Multi-scene meteorological virtual simulation method and system, storage medium and electronic equipment

Abstract

The invention provides a multi-scene meteorological virtual simulation method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting real-time meteorological data from a plurality of meteorological data sources, the real-time meteorological data comprising real-time multi-source vector meteorological data and multi-source grid meteorological data; based on the real-time meteorological data, carrying out real-time inspection on a meteorological simulation forecast result to obtain an inspection result; the meteorological simulation forecast result real-time inspection comprises multi-source observation data comparative analysis inspection, mode virtual simulation data inspection and model real-time forecast result inspection; performing multi-scene element simulation based on the real-time meteorological data, wherein the multi-scene element simulation comprises three-dimensional typhoon interaction analysis, three-dimensional wind power plant station meteorological simulation and single wind power plant wave height condition simulation; and carrying out meteorological display analysis control based on the real-time meteorological data. According to the invention, virtual display and real-time analysis of meteorological station data observed in real time can be realized.

Description

Technical Field

[0001] The present application relates to the field of meteorological management technology, and in particular to the field of multi-scenario meteorological virtual simulation methods, systems, storage media and electronic equipment technology. Background Art

[0002] my country's renewable energy must not only achieve continuous technological progress, continuous cost reduction, continuous efficiency improvement, and continuous competitiveness enhancement, and fully realize market-oriented development without subsidies, parity or even low prices, but also accelerate the resolution of key issues such as high-proportion consumption, key technology innovation, industrial chain and supply chain security, stability and reliability, further improve quality and efficiency, and accelerate the entry into a new stage of high-quality development. During the critical period and window period of carbon peak, it is necessary to build a clean, low-carbon, safe and efficient energy system, control the total amount of fossil energy, focus on improving utilization efficiency, implement renewable energy substitution actions, deepen power system reform, and build a new power system with new energy as the main body. With the rapid development of new energy and the widespread access of new energy-consuming equipment, the operating characteristics of the power system have changed significantly, and the wide-band coupling relationship between power electronic equipment and the power system has become more complex. Therefore, this also puts higher requirements on the "observable, measurable and controllable" power system. At the same time, the output of renewable energy such as wind power, solar energy, and hydropower depends on the weather, and the output on the power generation side becomes increasingly uncertain, and the reliable power generation capacity will be reduced: on the demand side, with the continuous advancement of electrification and the continuous development of flexible resources such as energy storage, hydrogen energy, and electric vehicles, the load structure is more diversified, and the power market mechanism is constantly advancing, making the two-way interaction between users more in-depth and complex. In summary, the operation mode of the power system will be diversified, decentralized, and differentiated. The planning and operation of a high-proportion renewable energy power system needs to handle the relationship between clean development and power security.

[0003] In the above context, the development orientation of water conservancy and hydropower, clean energy, ecological and environmental protection, etc. is gradually changing from extensive to intensive, and the correlation between business development and the improvement of meteorological service quality is becoming more and more obvious. The development planning relationship between meteorology and clean energy and ecological and environmental protection is becoming closer and closer, and the supporting role is becoming more prominent. The business has higher and higher requirements for the timeliness, accuracy and pertinence of meteorological forecasts. In the future development of meteorological services, we must not only rely on in-depth cooperation with the China Meteorological Administration, but also strengthen the construction of our own core meteorological business capabilities, form analytical research and application capabilities based on meteorological data, and have the professional ability to use meteorological data to support business development.

[0004] At present, high-precision, customized "integrated" services are the core goal of meteorology in the process of enterprise research and development and application. As the company's development model changes from "extensive" to "intensive", the role of meteorology in reducing costs and increasing efficiency in main business sectors such as new energy, water conservancy and hydropower, and ecological and environmental protection is gradually becoming more prominent. The new energy industry has an increasing demand for multi-source meteorological big data and refined and seamless meteorological resources and power forecasts, and is gradually forming a new energy meteorological ecological industry chain with meteorological data and algorithms as the core foundation. In addition, meteorological technology and products have also been integrated into multiple fields such as water conservancy and hydropower, ecological and environmental protection, big data, artificial intelligence, 5G, etc., and new meteorological products are continuously incubated to serve various production links and business operation systems of enterprises. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a multi-scenario meteorological virtual simulation method, system, storage medium and electronic device for realizing virtual display and real-time analysis of real-time meteorological observation station data.

[0006] To achieve the above-mentioned purpose and other related purposes, the present application provides a multi-scenario meteorological virtual simulation method, including: collecting real-time meteorological data from multiple meteorological data sources, the real-time meteorological data including real-time multi-source vector meteorological data and multi-source raster meteorological data; performing real-time verification of meteorological simulation forecast results based on the real-time meteorological data to obtain verification results; the real-time verification of meteorological simulation forecast results includes multi-source observation data comparison analysis verification, model virtual simulation data verification and model real-time forecast result verification; performing multi-scenario element simulation based on the real-time meteorological data, the multi-scenario element simulation including three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation and single wind farm wave height condition simulation; performing meteorological display analysis and control based on the real-time meteorological data.

[0007] In one embodiment of the present application, it also includes: detecting whether there is any missing data in the real-time meteorological data; in response to the presence of missing data in the real-time meteorological data, supplementing the missing data based on a preset supplementary sampling frequency and a supplementary sampling time range.

[0008] In one embodiment of the present application, the inspection indicators in the real-time inspection of the meteorological simulation forecast results include any multiple combinations of time correlation coefficient, root mean square error, TS score, and BS score. The inspection and evaluation methods include: any multiple combinations of time anomaly correlation, root mean square skill score, relative operating characteristics, and Brier skill score. The real-time forecast inspection method includes: any multiple combinations of spatial anomaly correlation coefficient, trend anomaly comprehensive score, symbol consistency rate score, and graded score.

[0009] In one embodiment of the present application, the comparison content in the model real-time forecast result verification includes any combination of multi-model wind resource forecast result verification comparison, multi-model light resource forecast result verification comparison, and multi-model precipitation forecast result verification comparison.

[0010] In one embodiment of the present application, the three-dimensional typhoon interactive analysis includes: three-dimensional GIS display of the real-time typhoon path, including using a three-dimensional pipeline to present the typhoon path, segmented coloring display of the three-dimensional path according to the wind speed of the path at the current moment, and three-dimensional display of the wind circle radius at the current point; synchronous display of real-time three-dimensional wind and rain data, including displaying the three-dimensional wind field in the form of a dynamic three-dimensional wind field map, a pipeline map of three-dimensional wind flow field tracking, and a three-dimensional wind pole map, and performing three-dimensional simulation in an extruded manner, and presenting the three-dimensional precipitation field display of the precipitation area in a convex manner; list display of the real-time typhoon path, including using a list to present the information of the currently selected typhoon at different positioning points, the information at least including the position of the positioning point, the wind circle radius and the wind speed; display of typhoon point details, including displaying the typhoon risk information at the point in the form of a pop-up bubble in response to a click operation on a point where a typhoon path exists, the typhoon risk information including multiple combinations of wind circle radius, wind speed, time, intensity, display forecast agency, and forecast time.

[0011] In one embodiment of the present application, the three-dimensional wind farm station meteorological simulation includes: three-dimensional simulation display of wind farm points, including superimposing multiple points on a three-dimensional earth at the same time, wherein the superposition is a two-dimensional picture superposition or a three-dimensional point model superposition, and in response to the mouse moving to the point, at least the location and name of the point are displayed; simulation display of the meteorological overview of the wind farm, including simulation display of the current meteorological observation situation, simulation display of the future weather forecast of the current specified numerical mode at the point, and simulation display of the current relevant power model data of the wind farm, presenting the model forecast or actual sampling data for the point in the form of a pop-up window.

[0012] In one embodiment of the present application, the meteorological display analysis control based on the real-time meteorological data includes: real-time observation data display analysis, satellite cloud image data display analysis and model data analysis display analysis.

[0013] To achieve the above-mentioned purpose and other related purposes, the present application also provides a multi-scenario meteorological virtual simulation system, including: a data collection and management module, which is used to collect real-time meteorological data from multiple meteorological data sources, and the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data; a meteorological simulation forecast result real-time verification module, which performs real-time verification of meteorological simulation forecast results based on the real-time meteorological data to obtain the verification results; the real-time verification of meteorological simulation forecast results includes multi-source observation data comparison and analysis verification, model virtual simulation data verification and model real-time forecast result verification; a multi-scenario element simulation module, which performs multi-scenario element simulation based on the real-time meteorological data, and the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation and single wind farm wave height condition simulation; a meteorological display analysis module, which performs meteorological display analysis control based on the real-time meteorological data.

[0014] In order to achieve the above-mentioned purpose and other related purposes, the present application also provides a computer storage medium storing program instructions, which, when executed, implement the steps of the multi-scenario meteorological virtual simulation method as described above.

[0015] To achieve the above-mentioned purpose and other related purposes, the present application also provides an electronic device, including a memory for storing a computer program; and a processor for running the computer program to implement the steps of the multi-scenario meteorological virtual simulation method as described above.

[0016] As described above, the multi-scenario meteorological virtual simulation method, system, storage medium and electronic device of the present application have the following beneficial effects:

[0017] 1. This application applies cutting-edge virtual simulation technology to the analysis of meteorological observation and forecast data. This technology can realize the virtual display and real-time analysis of real-time meteorological observation station data. Users can intuitively see the real-time results through computers or tablet devices. At the same time, the project also uses virtual simulation technology to display and analyze the numerical forecast model of short-term, medium-term and long-term meteorological data. The display method is scientific and intuitive, allowing users to better understand and apply meteorological information, thereby facilitating future decision-making.

[0018] 2. This application realizes real-time analysis and early warning of station-level data by constructing a meteorological data analysis system under different scenarios; and realizes analysis of the impact of typhoon paths on stations by analyzing the paths of historical and current typhoons. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 Shown is a schematic diagram of the overall process of a multi-scenario meteorological virtual simulation method in one embodiment of the present application;

[0021] Figure 2 Shown is a schematic diagram of the process of data supplementary acquisition in a multi-scenario meteorological virtual simulation method in an embodiment of the present application;

[0022] Figure 3 Shown is an example diagram of the wind speed deviation time series in the multi-scenario meteorological virtual simulation method in one embodiment of the present application;

[0023] Figure 4 Shown is a schematic flow chart of a three-dimensional typhoon interactive analysis in a multi-scenario meteorological virtual simulation method in an embodiment of the present application;

[0024] Figure 5 Shown is a schematic diagram of the process of three-dimensional wind farm meteorological simulation in a multi-scenario meteorological virtual simulation method in an embodiment of the present application;

[0025] Figure 6 Shown is a principle block diagram of a multi-scenario meteorological virtual simulation system in one embodiment of the present application;

[0026] Figure 7 It is a principle block diagram of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following describes the implementation of this embodiment through specific examples, and those skilled in the art can easily understand other advantages and effects of this embodiment from the content disclosed in this specification. This embodiment can also be implemented or applied through other different specific implementations, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of this embodiment. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0028] The embodiments of the present application provide a multi-scenario meteorological virtual simulation method, system, storage medium and electronic device for realizing virtual display and real-time analysis of real-time observed meteorological observation station data.

[0029] The principles and implementation methods of the multi-scenario meteorological virtual simulation method, system, storage medium and electronic device of this embodiment will be explained in detail below, so that those skilled in the art can understand the multi-scenario meteorological virtual simulation method, system, storage medium and electronic device of this embodiment without creative labor.

[0030] This embodiment provides a multi-scenario meteorological virtual simulation method, specifically, Figure 1 As shown, the multi-scenario meteorological virtual simulation method in this embodiment includes:

[0031] Step S100, collecting real-time meteorological data from multiple meteorological data sources, wherein the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data;

[0032] Step S200, performing a real-time test of the meteorological simulation forecast result based on the real-time meteorological data to obtain a test result; the real-time test of the meteorological simulation forecast result includes a comparative analysis test of multi-source observation data, a model virtual simulation data test, and a model real-time forecast result test;

[0033] Step S300, performing multi-scenario element simulation based on the real-time meteorological data, wherein the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation, and single wind farm wave height condition simulation;

[0034] Step S400: performing meteorological display analysis control based on the real-time meteorological data.

[0035] The above steps S100 to S400 in the multi-scenario meteorological virtual simulation method of this embodiment are described in detail below.

[0036] Step S100, collecting real-time meteorological data from a variety of meteorological data sources, wherein the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data.

[0037] Figure 2 The flowchart of data supplementation in the multi-scenario meteorological virtual simulation method in one embodiment of the present application is shown; Figure 2 As shown, in one embodiment of the present application, it also includes: detecting whether there is any missing data in the real-time meteorological data; in response to the presence of missing data in the real-time meteorological data, supplementing the missing data based on a pre-set supplementary sampling frequency and supplementary sampling time range.

[0038] 1) Multi-source vector data acquisition and processing

[0039] Real-time data collection includes data from more than 2,400 national meteorological automatic observation stations across the country, data from self-built offshore lidar buoy devices and onshore wind measurement equipment, and real-time data collection from artificial intelligence models.

[0040] Collected from more than 2,400 national automatic meteorological observation stations across the country: including: station-level real-time meteorological data including 2-minute average wind direction, 2-minute average wind speed, 10-minute average wind direction, 10-minute average wind speed, wind direction of maximum wind speed, maximum wind speed, time of maximum wind speed, instantaneous wind direction, instantaneous wind speed, wind direction of maximum wind speed, maximum wind speed, time of maximum wind speed. Supplementary provision of conventional meteorological elements such as precipitation, radiation, heat flux, etc.

[0041] Data collected from self-built offshore laser radar buoys and onshore wind measurement equipment: including data from offshore laser radar buoys and onshore wind measurement equipment in Jinshan, Yangjiang, Nanhui, Changle, etc. Data from 5 stations of the anniversary wave station, including hourly effective wave height H1 / 3, average wave height of one-tenth of the large wave H1 / 10, maximum wave height Hmax, average period Tmea and average wave direction Dir.

[0042] Real-time data collection from AI models: site-level wind resource / power forecast data output by AI models.

[0043] Since real-time data may be delayed or data sources may be missing, this embodiment should be able to automatically collect missing data after the data source returns to normal.

[0044] Automatic batch data collection: Automatically detect missing data and automatically collect missing data based on the pre-set collection frequency and collection time range.

[0045] This embodiment also includes normalizing the vector data:

[0046] a. Standardization of data format. Use a unified database to store and manage vector data.

[0047] b. Standardization of data naming. Prepare vector data naming standards and use the same naming rules for fields with the same meaning from different sources.

[0048] c. Standardization of field types. Standardize and classify the storage types of numerical, text, logical, and time data to ensure that data is stored in the most appropriate way.

[0049] 2) Multi-source raster data acquisition and processing

[0050] Real-time data collection includes: European Centre for Medium-Range Weather Forecasts releases 10-day forecast service analysis data, variable grid short-term numerical forecast model data, and variable grid medium- and long-term numerical forecast data.

[0051] The European Centre for Medium-Range Weather Forecasts releases forecast service analysis data for the next 10 days, including 100m east-west wind, 100m north-south wind, 10m east-west wind, 10m north-south wind, 2m temperature, mean sea level pressure, surface temperature, ground pressure, sea surface temperature, 10m gusts in the past 3 hours, 10m gusts in the past 6 hours, 10m east-west wind, and total precipitation. The data resolution is 0.125°×0.125°. Geopotential height, specific humidity, relative humidity, temperature, east-west wind, north-south wind, and vertical speed. The data resolution is 0.25°×0.25°. All data are updated at 07:00, 19:00, 0-144 hours (1 hour interval), and 150-240 hours (3 hours interval).

[0052] Variable grid short-term numerical forecast model data: resolution 3-10 kilometers, covering the forecast for the next 10 days, rolling storage of 7 days of historical data, variables include temperature, precipitation, air pressure, wind and other basic meteorological elements, covering the entire Chinese region and offshore areas.

[0053] Variable grid medium- and long-term numerical forecast data: resolution 10-100 kilometers, covering ensemble forecast information for the next 1 day to 10 years, data rolling forecast, storage of return information for the past 20 years, variables include basic meteorological element fields.

[0054] In this embodiment, the following functions are supported simultaneously in the data collection and processing:

[0055] 1. Support one-time supplementary collection of historical data from 2023 to the present.

[0056] 2. Supports the function of supplementary data collection for any customized time period.

[0057] 3. After data collection, the original data size must be compressed to be suitable for real-time network transmission and front-end rendering.

[0058] 4. Supports the collection of data in common meteorological formats such as NC\GRIB\TXT\HDF.

[0059] This embodiment also includes projection conversion of raster data to make it suitable for front-end rendering.

[0060] This embodiment also includes the following steps to standardize the raster data:

[0061] a. Standardization of file meta information. When entering data, it supports the storage of data meta information, including fixed information such as data storage path and update time, and also supports the storage of custom fields.

[0062] b. Standardized storage format. Data can be stored in both lossy and lossless image encoding modes to minimize image size.

[0063] c. Storage path standardization. Standardize the encoding of the data storage path. That is, design a reasonable path storage structure that can standardize and effectively express the source, field, level, forecast time, start time and other information of the numerical model.

[0064] In the meteorological system, the forecast data yi is the raster data provided by nc, and the observation data oi is the api data of the site (site data after mapping of more than 3,000 districts and counties).

[0065] Suppose you need to calculate the RMSE of the wind speed value at 2024-04-08 10:00.

[0066] For example, the nc data consists of 701*601 grids, with a total of 3218 stations.

[0067] Grids without stations are ignored and do not participate in the calculation. Grids with stations (with both forecast data and measured data) are calculated. That is, although the total number of grids is 701*601=421301, only 3218 grids are taken.

[0068]

[0069] K is 3218

[0070] yi is the forecast data of each grid with a station, and the wind speed data of nc at 2024-04-08 10:00 is taken. oi is the measured data of the station, and the wind speed data measured at the station at 2024-04-08 10:00 is taken. Note that yi and oi must correspond to each other, that is, they both correspond to one grid. If multiple stations are in the same grid, the predicted values ​​of these stations are all the data of this grid. When calculating, it is necessary to pay attention to the correspondence between the forecast data and the measured data, such as which two values ​​of the wind speed forecast and the measured value are.

[0071] It is important to recalculate according to the data update time. For example, if the future forecast data is updated at 07:00 and 19:00 every day, the RMSE should be recalculated with the new data after the forecast data is updated.

[0072] Taking wind speed as an example, we can get the rmse data at each time node. Figure 3 The x-axis is time, the time when the latest measured data appears and the 10 time nodes before it. For example, if the data update node is hourly, and the latest measured data update time is 2024-04-0810:00, then the x-axis is 2024-04-0800:00, 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, 8:00, 9:00, 10:00. The y-axis is the calculated rmse value, and the value of the axis needs to be automatically adjusted according to the upper and lower limits of the actual data.

[0073] Step S200, performing real-time verification of meteorological simulation forecast results based on the real-time meteorological data to obtain verification results; the real-time verification of meteorological simulation forecast results includes multi-source observation data comparison and analysis verification, model virtual simulation data verification and model real-time forecast result verification.

[0074] In one embodiment of the present application, the inspection indicators in the real-time inspection of the meteorological simulation forecast results include any multiple combinations of time correlation coefficient, root mean square error, TS score, and BS score. The inspection and evaluation methods include: any multiple combinations of time anomaly correlation, root mean square skill score, relative operating characteristics, and Brier skill score. The real-time forecast inspection method includes: any multiple combinations of spatial anomaly correlation coefficient, trend anomaly comprehensive score, symbol consistency rate score, and graded score.

[0075] In one embodiment of the present application, the comparison content in the model real-time forecast result verification includes any combination of multi-model wind resource forecast result verification comparison, multi-model light resource forecast result verification comparison, and multi-model precipitation forecast result verification comparison.

[0076] Specifically, in this embodiment, the real-time verification of the image simulation forecast result includes:

[0077] 1) Comparative analysis of multi-source observation data

[0078] Real-time accuracy verification is carried out between multi-source reanalysis data and station data. The verification indicators include but are not limited to time correlation coefficient, root mean square error, TS score, BS score, etc. Comprehensive verification and evaluation methods include: temporal anomaly correlation coefficient (TCC), mean square skill score (MSSS), relative operating characteristic (ROC), Brier skill score (BSS), etc. Real-time forecast verification methods include: spatial anomaly correlation coefficient (ACC), trend anomaly comprehensive score (Ps), symbol consistency rate score (Pc), graded score (Pg), etc.

[0079] 2) Model virtual simulation data verification

[0080] Multiple sets of simulation data are captured at regular intervals and dynamically verified with multi-source observation data. The inspection indicators include but are not limited to time correlation coefficient, root mean square error, TS score, BS score, etc. Comprehensive inspection and evaluation methods include: time anomaly correlation coefficient (TCC), mean square skill score (MSSS), relative operating characteristic (ROC), Brier skill score (BSS), etc.; time forecast inspection methods include: spatial anomaly correlation coefficient (ACC), trend anomaly comprehensive score (Ps), symbol consistency rate score (Pc), graded score (Pg), etc.

[0081] 3) Verification of multi-model real-time forecast results

[0082] Compare and verify the real-time forecast results of multiple meteorological models with the observation data to improve the accuracy and reliability of the forecast. The specific comparison content includes but is not limited to the following aspects: verification and comparison of wind resource forecast results of multiple models, verification and comparison of light resource forecast results of multiple models, and verification and comparison of precipitation forecast results of multiple models. Comprehensive inspection and evaluation methods include: temporal anomaly correlation coefficient (TCC), mean square skill score (MSSS), relative operating characteristic (ROC), Brier skill score (BSS), etc.; real-time forecast inspection methods include: spatial anomaly correlation coefficient (ACC), trend anomaly comprehensive score (Ps), symbol consistency score (Pc), graded score (Pg), etc.

[0083] In addition, this embodiment also includes monitoring of the forecast process of the forecast system and regularly generating evaluation and inspection reports.

[0084] Specifically, the forecasting system forecasting process monitoring includes:

[0085] a. Process tracking: real-time monitoring of the forecasting process of the numerical forecasting system and tracking of the mode operation status.

[0086] b. Abnormal reporting: Timely discover and report abnormal situations in the forecast process to ensure the transparency and traceability of the forecast process.

[0087] c. Recording and backtracking: Record the key nodes of the forecast process, provide a complete forecast production process log, and support process backtracking.

[0088] Specifically, the periodic generation of the assessment and inspection report includes:

[0089] a. Reanalysis data comparison report: summarizes the comparison results of multi-source reanalysis data and site data. Simulation data verification report: summarizes the comparison results of virtual simulation data and observation data of different models, and analyzes the time correlation coefficient, root mean square error, TS score and BS score of each model simulation data and observation data.

[0090] b. Forecast results comparison report: summarize and compare the forecast results of multiple models.

[0091] c. The report content includes data sources, test methods, statistical results, chart presentation, etc., and the export formats are PDF, Word, HTML, etc., which are convenient for archiving, sharing and further analysis.

[0092] Step S300, performing multi-scenario element simulation based on the real-time meteorological data, wherein the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation, and single wind farm wave height condition simulation.

[0093] Figure 4 The flowchart of the three-dimensional typhoon interactive analysis in the multi-scenario meteorological virtual simulation method in one embodiment of the present application is shown; Figure 4 As shown, in one embodiment of the present application, the three-dimensional typhoon interaction analysis includes the following steps S310 to S340.

[0094] Step S310, performing a three-dimensional GIS display on the real-time typhoon path, including presenting the typhoon path using a three-dimensional pipeline, displaying the three-dimensional path in sections according to the wind speed at the current moment of the path, and performing a three-dimensional display on the radius of the wind circle at the current point.

[0095] In this embodiment, the real-time three-dimensional GIS display of the typhoon path includes: 1) the typhoon path is presented using three-dimensional pipelines, and the thickness of the pipelines can be customized; 2) it supports segmented coloring of the three-dimensional path according to the wind speed at the current moment of the path; 3) it supports three-dimensional display of the wind circle radius at the current point.

[0096] Step S320, synchronously display the real-time three-dimensional wind and rain data, including displaying the three-dimensional wind field in the form of a dynamic three-dimensional wind field map, a pipeline map of the three-dimensional wind flow field tracking, and a three-dimensional wind pole map, and performing three-dimensional simulation in an extruded manner, and using a raised manner to present the three-dimensional precipitation field display of the precipitation area.

[0097] In this embodiment, the real-time three-dimensional wind and rain data synchronous display includes: 1) supporting three-dimensional wind field display, including wind volume in three directions of UVW. The display methods include dynamic three-dimensional wind field map, pipeline map of three-dimensional wind flow field tracking, and three-dimensional wind pole map; 2) supporting three-dimensional precipitation field display. Since precipitation is two-dimensional field data, it can support three-dimensional simulation using extrusion, and areas with high precipitation are presented in a convex manner.

[0098] Step S330, displaying the real-time typhoon path in a list, including presenting the information of the currently selected typhoon at different positioning points in a list format, wherein the information at least includes the position of the positioning point, the radius of the wind circle, and the wind speed.

[0099] In this embodiment, the real-time typhoon path list display includes: using a list to present the information of the currently selected typhoon at different positioning points, including the position of the positioning point, the radius of the wind circle, the wind speed, etc.

[0100] Step S340, displaying the typhoon point details, including responding to a click operation on a typhoon path point by displaying the typhoon risk information at the point in the form of a pop-up bubble, wherein the typhoon risk information includes multiple combinations of wind circle radius, wind speed, time, intensity, display of forecast agencies, and forecast time.

[0101] In this embodiment, the typhoon point details are displayed including: when there is a typhoon path point where the mouse is clicked, the typhoon risk situation at the point is displayed in the form of a pop-up bubble, including wind circle radius, wind speed, time, intensity and other information. If it is a forecast path point, it also supports the display of forecast agency, forecast time and other information.

[0102] Figure 5 The flowchart of the three-dimensional wind farm meteorological simulation in the multi-scenario meteorological virtual simulation method in one embodiment of the present application is shown; Figure 5 As shown, in one embodiment of the present application, the three-dimensional wind farm meteorological simulation includes the following steps S350 to S360.

[0103] Step S350, performing a three-dimensional simulation display of wind farm points, including superimposing multiple points on a three-dimensional earth at the same time, wherein the superposition is a two-dimensional picture superposition or a three-dimensional point model superposition, and in response to the mouse moving to a point, at least the location and name of the point are displayed.

[0104] In this embodiment, the three-dimensional display of wind farm points includes: 1) multiple points are superimposed on the three-dimensional earth at the same time; 2) the display method can be a two-dimensional picture superposition, or a three-dimensional point model superposition; 3) when the mouse moves to the point, it supports the display of the point's location, name and other inherent information.

[0105] Step S360, simulate and display the meteorological overview of the wind farm, including simulating and displaying the current meteorological observation situation, simulating and displaying the future weather forecast at the point with the current specified numerical model, and simulating and displaying the relevant power model data of the wind farm, and presenting the model forecast or actual sampling data for the point in the form of a pop-up window.

[0106] In this embodiment, the meteorological overview display of the wind farm includes: displaying the meteorological data collected at the wind farm point, including: 1) the current meteorological observation situation; 2) the future weather forecast at the point with the current specified numerical model, including common basic meteorological elements and ocean-related elements, such as wave height, wave direction, etc.; 3) if there is currently relevant power model data for the wind farm, it also supports presenting the model forecast or actual sampling data for the point in the form of a pop-up window.

[0107] In addition, when entering a specific wind farm through the wind farm point details pop-up window, the wave height conditions of the wind farm can be displayed, including:

[0108] 1) Display the numerical forecast data in a color-filled manner on a three-dimensional globe.

[0109] 2) The data is squeezed according to the wave height to show a wave effect with ups and downs.

[0110] This embodiment also includes a single wind farm flow field condition simulation. When entering a specific wind farm through the wind farm point details pop-up window, it supports displaying the flow field conditions of the wind farm, including:

[0111] 1) The flow velocity is displayed on the three-dimensional earth by coloring.

[0112] 2) Use dynamic wave lines to animate and simulate the flow field.

[0113] 5. Wind farm wind turbine model display

[0114] If the wind farm has three-dimensional wind turbine model data, it supports real-time rendering of the three-dimensional wind turbine model on the three-dimensional earth.

[0115] This embodiment also includes a custom scene:

[0116] A. Provide custom scene plug-ins

[0117] As research progresses, there may be more scene demonstration requirements in the future, so it is necessary to support custom management of scenes through plug-ins. Specifically:

[0118] 1) Expand the data types supported by the system through plug-ins.

[0119] 2) Supports expansion through display methods supported by the plug-in system.

[0120] 3) Support customized development of scenarios through plug-ins.

[0121] B. Provide custom scene plug-in management

[0122] Provides a visual page to manage custom scene plug-ins, mainly including:

[0123] 1) List display of scene plug-ins, including the list of all plug-ins, the list of currently installed plug-ins, etc.

[0124] 2) Display the details of the scene plug-in, including the introduction text and graphics of the scene plug-in.

[0125] 3) Support for installation and uninstallation of scene plug-ins. You can install or uninstall the connected scene plug-ins through the visual page.

[0126] C. Custom scene publishing

[0127] The developed scene can be released online only after being reviewed by the administrator.

[0128] Step S400: performing meteorological display analysis control based on the real-time meteorological data.

[0129] In one embodiment of the present application, the meteorological display analysis control based on the real-time meteorological data includes: real-time observation data display analysis, satellite cloud image data display analysis and model data analysis display analysis.

[0130] 1. Real-time observation data display and analysis

[0131] A. For national automatic station data, all display methods on the three-dimensional spherical screen are supported. Since this scene is a two-dimensional large-screen display, it has greater flexibility than the spherical surface. Therefore, in addition to this, the following functions should also be supported:

[0132] 1) Real-time data thinning display control. You can customize whether to display the data in real time.

[0133] 2) Time series analysis: Supports analysis of historical data for the point selected by the mouse, including time series within the specified time period, maximum and minimum values ​​and other related information.

[0134] 3) Table analysis: The data displayed on the current map can be presented in a table.

[0135] B. Satellite cloud image data

[0136] Access to real-time satellite cloud image data from Sunflower-8:

[0137] 1) In terms of bands, it supports display of three bands: visible light, infrared and water vapor.

[0138] 2) In terms of display mode, it supports the display of grid interpolation and grid filling.

[0139] 2. Self-built site data display and analysis

[0140] 1) Site marking: In addition to fully supporting the display requirements of the Weather 2400 site, a separate mark is used for the self-built site for personalized highlighting.

[0141] 2) Real-time access to elements: For self-built site data, in addition to supporting historical data analysis, it also supports access to real-time collected data for analysis.

[0142] 3) Time series analysis: The display method is the same as that of the National Meteorological Automatic Station.

[0143] 4) Data table display and download and export: For the self-built site data displayed on the map, it supports downloading and exporting in table format.

[0144] 3. Model data analysis

[0145] It includes short-term model data display and analysis, medium- and long-term model data display and analysis, and multiple artificial intelligence model data display and analysis.

[0146] 1) Short-term model data display and analysis include EC data analysis and display and variable grid model display analysis.

[0147] EC data analysis and display include: conventional coloring and other diversified displays; according to the background configuration, display the specified element switching options, support fast switching between multiple elements. (Including 100-meter east-west wind, 100-meter north-south wind, including 10-meter east-west wind, 10-meter north-south wind, 2-meter temperature, average sea level pressure, surface temperature, ground pressure, sea surface temperature, 10-meter gusts in the past 3 hours, 10-meter gusts in the past 6 hours, 10-meter east-west wind, total precipitation, etc., the data resolution is 0.125°×0.125°. Potential height, specific humidity, relative humidity, temperature, east-west wind, north-south wind, vertical speed, the data resolution is 0.25°×0.25°); support timeline animation playback, can quickly jump to the specified time and level of data; support the value of the mouse position. Display the data value of the current selected point.

[0148] The variable grid model display and analysis includes variable grid short-term numerical forecast model data: with a resolution of 3-10 kilometers, covering the forecast for the next 10 days, rolling storage of 7 days of historical data, and variables including basic meteorological elements such as temperature, precipitation, air pressure, and wind, covering the entire Chinese region and offshore areas.

[0149] 2) The display and analysis of medium- and long-term model data include time series analysis and multi-factor color map rendering.

[0150] Time series analysis includes displaying forecast information of any point in the form of time series, including: switching different numerical modes; switching different elements; switching different data time periods; switching different display effects (curve graphs, bar graphs, etc.); displaying the maximum line, minimum line, and average line in the current time period; and supporting the export and download of images.

[0151] Multi-element color spot map rendering includes: color spot map rendering is implemented using GPU to achieve fast zooming in and out rendering effects; color spot map rendering has custom color scales, and users can change the current color through the configuration file without modifying the code; for temperature and precipitation elements, contour lines are superimposed on the color spot map in real time through the GPU, so as to quickly view trends and keep the contour line animation in the animation synchronized (that is, it also supports animation using dynamic interpolation on contour lines).

[0152] 3) Multiple artificial intelligence model data display and analysis including power prediction series display and weather forecast display and analysis.

[0153] The power prediction sequence display presents the measured and forecasted power prediction data in the form of time series, including: switching between different artificial intelligence model results; comparative analysis of multiple artificial intelligence model results; and downloading and saving images.

[0154] The method and requirements of weather forecast display in weather forecast display analysis should be consistent with the power prediction curve.

[0155] In this embodiment, the meteorological virtual simulation platform applet can be used to display real-time meteorological conditions, numerical mode displays, and risk warnings.

[0156] Among them, the real-time weather display is to display the data of national automatic stations, self-built stations, and artificial intelligence models on the mobile terminal in the form of mini-programs. The numerical model display is to display the EC and self-developed medium- and long-term model data on the mobile terminal in the form of mini-programs; the risk warning is to display the national early warning signals, as well as the warning list of strong winds and precipitation elements that exceed the system threshold.

[0157] This embodiment also provides a multi-scenario meteorological virtual simulation system. Figure 6 The schematic diagram of the multi-scenario meteorological virtual simulation system in one embodiment of the present application is shown; Figure 6 As shown, the multi-scenario meteorological virtual simulation system includes: a data collection and management module, a meteorological simulation forecast result real-time verification module, a multi-scenario element simulation module and a meteorological display and analysis module.

[0158] Among them, the data collection and management module is used to collect real-time meteorological data from multiple meteorological data sources, and the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data; the meteorological simulation forecast result real-time verification module performs real-time verification of meteorological simulation forecast results based on the real-time meteorological data to obtain the verification results; the real-time verification of meteorological simulation forecast results includes multi-source observation data comparison and analysis verification, model virtual simulation data verification and model real-time forecast result verification; the multi-scenario element simulation module performs multi-scenario element simulation based on the real-time meteorological data, and the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation and single wind farm wave height condition simulation; the meteorological display analysis module performs meteorological display analysis control based on the real-time meteorological data.

[0159] In this embodiment, the implementation principles of each module of the multi-scenario meteorological virtual simulation system are the same as the implementation principles of each step of the above-mentioned multi-scenario meteorological virtual simulation method. The implementation principles and specific implementation methods of the multi-scenario meteorological virtual simulation system will not be repeated here.

[0160] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is cached. When the computer program is executed by a processor, the multi-scenario meteorological virtual simulation method provided in any embodiment of the present application is implemented.

[0161] In the embodiment of the present application, any combination of one or more storage media can be used. The storage medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0162] An embodiment of the present application also provides an electronic device. Figure 7 Shown is a schematic diagram of the structure of the electronic device 100 provided in the embodiment of the present application. In some embodiments, the electronic device can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA) and other terminal devices. In addition, the multi-scenario meteorological virtual simulation method provided in the present application can also be applied to databases, servers, and service response systems based on terminal artificial intelligence. The embodiment of the present application does not impose any restrictions on the specific application scenarios of the multi-scenario meteorological virtual simulation method.

[0163] like Figure 7 As shown, the electronic device 100 provided in the embodiment of the present application includes a memory 101 and a processor 102 .

[0164] The memory 101 is used to store computer programs; preferably, the memory 101 includes: ROM, RAM, disk, USB flash drive, memory card or CD-ROM and other media that can store program codes.

[0165] Specifically, the memory 101 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 100 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 101 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application.

[0166] The processor 102 is connected to the memory 101 and is used to execute the computer program stored in the memory 101 so that the electronic device 100 executes the multi-scenario meteorological virtual simulation method provided in any embodiment of the present application.

[0167] Optionally, the processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0168] Optionally, the electronic device 100 in this embodiment may further include a display 103. The display 103 is communicatively connected with the memory 101 and the processor 102, and is used to display a GUI interaction interface related to the multi-scenario meteorological virtual simulation method.

[0169] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0170] In summary, this application applies cutting-edge virtual simulation technology to the analysis of meteorological observation and forecast data. This technology can realize the virtual display of real-time observation of meteorological observation station data, and users can intuitively see the real-time results through computers or tablet devices. At the same time, the project also uses virtual simulation technology to display and analyze the numerical forecast model of short-term, medium-term and long-term meteorological data. This display method is scientific and intuitive, allowing users to better understand and apply meteorological information, thereby facilitating future decision-making; this application realizes real-time analysis and early warning of station-level data by constructing a meteorological data analysis system under different scenarios; through the analysis of the paths of historical and current typhoons, the impact of typhoon paths on stations is analyzed. Therefore, this embodiment effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0171] The above embodiments are merely illustrative of the principles and effects of the present embodiment, and are not intended to limit the present embodiment. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present embodiment. Therefore, all equivalent modifications or changes made by those of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present embodiment should still be covered by the claims of the present embodiment.

Claims

1. A multi-scenario meteorological virtual simulation method, characterized in that: include: Collecting real-time meteorological data from multiple meteorological data sources, the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data; Performing real-time verification of meteorological simulation forecast results based on the real-time meteorological data to obtain verification results; The real-time inspection of meteorological simulation forecast results includes comparison and analysis of multi-source observation data, model virtual simulation data inspection and model real-time forecast result inspection; Perform multi-scenario element simulation based on the real-time meteorological data, wherein the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation, and single wind farm wave height condition simulation; Weather display analysis and control are performed based on the real-time weather data.

2. The multi-scenario meteorological virtual simulation method according to claim 1, characterized in that: Also includes: Detecting whether the real-time meteorological data has data missing; In response to missing data in the real-time meteorological data, supplementary sampling is performed for the missing data based on a preset supplementary sampling frequency and a supplementary sampling time range.

3. The multi-scenario meteorological virtual simulation method according to claim 1 is characterized in that: The inspection indicators in the real-time inspection of the meteorological simulation forecast results include any combination of time correlation coefficient, root mean square error, TS score, and BS score. The inspection and evaluation methods include: any combination of time anomaly correlation, root mean square skill score, relative operating characteristics, and Brier skill score. The real-time forecast inspection method includes: any combination of spatial anomaly correlation coefficient, trend anomaly comprehensive score, symbol consistency rate score, and graded score.

4. The multi-scenario meteorological virtual simulation method according to claim 1, characterized in that: The comparison content in the model real-time forecast result verification includes any combination of multi-model wind resource forecast result verification comparison, multi-model light resource forecast result verification comparison, and multi-model precipitation forecast result verification comparison.

5. The multi-scenario meteorological virtual simulation method according to claim 1 is characterized in that: The three-dimensional typhoon interaction analysis includes: 3D GIS display of real-time typhoon paths, including the use of 3D pipelines to present typhoon paths, segmented coloring of 3D paths according to the wind speed at the current moment, and 3D display of the radius of the wind circle at the current point; Synchronous display of real-time 3D wind and rain data, including dynamic 3D wind field diagram, pipeline diagram of 3D wind flow field tracking, 3D wind pole diagram to display 3D wind field and 3D simulation in extrusion mode, and 3D precipitation field display in precipitation area in convex mode; Displaying the real-time typhoon path in a list, including presenting the information of the currently selected typhoon at different positioning points in a list format, wherein the information at least includes the position of the positioning point, the radius of the wind circle, and the wind speed; The typhoon point details are displayed, including in response to a click operation on a point where a typhoon path exists, the typhoon risk information at the point is displayed in the form of a pop-up bubble, and the typhoon risk information includes multiple combinations of wind circle radius, wind speed, time, intensity, display of forecast agencies, and forecast time.

6. The multi-scenario meteorological virtual simulation method according to claim 1 is characterized in that: The three-dimensional wind farm station meteorological simulation includes: Performing a three-dimensional simulation display of wind farm points, including superimposing multiple points on a three-dimensional earth at the same time, wherein the superposition is a two-dimensional picture superposition or a three-dimensional point model superposition, and in response to the mouse moving to a point, at least the position and name of the point are displayed; The meteorological overview of the wind farm is simulated and displayed, including the current meteorological observation situation, the future weather forecast of the point under the current specified numerical model, and the relevant power model data of the wind farm. The model forecast or actual sampling data for the point is presented in the form of a pop-up window.

7. The multi-scenario meteorological virtual simulation method according to claim 1, characterized in that: The meteorological display analysis control based on the real-time meteorological data includes: real-time observation data display analysis, satellite cloud image data display analysis and model data analysis display analysis.

8. A multi-scenario meteorological virtual simulation system, characterized by: include: A data collection management module, used to collect real-time meteorological data from multiple meteorological data sources, wherein the real-time meteorological data includes real-time multi-source vector meteorological data and multi-source raster meteorological data; A meteorological simulation forecast result real-time verification module performs a real-time verification of the meteorological simulation forecast result based on the real-time meteorological data to obtain a verification result; The real-time inspection of meteorological simulation forecast results includes comparison and analysis of multi-source observation data, model virtual simulation data inspection and model real-time forecast result inspection; A multi-scenario element simulation module performs multi-scenario element simulation based on the real-time meteorological data, wherein the multi-scenario element simulation includes three-dimensional typhoon interaction analysis, three-dimensional wind farm station meteorological simulation, and single wind farm wave height condition simulation; The meteorological display analysis module performs meteorological display analysis control based on the real-time meteorological data.

9. An electronic device, characterized in that: include: comprising a memory for storing a computer program; A processor is used to run the computer program to implement the steps of the multi-scenario meteorological virtual simulation method as described in any one of claims 1 to 7.

10. A computer storage medium storing program instructions, characterized in that: When the program instructions are executed, the steps of the multi-scenario meteorological virtual simulation method as described in any one of claims 1 to 7 are implemented.

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