A method for quickly correcting meteorological grids using unmanned aerial vehicle meteorological detection data

Abstract

The present invention belongs to the technical field of meteorological grids, and specifically discloses a method for quickly correcting meteorological grids by using unmanned aerial vehicle (UAV) meteorological detection data. The method includes: establishing a meteorological grid data set based on the meteorological grid data monitored by meteorological satellites, and then collecting position information and meteorological detection data by using a UAV to generate meteorological detection correction data, thereby constructing a rapid correction model for meteorological detection data. At the same time, the meteorological grid data set is imported into the rapid correction model for meteorological detection data to obtain corrected meteorological grid data, and accordingly, the correction evaluation of the meteorological grid data is carried out and corresponding feedback is provided. By using the meteorological grid data monitored by meteorological satellites and the meteorological detection data monitored by a UAV, the present invention corrects the meteorological grid data set, thereby avoiding the limitations of single data. At the same time, real-time meteorological data monitoring is carried out by using a UAV, ensuring the timeliness of obtaining meteorological data.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological grids, and relates to a method for quickly correcting meteorological grids by using unmanned aerial vehicle (UAV) meteorological detection data. Background Technique

[0002] A meteorological grid is a data set containing multiple meteorological elements constructed by a high-resolution numerical weather prediction system according to a certain horizontal grid spacing and vertical stratification. Although the meteorological grid is based on a numerical prediction system, there may be errors or it may not be accurate enough. Therefore, in order to ensure the accuracy of meteorological grid data, it is necessary to quickly correct it with the meteorological data detected by a UAV.

[0003] For example, Chinese Patent No. CN114564487A discloses a method for updating meteorological grid data by combining forecasting and prediction. The present invention discloses a method for updating meteorological grid data by combining forecasting and prediction. This method first obtains meteorological grid data from a server and defines the time series of the meteorological grid data. Then, a prediction model is established through stationary detection, model identification selection, model order determination, and model parameter estimation. Finally, the future meteorological grid data is predicted to achieve data update. The present invention uses a time series model to predict meteorological grid data, analyzes historical observation data to predict future meteorological data, and increases the accuracy of prediction according to the time dependence between data.

[0004] The above existing technologies have the following deficiencies: 1. Currently, meteorological grid data is only generated based on meteorological data monitored by meteorological satellites, and no further correction analysis of the meteorological grid data is carried out by collecting meteorological detection data with a UAV, resulting in the limitation of single data, and thus unable to ensure the accuracy of meteorological data and the comprehensiveness of meteorological data.

[0005] 2. Currently, meteorological grid data is monitored by meteorological satellites at fixed intervals, which cannot ensure the timeliness of obtaining meteorological data, and thus cannot ensure the real-time nature of meteorological data, further reducing the accuracy of subsequent meteorological-related decisions.

[0006] 3. When monitoring meteorological data currently, no further confirmation and analysis are carried out on the accuracy of meteorological data monitoring points, and the accuracy of the collected data cannot be ensured, further leading to the difficulty of subsequent data processing, thus affecting the spatial continuity and integrity of the data, and at the same time reducing the overall quality and usability of meteorological data. Summary of the Invention

[0007] In view of this, in order to solve the problems raised in the above background technique, a method for quickly correcting meteorological grids by using UAV meteorological detection data is proposed.

[0008] The object of the present invention can be achieved by the following technical solutions: The present invention provides a method for quickly correcting meteorological grids using unmanned aerial vehicle (UAV) meteorological detection data, including: Step 1, establishment of a meteorological grid dataset: Extract meteorological grid data monitored by meteorological satellites, and then establish a meteorological grid dataset through a high-resolution numerical weather prediction system.

[0009] Step 2, monitoring of meteorological detection data: Extract the flight data of the UAV, and then monitor the position information and meteorological detection data of the UAV at each meteorological monitoring point. The position information includes longitude coordinates, latitude coordinates, and altitude. The meteorological detection data includes temperature, pressure, humidity, wind speed, and wind direction angle.

[0010] Step 3, analysis of meteorological detection positions: Analyze the position accuracy of each meteorological monitoring point according to the position information of the UAV at each meteorological monitoring point.

[0011] Step 4, confirmation of meteorological detection positions: Confirm the accurate meteorological monitoring points at each position according to the position accuracy of each meteorological monitoring point.

[0012] Step 5, generation of meteorological detection corrections: Generate meteorological detection correction data according to the meteorological detection data of the accurate meteorological monitoring points at each position.

[0013] Step 6, correction of meteorological grid data: Construct a rapid correction model for meteorological detection data according to the meteorological detection correction data, and import the meteorological grid dataset into the rapid correction model for meteorological detection data to perform correction of the meteorological grid dataset and obtain corrected meteorological grid data.

[0014] Step 7, evaluation of meteorological grid corrections: Perform an evaluation of the correction of the meteorological grid data according to the corrected meteorological grid data to obtain the qualification degree of the correction of the meteorological grid data.

[0015] Step 8, feedback of meteorological grid data: Perform corresponding feedback according to the qualification degree of the correction of the meteorological grid data.

[0016] Preferably, the analysis of the position accuracy of each meteorological monitoring point includes: Denote the longitude coordinates, latitude coordinates, and altitude of the UAV at each meteorological monitoring point as , and , is the meteorological monitoring point number, .

[0017] Subtract the longitude coordinates, latitude coordinates, and altitude of each meteorological monitoring point from their set longitude coordinates, set latitude coordinates, and set altitude respectively to obtain the longitude difference, latitude difference, and altitude difference of each meteorological monitoring point, and denote them as , and 。

[0018] Statistically analyze the comprehensive position error of each meteorological monitoring point , 。

[0019] Statistically analyze the position accuracy of each meteorological monitoring point , 。

[0020] Preferably, the step of identifying each meteorological monitoring point with accurate position includes: comparing the position accuracy of each meteorological monitoring point with the set reference position accuracy.

[0021] If the position accuracy of a certain meteorological monitoring point is greater than or equal to the set reference position accuracy, then mark this meteorological monitoring point as a meteorological monitoring point with accurate position, and thus obtain each meteorological monitoring point with accurate position.

[0022] Preferably, the step of generating meteorological detection correction data includes: A1. Extract the meteorological detection data of the unmanned aerial vehicle at each meteorological monitoring point with accurate position from the meteorological detection data of the unmanned aerial vehicle at each meteorological monitoring point, and then statistically analyze the meteorological change rate of the meteorological detection data.

[0023] A2. Extract the temperature of each meteorological monitoring point with accurate position from the meteorological detection data of the unmanned aerial vehicle at each meteorological monitoring point with accurate position, and then confirm the monitored temperature of each meteorological grid point , Number the meteorological grid points 。

[0024] A3. Extract the air pressure, humidity, wind speed and wind direction angle of each meteorological monitoring point with accurate position from the meteorological detection data of the unmanned aerial vehicle at each meteorological monitoring point with accurate position.

[0025] A4. Similarly confirm the monitored air pressure, monitored humidity, monitored wind speed and monitored wind direction angle of each meteorological grid point according to the confirmation method of the monitored temperature of each meteorological grid point.

[0026] A5. Take the meteorological change rate of the meteorological detection data and the monitored temperature, monitored air pressure, monitored humidity, monitored wind speed and monitored wind direction angle of each meteorological grid point as the meteorological detection correction data column.

[0027] Preferably, the step of statistically analyzing the meteorological change rate of the meteorological detection data includes: extracting the temperature of each meteorological monitoring point with accurate position from the meteorological detection data of the unmanned aerial vehicle at each meteorological monitoring point with accurate position, and denoting it as , is the number of the meteorological monitoring point with accurate position 。

[0028] The temperature of each accurate meteorological monitoring point is subtracted from the temperature of its next accurate meteorological monitoring point to obtain the temperature difference of each accurate meteorological monitoring point, denoted as .

[0029] The monitoring interval duration is extracted from the flight data of the unmanned aerial vehicle, and then the monitoring interval duration between each accurate meteorological monitoring point and its next accurate meteorological monitoring point is extracted, denoted as .

[0030] Statistical temperature change rate of meteorological detection data , , is the number of accurate meteorological monitoring points at the location.

[0031] The air pressure, humidity, wind speed and wind direction angle of the unmanned aerial vehicle at each accurate meteorological monitoring point are extracted from the meteorological detection data of the unmanned aerial vehicle at each accurate meteorological monitoring point, and then the air pressure change rate, humidity change rate, wind speed change rate and wind direction angle change rate of the meteorological detection data are analyzed in the same way according to and are denoted as , , and .

[0032] Taking , , , and as the meteorological change rate of the meteorological detection data.

[0033] Preferably, the monitoring temperature of each meteorological grid point includes: extracting the longitude coordinate, latitude coordinate and altitude of each meteorological grid point from the meteorological grid dataset, and denoting them as , and , and denoting the longitude coordinate, latitude coordinate and altitude of each accurate meteorological monitoring point as , and .

[0034] Statistical Euclidean distance between each accurate meteorological monitoring point and each meteorological grid point , .

[0035] Statistical distance weight between each accurate meteorological monitoring point and each meteorological grid point , .

[0036] Statistical monitoring temperature of each meteorological grid point , .

[0037] Preferably, the correction evaluation of the meteorological grid data includes: S1. According to the meteorological grid data and the corrected meteorological grid data, the meteorological grid correction accuracy is statistically calculated .

[0038] S2. According to the meteorological grid data and the corrected meteorological grid data, the meteorological grid correction stability is statistically calculated .

[0039] S3. According to the corrected meteorological grid data, the meteorological grid correction smoothness is statistically calculated .

[0040] S4. The qualified degree of the correction evaluation of the meteorological grid data is statistically calculated , , , and are the weights of the set meteorological grid correction accuracy, meteorological grid correction stability, and meteorological grid correction smoothness respectively, , .

[0041] Preferably, the statistical calculation of the meteorological grid correction accuracy includes: extracting the temperature of each meteorological grid point from the meteorological grid data, denoted as , and extracting the corrected temperature of each meteorological grid point from the corrected meteorological grid data, denoted as .

[0042] The mean absolute error of the meteorological grid corrected temperature is statistically calculated , , is the number of meteorological grid points.

[0043] The root mean square error of the meteorological grid corrected temperature is statistically calculated , .

[0044] The temperature accuracy of the meteorological grid correction is statistically calculated , , and are the set reference mean absolute error and root mean square error respectively.

[0045] According to the statistical method of , the air pressure accuracy, humidity accuracy, wind speed accuracy, and wind direction angle accuracy of the meteorological grid correction are statistically calculated in the same way, and are denoted as , , and .

[0046] The meteorological grid correction accuracy is statistically calculated , .

[0047] Preferably, the statistical meteorological grid correction stability includes: taking for mean calculation to obtain the average meteorological grid correction temperature, denoted as .

[0048] The temperature standard deviation of the statistical meteorological grid correction , .

[0049] The temperature stability of the statistical meteorological grid correction , , is the temperature standard deviation set as a reference, is the natural constant.

[0050] According to the statistical method, the air pressure stability, humidity stability, wind speed stability and wind direction angle stability of the meteorological grid correction are statistically obtained in the same way, and are respectively denoted as , , and .

[0051] Select the minimum value from , , , and as the meteorological grid correction stability, and denote it as .

[0052] Preferably, the statistical meteorological grid correction smoothness includes: taking the difference between the temperature of each meteorological grid point and the temperature of its adjacent meteorological grid points respectively to obtain the temperature difference between each meteorological grid point and its adjacent meteorological grid points, and taking the absolute value to obtain the absolute value of the temperature difference.

[0053] Obtain the Euclidean distance between each meteorological grid point and its adjacent meteorological grid points through the Euclidean distance formula.

[0054] Take the ratio of the absolute value of the temperature difference between each meteorological grid and its adjacent meteorological grid points to the Euclidean distance as the meteorological grid temperature change rate.

[0055] If the meteorological grid temperature change rate between a certain meteorological grid and its adjacent meteorological grid point is less than the set reference meteorological grid temperature change rate, then mark the meteorological grid point and its adjacent meteorological grid point as temperature-smooth adjacent meteorological grids, and count the number of temperature-smooth adjacent meteorological grids, denoted as .

[0056] Count the number of adjacent meteorological grids, denoted as , take the and ratio as the temperature smoothness for meteorological grid correction .

[0057] In the same way as the statistical method of , statistically obtain the air pressure smoothness, humidity smoothness, wind speed smoothness and wind direction angle smoothness for meteorological grid correction, and record them as , , and respectively.

[0058] Select the minimum value from , , , and as the meteorological grid correction smoothness, and record it as .

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By correcting the meteorological grid data set based on the meteorological grid data monitored by meteorological satellites and the meteorological detection data monitored by unmanned aerial vehicles, the present invention avoids the deficiency of current meteorological grid data correction without collecting meteorological data by unmanned aerial vehicles, thus avoiding the limitation of single data, realizing the comprehensive analysis of meteorological grid data and meteorological detection data, further ensuring the accuracy of meteorological data, and at the same time ensuring the comprehensiveness of meteorological data.

[0060] (2) By using unmanned aerial vehicles to monitor real-time meteorological data, the present invention breaks through the deficiency of current meteorological grid data monitoring with a fixed cycle by meteorological satellites, ensures the timeliness of meteorological data acquisition, thus ensuring the real-time nature of meteorological data, and further improving the accuracy of subsequent meteorological-related decisions.

[0061] (3) By analyzing the position accuracy of each meteorological monitoring point according to the position information of the unmanned aerial vehicle at each meteorological monitoring point, the present invention avoids the deficiency of current failure to further confirm and analyze the accuracy of meteorological data monitoring points, ensures the effectiveness of the collected data, further reduces the difficulty of subsequent data processing, thus improving the spatial continuity and integrity of the data, and at the same time improving the overall quality and usability of meteorological data.

[0062] (4) By confirming the accurate meteorological monitoring points at each position according to the position accuracy of each meteorological monitoring point, the present invention reduces the interference of position deviation points, further improves the accuracy of meteorological detection data, and thus enhances the reliability of subsequent meteorological data analysis. Brief Description of the Drawings

[0063] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0064] Figure 1 It is a schematic connection diagram of each step of the method of the present invention.

[0065] Figure 2 It is a schematic connection diagram of the steps for generating the meteorological detection correction data of the present invention.

[0066] Figure 3 It is a schematic connection diagram of the process for correcting meteorological grid data of the present invention. Detailed implementation manners

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0068] Please refer to Figure 1 and Figure 3 As shown, the present invention provides a method for quickly correcting meteorological grids using unmanned aerial vehicle (UAV) meteorological detection data. The method includes: Step 1, establishing a meteorological grid data set: extracting meteorological grid data monitored by meteorological satellites, and then establishing a meteorological grid data set through a high-resolution numerical weather prediction system.

[0069] It should be added that the high-resolution numerical weather prediction system is an advanced weather prediction tool. It is based on a system of partial differential equations constructed by the principles of atmospheric dynamics and thermodynamics. After discretizing the equations using numerical methods, it uses high-performance computers to solve for weather prediction. Its feature lies in "high resolution", with an extremely small horizontal grid spacing and fine vertical stratification in space, like a fine net that can accurately capture the fine structure of the atmosphere.

[0070] It should be added that the meteorological grid data set includes: the longitude coordinates, latitude coordinates, and altitude of each meteorological grid point.

[0071] Step 2, monitoring meteorological detection data: extracting the flight data of the UAV, and then monitoring the position information and meteorological detection data of the UAV at each meteorological monitoring point. The position information includes longitude coordinates, latitude coordinates, and altitude, and the meteorological detection data includes temperature, pressure, humidity, wind speed, and wind direction angle.

[0072] It should be added that the flight data of the drone includes: the set longitude coordinates, set latitude coordinates, set altitude, and monitoring time interval of the drone at each meteorological monitoring point.

[0073] It should be added that the longitude coordinates, latitude coordinates, and altitude of the drone at each meteorological monitoring point are obtained by collecting through the global positioning system carried by the drone. The temperature, air pressure, humidity, wind speed, and wind direction angle of the drone at each meteorological monitoring point are respectively monitored by the corresponding meteorological sensors carried by the drone. For example, the temperature is monitored by the temperature sensor carried by the drone.

[0074] In the embodiment of the present invention, real-time meteorological data monitoring is carried out by the drone, breaking the deficiency of the current fixed-period meteorological grid data monitoring through meteorological satellites, ensuring the timeliness of obtaining meteorological data, thus ensuring the real-time nature of meteorological data, and further improving the accuracy of subsequent meteorological-related decisions.

[0075] Step 3. Meteorological detection position analysis: Analyze the position accuracy of each meteorological monitoring point according to the position information of the drone at each meteorological monitoring point.

[0076] Exemplarily, the analysis of the position accuracy of each meteorological monitoring point includes: respectively recording the longitude coordinates, latitude coordinates, and altitude of the drone at each meteorological monitoring point as 、 and , is the meteorological monitoring point number, 。

[0077] Extract the set longitude coordinates, set latitude coordinates, and set altitude of the drone at each meteorological monitoring point from the flight data of the drone, and respectively record them as 、 and 。

[0078] Subtract the longitude coordinates, latitude coordinates, and altitude of each meteorological monitoring point from their set longitude coordinates, set latitude coordinates, and set altitude respectively to obtain the longitude difference, latitude difference, and altitude difference of each meteorological monitoring point, and respectively record them as 、 and 。

[0079] Statistical comprehensive position error of each meteorological monitoring point , 。

[0080] In a specific embodiment, if the longitude coordinate, latitude coordinate, and altitude of a meteorological monitoring point are 120.567°E, 31.234°N, and 500 meters respectively, and the longitude coordinate, latitude coordinate, and altitude of an adjacent meteorological grid point are 120.565°E, 31.235°N, and 498 meters respectively.

[0081] The longitude difference is , and the latitude difference is , and the altitude difference is meters.

[0082] The distance corresponding to 1° of longitude is meters, so the longitude difference is converted to meters: , being the latitude value.

[0083] If the latitude value is 31.234°N, then meters.

[0084] The distance corresponding to 1° of latitude is 111194.93 meters, so the latitude difference is converted to meters: meters, then meters.

[0085] It should be added that the conversion process of longitude and latitude to meters: Longitude is the angle in the east-west direction of a location on the earth from the prime meridian, ranging from -180° to 180°, and latitude is the angle in the north-south direction of a location on the earth from the equator, ranging from -90° to 90°.

[0086] When performing the conversion of longitude and latitude to meters, the average radius value is usually adopted, and the average radius of the earth is about 6371000 meters.

[0087] At the equator, the distance corresponding to 1° of longitude is meters, where is the radius of the earth, that is meters, so at the equator, 1° of longitude is equivalent to 111319.49 meters. As the latitude increases, the distance between longitudes will gradually decrease. At a latitude of , the distance corresponding to 1° of longitude is meters.

[0088] The distance corresponding to 1° of latitude is relatively fixed, which is the distance along the meridian direction, that is meters, so 1° of latitude is equivalent to 111194.93 meters.

[0089] Statistical comprehensive error of meteorological monitoring points , meters.

[0090] Statistical position accuracy of each meteorological monitoring point , , is the comprehensive position error set as a reference.

[0091] It should be added that is extracted from the technical specification table of rapid correction of meteorological grid for UAV meteorological detection data.

[0092] In the embodiment of the present invention, by analyzing the position accuracy of each meteorological monitoring point according to the position information of the UAV at each meteorological monitoring point, the deficiency that the accuracy of the meteorological data monitoring point is not further confirmed and analyzed currently is avoided, the effectiveness of the collected data is ensured, and thus the difficulty of subsequent data processing is reduced, thereby improving the spatial continuity and integrity of the data, and at the same time improving the overall quality and usability of the meteorological data.

[0093] Step Four: Meteorological detection position confirmation: Confirm the accurate meteorological monitoring points at each position according to the position accuracy of each meteorological monitoring point.

[0094] Exemplarily, the confirmation of the accurate meteorological monitoring points at each position includes: comparing the position accuracy of each meteorological monitoring point with the position accuracy set as a reference.

[0095] If the position accuracy of a certain meteorological monitoring point is greater than or equal to the position accuracy set as a reference, then mark this meteorological monitoring point as an accurate meteorological monitoring point at the position, and thus obtain the accurate meteorological monitoring points at each position.

[0096] In the embodiment of the present invention, by confirming the accurate meteorological monitoring points at each position according to the position accuracy of each meteorological monitoring point, the interference of the position deviation points is reduced, and thus the accuracy of the meteorological detection data is improved, thereby enhancing the reliability of subsequent meteorological data analysis.

[0097] Step Five: Generation of meteorological detection correction: Generate meteorological detection correction data according to the meteorological detection data of the accurate meteorological monitoring points at each position.

[0098] Please refer to Figure 2 shown. Exemplarily, the generation of the meteorological detection correction data includes: A1. Extract the meteorological detection data of the UAV at each accurate meteorological monitoring point from the meteorological detection data of the UAV at each meteorological monitoring point, and then statistically analyze the meteorological change rate of the meteorological detection data.

[0099] Further, the statistical analysis of the meteorological change rate of the meteorological detection data includes: A1-1. Extract the temperature of each accurate meteorological monitoring point from the meteorological detection data of the UAV at each accurate meteorological monitoring point, and denote it as , is the number of the accurate meteorological monitoring point at the position, .

[0100] A1-2. Subtract the temperature of each accurate meteorological monitoring point from the temperature of its next accurate meteorological monitoring point to obtain the temperature difference of each accurate meteorological monitoring point, denoted as .

[0101] A1-3. Extract the monitoring interval duration from the flight data of the UAV, and then extract the monitoring interval duration between each accurate meteorological monitoring point and its next accurate meteorological monitoring point, denoted as .

[0102] It should be added that the specific method for obtaining the monitoring interval duration between each accurate meteorological monitoring point and its next accurate meteorological monitoring point is: extract the number of position deviation meteorological monitoring points between each accurate meteorological monitoring point and its next accurate meteorological monitoring point, denoted as .

[0103] Denote the monitoring interval duration as .

[0104] Statistical monitoring interval duration between each accurate meteorological monitoring point and its next accurate meteorological monitoring point , .

[0105] A1-4. Statistical temperature change rate of meteorological detection data , , is the number of accurate meteorological monitoring points.

[0106] A1-5. Extract the air pressure, humidity, wind speed and wind direction angle of the UAV at each accurate meteorological monitoring point from the meteorological detection data of the UAV at each accurate meteorological monitoring point, and then analyze and obtain the air pressure change rate, humidity change rate, wind speed change rate and wind direction angle change rate of the meteorological detection data in the same way as , and are denoted as , , and .

[0107] A1-6. Take , , , and as the meteorological change rate of the meteorological detection data.

[0108] A2. Extract the temperature of each accurate meteorological monitoring point from the meteorological detection data of the UAV at each accurate meteorological monitoring point, and then confirm the monitoring temperature of each meteorological grid point , is the meteorological grid point number, 。

[0109] Further, the step of confirming the monitored temperature of each meteorological grid point includes: A2-1. Extract the longitude coordinates, latitude coordinates, and altitude of each meteorological grid point from the meteorological grid dataset, and record them as 、 and , and record the longitude coordinates, latitude coordinates, and altitude of each accurate meteorological monitoring point at each location as 、 and respectively.

[0110] A2-2. Calculate the Euclidean distance , between each accurate meteorological monitoring point and each meteorological grid point.

[0111] A2-3. Calculate the distance weight , between each accurate meteorological monitoring point and each meteorological grid point.

[0112] A2-4. Calculate the monitored temperature , of each meteorological grid point.

[0113] In a specific embodiment, if the temperatures of the meteorological monitoring points are 、 and respectively, and the distance weights between the meteorological grid point and each meteorological monitoring point are 、 and respectively.

[0114] Calculate the monitored temperature , of the meteorological grid point.

[0115] A3. Extract the air pressure, humidity, wind speed, and wind direction angle of each accurate meteorological monitoring point from the meteorological detection data of the unmanned aerial vehicle at each accurate meteorological monitoring point.

[0116] A4. Similarly confirm the monitored air pressure, monitored humidity, monitored wind speed, and monitored wind direction angle of each meteorological grid point according to the confirmation method of the monitored temperature of each meteorological grid point.

[0117] A5. Use the meteorological change rate of the meteorological detection data and the monitored temperature, monitored air pressure, monitored humidity, monitored wind speed, and monitored wind direction angle of each meteorological grid point as the meteorological detection correction data column.

[0118] Step 6. Meteorological grid data correction: Based on the meteorological detection correction data, construct a rapid correction model for meteorological detection data, and import the meteorological grid data set into the rapid correction model for meteorological detection data to correct the meteorological grid data set and obtain the corrected meteorological grid data.

[0119] Step 7. Evaluation of meteorological grid correction: Based on the corrected meteorological grid data, conduct an evaluation of the correction of the meteorological grid data to obtain the qualification degree of the correction evaluation of the meteorological grid data.

[0120] Exemplarily, the evaluation of the correction of the meteorological grid data includes: S1. Statistically analyze the meteorological grid correction accuracy based on the meteorological grid data and the corrected meteorological grid data. .

[0121] Further, the statistical analysis of the meteorological grid correction accuracy includes: S1-1. Extract the temperature of each meteorological grid point from the meteorological grid data, denoted as , and extract the corrected temperature of each meteorological grid point from the corrected meteorological grid data, denoted as .

[0122] S1-2. Statistically analyze the mean absolute error of the corrected temperature of the meteorological grid , , where is the number of meteorological grid points.

[0123] S1-3. Statistically analyze the root mean square error of the corrected temperature of the meteorological grid , .

[0124] In a specific embodiment, if the temperatures of each meteorological grid are 25.2, 24.8, 26.1, 25.5, 24.7 respectively, and the corrected temperatures of each meteorological grid are 25.4, 25.0, 26.0, 25.7, 24.9 respectively.

[0125] Calculate respectively as: , , , , .

[0126] Statistically analyze the mean absolute error of the corrected temperature of the meteorological grid , .

[0127] Calculate respectively as: , , , , .

[0128] Root Mean Square Error of the Corrected Temperature in the Statistical Meteorological Grid , 。

[0129] S1-4, Temperature Accuracy of the Corrected Statistical Meteorological Grid , , and are the mean absolute error and root mean square error respectively with respect to the set reference.

[0130] It should be added that and are both extracted from the technical specification table for rapid correction of meteorological grids from unmanned aerial vehicle meteorological detection data.

[0131] S1-5, In the same statistical manner as , the pressure accuracy, humidity accuracy, wind speed accuracy, and wind direction angle accuracy of the corrected meteorological grid are statistically obtained and denoted as 、 、 and 。

[0132] S1-6, Statistical Accuracy of the Corrected Meteorological Grid , 。

[0133] S2. According to the meteorological grid data and the corrected meteorological grid data, the stability of the corrected meteorological grid is statistically obtained 。

[0134] Furthermore, the statistical stability of the corrected meteorological grid includes: S2-1. Calculating the mean value of to obtain the average corrected temperature of the meteorological grid, denoted as 。

[0135] S2-2. Statistical Standard Deviation of the Corrected Temperature of the Meteorological Grid , 。

[0136] In a specific embodiment, if the corrected temperatures of each meteorological grid are 25.4, 25.0, 26.0, 25.7, 24.9 respectively.

[0137] Statistical Average Corrected Temperature of the Meteorological Grid , 。

[0138] Calculating respectively gives: , , , , 。

[0139] Standard deviation of temperature for statistical meteorological grid correction , .

[0140] S2-3, Temperature stability of statistical meteorological grid correction , , is the standard deviation of temperature set as a reference, is the natural constant.

[0141] It should be added that, is extracted from the technical specification table for rapid correction of meteorological grids from UAV meteorological detection data.

[0142] S2-4, In the same way as the statistical method of , the pressure stability, humidity stability, wind speed stability and wind direction angle stability of the meteorological grid correction are statistically obtained, and are respectively denoted as , , and .

[0143] S2-5, Select the minimum value from , , , and as the meteorological grid correction stability, and denote it as .

[0144] S3. According to the corrected meteorological grid data, the smoothness of the meteorological grid correction is statistically calculated .

[0145] Furthermore, the statistical meteorological grid correction smoothness includes: S3-1. Subtract the temperature of each meteorological grid point from the temperature of each of its adjacent meteorological grid points to obtain the temperature difference between each meteorological grid point and each of its adjacent meteorological grid points, and take the absolute value to obtain the absolute value of the temperature difference.

[0146] S3-2. Obtain the Euclidean distance between each meteorological grid point and each of its adjacent meteorological grid points through the Euclidean distance formula.

[0147] S3-3. Take the ratio of the absolute value of the temperature difference between each meteorological grid and each of its adjacent meteorological grid points to the Euclidean distance as the temperature change rate of the meteorological grid.

[0148] S3-4. If the temperature change rate of a certain meteorological grid and a certain adjacent meteorological grid point is less than the set reference temperature change rate of the meteorological grid, then mark the meteorological grid point and the adjacent meteorological grid point as temperature-smooth adjacent meteorological grids, and count the number of temperature-smooth adjacent meteorological grids, denoted as .

[0149] S3-5. Count the number of adjacent meteorological grids and record it as , and use the ratio of and as the temperature smoothness for meteorological grid correction .

[0150] S3-6. Similarly, according to the statistical method of , statistically obtain the pressure smoothness, humidity smoothness, wind speed smoothness, and wind direction angle smoothness for meteorological grid correction, and record them as , , and respectively.

[0151] S3-7. Select the minimum value from , , , and as the meteorological grid correction smoothness, and record it as .

[0152] S4. Statistically obtain the qualified degree of correction evaluation of meteorological grid data , , , and are the weights of the set meteorological grid correction accuracy, meteorological grid correction stability, and meteorological grid correction smoothness respectively. , .

[0153] It should be noted that the accuracy directly measures the closeness between the corrected meteorological grid data and the actual meteorological conditions. The primary purpose of meteorological data is to accurately reflect the true state of the atmosphere. The stability reflects the consistency and reliability of the corrected data over time, and thus indirectly reflects the true meteorological conditions. The smoothness mainly focuses on whether the change of meteorological grid data between adjacent grid points is gentle. Although the smoothness can improve the aesthetics and readability of the data, in practical applications, it is not the most critical factor. Therefore, set . For the convenience of analysis, can be specifically set to 0.5, can be specifically set to 0.3, can be specifically set to 0.2.

[0154] Step Eight. Feedback of Meteorological Grid Data: Make corresponding feedback according to the qualified degree of correction evaluation of meteorological grid data.

[0155] In the embodiments of the present invention, by correcting the meteorological grid data set according to the meteorological grid data monitored by meteorological satellites and the meteorological detection data monitored by unmanned aerial vehicles, the deficiencies of the current meteorological grid data correction without collecting meteorological data by unmanned aerial vehicles are avoided, thereby avoiding the limitations of single data, realizing the comprehensive analysis of meteorological grid data and meteorological detection data, further ensuring the accuracy of meteorological data, and at the same time ensuring the comprehensiveness of meteorological data.

[0156] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for quickly correcting a meteorological grid using unmanned aerial vehicle meteorological detection data, characterized in that: The method includes: Step 1: Establishing meteorological grid data set: extracting meteorological grid data monitored by meteorological satellites, and then establishing meteorological grid data set through high-resolution numerical weather forecasting system; Step 2: Meteorological detection data monitoring: extract the flight data of the drone, and then monitor the location information and meteorological detection data of the drone at each meteorological monitoring point. The location information includes longitude coordinates, latitude coordinates and altitude, and the meteorological detection data includes temperature, air pressure, humidity, wind speed and wind direction angle; Step 3: Weather detection location analysis: Analyze the location accuracy of each weather monitoring point based on the location information of the drone at each weather monitoring point; Step 4: Confirmation of meteorological detection location: Confirm the accurate meteorological monitoring point at each location based on the location accuracy of each meteorological monitoring point; Step 5: Meteorological detection correction generation: Generate meteorological detection correction data based on the meteorological detection data of accurate meteorological monitoring points at each location; Step 6, meteorological grid data correction: according to the meteorological detection correction data, a meteorological detection data rapid correction model is constructed, and the meteorological grid data set is imported into the meteorological detection data rapid correction model to correct the meteorological grid data set to obtain the corrected meteorological grid data; Step 7, meteorological grid correction evaluation: according to the revised meteorological grid data, the meteorological grid data is corrected and evaluated to obtain the corrected evaluation qualification of the meteorological grid data; Step 8: Meteorological grid data feedback: Provide corresponding feedback based on the revised evaluation qualification of the meteorological grid data; The analysis of the location accuracy of each meteorological monitoring point includes: The longitude coordinates, latitude coordinates and altitude of the UAV at each meteorological monitoring point are recorded as , and , The number of the meteorological monitoring point. ; The longitude coordinates, latitude coordinates and altitude of the drone at each meteorological monitoring point are extracted from the drone’s flight data and recorded as , and ; The longitude coordinates, latitude coordinates and altitude of each meteorological monitoring point are respectively subtracted from its set longitude coordinates, set latitude coordinates and set altitude to obtain the longitude difference, latitude difference and altitude difference of each meteorological monitoring point, and are recorded as , and ; Statistical analysis of the comprehensive position error of each meteorological monitoring point , ; Statistical location accuracy of each meteorological monitoring point , , is the comprehensive position error of the setting reference; Confirming the accurate meteorological monitoring points at each location includes: Compare the location accuracy of each meteorological monitoring point with the location accuracy of the set reference; If the location accuracy of a certain meteorological monitoring point is greater than or equal to the location accuracy of the set reference, then the meteorological monitoring point is recorded as an accurate location meteorological monitoring point, and then each accurate location meteorological monitoring point is obtained; The generating of meteorological detection correction data comprises: A1. Extract the meteorological detection data of the UAV at each accurate meteorological monitoring point from the meteorological detection data of the UAV at each meteorological monitoring point, and then calculate the meteorological change rate of the meteorological detection data; A2. Extract the temperature of each accurate meteorological monitoring point from the meteorological detection data of the drone at each accurate meteorological monitoring point, and then confirm the monitoring temperature of each meteorological grid point , is the meteorological grid point number, ; A3. Extract the air pressure, humidity, wind speed and wind direction angle of each accurate meteorological monitoring point from the meteorological detection data of the drone at each accurate meteorological monitoring point; A4. Confirm the monitored air pressure, monitored humidity, monitored wind speed and monitored wind direction angle of each meteorological grid point in the same way as the monitoring temperature of each meteorological grid point; A5. The meteorological change rate of meteorological detection data and the monitored temperature, monitored air pressure, monitored humidity, monitored wind speed and monitored wind direction angle of each meteorological grid point are used as meteorological detection correction data; The meteorological change rate of the statistical meteorological detection data includes: The temperature of each accurate meteorological monitoring point is extracted from the meteorological detection data of the drone at each accurate meteorological monitoring point and recorded as , Number the meteorological monitoring points with accurate locations. ; The temperature difference of each accurate meteorological monitoring point is obtained by subtracting the temperature of each accurate meteorological monitoring point from the temperature of the next accurate meteorological monitoring point, which is recorded as ; The monitoring interval duration is extracted from the UAV flight data, and then the monitoring interval duration between each accurate meteorological monitoring point and its next accurate meteorological monitoring point is extracted, which is recorded as ; Statistical analysis of temperature change rate of meteorological detection data , , The number of meteorological monitoring points for accurate location; The air pressure, humidity, wind speed and wind direction angle of the accurate meteorological monitoring points of the drone at each location are extracted from the meteorological detection data of the drone at each location, and then The air pressure change rate, humidity change rate, wind speed change rate and wind direction angle change rate of the meteorological detection data are obtained by the same analysis method and recorded as , , and ; Will , , , and The meteorological change rate as meteorological detection data; The step of confirming the monitored temperature of each meteorological grid point includes: The longitude coordinates, latitude coordinates and altitude of each meteorological grid point are extracted from the meteorological grid data set and recorded as , and , and the longitude coordinates, latitude coordinates and altitude of each accurate meteorological monitoring point are recorded as , and ; Count the Euclidean distance between each accurate meteorological monitoring point and each meteorological grid point , ; Statistical distance weights between accurate meteorological monitoring points at each location and each meteorological grid point , ; Statistics of monitored temperatures at each meteorological grid point , ; The revised evaluation of meteorological grid data includes: S1. Statistical analysis of meteorological grid correction accuracy based on meteorological grid data and revised meteorological grid data ; S2. Statistical analysis of meteorological grid correction stability based on meteorological grid data and revised meteorological grid data ; S3. Calculate the smoothness of the meteorological grid correction based on the corrected meteorological grid data ; S4. Correction evaluation of statistical meteorological grid data , , , and are the weights of the set meteorological grid correction accuracy, meteorological grid correction stability and meteorological grid correction smoothness, respectively. , ; The statistical meteorological grid correction accuracy includes: Extract the temperature of each meteorological grid point from the meteorological grid data, and record it as , and extract the corrected temperature of each meteorological grid point from the corrected meteorological grid data, recorded as ; Mean absolute error of temperature correction of meteorological grid , , is the number of meteorological grid points; Root mean square error of temperature correction in statistical meteorological grid , ; Temperature accuracy of statistical meteorological grid correction , , and are the mean absolute error and root mean square error of the set reference, respectively; according to The statistical method of the meteorological grid is similar to the statistical method to obtain the accuracy of air pressure, humidity, wind speed and wind direction angle, and they are recorded as , , and ; Statistical meteorological grid correction accuracy , ; The statistical meteorological grid correction stability includes: Will Calculate the mean value and get the average meteorological grid corrected temperature, recorded as ; The temperature standard deviation of the statistical meteorological grid correction , ; Temperature stability corrected by statistical meteorological grid , , To set the reference temperature standard deviation, is a natural constant; according to The statistical method of the meteorological grid is similar to the statistical method to obtain the pressure stability, humidity stability, wind speed stability and wind direction angle stability, and they are recorded as , , and ; from , , , and The minimum value is selected as the meteorological grid correction stability and recorded as ; The statistical meteorological grid smoothing correction includes: Subtract the temperature of each meteorological grid point from that of each adjacent meteorological grid point to obtain the temperature difference between each meteorological grid point and each adjacent meteorological grid point, and take the absolute value to obtain the absolute value of the temperature difference; The Euclidean distance formula is used to obtain the Euclidean distance between each meteorological grid point and its adjacent meteorological grid points; The absolute value of the temperature difference between each meteorological grid and its adjacent meteorological grid points and the ratio of the Euclidean distance are taken as the meteorological grid temperature change rate; If the meteorological grid temperature change rate between a certain meteorological grid and a certain adjacent meteorological grid point is less than the set reference meteorological grid temperature change rate, then the meteorological grid point and the adjacent meteorological grid point are recorded as temperature smoothed adjacent meteorological grids, and the number of temperature smoothed adjacent meteorological grids is counted and recorded as ; The number of adjacent meteorological grids is recorded as ,Will and The ratio of is used as the temperature smoothness of the meteorological grid correction ; according to The statistical method of the meteorological grid is similar to the statistical method to obtain the air pressure smoothness, humidity smoothness, wind speed smoothness and wind direction angle smoothness, and they are recorded as , , and ; from , , , and The minimum value is selected as the meteorological grid correction smoothness and recorded as .

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