A Wide-Area CORS Service Quality Assessment Method and System Based on Virtual Observation
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNIV
- Filing Date
- 2026-05-29
- Publication Date
- 2026-06-30
Smart Images
Figure CN122307602A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of satellite navigation enhancement service monitoring technology, specifically to a wide-area CORS service quality assessment method and system based on virtual observation. Background Technology
[0002] Currently, Continuously Operating Reference Stations (CORS), Virtual Reference Stations (VRS), and various ground-based augmentation positioning services are widely used in surveying and mapping, natural resources, unmanned systems, intelligent transportation, engineering construction, and location services. For service providers, the ability to continuously, objectively, and cost-effectively evaluate service quality is a crucial foundation for ensuring commercial operation and large-scale service.
[0003] In existing technologies, differential service quality assessment typically relies on manual testing or user feedback. This approach is highly random in terms of test site selection, test periods, terminal equipment, antenna environment, communication links, and operator habits, resulting in problems such as low testing efficiency, large result dispersion, strong subjectivity, difficulty in continuous monitoring, and difficulty in forming objective assessment conclusions for full regional coverage.
[0004] To improve the objectivity of assessments, existing solutions propose deploying dedicated differential monitoring equipment or physical monitoring stations within the service coverage area. These stations receive differential correction data from the monitored service in real time, perform RTK positioning, and then upload the positioning results to a management platform for visualization (e.g., Chinese Patent CN116660953A, published on August 29, 2023, discloses an urban CORS intelligent monitoring system). While these solutions are more objective than manual assessments, they still rely on dedicated hardware deployments. Their monitoring range is limited by the location and number of devices installed, resulting in high construction and maintenance costs. They also struggle to cover arbitrary locations within the area and lack scalability in complex terrain, cross-regional monitoring, and large-scale grid-based monitoring scenarios. Summary of the Invention
[0005] To overcome the technical problems of existing service quality assessments, such as reliance on manual or physical monitoring stations, high monitoring costs, limited coverage, and difficulty in objectively reflecting service availability at any location in a region, this invention provides a wide-area CORS service quality assessment method and system based on virtual observation. It can complete the online assessment of the monitored service based solely on publicly available or accessible third-party virtual observation services, thereby achieving service quality assessment without relying on the deployment of physical monitoring stations, enabling the arbitrary setting of monitoring points within the service coverage area, and continuously outputting service quality assessment indicators such as accuracy, latency, and spatial availability.
[0006] According to one aspect of the present invention, a wide-area CORS service quality assessment method based on virtual observation is provided, comprising: step S1, determining the coordinates of at least one target monitoring point and generating service request coordinates corresponding to the target monitoring point; step S2, using the service request coordinates to request and receive first differential correction data from the monitored service, and simultaneously using the service request coordinates to request and receive second differential correction data from at least one third-party virtual observation service.
[0007] Step S3: Based on the data pattern of the monitored service, extract standardized reference coordinates from the first differential correction data; Step S4: Perform data standardization processing on the first differential correction data and the second differential correction data, and use the first differential correction data and standardized reference coordinates as the reference end and the second differential correction data as the flow end, and use a real-time dynamic positioning algorithm to calculate a predicted coordinate sequence of at least one target monitoring point; Step S5: Compare each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in sequence, and generate a quality assessment result based on the comparison result.
[0008] Furthermore, in step S1, the service request coordinates are the same as the target monitoring point coordinates, or are coarse initial coordinates generated based on the target monitoring point coordinates.
[0009] Further, step S3 includes: determining the data mode of the monitored service; the data mode includes grid enhancement mode and non-grid enhancement mode; when the monitored service is in grid enhancement mode, extracting the grid coordinates corresponding to the first differential correction data as standardized reference coordinates; when the monitored service is in non-grid enhancement mode, extracting the reference coordinates, virtual station coordinates or station center coordinates carried in the first differential correction data as standardized reference coordinates.
[0010] Furthermore, for multiple third-party virtual observation services, step S4, which uses the first differential correction data and standardized reference coordinates as the reference end and the second differential correction data as the flow end, and calculates the predicted coordinate sequence of at least one target monitoring point using a real-time dynamic positioning algorithm, includes: based on the first differential correction data and standardized reference coordinates, sequentially calculating multiple predicted coordinate sequences of the corresponding target monitoring point using a real-time dynamic positioning algorithm for the second differential correction data corresponding to each third-party virtual observation service; performing a consistency check on the calculated multiple predicted coordinate sequences; when the deviation between the solution result of the predicted coordinate sequence corresponding to a certain third-party virtual observation service and the predicted coordinate sequences corresponding to other third-party virtual observation services exceeds a preset threshold, marking the third-party virtual observation service as an anomaly source and reducing the weight of the third-party virtual observation service or directly removing it; and fusing the predicted coordinate sequences corresponding to each third-party virtual observation service based on a preset fusion method to obtain the final predicted coordinate sequence of the corresponding target monitoring point.
[0011] Furthermore, in step S4, the data standardization process includes data format standardization, coordinate reference frame unification, time synchronization, and outlier removal.
[0012] Further, step S5 includes: converting the predicted coordinate sequence and the target monitoring point coordinates to a unified coordinate framework; the predicted coordinate sequence is a set composed of predicted coordinates of target monitoring points at different time points; comparing each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in turn to obtain the offset of each predicted coordinate, and generating an accuracy index of the monitored service based on the minimum offset; generating a latency index and a spatial availability index of the monitored service based on the differential data reception time, solution completion time, ambiguity fixed state, epoch continuity and threshold compliance ratio; wherein, the quality assessment results include accuracy index, latency index and spatial availability index.
[0013] Furthermore, the evaluation method also includes: uploading the quality evaluation results to a monitoring platform, which is used to visualize the quality evaluation results in the form of maps, heat maps, graphs, alarm lists or reports, and automatically trigger alarms and record abnormal areas, abnormal services and abnormal time periods when any indicator in the quality evaluation results exceeds the corresponding preset threshold.
[0014] According to one aspect of this invention, a wide-area CORS service quality assessment system based on virtual observation is provided, comprising: a monitoring point generation module, which determines the coordinates of at least one target monitoring point and generates service request coordinates corresponding to the target monitoring point; a service request control module, which uses the service request coordinates to request and receive first differential correction data from a monitored service, and simultaneously uses the service request coordinates to request and receive second differential correction data from at least one third-party virtual observation service; a coordinate unification and standardization module, which extracts standardized reference coordinates from the first differential correction data according to the data pattern of the monitored service; a differential calculation module, which performs data standardization processing on the first differential correction data and the second differential correction data, and uses the first differential correction data and the standardized reference coordinates as a reference end and the second differential correction data as a flow end, and calculates a predicted coordinate sequence of at least one target monitoring point using a real-time dynamic positioning algorithm; and a quality assessment module, which compares each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in sequence, and generates a quality assessment result based on the comparison result.
[0015] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing program instructions executable by the processor, the processor invoking the program instructions to execute the aforementioned wide-area CORS service quality assessment method based on virtual observation.
[0016] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the aforementioned wide-area CORS service quality assessment method based on virtual observation.
[0017] The above technical solution first determines the coordinates of virtual target monitoring points based on monitoring point planning criteria such as user distribution areas and the coverage area of the monitored service, without being restricted by the physical equipment installation location, and generates service request coordinates based on the target monitoring point coordinates; using the service request coordinates, it requests differential correction data from the monitored service and at least one third-party virtual observation service to obtain first differential correction data and second differential correction data. That is to say, online evaluation of the monitored service can be completed solely based on publicly available or accessible third-party virtual observation services, without the need to deploy physical monitoring stations or dedicated differential terminals within the service coverage area; for grid-enhanced mode and non-grid-enhanced mode, from the first differential correction... Standardized reference coordinates are extracted from the data. That is, an evaluation mechanism that is compatible with both grid-enhanced and non-grid-enhanced modes is constructed to enable unified monitoring and comparison of different forms of differential services, which has good versatility. The first differential correction data and standardized reference coordinates are used as the reference end, and the second differential correction data is used as the flow end. A real-time dynamic relative positioning algorithm is used to calculate the predicted coordinate sequence. The predicted coordinate sequence is then compared with the target monitoring point to obtain the offset of the predicted coordinate sequence in real time. The results of various evaluation indicators such as accuracy, latency, and spatial availability are calculated and transmitted to the monitoring platform for visualization and alarm.
[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0019] (1) There is no need to deploy physical monitoring stations or dedicated differential terminals in the service coverage area. The online evaluation of the monitored service can be completed based solely on publicly available or accessible third-party virtual observation services, which significantly reduces construction and operation and maintenance costs.
[0020] (2) Virtual target monitoring points can be set arbitrarily within the service coverage area, without being restricted by the physical equipment installation location. This makes it easy to achieve full-area grid-based, corridor-based, and hotspot-based monitoring, thereby more realistically reflecting the availability of regional space.
[0021] (3) Through unified differential data standardization, grid / non-grid compatible processing and RTK predicted coordinate sequence inversion mechanism, different types of CORS services can be uniformly evaluated, which has good versatility.
[0022] (4) The evaluation process weakens the impact of differences in end-user working environment, manual operation habits and specific equipment models, and is therefore more objective, more continuous and easier to form a service quality report with consistent statistical standards than manual testing.
[0023] (5) By using the consistency verification and anomaly removal mechanism of multiple third-party virtual observation services, the dependence on a single external service can be reduced, and the stability and credibility of the evaluation results can be improved. Attached Figure Description
[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 This is a flowchart illustrating a wide-area CORS service quality assessment method based on virtual observation, provided as an embodiment of the present invention.
[0026] Figure 2 This is a schematic diagram illustrating the process of determining standardized reference coordinates under grid enhancement mode and non-grid enhancement mode, as provided in an embodiment of the present invention.
[0027] Figure 3 This is a schematic diagram of the overall architecture of a wide-area CORS service quality assessment system based on virtual observation, provided for an embodiment of the present invention. Detailed Implementation
[0028] It should be noted that:
[0029] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.
[0032] Please refer to the appendix. Figure 1 This invention provides a method for assessing the quality of service of a wide-area CORS based on third-party virtual observation, comprising the following steps:
[0033] Step S1: Determine the coordinates of at least one target monitoring point (coor2) and generate the service request coordinates (coor1) corresponding to the target monitoring point.
[0034] In step S1, based on the user distribution area, historical operation hotspots, administrative boundaries, road corridors, grid partitioning rules, or the coverage area of the monitored service, at least one target monitoring point coordinate (coor2) is determined, and a corresponding service request coordinate (coor1) is generated for each target monitoring point. The service request coordinate (coor1) can be the same as the target monitoring point coordinate (coor2), or it can be a coarsely initialized coordinate generated based on the target monitoring point coordinate (coor2). Understandably, this invention only requires setting virtual target monitoring points arbitrarily within the service coverage area, without deploying dedicated differential monitoring equipment or physical monitoring stations at the monitoring points. Therefore, this invention is not limited by the physical equipment installation location, easily achieving full-area grid-based, corridor-based, and hotspot-based monitoring, thus more realistically reflecting the spatial availability of the area.
[0035] It should be noted that coor2 is the known coordinates of the target monitoring point, serving as the target reference coordinates for quality assessment, while coor1 is the service request coordinates, used to initiate VRS (Virtual Reference Station) requests to the monitored service and third-party virtual observation services. The target monitoring point coordinates are generated through one or more of the following methods: regular grid placement, user hotspot placement, administrative boundary supplementation, and road corridor placement. Preferably, the placement of target monitoring points in step S1 adopts a combined strategy of "prioritizing densely populated user areas + supplementing boundary points + regular grid partitioning," so that the monitoring results can reflect both typical business scenarios and changes in spatial availability at service boundaries.
[0036] Step S2: Using the service request coordinates coor1, request and receive the first differential correction data data1 from the monitored service. At the same time, using the service request coordinates coor1, request and receive the second differential correction data data2 from at least one third-party virtual observation service.
[0037] In step S2, for each target monitoring point, a differential service request is initiated to the monitored service via the standard NTRIP interface or other equivalent interface using the service request coordinates coor1 to obtain the first differential correction data data1. Simultaneously, a differential service request is initiated to at least one third-party virtual observation service (i.e., a publicly available third-party VRS service) using the service request coordinates coor1 to obtain the second differential correction data data2 corresponding to the same location. It can be understood that this invention can complete the online evaluation of the monitored service based solely on publicly available or accessible third-party virtual observation services, without the need to deploy physical monitoring stations or dedicated differential terminals within the service coverage area, significantly reducing construction and maintenance costs.
[0038] It should be noted that data1 is the first differential correction data returned by the monitored service, which can be RTCM or its equivalent format. data2 is the second differential correction data returned by one or more third-party virtual observation services. Differential correction data refers to real-time correction data generated and broadcast by continuously operating reference stations (CORS) to correct GNSS (such as BeiDou, GPS, etc.) positioning errors, enabling the rover (user terminal) to achieve high-precision positioning at the centimeter or even decimeter level. The monitored service refers to the service that requires quality assessment, and the third-party virtual observation service refers to one or more external augmentation services that are independent of the monitored service and can request coordinates coor1 from the service to return differential correction data. Third-party virtual observation services may include one or more of the following: government public services, public services of natural resources authorities, commercial CORS (i.e., continuously operating reference station system) services, or operator CORS services.
[0039] Preferably, in step S2, at least two independent third-party virtual observation services are accessed. The consensus assessment based on multiple third-party virtual observation services will be further illustrated below with an example: In addition to the monitored service, the system simultaneously accesses three third-party virtual observation services, such as service A, service B, and service C. The system uses the same service request coordinate coor1 to request correction data from each third-party virtual observation service, obtaining multiple sets of second differential correction data data2a, data2b, and data2c.
[0040] Step S3: Based on the data pattern of the monitored service, extract the standardized reference coordinates from the first differential correction data.
[0041] Please refer to the appendix for details. Figure 2 It should be noted that step S3 aims to achieve coordinate compatibility processing between grid-enhanced and non-grid-enhanced modes. Specifically, it first determines the data mode of the monitored service, which includes both grid-enhanced and non-grid-enhanced modes. Further, in grid-enhanced mode, the first differential correction data (data1) returned by the monitored service is gridded differential correction information. In this case, directly using the user's requested location as the reference coordinate may deviate from the actual grid reference location used in the correction calculation. Therefore, this invention extracts the corresponding grid coordinates from the first differential correction data (data1) and uses them as standardized reference coordinates (coor1′) in subsequent relative positioning calculations, thus ensuring consistency between the reference coordinates and the differential correction data source. In non-grid-enhanced mode, the monitored service typically returns virtual reference station coordinates or equivalent reference coordinates. In this case, this invention directly extracts the corresponding reference coordinates, virtual station coordinates, or station center coordinates from the first differential correction data (data1) and uses them as standardized reference coordinates (coor1′), achieving a unified data processing interface with the grid mode. Therefore, this invention is compatible with multiple service implementation methods, improving the universality of the solution.
[0042] Step S4: Using the first differential correction data data1 and the standardized reference coordinate coor1′ as the reference end, and the second differential correction data data2 as the flow end, the predicted coordinate sequence coors3 of at least one target monitoring point is calculated using a real-time dynamic positioning algorithm.
[0043] In step S4, if there are multiple target monitoring points, real-time dynamic positioning calculation needs to be performed for each target monitoring point to obtain the predicted coordinate sequence coors3 for each target monitoring point. Simultaneously, if multiple third-party virtual observation services are used, each target monitoring point will calculate multiple predicted coordinate sequences coors3 corresponding to the multiple third-party virtual observation services. These multiple predicted coordinate sequences coors3 for each target monitoring point are then merged to obtain the final predicted coordinate sequence coors3 for each target monitoring point to participate in the coordinate comparison in the subsequent step S4.
[0044] Furthermore, before performing real-time dynamic positioning calculations, the first differential correction data (data1) and the second differential correction data (data2) need to be standardized. Data standardization includes: standardizing the data format of the first differential correction data (data1) and the second differential correction data (data2), unifying the coordinate reference frame, time synchronization, and outlier removal, ensuring that the first differential correction data (data1) and the second differential correction data (data2) meet the conditions for comparison within the same epoch and relative positioning calculation. It should be noted that the predicted coordinate sequence (coors3) represents the relative positioning result corresponding to the target monitoring point calculated by the third-party virtual observation service within the reference frame of the monitored service.
[0045] Furthermore, for multiple third-party virtual observation services, the first differential correction data (data1) and the standardized reference coordinates (coor1′) are used as the reference end, and the second differential correction data (data2) is used as the flow end. A real-time dynamic positioning algorithm (RTK algorithm) is used to calculate the predicted coordinate sequence of the corresponding target monitoring point epoch-by-epoch. This includes: based on the first differential correction data and the standardized reference coordinates, for each third-party virtual observation service, the real-time dynamic positioning algorithm is used to calculate multiple predicted coordinate sequences for the corresponding target monitoring point. Understandably, multiple predicted coordinate sequences can be obtained for each target monitoring point. A consistency check is performed on the calculated multiple predicted coordinate sequences. If the deviation between the predicted coordinate sequence of a certain third-party virtual observation service and the predicted coordinate sequences of other third-party virtual observation services exceeds a preset threshold, the third-party virtual observation service is marked as an anomaly source, and its weight is reduced or it is directly removed to improve the robustness of the evaluation conclusion. Based on a preset fusion method, the predicted coordinate sequences of each third-party virtual observation service are fused to generate the final predicted coordinate sequence (i.e., the consensus predicted coordinate sequence). Then, it is compared with the target monitoring point coordinates (coor2) to obtain more stable results in terms of accuracy, latency, and spatial availability. This method reduces the misleading influence of anomalies in a single third-party virtual observation service on the evaluation results. The preset fusion methods include, but are not limited to, weighted average, robust estimation, or median fusion methods.
[0046] It should be noted that existing methods require the establishment of a physical reference station. The coordinates of the monitoring point are obtained by combining the received satellite observation data with the differential data returned by the service request. However, this invention does not require the establishment of a physical reference station, nor does it require receiving satellite observation data. Instead, it obtains the first differential correction data returned by the monitored service and the second differential correction data returned by a third-party virtual observation service, and then calculates the coordinates of the target monitoring point by combining them with a real-time dynamic positioning algorithm.
[0047] It should also be noted that this invention is not limited to using only the RTK algorithm; it can also combine PPP-RTK, network RTK, or other relative positioning algorithms to achieve the solution of equivalent predicted coordinate sequences, as long as its technical essence is still based on the comparison of third-party virtual observation data rather than physical monitoring stations to complete the service quality assessment. This real-time dynamic RTK algorithm includes, but is not limited to, double-difference observation construction, cycle slip detection, ambiguity fixing, sliding window smoothing, epoch-level quality control, and solution status marking.
[0048] Step S5: Compare each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in turn, and generate a quality assessment result based on the comparison results.
[0049] In step S5, the predicted coordinate sequence coors3 and the target monitoring point coordinates coor2 are transformed into a unified coordinate framework. The predicted coordinate sequence is a set of predicted coordinates of the target monitoring point at different time points. Each predicted coordinate in the predicted coordinate sequence coors3 needs to be compared with the corresponding target monitoring point coordinates coor2 in turn to obtain the offset of each predicted coordinate. The accuracy index of the monitored service is generated based on the minimum offset (i.e., the offset corresponding to the predicted coordinate closest to the target monitoring point coordinates coor2). At the same time, the latency index and spatial availability index of the monitored service are generated based on the differential data reception time, solution completion time, ambiguity fixed state, epoch continuity and threshold compliance ratio.
[0050] In this embodiment, the predicted coordinate sequence coors3 and the target monitoring point coordinates coor2 are uniformly transformed to the local ENU coordinate system (i.e., the local East-North-Sky coordinate system), and the eastward offset between the predicted coordinates corresponding to each epoch in the predicted coordinate sequence coors3 and the target monitoring point coordinates coor2 is calculated sequentially. Northward offset and the upward offset ,in:
[0051] ,
[0052] ,
[0053] ,
[0054] in, This indicates the coordinates in the east direction. Indicates the coordinates in the north direction. This represents the coordinates in the elevation direction.
[0055] Furthermore, based on the eastward offset Northward offset and the upward offset Calculate the accuracy metrics of the monitored service, for example, calculate the plane error. Calculate the three-dimensional error .
[0056] It should be noted that the system can statistically analyze the evaluation results of various indicators (i.e., quality assessment results) at the second, minute, or custom sliding time window levels, and store these results in a database for subsequent visualization and analysis. The evaluation results include accuracy indicators, latency indicators, and spatial availability indicators. Accuracy indicators at least include eastward mean deviation, northward mean deviation, elevation mean deviation, plane error, three-dimensional error, 95th percentile error, and fixed solution ratio. Latency indicators at least include the first packet latency of differential data, epoch processing latency, fixed solution convergence time, and duration of continuous interruptions. Spatial availability indicators at least include the availability rate, regional raster availability rate, and time-period availability rate. It should also be noted that the quality assessment indicators are not limited to accuracy, latency, and spatial availability indicators; they can also be extended to indicators such as continuity, integrity, initialization success rate, ambiguity fixation stability, and anomaly recovery capability.
[0057] In some feasible embodiments, the evaluation method further includes: step S6, uploading the quality evaluation results to the monitoring platform so that the monitoring platform can visualize the results in the form of maps, heatmaps, graphs, alarm lists, or reports. When any indicator in the quality evaluation results exceeds a corresponding preset threshold, an alarm is automatically triggered and abnormal areas, abnormal services, and abnormal time periods are recorded. It is understood that each indicator in the quality evaluation results has its corresponding preset threshold, and the preset threshold for each indicator can be set according to actual needs, without limitation here.
[0058] Please refer to the appendix for details. Figure 3Based on the same technical concept as the aforementioned embodiments, this invention also provides a wide-area CORS service quality assessment system based on virtual observation, comprising: a monitoring point generation module, used to determine the coordinates of at least one target monitoring point and generate service request coordinates corresponding to the target monitoring point; a service request control module, used to request and receive first differential correction data from the monitored service using the service request coordinates, and simultaneously request and receive second differential correction data from at least one third-party virtual observation service using the service request coordinates; a coordinate unification and standardization module, used to extract grid coordinates, reference coordinates, or virtual station coordinates from the first differential correction data according to the data pattern of the monitored service to generate standardized reference coordinates; a differential calculation module, used to use the first differential correction data and the standardized reference coordinates as reference ends, and the second differential correction data as flow ends, to perform time synchronization, data standardization processing, and relative positioning calculation on the first differential correction data and the second differential correction data to obtain a predicted coordinate sequence; and a quality assessment module, used to compare each predicted coordinate in the predicted coordinate sequence sequentially with the corresponding target monitoring point coordinates, and generate a quality assessment result based on the comparison result. Based on this, this evaluation system overcomes the technical problems of existing service quality evaluations, which rely on manual or physical monitoring stations, have high monitoring costs, limited coverage, and are difficult to objectively reflect the service availability at any location in the region. It can complete the online evaluation of the monitored service based solely on publicly available or accessible third-party virtual observation services. It achieves service quality evaluation without relying on the deployment of physical monitoring stations, can set monitoring points arbitrarily within the service coverage area, and can continuously output indicators such as accuracy, latency, and spatial availability.
[0059] In some feasible practical examples, the evaluation system also includes a visualization and alarm module, which uploads the quality evaluation results to the monitoring platform so that the monitoring platform can visualize the results in the form of maps, heat maps, graphs, alarm lists or reports. When any indicator in the quality evaluation results exceeds the corresponding preset threshold, an alarm is automatically triggered and abnormal areas, abnormal services and abnormal time periods are recorded.
[0060] In the above embodiments, the evaluation system automatically traverses all target monitoring points according to a preset time period to form a regionalized continuous evaluation result. For each target monitoring point, the evaluation system determines whether the target monitoring point is available within the current time window based on preset thresholds. For example, when the plane error is not greater than the first threshold, the elevation error is not greater than the second threshold, the fixed solution ratio is not lower than the third threshold, and the first packet delay does not exceed the fourth threshold, the target monitoring point is determined to be available within the current time window.
[0061] The assessment system further calculates the availability of a specific administrative region, road corridor, construction area, or the entire service area, generating a spatial availability heat map. This heat map can be displayed by hour, day, week, or a custom period, and can be overlaid with timelines of abnormal events to assist maintenance personnel in locating service blind spots, weak areas, and areas with momentary anomalies.
[0062] At the visualization level, the evaluation system will , and The three-directional offsets are plotted as time-series curves, and the horizontal and vertical errors are plotted as statistical distribution maps. The service quality status is then displayed on the map interface using different colors, different level icons, or a grid coloring method. For target monitoring points that fail to meet the standards for multiple consecutive time windows, the evaluation system automatically generates alarm records and pushes them to the operation and maintenance terminal.
[0063] Based on the same technical concept as the foregoing embodiments, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute the aforementioned wide-area CORS service quality assessment method based on virtual observation.
[0064] Based on the same technical concept as the foregoing embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the aforementioned wide-area CORS service quality assessment method based on virtual observation.
[0065] In summary, this invention first determines the coordinates of virtual target monitoring points based on monitoring point planning criteria such as user distribution areas and the coverage area of the monitored service, without being limited by the physical equipment installation location, and generates service request coordinates based on the target monitoring point coordinates. Using the service request coordinates, it requests differential correction data from the monitored service and at least one third-party virtual observation service to obtain first and second differential correction data. That is, online evaluation of the monitored service can be completed solely based on publicly available or accessible third-party virtual observation services, without the need to deploy physical monitoring stations or dedicated differential terminals within the service coverage area. For both grid-enhanced and non-grid-enhanced modes, the first differential correction data... The process involves extracting standardized reference coordinates from positive data. This means constructing an evaluation mechanism that is compatible with both grid-enhanced and non-grid-enhanced modes to enable unified monitoring and comparison of different types of differential services, demonstrating good versatility. The first differential correction data and standardized reference coordinates are used as the reference end, and the second differential correction data as the flow end. A real-time dynamic relative positioning algorithm is used to calculate the predicted coordinate sequence. The predicted coordinate sequence is then compared with the target monitoring point to obtain the real-time offset of the predicted coordinate sequence. This is used to calculate various evaluation results, such as accuracy, latency, and spatial availability, and the evaluation results are transmitted to the monitoring platform for visualization and alerts.
[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.
Claims
1. A method for assessing the quality of service of a wide-area CORS based on virtual observation, characterized in that, include: Step S1: Determine the coordinates of at least one target monitoring point and generate the service request coordinates corresponding to the target monitoring point; Step S2: Using the service request coordinates, request and receive the first differential correction data from the monitored service; simultaneously, using the service request coordinates, request and receive the second differential correction data from at least one third-party virtual observation service. Step S3: Based on the data pattern of the monitored service, extract standardized reference coordinates from the first differential correction data; Step S4: Using the first differential correction data and standardized reference coordinates as the reference end and the second differential correction data as the flow end, a real-time dynamic positioning algorithm is used to calculate the predicted coordinate sequence of at least one target monitoring point. Step S5: Compare each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in turn, and generate a quality assessment result based on the comparison results.
2. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, In step S1, the service request coordinates are the same as the target monitoring point coordinates, or are coarse initial coordinates generated based on the target monitoring point coordinates.
3. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, Step S3 includes: Determine the data pattern of the monitored service; the data pattern includes grid-enhanced mode and non-grid-enhanced mode. When the monitored service is in grid enhancement mode, the grid coordinates corresponding to the first differential correction data are extracted as standardized reference coordinates. When the monitored service is in non-grid enhancement mode, the reference coordinates, virtual station coordinates, or station center coordinates carried in the first differential correction data are extracted as standardized reference coordinates.
4. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, For multiple third-party virtual observation services, step S4, which uses the first differential correction data and standardized reference coordinates as the reference end and the second differential correction data as the motion end, calculates the predicted coordinate sequence of at least one target monitoring point using a real-time dynamic positioning algorithm, including: Based on the first differential correction data and the standardized reference coordinates, for each third-party virtual observation service, the second differential correction data is used to calculate multiple predicted coordinate sequences of the corresponding target monitoring points using a real-time dynamic positioning algorithm. A consistency check is performed on the calculated multiple predicted coordinate sequences. When the deviation between the predicted coordinate sequence corresponding to a certain third-party virtual observation service and the predicted coordinate sequences corresponding to other third-party virtual observation services is greater than a preset threshold, the third-party virtual observation service is marked as an anomaly source, and the weight of the third-party virtual observation service is reduced or it is directly removed. The predicted coordinate sequences of various third-party virtual observation services are fused based on a preset fusion method to obtain the final predicted coordinate sequence of the corresponding target monitoring point.
5. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, In step S4, data standardization processing includes data format standardization, coordinate reference frame unification, time synchronization, and outlier removal.
6. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, Step S5 includes: The predicted coordinate sequence and the target monitoring point coordinates are transformed into a unified coordinate framework; the predicted coordinate sequence is a set composed of the predicted coordinates of the target monitoring points at different time points; Each predicted coordinate in the predicted coordinate sequence is compared with the corresponding target monitoring point coordinates in turn to obtain the offset of each predicted coordinate, and the accuracy index of the monitored service is generated based on the minimum offset. Based on the differential data reception time, solution completion time, ambiguity fixed state, epoch continuity, and threshold compliance rate, latency indicators and spatial availability indicators of the monitored service are generated; wherein, the quality assessment results include accuracy indicators, latency indicators, and spatial availability indicators.
7. The wide-area CORS service quality assessment method based on virtual observation as described in claim 1, characterized in that, The evaluation method also includes: The quality assessment results are uploaded to the monitoring platform. The monitoring platform is used to visualize the quality assessment results in the form of maps, heat maps, graphs, alarm lists or reports, and automatically trigger alarms and record abnormal areas, abnormal services and abnormal time periods when any indicator in the quality assessment results exceeds the corresponding preset threshold.
8. A wide-area CORS service quality assessment system based on virtual observation, characterized in that, include: The monitoring point generation module determines the coordinates of at least one target monitoring point and generates the service request coordinates corresponding to the target monitoring point. The service request control module is used to request and receive first differential correction data from the monitored service using the service request coordinates, and at the same time, to request and receive second differential correction data from at least one third-party virtual observation service using the service request coordinates. The coordinate unification and standardization module is used to extract standardized reference coordinates from the first differential correction data according to the data pattern of the monitored service. The differential calculation module is used to perform data standardization processing on the first differential correction data and the second differential correction data, and use the first differential correction data and the standardized reference coordinates as the reference end and the second differential correction data as the flow end, and use a real-time dynamic positioning algorithm to calculate the predicted coordinate sequence of at least one target monitoring point. The quality assessment module is used to compare each predicted coordinate in the predicted coordinate sequence with the corresponding target monitoring point coordinates in turn, and generate a quality assessment result based on the comparison result.
9. An electronic device, characterized in that, The system includes a memory and a processor, the memory storing program instructions that are executed by the processor, and the processor invoking the program instructions to execute a wide-area CORS service quality assessment method based on virtual observation as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the wide-area CORS service quality assessment method based on virtual observation as described in any one of claims 1 to 7.