Unmanned aerial vehicle low-altitude meteorological precise simulation and guarantee system of self-adaptive mobile grid

Abstract

The invention relates to the technical field of weather forecast information processing, in particular to an unmanned aerial vehicle low-altitude weather precise simulation and guarantee system of a self-adaptive mobile grid. The system comprises a WPR mode region design module and a prediction mode planning module. According to the invention, the WPR mode region design module is combined with the preset flight route of the unmanned aerial vehicle, WRF mode region grids at different time intervals are designed, and the prediction mode planning module is configured through the region high-resolution numerical weather forecast system. According to the method, a physical parameterization scheme and mode vertical encryption layering adaptive to a preset flight route of the unmanned aerial vehicle are selected, for short-time approaching weather forecast early warning, the forecast range is narrowed, the grid horizontal resolution is improved, an inner-layer nested grid domain is moved at each time, an area outside the flight route does not need to be simulated and forecasted, and the forecasting efficiency is improved. The integral operation time is greatly reduced, and various weather conditions on the air route of the low-altitude airspace aircraft can be effectively forecasted.

Description

Technical Field

[0001] The present invention relates to the technical field of weather forecast information processing, and in particular to an adaptive mobile grid UAV low-altitude weather precision simulation and support system. Background Art

[0002] In recent years, civil drones, represented by multi-rotor aircraft, have developed rapidly, and the related applications promoted by them include logistics, medical treatment, law enforcement operations, news collection, weather monitoring, and ground traffic assessment. However, for a long time, due to the constraints of the natural and social attributes of low-altitude airspace resources, low-altitude airspace resources have been an uncultivated field in the aviation industry. The natural attribute constraints of low-altitude airspace resources in the aviation industry are mainly reflected in the surface structure of low-altitude airspace and the near-ground low-altitude climate and meteorological environment and the interaction between the two. Different from medium and high-altitude airspace, low-altitude airspace is close to the surface, with steep changes and complex structures, dynamic legal and illegal buildings, and facilities such as power grids, high towers, and windmills, which will affect aviation safety; at the same time, the near-ground climate and meteorological conditions change more frequently, such as turbulent airflow and wind shear in the atmospheric boundary layer, and local extreme weather events, which restrict the development and utilization of low-altitude airspace resources by the aviation industry.

[0003] Since there are still many weak links in my country's low-altitude flight, the problems of low stability and high risk of low-altitude flight are more prominent. When low-altitude aircraft fly in a complex underlying surface environment, when disastrous weather such as thunderstorms, wind shear, and heavy precipitation appear in the airspace, it will seriously affect the safety of the aircraft. Horizontal wind shear has a serious impact on the attitude of the aircraft, while vertical wind shear will change the flight altitude of the aircraft in a short time; thunderstorms also damage the electronic components of the aircraft. With the development of meteorological observation technologies such as meteorological radars and satellites and the application of high-temporal and spatial resolution numerical models, disastrous weather systems in low-altitude airspace can be better predicted and warned.

[0004] At present, my country's low-altitude environment is complex and changeable, and the meteorological support system for low-altitude flight and the meteorological support for the safety of UAV flight are becoming more and more important. Flight in low-altitude airspace is often affected by meteorological factors such as thunderstorms, wind shear, visibility, temperature and humidity. The meteorological factors that affect low-altitude aircraft are mainly strong winds, thunderstorms, visibility, temperature, air pressure and humidity. During the flight, the aircraft will be disturbed by the wind. When the wind is calm or the wind speed is low, the aircraft's own flight control system can resist the interference of the wind and ensure its relatively stable flight. However, when the wind speed or wind force is high, especially when the wind speed exceeds the maximum flight speed of the aircraft and produces low-altitude wind shear, it will cause it to be unable to fly normally or even explode.

[0005] The existing meteorological numerical model can simulate and forecast the ground and high-altitude meteorological elements in a small area, and in the low-altitude airspace, there are automatic weather stations, wind profiler radars, Doppler radars and satellites to continuously monitor the weather conditions in a certain airspace. The latest technical means for the study of low-altitude airspace routes are the land-air-space integrated low-altitude flight safety meteorological guarantee system based on ground meteorological detection, low-altitude radar monitoring, Fengyun series meteorological satellites and Beidou navigation satellites, and most of them are statistical forecasts and empirical forecasts, and there are few applications for numerical simulation research. In addition, the existing technology also has some shortcomings in the meteorological safety guarantee of routes: the number of automatic weather stations near low-altitude routes is scarce, and automatic weather stations can only monitor the meteorological information on the aircraft route in real time, and cannot make forecasts and warnings; the Fengyun series meteorological satellites have a large detection range and high resolution, but the satellite transit time is discontinuous, and when there are clouds, the ground monitoring effect is very poor.

[0006] In summary, the problems existing in the prior art are: (1) There are few automatic weather stations near the flight paths of low-altitude aircraft. Automatic weather stations can only monitor the weather information on the route in real time, but cannot provide forecasts and warnings.

[0007] (2) Ground-based radar can accurately predict severe thunderstorms, heavy rainfall, hail and other disastrous weather within the take-off and landing range of aircraft (0-100km radius), but radar is better at short-term and nowcasting. The forecast level will be greatly reduced after more than 2 hours.

[0008] (3) Existing regional numerical models can perform high-resolution simulation and forecast of meteorological information on the flight paths of low-altitude aircraft. However, since the spatial range of regional numerical models is fixed, they cannot perform tracking forecasts based on the movement paths of aircraft. Furthermore, since the spatial range of regional numerical models is relatively wide, integration calculations for areas outside the flight paths of aircraft will waste machine time and increase forecast costs.

[0009] In order to address the above problems, there is an urgent need for an adaptive mobile grid UAV low-altitude meteorological accurate simulation and support system. Summary of the invention

[0010] The purpose of the present invention is to provide an adaptive mobile grid UAV low-altitude weather accurate simulation and support system to solve the problems raised in the above background technology.

[0011] To achieve the above objectives, an adaptive mobile grid UAV low-altitude meteorological precision simulation and support system is provided, including a meteorological data management platform, a UAV route acquisition module, an airspace longitude and latitude extraction module, a WPR mode area design module, a prediction mode planning module, and a low-altitude meteorological element forecast module; The meteorological data management platform is used to obtain meteorological data in the low-altitude flight area of ​​the UAV; The drone route acquisition module is used to obtain the preset flight route of the drone; The airspace longitude and latitude extraction module extracts the longitude and latitude on the route in combination with the preset route of the UAV flight; The WPR mode area design module combines the preset flight route of the UAV to design the WRF mode area grids at different time intervals, simulates the fixed route grid corresponding to the preset flight route of the UAV and the mobile grid under the flight state of the UAV, performs grid nesting processing, and automatically locates the longitude and latitude of the center of the mobile grid according to the longitude and latitude on the route; The prediction model planning module selects the physical parameterization scheme and model vertical encryption layering that are suitable for the preset flight route of the drone through the regional high-resolution numerical weather forecast system configuration; The low-altitude meteorological element forecast module combines the simulated fixed route grid and the mobile grid, matches the meteorological data of the corresponding area, and performs real-time forecast of meteorological data during the UAV flight period.

[0012] As a further improvement of the technical solution, the meteorological data management platform includes a meteorological data acquisition module and a meteorological data preprocessing module; The meteorological data acquisition module combines the automatic weather station near the low-altitude airspace and the global 1°×1° grid meteorological driving field data to obtain the meteorological data of the low-altitude airspace; The meteorological data preprocessing module performs preliminary missing measurement screening on the acquired meteorological data, and performs singular value filtering processing to eliminate noise and fluctuating data appearing in the meteorological data.

[0013] As a further improvement of the present technical solution, the airspace longitude and latitude extraction module obtains the preset flight route of the UAV using ArcGIS software to extract the longitude and latitude.

[0014] As a further improvement of the technical solution, the method for designing WRF mode area grids at different time intervals in the WPR mode area design module includes the following steps: S1. Combine the preset flight routes of the drone and set the corresponding interval time; S2, simulating the fixed route grid corresponding to the preset flight route of the drone and the mobile grid under the flight state of the drone according to the preset flight route of the drone; S3, using Shell script to write mobile grid parameters, and embed the mobile grid corresponding to each time into the airspace of the fixed route grid; S4. Change the longitude and latitude of the center of the mobile grid into the longitude and latitude of the airspace where the UAV is expected to arrive.

[0015] As a further improvement of the technical solution, the method of using Shell script to write the moving grid parameters in S3 includes the following steps: S301, estimate the total flight time, extract the longitude and latitude corresponding to the route hour by hour, and write them into a text file in the order of "year", "month", "day", "hour", "longitude" and "latitude" in rows; S302, write a Shell script, read the "year", "month", "day", "hour", "longitude", and "latitude" information written in the text file line by line, and perform loop processing; S303, write a Shell script, generate a WPS namelist file in the script, and call the "year", "month", "day", and "hour" values ​​read from the text file under the start_date parameter; Call the "year", "month", and "day" values ​​under the end_date parameter, and add 1 to the "hour" value; Call the "longitude" and "latitude" values ​​under the ref_lat, ref_lon, truelat1 and stand_lon parameters; S304, running WPS, initializing the meteorological data in the low-altitude area and interpolating it into the model grid area; S305, write a Shell script, and generate a WRF mode namelist file in the script; In the "start_year", "start_month", "start_day" and "start_hour" parameters under the "time_control" parameter section, call the "year", "month", "day" and "hour" values ​​read from the text file; Read the "year", "month", and "day" values ​​under "end_year", "end_month", and "end_day", and add 1 to the "hour" value in "end_hour"; S306, setting the integration step of "time_step"; S307, obtaining initial field and boundary field driving data in the WRF mode, and running the WRF mode according to the initial field and boundary field driving data; S308, running the WRF mode to complete the first time cycle.

[0016] As a further improvement of the present technical solution, the initial field and boundary field driving data in S307 are 0.25° data of the global forecast system of the US Environmental Forecast Center.

[0017] As a further improvement of the technical solution, the method for the prediction mode planning module to select a physical parameterization scheme adapted to the preset flight route of the drone includes the following steps: S10, using the WRF model system to obtain the physical parameter content of different regions; S20, identifying the area the drone passes through based on the preset flight route of the drone; S30, matching the physical parameterization scheme corresponding to the preset route through the area it passes through.

[0018] As a further improvement of the technical solution, the regional high-resolution numerical weather forecast system in the prediction model planning module adopts terrain following mass η coordinates, and the vertical level is set to 63 layers using the nonlinear hyperbolic tangent method.

[0019] Compared with the prior art, the present invention has the following beneficial effects: In the adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system, the WPR mode regional design module is combined with the preset UAV flight route to design the WRF mode regional grid at different time intervals, and the prediction mode planning module is configured through the regional high-resolution numerical weather forecast system to select the physical parameterization scheme and mode vertical encryption layering that are adapted to the preset UAV flight route. For short-term and imminent meteorological forecast warnings, the forecast range is narrowed and the horizontal resolution of the grid is improved. The inner nested grid domain is moved at each time, and there is no need to simulate and forecast the area outside the flight route, which greatly reduces the integration operation time and can effectively forecast various weather conditions on the low-altitude airspace aircraft route. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall system structure block diagram of the present invention; Figure 2 It is a schematic diagram of the Beijing-Shanghai low-altitude flight route of the present invention and a simulation diagram of the first-level grid area range.

[0021] The meaning of each number in the figure is: 10. Meteorological data collection module; 20. Meteorological data preprocessing module; 30. UAV route acquisition module; 40. Airspace longitude and latitude extraction module; 50. WPR mode area design module; 60. Prediction model planning module. DETAILED DESCRIPTION

[0022] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, an adaptive mobile grid UAV low-altitude meteorological precision simulation and support system is provided, including a meteorological data management platform, a UAV route acquisition module 30, an airspace longitude and latitude extraction module 40, a WPR mode area design module 50, a prediction mode planning module 60 and a low-altitude meteorological element forecasting module 70; The meteorological data management platform is used to obtain meteorological data in the low-altitude flight area of ​​drones; The drone route acquisition module 30 is used to acquire the preset flight route of the drone; The airspace longitude and latitude extraction module 40 extracts the longitude and latitude on the route in combination with the preset route of the UAV flight; The WPR mode area design module 50 designs the WRF mode area grids at different time intervals in combination with the preset flight route of the UAV, simulates the fixed route grid corresponding to the preset flight route of the UAV and the mobile grid under the flight state of the UAV, performs grid nesting processing, and automatically locates the longitude and latitude of the center of the mobile grid according to the longitude and latitude on the route; The prediction model planning module 60 selects the physical parameterization scheme and model vertical encryption layering adapted to the preset flight route of the UAV through the regional high-resolution numerical weather forecast system configuration; The low-altitude meteorological element forecasting module 70 combines the simulated fixed route grid and the mobile grid, matches the meteorological data of the corresponding area, and performs real-time forecasting of meteorological data during the UAV flight period.

[0024] When using it, during the process of processing the low-altitude meteorological information of the drone, firstly, the meteorological data in the low-altitude flight area of ​​the drone is obtained through the meteorological data management platform, including temperature, humidity, wind speed, wind direction, visibility and precipitation, etc., which will affect the flight status of the drone; After the meteorological data collection is completed, in order to perform positioning prediction processing, it is necessary to obtain the preset flight route of the drone through the drone route acquisition module 30, and extract the longitude and latitude on the route through the airspace longitude and latitude extraction module 40 as the flight position points of the positioning drone in different time periods; During the simulation process, the WPR mode area design module 50 is combined with the preset flight route of the UAV to design the WRF mode area grids at different time intervals, where WRF is the abbreviation of (Weather Research and Forecasting Model), that is, the mesoscale weather forecast. WRF is used as a substitute to simulate the fixed route grid corresponding to the preset flight route of the UAV and the mobile grid under the flight state of the UAV, and automatically locate the longitude and latitude of the center of the mobile grid according to the longitude and latitude on the route, where the fixed route grid is a coarse resolution grid, and the fixed route grid contains a rectangular grid of the entire flight path, and in the entire simulation and forecasting stage, the grid does not move, and the mobile grid is a fine resolution grid. Every 1 hour, the grid starts to move, and the longitude and latitude of the center of the grid is changed to the longitude and latitude of the airspace where the UAV is expected to arrive, and is combined with the longitude and latitude on the extracted route for real-time update; Since the flight routes of drones pass through different areas, the corresponding flight environments are different. Therefore, in order to adapt to different flight environments, the prediction model planning module 60 needs to select the physical parameterization scheme and model vertical encryption layering that are suitable for the preset flight routes of drones through the regional high-resolution numerical weather forecast system configuration. Different route flight environments correspond to different physical parameterization schemes, and corresponding vertical encryption layering is performed. The final WRF model regional grid is simulated according to the corresponding physical parameters. Finally, the low-altitude meteorological element forecast module 70 combines the simulated fixed route grid and mobile grid to match the meteorological data of the corresponding area, and performs real-time forecast of meteorological data during the drone flight time period. The longitude and latitude of the center of the mobile grid are changed to the longitude and latitude of the aircraft's expected arrival in the airspace, that is, at this time, the meteorological data corresponding to the updated longitude and latitude of the center of the mobile grid is forecasted.

[0025] In addition, the meteorological data management platform includes a meteorological data collection module 10 and a meteorological data preprocessing module 20; The meteorological data collection module 10 combines the automatic weather station near the low-altitude airspace and the global 1°×1° grid meteorological driving field data to obtain the meteorological data of the low-altitude airspace; The meteorological data preprocessing module 20 performs preliminary missing measurement screening on the acquired meteorological data, and performs singular value filtering processing to eliminate noise and fluctuating data appearing in the meteorological data.

[0026] Furthermore, the airspace longitude and latitude extraction module 40 obtains the preset flight route of the drone using ArcGIS software to extract the longitude and latitude.

[0027] The specific extraction steps are as follows: The first step is to convert the route data format to ensure that the route data format is vector format and contains the geometric information of the route; The second step is to adjust the attribute table in ArcGIS software, add longitude and latitude fields, and perform partition storage, one for longitude and one for latitude; Step 3. In the attribute table, click the longitude field, select Calculate Geometry, select the X coordinate as the attribute, and select the appropriate coordinate system. The same operation applies to the latitude field, and you need to select the Y coordinate as the attribute.

[0028] Furthermore, the method for designing WRF mode area grids at different time intervals in the WPR mode area design module 50 includes the following steps: S1. Combine the preset flight routes of the drone and set the corresponding interval time; S2, simulating the fixed route grid corresponding to the preset flight route of the drone and the mobile grid under the flight state of the drone according to the preset flight route of the drone; S3, using Shell script to write mobile grid parameters, and embed the mobile grid corresponding to each time into the airspace of the fixed route grid; S4. Change the longitude and latitude of the center of the mobile grid into the longitude and latitude of the airspace where the UAV is expected to arrive.

[0029] When using it specifically, in the process of designing the regional grid of the WRF mode, it is first necessary to combine the preset flight route of the drone and formulate the corresponding interval time. This scheme adopts the design method of 1 hour interval, that is, for different flight times, a double nested simulation area with different longitude and latitude of the route center is designed every 1 hour. The first nested grid corresponds to the fixed route grid, which is a coarse resolution grid with a height of 5km horizontally, including the entire preset flight route of the drone, and is a rectangular grid. During the processing, the entire first nested grid does not move; The second nested grid corresponds to the mobile grid, which is a fine-resolution grid with a height of 1 km horizontally. The mobile grid parameters are written using Shell scripts so that the mobile grid moves along the airspace in the fixed route grid every 1 hour. At the same time, in order to better locate the drone, this solution changes the longitude and latitude of the grid center in the mobile grid to the longitude and latitude of the drone's expected arrival airspace location, and updates the longitude and latitude in real time.

[0030] Specifically, the method of using Shell script to write mobile grid parameters in S3 includes the following steps: S301, estimate the total flight time, extract the longitude and latitude corresponding to the route hour by hour, and write them into a text file in the order of "year", "month", "day", "hour", "longitude" and "latitude" in rows; S302, write a Shell script, read the "year", "month", "day", "hour", "longitude", and "latitude" information written in the text file line by line, and perform loop processing; S303, write a Shell script, generate a WPS namelist file in the script, and call the "year", "month", "day", and "hour" values ​​read from the text file under the start_date parameter; Call the "year", "month", and "day" values ​​under the end_date parameter, and add 1 to the "hour" value; Call the "longitude" and "latitude" values ​​under the ref_lat, ref_lon, truelat1 and stand_lon parameters; S304, running WPS, initializing the meteorological data in the low-altitude area and interpolating it into the model grid area; S305, write a Shell script, and generate a WRF mode namelist file in the script; In the "start_year", "start_month", "start_day" and "start_hour" parameters under the "time_control" parameter section, call the "year", "month", "day" and "hour" values ​​read from the text file; Read the "year", "month", and "day" values ​​under "end_year", "end_month", and "end_day", and add 1 to the "hour" value in "end_hour"; S306, setting the integration step of "time_step"; S307, obtaining initial field and boundary field driving data in the WRF mode, and running the WRF mode according to the initial field and boundary field driving data; S308, running the WRF mode to complete the first time cycle.

[0031] When used specifically, in the process of writing mobile grid parameters through Shell script, since the total time spent on different routes is different and the flight date is also different, the corresponding meteorological data will be different. Therefore, it is necessary to estimate the total flight time first and extract the longitude and latitude corresponding to the route hour by hour. The present invention divides the table into "year", "month", "day", "hour", "longitude" and "latitude", and writes it into a text file in row arrangement order, and then writes a Shell script to read the "year", "month", "day", "hour", "longitude" and "latitude" information written in the text file for each line, and perform loop processing, and continue to write the Shell script, generate a WPS (WRF Preprocessing System) in the script, that is, a data preprocessing program, a namelist file, and call the "year", "month", "day" and "hour" values ​​read from the text file under the start_date parameter; Call the "year", "month", and "day" values ​​under the end_date parameter, and add 1 to the "hour" value; Call the "longitude" and "latitude" values ​​under the ref_lat, ref_lon, truelat1 and stand_lon parameters; And run WPS to initialize the meteorological data in the low-altitude area and interpolate it into the model grid area. The meteorological data include the high-resolution terrain field in the low-altitude area, the initial atmospheric field and the static surface data (the underlying vegetation type, soil type, terrain height and vegetation coverage); Then write the Shell script again and generate the WRF mode namelist file in the script; In the "start_year", "start_month", "start_day" and "start_hour" parameters under the "time_control" parameter section, call the "year", "month", "day" and "hour" values ​​read from the text file; Read the "year", "month" and "day" values ​​under "end_year", "end_month" and "end_day", and add 1 to the "hour" value in "end_hour", and set the integration step of "time_step". Finally, obtain the initial field and boundary field driving data in WRF mode, and run the WRF mode according to the initial field and boundary field driving data. Run the WRF mode to complete the first time cycle. Table 1 shows the nested grid parameters of the forecast area; Table 1 Nested grid parameters in forecast area Grid Domain Forecast time Center longitude and latitude Number of grid points Horizontal grid spacing (km) Integration time step (s) Terrain resolution 1 01、02、03、04、05、06、07、08、09、10 119°E, 35.5°N 130×225 5 30 10 minutes 2 01 117.09°E, 39.6°N 125×125 1 6 30 seconds 2 02 118.00°E, 38.6°N 125×125 1 6 30 seconds 2 03 117.90°E, 37.6°N 125×125 1 6 30 seconds 2 04 117.80°E, 36.6°N 125×125 1 6 30 seconds 2 05 118.40°E, 35.6°N 125×125 1 6 30 seconds 2 06 119.30°E, 34.6°N 125×125 1 6 30 seconds 2 07 120.59°E, 33.6°N 125×125 1 6 30 seconds 2 08 120.40°E, 32.6°N 125×125 1 6 30 seconds 2 09 120.80°E, 31.6°N 125×125 1 6 30 seconds 2 10 120.80°E, 31.1°N 125×125 1 6 30 seconds This table simulates the low-altitude area in the Beijing-Tianjin-Hebei region. The corresponding grid domain 1 is the first nested grid, with a horizontal grid spacing of 5km. The longitude and latitude of the grid center is in the middle of the Beijing-Shanghai route (119°E, 35.5°N). During the entire simulation forecast period, the first grid runs for 10 times, including all low-altitude flight time periods. Grid domain 2 is the second nested grid, with a grid center longitude and latitude of 117.09°E, 39.6°N, and a grid horizontal resolution of 1km, which only includes the first flight time. The following simulation times are analogous to this, until the 10th time, when the grid center longitude and latitude are 120.80°E, 31.1°N.

[0032] In addition, the initial and boundary field driving data in S307 are 0.25° data of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). The data at the initial moment are the assimilated analysis fields, and the data at subsequent times are all forecast fields. The forecast field includes 3-hour interval forecast data for the next 10 days. The time for each forecast release is 00, 06, 12 and 18 hours (UTC) every day.

[0033] Furthermore, the method for the prediction mode planning module 60 to select a physical parameterization scheme suitable for the preset flight route of the drone includes the following steps: S10, using the WRF model system to obtain the physical parameter content of different regions; S20, identifying the area the drone passes through based on the preset flight route of the drone; S30, matching the physical parameterization scheme corresponding to the preset route through the area it passes through.

[0034] During specific use, in the process of selecting the physical parameterization scheme, it is necessary to adapt to the area through which the UAV flight preset route passes. First, the WRF mode system is used to obtain the physical parameter content of different areas. The WRF mode provides a variety of physical parameterization schemes to choose from, including microphysical schemes, longwave and shortwave schemes, near-surface layer, planetary boundary layer schemes, cumulus convection parameterization schemes, land surface process parameterization schemes, and urban near-surface layer schemes. Combined with the UAV flight preset route, the area it passes through is identified, and the physical parameterization scheme is matched according to the UAV flight preset route.

[0035] Depend on Figure 2As shown in the figure, taking the Beijing-Shanghai low-altitude route test area as an example, the simulated first-level grid area includes the entire North China region, with a latitude and longitude range of 30-40°N, 115°-122°E. The terrain in this area is relatively flat, with only a few hilly areas and an altitude of about 0-200m. This route starts from Beijing, passes through Tianjin, Shandong hills, and the eastern coastal areas of Jiangsu Province, and finally reaches Shanghai. There are scattered towns in this area, most of which are farmland, woodland and farmland, dry land and grassland. The northern and western areas of this area are mostly mountainous, and the western and northern areas are distributed with deciduous broad-leaved forests and coniferous forests, which is also consistent with the actual underlying surface type.

[0036] Furthermore, the regional high-resolution numerical weather forecast system in the forecast model planning module 60 adopts terrain-following quality η coordinates, which is a commonly used technical means in numerical weather forecasting and climate models. It allows the bottom layer of the model to closely follow the undulations of the ground, thereby more accurately describing the meteorological phenomena near the ground. In this coordinate system, the vertical coordinate is usually recorded as η, which is a dimensionless variable that gradually changes from the ground surface (η=1) to the top of the model (η=0). In the terrain-following coordinate system, meteorological elements (such as temperature, humidity, and wind speed) are expressed as functions of spatial coordinates (x, y, η) and time t, that is, , and the vertical layer is set to 63 layers using the nonlinear hyperbolic tangent method.

[0037] Wherein, (η value is set to: 1.000, 0.9988, 0.9975, 0.9963, 0.995, 0.9938, 0.9926, 0.9913, 0.9901, 0.9889, 0.9876, 0.9864, 0.9849, 0.9831, 0.9809, 0.9783, 0.9752, 0.9715, 0.9672, 0.9622, 0.9564, 0.9497, 0.9419, 0.9329, 0.9227, 0.911, 0.8977, 0.8828, 0.8661, 0.8474, 0.8267, 0.804, 0.7793 , 0.7525, 0.7238, 0.6932, 0.661, 0.6274, 0.5927, 0.5571, 0.521, 0.4848, 0.4487, 0.413, 0.3781, 0.3442, 0.3115, 0.2802, 0.2505, 0.2225, 0.1961, 0.1692, 0.1466, 0.1261, 0.1076, 0.0909, 0.0758, 0.0623, 0.0501, 0.0392, 0.0295, 0.0208, 0.0131, 0.0061, 0.000), the model top atmospheric pressure is 50hPa.

[0038] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Adaptive mobile grid UAV low-altitude meteorological precision simulation and support system, characterized by: It includes a meteorological data management platform, a UAV route acquisition module (30), an airspace latitude and longitude extraction module (40), a WPR model area design module (50), a forecast model planning module (60) and a low-altitude meteorological element forecast module (70); The meteorological data management platform is used to obtain meteorological data in the low-altitude flight area of ​​the UAV; The drone route acquisition module (30) is used to acquire a preset flight route of the drone; The airspace longitude and latitude extraction module (40) extracts the longitude and latitude on the route in combination with the preset flight route of the drone; The WPR mode area design module (50) designs WRF mode area grids at different time intervals in combination with the preset flight route of the UAV, simulates the fixed route grid corresponding to the preset flight route of the UAV and the mobile grid under the UAV flight state, performs grid nesting processing, and automatically locates the longitude and latitude of the center of the mobile grid according to the longitude and latitude on the route; The prediction model planning module (60) selects a physical parameterization scheme and a model vertical encryption layering adapted to a preset flight route of the unmanned aerial vehicle through the regional high-resolution numerical weather forecast system configuration; The low-altitude meteorological element forecasting module (70) combines the simulated fixed route grid and the mobile grid, matches the meteorological data of the corresponding area, and performs real-time forecasting of meteorological data during the flight time period of the unmanned aerial vehicle.

2. The adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system according to claim 1 is characterized by: The meteorological data management platform comprises a meteorological data collection module (10) and a meteorological data preprocessing module (20); The meteorological data acquisition module (10) combines the automatic weather station near the low-altitude airspace and the global 1°×1° grid meteorological driving field data to obtain the meteorological data of the low-altitude airspace; The meteorological data preprocessing module (20) performs preliminary missing measurement screening on the acquired meteorological data and performs singular value filtering processing.

3. The adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system according to claim 1 is characterized by: The airspace longitude and latitude extraction module (40) obtains the preset flight route of the drone using ArcGIS software to extract the longitude and latitude.

4. The adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system according to claim 1 is characterized by: The method for designing WRF mode area grids at different time intervals in the WPR mode area design module (50) comprises the following steps: S1. Combine the preset flight routes of the drone and set the corresponding interval time; S2, simulating the fixed route grid corresponding to the preset flight route of the drone and the mobile grid under the flight state of the drone according to the preset flight route of the drone; S3, using Shell script to write mobile grid parameters, and embed the mobile grid corresponding to each time into the airspace of the fixed route grid; S4. Change the longitude and latitude of the center of the mobile grid into the longitude and latitude of the airspace where the UAV is expected to arrive.

5. The adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system according to claim 4 is characterized by: In S3, the method of using Shell script to write moving grid parameters includes the following steps: S301, estimate the total flight time, extract the longitude and latitude corresponding to the route hour by hour, and write it into a text file in the order of "year", "month", "day", "hour", "longitude", and "latitude" in row arrangement; S302, write a Shell script, read the "year", "month", "day", "hour", "longitude", "latitude" information written in the text file for each line, and perform loop processing; S303, write a Shell script, generate a WPS namelist file in the script, and call the "year", "month", "day", and "hour" values ​​read from the text file under the start_date parameter; Call the "year", "month", and "day" values ​​under the end_date parameter, and add 1 to the "hour" value; Call the "longitude" and "latitude" values ​​under the ref_lat, ref_lon, truelat1, and stand_lon parameters; S304, running WPS, initializing the meteorological data in the low-altitude area and interpolating it into the model grid area; S305, write a Shell script, and generate a WRF mode namelist file in the script; In the "start_year", "start_month", "start_day" and "start_hour" parameters under the "time_control" parameter section, call the "year", "month", "day", and "hour" values ​​read from the text file; Read the "year", "month", and "day" values ​​under "end_year", "end_month", and "end_day", and add 1 to the "hour" value in "end_hour"; S306, set the integration step size of "time_step"; S307, obtaining initial field and boundary field driving data in the WRF mode, and running the WRF mode according to the initial field and boundary field driving data; S308, running the WRF mode to complete the first time cycle.

6. The adaptive mobile grid UAV low-altitude meteorological precision simulation and support system according to claim 5 is characterized by: In S307, the initial field and boundary field driving data are 0.25° data of the global forecast system of the US Environmental Prediction Center.

7. The adaptive mobile grid UAV low-altitude meteorological precision simulation and guarantee system according to claim 1 is characterized by: The method for the prediction mode planning module (60) to select a physical parameterization scheme suitable for the preset flight route of the unmanned aerial vehicle comprises the following steps: S10, using the WRF model system to obtain the physical parameter content of different regions; S20, identifying the area the drone passes through based on the preset flight route of the drone; S30, matching the physical parameterization scheme corresponding to the preset route through the area it passes through.

8. The adaptive mobile grid UAV low-altitude meteorological precision simulation and support system according to claim 7 is characterized by: The regional high-resolution numerical weather forecast system in the prediction model planning module (60) adopts terrain following mass η coordinates, and the vertical level is set to 63 layers using a nonlinear hyperbolic tangent method.

Images