Method for Inverting Three-Dimensional Low-Altitude Atmospheric Wind Field Based on Vertical Observation of Wind Profiling Radar

Abstract

The present invention discloses a method for retrieving the three-dimensional wind field in the low-altitude atmosphere based on vertical observations of a wind profiler radar, which relates to the technical field of three-dimensional wind field retrieval. In order to solve the problem that inaccurate model parameters lead to poor retrieval effects during the wind field retrieval process, this application covers the root mean square error, correlation coefficient, and absolute error through differential analysis, evaluates the differences between the simulated data and the actual observed data from multiple perspectives, provides a comprehensive optimization basis, uses the particle swarm optimization method for parameter calculation optimization, which has the characteristics of fast convergence speed and strong global search ability, and is suitable for the optimization of complex problems. According to the wind field retrieval rules, the model requirements are confirmed, and the model is selected in combination with the evaluation results of the model library, ensuring that the selected model has high pertinence and applicability, which helps to improve the prediction accuracy and reliability of the model. The wind field fusion data is used for model initialization, fully integrating the data information from multiple sources and improving the comprehensiveness and accuracy of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional wind field inversion, and specifically to a method for inverting the three-dimensional wind field of the low-altitude atmosphere based on the vertical observation of a wind profiler radar. Background Art

[0002] Three-dimensional wind field inversion refers to inferring the wind field distribution in the three-dimensional space of the atmosphere from limited observation data through specific technologies and methods.

[0003] Chinese patent document with the publication number CN118091666B discloses a method and system for inverting the three-dimensional wind field of the full beam of a deep learning wind profiler radar. It mainly decodes and inverts the original data of the obtained wind profiler radar to obtain the initial information of the three-dimensional wind field, and then performs time-consistent averaging processing; matches and standardizes the historical wind field observation data and the original data of the wind profiler radar to form observation grids, and introduces terrain data as features to construct a training data set; constructs a three-dimensional wind field inversion model based on deep learning according to the improved deep neural network CU-Net Pro, and optimizes and trains the model using the training data set; standardizes and time-matches the three-dimensional inversion wind data of the wind profiler radar to be corrected and inputs them into the model to inversely solve the corrected values of the three-dimensional inversion wind. Although the above patent solves the problem of three-dimensional inversion, there are still the following problems in actual operation:

[0004] 1. Accurate data acquisition is not carried out through more and more perfect acquisition ports, resulting in inaccurate acquisition of the original wind field data.

[0005] 2. The collected wind field data is not further processed, and a targeted model is not established for the wind field data, resulting in poor inversion effect when performing wind field inversion.

[0006] 3. The wind field inversion model is not simulated more effectively, and targeted simulation optimization and adjustment are not carried out according to the simulation results, resulting in poor inversion effect. Summary of the Invention

[0007] The object of the present invention is to provide a method for retrieving the three-dimensional wind field of the low-altitude atmosphere based on vertical observation of a wind profiler radar. Through differential analysis covering root mean square error, correlation coefficient, and absolute error, the differences between simulated data and actual observation data are evaluated from multiple perspectives, providing a comprehensive basis for optimization. The particle swarm optimization method is used for parameter calculation and optimization, which has the characteristics of fast convergence speed and strong global search ability, and is suitable for the optimization of complex problems. According to the wind field inversion rules, the model requirements are confirmed, and the model is selected in combination with the evaluation results of the model library to ensure that the selected model has high pertinence and applicability, which helps to improve the prediction accuracy and reliability of the model. The wind field fusion data is used for model initialization, fully integrating data information from multiple sources, improving the comprehensiveness and accuracy of the data, and solving the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for retrieving the three-dimensional wind field of the low-altitude atmosphere based on vertical observation of a wind profiler radar. The three-dimensional wind field inversion method is to construct a dynamic model according to the acquired data, perform wind field simulation on the constructed dynamic model, generate a three-dimensional wind field by using the inversion algorithm for the simulation result and the actual observation data, and perform visual display after generation;

[0010] The three-dimensional wind field inversion method includes: collecting and quality controlling the data provided by the wind profiler radar and the ground meteorological station;

[0011] Performing data fusion on the data from different sources, and performing data preprocessing after the data fusion is completed;

[0012] Selecting a dynamic model from the model library, initializing the selected dynamic model by using the data after data preprocessing, and setting the initial state parameters;

[0013] Performing wind field simulation on the initialized dynamic model, performing differential analysis on the wind field simulation data and the actual observation data, and optimizing and adjusting the initialized dynamic model by using the optimization algorithm according to the differential analysis result;

[0014] Generating a three-dimensional wind field for the optimized and adjusted dynamic model, and displaying the inversion result of the generated three-dimensional risk by using visualization technology.

[0015] Preferably, collecting and quality controlling the data provided by the wind profiler radar and the ground meteorological station includes:

[0016] Collecting the vertical wind speed profile data in the wind profiler radar, and the vertical wind speed profile data includes the vertical wind speed and horizontal wind speed data in the time series;

[0017] Collect the data of the surface meteorological station, including the basic meteorological element data of temperature, pressure, humidity, wind speed and wind direction;

[0018] Conduct quality control on the data provided by the wind profiler radar and the surface meteorological station respectively;

[0019] The quality control of the wind profiler radar data includes: echo quality inspection, depolarization ratio inspection, vertical wind field consistency inspection and horizontal wind field comparison;

[0020] The quality control of the surface meteorological station data includes: internal consistency inspection, spatio-temporal continuity inspection, threshold inspection and statistical quality control;

[0021] At the same time, correct or remove the outliers in the data provided by the wind profiler radar and the surface meteorological station, and interpolate the missing data;

[0022] After the quality control is completed, the actual observed data is obtained.

[0023] Preferably, fuse the data from different sources, and after the data fusion is completed, conduct data preprocessing, including:

[0024] Fuse the wind profiler radar data and the surface meteorological station data with completed quality control in the actual observed data;

[0025] Among them, before data fusion, conduct quality assessment on the wind profiler radar data and the surface meteorological station data with completed quality control in the actual observed data. The quality assessment is to identify the outliers and noise points in the data such as wind speed and wind direction by using the isolation forest algorithm, mark or remove them, evaluate the reliability of the surface meteorological station data by using the Bayesian network model, and judge the rationality of the data according to the correlation relationship between different meteorological elements. Finally, select the data with quality assessment within the high-quality standard for data fusion;

[0026] The data fusion process includes: matching the wind profiler radar data and the surface meteorological station data. Among them, the data matching includes time matching and space matching; time matching is to match the wind profiler radar data and the surface meteorological station data in time according to the time stamp; space matching is to match the wind profiler radar data and the surface meteorological station data in space according to the geographical location.

[0027] Preferably, fuse the data from different sources, and after the data fusion is completed, conduct data preprocessing, which also includes:

[0028] Interpolate the wind profiler radar data and the surface meteorological station data with completed data matching. The data interpolation includes vertical interpolation and horizontal interpolation;

[0029] Among them, vertical interpolation is to use linear interpolation or bilinear interpolation for the wind profile radar's wind speed profile data, interpolate the data to the observation height of the surface meteorological station, and supplement the data at different height levels; horizontal interpolation is to expand the observation data of the surface meteorological station to the area covered by the radar through Kriging interpolation method;

[0030] For the wind profile radar data and surface meteorological station data after data interpolation, weighted processing is carried out using the weighted average method;

[0031] Before weighted processing, first formulate the weighting strategy, and the weighting strategy includes time weight, space weight and data quality weight;

[0032] The time weight is to assign a higher weight to the near-real-time observation data during the fusion process of the wind profile radar data and surface meteorological station data after data interpolation. If the timestamp of the data is very close to the current time, a higher weight can be given to this data;

[0033] The space weight is to calculate the spatial distance between the radar data and the meteorological station data, and assign a higher weight to the points based on the proximity;

[0034] The data quality weight is to dynamically adjust the weight according to the quality assessment of each data source;

[0035] Combine the data in the weighted processed wind profile radar data and surface meteorological station data, and after combination, obtain the fusion data of the wind profile radar data and surface meteorological station data;

[0036] Perform data preprocessing on the fused actual observation data;

[0037] After data preprocessing, wind field fusion data is obtained.

[0038] Preferably, select a dynamic model from the model library, initialize the selected dynamic model using the data after data preprocessing, and set the initial state parameters, including:

[0039] First evaluate the models in the model library, including physical characteristics, mathematical expressions, application scope and performance records;

[0040] According to the wind field inversion rules to confirm the model requirements and the evaluation results of the models in the model library, select the corresponding dynamic model;

[0041] Initialize the selected dynamic model using the wind field fusion data;

[0042] The initialization process includes: confirm the initialization indicators in the wind field fusion data, and the initialization criteria include wind speed, wind direction, temperature, pressure and humidity data;

[0043] Set the initial state parameters of the selected kinetic model according to the initialization indicators in the wind field fusion data;

[0044] Among them, the initial state parameters are set to use the kinetic model and its corresponding initialization method, and the initialization indicators in the wind field fusion data are input through functions or classes. After the input, the initial state configuration of the selected kinetic model is started;

[0045] After the initial state parameters are set, the wind field kinetic model is obtained.

[0046] Preferably, perform wind field simulation on the initialized kinetic model, perform difference analysis on the wind field simulation data and the actual observation data, and optimize and adjust the initialized kinetic model according to the difference analysis results, including:

[0047] Perform wind field simulation on the wind field kinetic model. Before performing the wind field simulation, confirm the simulation time and space range and resolution. At the same time, configure the physical parameters and boundary conditions of the wind field kinetic model, and finally set the simulation time step and total simulation duration;

[0048] After the above steps are completed, confirm the wind field data in the wind field kinetic model. The wind field data includes wind speed, wind direction, temperature and pressure of spatial distribution;

[0049] Establish a wind field model according to the wind field kinetic model, set the simulation conditions of the wind field model according to the wind field data, and finally obtain the target wind field model;

[0050] Divide the target area in the target wind field model into several grids by the structured grid method;

[0051] Through time evolution, calculate the wind field parameters of each grid. The calculation steps of the wind field parameters include:

[0052] Input the parameters of the wind field data into the target wind field model and set the initial state in time and space;

[0053] For each time step, use the discrete equation for time integration to calculate the state of the target wind field model at the next time step;

[0054] In each time step, perform iterative updates in sequence until the set total time is reached;

[0055] At each time step, save the calculated wind field data to the corresponding dataset and label the dataset as simulated wind field data;

[0056] Perform a difference analysis on the simulated wind field data and the actual observed data. The difference analysis includes root mean square error, correlation coefficient, and absolute error. The root mean square error is used to quantify the average deviation between the simulated wind field data and the actual observed data; the correlation coefficient is used to evaluate the similarity between the simulated wind field data and the actual observed data; the absolute error is used to calculate the absolute difference between the simulated wind field data and the actual observed data at each grid point or time step;

[0057] Confirm the data in the simulated wind field data that has the greatest impact on the target wind field model according to the difference analysis results;

[0058] Perform parameter calculation and optimization using the particle swarm optimization method based on the confirmed impact data;

[0059] Adjust the parameters of the target wind field model according to the optimized parameters. After the parameter adjustment, re - perform the wind field simulation and iterate the new simulation parameters again until the simulation accuracy of the target wind field model is within the preset range;

[0060] After the optimization and adjustment of the target wind field model are completed, a vertical wind field inversion model is obtained.

[0061] Preferably, confirm the simulation resolution, including:

[0062] Retrieve the available memory space corresponding to the wind field simulation system;

[0063] Extract the rated memory capacity of the wind field simulation system;

[0064] Determine the available memory occupancy ratio value corresponding to the wind field simulation according to the ratio between the available memory space corresponding to the wind field simulation system and the rated memory capacity;

[0065] Extract the change rate of the wind field data in the vertical direction, where the wind field data includes the wind speed, wind direction, temperature, and pressure distributed in space; and the change rate of the wind field data in the vertical direction includes the average change rate of wind speed, the average change rate of wind direction angle, the average change rate of temperature, and the average change rate of pressure;

[0066] Extract the change frequency of the wind field data per unit time;

[0067] Obtain the resolution of the wind field simulation in the vertical and horizontal directions according to the change frequency of the wind field data per unit time, the change rate of the wind field data in the vertical direction, and the available memory occupancy ratio value corresponding to the wind field simulation.

[0068] Preferably, obtaining the resolution of the wind field simulation in the vertical and horizontal directions according to the change frequency of the wind field data per unit time, the change rate of the wind field data in the vertical direction, and the available memory occupancy ratio value corresponding to the wind field simulation includes:

[0069] Extract the maximum rate of change in the rate of change of the wind field data in the vertical direction; wherein, the rate of change of the wind field data in the vertical direction includes the average rate of change of wind speed, the average rate of change of wind direction angle, the average rate of change of temperature, and the average rate of change of pressure;

[0070] Normalize the maximum rate of change in the rate of change of the wind field data in the vertical direction with the proportion of available memory corresponding to the wind field simulation to obtain the normalized maximum rate of change and the proportion of available memory value;

[0071] Compare the normalized maximum rate of change and the normalized proportion of available memory value;

[0072] When the normalized maximum rate of change does not exceed the normalized proportion of available memory value, confirm the simulation resolution according to the grid size corresponding to the preset initial vertical resolution and initial horizontal resolution;

[0073] When the normalized maximum rate of change exceeds the normalized proportion of available memory value, adjust the grid size corresponding to the initial vertical resolution and initial horizontal resolution by using the change frequency of the wind field data per unit time, the rate of change of the wind field data in the vertical direction, and the proportion of available memory value corresponding to the wind field simulation to obtain the grid size corresponding to the adjusted vertical resolution and horizontal resolution;

[0074] Wherein, the grid size corresponding to the adjusted vertical resolution and horizontal resolution is obtained through the following formula:

[0075] ;

[0076] Wherein, L c and L s represent the grid size corresponding to the adjusted vertical resolution and horizontal resolution; L c0 and L s0 represent the grid size corresponding to the initial vertical resolution and initial horizontal resolution; P represents the proportion of available memory value corresponding to the wind field simulation; B 01 、B 02 、B 03 and B 04 represent the average rate of change of wind speed, the average rate of change of wind direction angle, the average rate of change of temperature, and the average rate of change of pressure respectively; n represents the number of unit times experienced in the wind field data acquisition; f 01i 、f 02i 、f 03i and f 04i represent the change frequencies of wind speed, wind direction, temperature, and pressure corresponding to the i-th unit time; f 01c 、f02c , f 03c and f 04c represent the reference values of the change frequencies of the preset wind speed, wind direction, temperature, and pressure;

[0077] Confirm the simulation resolution according to the grid size corresponding to the adjusted vertical resolution and horizontal resolution.

[0078] Preferably, the target area in the target wind field model is divided into a number of grids by the structured grid method, including:

[0079] Extract the target area in the target wind field model;

[0080] Compare the area value of the target area in the target wind field model with the preset reference area value to obtain the ratio between the area value of the target area and the preset reference area value;

[0081] Compare the ratio between the area value of the target area and the preset reference area value with the preset ratio threshold;

[0082] When the ratio between the area value of the target area and the preset reference area value does not exceed the preset ratio threshold, the target area is structurally divided using the initial target area grid size to obtain a number of grids;

[0083] When the ratio between the area value of the target area and the preset reference area value exceeds the preset ratio threshold, retrieve the size change rate between the grid size of the adjusted vertical resolution and the grid size of the initial vertical resolution as the first size change rate value;

[0084] Retrieve the size change rate between the grid size of the adjusted horizontal resolution and the grid size of the initial horizontal resolution as the second size change rate value;

[0085] Use the ratio between the area value of the target area and the preset reference area value in combination with the first size change rate value and the second size change rate value to adjust the initial target area grid size to obtain the adjusted target area grid size;

[0086] Among them, the adjusted target area grid size is obtained through the following formula:

[0087] ;

[0088] Among them, D represents the adjusted target area grid size; D 0 represents the initial target area grid size; P d represents the ratio between the area value of the target area and the preset reference area value; Pc represents the first dimensional change rate value; P s represents the second dimensional change rate value;

[0089] The target area is structurally divided according to the adjusted target area grid size to obtain a number of grids.

[0090] Preferably, the optimized and adjusted kinetic model is used to generate a three-dimensional wind field, and the generated three-dimensional risk is displayed by using visualization technology for the inversion result, including:

[0091] Use a three-dimensional model generator to generate a three-dimensional wind field for the vertical wind field inversion model. Among them, the wind field parameter data in the vertical wind field inversion model is input into the three-dimensional model generator, and after starting the three-dimensional model generator, a three-dimensional wind field model generated by the vertical wind field inversion model is obtained;

[0092] Visualize the three-dimensional wind field model. The visualization process includes: using streamline diagrams to display the flow direction and intensity of the wind field, using vector field visualization to display the wind speed and wind direction at each grid point in three-dimensional space, and visualizing the spatial distribution of temperature and humidity;

[0093] Generate a visualization output for the three-dimensional wind field model after visualization processing, and after generation, obtain the three-dimensional wind field visualization data of the vertical wind field inversion model.

[0094] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0095] 1. The method for inverting the three-dimensional wind field of low-altitude atmosphere based on vertical observation of wind profiler radar provided by the present invention can supplement and refine data through data interpolation, making the data more refined and accurate in both vertical and horizontal directions. Horizontal interpolation can expand the data coverage range of ground meteorological stations so that it can cover the area monitored by the radar, thereby expanding the spatial coverage range of the data.

[0096] 2. The method for inverting the three-dimensional wind field of low-altitude atmosphere based on vertical observation of wind profiler radar provided by the present invention determines the model requirements according to the wind field inversion rules and selects the model in combination with the evaluation results of the model library, ensuring that the selected model has high pertinence and applicability. This helps to improve the prediction accuracy and reliability of the model. Using the wind field fusion data for model initialization fully integrates data information from multiple sources and improves the comprehensiveness and accuracy of the data.

[0097] 3. The low-altitude atmospheric three-dimensional wind field inversion method based on vertical observation of wind profiler radar provided by the present invention, the difference analysis covers the root mean square error, correlation coefficient and absolute error, evaluates the difference between the simulated data and the actual observed data from multiple angles, provides a comprehensive optimization basis, and uses the particle swarm optimization method to optimize parameter calculation. This is a new evolutionary computing algorithm with the characteristics of fast convergence speed and strong global search ability, and is suitable for the optimization of complex problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 It is a schematic diagram of the three-dimensional wind field inversion process for vertical observation of the wind profiler radar of the present invention;

[0099] Figure 2 It is a schematic diagram of the three-dimensional wind field inversion steps for vertical observation of the wind profiler radar of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0100] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0101] In order to solve the problem in the prior art that when obtaining the actual data of the wind field, accurate data is not obtained through more and more perfect acquisition ports, resulting in inaccurate acquisition of the original wind field data, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0102] A low-altitude atmospheric three-dimensional wind field inversion method based on vertical observation of wind profiler radar, the three-dimensional wind field inversion method is to construct a dynamic model according to the obtained data, simulate the wind field with the constructed dynamic model, generate a three-dimensional wind field with the simulated result and the actual observed data using an inversion algorithm, and perform visual display after generation;

[0103] The three-dimensional wind field inversion method includes: collecting and quality controlling the data provided by the wind profiler radar and the ground meteorological station;

[0104] Fusing the data from different sources, and performing data preprocessing after the data fusion is completed;

[0105] Select a dynamic model from the model library, initialize the selected dynamic model with the data after data preprocessing, and set the initial state parameters;

[0106] Perform wind field simulation on the initialized kinetic model, conduct difference analysis on the data from the wind field simulation and the actual observation data, and optimize and adjust the initialized kinetic model using an optimization algorithm according to the results of the difference analysis;

[0107] Generate a three-dimensional wind field using the optimized and adjusted kinetic model, and display the inversion results of the generated three-dimensional wind field using visualization technology.

[0108] Collect and perform quality control on the data provided by the wind profiler radar and the surface meteorological station, including:

[0109] Collect the vertical wind speed profile data in the wind profiler radar, and the vertical wind speed profile data includes the vertical wind speed and horizontal wind speed data in a time series;

[0110] Collect the data from the surface meteorological station, including the basic meteorological element data of temperature, pressure, humidity, wind speed, and wind direction;

[0111] Perform quality control on the data provided by the wind profiler radar and the surface meteorological station respectively;

[0112] The quality control of the wind profiler radar data includes: echo quality inspection, depolarization ratio inspection, vertical wind field consistency inspection, and horizontal wind field comparison;

[0113] The quality control of the surface meteorological station data includes: internal consistency inspection, spatio-temporal continuity inspection, threshold inspection, and statistical quality control;

[0114] Meanwhile, correct or eliminate the outliers in the data provided by the wind profiler radar and the surface meteorological station, and interpolate the missing data;

[0115] The actual observation data is obtained after the quality control is completed.

[0116] Specifically, by implementing strict quality control measures, such as echo quality inspection, depolarization ratio inspection, vertical wind field consistency inspection, and horizontal wind field comparison (for wind profiler radar data), as well as internal consistency inspection, spatio-temporal continuity inspection, threshold inspection, and statistical quality control (for surface meteorological station data), the accuracy of the data can be significantly improved, covering all key data collected from wind profiler radars and surface meteorological stations, including vertical wind speed profile data (vertical wind speed and horizontal wind speed) and basic meteorological element data (temperature, pressure, humidity, wind speed, and wind direction), ensuring the comprehensiveness and integrity of the data, clarifying the processing strategy for outliers, that is, correcting or removing them, which helps to eliminate errors or unreasonable values in the data, further improving the reliability of the data, interpolating missing data, ensuring the continuity and integrity of the data, which is crucial for subsequent data analysis and applications. Through a systematic data collection and quality control process, the automation level of data processing can be improved, manual intervention can be reduced, and work efficiency can be increased.

[0117] To solve the problem in the prior art that the collected wind field data is not further processed and no targeted model is established for the wind field data, resulting in poor inversion results when performing wind field inversion, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0118] Fuse data from different sources, and perform data preprocessing after data fusion, including:

[0119] Fuse the wind profiler radar data and surface meteorological station data with quality control completed in the actual observation data;

[0120] Among them, before data fusion, perform quality assessment on the wind profiler radar data and surface meteorological station data with quality control completed in the actual observation data. The quality assessment is to identify outliers and noise points in data such as wind speed and wind direction using the Isolation Forest algorithm, and mark or remove them, evaluate the reliability of the surface meteorological station data using the Bayesian network model, and judge the rationality of the data based on the correlation relationship between different meteorological elements. Finally, select the data with quality assessment within the high-quality standard for data fusion;

[0121] The data fusion process includes: matching the wind profiler radar data and surface meteorological station data. Among them, data matching includes time matching and spatial matching; time matching is to match the wind profiler radar data and surface meteorological station data in time according to the time stamp; spatial matching is to match the wind profiler radar data and surface meteorological station data in space according to the geographical location.

[0122] Interpolate the wind profiler radar data and the surface meteorological station data after data matching. The data interpolation includes vertical interpolation and horizontal interpolation;

[0123] Among them, the vertical interpolation is to use linear interpolation or bilinear interpolation for the wind speed profile data of the wind profiler radar, interpolate the data to the observation height of the surface meteorological station, and supplement the data of different height layers; the horizontal interpolation is to expand the observation data of the surface meteorological station to the area covered by the radar through the Kriging interpolation method;

[0124] Perform weighted processing on the wind profiler radar data and the surface meteorological station data after data interpolation using the weighted average method;

[0125] Before weighted processing, formulate the weighting strategy first. The weighting strategy includes time weight, spatial weight, and data quality weight;

[0126] The time weight is to assign a higher weight to the near-real-time observation data during the fusion process of the wind profiler radar data and the surface meteorological station data after data interpolation. If the timestamp of the data is very close to the current time, a higher weight can be given to this data;

[0127] The spatial weight is to calculate the spatial distance between the radar data and the meteorological station data, and assign a higher weight based on the points with a closer distance;

[0128] The data quality weight is to dynamically adjust the weight according to the quality assessment of each data source;

[0129] Combine the data in the weighted wind profiler radar data and the surface meteorological station data. After combination, the fusion data of the wind profiler radar data and the surface meteorological station data is obtained;

[0130] Perform data preprocessing on the fused actual observation data;

[0131] The wind field fusion data is obtained after data preprocessing.

[0132] Specifically, through data fusion, data from different sources (wind profiler radar and surface meteorological station) can be integrated to enhance data integrity. This integration can fill data gaps or deficiencies that may exist in a single data source, improving the overall usability of the data. Data matching (including time matching and spatial matching) ensures that the fused data is consistent in time and space, which helps reduce errors caused by inconsistent data sources. Data interpolation (vertical interpolation and horizontal interpolation) can supplement and refine the data, making the data more precise and accurate in both the vertical and horizontal directions. Horizontal interpolation (such as Kriging interpolation) can expand the data coverage of the surface meteorological station to cover the area monitored by the radar, thus expanding the spatial coverage of the data. Time matching ensures the continuity of the data in time, facilitating time series analysis or prediction. A variety of data fusion and preprocessing techniques (such as linear interpolation, bilinear interpolation, Kriging interpolation, and weighted average method) are adopted, and these methods can be selected and adjusted according to the characteristics and requirements of the data, thus improving the flexibility and adaptability of data fusion. By integrating and optimizing data from different sources, this solution can provide more comprehensive and accurate meteorological information, which is of great significance for fields such as meteorological forecasting, climate research, and environmental monitoring. Among them, quality assessment can significantly improve the accuracy of the data by identifying and processing outliers and noise points. This helps reduce data errors caused by factors such as equipment failures, data transmission errors, or environmental interference. Using the Bayesian network model to evaluate the reliability of surface meteorological station data can comprehensively consider the correlation relationships among multiple meteorological elements, thus more accurately judging the rationality of the data. This evaluation method helps screen out high-quality data, providing a reliable basis for subsequent data fusion. By conducting quality assessment on the data, it can be ensured that only high-quality data is used for fusion. This helps reduce error accumulation during the data fusion process and improve the accuracy and reliability of the fused data. Formulating a weighting strategy can ensure that in the data fusion process, data from different sources and of different qualities can be reasonably weighted according to their importance. This helps make the fusion result more in line with the actual situation and improve the scientific nature of data fusion. The formulation of the weighting strategy can be adjusted and optimized according to actual needs. For example, in terms of time weight, near-real-time data can be given a higher weight according to the real-time requirement of the data; in terms of spatial weight, it can be reasonably allocated according to the spatial distribution and data density of the radar and meteorological stations. This flexibility helps adapt to different application scenarios and data characteristics. By formulating a clear weighting strategy, the data fusion process can be simplified, reducing unnecessary calculation and processing steps. This helps improve the efficiency of data fusion, reducing the computational cost and time cost.

[0133] Select a kinetic model from the model library, initialize the selected kinetic model with the preprocessed data, and set the initial state parameters, including:

[0134] First, evaluate the models in the model library, including physical characteristics, mathematical expressions, scope of application, and performance records;

[0135] According to the wind field inversion rules, confirm the model requirements and the evaluation results of the models in the model library, and select the corresponding dynamic model;

[0136] Initialize the selected dynamic model using the wind field fusion data;

[0137] The initialization process includes: confirming the initialization indicators in the wind field fusion data, and the initialization criteria include wind speed, wind direction, temperature, pressure, and humidity data;

[0138] According to the initialization indicators in the wind field fusion data, set the initial state parameters of the selected dynamic model;

[0139] Among them, the initial state parameter setting is to use the dynamic model and its corresponding initialization method, input the initialization indicators in the wind field fusion data through functions or classes, and start the initial state configuration of the selected dynamic model after input;

[0140] After the initial state parameter setting is completed, a wind field dynamic model is obtained.

[0141] Specifically, by first evaluating the models in the model library, including physical characteristics, mathematical expressions, scope of application, and performance records, the scientificity and accuracy of the selected model are ensured. This helps to select the most suitable dynamic model for the current wind field characteristics, confirm the model requirements according to the wind field inversion rules, and select the model in combination with the evaluation results of the model library, ensuring that the selected model has high pertinence and applicability. This helps to improve the prediction accuracy and reliability of the model. Using the wind field fusion data for model initialization fully integrates data information from multiple sources, improving the comprehensiveness and accuracy of the data. This helps the model to more accurately reflect the actual situation of the wind field. The initialization indicators and criteria are clearly defined in the initialization process, including parameter data such as wind speed, wind direction, temperature, pressure, and humidity, ensuring the standardization and normalization of initialization. This helps to improve the efficiency and accuracy of model initialization. By inputting the initialization indicators in the wind field fusion data into the dynamic model through functions or classes, the flexible configuration and initialization of the model are realized. This helps to adapt to changes in different wind field characteristics and requirements, and is also convenient for subsequent expansion and optimization of the model. After the initial state parameter setting is completed, a wind field dynamic model can be obtained for subsequent wind field analysis and prediction. The entire process is efficient and practical, helping to quickly respond to the needs of wind field management.

[0142] To solve the problem in the prior art that the wind field inversion model is not simulated more effectively and there is no targeted simulation optimization and adjustment based on the simulation results, resulting in poor inversion effects, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0143] Perform wind field simulation on the initialized kinetic model, perform difference analysis on the wind field simulation data and the actual observation data, and optimize and adjust the initialized kinetic model using an optimization algorithm according to the difference analysis results, including:

[0144] Perform wind field simulation on the wind field kinetic model. Before performing the wind field simulation, confirm the simulation time and space range and resolution, configure the physical parameters and boundary conditions of the wind field kinetic model, and finally set the simulation time step and total simulation duration;

[0145] After the above steps are completed, confirm the wind field data in the wind field kinetic model. The wind field data includes wind speed, wind direction, temperature, and pressure in spatial distribution;

[0146] Establish a wind field model based on the wind field kinetic model, set the simulation conditions of the wind field model according to the wind field data, and finally obtain the target wind field model;

[0147] Divide the target area in the target wind field model into several grids using the structured grid method;

[0148] Through time evolution, calculate the wind field parameters of each grid. The calculation steps of the wind field parameters include:

[0149] Input the parameters of the wind field data into the target wind field model and set the initial state in time and space;

[0150] For each time step, perform time integration using the discrete equation to calculate the state of the target wind field model at the next time step;

[0151] In each time step, perform iterative updates in sequence until the set total time is reached;

[0152] At each time step, save the calculated wind field data to the corresponding dataset and label the dataset as simulated wind field data.

[0153] Perform difference analysis on the simulated wind field data and the actual observation data. The difference analysis includes root mean square error, correlation coefficient, and absolute error. The root mean square error is used to quantify the average deviation between the simulated wind field data and the actual observation data; the correlation coefficient is used to evaluate the similarity between the simulated wind field data and the actual observation data; the absolute error is used to calculate the absolute difference between the simulated wind field data and the actual observation data at each grid point or time step;

[0154] Confirm the data in the simulated wind field data that has the greatest impact on the target wind field model according to the difference analysis results;

[0155] Perform parameter calculation optimization using the particle swarm optimization method according to the confirmed impact data;

[0156] Adjust the parameters of the target wind field model according to the optimized parameters. After the parameter adjustment, re - conduct wind field simulation and iterate the new simulation parameters again until the simulation accuracy of the target wind field model is within the preset range;

[0157] After the optimization and adjustment of the target wind field model are completed, a vertical wind field inversion model is obtained.

[0158] Specifically, through refined initialization settings, including confirming the spatio - temporal range and resolution of the simulation, configuring physical parameters and boundary conditions, setting the time step and total simulation duration, the efficiency and accuracy of the simulation process are ensured. The structured grid method is used to divide the target area, improving the standardization of grid generation and calculation efficiency, while ensuring the accuracy of numerical operations. The simulated wind field data includes the spatially distributed wind speed, wind direction, temperature and pressure, comprehensively reflecting the characteristics of the wind field. The difference analysis covers the root - mean - square error, correlation coefficient and absolute error, evaluating the differences between the simulated data and the actual observed data from multiple angles, providing a comprehensive basis for optimization. The particle swarm optimization method is used for parameter calculation optimization, which is a new evolutionary computing algorithm with the characteristics of fast convergence speed and strong global search ability, suitable for the optimization of complex problems. According to the difference analysis results, optimize and adjust the data with the greatest impact, ensuring the pertinence and effectiveness of the optimization process. By iteratively adjusting parameters and re - conducting wind field simulation multiple times, continuously approaching the real wind field situation, the simulation accuracy of the model is improved. Presetting the range of simulation accuracy ensures the controllability of the optimization process and the reliability of the results.

[0159] Specifically, confirm the resolution of the simulation, including:

[0160] Retrieve the available memory space corresponding to the wind field simulation system;

[0161] Extract the rated memory capacity of the wind field simulation system;

[0162] Determine the value of the available memory occupancy ratio corresponding to the wind field simulation according to the ratio between the available memory space corresponding to the wind field simulation system and the rated memory capacity;

[0163] Extract the change rate of the wind field data in the vertical direction, where the wind field data includes the spatially distributed wind speed, wind direction, temperature and pressure; and the change rate of the wind field data in the vertical direction includes the average change rate of wind speed, the average change rate of wind direction angle, the average change rate of temperature and the average change rate of pressure;

[0164] Extract the change frequency of the wind field data per unit time;

[0165] Obtain the resolution of the wind field simulation in the vertical and horizontal directions according to the change frequency of the wind field data per unit time, the change rate of the wind field data in the vertical direction, and the available memory occupancy ratio value corresponding to the wind field simulation.

[0166] The technical effects of the above technical solution are as follows: By retrieving the available memory space and rated memory capacity of the wind field simulation system, the available memory occupancy ratio value is calculated. This enables the system to clearly understand its own memory resource status and reasonably plan the wind field simulation tasks according to the available degree of memory. For example, when the available memory occupancy ratio is relatively high, the simulation complexity can be appropriately increased; while when the memory is tight, optimization strategies can be adopted or the simulation scale can be reduced to avoid simulation interruption or system performance degradation caused by insufficient memory, effectively improving the utilization efficiency of memory resources. Extracting various change rates (average wind speed change rate, average wind direction angle change rate, average temperature change rate, and average pressure change rate) of the wind field data in the vertical direction, as well as the change frequency per unit time, can effectively improve the comprehensiveness and accuracy of depicting the dynamic characteristics of the wind field. At the same time, the vertical change rate reflects the characteristic differences of the wind field in the height dimension, and the change frequency reflects the fluctuation of the wind field data over time. The above technical solution provides rich and accurate inputs for the wind field simulation, making the simulation results closer to the actual wind field situation and improving the accuracy and reliability of the simulation.

[0167] On the other hand, comprehensively considering the change frequency of the wind field data per unit time, the change rate in the vertical direction, and the available memory occupancy ratio value to obtain the resolution of the wind field simulation in the vertical and horizontal directions. It is possible to adaptively adjust the simulation resolution according to the dynamic characteristics of the wind field data and the system memory resource status, improve the mobility of the simulation resolution adjustment, its matching with the dynamic characteristics of the wind field data, and the adjustment sensitivity, so as to balance the simulation accuracy and system performance under different conditions and enhance the overall performance and adaptability of the wind field simulation system.

[0168] Specifically, obtaining the resolution of the wind field simulation in the vertical and horizontal directions according to the change frequency of the wind field data per unit time, the change rate of the wind field data in the vertical direction, and the available memory occupancy ratio value corresponding to the wind field simulation includes:

[0169] Extract the maximum change rate among the change rates of the wind field data in the vertical direction; wherein, the change rate of the wind field data in the vertical direction includes the average wind speed change rate, the average wind direction angle change rate, the average temperature change rate, and the average pressure change rate;

[0170] Normalize the maximum value of the rate of change in the vertical direction of the wind field data with the percentage value of the available memory corresponding to the wind field simulation to obtain the normalized maximum value of the rate of change and the percentage value of the available memory;

[0171] Compare the normalized maximum value of the rate of change with the normalized percentage value of the available memory;

[0172] When the normalized maximum value of the rate of change does not exceed the normalized percentage value of the available memory, confirm the simulation resolution according to the grid size corresponding to the preset initial vertical resolution and initial horizontal resolution;

[0173] When the normalized maximum value of the rate of change exceeds the normalized percentage value of the available memory, adjust the grid size corresponding to the initial vertical resolution and initial horizontal resolution by using the change frequency of the wind field data per unit time, the rate of change of the wind field data in the vertical direction, and the percentage value of the available memory corresponding to the wind field simulation to obtain the grid size corresponding to the adjusted vertical resolution and horizontal resolution;

[0174] Among them, the grid size corresponding to the adjusted vertical resolution and horizontal resolution is obtained through the following formula:

[0175] ;

[0176] Among them, L c and L s represent the grid size corresponding to the adjusted vertical resolution and horizontal resolution; L c0 and L s0 represent the grid size corresponding to the initial vertical resolution and initial horizontal resolution; P represents the percentage value of the available memory corresponding to the wind field simulation; B 01 、B 02 、B 03 and B 04 represent the average rate of change of wind speed, the average rate of change of wind direction angle, the average rate of change of temperature, and the average rate of change of pressure respectively; n represents the number of unit times experienced in the wind field data acquisition; f 01i 、f 02i 、f 03i and f 04i represent the change frequencies of wind speed, wind direction, temperature, and pressure corresponding to the i-th unit time; f 01c 、f 02c 、f 03c and f 04c represent the reference values of the change frequencies of preset wind speed, wind direction, temperature, and pressure;

[0177] Confirm the simulation resolution according to the grid sizes corresponding to the adjusted vertical and horizontal resolutions.

[0178] The technical effects of the above technical solution are as follows: By extracting the maximum value in the vertical change rate of wind field data, the key factor with the most variability in the vertical direction of the wind field can be captured, providing a representative key indicator for subsequent analysis and decision-making, and enabling a more accurate grasp of the dynamic characteristics of the wind field. At the same time, comparing the maximum value of the change rate with the value of the available memory occupancy ratio and adopting different resolution confirmation strategies according to the comparison results enable the system to make more targeted and flexible decisions based on the actual situation of wind field changes and memory resources, avoiding a one-size-fits-all approach and improving the system's adaptability to different wind field conditions and memory situations.

[0179] On the other hand, normalizing the maximum value of the change rate and the value of the available memory occupancy ratio to compare and analyze them on the same scale enables a more reasonable determination of the simulation resolution based on memory resources. When the maximum value of the change rate does not exceed the value of the available memory occupancy ratio, the preset initial resolution is adopted, ensuring that the simulation can run in a relatively stable and efficient manner when resources are sufficient and the wind field changes relatively stably, making full use of system resources. When the maximum value of the change rate exceeds the value of the available memory occupancy ratio, it means that the wind field changes violently and more computing resources are required to ensure the accuracy of the simulation. At this time, adjusting the grid size corresponding to the initial resolution using multi-dimensional data can, on the premise of ensuring simulation accuracy, avoid excessive consumption of memory resources due to too high a resolution, prevent problems such as system freezing or crashing, and achieve a balance between simulation accuracy and system performance. At the same time, adjusting the grid size corresponding to the resolution according to the change frequency of wind field data, the vertical change rate, and the value of the available memory occupancy ratio enables the simulation resolution to be adjusted in real time with the dynamic changes of the wind field and memory resources. It can not only improve the accuracy, efficiency, and accuracy of capturing the details and changes of the wind field, but also improve the matching between the simulation and the memory occupancy ratio.

[0180] At the same time, calculating the grid sizes corresponding to the adjusted vertical and horizontal resolutions through formulas and confirming the simulation resolution according to the adjusted sizes provide an accurate quantitative basis for the adjustment of the simulation resolution, ensuring that appropriate resolutions can be obtained under various wind field conditions and memory resource situations, thus guaranteeing the quality and reliability of wind field simulation and making the simulation results closer to the actual wind field situation.

[0181] Specifically, divide the target area in the target wind field model into several grids using the structured grid method, including:

[0182] Extract the target area in the target wind field model;

[0183] Compare the area value of the target area in the target wind field model with a preset reference area value to obtain the ratio between the area value of the target area and the preset reference area value;

[0184] Compare the ratio between the area value of the target area and the preset reference area value with a preset ratio threshold;

[0185] When the ratio between the area value of the target area and the preset reference area value does not exceed the preset ratio threshold, the initial target area grid size is used to divide the structure of the target area to obtain a number of grids;

[0186] When the ratio between the area value of the target area and the preset reference area value exceeds the preset ratio threshold, the size change rate between the grid size of the adjusted vertical resolution and the grid size of the initial vertical resolution is retrieved as the first size change rate value;

[0187] Retrieve the size change rate between the grid size of the adjusted horizontal resolution and the grid size of the initial horizontal resolution as the second size change rate value;

[0188] Use the ratio between the area value of the target area and the preset reference area value in combination with the first size change rate value and the second size change rate value to adjust the initial target area grid size to obtain the adjusted target area grid size;

[0189] Among them, the adjusted target area grid size is obtained through the following formula:

[0190] ;

[0191] Among them, D represents the adjusted target area grid size; D 0 represents the initial target area grid size; P d represents the ratio between the area value of the target area and the preset reference area value; P c represents the first size change rate value; P s represents the second size change rate value;

[0192] Divide the structure of the target area according to the adjusted target area grid size to obtain a number of grids.

[0193] The technical effects of the above technical solution are as follows: By extracting the target area in the target wind field model and comparing its area value with the preset reference area value, it is possible to accurately grasp the size characteristics of the target area relative to the preset standard, providing a basic basis for the subsequent grid division strategy, enabling the division process to make reasonable decisions for target areas of different sizes. Comparing the above ratio with the preset ratio threshold and taking different processing methods according to the comparison results enables the system to flexibly select the grid division strategy according to the actual area of the target area, effectively improving the accuracy of grid size determination.

[0194] Meanwhile, when the ratio exceeds the threshold, the change rates between the grid sizes of the adjusted vertical resolution and horizontal resolution and the initial grid size are retrieved as the first size change rate and the second size change rate values, taking into account the influence of the resolution changes in the vertical and horizontal directions of the wind field simulation on the grid size, making the adjustment of the grid size more comprehensive and accurate. Using the ratio of the target area value to the preset reference area value, combined with the two size change rate values, the initial target area grid size is adjusted through a specific formula, providing an accurate quantitative method for grid size determination. This precise calculation based on multiple factors can accurately adjust the grid size according to the specific conditions of the target area and the resolution change requirements, ensuring that each grid can more reasonably cover the target area and improving the accuracy of grid division.

[0195] On the other hand, for areas with little difference in area from the preset reference value, the initial target area grid size is directly used for structural division, avoiding unnecessary complex calculations, improving the efficiency of grid division, enabling the rapid completion of the division of a large number of regular areas, and saving computing resources and time costs. For areas with a large area or a large difference from the preset value, the grid size is adjusted by introducing the size change rate, enabling a more refined grid division in these complex areas, thereby more accurately capturing the detailed information of the target area in the wind field simulation and improving the simulation accuracy. This way of adopting different strategies according to different situations achieves a balance between computing efficiency and simulation accuracy, ensuring both the overall computing speed and providing high-precision simulation results when needed.

[0196] The optimized and adjusted dynamic model is used to generate a three-dimensional wind field, and the generated three-dimensional risk is displayed by using visualization technology for the inversion result, including:

[0197] The three-dimensional model generator is used to generate a three-dimensional wind field from the vertical wind field inversion model. Among them, the wind field parameter data in the vertical wind field inversion model is input into the three-dimensional model generator, and after starting the three-dimensional model generator, a three-dimensional wind field model generated by the vertical wind field inversion model is obtained;

[0198] Visualize the three-dimensional wind field model. The visualization process includes: using streamline diagrams to show the flow direction and intensity of the wind field, visualizing the vector field to display the wind speed and direction at each grid point in three-dimensional space, and visualizing the spatial distribution of temperature and humidity;

[0199] Generate a visualization output of the three-dimensional wind field model after visualization processing. After generation, three-dimensional wind field visualization data of the vertical wind field inversion model is obtained.

[0200] Specifically, through a three-dimensional model generator and visualization processing technology, complex wind field data can be presented in an intuitive three-dimensional form. This intuitiveness enables researchers and managers to more easily understand and analyze the flow direction and intensity of the wind field, the distribution of wind speed and direction, and the spatial distribution of temperature and humidity. Using the wind field parameter data of the vertical wind field inversion model as input ensures the accuracy of the three-dimensional wind field model. This accuracy is crucial for fields such as scientific research, meteorological forecasting, and environmental assessment. The application of the three-dimensional model generator and visualization processing technology can efficiently process and analyze a large amount of wind field data. This improves the speed and efficiency of data processing, enabling researchers to obtain the required analysis results faster, supports real-time update and visualization output of the data, which means that researchers can obtain the latest wind field data at any time and conduct real-time analysis and display. This is particularly important for occasions that require real-time monitoring and early warning. It not only supports traditional wind field display methods such as streamline diagrams and vector field visualization but also can display additional information such as the spatial distribution of temperature and humidity. This versatility enables the solution to meet the needs of different fields and research purposes. Through the intuitive three-dimensional wind field visualization data, researchers and managers can better understand the characteristics and change trends of the wind field, thus making more informed decisions. This is of great significance for fields such as meteorological forecasting, wind energy development, and environmental protection. It can real-time monitor and early warn of potential risks in the wind field, such as extreme weather events and equipment failures. This helps to take measures in advance to reduce the losses and impacts caused by risks.

[0201] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0202] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A method for inverting low-altitude atmospheric three-dimensional wind fields based on vertical observation of wind profiler radar, characterized in that: The three-dimensional wind field inversion method is to construct a dynamic model based on the acquired data, simulate the wind field with the constructed dynamic model, generate a three-dimensional wind field using the simulation results and the actual observation data using an inversion algorithm, and then visualize the generated three-dimensional wind field; The three-dimensional wind field inversion method comprises: collecting and quality controlling data provided by wind profile radar and ground meteorological station; Data from different sources are fused and data preprocessed after data fusion is completed; Select a kinetic model from the model library, initialize the selected kinetic model using the data after data preprocessing, and set initial state parameters; The initialized dynamic model is used to simulate the wind field, and the difference between the wind field simulation data and the actual observation data is analyzed. According to the difference analysis results, the initialized dynamic model is optimized and adjusted using the optimization algorithm; The optimized and adjusted dynamic model is used to generate a three-dimensional wind field, and the generated three-dimensional wind field is inverted and displayed using visualization technology.

2. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 1 is characterized in that: Collect and quality control data from wind profiler radars and ground-based meteorological stations, including: The vertical wind speed profile data from the wind profile radar is collected, and the vertical wind speed profile data includes vertical wind speed and horizontal wind speed data of the time series; Collect data from ground weather stations, including basic meteorological element data such as temperature, pressure, humidity, wind speed and wind direction; Conduct quality control on the data provided by wind profiler radar and ground meteorological station respectively; The quality control of wind profile radar data includes: echo quality check, depolarization ratio check, vertical wind field consistency check and horizontal wind field comparison; The quality control of ground weather station data includes: internal consistency check, spatiotemporal continuity check, threshold check and statistical quality control; At the same time, outliers in the data provided by wind profiler radars and ground meteorological stations are corrected or eliminated, and missing data are interpolated; After quality control is completed, actual observation data is obtained.

3. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 2 is characterized in that: Data from different sources are fused and data preprocessed after data fusion is completed, including: Fuse the wind profile radar data and ground meteorological station data that have completed quality control in the actual observation data; Before data fusion, the wind profile radar data and ground meteorological station data that have completed quality control in the actual observation data are quality assessed. The quality assessment uses the isolation forest algorithm to identify outliers and noise points in the wind speed and wind direction data, and marks or removes them. The reliability of the ground meteorological station data is assessed using the Bayesian network model, and the rationality of the data is judged based on the correlation between different meteorological elements. Finally, the data with quality assessment within the high quality standard is selected for data fusion. The data fusion process includes: matching wind profiler radar data with ground meteorological station data, where data matching includes time matching and space matching; time matching is to match wind profiler radar data with ground meteorological station data in time according to timestamps; space matching is to match wind profiler radar data with ground meteorological station data in space according to geographic locations.

4. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 3 is characterized in that: Data from different sources are fused and data preprocessed after data fusion, including: Perform data interpolation on the wind profile radar data and ground meteorological station data after data matching, and the data interpolation includes vertical interpolation and horizontal interpolation; Among them, vertical interpolation is to use linear interpolation or bilinear interpolation on the wind speed profile data of the wind profile radar to interpolate the data to the observation height of the ground meteorological station and supplement the data of different altitude layers; horizontal interpolation is to expand the observation data of the ground meteorological station to the area covered by the radar through the Kriging interpolation method; The wind profile radar data and ground meteorological station data after data interpolation are weighted by using the weighted average method; Before weighted processing, a weighted strategy is formulated, which includes time weight, space weight and data quality weight; Time weight is the weight assigned to near-real-time observation data during the fusion process of wind profile radar data and ground meteorological station data after data interpolation. If the timestamp of the data is very close to the current time, the data will be given a higher weight; Spatial weights are calculated by calculating the spatial distance between radar data and weather station data, and weights are assigned based on points that are close; The data quality weight is dynamically adjusted based on the quality assessment of each data source; The weighted wind profile radar data and the data from the ground meteorological station data are combined to obtain fused data of the wind profile radar data and the ground meteorological station data; Perform data preprocessing on the fused actual observation data; After data preprocessing, wind field fusion data is obtained.

5. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 4 is characterized in that: Select a kinetic model from the model library, initialize the selected kinetic model using the data after data preprocessing, and set the initial state parameters, including: First, the models in the model library are evaluated, including physical properties, mathematical expressions, scope of application and performance records; Confirm the model requirements according to the wind field inversion rules and the evaluation results of the models in the model library, and select the corresponding dynamic model; Initialize the selected dynamic model using wind field fusion data; The initialization process includes: confirming the initialization indicators in the wind field fusion data, and the initialization standards include wind speed, wind direction, temperature, pressure and humidity data; According to the initialization index in the wind field fusion data, the initialization state parameters of the selected dynamic model are set; Among them, the initialization state parameters are set to use the dynamic model and its corresponding initialization method, and the initialization indicators in the wind field fusion data are input through a function or class method, and after the input, the initial state configuration of the selected dynamic model is started; After the initialization state parameters are set, the wind field dynamics model is obtained.

6. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 5, characterized in that: The initialized dynamic model is used to simulate the wind field, and the difference analysis is performed between the wind field simulation data and the actual observation data. According to the difference analysis results, the optimization algorithm is used to optimize and adjust the initialized dynamic model, including: The wind field dynamics model is used to simulate the wind field. Before the wind field simulation is performed, the temporal and spatial scope and resolution of the simulation are first confirmed. At the same time, the physical parameters and boundary conditions of the wind field dynamics model are configured. Finally, the simulation time step and total simulation time are set. After the steps are completed, the wind field data in the wind field dynamics model is confirmed, and the wind field data includes the spatially distributed wind speed, wind direction, temperature and pressure; A wind field model is established according to the wind field dynamics model, and simulation conditions of the wind field model are set according to the wind field data, and finally a target wind field model is obtained; Divide the target area in the target wind field model into a number of grids using a structured grid method; The wind field parameters of each grid are calculated through time deduction. The steps of calculating the wind field parameters include: Input the parameters of the wind field data into the target wind field model and set the initial state in time and space; For each time step, the discrete equation is used for time integration to calculate the state of the target wind field model at the next time step; In each time step, iterative updates are performed in sequence until the set total time is reached; At each time step, the calculated wind field data is saved in the corresponding data set, and the data set is marked as simulated wind field data; The simulated wind field data and the actual observation data are analyzed for differences. The difference analysis includes root mean square error, correlation coefficient and absolute error. The root mean square error is used to quantify the average deviation between the simulated wind field data and the actual observation data; the correlation coefficient is used to evaluate the similarity between the simulated wind field data and the actual observation data; the absolute error is used to calculate the absolute difference between the simulated wind field data and the actual observation data at each grid point or time step; According to the difference analysis results, the data in the simulated wind farm data that has the greatest impact on the target wind farm model is confirmed; According to the confirmed impact data, the particle swarm optimization method is used to optimize the parameter calculation; Adjust the parameters of the target wind farm model according to the optimized parameters, re-simulate the wind farm after the parameters are adjusted, and iterate the new simulation parameters again until the simulation accuracy of the target wind farm model is within a preset range; After the optimization and adjustment of the target wind field model is completed, the vertical wind field inversion model is obtained.

7. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 6, characterized in that: Verify the resolution of the simulation, including: Retrieve the available memory space corresponding to the wind farm simulation system; Extract the rated memory capacity of the wind farm simulation system; Determine the available memory percentage value corresponding to the wind farm simulation according to the ratio between the available memory space corresponding to the wind farm simulation system and the rated memory capacity; Extracting the change rate of wind field data in the vertical direction, wherein the wind field data includes spatially distributed wind speed, wind direction, temperature and pressure; and the change rate of wind field data in the vertical direction includes the average change rate of wind speed, the average change rate of wind direction angle, the average change rate of temperature and the average change rate of pressure; Extracting the frequency of change of the wind field data in each unit time; The resolution of the wind field simulation in the vertical and horizontal directions is obtained according to the change frequency of the wind field data in each unit time, the change rate of the wind field data in the vertical direction and the percentage value of the available memory corresponding to the wind field simulation.

8. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 7, characterized in that: The resolution of the wind field simulation in the vertical and horizontal directions is obtained according to the frequency of change of the wind field data in each unit time, the rate of change of the wind field data in the vertical direction, and the percentage of available memory corresponding to the wind field simulation, including: Extracting the maximum value of the change rate among the change rates of the wind field data in the vertical direction; wherein the change rate of the wind field data in the vertical direction includes the average change rate of wind speed, the average change rate of wind direction angle, the average change rate of temperature and the average change rate of pressure; Normalizing the maximum value of the rate of change of the wind field data in the vertical direction and the value of the available memory ratio corresponding to the wind field simulation to obtain the normalized maximum value of the rate of change and the value of the available memory ratio; Compare the normalized maximum value of the rate of change and the normalized value of the available memory percentage; When the maximum value of the normalized change rate does not exceed the value of the normalized available memory ratio, the simulation resolution is confirmed according to the grid size corresponding to the preset initial vertical resolution and initial horizontal resolution; When the maximum value of the change rate after normalization exceeds the value of the available memory ratio after normalization, the grid sizes corresponding to the initial vertical resolution and the initial horizontal resolution are adjusted using the change frequency of the wind field data in each unit time, the change rate of the wind field data in the vertical direction, and the value of the available memory ratio corresponding to the wind field simulation to obtain the grid sizes corresponding to the adjusted vertical resolution and horizontal resolution; The simulation resolution was confirmed by adjusting the grid size corresponding to the vertical and horizontal resolutions.

9. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 8, characterized in that: The target area in the target wind field model is divided into several grids using the structured grid method, including: Extracting a target area in a target wind field model; Comparing the area value of the target area in the target wind field model with a preset area value reference value to obtain a ratio between the area value of the target area and the preset area value reference value; Comparing a ratio between an area value of the target area and a preset area value reference value with a preset ratio threshold; When the ratio between the area value of the target area and the preset area value reference value does not exceed the preset ratio threshold, the target area is structurally divided using the initial target area grid size to obtain a plurality of grids; When the ratio between the area value of the target area and the preset area value reference value exceeds a preset ratio threshold, the size change rate between the grid size of the adjusted vertical resolution and the grid size of the initial vertical resolution is retrieved as a first size change rate value; Retrieving a size change rate between a grid size of the adjusted horizontal resolution and a grid size of the initial horizontal resolution as a second size change rate value; The initial target area grid size is adjusted by using the ratio between the area value of the target area and the preset area value reference value in combination with the first size change rate value and the second size change rate value to obtain an adjusted target area grid size; The target area is structurally divided according to the adjusted target area grid size to obtain a plurality of grids.

10. The method for inverting low-altitude atmospheric three-dimensional wind field based on vertical observation of wind profiler radar according to claim 9, characterized in that: The optimized and adjusted dynamic model is used to generate a three-dimensional wind field, and the generated three-dimensional wind field is inverted using visualization technology to display the results, including: The vertical wind field inversion model is used to generate a three-dimensional wind field using a three-dimensional model generator, wherein the wind field parameter data in the vertical wind field inversion model is input into the three-dimensional model generator, and after the three-dimensional model generator is started, a three-dimensional wind field model generated by the vertical wind field inversion model is obtained; Visualize the three-dimensional wind field model. The visualization includes: using streamline diagrams to show the flow direction and intensity of the wind field, using vector field visualization to show the wind speed and direction of each grid point in three-dimensional space, and visualizing the spatial distribution of temperature and humidity; The three-dimensional wind field model after visualization processing is visualized and outputted, and three-dimensional wind field visualization data of the vertical wind field inversion model is obtained.

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