Satellite data geographic positioning precision monitoring method and device, equipment and storage medium

Abstract

The invention discloses a satellite data geographic positioning precision monitoring method, device and equipment and a storage medium, and relates to the technical field of satellite geographic data monitoring, and the method comprises the steps: constructing a high-precision landmark library which comprises high-resolution landmark data which are uniformly distributed in a global range; based on a plurality of positioning precision test algorithms, with high-resolution landmark data as a reference, performing deviation test on different satellite remote sensing data; monitoring and tracking satellite remote sensing data of a specific landmark point, and generating a diversified statistical analysis result; providing a man-machine interaction image deviation inspection tool, and carrying out manual comparison verification on the diversified statistical analysis result; and generating a satellite data geographic positioning precision evaluation report based on the statistical analysis result and the manual comparison verification result.

Description

Technical Field

[0001] The present application relates to the technical field of satellite geographic data monitoring, and in particular to a method, device, equipment and storage medium for monitoring the accuracy of satellite data geographic positioning. Background Art

[0002] In recent years, with the rapid development of aerospace technology, remote sensing satellites have become an important tool in the fields of earth observation, environmental monitoring, resource investigation, disaster warning, etc. Remote sensing satellites can obtain high-resolution images and data of the earth's surface in real time or quasi-real time. These data are of great significance to scientific research, national defense construction, urban planning, agricultural production and many other aspects. However, the accuracy and availability of remote sensing satellite data largely depend on its geolocation accuracy. Geolocation accuracy is an important indicator to measure the quality of remote sensing satellite data, which determines whether the data can accurately reflect the real situation on the earth's surface.

[0003] At present, there are some mature methods and technologies for the inspection and evaluation of the geolocation accuracy of remote sensing satellite data. For example, the traditional manual collection method is to determine the geolocation accuracy through the interpretation and comparison of remote sensing images by professionals. In addition, there are some automated methods, such as using the location information of known landmark points to calculate the position deviation of corresponding points in the remote sensing image through algorithms to evaluate the geolocation accuracy. These methods can meet the inspection needs of the positioning accuracy of remote sensing satellite data to a certain extent.

[0004] However, the existing remote sensing satellite data geolocation accuracy verification and evaluation technology still has some defects. First, the traditional manual collection method is time-consuming, and the accuracy is greatly affected by human factors, which makes it difficult to meet the needs of large-scale, high-precision positioning accuracy verification. Secondly, although there are some automated methods, these methods are often only applicable to specific satellite payloads and observation conditions, and their applicability is limited for different types of satellites and payloads. In addition, the existing methods for processing and analyzing remote sensing data are often relatively simple, lacking diversified analysis methods and display methods, and are difficult to meet the complex and changing scientific research and application needs. Summary of the invention

[0005] The purpose of this application is to provide a satellite data geolocation accuracy monitoring method, device, equipment and storage medium.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for monitoring the accuracy of satellite data geolocation, comprising:

[0008] Building a high-precision landmark library, wherein the landmark library contains high-resolution landmark data evenly distributed around the world;

[0009] Based on a variety of positioning accuracy verification algorithms, different satellite remote sensing data are tested for deviations using high-resolution landmark data as a benchmark;

[0010] Monitor and track satellite remote sensing data of specific landmarks and generate diverse statistical analysis results;

[0011] Providing a human-computer interactive image deviation inspection tool to manually compare and verify the diversified statistical analysis results;

[0012] Based on the statistical analysis results and manual comparison and verification results, a satellite data geolocation accuracy assessment report is generated.

[0013] Optionally, the step of constructing a high-precision landmark library includes:

[0014] Using high-resolution satellite imagery data and mapping software, we initially screened and marked landmarks around the world;

[0015] Generate a unique identifier for each selected landmark and record its latitude and longitude information;

[0016] Periodically download the latest remote sensing satellite images and update landmark sample data to ensure the timeliness and accuracy of the landmark database.

[0017] Optionally, the positioning accuracy verification algorithm includes SIFT feature point matching method, optical flow method, and automatic landmark navigation.

[0018] Optionally, the step of monitoring and tracking the satellite remote sensing data of a specific landmark point includes:

[0019] Regularly obtain satellite observation data of specific landmark points;

[0020] Applying the positioning accuracy verification algorithm to perform deviation verification on the acquired data;

[0021] Record and analyze deviation data to generate long-term deviation result files.

[0022] Optionally, the step of generating diversified statistical analysis results comprises:

[0023] Use spatial distribution graphs, long time series graphs, circular probability error graphs and other charts to display deviation data;

[0024] It supports the elimination of outliers and the screening and analysis of data in specified areas or observation angles.

[0025] Optionally, the step of providing a human-computer interaction image deviation inspection tool includes:

[0026] The user selects satellite observation data at a specific time through the interface;

[0027] The system loads observation images and provides the function of overlaying the land-sea boundary;

[0028] The user can adjust the image position by moving the mouse to achieve sub-pixel level positioning accuracy deviation inspection.

[0029] Optionally, the step of generating a satellite data geolocation accuracy assessment report includes:

[0030] Comprehensive statistical analysis results and manual comparison and verification results;

[0031] Comprehensively evaluate the geolocation accuracy of satellite data and provide recommendations for improvement.

[0032] In a second aspect, the present application provides a satellite data geolocation accuracy monitoring device, comprising:

[0033] An acquisition module, used to construct a high-precision landmark library, wherein the landmark library contains high-resolution landmark data uniformly distributed around the world;

[0034] Based on a variety of positioning accuracy verification algorithms, different satellite remote sensing data are tested for deviations using high-resolution landmark data as a benchmark;

[0035] The analysis module is used to monitor and track satellite remote sensing data of specific landmarks and generate diversified statistical analysis results;

[0036] Providing a human-computer interactive image deviation inspection tool to manually compare and verify the diversified statistical analysis results;

[0037] A generation module is used to generate a satellite data geolocation accuracy assessment report based on the statistical analysis results and manual comparison and verification results.

[0038] Optionally, the acquisition module is further used to:

[0039] Using high-resolution satellite imagery data and mapping software, we initially screened and marked landmarks around the world;

[0040] Generate a unique identifier for each selected landmark and record its latitude and longitude information;

[0041] Periodically download the latest remote sensing satellite images and update landmark sample data to ensure the timeliness and accuracy of the landmark database.

[0042] Optionally, the positioning accuracy verification algorithm includes SIFT feature point matching method, optical flow method, and automatic landmark navigation.

[0043] Optionally, the analysis module is further used to:

[0044] Regularly obtain satellite observation data of specific landmark points;

[0045] Applying the positioning accuracy verification algorithm to perform deviation verification on the acquired data;

[0046] Record and analyze deviation data to generate long-term deviation result files.

[0047] Optionally, the analysis module is further used to:

[0048] Use spatial distribution graphs, long time series graphs, circular probability error graphs and other charts to display deviation data;

[0049] It supports the elimination of outliers and the screening and analysis of data in specified areas or observation angles.

[0050] Optionally, the analysis module is further used to:

[0051] The user selects satellite observation data at a specific time through the interface;

[0052] The system loads observation images and provides the function of overlaying the land-sea boundary;

[0053] The user can adjust the image position by moving the mouse to achieve sub-pixel level positioning accuracy deviation inspection.

[0054] Optionally, the generating module is further used to:

[0055] Comprehensive statistical analysis results and manual comparison and verification results;

[0056] Comprehensively evaluate the geolocation accuracy of satellite data and provide recommendations for improvement.

[0057] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the satellite data geolocation accuracy monitoring methods described above.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the satellite data geolocation accuracy monitoring methods described above.

[0059] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the satellite data geolocation accuracy monitoring methods described above.

[0060] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0061] The present application provides a satellite data geolocation accuracy monitoring method, device, equipment and storage medium. By constructing a high-precision landmark library and performing deviation inspection on satellite remote sensing data based on a variety of positioning accuracy inspection algorithms, comprehensive monitoring and evaluation of satellite data geolocation accuracy can be achieved. It can not only improve the detection accuracy of positioning accuracy, but also ensure the timeliness and accuracy of the landmark library, providing a reliable basis for the subsequent processing and application of satellite data. Monitoring and tracking the remote sensing data of specific landmark points and generating diversified statistical analysis results can more intuitively display the positioning accuracy performance of satellite data under different time and space conditions. These statistical analysis results not only include traditional reports, tables and other forms, but also use more professional and complex chart forms such as spatial distribution maps, long time series maps, and circular probability error maps, providing comprehensive data support for evaluating the geolocation accuracy of satellite data. In addition, a human-computer interactive image deviation inspection tool is provided, allowing users to manually compare and verify the diversified statistical analysis results, further improving the accuracy and credibility of the evaluation results. Through human-computer interaction, users can more intuitively understand the positioning accuracy of satellite data and make adjustments and optimizations according to actual needs. The satellite data geolocation accuracy assessment report generated based on statistical analysis results and manual comparison and verification results provides an important reference for the subsequent processing and application of satellite data. This report not only contains detailed data on landmark library construction, deviation testing, statistical analysis and other links, but also provides comprehensive assessment conclusions and improvement suggestions, which will help improve the application value and effect of satellite data. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A schematic diagram of a flow chart of a satellite data geolocation accuracy monitoring method provided in an embodiment of the present application;

[0064] Figure 2 A system schematic diagram of a satellite data geolocation accuracy monitoring method provided in one embodiment of the present application;

[0065] Figure 3 One of the interface schematic diagrams of a satellite data geolocation accuracy monitoring method provided in one embodiment of the present application;

[0066] Figure 4 A second schematic diagram of an interface of a method for monitoring the accuracy of satellite data geolocation provided in an embodiment of the present application;

[0067] Figure 5 A third schematic diagram of an interface of a method for monitoring the accuracy of satellite data geolocation provided in an embodiment of the present application;

[0068] Figure 6 A schematic diagram of functional modules of a satellite data geolocation accuracy monitoring device provided in one embodiment of the present application;

[0069] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] like Figure 1 As shown, some embodiments of the present application provide a method for monitoring the accuracy of satellite data geolocation, which includes the following steps 101 to 105. Among them:

[0072] Reference Figure 2, showing a system schematic diagram of a satellite data geolocation accuracy monitoring method provided by some embodiments of the present application. Among them, the system structure is divided into four core parts: P-Application (application layer), P-Service (service layer), P-Algorithm (algorithm layer) and P-Data (data layer). P-Application (application layer) covers a variety of satellite applications, such as meteorological satellites, ocean satellites, resource satellites and foreign satellites, and also includes specific tools such as optical imagers and microwave instruments. These applications use satellite data for environmental monitoring, resource management, etc., serving different fields and functions. P-Service (service layer) provides a series of technical support and data verification services, including high-precision landmark libraries, satellite data positioning accuracy inspection, human-computer interaction image deviation inspection, and diversified display of deviation results. These services ensure the accuracy and reliability of application layer data and provide solid guarantees for the effective operation of applications. P-Algorithm (algorithm layer) includes a variety of data processing and analysis algorithms, such as SIFT (scale-invariant feature transform) algorithm, optical flow algorithm and time series algorithm. These algorithms provide strong support for data processing and analysis, and are the key to realizing various service and application functions. P-Data (data layer) is the foundation of the entire system, involving data collection, standardization and storage. The original data is obtained through data collection, and the consistency and availability of the data are ensured through data standardization. Finally, the processed data is stored in a suitable medium to facilitate subsequent use and analysis.

[0073] Step 101 : construct a high-precision landmark library, wherein the landmark library contains high-resolution landmark data evenly distributed around the world.

[0074] In an embodiment of the present application, the system uses high-resolution satellite image data and map software to preliminarily screen and mark landmark points on a global scale. These landmark points should be stable, recognizable, and evenly distributed. Stability requires that the location, shape, and features of the landmarks remain unchanged for a long time and are not easily affected by natural factors (such as crustal movement, climate change) or human factors (such as farmland boundaries, temporary buildings, and transformations). Recognizability refers to the unique shape, texture, or color characteristics of the landmark, such as the outline of a lake, the direction of a river, the shape of the coastline, and the unique shape of an island. Distribution uniformity ensures that the landmarks are evenly distributed around the world to verify the accuracy of the satellite positioning system in different geographical environments and climatic conditions.

[0075] The system generates a unique identifier for each selected landmark and records its latitude and longitude information. This information will be used for subsequent data processing and positioning accuracy verification. The latest remote sensing satellite images are downloaded periodically to update the landmark sample data to ensure the timeliness and accuracy of the landmark library. By manually viewing the landmark sample image data, comparing and analyzing the stability, recognizability and distribution uniformity of different landmarks, the optimal landmark samples that meet the selection principles are screened out to generate a global landmark library.

[0076] Step 102 , based on a variety of positioning accuracy verification algorithms and taking high-resolution landmark data as a benchmark, conduct deviation verification on different satellite remote sensing data.

[0077] In the embodiments of the present application, a variety of satellite payload geographic positioning accuracy verification algorithms are selected, such as SIFT feature point matching method, optical flow method, automatic landmark navigation, etc. These algorithms have their own advantages and are applicable to different types of satellite data and payloads. Read high-resolution landmark data and satellite positioning data, and intercept the corresponding satellite image data and positioning data as the data to be verified based on the landmark longitude and latitude data. Apply the selected positioning accuracy verification algorithm to perform deviation verification on the data to be verified. For example, the SIFT feature point matching method is used to detect feature points in landmark images and satellite images, calculate the Euclidean distance between feature points, perform similarity judgment, extract matching point pairs, and calculate the pixel deviation results of matching points.

[0078] Step 103, monitor and track the satellite remote sensing data of specific landmark points, and generate diversified statistical analysis results.

[0079] In an embodiment of the present application, satellite observation data of a specific landmark point is obtained regularly, including image data and positioning data. Taking the Qinghai Lake landmark as an example, the longitude and latitude information of the landmark point is used to intercept the remote sensing data of the area corresponding to the satellite remote sensing data to be tested (such as FY4B_AGRI). The landmark point data is used as a benchmark, and the deviation of FY4B_AGRI at the landmark point is obtained through deviation test, and the deviation data is recorded and analyzed. Generate a long-term deviation result file, and use a variety of charts (such as spatial distribution maps, long-term series maps, circular probability error maps) to display the deviation data. Support the elimination of outliers, and screen and analyze the data of the specified area or observation angle to generate more accurate statistical analysis results.

[0080] Step 104, providing a human-computer interactive image deviation verification tool to perform manual comparison and verification on the diversified statistical analysis results.

[0081] In the embodiment of the present application, the system provides the user with a human-computer interactive image deviation inspection tool, and the user can select satellite observation data of a specific time through the interface. The system loads the observation image and provides the sea-land boundary overlay function. The user can adjust the image position by moving the mouse to achieve sub-pixel level positioning accuracy deviation inspection. The user's manual comparison verification results are recorded and used as part of the evaluation report.

[0082] Step 105: Generate a satellite data geolocation accuracy assessment report based on the statistical analysis results and manual comparison and verification results.

[0083] In the embodiment of the present application, the statistical analysis results and the manual comparison and verification results are integrated to form comprehensive evaluation data. Based on the comprehensive evaluation data, a satellite data geolocation accuracy evaluation report is generated. The report should include the construction of the landmark library, deviation test results, statistical analysis charts, manual comparison and verification results, and comprehensive evaluation conclusions. The evaluation report is published to relevant users and application scenarios to provide an important reference for the subsequent processing and application of satellite data.

[0084] In the embodiment of the present application, the device constructs a high-precision landmark library, and performs deviation inspection on satellite remote sensing data based on a variety of positioning accuracy inspection algorithms, which can realize comprehensive monitoring and evaluation of the geographic positioning accuracy of satellite data. It can not only improve the detection accuracy of positioning accuracy, but also ensure the timeliness and accuracy of the landmark library, providing a reliable basis for the subsequent processing and application of satellite data. Monitoring and tracking the remote sensing data of specific landmark points, and generating diversified statistical analysis results, can more intuitively display the positioning accuracy performance of satellite data under different time and space conditions. These statistical analysis results not only include traditional forms such as reports and tables, but also adopt more professional and complex chart forms such as spatial distribution maps, long time series maps, and circular probability error maps, which provide comprehensive data support for evaluating the geographic positioning accuracy of satellite data. In addition, a human-computer interactive image deviation inspection tool is provided, allowing users to manually compare and verify the diversified statistical analysis results, further improving the accuracy and credibility of the evaluation results. Through human-computer interaction, users can more intuitively understand the positioning accuracy of satellite data, and adjust and optimize according to actual needs. The satellite data geographic positioning accuracy evaluation report generated based on the statistical analysis results and the manual comparison and verification results provides an important reference for the subsequent processing and application of satellite data. This report not only includes detailed data on each link such as landmark library construction, deviation verification, and statistical analysis, but also provides comprehensive evaluation conclusions and improvement suggestions, which will help improve the application value and effectiveness of satellite data.

[0085] Optionally, the step 101 includes:

[0086] Step 1011, using high-resolution satellite image data and map software, preliminarily screen and mark landmark points worldwide.

[0087] In the embodiments of this application, refer to Figure 4 , you can use this high-precision landmark interface to mark landmarks. High-resolution satellite image data is high-resolution image data collected from different satellites. These data should cover the world to ensure the diversity and breadth of landmarks. Use professional map software as an auxiliary tool. The software should have accurate geographic coordinate system, layer management, landmark annotation and other functions.

[0088] The collected high-resolution satellite images are preprocessed, including denoising, contrast enhancement, and geometric distortion correction, to improve image quality and the accuracy of landmark recognition. Using map software, a preliminary search is conducted globally, and potential landmark points are preliminarily screened out on satellite images based on landmark selection principles (such as stability, recognizability, and uniform distribution). The selected landmark points are marked on the map, and their preliminary locations and feature descriptions are recorded.

[0089] Step 1012, generate a unique identifier for each selected landmark and record its latitude and longitude information.

[0090] In an embodiment of the present application, a unique identifier (ID) is generated for each selected landmark. This identifier can be a number, a letter, or a combination of numbers and letters to ensure that it is not repeated globally. The generation of the identifier should follow certain rules or algorithms to facilitate subsequent management and query. Use map software to accurately measure the longitude and latitude coordinates of each landmark and record them. Longitude and latitude information is a key parameter of the landmark location and is crucial for subsequent positioning accuracy inspection. The unique identifier of the landmark is associated with its longitude and latitude information and stored in a database for subsequent retrieval and use.

[0091] Step 1013, periodically download the latest remote sensing satellite images and update the landmark sample data to ensure the timeliness and accuracy of the landmark database.

[0092] In the embodiment of the present application, a reasonable period (such as weekly, monthly or quarterly) is set to regularly download the latest remote sensing satellite images. These images should cover all landmarks previously screened and marked. Use automated scripts or programs to automatically download image data from satellite data sources, reducing the errors and burdens of manual operations.

[0093] For each landmark, extract the image data of the corresponding location from the latest downloaded remote sensing satellite image as a new landmark sample. Compare the new and old landmark samples to check whether the location, shape and characteristics of the landmark have changed. If there is a change (such as the disappearance or deformation of the landmark due to natural factors or human modification), the landmark needs to be re-evaluated or replaced.

[0094] By periodically updating the landmark sample data, we ensure that the information in the landmark database is up-to-date, accurate, and able to reflect the current surface conditions. For landmarks that can no longer be used for various reasons (such as disappeared landmarks), they should be removed from the landmark database in a timely manner and new landmarks should be added to maintain the integrity and effectiveness of the landmark database.

[0095] Optionally, the positioning accuracy verification algorithm includes SIFT feature point matching method, optical flow method, and automatic landmark navigation.

[0096] In an embodiment of the present application, the remote sensing data and positioning data of AGRI at a certain time are read, the image data of the inspection channel is read from the remote sensing data, and the longitude and latitude and sea and land mask data of the corresponding time are extracted from the positioning data. The landmark data in the high-precision landmark library is read, including landmark images and longitude and latitude data. According to the longitude and latitude values ​​of the four corner points of the landmark longitude and latitude data, the area corresponding to the AGRI image data, longitude and latitude data and sea and land mask data is intercepted as the AGRI data to be verified. The local feature description operator that remains invariant to the rotation, scaling and illumination changes of the image - the scale invariant feature transform operator (Scale Invariant Feature Transform, i.e. SIFT operator): read the AGRI image data to be verified and the landmark image data respectively, detect the SIFT feature points, the feature points are composed of local extreme points in the image, and the detection points are compared with the adjacent points at the same scale, where the largest and smallest detection points are used as extreme points in the scale space, i.e. candidate points of the feature. The gradient direction of the neighborhood pixels is counted by histogram, and the peak of the histogram is taken as the size of the gradient of the feature point, and its direction is taken as the direction of the feature point. After the SIFT feature description of the two images is established, the Euclidean distance of the feature points of the two images is calculated, and the matching point pairs are extracted by similarity judgment. A feature point in the landmark image is taken, and the minimum value in the Euclidean distance matrix corresponding to the point is found. If the ratio of the two distances is less than a certain threshold, the pair of matching points is accepted, and the two feature points are considered to be the corresponding control point pairs with the same name, otherwise it is considered that the feature point has no matching point. The difference in the row and column numbers of the matching point in the landmark image and the AGRI image is considered to be the pixel deviation result of the matching point. If the difference in the row and column direction of the matching point is greater than a certain threshold, it is considered to be a mismatch and is eliminated. The row and column numbers, longitude and latitude and other information of the remaining valid matching points in the landmark image and the AGRI image are uniformly generated into a standardized format file. The long-term AGRI data is processed to generate a standardized deviation result file of the long-term series for subsequent long-term deviation statistics and diversified display analysis to achieve AGRI geographic positioning accuracy monitoring.

[0097] Optionally, the step 103 includes:

[0098] Step 1031, regularly obtain satellite observation data of specific landmark points.

[0099] In an embodiment of the present application, first, the specific landmark points that need to be monitored are identified. These landmark points are usually locations selected from a high-precision landmark library and have the characteristics of stability, recognizability, and uniform distribution. A detailed satellite observation plan is formulated based on the satellite's orbit, observation period, and the location of the landmark point. This includes determining the satellite's transit time, observation angle, sensor parameters, etc. During the satellite transit, observation data from the satellite is received. These data may include remote sensing images, positioning information, radiation measurements, etc. The received data needs to be preliminarily processed, such as denoising, format conversion, etc., to facilitate subsequent analysis. According to a pre-established observation plan, satellite observation data of specific landmark points is regularly acquired. This can be daily, weekly, or monthly, depending on monitoring needs and satellite resources.

[0100] Step 1032: Apply the positioning accuracy verification algorithm to perform deviation verification on the acquired data.

[0101] In an embodiment of the present application, a suitable positioning accuracy verification algorithm is selected according to the type of satellite payload and observation data. These algorithms may include SIFT feature point matching method, optical flow method, automatic landmark navigation, etc. The acquired satellite observation data is preprocessed, such as image calibration, radiation correction, etc., to ensure that the data quality meets the requirements of deviation verification. The selected positioning accuracy verification algorithm is applied to perform a deviation test on the preprocessed data. This usually involves comparing the observation data with a known high-precision landmark library and calculating the positioning deviation. The results of the deviation test are recorded, including information such as the deviation value, deviation direction, and deviation distribution.

[0102] Step 1033, record and analyze the deviation data, and generate a long time series deviation result file.

[0103] In the embodiment of the present application, the result of each deviation test is recorded in chronological order to form a deviation data set. This includes information such as the deviation value, deviation trend, etc. of each landmark point at different time points. The recorded deviation data is statistically analyzed, such as calculating the average deviation, standard deviation, maximum deviation, etc. At the same time, the time variation trend and spatial distribution characteristics of the deviation data are analyzed to identify possible problem areas or abnormal events. The analysis results are presented in the form of charts, such as spatial distribution maps, long time series maps, and circular probability error maps. These charts help to intuitively understand the characteristics and laws of the deviation data. The analysis results and charts are integrated into the deviation result file of the long time series. This file should contain detailed deviation data, analysis results, charts, and possible suggestions and improvement measures.

[0104] Optionally, the step 103 includes:

[0105] Step 1034, using a variety of chart forms such as spatial distribution chart, long time series chart, and circular probability error chart to display the deviation data.

[0106] In the embodiment of the present application, the spatial distribution map is a graphical form that displays deviation data based on geographic spatial position. It can represent the deviation values ​​of different landmark points on the map with visual elements such as color, size or shape, so as to intuitively display the spatial distribution characteristics of the deviation. Using geographic information system (GIS) software or professional drawing tools, the landmark points and their deviation data are imported, and different visual representation methods are set according to the size or type of the deviation value, such as color depth, point size or shape, etc., to generate a spatial distribution map.

[0107] A long time series graph is a graph that shows the trend of deviation data over time. It can arrange the deviation values ​​of a specific landmark point or a group of landmark points in chronological order and display them in the form of line graphs, bar graphs, etc., to help analyze the time evolution of deviations. Use data analysis software or programming languages ​​(such as Python, R, etc.) to organize the deviation data into tables or data sets in chronological order, and then select the appropriate chart type (such as line graphs, bar graphs, etc.) to draw a long time series graph.

[0108] The circular error probability map is a chart form used to represent positioning accuracy. It is centered on a landmark point and draws one or more circles based on the statistical characteristics of the deviation data, indicating the range in which the positioning point may deviate from the true position under a certain probability. Based on the statistical characteristics of the deviation data (such as the average deviation, standard deviation, etc.), the positioning error range under a certain probability level (such as 50%, 90%, etc.) is calculated, and then the corresponding circle is drawn on the map using a drawing tool to form a circular error probability map.

[0109] Step 1035 supports the elimination of outliers and the screening and analysis of data in a specified area or observation angle.

[0110] In the embodiments of the present application, there may be some outliers in the deviation data due to measurement errors, data errors or extreme cases. These outliers may affect the accuracy and reliability of data analysis, so they need to be eliminated. Use statistical methods (such as box plot method, Z score method, etc.) or judgment based on professional knowledge to identify and eliminate outliers in the deviation data. After elimination, the remaining data needs to be reanalyzed to ensure the accuracy of the analysis results. Data screening analysis of specified areas or observation angles:

[0111] In practical applications, it may be necessary to analyze only the deviation data of a specific area or observation angle. In this case, it is necessary to filter the original deviation data and extract the data subset that meets the requirements for analysis. According to the analysis requirements, set the filtering conditions (such as geographical scope, observation angle range, etc.). Then, use data analysis software or programming language to filter the deviation data and extract the data subset that meets the conditions. Finally, further analyze and visualize the filtered data.

[0112] Optionally, the step 104 includes:

[0113] Step 1041: The user selects satellite observation data of a specific time through the interface.

[0114] Step 1042, the system loads the observation image and provides a land-sea boundary overlay function.

[0115] Step 1043, the user adjusts the image position by moving the mouse to implement sub-pixel level positioning accuracy deviation inspection.

[0116] In the embodiments of this application, refer to Figure 4 The system provides a user-friendly interface that displays a list or timeline of satellite observations for selection. The list or timeline clearly marks the acquisition time and related description information of each observation. Users select the satellite observations they are interested in by clicking on a specific time on the interface or dragging the slider on the timeline. The system retrieves the corresponding observations from the database based on the user's selection. After the selection is completed, the system is ready to load the satellite observations of the selected time in preparation for subsequent deviation checks.

[0117] Based on the user's selection, the system loads the satellite observation images of the corresponding time from the storage. The images are displayed on the interface in high resolution for users to view and analyze. In order to enhance the readability of the images and the accuracy of the analysis, the system provides the land-sea boundary overlay function. This means that the system will automatically draw the land-sea boundary on the satellite observation image to help users more clearly identify the features of the land objects, especially the land-sea boundary area. Users can control the display and hiding of the land-sea boundary through the tools or buttons on the interface, as well as adjust its color and style to meet different analysis needs.

[0118] The user drags the satellite observation image on the interface with the mouse to adjust its position. The system responds to the user's dragging operation in real time, allowing the image to move smoothly. In order to achieve sub-pixel-level positioning accuracy deviation inspection, the system uses high-precision image processing and positioning algorithms. When the user moves the image with the mouse, the system can accurately calculate the offset of each pixel in the image relative to its original position, and can even identify tiny offsets less than one pixel. During the image movement process, the system calculates and displays the deviation between the image position and the expected position (such as the position determined according to the high-precision landmark library) in real time. This deviation inspection can be accurate to the sub-pixel level, helping users to discover and analyze positioning accuracy problems. The system feeds back the deviation inspection results to the user in an intuitive way (such as digital display, color change or graphic mark), so that the user can quickly understand the status of the image positioning accuracy and make adjustments or corrections as needed.

[0119] Optionally, the step 105 includes:

[0120] Step 1051, comprehensive statistical analysis results and manual comparison and verification results.

[0121] Step 1052, comprehensively evaluate the geolocation accuracy of the satellite data and provide improvement suggestions.

[0122] In the embodiments of this application, refer to Figure 5 , the comprehensive statistical analysis results can be displayed through the diversified analysis interface of deviation results.

[0123] The system or analysts need to collect all statistical analysis results and manual comparison and verification results on the geolocation accuracy of satellite data. Statistical analysis results may include statistical indicators such as the mean, standard deviation, maximum deviation, and deviation distribution of deviation data; manual comparison and verification results may include expert visual comparison results of specific landmarks or areas, field survey data, etc. Integrate the collected statistical analysis results and manual comparison and verification results. This may involve unifying the format, time synchronization, and spatial matching of data from different sources to ensure data consistency and comparability. Perform a comprehensive analysis of the integrated data. The focus of the analysis is to identify problem areas, outliers, trend deviations, etc. of positioning accuracy, and explore their possible causes. At the same time, it is also necessary to evaluate the consistency of manual comparison and verification results with statistical analysis results to verify the accuracy and reliability of statistical methods.

[0124] Determine the evaluation criteria for geolocation accuracy based on the application requirements, industry specifications or international standards of satellite data. These criteria may include the absolute value range of positioning accuracy, relative error limits, stability requirements, etc. Based on the comprehensive analysis results, conduct a comprehensive evaluation of the geolocation accuracy of satellite data. The evaluation includes the overall level of positioning accuracy, positioning performance in different regions or conditions, and whether there are significant deviations or errors. During the evaluation process, identify the main problems that lead to reduced positioning accuracy, such as sensor errors, data processing algorithm problems, and improper landmark selection.

[0125] Conduct in-depth analysis of the identified problems to find out their root causes and influencing factors. This may require further data analysis, experimental verification or expert consultation. Design improvement plans based on the results of the root cause analysis. Improvement plans may include optimizing sensor design, improving data processing algorithms, increasing the number of landmarks or improving landmark distribution, strengthening data quality control, etc. Put forward the improvement plan in the form of suggestions, clarifying specific improvement measures, implementation steps and expected results. At the same time, it is also necessary to consider the feasibility, cost-effectiveness and possible risks of the improvement plan. Communicate the improvement suggestions with relevant departments or teams, listen to their opinions and suggestions, and revise and improve the suggestions based on feedback.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a satellite data geolocation accuracy monitoring device for implementing the satellite data geolocation accuracy monitoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more satellite data geolocation accuracy monitoring device embodiments provided below can refer to the limitations of the satellite data geolocation accuracy monitoring method above, and will not be repeated here.

[0127] In an exemplary embodiment, Figure 6 As shown, a satellite data geolocation accuracy monitoring device 20 is provided, comprising:

[0128] An acquisition module 201 is used to construct a high-precision landmark library, wherein the landmark library contains high-resolution landmark data uniformly distributed around the world;

[0129] Based on a variety of positioning accuracy verification algorithms, different satellite remote sensing data are tested for deviations using high-resolution landmark data as a benchmark;

[0130] An analysis module 202 is used to monitor and track satellite remote sensing data of specific landmark points and generate diversified statistical analysis results;

[0131] Providing a human-computer interactive image deviation inspection tool to manually compare and verify the diversified statistical analysis results;

[0132] The generating module 203 is used to generate a satellite data geolocation accuracy assessment report based on the statistical analysis results and the manual comparison and verification results.

[0133] Optionally, the acquisition module 201 is further used to:

[0134] Using high-resolution satellite imagery data and mapping software, we initially screened and marked landmarks around the world;

[0135] Generate a unique identifier for each selected landmark and record its latitude and longitude information;

[0136] Periodically download the latest remote sensing satellite images and update landmark sample data to ensure the timeliness and accuracy of the landmark database.

[0137] Optionally, the positioning accuracy verification algorithm includes SIFT feature point matching method, optical flow method, and automatic landmark navigation.

[0138] Optionally, the analysis module 202 is further configured to:

[0139] Regularly obtain satellite observation data of specific landmark points;

[0140] Applying the positioning accuracy verification algorithm to perform deviation verification on the acquired data;

[0141] Record and analyze deviation data to generate long-term deviation result files.

[0142] Optionally, the analysis module 202 is further configured to:

[0143] Use spatial distribution graphs, long time series graphs, circular probability error graphs and other charts to display deviation data;

[0144] It supports the elimination of outliers and the screening and analysis of data in specified areas or observation angles.

[0145] Optionally, the analysis module 202 is further configured to:

[0146] The user selects satellite observation data at a specific time through the interface;

[0147] The system loads observation images and provides the function of overlaying the land-sea boundary;

[0148] The user can adjust the image position by moving the mouse to achieve sub-pixel level positioning accuracy deviation inspection.

[0149] Optionally, the generating module 203 is further used to:

[0150] Comprehensive statistical analysis results and manual comparison and verification results;

[0151] Comprehensively evaluate the geolocation accuracy of satellite data and provide recommendations for improvement.

[0152] The embodiment of the present application constructs a high-precision landmark library, and based on a variety of positioning accuracy verification algorithms, uses high-resolution landmark data as a benchmark to perform deviation verification on different satellite remote sensing data, so as to achieve comprehensive monitoring and evaluation of the geographic positioning accuracy of satellite data. It can not only improve the detection accuracy of positioning accuracy, but also ensure the timeliness and accuracy of the landmark library, and provide a reliable basis for the subsequent processing and application of satellite data. By monitoring and tracking the remote sensing data of specific landmark points and generating diversified statistical analysis results, the positioning accuracy performance of satellite data under different time and space conditions can be more intuitively displayed. These statistical analysis results not only include traditional forms such as reports and tables, but also adopt more professional and complex chart forms such as spatial distribution maps, long time series maps, and circular probability error maps, which provide comprehensive data support for evaluating the geographic positioning accuracy of satellite data. In addition, a human-computer interactive image deviation verification tool is provided, allowing users to manually compare and verify the diversified statistical analysis results, further improving the accuracy and credibility of the evaluation results. Through human-computer interaction, users can more intuitively understand the positioning accuracy of satellite data and make adjustments and optimizations according to actual needs. The satellite data geolocation accuracy assessment report generated based on statistical analysis results and manual comparison and verification results provides an important reference for the subsequent processing and application of satellite data. This report not only contains detailed data on landmark library construction, deviation testing, statistical analysis and other links, but also provides comprehensive assessment conclusions and improvement suggestions, which will help improve the application value and effect of satellite data.

[0153] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store satellite data geolocation accuracy monitoring data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a satellite data geolocation accuracy monitoring method is implemented.

[0154] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0156] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0157] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0160] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0161] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for monitoring satellite data geographic positioning accuracy, characterized in that: The satellite data geolocation accuracy monitoring method comprises: Building a high-precision landmark library, wherein the landmark library contains high-resolution landmark data evenly distributed around the world; Based on a variety of positioning accuracy verification algorithms, different satellite remote sensing data are tested for deviations using high-resolution landmark data as a benchmark; Monitor and track satellite remote sensing data of specific landmarks and generate diverse statistical analysis results; Providing a human-computer interactive image deviation inspection tool to manually compare and verify the diversified statistical analysis results; Based on the statistical analysis results and manual comparison and verification results, a satellite data geolocation accuracy assessment report is generated.

2. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The steps of constructing a high-precision landmark library include: Using high-resolution satellite imagery data and mapping software, we initially screened and marked landmarks around the world; Generate a unique identifier for each selected landmark and record its latitude and longitude information; Periodically download the latest remote sensing satellite images and update landmark sample data to ensure the timeliness and accuracy of the landmark database.

3. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The positioning accuracy verification algorithm includes SIFT feature point matching method, optical flow method, and automatic landmark navigation.

4. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The step of monitoring and tracking the satellite remote sensing data of a specific landmark point includes: Regularly obtain satellite observation data of specific landmark points; Applying the positioning accuracy verification algorithm to perform deviation verification on the acquired data; Record and analyze deviation data to generate long-term deviation result files.

5. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The step of generating diversified statistical analysis results comprises: Use spatial distribution graphs, long time series graphs, circular probability error graphs and other charts to display deviation data; It supports the elimination of outliers and the screening and analysis of data in specified areas or observation angles.

6. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The step of providing a human-computer interaction image deviation inspection tool comprises: The user selects satellite observation data at a specific time through the interface; The system loads observation images and provides the function of overlaying the land-sea boundary; The user can adjust the image position by moving the mouse to achieve sub-pixel level positioning accuracy deviation inspection.

7. The satellite data geolocation accuracy monitoring method according to claim 1, characterized in that: The step of generating a satellite data geolocation accuracy assessment report comprises: Comprehensive statistical analysis results and manual comparison and verification results; Comprehensively evaluate the geolocation accuracy of satellite data and provide recommendations for improvement.

8. A satellite data geographic positioning accuracy monitoring device, characterized in that: The satellite data geolocation accuracy monitoring device comprises: An acquisition module, used to construct a high-precision landmark library, wherein the landmark library contains high-resolution landmark data uniformly distributed around the world; Based on a variety of positioning accuracy verification algorithms, different satellite remote sensing data are tested for deviations using high-resolution landmark data as a benchmark; The analysis module is used to monitor and track satellite remote sensing data of specific landmarks and generate diversified statistical analysis results; Providing a human-computer interactive image deviation inspection tool to manually compare and verify the diversified statistical analysis results; A generation module is used to generate a satellite data geolocation accuracy assessment report based on the statistical analysis results and manual comparison and verification results.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the satellite data geolocation accuracy monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the satellite data geolocation accuracy monitoring method described in any one of claims 1 to 7 are implemented.

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