation manners of the present application are described by using specific examples in this document. The above description of the embodiments is only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed, and the above description of the present application should not be understood as a limitation of the present application.

Claims

1. A method for monitoring the quality of a GNSS-R constellation of networks, characterized in that, The method comprises: acquiring original data of a GNSS-R networking constellation; performing unified preprocessing on the original data to obtain multi-dimensional information of the networking constellation and a file name with a unified naming method; performing time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of a networking constellation segment; performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation segment to obtain a multi-dimensional visualization interface; performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

2. The method of claim 1, wherein, The unified preprocessing on the original data to obtain multi-dimensional information of the networking constellation and a file name with a unified naming method comprises: performing unified preprocessing on the original data to obtain independent multi-dimensional information files for different GNSS-R networking satellites; wherein the multi-dimensional information files contain the following multi-dimensional information of the networking constellation: spatial position information of the networking constellation satellite, spatial position information of the GNSS-R specular reflection point, spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, PRN code information of the GNSS satellite, in-orbit observation waveform information of the GNSS-R, and auxiliary information of the specular reflection point; the file name with the unified naming method of the multi-dimensional information file contains the following information: networking satellite number, observation start time and end time of the networking satellite, and reflection channel number of the GNSS-R receiver.

3. The method of claim 2, wherein, The time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of a networking constellation segment comprises: acquiring specified quality monitoring time range information; dividing the quality monitoring time range information into multiple time periods; respectively reading the multi-dimensional information of the networking constellation segment from the multi-dimensional information files corresponding to each time period.

4. The method of claim 3, wherein, The reading of the multi-dimensional information of the networking constellation segment from the multi-dimensional information files corresponding to each time period comprises: selecting the multi-dimensional information files corresponding to each time period according to the observation start time and the end time in the file name of each multi-dimensional information file; reading the sampling time matrix of the multi-dimensional information files corresponding to each time period; calculating the matrix subscripts of the sampling time matrix within each time period; reading the multi-dimensional information of the networking constellation segment according to the matrix subscripts; wherein the multi-dimensional information of the networking constellation segment contains: spatial position information of the networking constellation satellite, spatial position information of the GNSS-R specular reflection point, spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point, and PRN code information of the GNSS satellite.

5. The method of claim 4, wherein, The three-dimensional data rendering on the multi-dimensional information of the networking constellation segment to obtain a multi-dimensional visualization interface comprises: performing three-dimensional data rendering on the spatial position information of the networking constellation satellite, the spatial position information of the GNSS-R specular reflection point, and the spatial position information of the GNSS satellite corresponding to the GNSS-R specular reflection point to obtain a three-dimensional earth interface reflecting the type of ground objects as the background of a three-dimensional space geometry.

6. The method of claim 5, wherein, The two-dimensional visualization processing of the multi-dimensional information of the networking constellation segment obtains a multi-dimensional visualization interface, including: Obtaining playback synchronization time information of the three-dimensional data rendering, and obtaining running conditions of a networking constellation and a trajectory of a GNSS-R specular reflection point from the stereoscopic spatial geometric configuration interface; Determining a reflection channel number of a GNSS-R receiver of a networking constellation satellite to be monitored according to the running conditions and the trajectory; Reading out, from the multi-dimensional information file, GNSS-R in-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number; Two-dimensional visualization processing of the read-out GNSS-R in-orbit observation waveform information obtains a two-dimensional visualization projection interface.

7. The method of claim 6, wherein, The two-dimensional visualization processing of the multi-dimensional information of the networking constellation segment obtains a multi-dimensional visualization interface, further including: Reading out, from the multi-dimensional information file, auxiliary information of the specular reflection point corresponding to the playback synchronization time information and the reflection channel number; Projecting the read-out auxiliary information of the specular reflection point to the two-dimensional visualization projection interface.

8. The method of claim 5, wherein, The quality monitoring of the GNSS-R networking constellation according to the multi-dimensional visualization interface obtains a quality monitoring result, including: Determining a number and a number of networking constellation satellites currently in orbit and normally working according to a number and a number of satellites with continuously changing spatial positions in the stereoscopic spatial geometric configuration interface.

9. The method of claim 5, wherein, The quality monitoring of the GNSS-R networking constellation according to the multi-dimensional visualization interface obtains a quality monitoring result, including: Determining a number of reflection GNSS satellites currently visible to a networking constellation satellite according to a number of trajectories of GNSS-R specular reflection points that exist in the stereoscopic spatial geometric configuration interface.

10. The method of claim 5, wherein, The quality monitoring of the GNSS-R networking constellation according to the multi-dimensional visualization interface obtains a quality monitoring result, including: Determining a GNSS system type and a satellite PRN number corresponding to a reflection event according to a color depth of a GNSS-R specular reflection point and a navigation satellite number in the stereoscopic spatial geometric configuration interface; Wherein, a GPS satellite PRN number is equal to PRN+0, a BDS satellite PRN number is equal to PRN+100, and a GAL satellite PRN number is equal to PRN+200.

11. The method of claim 6, wherein, The quality monitoring of the GNSS-R networking constellation according to the multi-dimensional visualization interface obtains a quality monitoring result, including: Determining whether a reflection channel waveform generation function stops working according to GNSS-R in-orbit observation waveform information in the two-dimensional visualization projection interface.

12. The method of claim 7, wherein, The quality monitoring of the GNSS-R networking constellation according to the multi-dimensional visualization interface obtains a quality monitoring result, including: Determining in-orbit observation quality of a two-dimensional observation waveform of a reflection channel according to GNSS-R in-orbit observation waveform information in the two-dimensional visualization projection interface, auxiliary information of the specular reflection point, and a waveform peak signal-to-noise ratio corresponding to the specular reflection point.

13. A system for monitoring the quality of a GNSS-R constellation of networks, characterized in that it comprises: The system includes: An original data acquisition module configured to acquire original data of a GNSS-R networking constellation; The original data unified preprocessing module is configured to perform unified preprocessing on the original data to obtain multi-dimensional information of a network constellation and a file name in a unified naming manner. The multi-dimensional information slicing processing module is configured to perform time slicing processing on the multi-dimensional information of the network constellation to obtain multi-dimensional information of a network constellation segment. The multi-dimensional information visualization processing module is configured to perform three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the network constellation segment to obtain a multi-dimensional visualization interface. The quality monitoring module is configured to perform quality monitoring on the GNSS-R network constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

14. The system of claim 13, wherein, The original data unified preprocessing module is configured to perform unified preprocessing on the original data to obtain independent multi-dimensional information files for different GNSS-R network satellites. The multi-dimensional information file includes the following network constellation multi-dimensional information: spatial position information of a network constellation satellite, spatial position information of a GNSS-R mirror reflection point, spatial position information of a GNSS satellite corresponding to the GNSS-R mirror reflection point, PRN code information of the GNSS satellite, in-orbit observation waveform information of the GNSS-R, and auxiliary information of the mirror reflection point. The file name in the unified naming manner of the multi-dimensional information file includes the following information: a network satellite number, observation start and end times of the network satellite, and a reflection channel number of a GNSS-R receiver.

15. The system of claim 14, wherein, The multi-dimensional information slicing processing module includes: a monitoring time acquisition module configured to acquire specified quality monitoring time range information; a monitoring time division module configured to divide the quality monitoring time range information into multiple time periods; a segment multi-dimensional information reading module configured to read out the network constellation segment multi-dimensional information from the multi-dimensional information file corresponding to each time period, respectively.

16. The system of claim 15, wherein, The segment multi-dimensional information reading module includes: a multi-dimensional information file selection module configured to select the multi-dimensional information file corresponding to each time period according to the observation start and end times in the file name of each multi-dimensional information file; a sampling time matrix reading module configured to read a sampling time matrix of the multi-dimensional information file corresponding to each time period; a matrix subscript calculation module configured to calculate a matrix subscript of the sampling time matrix within each time period; a network constellation segment multi-dimensional information reading module configured to read the network constellation segment multi-dimensional information according to the matrix subscript; The network constellation segment multi-dimensional information includes: spatial position information of a network constellation satellite, spatial position information of a GNSS-R mirror reflection point, spatial position information of a GNSS satellite corresponding to the GNSS-R mirror reflection point, and PRN code information of the GNSS satellite.

17. The system of claim 16, wherein, The multi-dimensional information visualization processing module includes: The three-dimensional data rendering module is configured to perform three-dimensional data rendering according to the spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R specular reflection points, and the spatial position information of the GNSS satellites corresponding to the GNSS-R specular reflection points, so as to obtain a stereoscopic spatial geometric configuration interface with a three-dimensional earth reflecting a type of ground object as a background.

18. The system of claim 17, wherein, The multi-dimensional information visualization processing module further comprises: The playback time acquisition module is configured to acquire playback synchronization time information of the three-dimensional data rendering; The running condition and trajectory acquisition module is configured to acquire a running condition of the networking constellation and a trajectory of the GNSS-R specular reflection point from the stereoscopic spatial geometric configuration interface; The reflection channel number determination module is configured to determine a reflection channel number of a GNSS-R receiver of a networking constellation satellite to be monitored according to the running condition and the trajectory; The waveform information reading module is configured to read out, from the multi-dimensional information file, the GNSS-R in-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number; The two-dimensional visualization processing module is configured to perform two-dimensional visualization processing on the read-out GNSS-R in-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

19. The system of claim 18, wherein, The multi-dimensional information visualization processing module further comprises: The auxiliary information reading module is configured to read out, from the multi-dimensional information file, auxiliary information of the specular reflection point corresponding to the playback synchronization time information and the reflection channel number; The auxiliary information projection module is configured to project the read-out auxiliary information of the specular reflection point to the two-dimensional visualization projection interface.

20. The system of claim 17, wherein, The quality monitoring module comprises: The normal working satellite quality determination module is configured to determine a number and a number of networking constellation satellites currently in orbit and working normally according to a number and a number of satellites with continuously changing spatial positions in the stereoscopic spatial geometric configuration interface.

21. The system of claim 17, wherein, The quality monitoring module comprises: The reflected GNSS satellite number determination module is configured to determine a number of reflected GNSS satellites currently visible to the networking constellation satellites according to a number of trajectories of the GNSS-R specular reflection points with updated trajectories in the stereoscopic spatial geometric configuration interface.

22. The system of claim 17, wherein, The quality monitoring module comprises: The reflection event quality determination module is configured to determine a GNSS system type and a satellite PRN number corresponding to a reflection event according to a color depth of the GNSS-R specular reflection point and a navigation satellite number in the stereoscopic spatial geometric configuration interface. In the formula, a GPS satellite PRN number is equal to PRN+0, a BDS satellite PRN number is equal to PRN+100, and a GAL satellite PRN number is equal to PRN+200.

23. The system of claim 18, wherein, The quality monitoring module comprises: The reflection channel quality determination module is configured to determine whether a reflection channel waveform generation function stops working according to the GNSS-R in-orbit observation waveform information in the two-dimensional visualization projection interface.

24. The system of claim 19, wherein, The quality monitoring module comprises: The in-orbit observation quality determination module is configured to determine the in-orbit observation quality of the two-dimensional observation waveform of the reflection channel according to the in-orbit observation waveform information of the GNSS-R in the two-dimensional visualization projection interface, the auxiliary information of the specular reflection point, and the waveform peak signal-to-noise ratio corresponding to the specular reflection point.

25. An electronic device, comprising: Comprise: one or more processors; and one or more machine-readable media having stored thereon instructions, which when executed by the one or more processors, cause the electronic device to perform the quality monitoring method of the GNSS-R network constellation as claimed in any one of claims 1 to 12.

26. A computer-readable storage medium, characterized in that, The stored computer program causes the processor to perform the quality monitoring method of the GNSS-R network constellation as claimed in any one of claims 1 to 12.

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