Multi-radar collaborative analysis method, device and equipment for valley wind and storage medium

Abstract

The invention provides a valley wind-oriented multi-radar collaborative analysis method, which comprises the following steps of: acquiring model data of a target valley region, and selecting feature points as laser radar arrangement positions based on the model data; deploying a plurality of laser radars at the layout position, coordinating the scanning time sequence and the scanning mode of the plurality of laser radars, and constructing a collaborative observation network; the method comprises the following steps: acquiring atmospheric wind field point cloud data of a valley region through a collaborative observation network, preprocessing the point cloud data, and generating three-dimensional wind field observation data according to the preprocessed point cloud data; assimilating the three-dimensional wind field observation data into a bidirectional coupling numerical model formed by a mesoscale meteorological model and a microscale turbulence model, operating the numerical model, simulating a three-dimensional wind field of a valley region, and outputting a high-resolution four-dimensional wind field analysis result of the valley region. Through combination of multi-radar collaborative observation and multi-scale model bidirectional coupling assimilation, the problems of incomplete wind field observation and inaccurate simulation in a complex terrain are solved.

Description

Technical Field

[0001] This application belongs to the field of radio meteorological observation, specifically involving a multi-radar collaborative analysis method, device, equipment, and storage medium for valley winds. Background Technology

[0002] Valley winds are localized atmospheric circulation phenomena caused by temperature differences between mountain slopes and valleys. During the day, because the mountain slopes heat up faster than the air at the same altitude in the valley, the warm air rises along the slopes, forming a lower-level airflow that blows from the valley floor to the mountain slopes. This phenomenon, along with nighttime mountain winds, forms a 24-hour periodic alternation system, with average wind speeds reaching 2-10 meters per second and an influence depth of 500-1000 meters. It has a significant impact on agricultural production; the moisture transported during the day can increase slope humidity and induce rainfall, and in winter it can alleviate the effects of severe cold. It is an important meteorological phenomenon. Accurate observation and analysis of valley winds are of great significance for valley area weather forecasting, aviation safety, pollutant dispersion assessment, and wind energy resource utilization.

[0003] In valley terrain, the combined effects of surface thermal effects and topographic forcing create valley wind circulations with specific diurnal variations. These local circulations significantly impact regional weather, climate, air quality, and even the distribution of wind energy resources. Therefore, accurate three-dimensional observation and simulation of valley winds have significant scientific value and promising applications.

[0004] Currently, wind field observation mainly relies on direct measurements from meteorological / wind towers and indirect measurements from remote sensing equipment (such as acoustic Doppler radar and lidar). In mountainous terrain, establishing a high-density meteorological tower network is costly and difficult to achieve due to terrain limitations. Therefore, using mobile lidar for wind field observation has become an important technical means.

[0005] In practical applications, existing technologies typically employ a single lidar device for observation. Common scanning modes include Plane Position Indication (PPI) scanning and Range and Height Indication (RHI) scanning. Two-dimensional wind field information within a certain range can be derived from the scan data of a single lidar. However, for valley winds, which possess a significant three-dimensional structure and are strongly influenced by complex terrain, data from a single observation point has obvious limitations. It is difficult to capture the complete three-dimensional structure of the wind field throughout the entire valley area, as well as the entire process of the formation and dissipation of valley wind circulation.

[0006] In numerical simulation, mesoscale meteorological models can simulate large-scale meteorological fields, but their spatial resolution is relatively low, making it difficult to accurately depict the disturbance of airflow by small topographic features such as valleys and hillsides. While microscale models such as computational fluid dynamics (CFD) can provide high-resolution flow field details, their simulation results are highly dependent on the accuracy of boundary conditions. Without the constraints of actual observation data, the simulation results may deviate significantly from the actual wind field.

[0007] To overcome the limitations of single-radar observation, multi-radar collaborative observation technology has been developed in the industry. Existing technologies include solutions that utilize two lidar units to conduct joint detection of specific areas (such as valleys where bridges are located) through coordinated scanning strategies. Such methods typically involve: acquiring radial wind speed data from multiple spatial angles through simultaneous scanning by both radars; using wind field inversion algorithms (such as integral velocity-azimuth processing, IVAP) to initially calculate the horizontal wind field; and estimating the wind speed at target grid points by selecting neighboring raw data points within a defined spatial sphere.

[0008] While the aforementioned dual-radar collaborative technology expands the observation coverage to some extent, it still has several inherent limitations when dealing with complex valley terrain. Existing methods rely heavily on spatial geometry when selecting raw data for target point estimation, failing to adequately consider the physical non-uniformity of the flow field caused by complex terrain, thus introducing systematic errors. Regarding data quality control, existing methods only employ thresholding methods based on global or local statistical characteristics to remove outliers, making it difficult to effectively identify and correct spatial outliers caused by local obstruction or instrument noise. The core output of these methods is mostly direct inversion results based on observations, making it difficult to provide high-resolution four-dimensional wind field analysis data with physical consistency that transcends instantaneous observations, thus limiting their application in accurate forecasting and mechanism research of valley winds.

[0009] Therefore, there is an urgent need to develop a multi-radar collaborative analysis method, device, equipment, and storage medium for valley winds.

[0010] It should be noted that the above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0011] In view of the above problems, this application provides a multi-radar collaborative analysis method, apparatus, device and storage medium for valley winds. The technical solution adopted in the embodiments of this application is as follows.

[0012] The first aspect of this application relates to a multi-radar cooperative analysis method for valley winds, comprising the following steps: S100. Obtain model data of the target valley area, and select feature points as the deployment locations of the lidar based on the model data; S200: Deploy several lidars at the designated locations and coordinate the scanning sequence and scanning mode of these lidars to build a collaborative observation network. S300: Collect atmospheric wind field point cloud data in the valley area through a collaborative observation network, and preprocess the point cloud data to obtain preprocessed point cloud data. S400: Generate three-dimensional wind field observation data based on the preprocessed point cloud data; S500 assimilates the three-dimensional wind field observation data into a two-way coupled numerical model composed of a mesoscale meteorological model and a microscale turbulence model. The numerical model is run to simulate the three-dimensional wind field in the valley area and outputs high-resolution four-dimensional wind field analysis results for the valley area. The three-dimensional wind field observation data acquired by the multi-radar collaborative observation network is assimilated into the two-way coupled numerical model, and finally a four-dimensional wind field analysis result with high spatiotemporal resolution is generated. This enables a dynamic and three-dimensional description of the generation and dissipation process of valley winds, overcoming the shortcomings of single observation or single model in terms of resolution and accuracy.

[0013] In a specific feasible implementation, the step of selecting the location for the lidar deployment in S100 is as follows: S110. Obtain digital elevation model data of the target valley area, and calculate the terrain curvature of each grid point in the target valley area based on the model data. S120. Grid points with terrain curvature greater than a preset curvature threshold and horizontal distance between grid points greater than a preset distance threshold are identified as feature points.

[0014] By quantitatively calculating terrain curvature using a digital elevation model and setting a preset distance threshold, the arbitrariness and uncertainty of point selection based on subjective experience are overcome, ensuring the scientific nature of radar deployment locations. Preferred deployment of radars at grid points where the terrain curvature is greater than the preset curvature threshold and the horizontal distance between grid points is greater than the preset distance threshold enables precise capture of the most significant wind speed and direction changes and dynamic processes at key terrain features such as ridges and valleys.

[0015] In one specific implementation scheme, following S120, the following is also included: S130. Determine the prevailing wind direction in the target valley area based on historical meteorological data; S140. Among the feature points, select the feature points located upwind of the prevailing wind as the deployment locations for the lidar.

[0016] By combining prevailing wind direction information to optimize the deployment location, the observation network can prioritize coverage of the wind's incoming direction under limited resources, ensuring effective monitoring of key areas such as the wind source and inflow boundary conditions in valleys, thereby improving the overall effectiveness and resource utilization efficiency of the observation network.

[0017] In a specific implementation scheme, in S200, the scanning mode includes a combination mode of planar position display scanning and distance and height display scanning. Coordinating the scanning mode of several lidars is to control several lidars to alternately perform planar position display scanning and distance and height display scanning.

[0018] By coordinating multiple lidars to alternately execute different scanning modes, complementary scanning modes for planar position display and range / height display are achieved. This allows for the acquisition of both the horizontal distribution and vertical profile information of the wind field, enriching the three-dimensional dimension of the observation data. The timing control mechanism for alternating scanning avoids potential laser beam interference that can occur when multiple lidars operate simultaneously.

[0019] In a specific feasible implementation, the preprocessing of point cloud data in S300 includes: A filter is used to remove outliers from the radial wind speed time series measured by the collaborative observation network; based on The index performs consistency checks and corrections on wind speed data within a spatial range; The formula for calculating the filter is as follows:

[0020]

[0021]

[0022]

[0023] in, This refers to the sequence number of the instantaneous data in the radial wind speed time series measured by lidar. The width of the sliding window. For The central sliding window data set For sliding windows Any data point within, This represents the median of all radial wind speed values ​​within the sliding window. Sliding window The absolute deviation of the median, To adjust the threshold and The value range is (2, 2.5). The distribution uniformity constant and Set to 1.5, and median() is the median function.

[0024] The median and median absolute deviation filtering algorithm removes outliers from radial wind speed time series, improving data quality and reliability compared to traditional mean-based filters. Spatial consistency verification and correction can identify and correct data points that differ significantly from the surrounding wind field due to local disturbances or instrument errors, ensuring the spatial rationality and consistency of wind field data.

[0025] In one specific feasible implementation, preprocessing also includes: Call the terrain error correction parameter table generated in advance through fluid dynamics simulation; Compensation is performed on the flow field distortion error caused by terrain in the point cloud data.

[0026] Based on a pre-generated hydrodynamic simulation terrain error correction parameter table, point cloud data is compensated to correct flow field distortion errors caused by complex terrain, thereby reducing systematic errors in observation data and improving the physical authenticity and accuracy of the data.

[0027] In one specific feasible implementation, data assimilation in S500 includes: The three-dimensional wind field observation data were assimilated into a mesoscale meteorological model using an ensemble Kalman filter algorithm. Turbulence information simulated by the microscale turbulence model is fed back to the mesoscale meteorological model through a dynamic coupling interface. By employing an ensemble Kalman filter algorithm to assimilate observational data into a mesoscale meteorological model, and utilizing a dynamic coupling interface to feed back microscale turbulence information to the mesoscale model, the advantages of multi-scale models are complemented and they work in tandem. This mechanism allows the mesoscale meteorological model to incorporate microscale topographic disturbance details while obtaining observational constraints, thus improving the simulation accuracy of high-resolution wind fields under complex terrain while ensuring the accuracy of the macroscopic background field.

[0028] The second aspect of this application relates to a multi-radar cooperative analysis device for valley winds, the device comprising: The data acquisition module is used to acquire atmospheric wind field point cloud data in the valley area; The data preprocessing module is used to preprocess the acquired point cloud data; The data simulation module simulates the three-dimensional wind field in the valley area based on the numerical model; The data output module is used to output high-resolution four-dimensional wind field analysis results for valley areas.

[0029] The third aspect of this application relates to a computer device including a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement a multi-radar collaborative analysis method for valley winds.

[0030] The fourth aspect of this application relates to a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement a multi-radar collaborative analysis method for valley winds.

[0031] The beneficial effects of the technical solution provided in this application include at least the following: By deploying multiple lidars based on a scientific point selection method using terrain features, such as curvature, and constructing a collaborative observation network, the limited field of view of a single lidar is overcome. This allows for the simultaneous acquisition of wind field data from multiple angles and locations, thereby capturing the complex three-dimensional spatial structure and evolution of valley winds more completely and accurately. This application employs a preprocessing workflow that includes advanced filtering algorithms and spatial consistency checks, effectively eliminating outliers in the observation data and ensuring data consistency. Furthermore, by introducing a terrain error correction parameter table to compensate for point cloud data, the distortion error of the flow field caused by complex terrain is significantly reduced, providing a high-quality data foundation for subsequent analysis. Accurate simulation at multiple scales and high resolution is achieved by assimilating the three-dimensional wind field data obtained from multi-radar collaborative observations into a two-way coupled numerical model composed of a mesoscale meteorological model and a microscale turbulence model, combining the large-scale background field with the microscale terrain disturbance effect. This data assimilation and model coupling mechanism effectively overcomes the limitations of a single-scale model, thereby outputting wind field analysis results with high spatiotemporal resolution that simultaneously reflect macroscopic laws and local details, significantly improving simulation accuracy and reliability. This application enhances the analytical capabilities for key physical processes in valley winds. Because the collaborative observation network provides richer three-dimensional spatial data, and combined with microscale models capable of resolving turbulence mechanisms, this application not only describes the mean wind field but also helps to reveal details of key dynamic processes in valley winds, such as the recirculation zone, shear layer, and turbulent exchange, providing a powerful tool for scientific research and accurate forecasting. In summary, this application systematically solves the problems of incomplete wind field observations and inaccurate simulations under complex terrain by organically combining multi-radar collaborative observation with bidirectional coupling and assimilation of multi-scale models, ultimately achieving high-precision, high-resolution three-dimensional monitoring and accurate simulation of valley winds. Attached Figure Description

[0032] Figure 1 This application provides a schematic flowchart of a multi-radar collaborative analysis method for valley winds. Figure 2 This application provides a schematic diagram of the lidar deployment location selection process in its embodiments. Figure 3 This application provides a schematic diagram of the structure of a multi-radar collaborative analysis device for valley winds. Figure 4 This application provides a schematic diagram of a computer device structure. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0034] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0035] The following is in conjunction with the appendix Figures 1-4 This application will be described in further detail.

[0036] On the one hand, this application provides a multi-radar cooperative analysis method for valley winds, such as Figure 1 As shown, it includes the following steps: S100. Obtain model data of the target valley area, and select feature points as the deployment locations of the lidar based on the model data; like Figure 2 As shown: S110. Obtain digital elevation model data of the target valley area, and calculate the terrain curvature of each grid point in the target valley area based on the model data; S120. Grid points with terrain curvature greater than a preset curvature threshold and horizontal distance between grid points greater than a preset distance threshold are identified as feature points.

[0037] The preset curvature threshold is set at 0.001 m based on the actual terrain complexity. -1 up to 0.02 m -1 The preset distance threshold should be selected from the range specified above. It should be at least 1.5 times the effective detection radius of the lidar, typically set to 3km to 4km, to ensure that the observation coverage area has no overlapping blind spots.

[0038] S130. Determine the prevailing wind direction in the target valley area based on historical meteorological data; S140. Among the feature points, select the feature points located upwind of the prevailing wind as the deployment locations for the lidar.

[0039] Based on digital elevation models, terrain curvature is quantitatively calculated and distance thresholds are preset, providing objective and repeatable criteria for feature point selection. This overcomes the arbitrariness and uncertainty of relying on subjective experience in point selection, ensuring the scientific nature of radar deployment. Deploying radar at grid points where the terrain curvature exceeds the preset curvature threshold and the horizontal distance between grid points exceeds the preset distance threshold allows for precise capture of the most significant wind speed and direction changes and dynamic processes at key terrain features such as ridges and valley lines, thereby obtaining more representative and valuable observational data. Optimizing deployment locations by incorporating prevailing wind direction information allows the observation network to prioritize coverage of the wind's incoming direction within limited resources. This ensures effective monitoring of key areas such as valley wind sources and inflow boundary conditions, improving the overall effectiveness and resource utilization efficiency of the observation network.

[0040] S200. Deploy several lidars at the deployment location and coordinate the scanning sequence and scanning mode of the lidars to construct a collaborative observation network; the scanning mode includes a combination of planar position display scanning and distance and height display scanning, and coordinating the scanning mode of several lidars is to control the several lidars to alternately execute the planar position display scanning and distance and height display scanning modes.

[0041] By coordinating multiple lidars to alternately execute different scanning modes, complementary scanning modes for planar position display and range / height display are achieved. This allows for the acquisition of both the horizontal distribution and vertical profile information of the wind field, enriching the three-dimensional dimension of the observation data. The timing control mechanism for alternating scanning effectively avoids mutual interference between laser beams that may occur when multiple lidars are operating simultaneously, ensuring the reliability and quality of data acquisition.

[0042] S300. Collect atmospheric wind field point cloud data in the valley area through a collaborative observation network, and preprocess the point cloud data to obtain preprocessed point cloud data; Preprocessing includes: using filters to remove outliers from the radial wind speed time series measured by the collaborative observation network; based on The index performs consistency checks and corrections on wind speed data within a spatial range; the filter calculation formula is as follows:

[0043]

[0044]

[0045]

[0046] in, This refers to the sequence number of the instantaneous data in the radial wind speed time series measured by lidar. The width of the sliding window. For The central sliding window data set For sliding windows Any data point within, This represents the median of all radial wind speed values ​​within the sliding window. Sliding window The absolute deviation of the median, To adjust the threshold and The value range is (2, 2.5). The distribution uniformity constant and Set to 1.5, and median() is the median function.

[0047] A filtering algorithm based on the median and median absolute deviation is used to remove outliers from radial wind speed time series, improving data quality and reliability compared to traditional mean-based filters. Spatial consistency checks and corrections are introduced to identify and correct data points that differ significantly from the surrounding wind field due to local disturbances or instrument errors, ensuring the spatial rationality and consistency of the wind field data.

[0048] Preferably, this application uses the local Moran's I index. The local Moran's I index, also known as the local spatial autocorrelation index or LISA, is a core indicator in spatial statistics used to quantify the similarity of attribute values ​​between a single observation unit and its neighboring units in geospatial space, i.e., spatial autocorrelation. It overcomes the limitation of the global Moran's index, which can only characterize the overall spatial pattern and cannot locate specific anomalies. Based on the physical characteristics of wind fields having spatial continuity and correlation, this application selects the local Moran's I index for spatial consistency testing. Compared to traditional spatial interpolation residual analysis, this index can more sensitively identify spatial outliers hidden against a smooth wind field background. These outliers are often caused by local shading or transient instrument malfunctions.

[0049] The filter is preferably based on the absolute deviation of the median, which has the advantage of being insensitive to impulse outliers. In other embodiments of the invention, Gaussian filtering based on three times the standard deviation or the DBSCAN clustering algorithm can also be used for outlier removal. Although the robustness is not strong, the purpose of the invention can still be achieved.

[0050] This scheme aims to perform spatial consistency checks on 3D atmospheric wind field point cloud data acquired by a lidar collaborative observation network. The core objective is to identify and locate spatial outliers caused by measurement noise, local occlusion, or model assimilation errors. A spatial weight matrix is ​​constructed based on the spatial coordinates of observation points or analysis grid points. This matrix defines the "neighborhood" set of each spatial cell using either a distance threshold method or the K-nearest neighbor method, and typically employs binary weights or inverse distance weights to characterize the interaction strength between spatial cells. For each spatial cell, its local Moran's index statistic is calculated. The local Moran's index statistic assesses its spatial clustering pattern by comparing the deviation between the wind speed value of that cell and the weighted average of the wind speed values ​​of its neighboring cells. The calculation results can be summarized into four typical local spatial association patterns: High-high clustering: This indicates that the spatial cell itself has a high wind speed value, and its neighboring cells also have high values, showing a positive autocorrelation in space. This pattern usually corresponds to the main wind belt or acceleration zone of valley winds.

[0051] Low-low clustering: This indicates that the unit and its neighboring units have low wind speeds and also show positive spatial autocorrelation, which may correspond to a wind shadow area or a calm area.

[0052] High-low anomaly: This indicates that the cell has a high wind speed, but its neighboring cells all have low values, showing a significant spatial negative autocorrelation. This model identifies a potential spatial outlier.

[0053] Low-high anomaly: This indicates that the unit has a low wind speed value, but its neighboring units all have high values. It also shows spatial negative autocorrelation and is another typical type of spatial outlier.

[0054] The calculated local Moran index is subjected to statistical significance testing, such as a permutation test, to ensure that the identified spatial patterns are not randomly generated. Based on this, all "high-low anomalies" and "low-high anomalies" that pass the significance test are identified; these points are judged as spatial inconsistencies that disrupt the spatial continuity of the wind field. For these spatial inconsistencies, a data correction algorithm is initiated. Correction strategies may include direct removal followed by filling in the gaps using Kriging spatial interpolation, or spatial smoothing filtering based on the values ​​of neighboring points, thereby eliminating unreasonable data abrupt changes and enhancing the spatial consistency and physical plausibility of the entire wind field dataset.

[0055] This scheme utilizes the local Moran index, a spatial statistical analysis tool, to achieve objective and quantitative diagnosis and correction of spatial outliers in multi-source radar wind field data. This process significantly improves the quality of the raw observation data.

[0056] Preprocessing also includes: calling the terrain error correction parameter table generated in advance through fluid dynamics simulation; and compensating for flow field distortion errors caused by terrain in the point cloud data.

[0057] Based on the terrain error correction parameter table pre-generated by fluid dynamics simulation, point cloud data can be compensated, which can specifically correct the flow field distortion error caused by complex terrain, significantly reduce the systematic error of the observation data, and improve the physical authenticity and accuracy of the data.

[0058] S400. Generate three-dimensional wind field observation data based on the preprocessed point cloud data; S500 assimilates three-dimensional wind field observation data into a two-way coupled numerical model composed of a mesoscale meteorological model and a microscale turbulence model. The numerical model is run to simulate the three-dimensional wind field in the valley area and outputs high-resolution four-dimensional wind field analysis results for the valley area. The preferred mesoscale meteorological model is Weather Research and Forecasting (WRF) model V4.0, and the preferred microscale turbulence model is the Large Eddy Simulation (LES) model. The two are dynamically coupled through a Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer scheme. Specifically, WRF provides the large-scale background field and boundary conditions to LES, while LES feeds back the simulated high-resolution turbulence information to the WRF model every 10 minutes, thus achieving bidirectional information exchange from scales of hundreds of meters to kilometers.

[0059] The three-dimensional wind field observation data are assimilated into the mesoscale meteorological model using an ensemble Kalman filter algorithm; the turbulence information simulated by the microscale turbulence model is fed back into the mesoscale meteorological model through a dynamic coupling interface.

[0060] The dynamic coupling interface is implemented as follows: at each coupling time step, such as 5 minutes, the turbulent kinetic energy field of the layer 40m above the ground in the LES simulation domain is mapped to the boundary layer parameterization scheme of the corresponding grid of the WRF model through bilinear interpolation, as a correction term for its turbulence intensity. By employing an ensemble Kalman filter algorithm, observational data is assimilated into a mesoscale meteorological model, and microscale turbulence information is fed back to the mesoscale model via a dynamic coupling interface, achieving complementary advantages and bidirectional synergy among multi-scale models. This mechanism allows the mesoscale model to incorporate microscale topographic disturbance details while obtaining observational constraints, thus improving the simulation accuracy of high-resolution wind fields under complex terrain while ensuring the accuracy of the macroscopic background field.

[0061] The three-dimensional wind field observation data acquired by the multi-radar collaborative observation network is assimilated into the two-way coupled numerical model, and finally a four-dimensional wind field analysis result with high spatiotemporal resolution is generated. This enables a dynamic and three-dimensional description of the generation and dissipation process of valley winds, effectively overcoming the shortcomings of single observation or single model in terms of resolution and accuracy.

[0062] Another aspect involves a multi-radar collaborative analysis device for valley winds, such as... Figure 3 As shown, the device includes: The data acquisition module is used to acquire atmospheric wind field point cloud data in the valley area; The data preprocessing module is used to preprocess the acquired point cloud data; The data simulation module simulates the three-dimensional wind field in the valley area based on the numerical model; The data output module is used to output high-resolution four-dimensional wind field analysis results for valley areas.

[0063] Another aspect relates to a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the processor loads and executes at least one instruction, at least one program, code set or instruction set to realize a multi-radar collaborative analysis method for valley winds.

[0064] Another aspect relates to a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement a multi-radar collaborative analysis method for valley winds.

[0065] By deploying multiple lidars based on scientific point selection methods using terrain features such as curvature and constructing a collaborative observation network, the limitations of single-radar observation are overcome. This allows for the simultaneous acquisition of wind field data from multiple angles and locations, thus capturing the complex three-dimensional spatial structure and evolution of valley winds more completely and accurately. This application employs a preprocessing workflow that includes advanced filtering algorithms and spatial consistency checks, effectively eliminating outliers in the observation data and ensuring data consistency. Furthermore, by introducing a terrain error correction parameter table to compensate for point cloud data, the distortion error of the flow field caused by complex terrain is significantly reduced, providing a high-quality data foundation for subsequent analysis. Accurate simulation at multiple scales and high resolution is achieved by assimilating the three-dimensional wind field data obtained from multi-radar collaborative observations into a two-way coupled numerical model composed of a mesoscale meteorological model and a microscale turbulence model, combining the large-scale background field with the microscale terrain disturbance effect. This data assimilation and model coupling mechanism effectively overcomes the limitations of single-scale models, thereby outputting high spatiotemporal resolution wind field analysis results that simultaneously reflect macroscopic laws and local details, significantly improving simulation accuracy and reliability. This application enhances the analytical capabilities for key physical processes in valley winds. Because the collaborative observation network provides richer three-dimensional spatial data, and combined with microscale models capable of resolving turbulence mechanisms, this application not only describes the mean wind field but also helps to reveal details of key dynamic processes in valley winds, such as the recirculation zone, shear layer, and turbulent exchange, providing a powerful tool for scientific research and accurate forecasting. In summary, this application systematically solves the problems of incomplete wind field observations and inaccurate simulations under complex terrain by organically combining multi-radar collaborative observation with bidirectional coupling and assimilation of multi-scale models, ultimately achieving high-precision, high-resolution three-dimensional monitoring and accurate simulation of valley winds.

[0066] It should be noted that the real-time ranging device for moving targets provided in this embodiment is only an example of the above-described division of functional modules / units. In practical applications, the above functions can be assigned to different functional modules / units as needed, that is, the internal structure of the real-time ranging device can be divided into different functional modules / units to complete all or part of the functions described above. Furthermore, the implementation method of the multi-radar cooperative analysis method for valley winds provided in the above method embodiment and the implementation method of the multi-radar cooperative analysis device for valley winds provided in this embodiment belong to the same concept. For details of the specific implementation process of the multi-radar cooperative analysis device for valley winds provided in this embodiment, please refer to the above method embodiment, which will not be repeated here.

[0067] Figure 4This illustration shows a structural block diagram of a computer device provided in an exemplary embodiment of this application. The computer device can be a desktop computer, a laptop computer, a handheld computer, or a cloud server, etc. The computer device may include, but is not limited to, a processor and memory. The processor and memory can be connected via a bus or other means. The processor can be a Central Processing Unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, graphics processing units (GPUs), embedded neural network processing units (NPUs) or other dedicated deep learning coprocessors, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0068] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0069] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above embodiments. The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] In some embodiments, the computer device may also optionally include: a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface can be connected via a bus or signal lines. Each peripheral device can be connected to the peripheral device interface via a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of: a radio frequency circuit, a display screen, and a keyboard.

[0071] Peripheral device interfaces can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory. In some embodiments, the processor, memory, and peripheral device interface are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor, memory, and peripheral device interface can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0072] The display screen is used to display the UI (User Interface). This UI can include graphics, text, icons, videos, and any combination thereof. When the display screen is a touch screen, it also has the ability to collect touch signals on or above the surface of the display. These touch signals can be input as control signals to a processor for processing. In this case, the display screen can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen, located on the front panel of the computer device; in other embodiments, there may be at least two display screens, respectively located on different surfaces of the computer device or in a folded design; in still other embodiments, the display screen may be a flexible display screen, located on a curved or folded surface of the computer device. Furthermore, the display screen can be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0073] A power supply is used to power the various components in a computer device. The power supply can be alternating current (AC), direct current (DC), a disposable battery, or a rechargeable battery. When the power supply includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is charged via a wired connection, while a wireless rechargeable battery is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0074] Those skilled in the art will understand that the structure shown in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0075] This application also discloses a computer-readable storage medium. Specifically, the computer-readable storage medium is used to store a computer program, which, when executed by a processor, implements the methods described in the above-described method embodiments. Those skilled in the art will understand that implementing all or part of the processes in the methods described above can be accomplished by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0076] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A multi-radar collaborative analysis method for valley winds, characterized in that, Includes the following steps: S100: Obtain model data of the target valley area, and select feature points as the deployment locations of the lidar based on the model data; S200. Deploy several lidars at the deployment location and coordinate the scanning sequence and scanning mode of the lidars to construct a collaborative observation network. S300. Collect atmospheric wind field point cloud data of the valley area through the collaborative observation network, and preprocess the point cloud data to obtain preprocessed point cloud data. S400. Generate three-dimensional wind field observation data based on the preprocessed point cloud data; S500. Assimilate the three-dimensional wind field observation data into a two-way coupled numerical model composed of a mesoscale meteorological model and a microscale turbulence model, run the numerical model, simulate the three-dimensional wind field of the valley area, and output high-resolution four-dimensional wind field analysis results of the valley area.

2. The multi-radar collaborative analysis method for valley winds according to claim 1, characterized in that, The steps for selecting the deployment location of the lidar as described in S100 are as follows: S110. Obtain digital elevation model data of the target valley area, and calculate the terrain curvature of each grid point in the target valley area based on the model data; S120. The grid points whose terrain curvature is greater than a preset curvature threshold and whose horizontal distance between grid points is greater than a preset distance threshold are identified as the feature points.

3. The multi-radar collaborative analysis method for valley winds according to claim 2, characterized in that, Following S120, the following is also included: S130. Determine the prevailing wind direction of the target valley area based on historical meteorological data; S140. Among the feature points, select the feature point located upwind of the prevailing wind direction as the location for deploying the lidar.

4. The multi-radar collaborative analysis method for valley winds according to claim 1, characterized in that: In S200, the scanning mode includes a combination of planar position display scanning and distance / height display scanning. Coordinating the scanning modes of several lidars involves controlling several lidars to alternately perform planar position display scanning and distance / height display scanning.

5. The multi-radar collaborative analysis method for valley winds according to claim 1, characterized in that, The preprocessing of the point cloud data in step S300 includes: An outlier was removed from the radial wind speed time series measured by the collaborative observation network using a filter. based on The index performs consistency checks and corrections on wind speed data within a spatial range; The formula for calculating the filter is as follows: For all in, This refers to the sequence number of the instantaneous data in the radial wind speed time series measured by lidar. The width of the sliding window. For The central sliding window data set For sliding windows Any data point within, This represents the median of all radial wind speed values ​​within the sliding window. Sliding window The absolute deviation of the median, To adjust the threshold and The value range is (2, 2.5). The distribution uniformity constant and Set to 1.5, and median() is the median function.

6. The multi-radar collaborative analysis method for valley winds according to claim 5, characterized in that, The preprocessing also includes: Call the terrain error correction parameter table generated in advance through fluid dynamics simulation; The point cloud data is compensated for flow field distortion errors caused by terrain.

7. The multi-radar collaborative analysis method for valley winds according to claim 1, characterized in that, The data assimilation in S500 includes: The three-dimensional wind field observation data were assimilated into a mesoscale meteorological model using an ensemble Kalman filter algorithm. The turbulence information simulated by the microscale turbulence model is fed back to the mesoscale meteorological model through a dynamic coupling interface.

8. A multi-radar collaborative analysis device for valley winds, characterized in that, The device includes: The data acquisition module is used to acquire atmospheric wind field point cloud data in the valley area; The data preprocessing module is used to preprocess the acquired point cloud data; The data simulation module simulates the three-dimensional wind field in the valley area based on the numerical model; The data output module is used to output high-resolution four-dimensional wind field analysis results for valley areas.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, a code set, or an instruction set, the at least one instruction, the at least one program, the code set, or the instruction set being loaded and executed by the processor to implement a multi-radar collaborative analysis method for valley winds as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement a multi-radar collaborative analysis method for valley winds as described in any one of claims 1 to 7.

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