Method, device and equipment for real-time detection of consistency between weather radars and storage medium

Abstract

The application discloses a kind of weather radar consistency real-time detection method, device, equipment and storage medium, the detection method includes obtaining space-time matching A radar standard data and virtual B radar standard data;Wherein, the virtual B radar standard data is obtained by mapping B radar standard data to A radar polar coordinate system;From space-time matching A radar standard data and virtual B radar standard data, A radar effective data and virtual B radar effective data are extracted;According to the A radar effective data and virtual B radar effective data, calculate minimum index distance;According to the minimum index distance and index distance threshold, whether the consistency of A radar and B radar is abnormal is judged in real time.The application solves the problem that for the weather type of flow pattern precipitation and stratiform cloud precipitation, due to the fluctuation of mean absolute error, standard deviation and correlation coefficient, etc. Consistency index fluctuation is larger, and suitable judgment basis cannot be selected.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of weather radar, and particularly relates to a weather radar inter-consistency real-time detection method, device, equipment and storage medium. BACKGROUND

[0002] Multi-radar data networking fusion can provide more extensive and more accurate weather monitoring and forecasting information, and also provides support for better and faster capturing and tracking of the formation and development process of extreme weather. The key technology of multi-radar networking is to keep the data quality of each radar stable and the intensity consistency of each radar, however, in the actual operation process of weather radar, different wave bands of weather radar and the same wave band of weather radar selecting different reference standards for radar data calibration, etc. may cause great differences in radar intensity between radars and lead to poor radar intensity consistency. In addition, natural aging of components, adjustment and modification of radar hardware facilities, and changes in radar operation mode may cause changes in radar calibration values, leading to poor intensity consistency between radars and affecting the effect of radar networking fusion.

[0003] Document 1 (see: Zhang Zhiqiang, Liu Liping, Wang Hongyan, et al. North China regional four radar detection intensity and positioning consistency analysis [J]. Meteorology, 2008, (09): 22-27+130.) interpolates the data of four weather radars in North China region into grid network product data, selects strong echoes detected in the public area, and compares and analyzes the strong echo structure and correlation coefficient of the radar detection area by calculating the average value of echo difference. However, the matching accuracy of this method is low, and it cannot analyze the detection sensitivity and vertical structure of the radar.

[0004] Reference 2 (for details, see: Wu Chong, Liu Liping, Zhang Zhiqiang. Quantitative comparison method and preliminary application of S-band phased array weather radar and new generation Doppler weather radar [J]. Acta Meteorologica Sinica, 2014, 72(02): 390-401.) proposes a radar spatial interpolation matching method to spatially map the data of one S-band polar coordinate radar to the spatial coordinate system of another S-band phased array radar. The basic principle is to convert the polar coordinate data of radar A into geodetic coordinates, then find the corresponding radar range library in the polar coordinate system of radar B, and then map the radar data in the spatial polar coordinate system of radar A to the spatial coordinate system of radar B, obtaining the radar data in the virtual spatial coordinate system of radar B, which is used for quantitative comparison of the radar data. This method can obtain the virtual data of radar B in the spatial coordinate system of radar A, and then conduct qualitative and quantitative comparative analysis of the detection capabilities of the two radars. However, this method requires traversing every radial distance library in the three-dimensional polar coordinate system of radar A to find the corresponding radar data in the spatial coordinate system of radar B. The data volume is large and the matching time is long, which makes it difficult to meet the requirements of real-time analysis and comparison.

[0005] Chinese patent publication CN118393443A proposes a weather radar consistency assessment method. Given a given region of radar observation overlap, the method then searches for overlapping observation points. The method then searches for observation points and candidate observation points corresponding to the polar coordinate system for the first and second weather radars, respectively. After data preprocessing and screening, the method uses the mean and standard deviation of the differences in the common overlapping region, as well as the correlation coefficient of the reflectivity observed by adjacent weather radars at the overlapping points, as consistency assessment metrics. However, actual operational testing revealed that using standard deviation and correlation coefficient as consistency assessment metrics resulted in significant fluctuations in the mean absolute error, standard deviation, and correlation coefficient between radar data for different weather types, such as stratiform and convective precipitation. This made it difficult to select a suitable threshold to measure the differences in radar data for different weather types, and the method was unable to achieve automatic calibration of weather radar consistency. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, device, equipment and storage medium for real-time detection of consistency between weather radars to solve the problem that traditional methods are difficult to select appropriate judgment basis.

[0007] The present invention solves the above technical problems through the following technical solutions: a method for real-time consistency detection between weather radars, comprising:

[0008] Obtaining time-space matched radar A standard data and virtual radar B standard data; wherein the virtual radar B standard data is obtained by mapping the radar B standard data to the A radar polar coordinate system;

[0009] extracting A radar effective data and virtual B radar effective data from the spatio-temporal matched A radar standard data and virtual B radar standard data;

[0010] calculating a minimum index distance according to the A radar effective data and virtual B radar effective data;

[0011] judging whether the consistency of the A radar and the B radar is abnormal in real time according to the minimum index distance and an index distance threshold.

[0012] Further, the obtaining of the spatio-temporal matched A radar standard data and virtual B radar standard data comprises:

[0013] obtaining A radar volume scan data and B radar volume scan data;

[0014] temporally matching the A radar volume scan data and the B radar volume scan data;

[0015] respectively performing standardization processing on the temporally matched A radar volume scan data and B radar volume scan data to obtain A radar standard data and B radar standard data;

[0016] mapping the B radar standard data to an A radar polar coordinate system by using an inverse distance weighting method to realize spatial matching.

[0017] Further, the mapping of the B radar standard data to the A radar polar coordinate system by using the inverse distance weighting method is specifically a mapping formula:

[0018] ;

[0019] , ;

[0020] wherein, denotes a radar parameter value of an i-th grid point of an A radar, denotes a distance bin number corresponding to the i-th grid point of the A radar, denotes an azimuth angle value corresponding to the i-th grid point of the A radar, denotes a radial elevation angle value corresponding to the i-th grid point of the A radar; denotes a radar parameter value of an i-th grid point of a B radar, denotes a distance bin number corresponding to the i-th grid point of the B radar, denotes an azimuth angle value corresponding to the i-th grid point of the B radar, denotes a radial elevation angle value corresponding to the i-th grid point of the B radar; denotes an upper radial elevation angle value adjacent to the radial elevation angle value ; denotes a lower radial elevation angle value adjacent to the radial elevation angle value ; Indicates the upper radial elevation angle value Interpolate to radial elevation value The weight of Indicates the lower radial elevation angle value Interpolate to radial elevation value The weight of .

[0021] Furthermore, the A radar volume scan data or the B radar volume scan data is subjected to standardization processing, including:

[0022] Determine whether the PPI radial scan data of each layer in the A radar volume scan data or the B radar volume scan data exceeds 360;

[0023] If so, the radar parameter values ​​of each layer are standardized;

[0024] If not, the radar parameter value is calculated based on the circular variance between the two azimuths.

[0025] Furthermore, the radar parameter values ​​of each layer are standardized. The specific standardization formula is:

[0026] , ;

[0027] in, represents the radar parameter value of the i-th azimuth after standardization, Indicates that before standardization The radar parameter value of the azimuth angle within the range, n represents the number of radial scans.

[0028] Furthermore, the radar parameter value is calculated based on the circular variance between the two azimuths. The specific calculation formula is:

[0029] ;

[0030] ;

[0031] ;

[0032] in, represents the radar parameter value of the i-th azimuth after standardization; represents the radar parameter value of one of the two azimuths that are most adjacent to the i-th azimuth before normalization, represents the radar parameter value of the other of the two azimuths that are most adjacent to the i-th azimuth before normalization; represents the circular variance between the i-th azimuth and one of the two azimuths most adjacent to the i-th azimuth, represents the circular variance between the i-th azimuth and the other of the two azimuths most adjacent to the i-th azimuth; represents the circular variance between two azimuth angles, i.e. is or ; represents the module of the resultant vector of the two unit vectors corresponding to the two azimuth angles for which the circular variance is to be calculated; , represents the two azimuth angles for which the circular variance is to be calculated, when is , , respectively represent the i-th azimuth angle, and one of the two azimuth angles most adjacent to the i-th azimuth angle; when is , , respectively represent the i-th azimuth angle, and the other of the two azimuth angles most adjacent to the i-th azimuth angle.

[0033] Further, the A-radar effective data and the virtual B-radar effective data are extracted from the spatio-temporally matched A-radar standard data and the virtual B-radar standard data, comprising:

[0034] respectively pre-process the spatio-temporally matched A-radar standard data and the virtual B-radar standard data;

[0035] extract the common part from the pre-processed A-radar standard data and the virtual B-radar standard data to obtain A-radar common standard data and virtual B-radar common standard data;

[0036] extract standard data with effective reflectivity greater than and less than from the A-radar common standard data and the virtual B-radar common standard data respectively to obtain A-radar effective data and virtual B-radar effective data; wherein, and both represent reflectivity threshold values.

[0037] Further, the spatio-temporally matched A-radar standard data and the virtual B-radar standard data are respectively pre-processed, comprising:

[0038] extract standard data with PPI layer number greater than 3 and less than 24 from the spatio-temporally matched A-radar standard data and the virtual B-radar standard data respectively, and perform denoising processing on the extracted standard data.

[0039] Further, the calculation formula of the minimum index distance is:

[0040] ;

[0041] , ;

[0042] ;

[0043] in, Indicates the minimum index distance; Indicates the calibration value offset; represents the optimal effective reflectivity error, The minimum MAE ; represents the minimum mean absolute error; Indicates the effective reflectivity in the effective data of the virtual B radar; Indicates the effective reflectivity in the effective data of radar A; represents the effective reflectivity error; Indicates the number of data points in the effective data of radar A or virtual radar B.

[0044] Furthermore, the detection method further includes automatically calibrating radar A according to the minimum index distance when the consistency between radar A and radar B is abnormal. The specific calibration formula is:

[0045] ;

[0046] in, Indicates the new calibration value of radar A, Indicates the original calibration value of radar A, Indicates the minimum index distance.

[0047] Based on the same concept, the present invention also provides a real-time detection device for consistency between weather radars, comprising:

[0048] An acquisition unit is used to acquire the time-space matched A radar standard data and virtual B radar standard data; wherein the virtual B radar standard data is obtained by mapping the B radar standard data to the A radar polar coordinate system;

[0049] An extraction unit, configured to extract effective radar A data and effective virtual radar B data from the temporally and spatially matched standard radar A data and virtual radar B data;

[0050] a calculation unit, configured to calculate a minimum index distance based on the effective data of the A radar and the effective data of the virtual B radar;

[0051] A judgment unit is used to judge in real time whether the consistency between radar A and radar B is abnormal based on the minimum index distance.

[0052] Based on the same concept, the present application also provides an electronic device comprising a memory, a processor and a computer program / instructions stored on the memory, the processor executing the computer program / instructions to implement the weather radar inter-consistency real-time detection method as described above.

[0053] Based on the same concept, the present application also provides a computer readable storage medium having stored thereon a computer program / instructions, the computer program / instructions being executed by a processor to implement the weather radar inter-consistency real-time detection method as described above.

[0054] Compared with the prior art, the present application has the beneficial effects that:

[0055] The present application calculates the minimum index distance according to the spatio-temporal matched A radar standard data and virtual B radar standard data, and uses whether the minimum index distance exceeds the index distance threshold as the basis for judging the consistency of the two radars, thereby realizing real-time detection of the consistency of the two radars, solving the problem that the weather types of the flow pattern precipitation and the stratiform cloud precipitation cannot be selected as appropriate judgment basis due to the large fluctuations of the consistency indicators such as mean absolute error, standard deviation and correlation coefficient.

[0056] When the consistency of the two radars is abnormal, the present application can calculate the new radar calibration value in real time, thereby realizing automatic calibration of the consistency of the two radars. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only one embodiment of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0058] Figure 1 is the flow chart of the weather radar inter-consistency real-time detection method in the embodiment of the present application;

[0059] Figure 2 is the radar reflectivity diagram in the rectangular coordinate system before standardization of the S-band mechanical radar in the embodiment of the present application; wherein the color band represents the echo intensity;

[0060] Figure 3 is the radar reflectivity diagram in the rectangular coordinate system after standardization of the S-band mechanical radar in the embodiment of the present application; wherein the color band represents the echo intensity;

[0061] Figure 4 is the radar reflectivity diagram in the polar coordinate system before standardization of the S-band mechanical radar in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity;

[0062] Figure 5 is a radar reflectivity diagram in polar coordinate system after S-band mechanical radar standardization in the embodiment of the present application; wherein, color band represents echo intensity;

[0063] Figure 6 is a radar reflectivity diagram in rectangular coordinate system before X-band phased array radar standardization in the embodiment of the present application; wherein, color band represents echo intensity;

[0064] Figure 7 is a radar reflectivity diagram in rectangular coordinate system after X-band phased array radar standardization in the embodiment of the present application; wherein, color band represents echo intensity;

[0065] Figure 8 is a radar reflectivity diagram in polar coordinate system before X-band phased array radar standardization in the embodiment of the present application; wherein, angle represents azimuth angle deviating from the north direction, and color band represents echo intensity;

[0066] Figure 9 is a radar reflectivity diagram in polar coordinate system after X-band phased array radar standardization in the embodiment of the present application; wherein, angle represents azimuth angle deviating from the north direction, and color band represents echo intensity;

[0067] Figure 10 is a space matching diagram in the embodiment of the present application; wherein, represents a radar data point of fixed point C in space detected by B radar, 、 represents two radar data points with the same radial length and adjacent elevation angle of fixed point C detected by B radar, represents a radar data point of fixed point C in space detected by A radar;

[0068] Figure 11 is a radar reflectivity PPI diagram of X-band phased array radar in the embodiment of the present application; wherein, angle represents azimuth angle deviating from the north direction, and color band represents echo intensity;

[0069] Figure 12 is a virtual S-band mechanical radar reflectivity diagram in the embodiment of the present application; wherein, angle represents azimuth angle deviating from the north direction, and color band represents echo intensity;

[0070] Figure 13 is a virtual S-band radar data mapping to X-band a radar coordinate system reflectivity intensity PPI diagram in the embodiment of the present application; wherein, angle represents azimuth angle deviating from the north direction, and color band represents echo intensity;

[0071] Figure 14Fig. 1 is a radar reflectivity intensity PPI diagram obtained by using a business calibration value of an X-band a radar in an embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0072] Figure 15 Fig. 2 is a radar reflectivity intensity PPI diagram obtained by adding +1 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0073] Figure 16 Fig. 3 is a radar reflectivity intensity PPI diagram obtained by adding +2 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0074] Figure 17 Fig. 4 is a radar reflectivity intensity PPI diagram obtained by adding +3 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0075] Figure 18 Fig. 5 is a radar reflectivity intensity PPI diagram obtained by adding +4 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0076] Figure 19 Fig. 6 is a radar reflectivity intensity PPI diagram obtained by adding +5 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0077] Figure 20 Fig. 7 is a radar reflectivity intensity PPI diagram obtained by adding -1 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0078] Figure 21 Fig. 8 is a radar reflectivity intensity PPI diagram obtained by adding -2 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0079] Figure 22 Fig. 9 is a radar reflectivity intensity PPI diagram obtained by adding -3 to the business calibration value of the X-band a radar in the embodiment of the present application; wherein the angle represents a bearing angle deviating from the north direction, and the color band represents echo intensity;

[0080] Figure 23is a radar reflectivity intensity PPI diagram obtained by adding-4 to the X-band a radar business calibration value in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity;

[0081] Figure 24 is a radar reflectivity intensity PPI diagram obtained by adding-5 to the X-band a radar business calibration value in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity;

[0082] Figure 25 is a radar reflectivity intensity PPI diagram obtained by mapping the virtual S-band radar data to the X-band b radar coordinate system in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity;

[0083] Figure 26 is a radar reflectivity intensity PPI diagram of the X-band b radar before automatic calibration in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity;

[0084] Figure 27 is a radar reflectivity intensity PPI diagram of the X-band b radar after automatic calibration in the embodiment of the present application; wherein the angle represents the azimuth angle deviating from the north direction, and the color band represents the echo intensity. DETAILED DESCRIPTION

[0085] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0086] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0087] Embodiment one

[0088] As shown in the figure, the weather radar consistency real-time detection method provided by the embodiment of the present application includes the following steps: Figure 1

[0089] Step 1: Obtain the spatiotemporally matched A radar standard data and virtual B radar standard data.

[0090] In the specific embodiments of the present application, obtaining the spatiotemporally matched A radar standard data and virtual B radar standard data includes:

[0091] ​Step 1.1: Obtain A radar volume scan data and B radar volume scan data.

[0092] A radar and B radar are two of mechanical radar, S / X / C wave band phased array radar, and the embodiment takes S wave band mechanical radar (large radar) and X wave band phased array radar (small radar) as B radar and A radar respectively.

[0093] Step 1.2: Time match A radar volume scan data and B radar volume scan data.

[0094] Before the consistency analysis of different modes and different systems of radars, the volume scan data of two radars need to be time matched. The time matching rule is: taking the time point T1 with coarser time resolution as the standard, find the time point T2 with finer time resolution, if the difference between T2 and T1 is less than half of the time resolution of the time resolution finer, then the time point T1 is successfully matched to the time point T2.

[0095] For example, the volume scan time resolution of X wave band phased array radar is 1 min, and the volume scan time resolution of S wave band mechanical radar is 6 min. In time matching, the volume scan time resolution of X wave band phased array radar is the time resolution finer, and the volume scan time resolution of S wave band mechanical radar is the time resolution finer. The volume scan data of S wave band mechanical radar has a time point T1 = 11:52:33, and the volume scan data of X wave band phased array radar has a time point T2 = 11:53:00. Since T2-T1 < 1 / 2 min, the time point T1 is successfully matched to the time point T2.

[0096] Step 1.3: Standardize the time matched A radar volume scan data and B radar volume scan data respectively to obtain A radar standard data and B radar standard data.

[0097] When the radar volume scans, the volume scan azimuth resolution and the number of volume scan distance bins may not be stable, resulting in that the corresponding azimuth angle and radial number are not completely the same each time the volume scans. But in the case of unchanged radar mode, the three-dimensional space of the entire volume scan is basically unchanged. Due to the inconsistent characteristics of the radial azimuth angle of radar volume data at different times, when the volume scan data of two radars are spatially matched, each spatial matching needs to find out whether all the grid points of the spatial coordinate system of one radar can find the corresponding radar distance bin in the spatial coordinate system of the other radar. For phased array radar, it has the characteristics of fast scanning speed and high radial resolution, and the corresponding volume scan data is also larger. Therefore, when the volume scan data of two radars are spatially matched, especially the phased array radar volume scan data, the time consumed is longer. Taking the spatial matching with S wave band mechanical radar volume scan data as an example, the shortest time for spatial matching is 1 minute and 30 seconds, which is far from the demand of real-time monitoring.

[0098] In order to solve the above technical problems, the present application first normalizes the two radar volume scan data before the two radar volume scan data spaces are matched, and then respectively normalizes the radar volume scan data needing space matching into standard three-dimensional data blocks with 1° azimuth resolution and 360 radial directions through interpolation and sampling average, and then generates a space matching file according to the standard three-dimensional data blocks in the first run, and directly calls the space matching file to perform space matching, so that the space matching time is shortened to about 15s, which is more than 6 times less than the traditional space matching time, and is beneficial to the real-time detection of the consistency between weather radars. If multiple radar parameters are simultaneously space matched, the time advantage is more obvious.

[0099] In the specific embodiment of the present application, the A radar volume scan data or the B radar volume scan data is normalized, which comprises:

[0100] judging whether each layer of PPI radial scan data in the A radar volume scan data or the B radar volume scan data exceeds 360;

[0101] If it exceeds 360, it indicates that the radial resolution of the radar azimuth angle is less than 1°, and the radial resolution is higher, and the radar parameter values of each layer are normalized, and the specific normalization formula is:

[0102] , (1)

[0103] wherein, represents the radar parameter value of the i th azimuth angle after normalization, represents the radar parameter value of the azimuth angle belonging to the range of , and n represents the radial scan number. The radar parameters include reflectivity factor, specific differential phase, differential reflectivity and correlation coefficient.

[0104] Taking a 720-radial S-band mechanical radar as an example, the radar reflectivity in the rectangular coordinate system before and after normalization is shown in Figure 2 and Figure 3 , wherein the horizontal coordinate represents the radar distance library, the vertical coordinate represents the radial number, and the color band represents the radar reflectivity intensity value. It can be known from Figure 2 and Figure 3 that the radial number after normalization is changed from 720 to 360, but the overall echo shape is exactly the same. The radar reflectivity in the polar coordinate system before and after normalization is shown in Figure 4 and Figure 5 , and the color band represents the radar reflectivity intensity value. It can be known from the polar coordinate system of Figure 4 and Figure 5 that the radar reflectivity in the polar coordinate system before and after normalization is exactly the same, which indicates that the radar normalization result is correct.

[0105] If the number is not more than 360, it indicates that the radial resolution of the radar azimuth angle is greater than 1°, the radial resolution is low, and the number of normalized radials is 360, wherein the azimuth angle value corresponding to the ith radial is i°, the radial serial number and the azimuth angle are equal in value. Calculate the circular variance of the azimuth angle corresponding to the ith normalized radial and the azimuth angle array corresponding to all radials before normalization, and get the two azimuth angles with the minimum circular variance, which are the closest to the ith normalized radial in space before normalization. The radar parameter after normalization is obtained by the inverse distance weighted interpolation method. The circular variance between the two azimuth angles is used to measure the direction dispersion degree of the two azimuth angles, which can describe the direction consistency degree of the two azimuth angles. The greater the angle difference, the greater the circular variance, and the maximum circular variance is 1, at this time the azimuth angle vectors are opposite, and the angle difference is (2n+1) ; the minimum circular variance is 0, at this time the azimuth angle vectors coincide, and the angle difference is The circular variance is used to measure the weight value of the radial close to the ith normalized radial. The circular variance calculation formula between the two azimuth angles is:

[0106] (2)

[0107] (3)

[0108] (4)

[0109] Wherein, represents the radar parameter value of the ith normalized azimuth angle; represents the radar parameter value of one of the two most adjacent azimuth angles before normalization and the ith azimuth angle, represents the radar parameter value of the other of the two most adjacent azimuth angles before normalization and the ith azimuth angle; represents the circular variance between the ith azimuth angle and one of the two most adjacent azimuth angles of the ith azimuth angle, represents the circular variance between the ith azimuth angle and the other of the two most adjacent azimuth angles of the ith azimuth angle; represents the circular variance between the two azimuth angles, that is or ; represents the module length of the resultant vector of the unit vectors corresponding to the two azimuth angles which need to calculate the circular variance; , represents the two azimuth angles which need to calculate the circular variance, when , , , ​respectively represent the i-th azimuth angle, and the other of the two azimuth angles most adjacent to the i-th azimuth angle. is , , respectively represent the i-th azimuth angle, and the other of the two azimuth angles most adjacent to the i-th azimuth angle.

[0110] Taking 270 radial X-band phased array radars as an example, the radar reflectivity in the rectangular coordinate system before and after standardization is as shown in Figure 6 and Figure 7 , wherein the abscissa represents the radar distance library, the ordinate represents the radial number, and the color band represents the radar reflectivity intensity value. Figure 6 and Figure 7 It can be seen that the radial number after standardization is changed from 270 to 360, but the overall echo shape is exactly the same. The radar reflectivity in the polar coordinate system before and after standardization is as shown in Figure 8 and Figure 9 , and the color band represents the radar reflectivity intensity value. From the polar coordinate system of Figure 8 and Figure 9 , it can be seen that the radar reflectivity in the polar coordinate system before and after standardization is exactly the same, indicating that the radar standardization result is correct.

[0111] Compared with the traditional spatial interpolation matching method, the present application first performs standardization processing on the radar volume scanning data before spatial matching, greatly shortens the spatial matching time of the radar volume scanning data, improves the spatial matching efficiency, and meets the requirements of real-time detection of radar consistency. The circular variance is used to calculate the interpolation weight, which better solves the problem of difficult judgment of interpolation radial and weight when the radar azimuth angle does not exceed 360°.

[0112] Step 1.4: The inverse distance weighting method is used to map the B radar standard data to the A radar polar coordinate system to realize spatial matching.

[0113] In the common scanning area of the A and B radars, an arbitrary distance library in the A radar polar coordinate system can find one and / or multiple spatial distance libraries of the B radar adjacent or overlapping thereto, as shown in Figure 10 . The mapping relationship between the distance libraries in the polar coordinate systems of the two radars is established by interpolation matching, the B radar standard data is mapped to the A radar polar coordinate system, and virtual B radar standard data is obtained. The present application uses the inverse distance weighting method to map the B radar standard data to the A radar polar coordinate system to realize spatial matching. The specific mapping formula is:

[0114] (5)

[0115] (6)

[0116] (7)

[0117] wherein, represents the radar parameter value of the i-th grid point of the A radar, represents the distance bin number corresponding to the i-th grid point of the A radar, represents the azimuth angle value corresponding to the i-th grid point of the A radar, represents the radial elevation angle value corresponding to the i-th grid point of the A radar; represents the radar parameter value of the i-th grid point of the B radar, represents the distance bin number corresponding to the i-th grid point of the B radar, represents the azimuth angle value corresponding to the i-th grid point of the B radar, represents the radial elevation angle value corresponding to the i-th grid point of the B radar; represents the upper radial elevation angle value adjacent to the radial elevation angle value ; represents the lower radial elevation angle value adjacent to the radial elevation angle value ; represents the weight of the upper radial elevation angle value interpolated to the radial elevation angle value ; represents the weight of the lower radial elevation angle value interpolated to the radial elevation angle value .

[0118] According to the site number and the elevation layer number of the two radars required for spatial matching, a spatial matching file between the two radars is generated, which contains the mapping information of each standardized grid point in the polar coordinate system of the A radar and the corresponding standardized grid point in the polar coordinate system of the B radar. The mapping information of any parameter of the two radars can be obtained by formula (5).

[0119] Taking an S-band mechanical radar (large radar) as the B radar and an X-band phased array radar (small radar) as the A radar, the X-band phased array radar reflectivity PPI diagram is shown in Figure 11 , and the virtual S-band mechanical radar reflectivity obtained by mapping the standard data of the S-band mechanical radar to the polar coordinate system of the A radar is shown in Figure 12 . As can be seen from Figure 11 and Figure 12 , the pseudo S-band mechanical radar reflectivity is basically consistent with the X-band phased array radar reflectivity in echo intensity and shape.

[0120] Step 2: Extract the A radar effective data and the virtual B radar effective data from the spatio-temporally matched A radar standard data and virtual B radar standard data.

[0121] In the specific embodiments of the present application, the A-radar effective data and the virtual B-radar effective data are extracted from the spatio-temporally matched A-radar standard data and virtual B-radar standard data, comprising:

[0122] Step 2.1: Preprocessing the spatio-temporally matched A-radar standard data and virtual B-radar standard data respectively.

[0123] In this embodiment, the standard data with PPI layer number greater than 3 and less than 24 are extracted from the spatio-temporally matched A-radar standard data and virtual B-radar standard data respectively, and the extracted standard data is denoised. By extracting the standard data with PPI layer number greater than 3 and less than 24, the ground clutter and the data susceptible to beam shielding are eliminated, and by denoising the standard data with signal-to-noise ratio less than 10 dB is eliminated, the reliability of the data is enhanced.

[0124] Step 2.2: Extracting the common part from the preprocessed A-radar standard data and virtual B-radar standard data to obtain A-radar common standard data and virtual B-radar common standard data.

[0125] Step 2.3: Extracting the standard data with effective reflectivity greater than and less than from the A-radar common standard data and virtual B-radar common standard data respectively to obtain A-radar effective data and virtual B-radar effective data; wherein, and both represent the reflectivity threshold.

[0126] In this embodiment, is 15 dB, is 45 dB. Because different waveband radar data will have certain differences in detecting weak echoes due to different radar detection sensitivities, and the strong echo attenuation degree of different radar wavebands is different, in order to eliminate the influence of attenuation as much as possible, the strong echo area above 45 dB is eliminated.

[0127] Step 3: Calculating the minimum index distance according to the A-radar effective data and the virtual B-radar effective data.

[0128] In this embodiment, the calculation formula of the minimum index distance is:

[0129] (8)

[0130] , (9)

[0131] (10)

[0132] wherein, represents the minimum index distance; denotes the calibration value offset; denotes the optimal effective reflectivity error, is the MAE when the ; denotes the minimum mean absolute error; denotes the effective reflectivity in the virtual B radar effective data; denotes the effective reflectivity in the A radar effective data; denotes the effective reflectivity error; denotes the number of data points in the A radar effective data or the virtual B radar effective data.

[0133] In this embodiment, the value of M is 5dB, and the iteration step is 0.2dB, so that , .

[0134] Step 4: Real-time judge whether the consistency of the A radar and the B radar is abnormal according to the minimum index distance and the index distance threshold.

[0135] When the absolute value of the minimum index distance is less than or equal to the index distance threshold, the consistency of the A radar and the B radar is normal; when the absolute value of the minimum index distance is greater than the index distance threshold, the consistency of the A radar and the B radar is abnormal.

[0136] In this embodiment, the index distance threshold is set to 2.

[0137] Step 5: When the consistency of the A radar and the B radar is abnormal, automatically calibrate the A radar according to the minimum index distance.

[0138] The automatic calibration formula is:

[0139] (11)

[0140] wherein, denotes the new calibration value of the A radar, denotes the original calibration value of the A radar.

[0141] In order to verify the consistency monitoring effect, the present application maps the S-band large radar data to the X-band small radar spatial coordinate system to obtain virtual S-band large radar data (as shown in Figure 13 ) as a consistency reference standard, takes the X-band small radar business calibration value (as shown in Figure 14 ) as a reference, respectively adds (+1, +2, +3, +4, +5, -1, -2, -3, -4, -5) different offset values to the X-band small radar business calibration value, and performs quality control comparison, which corresponds to Figure 15 , Figure 16 , respectively.Figure 17 、 Figure 18 、 Figure 19 、 Figure 20 、 Figure 21 、 Figure 22 、 Figure 23 、 Figure 24 , and each calibration value is tested and verified in turn by the consistency detection method of the application.

[0142] As shown in Figures 13 to 24 , the X-band small radar business calibration value and the virtual S-band large radar consistency monitoring result are normal, indicating that the consistency of the two radars is consistent, and the comparison results of each PPI diagram can also clearly show that the echo heights of the two radars are consistent. At the same time, the results of the consistency monitoring after the quality control of the calibration values with small offsets +1, +2, -1, and -2 are normal. From the radar echoes, the radar echoes corresponding to these offsets (as shown in Figure 15 、 Figure 16 、 Figure 20 and Figure 21 ) are also basically consistent with the S-band large radar echoes (as shown in Figure 13 ) in shape and intensity. When the calibration value of the radar echo has a large offset, such as +3, +4, +5, -3, -4, and -5, the echo consistency monitoring result is abnormal, and by comparing the radar echoes (as shown in Figure 17 、 Figure 18 、 Figure 19 、 Figure 22 、 Figure 23 and Figure 24 ) after the quality control of these calibration values, it is found that these radar echoes and the echoes of the S-band large radar data (as shown in Figure 13 ) have large differences in echo intensity and echo shape. The above analysis and comparison results show that the consistency detection method of the application can accurately identify whether the consistency between radars is abnormal.

[0143] In order to verify the consistency automatic calibration effect of the application, the X-band small radar echo triggers the consistency automatic calibration, as shown in Figures 25 to 27 , wherein the automatic calibration offset is 5. As shown in Figures 25 to 27 , the X-band small radar data (as shown in Figure 26 ) before automatic calibration is obviously stronger than the virtual S-band large radar data (as shown in Figure 25 ), and after triggering the radar automatic calibration, the result (as shown in Figure 27 ) after the quality control of the radar calibration value with an offset of 5 is basically consistent with the virtual S-band large radar (as shown in Figure 25 ) in echo intensity and shape. The results show that the inter-radar consistency automatic calibration method of the application can maintain the stability of the consistency between the two radars.

[0144] Embodiment Two

[0145] The weather radar inter-consistency real-time detection device provided by the embodiment of the present application comprises an acquisition unit, an extraction unit, a calculation unit and a judgment unit.

[0146] The acquisition unit is configured to acquire the spatio-temporally matched A-radar standard data and virtual B-radar standard data, wherein the virtual B-radar standard data is obtained by mapping the B-radar standard data to the A-radar polar coordinate system.

[0147] The extraction unit is configured to extract A-radar effective data and virtual B-radar effective data from the spatio-temporally matched A-radar standard data and virtual B-radar standard data.

[0148] The calculation unit is configured to calculate a minimum index distance according to the A-radar effective data and the virtual B-radar effective data.

[0149] The judgment unit is configured to judge whether the consistency of the A-radar and the B-radar is abnormal in real time according to the minimum index distance.

[0150] In the specific embodiments of the present application, the real-time detection device further comprises a calibration unit configured to automatically calibrate the A-radar according to the minimum index distance when the consistency of the A-radar and the B-radar is abnormal.

[0151] In some specific embodiments of the present application, the weather radar inter-consistency real-time detection device can combine the features of the weather radar inter-consistency real-time detection method in Embodiment One of the present application, and vice versa.

[0152] Embodiment Three

[0153] The embodiment of the present application further provides an electronic device, which comprises a memory, a processor and a computer program / instruction stored in the memory, and the processor executes the computer program / instruction to implement the weather radar inter-consistency real-time detection method in Embodiment One of the present application.

[0154] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes according to programs and / or data stored in a read-only memory (ROM) or programs and / or data loaded from a storage section into a random access memory (RAM). The processor can be one multi-core processor or can include a plurality of processors. In some embodiments, the processor can include a general-purpose main processor and one or more special-purpose co-processors, such as a central processing unit, a graphics processing unit (GPU), a neural processing unit (NPU), a digital signal processor (DSP), and the like. In the RAM, various programs and data required for device operations are also stored. The processor, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0155] The above processor and memory are used together to execute programs / instructions stored in the memory, which, when executed by a computer, can implement the methods, steps, or functions described in the above embodiments.

[0156] Although not shown, the embodiments of the present application also provide a computer-readable storage medium having stored thereon computer programs / instructions, which, when executed by a processor, implement the weather radar consistency real-time detection method in the first embodiment of the present application.

[0157] The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0158] The above disclosure is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or modifications within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A real-time detection method for consistency between weather radars, characterized in that: The detection method comprises: Obtaining time-space matched radar A standard data and virtual radar B standard data; wherein the virtual radar B standard data is obtained by mapping the radar B standard data to the A radar polar coordinate system; Extracting effective data of radar A and effective data of virtual radar B from the standard data of radar A and the standard data of virtual radar B that are matched in time and space; Calculate the minimum index distance based on the effective data of the A radar and the effective data of the virtual B radar; Determine in real time whether the consistency between radar A and radar B is abnormal based on the minimum index distance and the index distance threshold; The calculation formula of the minimum index distance is: ； ， ； ； in, Indicates the minimum index distance; Indicates the calibration value offset; represents the optimal effective reflectivity error, The minimum MAE ; represents the minimum mean absolute error; Indicates the effective reflectivity in the effective data of the virtual B radar; Indicates the effective reflectivity in the effective data of radar A; represents the effective reflectivity error; Indicates the number of data points in the effective data of radar A or virtual radar B.

2. The method for real-time detection of consistency between weather radars according to claim 1, characterized in that: The step of obtaining the time-space matched A radar standard data and virtual B radar standard data includes: Obtain volume scan data of radar A and radar B; Performing time matching on the A radar volume scan data and the B radar volume scan data; The time-matched volume scan data of radar A and radar B are standardized to obtain standard data of radar A and radar B respectively; The inverse distance weighted method is used to map the standard data of radar B to the polar coordinate system of radar A to achieve spatial matching.

3. The method for real-time consistency detection between weather radars according to claim 2, characterized in that: The standardization process is performed on the A radar volume scan data or the B radar volume scan data, including: Determine whether the PPI radial scan data of each layer in the A radar volume scan data or the B radar volume scan data exceeds 360; If so, the radar parameter values ​​of each layer are standardized; If not, the radar parameter value is calculated based on the circular variance between the two azimuths.

4. The method for real-time detection of consistency between weather radars according to claim 1, characterized in that: Extracting effective radar A data and virtual radar B data from the time-space matched standard radar A data and virtual radar B standard data, including: Preprocess the time-space matched radar A standard data and virtual radar B standard data respectively; Extracting a common part from the preprocessed radar A standard data and virtual radar B standard data to obtain radar A common standard data and virtual radar B common standard data; Extract the effective reflectivity greater than 100 from the public standard data of radar A and the public standard data of virtual radar B respectively. and less than The standard data of radar A and virtual radar B are obtained; among them, and Both represent reflectivity thresholds.

5. The method for real-time consistency detection between weather radars according to claim 4, characterized in that: The time-space matched radar A standard data and virtual radar B standard data are preprocessed separately, including: Standard data with PPI layers above 3 and below 24 are extracted from the time-space matched A radar standard data and virtual B radar standard data, and denoising is performed on the extracted standard data.

6. The method for real-time consistency detection between weather radars according to any one of claims 1 to 5, characterized in that: The detection method further includes automatically calibrating radar A according to the minimum index distance when the consistency between radar A and radar B is abnormal. The specific calibration formula is: ； in, Indicates the new calibration value of radar A, Indicates the original calibration value of radar A, Indicates the minimum index distance.

7. A real-time detection device for consistency between weather radars, characterized in that: The detection device comprises: An acquisition unit is used to acquire the time-space matched A radar standard data and virtual B radar standard data; wherein the virtual B radar standard data is obtained by mapping the B radar standard data to the A radar polar coordinate system; An extraction unit, configured to extract effective radar A data and effective virtual radar B data from the temporally and spatially matched standard radar A data and virtual radar B data; a calculation unit, configured to calculate a minimum index distance based on the effective data of the A radar and the effective data of the virtual B radar; a judgment unit, configured to judge in real time whether the consistency between radar A and radar B is abnormal based on the minimum index distance; The calculation formula of the minimum index distance is: ； ， ； ； in, Indicates the minimum index distance; Indicates the calibration value offset; represents the optimal effective reflectivity error, The minimum MAE ; represents the minimum mean absolute error; Indicates the effective reflectivity in the effective data of the virtual B radar; Indicates the effective reflectivity in the effective data of radar A; represents the effective reflectivity error; Indicates the number of data points in the effective data of radar A or virtual radar B.

8. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instruction to implement the real-time detection method for consistency between weather radars according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method for real-time detection of consistency between weather radars according to any one of claims 1 to 6 is implemented.

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