Quality monitoring method, system and device for GNSS-R constellation and medium

Abstract

The embodiment of the invention provides a quality monitoring method and system for a GNSS-R (Global Navigation Satellite System-Radio) networking constellation, and the method comprises the steps: obtaining original data of the GNSS-R networking constellation; performing unified preprocessing on the original data to obtain networking constellation multi-dimensional information and a file name of a unified naming mode; performing time slicing processing on the networking constellation multi-dimensional information to obtain networking constellation fragment multi-dimensional information; performing three-dimensional data rendering and two-dimensional visualization processing on the networking constellation fragment multi-dimensional information to obtain a multi-dimensional visualization interface; and performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visual interface to obtain a quality monitoring result. According to the embodiment of the invention, quick, accurate, convenient and visual quality monitoring of the GNSS-R large-scale networking constellation compatible with multiple GNSS systems is realized, the monitoring efficiency, accuracy and convenience are improved, and powerful technical support is provided for future satellite networking constellation management.

Description

Technical Field

[0001] The present invention relates to the field of global navigation satellite system (GNSS) reflected signal remote sensing technology, and in particular to a quality monitoring method for a global navigation satellite system reflected signal remote sensing (GNSS-R) network constellation, a quality monitoring system for a GNSS-R network constellation, an electronic device, and a computer-readable storage medium. Background Art

[0002] In the early 1990s, research revealed that in addition to traditional positioning, navigation, and timing functions, GNSS signals can also be received through surface reflections, and then used for remote sensing of physical parameters of surface reflection surfaces. This discovery gave birth to GNSS-R technology. Currently, GNSS-R technology has been widely used in remote sensing of various physical parameters, such as GNSS-R altimetry, sea surface wind field, soil moisture, sea ice cover, total surface biomass, surface freeze-thaw, etc.

[0003] Since GNSS-R technology uses existing navigation satellite signals for remote sensing detection without the need for signal transmitters, the receiver is lightweight, consumes little power, and is cost-effective, making it very suitable for being carried on small satellite platforms to form large-scale network detection. In recent years, with the increasing maturity of GNSS-R receiver technology and inversion algorithms, the GNSS-R large-scale network constellation compatible with multiple GNSS systems based on micro-nano satellite platforms, as well as the GNSS-R detection network technology based on the last-stage cluster on orbit, have made significant progress, and a GNSS-R large-scale network constellation compatible with multiple GNSS systems is taking shape.

[0004] However, due to the large number of satellites and reflection channels in the GNSS-R large-scale networking constellation compatible with multiple GNSS systems, and the design life of the micro-nano satellite, the final stage and the GNSS-R receiver, the platform and GNSS-R receiver are more prone to failures and quality problems during in-orbit operation. Therefore, how to achieve fast, accurate, convenient and intuitive quality monitoring of the GNSS-R large-scale networking constellation compatible with multiple GNSS systems in orbit has become an urgent problem to be solved. Summary of the invention

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

[0006] In order to solve the above problems, an embodiment of the present invention discloses a quality monitoring method for a GNSS-R networking constellation, the method comprising: acquiring raw data of the GNSS-R networking constellation; performing unified preprocessing on the raw data to obtain multi-dimensional information of the networking constellation and a file name in a unified naming method; performing time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of networking constellation fragments; performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragments 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 present invention also discloses a quality monitoring system for a GNSS-R networking constellation, the system comprising: a raw data acquisition module, used to acquire raw data of a GNSS-R networking constellation; a raw data unified preprocessing module, used to perform unified preprocessing on the raw data to obtain multi-dimensional information of a networking constellation and a file name in a unified naming method; a multi-dimensional information slicing processing module, used to perform time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of networking constellation fragments; a multi-dimensional information visualization processing module, used to perform three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface; and a quality monitoring module, used to perform quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0008] An embodiment of the present invention also discloses an electronic device, comprising: one or more processors; and one or more machine-readable media having instructions stored thereon, which, when executed by the one or more processors, enables the electronic device to execute the quality monitoring method of the GNSS-R networking constellation as described above.

[0009] An embodiment of the present invention further discloses a computer-readable storage medium, wherein a computer program stored in the storage medium enables a processor to execute the quality monitoring method for a GNSS-R networking constellation as described above.

[0010] The embodiments of the present invention include the following advantages:

[0011] The quality monitoring solution for the GNSS-R networking constellation provided in the embodiment of the present invention can be applied to a monitoring system. The original data of the GNSS-R networking constellation is obtained through the monitoring system; the original data is uniformly preprocessed to obtain multi-dimensional information of the networking constellation and a file name with a unified naming method; the multi-dimensional information of the networking constellation is time-sliced ​​to obtain multi-dimensional information of the networking constellation fragments; three-dimensional data rendering and two-dimensional visualization processing are performed on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface; and the quality of the GNSS-R networking constellation is monitored according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0012] The embodiment of the present invention can perform refined analysis on the constellation status in different time periods by performing time slicing processing on the multi-dimensional information of the networked constellation after unified preprocessing, thereby improving the accuracy of quality monitoring. Time slicing processing allows the monitoring system to adapt to GNSS-R networked constellations with larger scale and more reflection channels, and can capture potential faults or quality problems more carefully, avoiding system crashes caused by excessive data volume or omissions caused by insufficient analysis. Three-dimensional data rendering and two-dimensional visualization processing are performed on the multi-dimensional information of the networked constellation fragments, so that the data is not only presented in the form of text or tables, but also multi-dimensional information is presented through two-dimensional and three-dimensional visualization interfaces. Such a visualization processing method improves the intuitiveness of the data, making it easier for users to understand and analyze monitoring data more quickly, so as to make more timely decisions. It is possible to monitor data of multiple dimensions at the same time, such as time dimension, space dimension and various physical parameter dimensions. Multi-dimensional comprehensive monitoring can more comprehensively grasp the overall operating status of the constellation, and help to quickly identify the specific source of the fault and the scope of influence. Through the comprehensive application of automated unified preprocessing, time slicing processing, and multi-dimensional visualization, the steps of manual intervention are reduced, making the quality monitoring process more convenient. Users can quickly obtain monitoring results through an intuitive multi-dimensional visualization interface, simplifying the processing and analysis of large amounts of complex data.

[0013] In summary, compared with the background technology, the embodiments of the present invention, through technical means such as unified data preprocessing, time slicing, multi-dimensional information rendering, and multi-dimensional visualization interface, realize fast, accurate, convenient and intuitive quality monitoring of GNSS-R large-scale networking constellations compatible with multiple GNSS systems, greatly improves the efficiency, accuracy and convenience of monitoring, and provides strong technical support for future satellite networking constellation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the steps of a quality monitoring method of a GNSS-R networking constellation according to an embodiment of the present invention;

[0015] Figure 2 It is a flowchart of the steps of a multi-dimensional information fusion GNSS-R networking constellation visualization quality monitoring solution according to an embodiment of the present invention;

[0016] Figure 3 It is a three-dimensional visualization effect diagram of spatial geometric information of a GNSS-R networking constellation according to an embodiment of the present invention;

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

[0018] Figure 5 It is a two-dimensional visualization effect diagram of GNSS-R mirror reverse point auxiliary information in an embodiment of the present invention;

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

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] The quality monitoring solution for the GNSS-R networking constellation proposed in the embodiment of the present invention uniformly preprocesses the raw data of the GNSS-R networking constellation, extracts multi-dimensional information, and processes it in segments through time slicing technology, thereby realizing three-dimensional rendering and two-dimensional visualization of multi-dimensional data, thereby constructing an intuitive multi-dimensional visualization interface. With the help of this interface, it is possible to perform real-time and precise quality monitoring of large-scale networking constellations, and effectively identify and analyze potential faults or quality problems. Compared with traditional methods, this solution improves data processing efficiency, monitoring accuracy and convenience, and is particularly suitable for quality management of large-scale GNSS-R networking constellations compatible with multiple GNSS systems.

[0022] Reference Figure 1 , shows a flow chart of the steps of a quality monitoring method for a GNSS-R network constellation according to an embodiment of the present invention. The quality monitoring method for a GNSS-R network constellation can be applied to a monitoring system, referred to as a system. The quality monitoring method for a GNSS-R network constellation can specifically include the following steps:

[0023] Step 101: Acquire raw data of the GNSS-R network constellation.

[0024] In the embodiment of the present invention, the key to obtaining the raw data of the GNSS-R network constellation is to collect complete and accurate raw data, which can be directly obtained from the capture of the surface reflection signal by the satellite receiver. The principle of GNSS-R technology is to receive the reflection wave of the global navigation satellite system signal on the surface, and the reflection signal will carry information about the physical parameters of the surface, such as sea level, wind field, soil moisture, etc.

[0025] In practical applications, the acquisition of raw data from the GNSS-R network constellation can be divided into the following main links: First, the reflected signals on the earth are captured by satellite receivers, which are devices specially designed to receive these reflected signals. These receivers are usually carried on micro-nano satellite platforms. Due to the size and weight restrictions of these platforms, the design of the receivers must take into account both high sensitivity and low power consumption. Secondly, the received signal data is transmitted to the ground station or other relay stations via satellite. During this process, the signal may be forwarded and processed multiple times to ensure the integrity and accuracy of the data transmission. In order to obtain high-quality raw data, the normal working state of the satellite receiver must be guaranteed during orbital operation to avoid data loss or distortion caused by receiver failure, signal interference or other external factors.

[0026] In addition, the acquisition of raw data also needs to consider the compatibility between different GNSS systems. At present, GNSS systems include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the Galileo system (Galileo), the Beidou Navigation Satellite System (BDS), and other systems. In order to achieve compatibility with multiple GNSS systems, the receiver must be able to simultaneously receive and process signals from different systems, which places 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 surface, thus laying 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 network constellation quality monitoring system.

[0028] Step 102, pre-processing the original data in a unified manner to obtain multi-dimensional information of the networking constellation and a file name in a unified naming method.

[0029] In an embodiment of the present invention, after the raw data of the GNSS-R network constellation is obtained, it is necessary to perform unified preprocessing on the raw data in order to extract effective multi-dimensional information. The purpose of unified preprocessing is to remove noise and irrelevant information from the raw data, and to perform structured processing on the useful information so that it can be effectively used in subsequent analysis steps. Since the amount of GNSS-R raw data is large and complex, the unified preprocessing step is crucial to improving 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 many factors during transmission, such as atmospheric interference, electromagnetic noise, etc., which can cause the quality of the original data to deteriorate. Through denoising, these interference signals can be filtered out, so as to extract purer reflected signal information. Common denoising methods include filter design, waveform analysis, and spectrum analysis, which can be selectively applied according to different interference types. Secondly, data calibration and normalization are also important links in unified preprocessing. Due to changes in the sensitivity of satellite receivers and environmental conditions, the received signal strength may vary. Through calibration, these differences can be eliminated to ensure the comparability between different data sets. Normalization maps the data to a standardized range, usually between 0 and 1, so as to avoid analysis errors caused by different data scales. Next, unified preprocessing also includes data resampling and interpolation. The sampling rate of the original data may be inconsistent, and data may even be missing in certain time periods. Through resampling, the data can be rearranged to a unified time axis, and interpolation can fill in the missing parts of the data to ensure the continuity and integrity of the data. Resampling and interpolation can effectively improve the accuracy of subsequent analysis, especially in time series analysis. Finally, unified preprocessing will also extract multidimensional information. This step is to further analyze the denoised, calibrated and resampled data to extract multidimensional information related to surface physical parameters, such as reflection intensity, delay time, phase offset, etc. This multidimensional information will provide a basis for subsequent time slice processing and visualization analysis.

[0031] Through unified preprocessing, the raw 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 slice processing and visual analysis, and ultimately helps to achieve efficient quality monitoring of the GNSS-R network constellation.

[0032] Step 103: Time-slice the multi-dimensional information of the networking constellation to obtain multi-dimensional information of the networking constellation fragments.

[0033] In the embodiment of the present invention, after the unified data preprocessing is completed and multi-dimensional information is obtained, the next step is to perform time slicing processing on the multi-dimensional information to generate multi-dimensional information of networked constellation segments 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 within 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 slicing. The selection of this time interval needs to consider many factors, such as the orbit of the satellite, 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 the key change information of the network constellation can be captured, while avoiding the computational burden caused by too fragmented data. For rapidly changing parameters, a shorter time interval may be required, while for slowly changing parameters, the time interval can be appropriately extended. Secondly, after determining the time interval, the multidimensional information is divided into several fragments in chronological order. Each fragment represents the operating status of the network constellation during that time period. In this way, complex multidimensional information can be decomposed into small pieces of data that are easier to analyze, so as to better understand the trend of data changes over time.

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

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

[0037] In general, time slicing processing divides continuous multi-dimensional information into time dimensions, allowing data analysis to better focus on the changing process rather than just the static results. This processing method provides a time series perspective for subsequent 3D rendering and visualization analysis, allowing the quality monitoring system to more accurately and comprehensively grasp the operating status of the GNSS-R network constellation.

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

[0039] In the embodiment of the present invention, after completing the time slice processing and obtaining the multi-dimensional information of the networked constellation fragments, it is necessary to perform three-dimensional data rendering and two-dimensional visualization processing on the information to generate a multi-dimensional visualization interface. The core of this step is to intuitively display complex multi-dimensional data in front of the user through visualization technology, so as to help the user quickly understand and analyze the status of the networked constellation.

[0040] In practical applications, three-dimensional data rendering is the process of converting fragmented multidimensional information into three-dimensional images. Multidimensional information usually includes spatial coordinates, time dimensions, and other physical parameters such as signal strength, delay, etc. Through three-dimensional rendering technology, this information can be mapped into a three-dimensional space to generate a three-dimensional visualization effect. For example, the trajectory of a satellite can be rendered as a curve in three-dimensional space, and the intensity of the reflected signal can be marked by color or transparency, so as to intuitively show the signal changes at different times and locations. Secondly, two-dimensional visualization processing is the process of flattening fragmented multidimensional information. 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 shown by a line graph, and 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, and is particularly suitable for displaying data changes that require quick decision-making or monitoring.

[0041] In actual operation, 3D rendering and 2D visualization are often used in combination. 3D rendering provides a global view and stereoscopic sense of data, allowing users to observe data changes from different angles; while 2D visualization helps users quickly capture key information through simple and clear charts and graphs. For example, in a quality monitoring system, 3D rendering can be used to display the real-time status of the entire GNSS-R network constellation, while 2D charts can be used to monitor abnormal data within a specific time period.

[0042] In order to realize this multi-dimensional visualization interface, advanced data processing and graphics rendering technologies are needed. Common 3D rendering technologies include ray tracing and volume rendering, while 2D visualization usually relies on chart libraries and data visualization tools such as Matplotlib and D3.js. These technologies can be seamlessly integrated with uniformly pre-processed data through programming interfaces to generate dynamic and highly 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 a time period, or zoom in on a specific area of ​​data by clicking, dragging, zooming, etc. This interactive design not only improves the 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 anomalies.

[0044] By performing 3D rendering and 2D visualization of multi-dimensional information of networked constellation fragments, the resulting multi-dimensional visualization interface makes complex data easy to understand and analyze. Users can use this interface to intuitively observe the state changes of the networked constellation, thereby better performing real-time quality monitoring and problem diagnosis. This approach not only improves the accuracy and efficiency of monitoring, but also provides users with a powerful tool to promptly respond to and resolve quality issues that may arise in the networked constellation.

[0045] Step 105: Perform quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0046] In the embodiment of the present invention, after obtaining the multi-dimensional visualization interface, the core task of quality monitoring is to perform real-time quality monitoring on the GNSS-R network constellation through this interface, and finally generate quality monitoring results. The focus of this process is to use the information provided by the visualization interface to quickly and accurately evaluate the operating status of the network constellation, 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 orbit, signal strength, reflection parameters, time series changes, etc., which can help users fully understand the operation status of the constellation. For example, if the reflection signal of a certain satellite is found to be suddenly weakened or disappeared on the interface, this may indicate that the receiver of the satellite has failed or the signal transmission has been abnormal. Through this real-time monitoring, users can quickly locate the source of the problem. Secondly, when conducting quality monitoring, some key indicators and thresholds are usually set for automatic detection and alarm. When certain key indicators exceed the preset safety range, the system can automatically generate an alarm and highlight the problem area on the visualization interface. For example, if the signal delay increases abnormally in certain time periods, 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, the laws and trends in constellation operation can be summarized, thus providing a basis for future fault prevention and performance optimization. For example, if the analysis results show that certain satellites have frequent signal interruptions over a period of time, this may indicate that there is continuous external interference in the area, or that there are design defects in the satellite equipment of this batch. Through this historical data analysis, users can take corresponding improvement measures, such as adjusting satellite configuration, optimizing receiver design, or enhancing anti-interference capabilities.

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

[0050] In summary, the quality monitoring method of the multi-dimensional visual interface can not only evaluate the status of the GNSS-R network constellation in real time and accurately, but also generate comprehensive monitoring results to provide users with valuable decision support. This method greatly improves the efficiency and reliability of quality monitoring, allowing users to easily manage complex network constellations and ensure efficient and stable operation of the system.

[0051] The quality monitoring solution for the GNSS-R networking constellation provided in the embodiment of the present invention can be applied to a monitoring system. The original data of the GNSS-R networking constellation is obtained through the monitoring system; the original data is uniformly preprocessed to obtain multi-dimensional information of the networking constellation and a file name with a unified naming method; the multi-dimensional information of the networking constellation is time-sliced ​​to obtain multi-dimensional information of the networking constellation fragments; three-dimensional data rendering and two-dimensional visualization processing are performed on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface; and the quality of the GNSS-R networking constellation is monitored according to the multi-dimensional visualization interface to obtain a quality monitoring result.

[0052] The embodiment of the present invention can perform refined analysis on the constellation status in different time periods by performing time slicing processing on the multi-dimensional information of the networked constellation after unified preprocessing, thereby improving the accuracy of quality monitoring. Time slicing processing allows the monitoring system to adapt to GNSS-R networked constellations with larger scale and more reflection channels, and can capture potential faults or quality problems more carefully, avoiding system crashes caused by excessive data volume or omissions caused by insufficient analysis. Three-dimensional data rendering and two-dimensional visualization processing are performed on the multi-dimensional information of the networked constellation fragments, so that the data is not only presented in the form of text or tables, but also multi-dimensional information is presented through two-dimensional and three-dimensional visualization interfaces. Such a visualization processing method improves the intuitiveness of the data, making it easier for users to understand and analyze monitoring data more quickly, so as to make more timely decisions. It is possible to monitor data of multiple dimensions at the same time, such as time dimension, space dimension and various physical parameter dimensions. Multi-dimensional comprehensive monitoring can more comprehensively grasp the overall operating status of the constellation, and help to quickly identify the specific source of the fault and the scope of influence. Through the comprehensive application of automated unified preprocessing, time slicing processing, and multi-dimensional visualization, the steps of manual intervention are reduced, making the quality monitoring process more convenient. Users can quickly obtain monitoring results through an intuitive multi-dimensional visualization interface, simplifying the processing and analysis of large amounts of complex data.

[0053] In summary, compared with the background technology, the embodiments of the present invention, through technical means such as unified data preprocessing, time slicing, multi-dimensional information rendering, and multi-dimensional visualization interface, realize fast, accurate, convenient and intuitive quality monitoring of GNSS-R large-scale networking constellations compatible with multiple GNSS systems, 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 present invention, a unified preprocessing of raw data to obtain multi-dimensional information of networking constellations and a file name with a unified naming method is performed to uniformly preprocess the raw data to obtain independent multi-dimensional information files for different GNSS-R networking satellites; wherein the multi-dimensional information file includes the following multi-dimensional information of the networking constellation: spatial position information of the networking constellation satellites, 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 coding information of the GNSS satellite, GNSS-R on-orbit observation waveform information, and auxiliary information of the mirror reflection point. The file name of the unified naming method of the multi-dimensional information file includes 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 unified preprocessing of raw data is to convert the raw GNSS-R data into multidimensional information files that are easy to analyze and process. Each independent multidimensional information file is specific to different networking satellites and contains a series of key information for subsequent data processing, analysis and quality monitoring. During the implementation process, the data from multiple satellites must first be preliminarily sorted and cleaned. The raw data may include various types of signal strength, noise level, timestamp and other information. The quality and integrity of this data directly affect the accuracy of subsequent processing. Therefore, the first step of unified preprocessing is usually data cleaning to remove or correct outliers caused by noise or equipment errors. For missing data, interpolation or other methods can be used to complete it to ensure the continuity and consistency of the data. Next, the core part of unified preprocessing is to generate independent multidimensional information files for different networking satellites.

[0056] These files contain the following key multi-dimensional information about the network constellation:

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

[0058] 2. Spatial location information of GNSS-R mirror reflection points: The mirror reflection point is the intersection of the surface and the GNSS signal path, and is the core area for remote sensing detection. By calculating the position of the reflection point, the physical parameters of the surface, such as soil moisture, sea level, etc., can be inferred. These location information are usually stored in the form of three-dimensional coordinates.

[0059] 3. Spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point: In order to accurately analyze the reflected signal, it is necessary to know the source of the signal, that is, the position of the GNSS satellite. With this information, the path that the signal takes during transmission can be calculated, including parameters such as reflection angle and propagation delay, which is crucial for accurate decoding of the signal and inversion of physical parameters.

[0060] 4. GNSS satellite pseudo-random noise (PRN) coding information: PRN coding is a unique identifier for each GNSS satellite transmission signal, which is used to distinguish signals from different satellites. In data processing, PRN coding is used to match the received signal with the corresponding satellite to ensure the correctness and integrity of the data.

[0061] 5. GNSS-R on-orbit observation waveform information: This is the core data of GNSS-R technology, which includes the signal waveform after being reflected by the surface. By analyzing these waveforms, the physical characteristics of the reflecting surface, such as reflection intensity, delay, etc., can be obtained. This information is the basis for subsequent inversion algorithms.

[0062] 6. Auxiliary information of specular reflection points: Auxiliary information may include environmental characteristics around the reflection point, such as terrain type, altitude, vegetation coverage, etc. This information helps to further refine the inversion and analysis of surface physical parameters and improve the accuracy of remote sensing detection.

[0063] The embodiment of the present invention generates independent multi-dimensional information files for different networked satellites by uniformly preprocessing the original data, which not only makes the management and processing of data more systematic, but also provides a clear input data source for subsequent analysis. This structured multi-dimensional information file enables the data of different satellites to be analyzed independently, facilitates distributed monitoring and diagnosis of the status of the entire networked constellation, and effectively improves the maintainability and reliability of the system. The multi-dimensional file containing a variety of 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 invention, an implementation method of time slicing the networking constellation multidimensional information to obtain the networking constellation fragment multidimensional information is to obtain the specified quality monitoring time range information; divide the quality monitoring time range information into multiple time periods; and read the networking constellation fragment multidimensional information from the multidimensional information file corresponding to each time period.

[0065] The purpose of time slicing the multi-dimensional information of the networked constellation is to divide the continuously changing multi-dimensional information into multiple time segments for more fine-grained analysis and monitoring. Through time slicing, the state of the networked constellation can be monitored with high precision within a specified time range, thereby improving the efficiency and accuracy of monitoring. In practical applications, it is necessary to obtain a specified quality monitoring time range information. 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, hours, or even days. The setting of this time range is very critical because it determines the starting and ending points of the time slicing processing and lays the foundation for the subsequent time period division. After the time range is clarified, the next step is to divide this 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 period can be divided into shorter time periods, each of which may be only a few minutes; while in a more relaxed monitoring scenario, the time period can be longer, even up to several hours. The division of each time period not only affects the amount of data processed, but also affects the accuracy and real-time performance of subsequent analysis. Therefore, the division of time periods needs to be weighed according to specific application scenarios. After the time period division is completed, the corresponding multi-dimensional information of the networked constellation fragments will be read from the multi-dimensional information files corresponding to each time period. These multi-dimensional information files are usually generated through unified preprocessing and contain all key data related to each time period, including satellite positions, GNSS-R mirror reflection point positions, PRN codes, observation waveform information, etc. By reading this information, a complete networked constellation fragment view can be constructed in each time period to ensure real-time tracking and monitoring of the constellation status.

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

[0067] In an exemplary embodiment of the present invention, an implementation method of reading out the multidimensional information of the networking constellation fragment from the multidimensional information file corresponding to each time period is to select the multidimensional information file corresponding to each time period according to the observation start time and end time in the file name of each multidimensional information file; read the sampling time matrix of the multidimensional information file corresponding to each time period; calculate the matrix subscript of the sampling time matrix in each time period; read the multidimensional information of the networking constellation fragment according to the matrix subscript; wherein the multidimensional information of the networking constellation fragment includes: the spatial position information of the networking constellation satellite, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding information of the GNSS satellite.

[0068] In practical applications, the time information in the file name of the multidimensional information file is used to select the file corresponding to each time period. During data collection and storage, the file name usually carries key information such as timestamps or time intervals. This time information is an important basis for file classification and management. When performing time slicing processing, the multidimensional information files with qualified time information in the file name are matched and selected according to the pre-defined time period. This step ensures the accuracy and pertinence of the data for subsequent processing, because only files with matching time ranges will be selected and further processed. Next, the sampling time matrix of the multidimensional information file corresponding to each time period is read. The sampling time matrix refers to the time series during data collection. These time series are usually organized in the form of a matrix and mark the specific time points of data collection. By reading these time matrices, the time axis of data collection can be accurately determined, and an accurate time reference can be provided for subsequent data extraction. After obtaining the sampling time matrix, the matrix subscripts in each time period need to be calculated. The matrix subscript refers to the position or index of the data in a specific time period in the sampling time matrix. By calculating these subscripts, it is possible to determine which data points belong to the specified time period. This step is critical because it ensures that only data matching the time period will be extracted and analyzed, avoiding interference from irrelevant data. Finally, according to the calculated matrix subscript, the corresponding multi-dimensional information of the networked constellation fragment will be read. This information includes the spatial position information of the networked constellation satellite, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding information of the GNSS satellite. The spatial position information of the networked 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 mirror reflection point helps determine the location of the surface reflection area; the spatial position information of the GNSS satellite corresponding to the mirror reflection point is used to calculate the signal path and reflection angle; the PRN coding information is used to identify and distinguish the signals of different satellites. Together, this information constitutes a fragment view of the networked constellation in a specific time period, providing detailed data support for further quality monitoring.

[0069] The embodiments of the present invention can perform more detailed and accurate quality monitoring in the time dimension through accurate time period matching and data extraction. The sampling time matrix and matrix subscript can be used to effectively filter and extract relevant data, thereby improving the efficiency and accuracy of data processing. In addition, by reading and analyzing the multi-dimensional information of the networked constellation fragments, the operating status of the constellation can be tracked and monitored in real time, which helps to quickly identify and locate potential abnormalities and faults.

[0070] In an exemplary embodiment of the present invention, one implementation method of performing three-dimensional data rendering on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface is to perform three-dimensional data rendering based on the spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R mirror reflection points, and the spatial position information of the GNSS satellites corresponding to the GNSS-R mirror reflection points to obtain a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting the type of land object as the background. Performing three-dimensional data rendering on the multi-dimensional information of the networking constellation fragments is a key step in converting complex multi-dimensional information into an intuitive and visual interface. By performing three-dimensional data rendering on different types of spatial position information, a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting the type of land object as the background is constructed, providing users with an intuitive quality monitoring tool.

[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 mirror reflection points, and the spatial position information of the GNSS satellites corresponding to the GNSS-R mirror reflection points. The spatial position information of the networking constellation satellites reflects the orbit and position of the satellite in a specific time period. It is the key data of the entire rendering process because the position of the satellite directly affects the position of the reflection point and the path of signal transmission. The spatial position information of the GNSS-R mirror reflection points identifies the position of the surface reflection signal. These reflection points are important reference points for detecting surface physical parameters. The spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point is also crucial because it provides the position data of the signal source and is the basis for calculating the reflection path.

[0072] After obtaining these spatial position information, three-dimensional data rendering is performed based on this information. Specifically, with the three-dimensional earth reflecting the type of land objects as the background, the positions of the networking constellation satellites, GNSS-R mirror reflection points and corresponding GNSS satellites are geometrically configured in three-dimensional space. This rendering method allows users to intuitively observe the relative positions and geometric relationships between satellites, reflection points and signal sources against the background of the three-dimensional earth reflecting the type of land objects. For example, users can use the three-dimensional interface to view the position of a satellite in its orbit during a specific time period, and its relative relationship with the reflection points on the surface. This three-dimensional spatial geometric configuration not only shows the spatial relationship between the satellite and the surface, but also reflects the propagation path and reflection process of the signal.

[0073] The embodiment of the present invention uses three-dimensional data rendering to enable users to obtain an intuitive and three-dimensional quality monitoring interface. Compared with traditional two-dimensional graphics, the three-dimensional rendering interface provides a richer information dimension. Users can observe and analyze the spatial configuration of the constellation from different angles, thereby improving the accuracy and efficiency of monitoring. Secondly, the rendering method with a three-dimensional earth reflecting the type of land object as the background also enhances the operability and comprehensibility of the system. Users can freely explore and analyze the operating status of the constellation through operations such as rotation and scaling. Three-dimensional data rendering can also improve the real-time monitoring capability of the system to a certain extent. Through the display of three-dimensional space, users can more quickly identify and locate potential anomalies and fault problems, such as satellite position deviation, reflection point anomalies, etc. This not only provides strong support for quality monitoring, but also provides a basis for engineers to respond quickly and solve problems.

[0074] In an exemplary embodiment of the present invention, an implementation method of performing two-dimensional visualization processing on multi-dimensional information of a networking constellation fragment to obtain a multi-dimensional visualization interface is to obtain playback synchronization time information of three-dimensional data rendering, and obtain the operating status of the networking constellation and the trajectory of the GNSS-R mirror reflection point from the three-dimensional space geometric configuration interface; determine the reflection channel number of the GNSS-R receiver of the networking constellation satellite to be monitored according to the operating status and the trajectory; read out the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file; and perform two-dimensional visualization processing on the read GNSS-R on-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

[0075] In practical applications, 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 the three-dimensional data rendering process. These time points correspond to the spatial positions of the constellation satellites and their reflection points at specific times, and are the key basis for subsequent two-dimensional visualization processing. Through these synchronized time information, the trajectories of the constellation satellites and the GNSS-R mirror reflection points can be accurately located, thereby ensuring the consistency of the time axis of the two-dimensional visualization processing with the three-dimensional rendering. Next, the operating status of the networked constellation and the trajectory of the GNSS-R mirror reflection points are obtained from the three-dimensional space geometric configuration interface. The purpose of this step is to provide the necessary geometric and physical background information for two-dimensional visualization processing. The operating status reflects the motion state of the constellation satellite in orbit, including parameters such as speed, trajectory and attitude, while the trajectory of the GNSS-R mirror reflection point describes the moving path of the reflected signal on the surface. This information lays the foundation for the subsequent determination of the reflection channel number and the extraction of waveform information.

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

[0077] After the reflection channel number is determined, the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number 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 reflection signal, which contains multi-dimensional information such as the amplitude, frequency, and phase of the reflection signal. In this step, the system extracts this waveform information and provides basic data for two-dimensional visualization processing.

[0078] Finally, the read GNSS-R on-orbit observation waveform information is processed in two dimensions to obtain an intuitive two-dimensional visualization projection interface. On this interface, users can clearly see the waveform characteristics of the reflected signal and judge the quality and stability of the signal by observing these waveforms. For example, users can identify possible signal attenuation problems by observing the amplitude changes of the waveform, or detect possible interference sources by frequency offset.

[0079] The embodiment of the present invention uses two-dimensional visualization processing, so that users can quickly and intuitively obtain key waveform information without having to deeply understand complex three-dimensional data. This interface design enhances the user-friendliness of the system and makes quality monitoring more concise and efficient. Through the application of playback synchronization time information, the two-dimensional visualization interface and the three-dimensional rendering interface maintain good time consistency, thereby ensuring the continuity and accuracy of data analysis. In addition, by focusing on a specific reflection channel number, it is possible to accurately analyze a specific signal source, thereby improving the pertinence and effectiveness of quality monitoring.

[0080] In an exemplary embodiment of the present invention, an implementation method of performing two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragment to obtain a multi-dimensional visualization interface is to read out 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; and project the read auxiliary information of the mirror reflection point onto the two-dimensional visualization projection interface.

[0081] In practical applications, it is necessary to obtain 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 to ensure that the information extracted from the multi-dimensional information file is consistent with the actual time. These data are crucial to understanding the status and behavior of the reflection point because they provide background information on the quality of the reflected signal.

[0082] After obtaining this auxiliary information, it is projected onto a two-dimensional visualization projection interface. The purpose of two-dimensional visualization processing is to convert complex multidimensional data into easy-to-understand planar graphics, so that users can view and analyze the data in a flat view. This process involves mapping the auxiliary information of the reflection point into a two-dimensional space, which usually includes drawing the location of the reflection point, displaying its related characteristic data, and providing comparison with other key data.

[0083] For example, a two-dimensional visualization interface can display the spatial distribution of reflection points, changes in signal strength, and reflection patterns related to time. These graphics may include marking the location of reflection points, plotting the intensity of reflected signals, or showing the changes of reflection points at different time points. Through these visualizations, users can quickly identify changes in the status of reflection points, detect signal anomalies, and evaluate the performance of reflection points.

[0084] The embodiment of the present invention projects the auxiliary information of the mirror reflection point onto a two-dimensional interface, so that the complex reflection data is converted into simple and easy-to-understand graphics. This method enhances the visualization effect of the data, allowing users to more intuitively analyze the status and performance of the reflection point. Through the two-dimensional visualization interface, users can quickly identify and locate anomalies or problems of the reflection point. Such an interface provides an efficient data analysis tool, allowing users to monitor the operating status of the system in real time and quickly discover and solve potential problems. The two-dimensional visualization interface provides a wealth of reflection point data and feature displays to help users make detailed analysis and decisions. Users can make optimizations and adjustments based on these graphical data to improve the performance and stability of the GNSS-R system. Converting complex multidimensional data into two-dimensional graphics reduces the complexity of data processing. Users do not need to deeply analyze each complex data dimension, but can effectively monitor and analyze through intuitive two-dimensional graphics.

[0085] In an exemplary embodiment of the present invention, an implementation method for performing quality monitoring on a GNSS-R networking constellation according to a multi-dimensional visualization interface to obtain a quality monitoring result is to determine the number and number of networking constellation satellites currently in orbit and operating normally according to the number and number of satellites whose spatial positions continuously change in a three-dimensional space geometric configuration interface.

[0086] In practical applications, the three-dimensional spatial geometry configuration interface is a three-dimensional visualization interface that displays the spatial position of the satellites in the networking constellation and their continuous changes. This interface provides a comprehensive perspective that can display the position, trajectory and relationship of each satellite in real time. In this interface, the spatial position of the satellite changes dynamically, which means that the satellite has its specific spatial position data at every moment on the trajectory. By analyzing these dynamic data, the real-time status and movement of each satellite can be obtained. The number and number of satellites need to be extracted from the three-dimensional spatial geometry 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 networking constellation. Next, analyze this data 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 is consistent with the expected trajectory and determining whether the satellite is working normally on the predetermined orbit. This can be verified by comparing the real-time data with the preset normal working status. If the position and number of the satellite are consistent with the expected, it means that the satellite is working normally in orbit; otherwise, there may be a fault or other problem.

[0087] For example, some satellites may not work properly due to technical failures, orbital deviations or other reasons. By analyzing the data in the three-dimensional space geometry configuration interface, these abnormalities can be discovered in time and further measures can be taken to repair or adjust them. This real-time monitoring can help operators maintain the stability and reliability of the system.

[0088] By analyzing the satellite data in the three-dimensional space geometric configuration interface, the embodiment of the present invention can understand the operating status of the satellites in the networked constellation in real time. This real-time performance ensures the timely discovery and processing 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 to ensure the normal operation of all satellites in orbit and avoid 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. In this way, remedial measures can be taken in a timely manner to ensure the reliability and continuity of system operation. By monitoring the normal working status of satellites in orbit, operators can obtain valuable data on system performance. These data can be used to optimize system configuration and improve operating procedures, thereby improving the efficiency and performance of the overall system.

[0089] In an exemplary embodiment of the present invention, an implementation method for performing quality monitoring on a GNSS-R networking constellation according to a multi-dimensional visualization interface to obtain a quality monitoring result is to determine the number of reflecting GNSS satellites currently visible to the networking constellation satellites based on the number of trajectories of updated GNSS-R mirror reflection points in a three-dimensional space geometric configuration interface.

[0090] Mirror reflection points are key observation objects in GNSS-R technology. They 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 propagation path of the reflected signal, ground physical parameters, etc. can be obtained. In the multi-dimensional visualization interface, the trajectories of the mirror reflection points will be dynamically updated over time to reflect the reflection of the signal in the current system. In the three-dimensional interface, when the number of trajectories is updated, it reflects how many GNSS satellites' signals are currently reflected by the ground and received by the GNSS-R receiver. By counting the number of these updated trajectories, the number of reflecting satellites of the GNSS signals received by each micro-nano satellite in the current network constellation can be determined. The change in this number can show the coverage of the satellite observation network and the current signal reception quality. Visible satellites are those satellites whose signals can be reflected by the earth's surface and effectively captured by the GNSS-R receiver during the current time period. By analyzing the number of trajectories of GNSS-R mirror reflection points, users can determine the visibility of these reflecting satellites. This is of great significance for monitoring the health and working status of the satellite network.

[0091] In the embodiment of the present invention, the user can quickly determine the number of currently visible reflection GNSS satellites through the real-time updated mirror reflection point trajectory. This real-time monitoring capability can ensure that the system has a timely understanding of the working status of the satellite, improve the accuracy of the data, and provide a reliable basis for system maintenance. By analyzing the number of visible satellites, the user can evaluate the signal coverage of the current GNSS-R networking constellation. If the number of visible satellites decreases, it may mean that there is a problem with some satellites or reflection channels, which provides a direct reference for monitoring and optimizing the signal coverage capability of the networking constellation. The reflection point trajectory dynamically displayed by the three-dimensional space geometric configuration interface not only shows the normal working status of the satellite network, but also can identify abnormal working status. For example, when the number of trajectories decreases abnormally, the user can be prompted to check whether some satellites or receiving devices are faulty. This timely feedback mechanism helps to 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 judge the satellite status and signal reception status from the interface without processing complex text data. This not only improves work efficiency, but also reduces the professional threshold requirements for technicians. By monitoring and analyzing the number of visible satellites, users can effectively understand the operating status of satellites and the quality of signal transmission, and make corresponding adjustments and optimization decisions. For example, in areas with insufficient signal coverage, adding satellites or adjusting satellite orbits can optimize network performance and ensure the normal operation of the system.

[0092] In an exemplary embodiment of the present invention, one implementation method of quality monitoring the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain the quality monitoring result 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 mirror reflection point in the three-dimensional space 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 mirror reflection point is generally used to indicate the intensity or reflection characteristics of the reflection signal. For example, the depth of the color can reflect the reflection intensity of the signal, and the darker the color, the stronger the reflection 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 for its motion trajectory, which is used to identify the specific reflection point and its changes in time. The trajectory number helps users track and analyze the behavior pattern of a specific reflection point and match it with other data in the GNSS system.

[0093] Based on 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 usually includes the following steps:

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

[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 a preset database or system to confirm the specific system type and satellite PRN number.

[0096] Data integration: The color depth and navigation satellite number information are integrated 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 the related GNSS system.

[0097] The embodiment 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 the GNSS-R mirror reflection point. This precise identification helps to ensure the accuracy and validity of the data. Real-time tracking of the color and trajectory number of the reflection point can provide instant information about the GNSS system type and satellite status. This real-time performance enables monitoring personnel to quickly discover and handle any anomalies 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 and can help evaluate the data quality of the reflection event. Through this evaluation, the system can judge the reliability of the data and ensure the accuracy of subsequent analysis and application. Through detailed analysis of the reflection point, faults in the system can be quickly identified and located. For example, if the signal strength of the reflection point of a satellite is abnormal or the trajectory does not match, it may indicate that there is a problem with the satellite. The system can perform fault diagnosis and repair based on this information.

[0098] In an exemplary embodiment of the present invention, one implementation method of quality monitoring the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain the quality monitoring result is to determine whether the reflection channel waveform generation function stops working according to the GNSS-R on-orbit observation waveform information in the two-dimensional visualization projection interface. The two-dimensional visualization projection interface is an interface that displays the GNSS-R on-orbit observation waveform information. The interface converts complex three-dimensional data into two-dimensional graphics, and intuitively displays the waveform characteristics and dynamic changes of the reflection signal. In this way, the user can more clearly observe the signal strength, frequency and other key parameters.

[0099] GNSS-R on-orbit observation waveform information refers to the real-time observation data of the reflected signal on the satellite orbit. These waveform information include the signal strength change, frequency characteristics and dynamic situation of the reflection point. By analyzing these waveforms, detailed information about the working status of the reflection track can be obtained. In order to determine whether the reflection track has stopped working, the following steps can be taken:

[0100] Data collection: Obtain GNSS-R on-orbit observation waveform information from a two-dimensional visual projection interface. This information is usually displayed as graphical waveform data, including changes in signal strength and frequency.

[0101] Waveform analysis: Analyze the characteristics in the waveform information. For example, if the reflector track is working properly, the waveform should show stable periodic changes and certain signal strength. If the waveform shows obvious interruptions, missing or irregular changes, it may indicate that there is a problem with the reflector track.

[0102] Track status judgment: Determine the status of the reflection channel waveform generation function based on the analysis results of the waveform information. If the waveform information shows that the signal strength is continuously zero or significantly decreases, and there is no periodic change, it can be inferred that the reflection channel waveform generation function may have stopped working. Further verification is required to determine whether there is a hardware failure or data transmission problem.

[0103] The embodiment of the present invention can monitor the working status of the reflection channel waveform generation function in real time and promptly detect any abnormality or shutdown by analyzing the waveform information of the two-dimensional visual projection interface. This timeliness helps to quickly locate problems and reduce system downtime and potential losses. The analysis of waveform information can provide detailed status data about the reflection channel waveform generation function, including changes in signal strength and waveform anomalies. With this information, the fault point of the reflection channel waveform generation function can be accurately located to help technicians quickly repair and maintain it. Improve the overall system reliability of the GNSS-R networking constellation. By continuously monitoring the status 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 invention, an implementation method of quality monitoring the GNSS-R networking constellation according to a multi-dimensional visualization interface to obtain a quality monitoring result is to determine the on-orbit observation quality of the two-dimensional observation waveform of the reflection channel according to the GNSS-R on-orbit observation waveform information, the auxiliary information of the mirror reflection point and the waveform peak signal-to-noise ratio corresponding to the mirror reflection point in the two-dimensional visualization projection interface. 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. A higher signal-to-noise ratio generally means clearer signals and higher observation quality. In the two-dimensional visualization projection interface, waveform peak signal-to-noise ratio data can help determine the quality of the signal. Determining the on-orbit observation quality of the two-dimensional observation waveform of the reflection channel can specifically include the following steps:

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

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

[0107] Quality Assessment: Based on the analysis results, determine the observation quality of the reflection channel. If the signal-to-noise ratio is high and the waveform characteristics are stable and consistent, the reflection channel can be judged to be of good quality. 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 requires further investigation and repair.

[0108] The embodiment of the present invention can obtain high-quality observation data about the reflection channel by performing a detailed analysis of the waveform information of the two-dimensional visual projection interface. 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 network constellation. This optimization can improve the overall efficiency and reliability of the system.

[0109] Based on the above description of the embodiment of a quality monitoring method for a GNSS-R networking constellation, a visual quality monitoring scheme for a GNSS-R networking constellation with multi-dimensional information fusion is introduced below. This visual quality monitoring scheme for a GNSS-R networking constellation with multi-dimensional information fusion uses a GNSS-R networking constellation consisting of 10 satellites as an example. These satellites are compatible with the four major navigation systems: BDS, GPS, GAL, and GLO. Assume that the on-orbit operation quality of 10 GNSS-R networking constellations within 1 hour starting at 00:00:00 on February 1, 2024 is specified to be monitored and evaluated.

[0110] Reference Figure 2 , showing a flowchart of the steps of a GNSS-R networking constellation visualization quality monitoring solution with multi-dimensional information fusion according to an embodiment of the present invention.

[0111] (1) Unified data preprocessing

[0112] All the received raw code stream data of the GNSS-R networking constellation are processed into a unified preprocessing file with the same format. The unified preprocessing file should contain multi-dimensional information of the networking constellation, including the spatial position information of the networking constellation satellite, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, the PRN coding information of the GNSS satellite, the GNSS-R on-orbit observation waveform information, the auxiliary information of the mirror reflection point and other data products with unified data format. 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 GPS satellites is uniformly processed as PRN+0, the PRN number of BDS satellites is uniformly processed as PRN+100, and the PRN number of GAL satellites is uniformly processed as PRN+200. In order to distinguish different satellites of the networking constellation, different networking satellites generate independent unified preprocessing files, and the unified 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 unified preprocessing file should also identify the observation start and end time of the satellite. For example, the file name of satellite No. 1 is designed to be GNSSR_01_20240201_000000_20240201_010000_C3.NC, where GNSSR represents the networking constellation type, 01 represents networking satellite No. 1, 20240201_000000 represents the observation start time 00:00:00 on February 1, 2024, 20240201_010000 represents the observation end time 01:00:00 on February 1, 2024, C3 represents reflection channel 3, and NC represents the file type is in 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, of which the initial time period is from 00:00:00 on February 1, 2024 to 00:10:00 on February 1, 2024. Read the observation time range in the file name of the unified preprocessing file generated in step (1) one by one, select the unified preprocessing file with a time range within the quality monitoring period, then read the sampling time matrix in the file, calculate the matrix subscript of the time period within the quality monitoring time range, read the spatial position of the network constellation satellite, the spatial position of the GNSS-R mirror reflection point, the spatial position of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding variable information of the GNSS satellite according to the matrix subscript, and push the information in each small time segment to the 3D visualization system after the information in each small time segment is read, and then read the information in the next time segment until all time segments are traversed.

[0115] (3) Network constellation space geometric information data rendering and three-dimensional visualization processing

[0116] The GNSS-R networking constellation information within a complete time segment pushed from step (2) is rendered in three dimensions in chronological order based on the spatial positions of the constellation satellites, the spatial positions of the GNSS-R mirror reflection points, and the spatial positions of the GNSS satellites corresponding to the GNSS-R mirror reflection points, to form a three-dimensional spatial geometric configuration with the three-dimensional earth reflecting the type of landform as the background. The three-dimensional earth surface reflecting the type of landform uses colors of different depths to represent different landforms. The spatial position of the GNSS-R mirror reflection point is retained on the three-dimensional earth surface reflecting the type of landform. The positions of the networking constellation satellites and the positions of the GNSS satellites only retain the position points at the current moment. While the networking constellation information data is rendered and the three-dimensional visualization is played back, the playback synchronization time information is synchronously transmitted to the GNSS-R on-orbit observation waveform visualization module and the auxiliary data information visualization module.

[0117] The above data rendering and 3D visualization operations are implemented using Cesium, an open source JavaScript-based 3D map framework. Figure 3 , showing a three-dimensional visualization effect diagram of the spatial geometric information of a GNSS-R networking constellation according to an embodiment of the present invention.

[0118] After the constellation data in the initial time period from 00:00:00 on February 1, 2024 to 00:10:00 on February 1, 2024 is rendered, read the data from 00:10:01 on February 1, 2024 to 00:20:00 on February 1, 2024 and start rendering. After the rendering is completed, the sample data is put into the playback sequence, and the quality monitoring visualization interface is continuously updated according to step (3). The same steps are repeated for the remaining 4 subsequent time segments until the task of replaying the on-orbit operation status of the 10 GNSS-R network constellations within 1 hour starting from 00:00:00 on February 1, 2024 is completed.

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

[0120] While the network constellation spatial geometry information data is rendered and played back in three-dimensional visualization, according to the playback synchronization time information transmitted in step (3), in order to judge the on-orbit observation performance of the reflection channel 1 of the 01 satellite, the network constellation 01 satellite to be monitored and the GNSS-R receiver reflection channel number 1 are selected, and the GNSS-R on-orbit observation waveform of the corresponding time and channel is selected and read from the unified preprocessing file generated in step (1), and the waveform data is processed by two-dimensional visualization projection according to the size of the waveform data. Figure 4 , showing a two-dimensional visualization effect diagram of GNSS-R waveform data information in an embodiment of the present invention. And synchronously replay with the spatial geometric information of step (3), and at the same time pass the playback synchronization time information, the satellite number 01 of the constellation to be monitored and the reflection channel number 1 of the GNSS-R receiver 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 network constellation spatial geometry information data rendering and three-dimensional visualization are being played back, according to the playback synchronization time information transmitted in step (4), the network constellation satellite No. 01 to be monitored and the GNSS-R receiver reflection channel No. 1, a unified pre-processing file is selected from step (1), and the GNSS-R mirror reflection point auxiliary information of the corresponding time and channel is read, and the value of the information is projected onto the map used in step (2) using points of different colors, and the map is locally enlarged, and the position of the auxiliary information on the map is the same as the position of the GNSS-R mirror reflection point. Figure 5 , showing a two-dimensional visualization effect diagram of GNSS-R mirror reverse point auxiliary information according to an embodiment of the present invention.

[0123] (6) Determination of the quality of network constellations

[0124] The on-orbit working status of the GNSS-R of the 01 channel of the 01 satellite of the GNSS-R large-scale networking constellation is judged by comprehensively utilizing multi-dimensional information such as the spatial geometry information of the networking constellation, the on-orbit observed waveform data information, and the auxiliary data information of the mirror reflection point:

[0125] 1) Since the three-dimensional geospatial positions of the 10 networking satellites reflecting the types of ground objects in step (3) are continuously changing, and the trajectories of the mirror reflection points are constantly updated, it can be determined that the current networking constellation of 10 satellites numbered 01-10 is operating normally, among which satellite 01 has three reflection point observation trajectories at the current moment, so satellite 01 currently has three reflection channels receiving the reflection satellite.

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

[0127] 3) Since the observed waveform of the reflection channel 1 of satellite No. 01 is a typical "horseshoe-shaped" distribution, and the GNSS-R mirror reflection point auxiliary data information shows that the position of the mirror reflection point at this moment 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 expectations and has good quality.

[0128] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0129] Reference Figure 6 , shows a structural block diagram of a quality monitoring system for a GNSS-R network constellation according to an embodiment of the present invention. The quality monitoring system for a GNSS-R network constellation may specifically include the following modules.

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

[0131] The raw data unified preprocessing module 62 is used to perform unified preprocessing on the raw data to obtain multi-dimensional information of the network constellation and a file name in a unified naming method;

[0132] A multi-dimensional information slicing processing module 63 is used to perform time slicing processing on the networking constellation multi-dimensional information to obtain networking constellation fragment multi-dimensional information;

[0133] A multi-dimensional information visualization processing module 64 is used to perform three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragment to obtain a multi-dimensional visualization interface;

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

[0135] In an exemplary embodiment of the present invention, the raw data unified preprocessing module 61 is used to perform unified preprocessing on the raw data to obtain independent multi-dimensional information files for different GNSS-R networking satellites;

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

[0137] The spatial position information of the network constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, the PRN coding information of the GNSS satellite, the GNSS-R on-orbit observation waveform information, and the auxiliary information of the mirror reflection point;

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

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

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

[0141] The monitoring time acquisition module is used to obtain the specified quality monitoring time range information;

[0142] A monitoring time division module, used to divide the quality monitoring time range information into multiple time periods;

[0143] The segment multi-dimensional information reading module is used to read the networking constellation segment multi-dimensional information from the multi-dimensional information file corresponding to each of the time periods.

[0144] In an exemplary embodiment of the present invention, the fragment multi-dimensional information reading module includes:

[0145] A multidimensional information file selection module, configured to select the multidimensional information file corresponding to each of the time periods according to the observation start time and the end time in the file name of each of the multidimensional information files;

[0146] A sampling time matrix reading module, used for reading the sampling time matrix of the multidimensional information file corresponding to each of the time periods;

[0147] A matrix subscript calculation module, used for calculating the matrix subscript of the sampling time matrix in each of the time periods;

[0148] A networking constellation fragment multi-dimensional information reading module, used to read the networking constellation fragment multi-dimensional information according to the matrix subscript;

[0149] The multi-dimensional information of the networking constellation fragments includes:

[0150] The spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding information of the GNSS satellite.

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

[0152] The three-dimensional data rendering module is used 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 mirror reflection point, and the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point to obtain a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting the type of landform as the background.

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

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

[0155] An operating status and trajectory acquisition module, used to acquire the operating status of the networking constellation and the trajectory of the GNSS-R mirror reflection point from the three-dimensional space geometric configuration interface;

[0156] A reflection channel number determination module, used to determine the reflection channel number of the GNSS-R receiver of the networked constellation satellite to be monitored according to the operating status and the trajectory;

[0157] A waveform information reading module, used for reading the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file;

[0158] The two-dimensional visualization processing module is used to perform two-dimensional visualization processing on the read GNSS-R on-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

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

[0160] An auxiliary information reading module, used for reading 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;

[0161] The auxiliary information projection module is used to project the auxiliary information of the mirror reflection point read out onto the two-dimensional visualization projection interface.

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

[0163] The normal working satellite quality determination module is used to determine the number and number of networked constellation satellites currently in orbit and working normally according to the number and number of satellites whose spatial positions continuously change in the three-dimensional space geometric configuration interface.

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

[0165] The module for determining the number of reflecting GNSS satellites is used to determine the number of reflecting GNSS satellites currently visible to the satellites of the networked constellation according to the number of tracks of updated GNSS-R mirror reflection points in the three-dimensional space geometric configuration interface.

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

[0167] A reflection event quality determination module, used to determine the GNSS system type and satellite PRN number corresponding to the reflection event according to the color depth of the GNSS-R mirror reflection point in the three-dimensional space geometric configuration interface and the navigation satellite number;

[0168] 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.

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

[0170] The reflection channel quality determination module is used to determine whether the reflection channel waveform generation function stops working according to the GNSS-R on-orbit observation waveform information in the two-dimensional visual projection interface.

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

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

[0173] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0174] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0175] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0176] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0179] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0180] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used 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 "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0181] A quality monitoring method for a GNSS-R networking constellation and a quality monitoring system for a GNSS-R networking constellation provided by the present invention are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring the quality of a GNSS-R network constellation, characterized in that: The method comprises: Obtain the raw data of the GNSS-R network constellation; Performing unified preprocessing on the raw data to obtain multi-dimensional information of the networking constellation and a file name in a unified naming method; Performing time slicing processing on the networking constellation multi-dimensional information to obtain networking constellation fragment multi-dimensional information; Performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragment to obtain a multi-dimensional visualization interface; The quality of the GNSS-R networking constellation is monitored according to the multi-dimensional visualization interface to obtain a quality monitoring result.

2. The method according to claim 1, characterized in that The unified preprocessing of the raw data to obtain multi-dimensional information of the networking constellation and a file name in a unified naming method includes: The raw data are uniformly preprocessed to obtain independent multi-dimensional information files for different GNSS-R networking satellites; The multi-dimensional information file includes the following multi-dimensional information of the networking constellation: The spatial position information of the network constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, the PRN coding information of the GNSS satellite, the GNSS-R on-orbit observation waveform information, and the auxiliary information of the mirror reflection point; The file name of the unified naming method of the multi-dimensional information file includes the following information: Networking satellite number, observation start and end time of the networking satellite, and reflection channel number of the GNSS-R receiver.

3. The method according to claim 2, characterized in that The step of performing time slicing processing on the networking constellation multi-dimensional information to obtain networking constellation fragment multi-dimensional information includes: Get the specified quality monitoring time range information; Dividing the quality monitoring time range information into a plurality of time periods; The networking constellation segment multidimensional information is read out from the multidimensional information file corresponding to each of the time periods respectively.

4. The method according to claim 3, characterized in that The step of reading the multi-dimensional information of the networking constellation fragments from the multi-dimensional information files corresponding to each of the time periods comprises: According to the observation start time and the end time in the file name of each multidimensional information file, select the multidimensional information file corresponding to each time period; Reading a sampling time matrix of the multidimensional information file corresponding to each of the time periods; Calculating the matrix subscript of the sampling time matrix in each of the time periods; Reading the multi-dimensional information of the networking constellation fragment according to the matrix subscript; The multi-dimensional information of the networking constellation fragments includes: The spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding information of the GNSS satellite.

5. The method according to claim 4, characterized in that The performing three-dimensional data rendering on the multi-dimensional information of the networking constellation fragment to obtain a multi-dimensional visualization interface includes: Three-dimensional data rendering is performed based on the spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R mirror reflection point, and the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point to obtain a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting the type of land feature as the background.

6. The method according to claim 5, characterized in that The performing two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface includes: Obtain playback synchronization time information of the three-dimensional data rendering, and obtain the operation status of the networking constellation and the trajectory of the GNSS-R mirror reflection point from the three-dimensional space geometric configuration interface; Determine the reflection channel number of the GNSS-R receiver of the networked constellation satellite to be monitored according to the operating status and the trajectory; Reading the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file; The read GNSS-R on-orbit observation waveform information is subjected to two-dimensional visualization processing to obtain a two-dimensional visualization projection interface.

7. The method according to claim 6, characterized in that The performing two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragments to obtain a multi-dimensional visualization interface also includes: Reading out 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 read-out mirror reflection point is projected onto the two-dimensional visualization projection interface.

8. The method according to claim 5, characterized in that The performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result includes: According to the number and number of satellites whose spatial positions continuously change in the three-dimensional space geometric configuration interface, the number and number of networked constellation satellites currently in orbit and operating normally are determined.

9. The method according to claim 5, characterized in that The performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result includes: The number of reflecting GNSS satellites currently visible to the network constellation satellites is determined according to the number of updated GNSS-R mirror reflection point trajectories in the three-dimensional space geometric configuration interface.

10. The method according to claim 5, characterized in that The performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result includes: Determine the GNSS system type and satellite PRN number corresponding to the reflection event according to the color depth of the GNSS-R mirror reflection point in the three-dimensional space 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.

11. The method according to claim 6, characterized in that The performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result includes: According to the GNSS-R on-orbit observation waveform information in the two-dimensional visual projection interface, it is determined whether the reflection channel waveform generation function stops working.

12. The method according to claim 7, characterized in that The performing quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result includes: The on-orbit observation quality of the two-dimensional observation waveform of the reflection channel is determined based on the GNSS-R on-orbit observation waveform information in the two-dimensional visualization projection interface, the auxiliary information of the mirror reflection point and the waveform peak signal-to-noise ratio corresponding to the mirror reflection point.

13. A quality monitoring system for a GNSS-R network constellation, characterized in that: The system comprises: The raw data acquisition module is used to obtain the raw data of the GNSS-R network constellation; A raw data unified preprocessing module, used for performing unified preprocessing on the raw data to obtain multi-dimensional information of the network constellation and a file name in a unified naming method; A multi-dimensional information slicing processing module, used for performing time slicing processing on the multi-dimensional information of the networking constellation to obtain multi-dimensional information of the networking constellation fragments; A multi-dimensional information visualization processing module, used for performing three-dimensional data rendering and two-dimensional visualization processing on the multi-dimensional information of the networking constellation fragment to obtain a multi-dimensional visualization interface; The quality monitoring module is used to perform quality monitoring on the GNSS-R networking constellation according to the multi-dimensional visualization interface to obtain a quality monitoring result.

14. The system according to claim 13, characterized in that The raw data unified preprocessing module is used to perform unified preprocessing on the raw data to obtain independent multi-dimensional information files for different GNSS-R networking satellites; The multi-dimensional information file includes the following multi-dimensional information of the networking constellation: The spatial position information of the network constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, the PRN coding information of the GNSS satellite, the GNSS-R on-orbit observation waveform information, and the auxiliary information of the mirror reflection point; The file name of the unified naming method of the multi-dimensional information file includes the following information: Networking satellite number, observation start and end time of the networking satellite, and reflection channel number of the GNSS-R receiver.

15. The system according to claim 14, characterized in that The multi-dimensional information slicing processing module comprises: The monitoring time acquisition module is used to obtain the specified quality monitoring time range information; A monitoring time division module, used to divide the quality monitoring time range information into multiple time periods; The segment multi-dimensional information reading module is used to read the networking constellation segment multi-dimensional information from the multi-dimensional information file corresponding to each of the time periods.

16. The system according to claim 15, characterized in that The fragment multi-dimensional information reading module comprises: A multidimensional information file selection module, configured to select the multidimensional information file corresponding to each of the time periods according to the observation start time and the end time in the file name of each of the multidimensional information files; A sampling time matrix reading module, used for reading the sampling time matrix of the multidimensional information file corresponding to each of the time periods; A matrix subscript calculation module, used for calculating the matrix subscript of the sampling time matrix in each of the time periods; A networking constellation fragment multi-dimensional information reading module, used to read the networking constellation fragment multi-dimensional information according to the matrix subscript; The multi-dimensional information of the networking constellation fragments includes: The spatial position information of the networking constellation satellites, the spatial position information of the GNSS-R mirror reflection point, the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point, and the PRN coding information of the GNSS satellite.

17. The system according to claim 16, characterized in that The multi-dimensional information visualization processing module comprises: The three-dimensional data rendering module is used 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 mirror reflection point, and the spatial position information of the GNSS satellite corresponding to the GNSS-R mirror reflection point to obtain a three-dimensional spatial geometric configuration interface with a three-dimensional earth reflecting the type of landform as the background.

18. The system according to claim 17, characterized in that The multi-dimensional information visualization processing module further includes: A playback time acquisition module, used to acquire playback synchronization time information of the three-dimensional data rendering; An operating status and trajectory acquisition module, used to acquire the operating status of the networking constellation and the trajectory of the GNSS-R mirror reflection point from the three-dimensional space geometric configuration interface; A reflection channel number determination module, used to determine the reflection channel number of the GNSS-R receiver of the networked constellation satellite to be monitored according to the operating status and the trajectory; A waveform information reading module, used for reading the GNSS-R on-orbit observation waveform information corresponding to the playback synchronization time information and the reflection channel number from the multi-dimensional information file; The two-dimensional visualization processing module is used to perform two-dimensional visualization processing on the read GNSS-R on-orbit observation waveform information to obtain a two-dimensional visualization projection interface.

19. The system according to claim 18, characterized in that The multi-dimensional information visualization processing module further includes: An auxiliary information reading module, used for reading 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 projection module is used to project the auxiliary information of the mirror reflection point read out onto the two-dimensional visualization projection interface.

20. The system according to claim 17, characterized in that The quality monitoring module comprises: The normal working satellite quality determination module is used to determine the number and number of networked constellation satellites currently in orbit and working normally according to the number and number of satellites whose spatial positions continuously change in the three-dimensional space geometric configuration interface.

21. The system according to claim 17, characterized in that The quality monitoring module comprises: The module for determining the number of reflecting GNSS satellites is used to determine the number of reflecting GNSS satellites currently visible to the satellites of the networked constellation according to the number of tracks of updated GNSS-R mirror reflection points in the three-dimensional space geometric configuration interface.

22. The system according to claim 17, characterized in that The quality monitoring module comprises: A reflection event quality determination module, used to determine the GNSS system type and satellite PRN number corresponding to the reflection event according to the color depth of the GNSS-R mirror reflection point in the three-dimensional space 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.

23. The system according to claim 18, characterized in that The quality monitoring module comprises: The reflection channel quality determination module is used to determine whether the reflection channel waveform generation function stops working according to the GNSS-R on-orbit observation waveform information in the two-dimensional visual projection interface.

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

25. An electronic device, characterized in that: include: one or more processors; and One or more machine-readable media having instructions stored thereon, when executed by the one or more processors, enable the electronic device to perform the quality monitoring method for the GNSS-R networking constellation as described in any one of claims 1 to 12.

26. A computer-readable storage medium, characterized in that: The computer program stored therein enables the processor to execute the quality monitoring method for the GNSS-R networking constellation as described in any one of claims 1 to 12.

Images