Dual-laser wind-finding radar wind field inversion method based on valley bridge wind observation

Abstract

The invention relates to a dual-laser wind-finding radar wind field inversion method based on valley bridge wind observation, and belongs to the field of radar meteorology, and the method comprises the steps: carrying out the coordinated scanning of two radars, obtaining radar radial data, and calculating the coordinate information of radar radial data points; calculating target three-dimensional wind field coordinate information, and performing inversion on radar radial data through an IVAP technology to obtain horizontal wind data; and screening original data points for estimating single target three-dimensional wind field data one by one, performing quality control on the screened original data points, and finally estimating wind speed data of the target wind field data points. According to the method, key data points most valuable for unknown point wind speed estimation can be rapidly extracted from a large number of radar scanning data points, data redundancy is remarkably reduced, calculation efficiency is improved, and the method has great significance in real-time wind field inversion and dynamic monitoring.

Description

Technical Field

[0001] The present invention relates to the field of radar meteorology, and particularly to a method for wind field inversion of a dual-laser wind lidar based on valley bridge wind observation. Background Art

[0002] In the fields of meteorological observation and wind engineering, the wind field characteristics in the valley bridge area have an important impact on the safety of bridge structures, traffic operation, and the surrounding environment. Due to the complex terrain, the wind field in the valley bridge area often exhibits a high degree of non-uniformity and dynamic changes. In recent years, laser wind lidar technology has gradually become an important means of wind field observation due to its advantages such as high precision, non-contact measurement, and high spatial resolution. However, when a single laser wind lidar observes complex terrain, there are still certain limitations, such as observation blind spots and limited data coverage. Therefore, traditional single-point wind speed measurement methods are difficult to comprehensively and accurately reflect the spatial distribution and change characteristics of the wind field. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for wind field inversion of a dual-laser wind lidar based on valley bridge wind observation, which solves the deficiencies existing in the prior art.

[0004] The purpose of the present invention is achieved through the following technical solutions: A method for wind field inversion of a dual-laser wind lidar based on valley bridge wind observation, the method comprising:

[0005] S1. Coordinating scans by two radars to obtain radar radial data, and calculating the coordinate information of radar radial data points;

[0006] S2. Calculating the three-dimensional wind field coordinate information of the target, and inversely calculating the horizontal wind data from the radar radial data through the IVAP technology;

[0007] S3. Screening the original data points for estimating the three-dimensional wind field data of a single target one by one, performing quality control on the screened original data points, and finally estimating the wind speed data of the target wind field data points.

[0008] The calculation of the coordinate information of the radar radial data points specifically includes the following content:

[0009] A1. Assume that the two radars are radar A and radar B respectively, where the longitude, latitude, and altitude information of radar A is , and the longitude, latitude, and altitude information of radar B is . Taking the due east direction as the positive x-axis direction, the due north direction as the positive y-axis direction, and the vertically upward direction as the positive z-axis direction, create Cartesian rectangular coordinate systems with the positions of the two radars as the coordinate origins respectively;

[0010] A2. The elevation angle, azimuth angle Az, number of range bins, and range bin length in the multi-layer RPI scanning mode are used to calculate the coordinates of the radial data points in a single scan relative to the radar in a rectangular coordinate system. Here, n = 1, 2, 3,..., N, where N is the number of radial data points, and RPI represents azimuth sector scanning. The elevation angle, azimuth angle Az, number of range bins and range bin length information are used to calculate the coordinates of the radial data points in a rectangular coordinate system relative to the radar. Here, n = 1, 2, 3,..., N, where N is the number of radial data points, and RPI represents azimuth sector scanning.

[0011] A3. The coordinate information of the single-radar data points obtained in A2 relative to the radar is calculated with the longitude, latitude, and altitude information of the radar itself to obtain the longitude, latitude, and altitude information of the radial data points. The longitude, latitude, and altitude information of the radial data points .

[0012] The calculation of the target three-dimensional wind field coordinate information specifically includes the following:

[0013] B1. The longitude, latitude, and altitude information of the center of the measured space is calculated from the longitude, latitude, and altitude information of two radars as ;

[0014] B2. Assuming that the distance resolution of the target three-dimensional wind field in the radial, latitudinal, and vertical directions is Dis, with the center of the measured space as the origin and Dis as the step size, the minimum values in the radial, latitudinal, and vertical directions of the three-dimensional wind field are calculated as , , and the maximum values , , ;

[0015] B3. The longitude, latitude, and altitude information of a single data point of the target three-dimensional wind field is expressed as where , , , n 1 , n 2 , n 3 are all positive integers.

[0016] The inversion of the radar radial data to obtain horizontal wind data through the integrated velocity-azimuth processing technique (IVAP) specifically includes the following:

[0017] C1. The IVAP technique uses the radar-observed radial velocity data and azimuth angle information. When the radar scanning elevation angle is less than the set value, the velocity components u and v of the wind field are inverted using the least squares method. The radial velocity V observed by the radar rDenote the projections of the velocity components \(u\) and \(v\) of the wind field onto the radar beam direction , where \(\theta\) is the elevation angle of the radar beam , \(\varphi\) is the azimuth angle of the radar beam, representing the angle between the radar beam and the due north direction, \(u\) is the velocity component in the east - west direction, and \(v\) is the velocity component in the north - south direction;

[0018] C2. Assume that the wind field is uniform within the range of the azimuth angle , then the velocity components of the wind field are calculated by the formula as and , where and are the ranges of the azimuth angle.

[0019] The specific content of S3 is as follows:

[0020] S31. Take the due east direction as the positive x - axis direction, the due north direction as the positive y - axis direction, and the vertically upward direction as the positive z - axis direction, create a Cartesian rectangular coordinate system with the current radar location as the origin , traverse the target data points, and use the longitude, latitude, and altitude information of the data points and the current radar to calculate the coordinate information \((dx, dy, dz)\) of a certain target data point in the rectangular coordinate system;

[0021] S32. In the three - dimensional rectangular coordinate system, project the sphere with the data point as the center and \(R\) as the radius onto the plane \(xOy\) formed by the x - axis, y - axis, and the origin , project the sphere onto the plane \(zOm\) formed by the z - axis, the ray and the origin , and calculate the values of four auxiliary angles \(\alpha\) 1 , \(\alpha\) 2 , \(\beta\) 1 , \(\beta\) 2 from the two projections to determine whether there is an intersection between the target data point and the scanning range of the radar at the origin;

[0022] S33. Judge whether the target data point is within the single - scan range of the radar. If both criterion a and criterion b are satisfied, the data point can obtain an estimated value through subsequent steps, and continue to perform step S34 to screen the elevation angle of the original data point. Otherwise, analyze the next target data point and repeat the judgment in step S33 until a target data point that meets the conditions is obtained;

[0023] S34. Traverse starting from the first scanning layer of the two radars respectively, and judge whether each scanning layer intersects with the sphere with the current target data point There is an intersection with a sphere centered at the sphere center and with a radius of R. If the elevation angle of the scanning layer satisfies criterion c, then the original data available for estimating the target data point is included in this scanning layer , and continue with step S35 to screen the azimuth angle of the original data point. Otherwise, analyze the next scanning layer and repeat the judgment in step S34 until a scanning layer that meets the conditions is obtained;

[0024] S35. Traverse all the radials on the radar scanning layer obtained in step S34, and judge the azimuth angle of each radial relative to the radar whether it meets criterion d. If the radial azimuth angle meets criterion d, then continue with step S36 to screen the range bins of the radial data points. Otherwise, analyze the next radial and repeat the judgment in step S35 until a radial that meets the conditions is obtained;

[0025] S36. Traverse all the range bins on a single radial obtained in step S35, and screen out the original data points within a distance of R from the target data point , and judge whether each range bin number meets criterion e. If the bin number meets criterion e, and the wind speed of the original data points on this range bin is a valid value, then continue with step S37 to screen the original data points. Otherwise, analyze the next range bin and repeat the judgment in step S36 until a range bin number that meets the conditions is obtained, represents the coordinates of the original data in the current coordinate system;

[0026] S37. Calculate for a single target data point and the original data points obtained in step S36 to obtain the spatial distance between the two points. If the distance meets criterion f, then record this original data point. Otherwise, analyze the next original data point. Repeat the judgment in step S37 until all the original data points that meet the conditions are obtained, and then continue with step S38;

[0027] S38. Statistically calculate all the original data points that meet the conditions obtained in step S37 to obtain the number N of the original data points used to estimate the target data point 1 , set a quantity threshold N 0 . If N 1 ≥N 0 , it is considered that the amount of original data is sufficient. Otherwise, directly use this data point Set the wind speed to an invalid value, and then execute step S32 to process the next target data point. If the amount of original data is sufficient, calculate the mean values of the u and v velocity components of the original data points respectively 、 and the standard deviation 、 , set the threshold , remove the data points whose values in the u and v velocity components exceed the corresponding thresholds. After the processing is completed, record the number of remaining original data points as N 2 ;

[0028] S39. Calculate the weight values of each original data point and estimate the target data point. Select the exponent q. From the formulas and , obtain the weight values of the original data points used to estimate the data point , and the u and v velocity components of the data point . represents the weight of the original data point , z = 1, 2,..., N 2 , N 2 is the number of original data points for calculating the current data point , represents the velocity component u or v of the original data point , represents the velocity component u or v of the data point ;

[0029] S310. Synthesize the horizontal wind at the target data point from the u and v velocity components.

[0030] The expression of the criterion a includes: , L represents the spatial distance between the target data point and the origin, is the maximum number of distance bins, is the length of the distance bin;

[0031] The expression of the criterion b includes: or , and correspond to the elevation angles of the first scan and the last scan in the multi-layer RPI scan of the radar respectively. I represents the total number of radar scan layers;

[0032] The expression of the decision c includes: , where is the elevation angle value of the i-th layer of scan data, i = 1, 2,..., I, and I is the total number of scan layers;

[0033] The expression of the criterion d includes: , where is the azimuth angle of the i-th scanning layer and the j-th radial direction, j = 1, 2, ..., J, where J is the total number of radial directions of the i-th scanning layer;

[0034] The expression of the criterion e includes: , roughly screening out the original data points within a distance of R from the target data point , represents the k-th distance bin on the j-th radial direction of the i-th scanning layer, k = 1, 2, ..., K, where K is the total number of distance bins on the j-th radial direction of the i-th scanning layer;

[0035] The expression of the criterion f includes: .

[0036] The specific process of obtaining radar radial data through coordinated scanning by two radars includes:

[0037] Place the two radars at both ends of the bridge respectively, and use the multi-layer RPI mode for coordinated scanning simultaneously to obtain the data near the bridge deck;

[0038] Preprocess and quality control the obtained data to ensure the accuracy and reliability of the data, and obtain the radar radial data.

[0039] The present invention has the following advantages: A method for wind field inversion of a dual-laser anemometer radar based on valley bridge wind observation can quickly extract the key data points most valuable for estimating the wind speed of unknown points from a large number of radar scan data points, significantly reducing data redundancy and improving calculation efficiency, which is of great significance for real-time wind field inversion and dynamic monitoring; By calculating the mean and standard deviation of wind field data points and setting dynamic thresholds based on these statistics, the quality control of radar scan data points is realized, effectively removing abnormal data points. This not only reduces the interference of redundant data on the wind field inversion results, but also improves the automation degree and reliability of data screening, and can better adapt to the non-uniformity and dynamic changes of the wind field, providing more efficient and accurate data support for real-time wind field inversion and dynamic monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the flow schematic diagram of the present invention;

[0041] Figure 2 is the schematic diagram of a three-dimensional rectangular coordinate system;

[0042] Figure 3 is the schematic diagram of the projection coordinates on the xOy plane;

[0043] Figure 4 is the schematic diagram of the projection coordinates on the zOm plane. DETAILED DESCRIPTION OF THE INVENTION

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part rather than all of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of this application provided below with reference to the accompanying drawings is not intended to limit the protection scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts fall within the protection scope of this application. The following further describes the present invention with reference to the accompanying drawings.

[0045] As Figure 1 shown, the present invention specifically relates to a method for wind field inversion of a dual-laser wind lidar based on valley bridge wind observation. By optimizing the cooperative observation strategy and data processing algorithm of the dual lidar, the inversion of the wind field in the valley bridge area is realized, the wind field environment around the bridge is monitored, and the safety of bridge construction workers and vehicle traffic is ensured; specifically, it includes the following contents.

[0046] Step 1: The two lidars perform coordinated scanning to obtain radial data;

[0047] 1.1. The two lidars are respectively located at both ends of the bridge and simultaneously use the multi-layer RPI (azimuth sector scan) mode for cooperative scanning to obtain data near the bridge deck.

[0048] 1.2. Preprocess and quality control the data, including removing noise, interference, etc., to ensure the accuracy and reliability of the data and obtain the radar radial data.

[0049] Step 2: Calculate the coordinate information of the radar radial data points;

[0050] 2.1. Let the two lidars be lidar A and lidar B respectively, where the longitude, latitude, and altitude information of lidar A is , and the longitude, latitude, and altitude information of lidar B is . With the due east direction as the positive x-axis direction, the due north direction as the positive y-axis direction, and the vertically upward direction as the positive z-axis direction, a Cartesian rectangular coordinate system with the positions of the two lidars as the coordinate origins is respectively created.

[0051] 2.2. Calculate the coordinate of the data points of a single lidar. From the elevation angle , azimuth angle Az, number of range bins , and range bin length information in the multi-layer RPI scan mode, calculate the radial data points Coordinates relative to the radar in a rectangular coordinate system , where n = 1, 2, 3, ..., N, and N is the number of radial data points.

[0052] 2.3. Calculate the longitude, latitude, and altitude information of the radial data points by calculating the coordinate information of the single radar data points obtained in 2.2 relative to the radar and the longitude, latitude, and altitude information of the radar itself. The longitude, latitude, and altitude information , where n = 1, 2, 3, ..., N, and N is the number of radial data points.

[0053] Step 3. Calculate the target three-dimensional wind field coordinate information;

[0054] 3.1. Calculate the longitude, latitude, and altitude information of the center of the measured space from the longitude, latitude, and altitude information of the two radars:

[0055] ,

[0056] 3.2. Based on the terrain information near the bridge, preliminarily calculate the maximum range of the three-dimensional wind field. Set the distance resolution of the target three-dimensional wind field in the radial, latitudinal, and vertical directions to Dis. Starting from the center of the measured space as the origin and with Dis as the step size, expand in the radial, latitudinal, and vertical directions respectively to calculate the minimum values , , and the maximum values , , .

[0057] 3.3. The longitude, latitude, and altitude information of a single data point in the target three-dimensional wind field can be expressed as:

[0058] ,

[0059] where , , , n 1 , n 2 , n 3 are all positive integers.

[0060] Step 4. Invert the horizontal wind data from the radial wind data;

[0061] Based on the assumption of local wind field uniformity, the integral velocity-azimuth processing technique (IVAP) is used to invert the horizontal wind from the radial wind data. When the radar scanning elevation angle is less than 20°, the vertical velocity is not considered. The IVAP technique uses the radial velocity data and azimuth angle information to invert the horizontal components u and v of the wind field by the least squares method. The radial velocity V observed by the radarr It can be expressed as the projections of the velocity components u and v of the wind field in the direction of the radar beam:

[0062] ,

[0063] where θ is the elevation angle of the radar beam; is the azimuth angle of the radar beam, representing the angle between the radar beam and the due north direction; u is the velocity component in the east - west direction, and v is the velocity component in the north - south direction.

[0064] During the calculation process, first, the radial velocity data observed by the radar are screened and averaged to remove invalid values.

[0065] Assume that the wind field is uniform within the azimuth angle range. The velocity components of the wind field can be calculated through the following formulas:

[0066] ,

[0067] ,

[0068] where, and are the azimuth angle ranges.

[0069] Step 5: Using the original data points obtained in Step 2 and the longitude, latitude, and altitude coordinate information of the target three - dimensional wind field in Step 3, calculate the set of original data points used to estimate a single data point in the target wind field. When setting interpolation estimation, select the original data points within a spatial distance of R or less (including R) from the estimation point within the detection range of the two radars. To improve the screening efficiency, by projecting onto multiple coordinate planes, quickly obtain the range information of the scan layer number, radial number, and distance bin number where the qualified original data points are located, and then calculate the coordinates of the original data points and the target wind field data points to obtain the set of original data points within a distance of R or less from the target data point. The specific method is as follows:

[0070] 5.1. Screen the original data points of the two radars respectively: As Figure 2 shown, with the due east direction as the positive x - axis direction, the due north direction as the positive y - axis direction, and the vertically upward direction as the positive z - axis direction, create a Cartesian rectangular coordinate system with the current radar location as the origin . Traverse the target data points, and use the longitude, latitude, and altitude information of the data points and the radar to calculate the coordinate information (dx, dy, dz) of a certain data point in the rectangular coordinate system. Connect the center of the sphere and the origin to make a ray , and project onto the plane formed by the x - axis, y - axis, and the origin The projection of the formed plane on the xOy plane yields a ray .

[0071] 5.2. In a three-dimensional rectangular coordinate system, project the sphere with the data point as the center of the sphere and R as the radius onto the plane xOy formed by the x-axis, y-axis, and the origin . The projection is as shown in Figure 3 ; in a three-dimensional rectangular coordinate system, project the sphere onto the plane zOm formed by the z-axis, the ray , and the origin . The projection is as shown in Figure 4 . Calculate the values of four auxiliary angles from the two projections to determine whether there is an intersection between the target data point and the scanning range of the radar at the origin. The four auxiliary angles α 1 , α 2 , β 1 , and β 2 are calculated as shown in the following equations:

[0072] ,

[0073] ,

[0074] ,

[0075] ,

[0076] ,

[0077] ,

[0078] where (dx, dy, dz) represents the coordinates of the target data point in the current coordinate system, and L represents the spatial distance between the target data point and the origin.

[0079] 5.3. Determine whether the target data point is within the single scan range of the radar. If both criterion a and criterion b are satisfied, the target data point can be used to calculate the estimated value through subsequent steps, and continue with the screening of the elevation angle of the original data point in 5.4. Otherwise, analyze the next target data point and repeat the judgment in 5.3 until a target data point that meets the conditions is obtained. The expressions for criterion a and b are as follows:

[0080] a. ,

[0081] b. or ,

[0082] where is the maximum number of distance bins, is the distance library length, and correspond to the elevation angles of the first scan layer and the last scan layer of the radar in the multi-layer RPI scan respectively. I represents the total number of scan layers of the radar.

[0083] 5.4. Starting from the first scan layer of each of the two radars, traverse to determine whether each scan layer intersects with a sphere centered at the current target data point with a radius of R. If the elevation angle of the scan layer satisfies criterion c, then the scan layer contains the raw data that can be used to estimate the target data point . Continue with the screening of the azimuth angle of the raw data points in 5.5. Otherwise, analyze the next scan layer and repeat the judgment in 5.4 until a scan layer that meets the conditions is obtained. The expression of criterion c is as follows:

[0084] c, ,

[0085] where is the elevation angle value of the i-th layer of scan data, i = 1, 2,..., I, and I is the total number of scan layers.

[0086] 5.5. Traverse all the radials on the radar scan layer obtained in 5.4 to determine whether the azimuth angle of each radial relative to the radar meets criterion d. If the azimuth angle of the radial satisfies criterion d, then continue with the screening of the distance libraries of the radial data points in 5.6. Otherwise, analyze the next radial and repeat the judgment in 5.5 until a radial that meets the conditions is obtained. The expression of criterion d is as follows:

[0087] d, ,

[0088] where is the azimuth angle of the j-th radial in the i-th scan layer, j = 1, 2,..., J, and J is the total number of radials in the i-th scan layer.

[0089] 5.6. Traverse all the distance libraries on the single radial obtained in 5.5, roughly screen out the raw data points within a distance of R from the target data point , and judge whether each distance library number meets criterion e. If the library number satisfies criterion e and the wind speed of the raw data points on this distance library is a valid value, then continue with the screening of the raw data points in 5.7. Otherwise, analyze the next distance library and repeat the judgment in 5.6 until a distance library number that meets the conditions is obtained. The expression of criterion e is as follows:

[0090] e, ,

[0091] Where L is the target data point obtained in 5.2 The spatial distance from the origin of the coordinate system, represents the kth distance bin on the i-th scanning layer and the j-th radial direction, k=1,2,...,K, and K is the total number of distance bins on the i-th scanning layer and the j-th radial direction.

[0092] 5.7. For a single target data point Compared with the original data points obtained in 5.6 Calculate the spatial distance between two points If the distance If the criterion f is met, the original data point is recorded, otherwise the next original data point is analyzed and the judgment in 5.7 is repeated until the original data point that meets the conditions is obtained, and then the content of 5.8 is continued. The expression of criterion f is as follows:

[0093] f. ,

[0094] Among them, (dx,dy,dz) and Represent the target data points With the original data points Coordinates in the current coordinate system.

[0095] 5.8. Perform statistics and calculations on all the original data points that meet the conditions obtained in 5.7 to obtain the target data points for estimation The number of original data points N 1 In order to improve the accuracy of three-dimensional wind field data and avoid the problem of excessive wind field error caused by too little effective data during estimation, a quantity threshold N is set. 0 , if N 1 ≥N 0 , then the original data volume is considered sufficient, otherwise the data point is directly Set the wind speed to an invalid value, and then execute 5.2 to process the next target data point. If the amount of original data is sufficient, calculate the mean of the u and v velocity components of the original data points respectively. , With standard deviation , , set the threshold , remove the data points whose values ​​in the u and v velocity components exceed the corresponding threshold. After the processing is completed, the number of remaining original data points is recorded as N 2 The expression of the threshold is as follows:

[0096] ,

[0097] in, Denote the z-th original data point for the velocity component u or v, where z = 1, 2, ..., N 1 , N 1 is the number of original data points for calculating the threshold value.

[0098] 5.9. Calculate the weight values of each original data point and estimate the target data point. Select an exponent q (e.g., q = 2), and obtain the weight values of the original data points used to estimate the data point respectively, and the u and v velocity components of the data point from the following two equations.

[0099] ,

[0100] ,

[0101] where denotes the weight of the original data point , where z = 1, 2, ..., N 2 , N 2 is the number of original data points for calculating the current data point , denotes the velocity component u or v of the original data point , denotes the velocity component u or v of the data point .

[0102] 5.10. Synthesize the horizontal wind at the target data point from the u and v velocity components. The magnitude Vh of the horizontal wind speed and the direction Dir of the horizontal wind are calculated as shown in the following equations:

[0103] ,

[0104] ,

[0105] ,

[0106] where it is stipulated that the incoming direction of the horizontal wind is the wind direction of the horizontal wind, taking the due north direction , and the direction of the horizontal wind is .

[0107] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and improvements, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. And any changes and variations made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A wind field inversion method based on dual laser wind radar wind observation on valley bridges, characterized in that: The method comprises: S1. Obtain radar radial data through coordinated scanning of two radars, and calculate the coordinate information of radar radial data points; S2, calculate the target three-dimensional wind field coordinate information, and invert the radar radial data through IVAP technology to obtain horizontal wind data; S3. Filter the original data points used to estimate the single target three-dimensional wind field data one by one, perform quality control on the filtered original data points, and finally estimate the wind speed data of the target wind field data points.

2. The method for wind field inversion based on valley bridge wind observation by dual laser wind radar according to claim 1 is characterized in that: The calculation of radar radial data point coordinate information specifically includes the following contents: A1. Assume that there are two radars, radar A and radar B. The longitude, latitude and altitude information of radar A are: , the longitude, latitude, and altitude information of radar B are: , with the east direction as the positive direction of the x-axis, the north direction as the positive direction of the y-axis, and the vertical upward direction as the positive direction of the z-axis, respectively create a Cartesian rectangular coordinate system with the locations of the two radars as the coordinate origin; A2, elevation angle in multi-layer RPI scanning mode , azimuth angle Az, distance library number , Distance to library Information, calculate the radial data points in a single scan Coordinates relative to the radar in rectangular coordinates , where n=1,2,3,...,N, N is the number of radial data points, and RPI represents azimuth sector scanning; A3: Calculate the coordinate information of the single radar data point relative to the radar obtained in A2 with the latitude, longitude and altitude information of the radar itself to obtain the radial data point Latitude, longitude and altitude information .

3. The method for wind field inversion based on valley bridge wind observation by dual laser wind radar according to claim 2 is characterized in that: The target three-dimensional wind field coordinate information is calculated to include the following: B1. The longitude, latitude and altitude information of the measured space center are calculated from the longitude, latitude and altitude information of the two radars: ; B2. Set the distance resolution of the target three-dimensional wind field in the radial, latitudinal and vertical directions to Dis. Take the center of the measured space as the origin and Dis as the step size, and expand in the radial, latitudinal and vertical directions respectively. The minimum value of the three-dimensional wind field in the radial, latitudinal and vertical directions is calculated to be , , With the maximum value , , ; B3. The longitude, latitude and altitude information of a single data point in the target three-dimensional wind field is expressed as ,in, , , , n1, n2, and n3 are all positive integers.

4. The method for wind field inversion based on valley bridge wind observation using dual laser wind radar according to claim 1 is characterized in that: The inversion of radar radial data by IVAP technology to obtain horizontal wind data specifically includes the following contents: C1, IVAP technology uses radar to observe radial velocity data and azimuth information. When the radar scanning elevation angle is less than the set value, the least squares method is used to invert the velocity components u and v of the wind field. The radial velocity V observed by the radar r Expressed as the projection of the velocity components u and v of the wind field in the direction of the radar beam , where θ is the elevation angle of the radar beam, is the azimuth of the radar beam, which indicates the angle between the radar beam and the true north direction, u is the velocity component in the east-west direction, and 𝑣 is the velocity component in the north-south direction; C2, set in azimuth The wind field is uniform within the range of , and the velocity component of the wind field is calculated by the formula: and ,in, and is the range of azimuth angles.

5. The method for wind field inversion based on valley bridge wind observation using dual laser wind radar according to claim 1 is characterized in that: The S3 specifically includes the following contents: S31, with the east direction as the positive direction of the x-axis, the north direction as the positive direction of the y-axis, and the vertical upward direction as the positive direction of the z-axis, create a line with the current radar location as the origin The Cartesian rectangular coordinate system is used to traverse the target data points, and the longitude, latitude, and altitude information of the data points and the radar are used to calculate a target data point. Coordinate information in the rectangular coordinate system (dx, dy, dz); S32. In the three-dimensional rectangular coordinate system, the data points A sphere with R as the radius and x-axis, y-axis and origin The surface xOy formed by the projection of the sphere is projected along the z-axis and the ray and the origin The projection of the surface zOm is constructed, and the values ​​of the four auxiliary angles α1, α2, β1, and β2 are calculated by two projections to determine whether the target data point intersects with the scanning range of the radar at the origin; S33, determine the target data point Is it within the single scanning range of the radar? If both criteria a and b are met, then the target data point The estimated value is calculated through the subsequent steps, and the elevation angle of the original data point is screened in step S34. Otherwise, the next target data point is analyzed, and the judgment of step S33 is repeated until the target data point that meets the conditions is obtained; S34, start traversing from the first scanning layer of the two radars respectively, and determine whether each scanning layer is consistent with the current target data point The spheres with the center and radius R intersect. If the elevation angle of the scanning layer is If the criterion c is satisfied, then the scan layer contains data points that can be used to estimate the target If the original data is obtained, the azimuth angle of the original data point is screened in step S35. Otherwise, the next scanning layer is analyzed and the judgment in step S34 is repeated until a scanning layer that meets the conditions is obtained. S35, traverse all radials on the radar scanning layer obtained in step S34, and determine the azimuth of each radial relative to the radar Whether it meets the criterion d, if the radial azimuth meets the criterion d, then continue to step S36 to screen the distance library of radial data points, otherwise, analyze the next radial and repeat the judgment of step S35 until a radial that meets the conditions is obtained; S36: Traverse all distance libraries on a single radial obtained in step S35, and filter out the distance libraries that are closest to the target data point. Original data points within R , determine the number of libraries for each distance Whether it meets the criterion e, if the number of stocks Satisfies criterion e, and the original data point on the distance library If the wind speed is a valid value, then the step S37 will continue to screen the original data points. Otherwise, the next distance library will be analyzed and the judgment of step S36 will be repeated until the number of distance libraries that meet the conditions is obtained. Represents the original data Coordinates in the current coordinate system; S37. For a single target data point The original data points obtained in step S36 Calculate the spatial distance between two points , if the distance If the criterion f is met, the original data point is recorded, otherwise the next original data point is analyzed, and the judgment of step S37 is repeated until an original data point that meets the condition is obtained, and step S38 is continued; S38: Perform statistics and calculation on all original data points that meet the conditions obtained in step S37 to obtain the data points used to estimate the target data points. The number of original data points N1 is set, and a number threshold N0 is set. If N1 ≥ N0, the original data volume is considered sufficient, otherwise the data point is directly The wind speed is set to an invalid value, and then step S32 is executed to process the next target data point. If the amount of original data is sufficient, the mean of the velocity components u and v of the original data points are calculated respectively. , With standard deviation , , set the threshold , remove the data points whose values ​​in the u and v velocity components exceed the corresponding thresholds. After the processing is completed, the number of remaining original data points is recorded as N2; S39, calculate the weight value of each original data point and estimate the target data point, select the index q, and use the formula and The data points used to estimate The original data point weights, and the data points The u and v velocity components of Represents the original data point The weight, z=1,2,...,N2, N2 is the weight for calculating the current data point The number of original data points, Represents the original data point The velocity component u or v, Represents data points The velocity component u or v; S310, synthesize the horizontal wind at the target data point by using the u and v velocity components.

6. The method for wind field inversion based on valley bridge wind observation using dual laser wind radar according to claim 5 is characterized by: The expression of the criterion a includes: , L represents the spatial distance between the target data point and the origin, is the maximum distance library number, is the distance library length; The expression of the criterion b includes: or , and They correspond to the first and last scanning elevation angles of the radar in multi-layer RPI scanning, respectively. I is the total number of radar scanning layers. The expression of the criterion c includes: ,in, is the elevation angle value of the i-th layer of scanning data, i=1,2,...,I, I is the total number of scanning layers; The expression of the criterion d includes: ,in, is the azimuth of the jth radial line in the i-th scanning layer, j=1,2,...,J, J is the total number of radial lines in the i-th scanning layer; The expression of the criterion e includes: , represents the kth distance bin on the i-th scanning layer and the j-th radial direction, k=1,2,...,K, K is the total number of distance bins on the i-th scanning layer and the j-th radial direction; The expression of the criterion f includes: .

7. A dual laser wind radar wind field inversion method based on valley bridge wind observation according to any one of claims 1 to 6, characterized in that: The step of obtaining radar radial data by coordinated scanning of two radars specifically includes: Two radars are set up at both ends of the bridge and coordinated scanning is performed using multi-layer RPI mode to obtain data near the bridge deck; The acquired data is preprocessed and quality controlled to ensure the accuracy and reliability of the data and obtain radar radial data.

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