GNSS meteorological parameter optimization method and system

Abstract

The application discloses a kind of GNSS meteorological parameter optimization method and system, the present application relates to the technical field of meteorological parameter optimization method, corresponding meteorological type is determined based on the identification of each meteorological region, and according to the meteorological parameter corresponding to each meteorological region, position and meteorological type, the theoretical meteorological event of GNSS observation area is determined, the accuracy of the theoretical meteorological event of GNSS observation area is guaranteed.Therefore, according to the identification of the current image of GNSS observation area, a plurality of actual meteorological characteristics are determined, and the actual meteorological event of GNSS observation area is determined according to the synthesis of a plurality of actual meteorological characteristics;Abnormal meteorological content is determined based on the comparison between the theoretical meteorological event and the actual meteorological event, the abnormal meteorological region is determined according to the tracing of abnormal meteorological content, and the corresponding abnormal meteorological parameter is marked, the optimized meteorological parameter is determined based on the optimization of abnormal meteorological parameter and abnormal meteorological region, and the accuracy of the optimized meteorological parameter is guaranteed.

Description

Technical Field

[0001] This invention relates to the technical field of meteorological parameter optimization methods, and more particularly to a GNSS meteorological parameter optimization method and system. Background Technology

[0002] With the development of technology, GNSS (Global Navigation Satellite System) has been gradually applied to people's lives and has been used to detect the weather in GNSS observation areas. This has introduced multiple meteorological parameters. In the current technology, the real-time monitoring of the weather in the GNSS observation area and the collection of multiple meteorological parameters are used to evaluate the theoretical meteorological events in the GNSS observation area. However, the evaluation is only carried out along a single dimension of the meteorological parameters, which affects the accuracy of the theoretical meteorological events. The theoretical meteorological events are prone to inconsistencies with the actual meteorological events and cannot be optimized for abnormal meteorological parameters. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and system for optimizing GNSS meteorological parameters.

[0004] This invention provides a meteorological parameter optimization method, including:

[0005] The location of the GNSS observation area is collected, and multiple GNSS observation points are determined based on the location and morphology of the GNSS observation area.

[0006] Based on meteorological observations at each GNSS observation point, the corresponding combination of meteorological parameters is determined. Multiple meteorological regions are determined according to the relative positions of each GNSS observation point and the corresponding combination of meteorological parameters. These multiple meteorological regions are presented at different locations within the GNSS observation area.

[0007] Based on the identification of each meteorological region, the corresponding meteorological type is determined, and the theoretical meteorological events in the GNSS observation area are determined according to the meteorological parameters, location and meteorological type of each meteorological region.

[0008] Multiple actual meteorological features are identified based on the current image of the GNSS observation area, and the actual meteorological events in the GNSS observation area are determined by the synthesis of multiple actual meteorological features.

[0009] Abnormal meteorological content is determined by comparing theoretical meteorological events with actual meteorological events. Abnormal meteorological areas are determined by tracing the abnormal meteorological content, and the corresponding abnormal meteorological parameters are marked. Optimized meteorological parameters are determined based on the optimization of abnormal meteorological parameters and abnormal meteorological areas.

[0010] This invention provides a GNSS meteorological parameter optimization system, which is applied to the above-described GNSS meteorological parameter optimization method. The GNSS meteorological parameter optimization system includes:

[0011] The GNSS observation point module is used to collect the location of the GNSS observation area and determine multiple GNSS observation points based on the location and shape of the GNSS observation area.

[0012] The meteorological region module is used to determine the corresponding combination of meteorological parameters based on meteorological detection at each GNSS observation point. Multiple meteorological regions are determined according to the relative position of each GNSS observation point and the corresponding combination of meteorological parameters. These multiple meteorological regions are presented at different locations within the GNSS observation area.

[0013] The theoretical meteorological event module is used to determine the corresponding meteorological type based on the identification of each meteorological region, and to determine the theoretical meteorological events of the GNSS observation area according to the meteorological parameters, location and meteorological type of each meteorological region.

[0014] The actual meteorological event module is used to determine multiple actual meteorological features based on the identification of the current image of the GNSS observation area, and to determine the actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features.

[0015] The optimization module is used to determine abnormal meteorological content based on the comparison between theoretical meteorological events and actual meteorological events, determine abnormal meteorological areas by tracing the abnormal meteorological content, mark the corresponding abnormal meteorological parameters, and determine the optimized meteorological parameters based on the optimization of abnormal meteorological parameters and abnormal meteorological areas.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In this embodiment of the invention, the method determines multiple GNSS observation points based on the location and morphology of the GNSS observation area; it determines corresponding meteorological parameter combinations based on meteorological detection at each GNSS observation point; it determines multiple meteorological regions based on the relative positions of each GNSS observation point and the corresponding meteorological parameter combinations; it determines corresponding meteorological types based on the identification of each meteorological region; and it determines theoretical meteorological events for the GNSS observation area based on the meteorological parameters, locations, and meteorological types corresponding to each meteorological region. The introduction of multiple meteorological regions, located at different positions within the GNSS observation area, accommodates a holistic consideration of the meteorological parameters, locations, and meteorological types corresponding to each meteorological region, ensuring the accuracy of theoretical meteorological events for the GNSS observation area.

[0018] Therefore, multiple actual meteorological features are identified based on the current image of the GNSS observation area, and actual meteorological events in the GNSS observation area are determined by synthesizing these features. Abnormal meteorological content is determined by comparing theoretical and actual meteorological events, and abnormal meteorological areas are identified by tracing the abnormal meteorological content. Corresponding abnormal meteorological parameters are marked, and optimized meteorological parameters are determined based on the optimization of abnormal meteorological parameters and areas. This further controls theoretical and actual meteorological events, introduces abnormal meteorological content, ensures the accuracy of abnormal meteorological parameters, and optimizes both parameters and areas, guaranteeing the accuracy of the optimized meteorological parameters. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the GNSS meteorological parameter optimization method in an embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating step S11 in the GNSS meteorological parameter optimization method of this invention.

[0021] Figure 3 This is a flowchart illustrating step S12 in the GNSS meteorological parameter optimization method of this invention.

[0022] Figure 4 This is a flowchart illustrating step S13 in the GNSS meteorological parameter optimization method of this invention.

[0023] Figure 5 This is a flowchart illustrating step S14 in the GNSS meteorological parameter optimization method of this invention.

[0024] Figure 6 This is a flowchart illustrating step S15 in the GNSS meteorological parameter optimization method of this invention.

[0025] Figure 7 This is a schematic diagram of the structural composition of the GNSS meteorological parameter optimization system in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] Please see Figures 1 to 7 A GNSS meteorological parameter optimization method is proposed and applied to a GNSS meteorological parameter optimization scenario. The GNSS meteorological parameter optimization method includes:

[0028] Step S11: Collect the location of the GNSS observation area, and determine multiple GNSS observation points based on the location and morphology of the GNSS observation area;

[0029] Step S12: Determine the corresponding meteorological parameter combination based on the meteorological detection of each GNSS observation point, and determine multiple meteorological regions according to the relative position of each GNSS observation point and the corresponding meteorological parameter combination. The multiple meteorological regions are presented in different positions of the GNSS observation area.

[0030] Step S13: Based on the identification of each meteorological region, determine the corresponding meteorological type, and determine the theoretical meteorological events in the GNSS observation area according to the meteorological parameters, location and meteorological type corresponding to each meteorological region;

[0031] Step S14: Determine multiple actual meteorological features based on the identification of the current image of the GNSS observation area, and determine the actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features;

[0032] Step S15: Determine the abnormal meteorological content based on the comparison between theoretical meteorological events and actual meteorological events, determine the abnormal meteorological area based on the tracing of the abnormal meteorological content, mark the corresponding abnormal meteorological parameters, and determine the optimized meteorological parameters based on the optimization of the abnormal meteorological parameters and the abnormal meteorological area.

[0033] refer to Figure 2 In step S11, the location of the GNSS observation area is collected, and multiple GNSS observation points are determined based on the location and shape of the GNSS observation area.

[0034] In the specific implementation of this invention, the specific steps are as follows:

[0035] S111: Collect GNSS observation markers, determine the location of the GNSS observation area based on the location detection of the GNSS observation markers, determine multiple environmental parameters based on the environmental detection of the location of the GNSS observation area, and determine the environmental scene corresponding to the GNSS observation area based on the identification of multiple environmental parameters.

[0036] S112: Determine the regional shape of the GNSS observation area based on the detection of the GNSS observation area, determine the observation point identification pattern of the GNSS observation area according to the regional shape of the GNSS observation area, the location of the GNSS observation area and the environmental scene corresponding to the GNSS observation area, and mark multiple GNSS observation points in the GNSS observation area.

[0037] In the embodiments of this application, a high-precision GNSS receiver (such as Trimble R10, Leica GS18, etc.) is used, which can provide centimeter-level positioning accuracy; multiple marker points are set in the observation area, which can be fixed locations that are preset (such as corners of buildings, mountain tops, valleys, etc.) or moving observation points (such as vehicle-mounted GNSS receivers); the coordinate data (longitude, latitude, elevation) of each marker point is collected using the GNSS receiver and recorded in the data acquisition device.

[0038] Import the collected GNSS marker coordinates into GIS software (such as ArcGIS or QGIS); draw the boundary of the observation area based on the marker positions. You can use polygon tools to connect the markers to form a closed region; calculate the geometric center or centroid of the observation area as a reference location.

[0039] Multiple sensors are deployed within the observation area to collect environmental parameters. Common environmental parameters include: terrain elevation: elevation data is acquired via GNSS receivers; vegetation cover: acquired using vegetation index sensors (such as NDVI sensors) or satellite remote sensing data (such as MODIS, Landsat); soil moisture: acquired using soil moisture sensors (such as TDR sensors); air temperature and humidity.

[0040] Use a temperature and humidity sensor (such as DHT22).

[0041] Data on the aforementioned environmental parameters are collected at each marker point or key location and recorded in the data acquisition device. The collected environmental parameter data is standardized for subsequent classification analysis. Machine learning algorithms (such as K-nearest neighbor algorithm, random forest, etc.) are used to classify the environmental parameters and identify the environmental scene of the observation area. A classification model can be pre-trained, taking environmental parameters (such as temperature, humidity, vegetation cover, etc.) as input and outputting environmental scene (such as city, mountain, forest, etc.). The classification model is trained using known environmental scene data (such as historical data or field survey data), and the accuracy of the model is verified through methods such as cross-validation.

[0042] Optionally, suppose we are conducting meteorological observations in a mountainous area and need to determine the location of the observation area. We selected the following marker points: a meteorological station on a mountaintop (marker point A); beside a river in a valley (marker point B); and an open area on the mountainside (marker point C). Using a Trimble R10 GNSS receiver, data was collected at each of these marker points. The collected coordinates are as follows: Marker point A: Longitude 116.39°E, Latitude 39.91°N, Elevation 1500 meters; Marker point B: Longitude 116.40°E, Latitude 39.90°N, Elevation 800 meters; Marker point C: Longitude 116.41°E, Latitude 39.92°N, Elevation 1200 meters. These coordinate data will be used to determine the location of the observation area. In the GIS software, the above three marker points (A, B, and C) are imported, and a polygonal area is drawn. Using the software's geometric analysis tools, the centroid of the region was calculated to be: longitude 116.40°E, latitude 39.91°N, and elevation 1200 meters; this centroid location will serve as a reference location for the observation area.

[0043] In our mountainous observation area, we deployed the following sensors: a temperature and humidity sensor and a vegetation index sensor at the mountain top (marker point A); a soil moisture sensor and a temperature and humidity sensor at the valley (marker point B); and a temperature and humidity sensor and a vegetation index sensor at the mountainside (marker point C).

[0044] The collected environmental parameter data are as follows: Point A: air temperature 15℃, humidity 60%, vegetation coverage 70%, soil moisture 30%; Point B: air temperature 20℃, humidity 80%, vegetation coverage 40%, soil moisture 70%; Point C: air temperature 18℃, humidity 70%, vegetation coverage 60%, soil moisture 50%. These data will be used for further analysis of the environmental characteristics of the observation area.

[0045] We used the random forest algorithm to classify environmental parameters. Input features included temperature, humidity, vegetation cover, and soil moisture. By training the model, we obtained the following classification results: Marker A: Environmental scene "mountainous"; Marker B: Environmental scene "valley"; Marker C: Environmental scene "open area on the mountainside". Based on these classification results, we can determine that the environmental scene of the entire observation area is "mountainous", which includes sub-scenes such as mountaintops, valleys, and open areas on the mountainside.

[0046] In the embodiments of this application, the GNSS marker coordinate data collected in the previous step S111 is used; the marker coordinates are imported into GIS software (such as ArcGIS or QGIS), and the boundaries of the observation area are drawn using these points; the geometric features of the area, such as area, perimeter, and aspect ratio, are calculated using the GIS software. The area morphology is classified according to the geometric features. Common morphologies include: regular shapes such as rectangles and circles; and irregular shapes such as polygons and complex terrain areas. A visual map of the area morphology is generated to provide an intuitive understanding of the area's shape.

[0047] Review the environmental scenes identified in step S111 (such as mountainous areas, valleys, open areas, etc.); determine the observation point identification mode of the GNSS observation area based on the regional shape, location, and corresponding environmental scene of the GNSS observation area. The observation point layout modes are as follows: Regular grid mode: suitable for areas with flat terrain and relatively uniform environment. For example, the area can be divided into uniform square or rectangular grids, with one observation point set at the center of each grid; Irregular grid mode: suitable for areas with complex terrain or large environmental changes. For example, observation points can be arranged along terrain features (such as valleys, mountain tops, rivers, etc.); Environmental feature-based mode: design the observation point layout according to the specific characteristics of the environmental scene; for example, increase the observation point density in areas with high vegetation coverage, and set observation points in key locations such as valleys or mountain tops.

[0048] refer to Figure 3 In step S12, the corresponding meteorological parameter combination is determined based on the meteorological detection of each GNSS observation point, and multiple meteorological regions are determined according to the relative position of each GNSS observation point and the corresponding meteorological parameter combination. The multiple meteorological regions are presented in different positions of the GNSS observation area.

[0049] In the specific implementation of this invention, the specific steps are as follows:

[0050] S121: Real-time monitoring of each GNSS observation point and triggering meteorological detection at each GNSS observation point. At this time, multiple meteorological parameters are determined based on the meteorological detection at each GNSS observation point, and the corresponding combination of meteorological parameters is determined based on the multiple meteorological parameters and the environmental scene corresponding to the GNSS observation point.

[0051] S122: Collect the current position of each GNSS observation point, and determine the relative position of each GNSS observation point based on the comparison of the current positions of each GNSS observation point;

[0052] S123: Based on the relative positions of each GNSS observation point and the corresponding combination of meteorological parameters, multiple meteorological regions are determined. These multiple meteorological regions are part of the GNSS observation area and are presented in different positions within the GNSS observation area. Each meteorological region is enclosed by a corresponding GNSS observation point.

[0053] In the embodiments of this application, each GNSS observation point is monitored in real time, and meteorological detection at each GNSS observation point is triggered. At this time, multiple meteorological parameters are determined based on the meteorological detection at each GNSS observation point, and a combination of meteorological parameters is determined based on the multiple meteorological parameters and the environmental scene corresponding to the GNSS observation point. This takes into account the overall consideration of multiple meteorological parameters and the environmental scene corresponding to the GNSS observation point, ensuring the accuracy of the corresponding combination of meteorological parameters.

[0054] At this point, meteorological sensors (such as temperature sensors, humidity sensors, barometric pressure sensors, wind speed sensors, and wind direction sensors) should be installed at each GNSS observation point; ensure that these sensors can transmit data to the central data acquisition system in real time. Data transmission can be performed using wireless sensor networks (WSN), satellite communication, or wired networks; configure the sensor sampling frequency (e.g., per minute, per hour), and adjust the frequency according to actual needs.

[0055] Use a data acquisition system (such as a data logger, IoT platform, etc.) to receive sensor data in real time; ensure the stability and reliability of data transmission, and avoid data loss or transmission delay; for key meteorological parameters (such as heavy rain, strong winds, etc.), a trigger mechanism can be set to automatically trigger high-frequency data acquisition when the parameter exceeds a preset threshold. Optionally, thresholds for meteorological parameters can be set (such as temperature exceeding 35℃, humidity exceeding 90%, wind speed exceeding 10m / s, etc.); when a certain meteorological parameter exceeds the threshold, high-frequency data acquisition (such as once per second) is automatically triggered to record meteorological changes in more detail.

[0056] The collected meteorological parameter data is recorded in the database in real time, including timestamps, observation point numbers, temperature, humidity, air pressure, wind speed, and wind direction; the integrity and accuracy of the data are ensured, and abnormal data is marked and processed; the collected data is filtered to remove noise and outliers; and the statistical characteristics of the meteorological parameters for each observation point (such as mean, standard deviation, maximum, and minimum values) are calculated.

[0057] Review the environmental scenario (e.g., mountaintop, valley, open area, etc.) for each observation point determined in step S11; select relevant meteorological parameters based on the characteristics of the environmental scenario; define combinations of meteorological parameters based on the importance of the environmental scenario and the meteorological parameters. For example:

[0058] Mountain top area: temperature, humidity, air pressure; valley area: temperature, humidity, wind speed; mountainside area: temperature, humidity, wind direction; each combination can contain multiple meteorological parameters, and the combination content can be adjusted according to actual needs; for each combination of meteorological parameters, calculate its characteristic values ​​(such as average value, standard deviation, etc.); these characteristic values ​​will be used for subsequent meteorological regional division and analysis.

[0059] Furthermore, the current positions of each GNSS observation point are collected, and the geographic coordinates (longitude and latitude) of the GNSS observation points are converted into a plane coordinate system (such as UTM, Cartesian coordinate system, etc.) to calculate distance and direction. The distance between each pair of observation points is calculated using distance formulas (such as the Euclidean distance formula). At the same time, the direction (angle) between each pair of observation points is calculated using the arctangent function. Through these calculations, we determine the relative position of each observation point, including distance and direction. This information will be used for subsequent meteorological region division and analysis.

[0060] Therefore, multiple meteorological regions are determined based on the relative positions of each GNSS observation point and the corresponding combinations of meteorological parameters. These multiple meteorological regions are part of the GNSS observation area and are located at different positions within the GNSS observation area. Each meteorological region is enclosed by a corresponding GNSS observation point, which takes into account the overall consideration of the relative positions of each GNSS observation point and the corresponding combinations of meteorological parameters, thus ensuring the accuracy of multiple meteorological regions.

[0061] At this point, the current location (longitude, latitude, elevation) of each GNSS observation point is collected; the meteorological parameter combinations of each observation point (such as temperature, humidity, air pressure, wind speed, wind direction, etc.) are collected; clustering algorithms (such as K-Means, DBSCAN, etc.) are used to cluster the observation points. The clustering is based on the relative position of the observation points and the combination of meteorological parameters; appropriate clustering algorithms and parameters (such as the number of clusters, distance threshold, etc.) are selected; based on the clustering results, the boundary of each meteorological region is determined. The convex hull algorithm or the alpha shape algorithm can be used to generate the region boundary; ensure that each meteorological region is enclosed by the corresponding GNSS observation points, and at the same time, calculate the average meteorological parameters of each meteorological region (such as average temperature, average humidity, etc.); describe the location of each meteorological region in the GNSS observation area (such as mountain top region, valley region, hillside region, etc.).

[0062] refer to Figure 4 In step S13, the corresponding meteorological type is determined based on the identification of each meteorological region, and the theoretical meteorological events of the GNSS observation area are determined according to the meteorological parameters, location and meteorological type of each meteorological region.

[0063] In the specific implementation of this invention, the specific steps are as follows:

[0064] S131: In each meteorological region, the corresponding meteorological parameters are determined based on the meteorological region's detection, and the corresponding meteorological type is determined based on the meteorological parameters corresponding to each meteorological region and the past meteorological types of the meteorological region;

[0065] S132: Determine the first meteorological event based on the meteorological parameters and location corresponding to each meteorological region, and determine the second meteorological event based on the location and meteorological type of each meteorological region; determine the theoretical meteorological event of the GNSS observation area based on the mapping relationship between the first meteorological event, the second meteorological event and the theoretical meteorological event.

[0066] In the embodiments of this application, in each meteorological region, the corresponding meteorological parameters are determined based on the detection of the meteorological region, and the corresponding meteorological type is determined according to the meteorological parameters corresponding to each meteorological region and the past meteorological types of the meteorological region. This takes into account the overall consideration of the meteorological parameters corresponding to each meteorological region and the past meteorological types of the meteorological region, and ensures the accuracy of the corresponding meteorological type.

[0067] At this point, current meteorological parameters for each meteorological region are collected, including temperature, humidity, air pressure, wind speed, and wind direction. To ensure the real-time nature and accuracy of the data, data can be transmitted to the data processing center in real time via a sensor network. Average meteorological parameters (such as average temperature and average humidity) for each meteorological region are calculated. The changing trends and characteristics of the meteorological parameters are analyzed, such as calculating standard deviation, maximum value, and minimum value. Simultaneously, the collected data is filtered to remove noise and outliers. To ensure the integrity and consistency of the data, missing data is interpolated or filled in.

[0068] Based on the characteristics of meteorological parameters, different meteorological types are defined (such as sunny, rainy, foggy, and windy). Historical meteorological data is used to establish a mapping relationship between meteorological types and meteorological parameters. A matching table is created to correspond the range of meteorological parameters to meteorological types. For example: temperature > 25℃ and humidity < 60%: sunny; temperature < 10℃ and humidity > 80%: rainy; wind speed > 15m / s: windy; humidity > 90%: foggy. Weights are assigned to each meteorological parameter, and the score for each meteorological region is calculated based on the weights. The meteorological type with the highest score is selected as the meteorological type for the current meteorological region.

[0069] Furthermore, the first meteorological event is determined based on the meteorological parameters and location corresponding to each meteorological region, and the second meteorological event is determined based on the location and meteorological type of each meteorological region. The theoretical meteorological event of the GNSS observation area is determined based on the mapping relationship between the first meteorological event, the second meteorological event, and the theoretical meteorological event. This approach takes into account the overall consideration of the mapping relationship between the first meteorological event, the second meteorological event, and the theoretical meteorological event, ensuring the accuracy of the theoretical meteorological event of the GNSS observation area. At the same time, multiple meteorological regions are introduced, which are presented in different locations within the GNSS observation area. This approach takes into account the overall consideration of the meteorological parameters, location, and meteorological type corresponding to each meteorological region, ensuring the accuracy of the theoretical meteorological event of the GNSS observation area.

[0070] At this point, meteorological parameters (such as temperature, humidity, air pressure, wind speed, wind direction, etc.) are collected for each meteorological region; location information (such as longitude, latitude, elevation, etc.) for each meteorological region is collected; based on the characteristics and spatial distribution of meteorological parameters, the first meteorological event (such as localized heavy rain, localized strong wind, etc.) is defined; and cluster analysis or spatial analysis methods are used to identify areas with abnormal meteorological parameters.

[0071] Use GIS software or spatial analysis tools to analyze the spatial distribution characteristics of meteorological parameters; for example, generate spatial distribution maps of meteorological parameters through interpolation methods (such as Kriging interpolation) to identify abnormal areas; identify the first meteorological event based on the threshold of meteorological parameters (such as temperature exceeding 35℃, humidity exceeding 90%, wind speed exceeding 15m / s, etc.); for example, if the humidity of a certain area exceeds 90% and the wind speed exceeds 10m / s, it can be identified as a "localized rainstorm" event.

[0072] Collect location information (such as longitude, latitude, and elevation) for each meteorological region; collect meteorological types (such as sunny, rainy, foggy, and windy) for each meteorological region; define secondary meteorological events (such as mountain torrential rain and valley winds) based on the spatial distribution of meteorological types; use spatial analysis methods to identify areas with abnormal meteorological types; and identify secondary meteorological events based on meteorological types and location information. For example, if the meteorological type of a certain mountain area is "torrential rain," it can be identified as a "mountain torrential rain" event.

[0073] Establish a mapping relationship between the first meteorological event, the second meteorological event, and the theoretical meteorological event; for example: localized heavy rain + mountain heavy rain = regional heavy rain; localized strong wind + valley strong wind = regional strong wind; assign weights to each meteorological event and calculate the score of the theoretical meteorological event based on the weights; select the theoretical meteorological event with the highest score as the final result; verify the rationality of the theoretical meteorological event to ensure that it conforms to the actual meteorological conditions; and conduct comparative analysis with historical meteorological data to verify the accuracy of the results.

[0074] refer to Figure 5In step S14, multiple actual meteorological features are determined based on the identification of the current image of the GNSS observation area, and the actual meteorological events in the GNSS observation area are determined based on the synthesis of multiple actual meteorological features.

[0075] In the specific implementation of this invention, the specific steps are as follows:

[0076] S141: Real-time monitoring of the GNSS observation area and acquisition of the current image of the GNSS observation area. Based on the division of the current image of the GNSS observation area, multiple meteorological feature areas are determined. Based on the identification of multiple meteorological feature areas, multiple actual meteorological features are determined. At this time, each meteorological feature area contains at least one actual meteorological feature.

[0077] S142: Collect multiple actual meteorological features and determine the actual meteorological multimodal data based on the morphology, location and time of the multiple actual meteorological features;

[0078] S143: Based on the identification of actual meteorological multimodal data, determine multiple sub-meteorological events in the GNSS observation area, and determine the actual meteorological events based on the mapping relationship between multiple sub-meteorological events and actual meteorological events.

[0079] In the embodiments of this application, the GNSS observation area is monitored in real time and the current image of the GNSS observation area is collected. Multiple meteorological feature areas are determined based on the division of the current image of the GNSS observation area. Multiple actual meteorological features are determined based on the identification of multiple meteorological feature areas. At this time, each meteorological feature area contains at least one actual meteorological feature, which takes into account the overall consideration of the identification of multiple meteorological feature areas and ensures the accuracy of multiple actual meteorological features.

[0080] At this point, install high-resolution cameras or satellite image receiving equipment in the GNSS observation area to ensure coverage of the entire observation area; configure the camera's shooting frequency (e.g., per minute, per hour, etc.) and adjust the frequency according to actual needs; collect current images of the observation area in real time through the camera or satellite image receiving equipment; ensure the images are clear and reflect the current weather conditions (e.g., clouds, precipitation, wind direction, etc.); transmit the collected image data to the data processing center in real time, using either a wireless or wired network; ensure the stability and reliability of data transmission to avoid data loss or transmission delays.

[0081] The acquired images undergo preprocessing, including noise reduction, contrast enhancement, and cropping, to improve image quality. Image segmentation algorithms (such as edge detection, region growing, and deep learning segmentation algorithms) are used to divide the image into multiple meteorological feature regions. Each region should contain at least one actual meteorological feature, such as clouds, precipitation, or wind direction. For example, convolutional neural networks (CNNs) can be used for image segmentation to identify different meteorological feature regions. A unique identifier is assigned to each meteorological feature region, and its location and extent are recorded. For example, polygons or rectangles can be used to label each meteorological feature region.

[0082] Image recognition algorithms (such as convolutional neural networks, CNNs) are used to identify the actual meteorological features in each meteorological feature region. For example, the type of cloud, the intensity of precipitation, and the wind direction are identified. Each actual meteorological feature is described, including its shape, location, and time information. For example, the shape, location, direction of movement, and speed of cloud are described. The identified actual meteorological features and their descriptions are recorded in a database, including timestamps, locations, and feature types.

[0083] Furthermore, multiple actual meteorological features are collected, and actual meteorological multimodal data are determined based on the morphology, location, and time of these features. This comprehensive consideration of the morphology, location, and time of multiple actual meteorological features ensures the accuracy of the actual meteorological multimodal data.

[0084] At this stage, the actual meteorological characteristics of each meteorological feature area are collected, including morphology, location, and time information; the integrity and accuracy of the data are ensured, and missing data is interpolated or completed; the collected actual meteorological characteristics are recorded in the database, including timestamps, locations, and feature types; for example, the shape, location, direction of movement, and speed of clouds, the intensity and extent of precipitation, wind direction, and wind speed are recorded; image data is fused with other meteorological data (such as temperature, humidity, and air pressure) to form multimodal data; for example, the formation and development of clouds are analyzed by combining cloud information and temperature data in the image; each actual meteorological feature is described in detail, including its morphology, location, and temporal changes; for example, the shape, location, direction of movement, and speed of clouds, changes in precipitation intensity, and changes in wind direction and speed are described; data from different modalities (such as images, temperature, humidity, and air pressure) are integrated into a unified data structure.

[0085] Optional data is available for Region 1: Thick cloud cover, significant precipitation, temperature 15℃, humidity 80%, air pressure 1010hPa, wind speed 5m / s, wind direction 180°, timestamp 2025-06-23 10:00. Region 2: Thin cloud cover, no precipitation, temperature 20℃, humidity 60%, air pressure 1012hPa, wind speed 3m / s, wind direction 270°, timestamp 2025-06-23 10:00. Region 3: Thick cloud cover, significant precipitation, temperature 18℃, humidity 85%, air pressure 1008hPa, wind speed 6m / s, wind direction 190°, timestamp 2025-06-23 10:00. The integrated data will be stored in a database to form actual meteorological multimodal data. A schematic table of the actual meteorological multimodal data is shown in Table 1.

[0086] Table 1. Schematic diagram of actual meteorological multimodal data

[0087]

[0088] Therefore, multiple sub-meteorological events in the GNSS observation area are determined based on the identification of actual meteorological multimodal data, and the actual meteorological events are determined based on the mapping relationship between multiple sub-meteorological events and actual meteorological events. This approach takes into account the overall mapping relationship between multiple sub-meteorological events and actual meteorological events, ensuring the accuracy of actual meteorological events.

[0089] At this stage, actual meteorological multimodal data is collected and organized, including image features, temperature, humidity, air pressure, wind speed, and wind direction; ensuring temporal synchronization and spatial consistency of the data; analyzing the actual meteorological multimodal data for each meteorological characteristic region to identify sub-meteorological events; using clustering analysis, rule matching, or machine learning algorithms to identify sub-meteorological events; and defining sub-meteorological events based on combinations of meteorological features. For example: Localized heavy rain: thick cloud cover, significant precipitation, humidity >80%, wind speed >5m / s. Localized strong wind: wind speed >10m / s, thin cloud cover, no precipitation. Localized clear skies: thin cloud cover, no precipitation, humidity <60%.

[0090] Matching actual meteorological multimodal data for each meteorological characteristic region identifies sub-meteorological events; for example, if a region has thick cloud cover, significant precipitation, humidity of 85%, and wind speed of 6 m / s, it is identified as "localized heavy rain"; establishing a mapping relationship between sub-meteorological events and actual meteorological events. For example: localized heavy rain + localized heavy rain = regional heavy rain; localized strong wind + localized strong wind = regional strong wind; localized heavy rain + localized strong wind = regional heavy rain and strong wind; based on the mapping relationship, multiple sub-meteorological events are combined into an actual meteorological event; for example, if multiple sub-meteorological events are all localized heavy rain, they are combined into a regional heavy rain; verifying the rationality of the actual meteorological event to ensure it conforms to actual meteorological conditions; comparative analysis can be performed using historical meteorological data to verify the accuracy of the results.

[0091] Specifically, the sub-meteorological events are as follows: Region 1: Thick cloud cover, significant precipitation, humidity 80%, wind speed 5m / s → localized heavy rain; Region 2: Thin cloud cover, no precipitation, humidity 60%, wind speed 3m / s → localized sunny weather; Region 3: Thick cloud cover, significant precipitation, humidity 85%, wind speed 6m / s → localized heavy rain; The mapping relationship is as follows: localized heavy rain + localized heavy rain = regional heavy rain; localized strong wind + localized strong wind = regional strong wind; localized heavy rain + localized strong wind = regional heavy rain and strong wind; Region 1 and Region 3: both localized heavy rain → regional heavy rain; Region 2: localized sunny weather (not participating in the regional event); Actual meteorological event: regional heavy rain.

[0092] refer to Figure 6 In step S15, abnormal meteorological content is determined based on the comparison between theoretical meteorological events and actual meteorological events, abnormal meteorological areas are determined based on the tracing of abnormal meteorological content, and the corresponding abnormal meteorological parameters are marked. Optimized meteorological parameters are determined based on the optimization of abnormal meteorological parameters and abnormal meteorological areas.

[0093] In the specific implementation of this invention, the specific steps are as follows:

[0094] S151: Collect theoretical meteorological events and actual meteorological events, compare the content of theoretical meteorological events and actual meteorological events to determine abnormal meteorological content, determine multiple abnormal locations of multiple meteorological characteristic areas based on the tracing of abnormal meteorological content, and determine abnormal meteorological areas based on the synthesis of multiple abnormal locations.

[0095] S152: Collect multiple meteorological parameters associated with the abnormal weather area, determine the corresponding abnormal meteorological parameters based on the abnormal screening of multiple meteorological parameters, and mark the meteorological parameter type corresponding to the abnormal meteorological parameter.

[0096] S153: Determine the first optimization coefficient based on the abnormal meteorological parameters and the corresponding meteorological parameter type; determine the second optimization coefficient based on the abnormal meteorological parameters and the abnormal meteorological area; determine the corresponding optimization method based on the mapping relationship between the first optimization coefficient, the second optimization coefficient and the optimization method of the meteorological parameters; trigger the optimization of the abnormal meteorological parameters based on the execution of the optimization method; and output the optimized meteorological parameters.

[0097] In the embodiments of this application, theoretical meteorological events and actual meteorological events are collected, and the contents of the theoretical meteorological events and actual meteorological events are compared to determine abnormal meteorological content. Based on the tracing of abnormal meteorological content, multiple abnormal locations of multiple meteorological feature areas are determined. Based on the synthesis of multiple abnormal locations, an abnormal meteorological area is determined. This approach incorporates the overall consideration of tracing abnormal meteorological content and ensures the accuracy of multiple abnormal locations of multiple meteorological feature areas.

[0098] At this point, theoretical meteorological events in the GNSS observation area are collected (determined via step S13); actual meteorological events in the GNSS observation area are collected (determined via step S14); the temporal synchronization and spatial consistency of the data are ensured; the theoretical and actual meteorological events are recorded in the database, including timestamps, event types, affected areas, etc.; the contents of the theoretical and actual meteorological events are compared to identify differences; for example, the theoretical meteorological event is "regional rainstorm", while the actual meteorological event is "regional rainstorm and strong wind".

[0099] The process involves identifying discrepancies between theoretical and actual meteorological events to determine anomalous meteorological content. For example, a theoretical meteorological event might not include "strong winds," but an actual event might. The identified anomalous meteorological content is recorded in a database, including the anomaly type and affected area. Based on the anomalous meteorological content, specific meteorological characteristic areas are traced. For example, strong winds occurred in areas 2 and 3. Anomalous locations are recorded in the database, including area identifiers and specific locations (such as latitude and longitude). Multiple anomalous locations are combined to determine anomalous meteorological areas. For example, areas 2 and 3 are identified as anomalous meteorological areas. These anomalous meteorological areas are then recorded in the database, including their geographical extent and affected area.

[0100] Specifically, suppose we have the following theoretical meteorological events and actual meteorological events: the theoretical meteorological event is a regional rainstorm; the actual meteorological event is a regional rainstorm and strong wind; and we collect a preset meteorological feature regional matching table, as shown in Table 2:

[0101] Table 2. Meteorological Feature Regional Matching Table

[0102]

[0103] The theoretical meteorological event was "regional rainstorm", while the actual meteorological event was "regional rainstorm and strong wind". The abnormal meteorological content was "strong wind". Tracing the abnormal location: the wind speed in region 2 was 12 m / s and the wind speed in region 3 was 11 m / s, both exceeding the threshold of 10 m / s. By combining multiple abnormal locations, the abnormal meteorological region was determined. Abnormal meteorological region: Abnormal meteorological content of regions 2 and 3: strong wind.

[0104] Furthermore, multiple meteorological parameters associated with the abnormal weather area are collected, and the corresponding abnormal meteorological parameters are determined based on the anomaly screening of multiple meteorological parameters. The meteorological parameter type corresponding to the abnormal meteorological parameter is then marked, which takes into account the overall consideration of anomaly screening of multiple meteorological parameters and ensures the accuracy of the corresponding abnormal meteorological parameters.

[0105] At this point, meteorological parameters of the abnormal weather areas (determined through step S151) are collected, including temperature, humidity, air pressure, wind speed, and wind direction. To ensure data synchronization and spatial consistency, the collected meteorological parameters are recorded in the database, including timestamps, area identifiers, parameter types, and parameter values. Anomaly thresholds are defined for each meteorological parameter. For example: wind speed: >10 m / s; humidity: >80%; temperature: >30℃. Anomaly screening is performed on each meteorological parameter to identify parameters exceeding the thresholds. For example, the wind speed in area 2 is 12 m / s, and the wind speed in area 3 is 11 m / s, both exceeding the threshold of 10 m / s. Based on the anomaly screening results, abnormal meteorological parameters are determined. For example, the wind speeds in areas 2 and 3 are both abnormal meteorological parameters. The meteorological parameter type corresponding to the abnormal meteorological parameter is marked. For example, "wind speed" is marked as an abnormal meteorological parameter type.

[0106] Specifically, meteorological parameters were collected for regions 2 and 3: Region 2: temperature 20℃, humidity 60%, air pressure 1012hPa, wind speed 12m / s, wind direction 270°; Region 3: temperature 18℃, humidity 85%, air pressure 1008hPa, wind speed 11m / s, wind direction 190°; Abnormal thresholds were defined as: wind speed >10m / s; humidity >80%; temperature >30℃; the wind speed in region 2 was 12m / s, exceeding the threshold of 10m / s; The wind speed in area 3 is 11 m / s, exceeding the threshold of 10 m / s; the humidity in area 3 is 85%, exceeding the threshold of 80%; the abnormal meteorological parameters in area 2 are wind speed and humidity. Area 2: "Wind speed" is marked as the abnormal meteorological parameter type. Area 3: "Wind speed" and "humidity" are marked as the abnormal meteorological parameter types. Abnormal meteorological parameters: Area 2: Wind speed (12 m / s); Area 3: Wind speed (11 m / s), humidity (85%).

[0107] Therefore, a first optimization coefficient is determined based on the abnormal meteorological parameters and their corresponding types, and a second optimization coefficient is determined based on the abnormal meteorological parameters and their corresponding regions. The corresponding optimization method is determined based on the mapping relationship between the first and second optimization coefficients and the optimization methods of the meteorological parameters. The execution of the optimization method triggers the optimization of the abnormal meteorological parameters, and the optimized meteorological parameters are output. This approach considers the overall relationship between the first and second optimization coefficients and the optimization methods of the meteorological parameters, ensuring the accuracy of the corresponding optimization methods. Simultaneously, it ensures the accuracy of the abnormal meteorological parameters and achieves the optimization of both the abnormal meteorological parameters and their corresponding regions, thus guaranteeing the accuracy of the optimized meteorological parameters.

[0108] Collect abnormal meteorological parameters and their corresponding meteorological parameter types; for example, the abnormal meteorological parameters in region 2 are wind speed (12 m / s) and the abnormal meteorological parameters in region 3 are wind speed (11 m / s) and humidity (85%); define the calculation rules for the first optimization coefficient according to the meteorological parameter type and the degree of abnormality; for example: wind speed: for the part exceeding the threshold of 10 m / s, the first optimization coefficient is reduced by 0.1 for every 1 m / s exceeding the threshold; humidity: for the part exceeding the threshold of 80%, the first optimization coefficient is reduced by 0.05 for every 5% exceeding the threshold; calculate the first optimization coefficient according to the value of the abnormal meteorological parameters; for example: the wind speed in region 2 is 12 m / s, exceeding the threshold of 2 m / s, the first optimization coefficient is 1 - 0.1 × 2 = 0.8; the wind speed in region 3 is 11 m / s, exceeding the threshold of 1 m / s, the first optimization coefficient is 1 - 0.1 × 1 = 0.9; the humidity in region 3 is 85%, exceeding the threshold of 5%, the first optimization coefficient is 1 - 0.05 × 1 = 0.95.

[0109] Collect location information on abnormal meteorological parameters and areas; for example, the specific locations of areas 2 and 3 are (116.40, 39.90) and (116.41, 39.92), respectively. Define the calculation rules for the second optimization coefficient based on the location of the abnormal meteorological area and the type of abnormal meteorological parameter; for example: wind speed: if the abnormal area is located in a mountainous area, the second optimization coefficient is 0.9; if it is located in a plain, the second optimization coefficient is 0.8; humidity: if the abnormal area is located in a valley, the second optimization coefficient is 0.85; if it is located in an open area, the second optimization coefficient is 0.95. Calculate the second optimization coefficient based on the location of the abnormal meteorological area; for example, if area 2 is located in a mountainous area, the second optimization coefficient for wind speed is 0.9; if area 3 is located in a valley, the second optimization coefficient for wind speed is 0.85, and the second optimization coefficient for humidity is 0.85. At this point, the first optimization coefficient is calculated based on the degree of abnormality of the abnormal meteorological parameter, reflecting the degree to which the abnormal meteorological parameter deviates from the normal range. The first optimization coefficient is typically a value between 0 and 1; a smaller value indicates a higher degree of anomaly and requires greater optimization adjustments. The second optimization coefficient is calculated based on the geographical location of the anomalous weather area and the type of anomalous weather parameters, reflecting the influence of the geographical environment of the anomalous weather area on the meteorological parameters. It is also a value between 0 and 1; a smaller value indicates a greater influence of the geographical environment on the anomalous weather parameters and requires greater optimization adjustments.

[0110] Establish a mapping relationship between the first optimization coefficient, the second optimization coefficient, and the optimization method; for example: Optimization method A: first optimization coefficient < 0.85 and second optimization coefficient < 0.9 → adjust the wind speed model; Optimization method B: first optimization coefficient ≥ 0.85 and second optimization coefficient ≥ 0.9 → adjust the humidity model; select the corresponding optimization method based on the first and second optimization coefficients; for example: Region 2: first optimization coefficient 0.8, second optimization coefficient 0.9 → optimization method A; Region 3: first optimization coefficient of wind speed 0.9, second optimization coefficient 0.85 → optimization method B. Optimization Method A; Region 3: Humidity first optimization coefficient 0.95, second optimization coefficient 0.85 → Optimization Method B; Optimize abnormal meteorological parameters according to the selected optimization method; For example: Optimization Method A: Adjust the wind speed model to reduce the wind speed by 20%; Optimization Method B: Adjust the humidity model to reduce the humidity by 10%; Record the optimized meteorological parameters and output the results; For example: Wind speed in Region 2 is optimized from 12m / s to 9.6m / s; Wind speed in Region 3 is optimized from 11m / s to 8.8m / s; Humidity in Region 3 is optimized from 85% to 76.5%.

[0111] Specifically, the anomalous meteorological parameters for region 2 are wind speed (12 m / s) and for region 3 are wind speed (11 m / s) and humidity (85%). A pre-set first optimization coefficient matching table was collected, as shown in Table 3.

[0112] Table 3 First Optimization Coefficient Matching Table

[0113] Meteorological parameter types Range of outliers First optimization coefficient wind speed >10m / s 0.8 humidity >80％ 0.9 temperature >30℃ 0.7

[0114] Based on the values ​​of abnormal meteorological parameters, the first optimization coefficient matching table is searched to determine the first optimization coefficient; at this time, the wind speed in area 2 is 12m / s, and the first optimization coefficient is 0.8; the wind speed in area 3 is 11m / s, and the first optimization coefficient is 0.8; the humidity in area 3 is 85%, and the first optimization coefficient is 0.9.

[0115] Collect abnormal meteorological parameters and location information of abnormal meteorological areas. The specific locations of areas 2 and 3 are (116.40, 39.90) and (116.41, 39.92), respectively.

[0116] A pre-defined second optimization coefficient matching table is collected, as shown in Table 4:

[0117] Table 4. Second Optimization Coefficient Matching Table

[0118] Abnormal weather areas Meteorological parameter types Second optimization coefficient mountainous areas wind speed 0.9 mountainous areas humidity 0.95 valley wind speed 0.85 valley humidity 0.85

[0119] Region 2 is located in a mountainous area, with a second optimization coefficient of 0.9 for wind speed; Region 3 is located in a valley, with a second optimization coefficient of 0.85 for both wind speed and humidity.

[0120] Create an optimization method matching table to map the first optimization coefficient, the second optimization coefficient, and the optimization method; the optimization method matching table is shown in Table 5:

[0121] Table 5: Optimization Method Matching Table

[0122] First optimization coefficient Second optimization coefficient Optimization methods <0.85 <0.9 Optimization Method A ≥0.85 ≥0.9 Optimization Method B <0.85 ≥0.9 Optimization method C

[0123] Based on the first and second optimization coefficients, search the matching table and select the corresponding optimization method; Region 2: First optimization coefficient 0.8, second optimization coefficient 0.9 → Optimization method A; Region 3: First optimization coefficient for wind speed 0.8, second optimization coefficient 0.85 → Optimization method A; Region 3: First optimization coefficient for humidity 0.9, second optimization coefficient 0.85 → Optimization method B; Optimize the abnormal meteorological parameters according to the selected optimization method; For example: Optimization method A: Adjust the wind speed model to reduce the wind speed by 20%; Optimization method B: Adjust the humidity model to reduce the humidity by 10%. Record the optimized meteorological parameters and output the results: Wind speed in Region 2 optimized from 12m / s to 9.6m / s; Wind speed in Region 3 optimized from 11m / s to 8.8m / s; Humidity in Region 3 optimized from 85% to 76.5%.

[0124] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of the GNSS meteorological parameter optimization system in an embodiment of the present invention; the GNSS meteorological parameter optimization system includes:

[0125] GNSS observation point module 21 is used to collect the location of the GNSS observation area and determine multiple GNSS observation points based on the location and shape of the GNSS observation area.

[0126] Meteorological region module 22 is used to determine the corresponding combination of meteorological parameters based on meteorological detection at each GNSS observation point. Multiple meteorological regions are determined according to the relative position of each GNSS observation point and the corresponding combination of meteorological parameters. The multiple meteorological regions are presented at different positions in the GNSS observation area.

[0127] The theoretical meteorological event module 23 is used to determine the corresponding meteorological type based on the identification of each meteorological region, and to determine the theoretical meteorological events of the GNSS observation area according to the meteorological parameters, location and meteorological type of each meteorological region.

[0128] The actual meteorological event module 24 is used to determine multiple actual meteorological features based on the identification of the current image of the GNSS observation area, and to determine the actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features.

[0129] The optimization module 25 is used to determine abnormal meteorological content based on the comparison between theoretical meteorological events and actual meteorological events, determine abnormal meteorological areas based on the tracing of abnormal meteorological content, mark the corresponding abnormal meteorological parameters, and determine the optimized meteorological parameters based on the optimization of abnormal meteorological parameters and abnormal meteorological areas.

[0130] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all 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.

Claims

1. A method for optimizing GNSS meteorological parameters, characterized in that, include: The location of the GNSS observation area is collected, and multiple GNSS observation points are determined based on the location and morphology of the GNSS observation area. Based on meteorological observations at each GNSS observation point, the corresponding combination of meteorological parameters is determined. Multiple meteorological regions are determined according to the relative positions of each GNSS observation point and the corresponding combination of meteorological parameters. These multiple meteorological regions are presented at different locations within the GNSS observation area. Based on the identification of each meteorological region, the corresponding meteorological type is determined, and the theoretical meteorological events in the GNSS observation area are determined according to the meteorological parameters, location and meteorological type of each meteorological region. Multiple actual meteorological features are identified based on the current image of the GNSS observation area, and the actual meteorological events in the GNSS observation area are determined by the synthesis of multiple actual meteorological features. The process involves identifying anomalous meteorological content based on a comparison of theoretical and actual meteorological events, determining anomalous meteorological regions by tracing the anomalous meteorological content, marking corresponding anomalous meteorological parameters, and determining optimized meteorological parameters based on the optimization of anomalous meteorological parameters and anomalous meteorological regions. This includes: collecting theoretical and actual meteorological events; comparing the content of theoretical and actual events to identify anomalous meteorological content; identifying multiple anomalous locations in multiple meteorological characteristic regions by tracing the anomalous meteorological content; determining the anomalous meteorological region by combining multiple anomalous locations; collecting multiple meteorological parameters associated with the anomalous meteorological region; determining the corresponding anomalous meteorological parameters by filtering the anomalies of multiple meteorological parameters and marking the meteorological parameter type corresponding to the anomalous meteorological parameter; determining a first optimization coefficient based on the anomalous meteorological parameters and corresponding meteorological parameter types; determining a second optimization coefficient based on the anomalous meteorological parameters and anomalous meteorological regions; determining the corresponding optimization method based on the mapping relationship between the first optimization coefficient, second optimization coefficient, and meteorological parameter optimization methods; triggering the optimization of anomalous meteorological parameters based on the execution of the optimization method; and outputting the optimized meteorological parameters. The first optimization coefficient reflects the degree to which the anomalous meteorological parameters deviate from the normal range; the second optimization coefficient reflects the degree of influence of the geographical environment of the anomalous meteorological region on the meteorological parameters.

2. The GNSS meteorological parameter optimization method according to claim 1, characterized in that, The location of the GNSS observation area is determined based on its location and morphology, including multiple GNSS observation points: Collect GNSS observation markers, determine the location of the GNSS observation area based on the location detection of the GNSS observation markers, determine multiple environmental parameters based on the environmental detection of the location of the GNSS observation area, and determine the environmental scene corresponding to the GNSS observation area based on the identification of multiple environmental parameters. Based on the detection of the GNSS observation area, the regional shape of the GNSS observation area is determined. According to the regional shape of the GNSS observation area, the location of the GNSS observation area and the environmental scene corresponding to the GNSS observation area, the observation point identification pattern of the GNSS observation area is determined, and multiple GNSS observation points in the GNSS observation area are marked.

3. The GNSS meteorological parameter optimization method according to claim 1, characterized in that, The meteorological parameter combinations are determined based on meteorological observations at each GNSS observation point. Multiple meteorological regions are then determined according to the relative positions of the GNSS observation points and the corresponding meteorological parameter combinations. These multiple meteorological regions are located at different positions within the GNSS observation area, including: Real-time monitoring of each GNSS observation point and triggering meteorological detection at each GNSS observation point. At this time, multiple meteorological parameters are determined based on the meteorological detection at each GNSS observation point, and the corresponding combination of meteorological parameters is determined based on the multiple meteorological parameters and the environmental scene corresponding to the GNSS observation point. The current position of each GNSS observation point is collected, and the relative position of each GNSS observation point is determined by comparing the current positions of each GNSS observation point. Multiple meteorological regions are determined based on the relative positions of each GNSS observation point and the corresponding combination of meteorological parameters. These multiple meteorological regions are part of the GNSS observation area and are located at different positions within the GNSS observation area. Each meteorological region is enclosed by a corresponding GNSS observation point.

4. The GNSS meteorological parameter optimization method according to claim 1, characterized in that, The process of identifying the corresponding meteorological type based on the identification of each meteorological region, and determining the theoretical meteorological events in the GNSS observation area based on the meteorological parameters, location, and meteorological type corresponding to each meteorological region, includes: In each meteorological region, the corresponding meteorological parameters are determined based on the meteorological region's monitoring data, and the corresponding meteorological type is determined based on the meteorological parameters corresponding to each meteorological region and the region's past meteorological types.

5. The GNSS meteorological parameter optimization method according to claim 4, characterized in that, The process of identifying the corresponding meteorological type based on the identification of each meteorological region, and determining the theoretical meteorological events in the GNSS observation area based on the meteorological parameters, location, and meteorological type corresponding to each meteorological region, also includes: The first meteorological event is determined based on the meteorological parameters and location corresponding to each meteorological region, and the second meteorological event is determined based on the location and meteorological type of each meteorological region. The theoretical meteorological event of the GNSS observation area is determined based on the mapping relationship between the first meteorological event, the second meteorological event and the theoretical meteorological event.

6. The GNSS meteorological parameter optimization method according to claim 1, characterized in that, The process of determining multiple actual meteorological features based on the identification of current images of the GNSS observation area, and determining actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features, includes: The system monitors the GNSS observation area in real time and collects current images of the GNSS observation area. Based on the division of the current images of the GNSS observation area, multiple meteorological feature areas are determined. Based on the identification of multiple meteorological feature areas, multiple actual meteorological features are determined. At this time, each meteorological feature area contains at least one actual meteorological feature.

7. The GNSS meteorological parameter optimization method according to claim 6, characterized in that, The method of determining multiple actual meteorological features based on the identification of the current image of the GNSS observation area, and determining the actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features, further includes: Collect multiple actual meteorological features, and determine the actual meteorological multimodal data based on the morphology, location and time of the multiple actual meteorological features; Multiple sub-meteorological events in the GNSS observation area are determined based on the identification of actual meteorological multimodal data, and the actual meteorological events are determined based on the mapping relationship between the multiple sub-meteorological events and the actual meteorological events.

8. A GNSS meteorological parameter optimization system, characterized in that, The GNSS meteorological parameter optimization system is applied to the GNSS meteorological parameter optimization method as described in any one of claims 1-7, and the GNSS meteorological parameter optimization system comprises: The GNSS observation point module is used to collect the location of the GNSS observation area and determine multiple GNSS observation points based on the location and shape of the GNSS observation area. The meteorological region module is used to determine the corresponding combination of meteorological parameters based on meteorological detection at each GNSS observation point. Multiple meteorological regions are determined according to the relative position of each GNSS observation point and the corresponding combination of meteorological parameters. These multiple meteorological regions are presented at different locations within the GNSS observation area. The theoretical meteorological event module is used to determine the corresponding meteorological type based on the identification of each meteorological region, and to determine the theoretical meteorological events of the GNSS observation area according to the meteorological parameters, location and meteorological type of each meteorological region. The actual meteorological event module is used to determine multiple actual meteorological features based on the identification of the current image of the GNSS observation area, and to determine the actual meteorological events in the GNSS observation area based on the synthesis of multiple actual meteorological features. The optimization module is used to determine abnormal meteorological content based on the comparison between theoretical meteorological events and actual meteorological events, determine abnormal meteorological areas based on the tracing of abnormal meteorological content, mark the corresponding abnormal meteorological parameters, and determine the optimized meteorological parameters based on the optimization of abnormal meteorological parameters and abnormal meteorological areas.

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