A GNSS system maintenance method and system

Abstract

The present invention provides a GNSS system maintenance method and system, which relates to the technical field of global navigation satellite systems, including: obtaining first information, the first information including satellite data collected by a GNSS base station receiver; performing frequency domain analysis processing on the first information to obtain second information; performing pattern recognition processing on the second information based on a preset machine learning algorithm to obtain third information; performing signal feature map construction processing based on the third information to obtain fourth information; performing spatial optimization processing based on the fourth information, performing spatial optimization processing on the signal feature map by using a preset spatial statistical difference mathematical model to obtain fifth information; performing dynamic signal adjustment processing on the GNSS system based on the fifth information and the fourth information to obtain maintenance results. The present invention optimizes the signal feature map using spatial interpolation and statistical analysis technology, determines the optimal receiving area and signal coverage range, and improves the positioning accuracy and stability of the GNSS receiver.

Description

Technical Field

[0001] The present invention relates to the technical field of global navigation satellite systems, and in particular to a GNSS system maintenance method and system. Background Art

[0002] Currently, the Global Navigation Satellite System (GNSS) plays an important role in the field of geographic positioning and navigation. However, due to the existence of signal interference and multipath effects, the performance and accuracy of GNSS receivers in complex environments are often challenged. Existing GNSS system maintenance methods mainly rely on simple signal processing techniques and pattern recognition methods, which are often difficult to effectively deal with complex signal interference and multipath effects. Current solutions are often limited to simple signal filtering and interference detection, lacking in-depth mining and accurate identification of signal features, resulting in positioning accuracy and robustness in complex environments that cannot meet actual needs. In addition, existing technologies often ignore the characteristics of spatial distribution and dynamic changes, resulting in limitations in the signal processing and maintenance process, and unable to achieve optimization and dynamic adjustment of the receiving area.

[0003] Based on the shortcomings of the prior art, a GNSS system maintenance method and system are urgently needed. Summary of the invention

[0004] The purpose of the present invention is to provide a GNSS system maintenance method and system to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a GNSS system maintenance method, comprising:

[0006] Acquiring first information, wherein the first information includes satellite data collected by a GNSS reference station receiver;

[0007] Performing frequency domain analysis on the first information to obtain second information, where the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite;

[0008] Performing pattern recognition processing on the second information based on a preset machine learning algorithm, and obtaining third information by identifying signal interference and multipath effects, wherein the third information includes an abnormal signal identification result;

[0009] Performing signal characteristic graph construction processing according to the third information to obtain fourth information, wherein the fourth information includes a spatial distribution graph of satellite signal strength, a change graph of signal Doppler frequency, and a timing graph of antenna phase center offset;

[0010] Performing spatial optimization processing on the fourth information, and obtaining fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, wherein the fifth information includes an optimal receiving area of ​​the GNSS receiver;

[0011] Dynamic signal adjustment processing is performed on the GNSS system based on the fifth information and the fourth information to obtain a maintenance result.

[0012] In a second aspect, the present application also provides a GNSS system maintenance system, including:

[0013] An acquisition module, configured to acquire first information, wherein the first information includes satellite data collected by a GNSS reference station receiver;

[0014] An analysis module, configured to perform frequency domain analysis on the first information to obtain second information, wherein the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite;

[0015] an identification module, performing pattern recognition processing on the second information based on a preset machine learning algorithm, and obtaining third information by identifying signal interference and multipath effects, wherein the third information includes an abnormal signal identification result;

[0016] A construction module, configured to construct a signal characteristic graph according to the third information to obtain fourth information, wherein the fourth information includes a spatial distribution graph of satellite signal strength, a change graph of signal Doppler frequency, and a timing graph of antenna phase center offset;

[0017] an optimization module, configured to perform spatial optimization processing according to the fourth information, and obtain fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, wherein the fifth information includes an optimal receiving area of ​​the GNSS receiver;

[0018] The adjustment module performs dynamic signal adjustment processing on the GNSS system based on the fifth information and the fourth information to obtain a maintenance result.

[0019] The beneficial effects of the present invention are:

[0020] The present invention uses frequency domain analysis to process satellite signal data, combines machine learning algorithms to accurately identify signal interference and multipath effects, and then generates a clear signal feature map. Based on this information, the present invention uses spatial interpolation and statistical analysis techniques to optimize the signal feature map, determine the optimal receiving area and signal coverage, and improve the positioning accuracy and stability of the GNSS receiver.

[0021] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a flowchart of the GNSS system maintenance method described in an embodiment of the present invention;

[0024] Figure 2 A genetic algorithm topology diagram described in an embodiment of the present invention; DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] Embodiment 1:

[0028] This embodiment provides a GNSS system maintenance method.

[0029] See also Figure 1, the figure shows that the method includes step S100, step S200, step S300, step S400, step S500 and step S600.

[0030] Step S100: Acquire first information, where the first information includes satellite data collected by a GNSS reference station receiver.

[0031] It is understandable that in the Global Navigation Satellite System (GNSS), the base station receiver is responsible for receiving signals from satellites and recording relevant information, including the satellite's position, signal strength, Doppler frequency, etc.

[0032] Step S200: Perform frequency domain analysis on the first information to obtain second information, where the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite.

[0033] It can be understood that frequency domain analysis is used to convert time domain signals into frequency domain signals in order to better understand the frequency components and characteristics of the signals. Among them, the satellite signal strength time series records the signal strength of each satellite over time, while the signal Doppler frequency time series reflects the change of the Doppler frequency of the satellite signal over time. In addition, the antenna phase center offset time series of each satellite describes the offset of the phase center, which is crucial to maintaining signal phase stability and reducing the impact of phase drift. This step processes the first information through frequency domain analysis, effectively extracts the key feature information of the satellite signal, and provides the necessary data basis for subsequent pattern recognition and signal feature analysis.

[0034] Step S300: Perform pattern recognition processing on the second information based on a preset machine learning algorithm, and obtain third information by identifying signal interference and multipath effects. The third information includes an abnormal signal recognition result.

[0035] Furthermore, this process trains an intelligent system to learn and understand the difference between normal and abnormal signal patterns from satellite signals. Machine learning algorithms can automatically extract features from large amounts of data and classify signals based on these features. By performing pattern recognition processing on the second information, abnormal signals can be quickly and accurately identified, and the causes and effects of these abnormal signals can be further analyzed. This intelligent signal processing method can effectively cope with complex and changing environmental conditions, providing important support for the performance optimization of GNSS systems.

[0036] Step S400, constructing and processing a signal characteristic graph according to the third information to obtain fourth information, wherein the fourth information includes a spatial distribution graph of satellite signal strength, a graph of changes in signal Doppler frequency, and a timing graph of antenna phase center offset.

[0037] It can be understood that this step provides a comprehensive description and visual analysis of signal characteristics, which can more clearly understand the change patterns of signals in space and time. By analyzing the spatial distribution diagram and change diagram, the distribution and change trend of the signal can be found, which helps to better understand the propagation characteristics of the signal in different environments. At the same time, the generation of the timing diagram makes it possible to track the changes of signal characteristics over time, providing an important reference for further signal processing and optimization.

[0038] Step S500, performing spatial optimization processing according to the fourth information, and obtaining fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, wherein the fifth information includes an optimal receiving area of ​​the GNSS receiver.

[0039] It can be understood that this step provides accurate positioning and reliability analysis of the optimal reception area of ​​the GNSS receiver. Through spatial optimization processing, we can more accurately determine the reception area and improve the performance of the receiver in this area.

[0040] Step S600: Dynamically adjust the signal of the GNSS system based on the fifth information and the fourth information to obtain a maintenance result.

[0041] Furthermore, this step can respond to environmental changes and signal fluctuations in a timely manner and optimize system parameters through dynamic adjustments based on the optimal receiving area and signal characteristics, thereby ensuring that the system can provide stable and reliable positioning services under different conditions.

[0042] In addition, the present application also discloses an embodiment of static adjustment. In the embodiment of static signal adjustment, first, according to the optimized receiver parameter configuration scheme and the optimal receiving area, the GNSS system is parameterized, including the working mode, receiving sensitivity, tracking threshold, etc. of the receiver. Subsequently, the system performs signal adjustment processing, but not dynamically, but fixedly adjusts according to pre-set parameters. In this process, it is crucial to output a work log, which records key events, parameter settings, signal adjustment results, and other information of the system during the static adjustment process, providing valuable reference for operators. Specifically, the parameter setting record includes the parameter settings adopted by the system during the static adjustment process, such as the working mode, transmission power, tracking threshold, etc. of the receiver. These parameter settings have a significant impact on the signal adjustment and performance of the system, and the operator can understand the working status of the system based on the recorded parameter settings. Key events include important events that occur during the static adjustment process of the system, such as signal interference, abnormal signals received, etc. These event records can help operators understand the performance of the system in different situations, and then take corresponding measures to perform quiet settings. The signal adjustment result records the signal characteristics, signal quality, and other information of the system after static adjustment. By analyzing these results, operators can evaluate the performance and stability of the system and optimize the quiet settings accordingly.

[0043] By analyzing the work log, operators can understand the performance of the system during the static adjustment process and make further optimization or adjustments accordingly. In addition, quiet adjustment is also a key step, which allows the system to make specific adjustments to the signal within a specific time period to adapt to environmental changes or special needs. Through work logs or reports, operators can better understand the working status of the system, assist in quiet settings, and ensure stable and reliable operation of the system in various working environments.

[0044] It should be noted that step S200 includes step S210, step S220, step S230 and step S240.

[0045] Step S210: Perform Fourier transform processing on the first information to obtain frequency domain information.

[0046] Specifically, this step uses the Fourier transform algorithm to process the satellite data collected from the GNSS base station receiver. These data are usually signals sampled in the time domain, representing the changes in the signal strength emitted by different satellites over time. By applying the Fourier transform, these time domain signals are converted into frequency domain signals, and the spectrum information of the satellite signal is obtained. This step can better understand the frequency components and spectrum characteristics of the satellite signal by converting the signal from the time domain to the frequency domain. This provides a basis for subsequent signal analysis and processing, and can more accurately identify signal characteristics and anomalies, thereby providing a more reliable data basis for the maintenance and optimization of the GNSS system.

[0047] Among them, the formula for Fourier transform processing satellite signal strength information is as follows:

[0048]

[0049] Among them, X s (f, t) represents the frequency domain information of satellite signal strength; f represents frequency; t represents time; s(t) represents the input signal.

[0050] Step S220: Calculate the model spectrum of each satellite according to the frequency domain information, divide the frequency domain information into at least two time windows and calculate the power spectrum density of the signal in each window to obtain a satellite signal strength time series, wherein the satellite signal strength time series includes the average signal strength of each satellite.

[0051] It should be noted that step S220 includes step S221, step S222, step S223 and step S224.

[0052] Step S221: divide the frequency domain information into time windows.

[0053] First, the frequency domain information is divided into multiple time windows according to a given time window size. The size of the time window depends on the required time resolution and the characteristics of the signal. These windows can be fixed in size or dynamically adjusted according to specific criteria or algorithms.

[0054] Step S222: Perform inverse Fourier transform on each time window:

[0055] For the frequency domain information in each time window, an inverse Fourier transform is performed to convert it back to the time domain. The inverse Fourier transform converts the frequency domain information into a time domain signal, so that the original signal in each time window can be obtained.

[0056] Step S223: Calculate the power spectrum density.

[0057] For the original signal in each time window, calculate its power spectrum density. Power spectrum density is the energy distribution of the signal in the frequency domain, reflecting the energy of the signal at different frequencies. Fourier transform or other spectrum analysis methods can be used to calculate power spectrum density.

[0058] Step S224: Calculate the average strength of satellite signals.

[0059] The power spectrum density calculated in each time window is integrated or summed to obtain the total power of the satellite signal in the time window. Then, the total power of the satellite signal in all time windows is averaged to obtain the average signal strength of each satellite. The average signal strength time series obtained in this way reflects the change of the average signal strength of each satellite in different time periods.

[0060] Through these four steps, the frequency domain information can be converted into time domain information, and the average signal strength time series of each satellite can be obtained, which provides a basis for subsequent analysis and processing. This processing flow can help us better understand the temporal variation trend of satellite signals and thus optimize the performance of the GNSS system.

[0061] Step S230: Fit the frequency components of each satellite according to the frequency domain information, convert the frequency domain information into a time domain signal, use the frequency fitting method to find the best fitting curve, and further calculate the Doppler frequency at each time point to obtain the signal Doppler frequency time series.

[0062] It can be understood that in this step, the frequency domain information is first converted into a time domain signal, so that the changes in the signal over time can be better observed. Then, the best fitting curve is found by using the least squares method. This curve reflects the frequency changes of the signal at different time points. Using the fitting curve, the Doppler frequency at each time point can be further calculated. The Doppler frequency is the change in signal frequency caused by the movement of the GNSS satellite, which is related to the relative movement speed between the receiver and the satellite. By recording the Doppler frequency of each satellite at different time points, the motion state of the satellite relative to the receiver can be understood, and the position information of the receiver can be inferred.

[0063] Step S240: perform phase demodulation processing according to the frequency domain information, calculate the phase information at each moment in the frequency domain information, and track and adjust the phase information in real time based on a preset tracking loop mathematical model to obtain the antenna phase center offset time series of each satellite.

[0064] Specifically, the phase information of the satellite signal at each moment is extracted from the frequency domain information, which is achieved by demodulating the frequency domain information. The demodulation operation involves inverse transformation or phase extraction technology to extract the phase information from the frequency domain signal. Further, a phase tracking system is established according to a preset tracking loop mathematical model to track and adjust the phase information of the satellite signal in real time. The tracking loop model usually includes a phase-locked loop (PLL) or its variants, which are used to track the phase change of the signal and keep the tracking stable. After the tracking loop model is established, the extracted phase information is input into the tracking loop to perform phase tracking and adjustment in real time. The tracking loop continuously adjusts its internal parameters according to the input phase information and the preset model parameters to track the signal phase and calculate the antenna phase center offset at each moment. Finally, according to the output of the tracking loop, the antenna phase center offset time series of each satellite is generated. This time series records the phase center offset of each satellite at the receiver end, which can be used for subsequent signal processing and positioning calculations.

[0065] It should be noted that step S300 includes step S310, step S320, step S330 and step S340.

[0066] Step S310: Perform peak detection and threshold determination processing according to the satellite signal strength time series in the second information to obtain first time point information, where the first time point information includes a time point affected by signal interference.

[0067] It is understandable that these time points represent time points at which the signal strength is affected by interference or anomalies and may require further analysis and processing.

[0068] Step S320: According to the signal Doppler frequency time series in the second information, spectrum analysis and frequency change trend judgment are used to obtain second time point information, where the second time point information includes a time point where a multipath effect exists.

[0069] Specifically, first, the signal Doppler frequency time series is subjected to spectrum analysis, and the time series is converted into a spectrum diagram in the frequency domain. The spectrum diagram can display the energy distribution of the signal at different frequencies, which helps to identify the main frequency components of the signal. Based on the spectrum diagram, the frequency change trend of the signal is judged. By analyzing the change of frequency over time, it can be found whether there is a change in signal frequency caused by multipath effect. The multipath effect will cause the signal to propagate in multiple paths at the receiving end, thereby causing a change in signal frequency. By observing the irregular changes in frequency, it can be judged whether there is the influence of multipath effect. This step identifies the time point when the multipath effect exists by performing spectrum analysis and judging the frequency change trend on the signal Doppler frequency time series, providing basic data for subsequent abnormal signal identification.

[0070] Step S330: perform data fusion processing based on the first time point information and the second time point information, and perform space-time correlation analysis on the fused data to obtain third time point information, where the third time point information is a time point affected by signal interference and multipath effect.

[0071] It is understandable that fusing the first time point information and the second time point information involves integrating and merging the data of the two time point information so that the influence of signal interference and multipath effects can be considered simultaneously in subsequent analysis. The spatial-temporal correlation analysis of the fused data is aimed at determining the time points where signal interference and multipath effects jointly affect, that is, points that are affected by interference and multipath effects in space and time. By combining spatial and temporal information for analysis, these affected points can be determined more accurately. The time points where signal interference and multipath effects jointly affect are crucial for identifying signal data points that are doubly affected. In the GNSS system, accurately identifying these affected points can help the system better perform error correction and positioning processing.

[0072] Step S340: Perform pattern recognition processing on the third time point information based on a preset support vector machine mathematical model, and obtain third information by combining the satellite signal strength change trend and the Doppler frequency change law.

[0073] It is understandable that the support vector machine is a supervised learning algorithm used for pattern recognition and classification problems. It should be noted that before applying the support vector machine, the information at the third time point needs to be feature extracted and preprocessed, which involves extracting the changing trend of satellite signal strength and the changing law of Doppler frequency as features, and standardizing or normalizing these features for the input of the support vector machine model. The preprocessed information at the third time point is then input into the support vector machine model for pattern recognition processing. The support vector machine will learn how to classify the input features with different categories or patterns, thereby identifying abnormal or affected data points. Finally, the output results of the support vector machine are combined to obtain the third information. This information includes the identification results of the abnormal points, the classification labels of the affected data points, or other relevant information. By combining the changing trend of satellite signal strength and the changing law of Doppler frequency, the third information can provide a deeper understanding of the abnormal points and affected data points.

[0074] It should be noted that step S400 includes step S410, step S420, step S430 and step S440.

[0075] Step S410: spatially locate the satellite signal strength, satellite signal strength, Doppler frequency and antenna phase center offset characteristics at each time point according to the third information to obtain spatial coordinate information of the satellite signal feature points.

[0076] Furthermore, before performing spatial positioning, it is necessary to prepare the satellite signal strength, Doppler frequency and antenna phase center offset characteristic data in the third information, and these characteristic data are distributed in space. Furthermore, the satellite signal characteristics at each time point in the third information are associated with the corresponding spatial position to obtain the spatial coordinate information of the satellite signal characteristic point. This can be achieved by performing spatial positioning in a known geographic coordinate system in the GNSS system, and the time information of the satellite signal characteristic point is corresponded to its position in the geographic space. Then, based on the results of spatial positioning, the spatial coordinate information of each satellite signal characteristic point in the geographic coordinate system is obtained. This information will include spatial position parameters such as longitude, latitude and altitude of each characteristic point for subsequent spatial analysis and processing. The acquired spatial coordinate information needs to be further processed, including data cleaning, coordinate conversion or other necessary preprocessing steps to ensure the accuracy and consistency of the data.

[0077] Step S420: perform spatial interpolation processing on the satellite signal strength of the spatial coordinate information based on a preset radial basis function interpolation mathematical model, and perform parameter adjustment processing based on the uneven distribution of satellites and the spatial correlation of the signal strength to generate a spatial distribution map.

[0078] It can be understood that the interpolation process is to smooth the signal strength in space, while taking into account the spatial distribution of satellites and the spatial correlation of signal strength, so as to more accurately estimate the signal strength value of each spatial position. In this process, according to the actual situation and prior knowledge, the parameters of the radial basis function interpolation model are adjusted, which involves the parameter adjustment of the interpolation method, the setting of the interpolation weight, etc., to ensure that the interpolation result can reflect the true distribution of satellite signal strength in space. Finally, based on the results of the interpolation process, a spatial distribution map of satellite signal strength is generated. This map intuitively shows the signal strength in different geographical locations, helps us understand the spatial variation of signal strength, and provides an important reference for subsequent spatial optimization processing.

[0079] The radial basis function formula is as follows:

[0080]

[0081] Where f(x) represents the function value to be estimated; x represents the input spatial coordinate information; N represents the number of radial basis functions; w i represents the weight of the radial basis function; c irepresents the center of the radial basis function; φ(||xc i ||) represents the multi-aperture radial basis function; i represents the index of the radial basis function.

[0082] Through the linear combination of radial basis functions, the formula models the spatial distribution of signal strength, so that this model can be used to perform spatial interpolation processing on the signal strength. In specific applications, by adjusting the parameter w of the radial basis function i and c i , it is possible to achieve accurate estimation and adjustment of the spatial distribution of signal strength, and then generate a spatial distribution map.

[0083] Step S430, the Doppler frequency of the spatial coordinate information is processed based on a preset spatial average filtering mathematical model, and the size of the field range and the weighting method are adjusted in combination with the spatial distribution of the satellite signal feature points to obtain an average filtering result that reflects the spatial variation law of the Doppler frequency, and a Doppler frequency variation graph is generated based on the average filtering result.

[0084] It should be noted that the mathematical model of the spatial average filter is a spatial filter based on weighted average, in which the weight of each satellite signal feature point will be adjusted according to its distribution in space. The spatial coordinate information obtained in step S410 is combined with the corresponding satellite Doppler frequency data, and the Doppler frequency is processed using the spatial average filter model. In this process, not only the Doppler frequency values ​​of each satellite signal feature point should be considered, but also their distribution in space. According to the spatial distribution of the satellite signal feature points, the domain range and weighting method in the spatial average filter model are adjusted, which specifically involves determining the filter window size, neighborhood distance weight and other parameters to ensure that the filtering effect can reflect the true change law of the Doppler frequency in space. The average filtering result is used to describe the Doppler frequency situation at each geographical location, and the Doppler frequency change diagram intuitively shows the change trend of the Doppler frequency with the spatial position.

[0085] Step S440: Perform timing analysis processing according to the spatial coordinate information, and obtain a timing diagram of the antenna phase center offset by performing timing analysis on the antenna phase center offset of the satellite signal.

[0086] It can be understood that this step uses the timing analysis method to process the antenna phase center offset data, extracts the periodicity, trend and other information, and reveals the regular changes of the antenna phase center offset. The generated antenna phase center offset timing diagram intuitively shows the changes of the antenna phase center offset over time, including possible periodic fluctuations, trend changes, etc.

[0087] It should be noted that step S500 includes step S510, step S520, step S530 and step S540.

[0088] Step S510: According to the spatial distribution diagram in the fourth information, the signal feature diagram is processed by using a spatial statistical difference method, and the outlier identification result is obtained by evaluating the difference between the signal characteristics at each position and the surrounding positions.

[0089] It is understandable that processing the signal feature map using the spatial statistical difference method involves comparing the signal features at each location with the feature values ​​of the surrounding locations to evaluate the differences between them. Based on the results of the spatial statistical difference, the signal feature points that are significantly different from the surrounding locations are identified and regarded as outliers. These outliers indicate a sudden increase or decrease in signal strength, abnormal changes in Doppler frequency, etc., which are abnormal conditions caused by factors such as signal interference and multipath effects. Finally, the outlier point identification result is obtained, that is, the location of the outlier point is identified in the spatial distribution map. The identification of these outliers can help to timely detect and eliminate abnormal conditions and improve the stability and reliability of the GNSS system.

[0090] Step S520: cluster the outlier recognition results based on a preset outlier detection mathematical model, and obtain filtering results by identifying and filtering outliers in the heterogeneous space feature map.

[0091] It should be noted that, since the outlier recognition results need to be clustered to filter outliers, a density-based outlier detection model can be selected, such as the density-based spatial clustering algorithm for noise applications (DBSCAN). The clustering process and recognition filtering in this step can be divided into the following steps:

[0092] Step S521: First, before using the density-based outlier detection model, two parameters need to be defined: neighborhood radius (eps) and minimum number of samples (min samples).

[0093] The neighborhood radius is used to determine the neighborhood range of a point, and the minimum number of samples is used to specify the minimum number of samples required in the neighborhood of a core point.

[0094] Step S522: taking the outlier point recognition result as input data, the spatial coordinate information of each outlier point is represented as a multi-dimensional feature vector.

[0095] It can be understood that each outlier point is described as a vector containing relevant features such as its spatial location. These feature vectors include information such as the coordinates of the outlier point, signal strength, Doppler frequency, etc. By combining these feature vectors into a data set, further clustering and processing can be performed to identify and filter out points that exhibit abnormal performance.

[0096] Step S523: The defined parameters and input data are passed into the DBSCAN algorithm for processing. DBSCAN divides the data points into core points, boundary points and noise points according to the density and neighborhood relationship of each point.

[0097] Among them, core points represent data points with the minimum density threshold, which represent the stable source of the signal. Boundary points represent the change or transition area of ​​certain signals, while noise points represent subtle signal anomalies or interference.

[0098] Step S524: According to the output result of DBSCAN, the data points can be divided into different clusters, wherein the core points belong to one cluster, the boundary points belong to one cluster or are regarded as noise points, and the noise points are excluded.

[0099] Step S525: Filter out the data points determined as noise points to obtain filtering results. These filtered data points correspond to points that are abnormal in the heterogeneous space feature map, and they represent abnormal interference or other abnormal conditions in the data.

[0100] It is understandable that these outliers represent abnormal interference, erroneous measurements or other abnormal conditions in the signal data. By filtering out these outliers, the quality and reliability of the data can be improved, allowing for more accurate subsequent analysis and processing. The result of this step is a cleaned and screened data set in which potential outliers have been excluded, making subsequent data processing and analysis more reliable and effective.

[0101] Step S530: Optimize the receiving area of ​​the receiver according to the filtering result, and obtain the evaluation result by evaluating the signal quality and stability of each area in the signal characteristic graph.

[0102] This step involves evaluating the signal quality and stability of each area in the signal profile to determine the best reception area. By analyzing the filtered data, areas with higher signal quality and better stability can be identified and used as the optimal reception area.

[0103] Step S540: Perform space optimization processing according to the evaluation result to obtain fifth information.

[0104] It is understood that this step involves converting the evaluation results into a specific spatial optimization plan to determine the optimal reception area and signal coverage. By comprehensively considering factors such as signal quality, stability, and receiver deployment, the layout and parameter settings of the reception area can be optimized to maximize the performance and coverage of the GNSS system. Such spatial optimization processing can effectively improve the quality and stability of signal reception, thereby improving the positioning accuracy and reliability of the GNSS system.

[0105] It should be noted that step S540 includes step S541, step S542 and step S543.

[0106] Step S541: clustering the signal strength distribution and spatial correlation in the evaluation result, and obtaining clustering results of different areas by dividing the receiving area.

[0107] It is understandable that cluster analysis can identify areas with similar signal characteristics and spatial distribution characteristics and classify them into the same category. Such division helps to better understand the spatial distribution of signals and provides a basis for subsequent spatial optimization processing. The clustering results will reflect the signal characteristics and spatial distribution characteristics of different areas, providing an important reference for the optimization of the receiving area.

[0108] Step S542: perform interpolation processing according to the clustering result, use the nearest neighbor interpolation algorithm to interpolate the signal strength in each area, and obtain a signal strength distribution map by filling in gaps and smoothing signal distribution.

[0109] Preferably, this step uses the nearest neighbor interpolation algorithm, by finding the nearest known data point in each region, and using its value to fill in the gap or smooth the signal distribution. The purpose of this is to make the signal strength distribution diagram more continuous and smooth in each region, so as to more accurately reflect the distribution of the signal. Through interpolation processing, data gaps can be filled, and information loss caused by uneven sampling or missing data can be compensated, thereby obtaining a more complete and reliable signal strength distribution diagram.

[0110] Step S543: perform spatial optimization processing on the signal strength distribution map based on the preset geographic information data, form an optimal network structure connecting all location points by constructing a graph model and applying a minimum spanning tree algorithm, and determine the center point of the receiving area to obtain the optimal receiving area.

[0111] Specifically, first, a graph model is constructed using geographic information data, in which the nodes of the graph represent different location points, and the edges represent the connection relationship between the location points. Then the minimum spanning tree algorithm is applied to find the optimal network structure connecting all location points. The goal of the minimum spanning tree algorithm is to find a tree that contains all nodes and has the smallest sum of edge weights. In this embodiment, this tree represents the optimal connection scheme, which can optimize the signal coverage of the entire receiving area. Through the minimum spanning tree algorithm, the optimal network structure connecting all location points is determined, and the center point of the receiving area can be determined based on this network structure. This center point is the ideal location for the optimal receiving area because it can maximize the coverage of the entire receiving area while minimizing the change in signal quality.

[0112] It should be noted that step S600 includes step S610, step S620, step S630 and step S640.

[0113] Step S610: According to the optimal receiving area and signal characteristic diagram in the fourth information and the fifth information, a first adjustment scheme is obtained through parameter optimization processing using a genetic algorithm. The first adjustment scheme is an optimized receiver parameter configuration scheme.

[0114] It is understandable that if Figure 2 As shown, this step uses the optimal receiving area and signal characteristic diagram in the fourth information as a reference for the optimization target, and combines the spatial optimization results in the fifth information to construct an optimization objective function suitable for the genetic algorithm. Then, a set of initial receiver parameter configuration schemes are defined, and they are used as the population of the genetic algorithm for evolutionary optimization. In each generation of the genetic algorithm, the pros and cons of each individual are evaluated according to the fitness function, and genetic operations (such as selection, crossover and mutation) are used to generate a new generation of populations. After multiple rounds of iterative optimization, the genetic algorithm will gradually converge to the optimal receiver parameter configuration scheme. The first adjustment scheme finally obtained after the parameter optimization processing of the genetic algorithm is an optimized receiver parameter configuration scheme. Among them, the optimization objective function is:

[0115]

[0116] Where f is the optimization objective function; N is the number of locations within the acceptance region; QD i Indicates the difference in signal quality at each location compared to surrounding locations; SOI i It indicates the influence of spatial optimization results on signal quality, and α indicates the influence weight of spatial optimization results on the objective function.

[0117] Step S620: Perform simulation processing according to the first adjustment scheme, and obtain a second adjustment scheme through a dynamic adjustment strategy based on reinforcement learning. The second adjustment scheme is a real-time receiver working mode and parameter adjustment scheme.

[0118] It is understandable that this step uses the first adjustment scheme to perform simulation processing to evaluate its performance in the actual scenario. The simulation involves applying the receiver parameter configuration determined in the first adjustment scheme to the simulation environment of the actual scenario and simulating the operation of the receiver under different working conditions. During the simulation process, different working scenarios and receiver working modes will be simulated according to the actual operation of the GNSS system, and the performance of the receiver will be comprehensively evaluated in combination with the parameter configuration in the first adjustment scheme. Through simulation analysis, the applicability and stability of the first adjustment scheme in various situations can be evaluated, and possible deficiencies can be found. Based on the results of the simulation, a dynamic adjustment strategy based on reinforcement learning is adopted to design an intelligent receiver working mode and parameter adjustment scheme, namely the second adjustment scheme. Reinforcement learning is a machine learning method whose goal is to learn the optimal decision-making strategy through interaction with the environment. Here, the reinforcement learning algorithm is used to dynamically adjust the working mode and parameter configuration of the receiver according to the results of the simulation and real-time observation data so that it can adapt to the changing working environment and signal conditions. This step can achieve real-time optimization and adaptive adjustment of the receiver through a dynamic adjustment strategy based on reinforcement learning, thereby improving its performance and robustness, enabling it to operate stably in complex and changing environments and adapt to different working requirements and signal conditions in real time.

[0119] Step S630: Perform prediction processing according to the second adjustment scheme to obtain a third adjustment scheme, where the third adjustment scheme is a dynamic signal adjustment scheme.

[0120] It can be understood that this step is intended to adjust the working mode and parameter configuration of the receiver in advance by predicting possible signal conditions and working environment changes in the future, so that it can better adapt to future working requirements and signal conditions.

[0121] Step S640: configure the receiver according to the first adjustment scheme, the second adjustment scheme, and the third adjustment scheme to obtain a maintenance result.

[0122] It should be noted that, first, according to the optimized receiver parameter configuration scheme, the hardware equipment of the receiver is set and adjusted accordingly, including adjusting the receiver's receiving frequency, gain, filter settings and other parameters to ensure that the receiver can better receive and process signals. Secondly, according to the changes in the current environment and signal conditions, the working mode and parameter configuration of the receiver are adjusted to adapt to different signal scenarios and working requirements. Finally, according to the predicted signal conditions and working environment changes, the working mode and parameter configuration of the receiver are adjusted to ensure that the receiver maintains stable performance and efficient signal reception capabilities in the future working environment. Through these adjustments and configuration processing, the maintenance and optimization of the receiver is achieved, so that it can maintain stable performance and reliable working status in different signal scenarios and working environments, and improve the quality and stability of signal reception.

[0123] Embodiment 2:

[0124] like Figure 2 As shown, this embodiment provides a GNSS system maintenance system, the system comprising:

[0125] The acquisition module is used to acquire first information, where the first information includes satellite data collected by a GNSS reference station receiver.

[0126] The analysis module is used to perform frequency domain analysis on the first information to obtain second information, where the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite.

[0127] The identification module performs pattern recognition processing on the second information based on a preset machine learning algorithm, and obtains third information by identifying signal interference and multipath effects. The third information includes abnormal signal identification results.

[0128] A construction module is used to construct and process a signal characteristic diagram based on the third information to obtain fourth information, wherein the fourth information includes a spatial distribution diagram of satellite signal strength, a change diagram of signal Doppler frequency, and a timing diagram of antenna phase center offset.

[0129] The optimization module is used to perform spatial optimization processing according to the fourth information, and obtain the fifth information by performing spatial optimization processing on the signal characteristic diagram using a preset spatial statistical difference mathematical model. The fifth information includes the optimal receiving area of ​​the GNSS receiver.

[0130] The adjustment module performs dynamic signal adjustment processing on the GNSS system based on the fifth information and the fourth information to obtain a maintenance result.

[0131] In a specific embodiment of the present disclosure, the analysis module includes:

[0132] The first processing unit is used to perform Fourier transform processing on the first information to obtain frequency domain information.

[0133] The first calculation unit is used to calculate the model spectrum of each satellite according to the frequency domain information, and obtain the satellite signal strength time series by dividing the frequency domain information into at least two time windows and calculating the power spectrum density of the signal in each window. The satellite signal strength time series includes the average signal strength of each satellite.

[0134] The first fitting unit is used to fit the frequency components of each satellite according to the frequency domain information, convert the frequency domain information into a time domain signal, use the frequency fitting method to find the best fitting curve, and further calculate the Doppler frequency at each time point to obtain the signal Doppler frequency time series.

[0135] The second calculation unit is used to perform phase demodulation processing according to the frequency domain information. By calculating the phase information at each moment in the frequency domain information and tracking and adjusting the phase information in real time based on a preset tracking loop mathematical model, the antenna phase center offset time series of each satellite is obtained.

[0136] In a specific embodiment of the present disclosure, the identification module includes:

[0137] The first detection unit is used to perform peak detection and threshold determination processing according to the satellite signal strength time series in the second information to obtain first time point information, wherein the first time point information includes a time point affected by signal interference.

[0138] The second processing unit is used to obtain second time point information by spectrum analysis and frequency change trend judgment according to the signal Doppler frequency time series in the second information, and the second time point information includes the time point when the multipath effect exists.

[0139] The first fusion unit is used to perform data fusion processing according to the first time point information and the second time point information, and perform space-time correlation analysis on the fused data to obtain third time point information, where the third time point information is a time point affected by signal interference and multipath effect.

[0140] The first recognition unit performs pattern recognition processing on the third time point information based on a preset support vector machine mathematical model, and obtains the third information by combining the satellite signal strength change trend and the Doppler frequency change law.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0142] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A GNSS system maintenance method, characterized in that: include: Acquiring first information, wherein the first information includes satellite data collected by a GNSS reference station receiver; Performing frequency domain analysis on the first information to obtain second information, where the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite; Performing pattern recognition processing on the second information based on a preset machine learning algorithm, and obtaining third information by identifying signal interference and multipath effects, wherein the third information includes an abnormal signal identification result; Performing signal characteristic graph construction processing according to the third information to obtain fourth information, wherein the fourth information includes a spatial distribution graph of satellite signal strength, a change graph of signal Doppler frequency, and a timing graph of antenna phase center offset; Performing spatial optimization processing on the fourth information, and obtaining fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, wherein the fifth information includes an optimal receiving area of ​​the GNSS receiver; Performing dynamic signal adjustment processing on the GNSS system based on the fifth information and the fourth information to obtain a maintenance result; Wherein, performing spatial optimization processing according to the fourth information, and obtaining fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, includes: According to the fourth information, the signal feature map is processed by using a spatial statistical difference method, and the outlier identification result is obtained by evaluating the difference between the signal feature at each position and the surrounding positions; Clustering the outlier recognition results based on a preset outlier detection mathematical model, and obtaining filtering results by identifying and filtering outliers in the signal feature graph; Optimizing the receiving area of ​​the receiver according to the filtering result, and obtaining an evaluation result by evaluating the signal quality and stability of each area in the signal characteristic graph; Performing space optimization processing according to the evaluation result to obtain fifth information; The fifth information is obtained by performing space optimization processing according to the evaluation result, including: Performing clustering processing according to the signal strength distribution and spatial correlation in the evaluation results, and obtaining clustering results of different areas by dividing the receiving area; Performing interpolation processing according to the clustering results, using a nearest neighbor interpolation algorithm to interpolate the signal strength in each area, and obtaining a signal strength distribution map by filling in gaps and smoothing signal distribution; The signal strength distribution map is spatially optimized based on preset geographic information data, and an optimal network structure connecting all location points is formed by constructing a graph model and applying a minimum spanning tree algorithm, and the center point of the receiving area is determined to obtain the optimal receiving area.

2. The GNSS system maintenance method according to claim 1, characterized in that , performing frequency domain analysis on the first information to obtain second information, including: Performing Fourier transform processing on the first information to obtain frequency domain information; Calculating the signal spectrum of each satellite according to the frequency domain information, dividing the frequency domain information into at least two time windows and calculating the power spectrum density of the signal in each window to obtain a satellite signal strength time series, wherein the satellite signal strength time series includes an average signal strength of each satellite; Fitting the frequency components of each satellite according to the frequency domain information, converting the frequency domain information into a time domain signal, finding the best fitting curve using a frequency fitting method, and further calculating the Doppler frequency at each time point to obtain a signal Doppler frequency time series; Phase demodulation processing is performed according to the frequency domain information, the phase information at each moment in the frequency domain information is calculated, and the phase information is tracked and adjusted in real time based on a preset tracking loop mathematical model to obtain the antenna phase center offset time series of each satellite.

3. The GNSS system maintenance method according to claim 1, characterized in that , performing pattern recognition processing on the second information based on a preset machine learning algorithm, and obtaining third information by identifying signal interference and multipath effects, including: Perform peak detection and threshold determination processing according to the satellite signal strength time series in the second information to obtain first time point information, wherein the first time point information includes a time point affected by signal interference; According to the signal Doppler frequency time series in the second information, spectrum analysis and frequency change trend judgment are used to obtain second time point information, where the second time point information includes a time point when a multipath effect exists; Perform data fusion processing according to the first time point information and the second time point information, and perform space-time correlation analysis on the fused data to obtain third time point information, where the third time point information is a time point affected by signal interference and multipath effect; The third time point information is subjected to pattern recognition processing based on a preset support vector machine mathematical model, and the third information is obtained by combining the satellite signal strength variation trend and the Doppler frequency variation law.

4. The GNSS system maintenance method according to claim 1, characterized in that , constructing and processing a signal characteristic graph according to the third information to obtain fourth information, including: According to the third information, the satellite signal strength, satellite signal strength, Doppler frequency and antenna phase center offset characteristics at each time point are spatially positioned to obtain spatial coordinate information of satellite signal feature points; Based on a preset radial basis function interpolation mathematical model, the satellite signal strength of the spatial coordinate information is spatially interpolated, and parameter adjustment is performed in combination with the uneven distribution of satellites and the spatial correlation of signal strength to generate a spatial distribution map; The Doppler frequency of the spatial coordinate information is processed based on a preset spatial average filtering mathematical model, and the size of the domain range and the weighting method are adjusted in combination with the spatial distribution of the satellite signal feature points to obtain an average filtering result that reflects the spatial variation law of the Doppler frequency, and a Doppler frequency variation graph is generated based on the average filtering result; A timing analysis process is performed according to the spatial coordinate information, and a timing diagram of the antenna phase center offset is obtained by performing a timing analysis on the antenna phase center offset of the satellite signal.

5. The GNSS system maintenance method according to claim 1, characterized in that ,Based on the fifth information and the fourth information, the GNSS system is dynamically adjusted to obtain maintenance results, including: According to the optimal receiving area and the signal characteristic diagram in the fourth information, a first adjustment scheme is obtained through parameter optimization processing of a genetic algorithm, wherein the first adjustment scheme is an optimized receiver parameter configuration scheme; Performing simulation processing according to the first adjustment scheme, obtaining a second adjustment scheme through a dynamic adjustment strategy based on reinforcement learning, wherein the second adjustment scheme is a real-time receiver working mode and parameter adjustment scheme; Performing prediction processing according to the second adjustment scheme to obtain a third adjustment scheme, wherein the third adjustment scheme is a dynamic signal adjustment scheme; The receiver is configured and processed according to the first adjustment scheme, the second adjustment scheme and the third adjustment scheme to obtain a maintenance result.

6. A GNSS system maintenance system, characterized in that: include: An acquisition module, configured to acquire first information, wherein the first information includes satellite data collected by a GNSS reference station receiver; An analysis module, configured to perform frequency domain analysis on the first information to obtain second information, wherein the second information includes a satellite signal strength time series, a signal Doppler frequency time series, and an antenna phase center offset time series of each satellite; an identification module, performing pattern recognition processing on the second information based on a preset machine learning algorithm, and obtaining third information by identifying signal interference and multipath effects, wherein the third information includes an abnormal signal identification result; A construction module, configured to construct a signal characteristic graph according to the third information to obtain fourth information, wherein the fourth information includes a spatial distribution graph of satellite signal strength, a change graph of signal Doppler frequency, and a timing graph of antenna phase center offset; an optimization module, configured to perform spatial optimization processing according to the fourth information, and obtain fifth information by performing spatial optimization processing on the signal characteristic graph using a preset spatial statistical difference mathematical model, wherein the fifth information includes an optimal receiving area of ​​the GNSS receiver; An adjustment module performs dynamic signal adjustment processing on the GNSS system based on the fifth information and the fourth information to obtain a maintenance result; Wherein, the optimization module includes: A first evaluation unit is used to process the signal feature map using a spatial statistical difference method according to the spatial distribution map in the fourth information, and obtain an abnormal point identification result by evaluating the difference between the signal feature at each position and the surrounding positions; A first clustering unit performs clustering processing on the abnormal point recognition result based on a preset outlier detection mathematical model, and obtains a filtering result by identifying and filtering points showing abnormalities in the signal characteristic graph; A first optimization unit, configured to optimize the receiving area of ​​the receiver according to the filtering result, and obtain an evaluation result by evaluating the signal quality and stability of each area in the signal characteristic graph; A second optimization unit, configured to perform space optimization processing according to the evaluation result to obtain fifth information; Wherein, the second optimization unit includes: A first division unit, configured to perform clustering processing according to the signal strength distribution and spatial correlation in the evaluation result, and obtain clustering results of different areas by dividing the receiving area; A first interpolation unit, configured to perform interpolation processing according to the clustering result, interpolate the signal strength in each area using a nearest neighbor interpolation algorithm, and obtain a signal strength distribution map by filling in gaps and smoothing signal distribution; The third optimization unit performs spatial optimization processing on the signal strength distribution map based on preset geographic information data, forms an optimal network structure connecting all location points by constructing a graph model and applying a minimum spanning tree algorithm, and determines the center point of the receiving area to obtain an optimal receiving area.

7. The GNSS system maintenance system according to claim 6, characterized in that: The analysis module comprises: A first processing unit, configured to perform Fourier transform processing on the first information to obtain frequency domain information; A first calculation unit is used to calculate the model spectrum of each satellite according to the frequency domain information, and obtain a satellite signal strength time series by dividing the frequency domain information into at least two time windows and performing power spectrum density calculation on the signal in each window, wherein the satellite signal strength time series includes an average signal strength of each satellite; A first fitting unit is used to perform fitting processing on the frequency component of each satellite according to the frequency domain information, by converting the frequency domain information into a time domain signal, using a frequency fitting method to find the best fitting curve, and further calculating the Doppler frequency at each time point to obtain a signal Doppler frequency time series; The second calculation unit is used to perform phase demodulation processing according to the frequency domain information, calculate the phase information at each moment in the frequency domain information, and track and adjust the phase information in real time based on a preset tracking loop mathematical model to obtain the antenna phase center offset time series of each satellite.

8. The GNSS system maintenance system according to claim 7, characterized in that: The identification module comprises: a first detection unit, configured to perform peak detection and threshold determination processing according to a satellite signal strength time series in the second information to obtain first time point information, wherein the first time point information includes a time point affected by signal interference; A second processing unit is used to obtain second time point information by spectrum analysis and frequency change trend judgment according to the signal Doppler frequency time series in the second information, wherein the second time point information includes a time point when a multipath effect exists; A first fusion unit is used to perform data fusion processing according to the first time point information and the second time point information, and perform space-time correlation analysis on the fused data to obtain third time point information, where the third time point information is a time point affected by signal interference and multipath effect; The first recognition unit performs pattern recognition processing on the third time point information based on a preset support vector machine mathematical model, and obtains the third information by combining the satellite signal strength change trend and the Doppler frequency change law.

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