Weather radar base data quality analysis and standard exceeding early warning system

Abstract

The invention discloses a weather radar-based data quality analysis and standard exceeding early warning system, which comprises a weather radar-based data collection and classification subsystem, a weather radar-based data quality analysis subsystem and a visual service early warning subsystem, the weather radar base data collection and classification subsystem is used for acquiring weather radar base data from a real-time running weather radar and classifying the weather radar base data; the weather radar base data quality analysis subsystem is used for carrying out quality analysis on the weather radar base data; and the visual service early warning subsystem is used for displaying the weather radar base data, the classification result of the weather radar base data and the analysis result of the weather radar base data quality analysis subsystem, calculating a comprehensive score, identifying and displaying early warning information and generating a solution suggestion. According to the invention, the quality of the gas radar base data can be accurately analyzed in real time, and early warning and suggestion can be given out for abnormal conditions in time.

Description

Technical Field

[0001] The present invention relates to the technical field of weather radars, and more particularly to a weather radar base data quality analysis and over-standard early warning system. Background Art

[0002] At present, most of the existing weather radar-based data quality analysis systems cannot achieve real-time analysis results, and there are problems such as low accuracy of analysis results and inability to promptly warn of abnormal situations and give suggestions.

[0003] Therefore, how to provide a weather radar base data quality analysis and over-standard warning system that can analyze the quality of weather radar base data in real time and accurately, and promptly warn of abnormal situations and give suggestions is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a weather radar-based data quality analysis and over-standard early warning system.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A weather radar base data quality analysis and over-standard early warning system, including a weather radar base data collection and classification subsystem, a weather radar base data quality analysis subsystem and a visual business early warning subsystem;

[0007] The weather radar base data collection and classification subsystem is used to obtain weather radar base data from the real-time operating weather radar and classify it;

[0008] The weather radar base data quality analysis subsystem is used to perform quality analysis on weather radar base data; wherein the quality analysis includes analysis of isolated noise point removal effect, ground object echo suppression effect, minimum measurable echo intensity, co-location radar echo consistency, differential reflectivity system deviation, radial velocity standard deviation, and antenna pointing accuracy.

[0009] The visual business warning subsystem is used to display the weather radar base data and its classification results, and the analysis results of the weather radar base data quality analysis subsystem;

[0010] The visual business warning subsystem is also used to calculate the comprehensive score, identify warning information and generate solution suggestions based on the analysis results of the weather radar base data quality analysis subsystem;

[0011] The visual business warning subsystem is also used to display the comprehensive score, the warning information and the solution suggestions.

[0012] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a weather radar base data quality analysis and over-standard warning system; it can analyze the quality of weather radar base data in real time and accurately, and promptly warn of abnormal situations and give suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0014] Figure 1 A schematic diagram of the structure of a weather radar-based data quality analysis and over-standard early warning system provided by the present invention;

[0015] Figure 2 A schematic diagram of a process of matching two radar range libraries with resolutions of 0.25km and 0.15km provided in one embodiment of the present invention;

[0016] Figure 3 A probability density diagram of a differential reflectivity system deviation provided in a certain embodiment of the present invention;

[0017] Figure 4 It is an interface of a weather radar base data quality analysis and over-standard early warning system provided in a certain embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 and Figure 4 As shown, the embodiment of the present invention discloses a weather radar base data quality analysis and over-standard warning system, including a weather radar base data collection and classification subsystem, a weather radar base data quality analysis subsystem and a visual business warning subsystem;

[0020] The weather radar base data collection and classification subsystem is used to obtain weather radar base data from the real-time operating weather radar and classify it;

[0021] The weather radar base data quality analysis subsystem is used to perform quality analysis on weather radar base data; wherein the quality analysis includes analysis of isolated noise point removal effect, ground object echo suppression effect, minimum measurable echo intensity, co-location radar echo consistency, differential reflectivity system deviation, radial velocity standard deviation, and antenna pointing accuracy.

[0022] The visual business warning subsystem is used to display the weather radar base data and its classification results, and the analysis results of the weather radar base data quality analysis subsystem;

[0023] The visual business warning subsystem is also used to calculate the comprehensive score, identify warning information and generate solution suggestions based on the analysis results of the weather radar base data quality analysis subsystem;

[0024] The visual business warning subsystem is also used to display the comprehensive score, the warning information and the solution suggestions.

[0025] In one embodiment, the weather radar-based data collection and classification subsystem includes a weather radar configuration module and a data collection module;

[0026] The weather radar configuration module is used to manage and adjust the working parameters of the weather radar;

[0027] The data collection module is used to collect weather radar basic data of various meteorological conditions in real time and classify and manage them.

[0028] It can be understood that the sky radar configuration module is an important part of the weather radar base data collection and classification subsystem. It is mainly responsible for managing and adjusting the working parameters of the weather radar so that the weather radar can collect accurate weather radar base data under different weather and environmental conditions. The sky radar configuration module not only needs to handle the basic settings of the weather radar, but also needs to flexibly configure the weather radar according to different observation requirements and precipitation processes (such as clear sky, precipitation, etc.). The specific functions and roles are as follows:

[0029] (1)Work parameter management.

[0030] The Sky Radar Configuration Module is responsible for managing various working parameters of the weather radar, including but not limited to: a) Scanning mode: Select different scanning modes (such as directional scanning, rotation scanning, etc.) to meet different observation needs. For example, when monitoring precipitation, a higher scanning density may be required, while a looser scanning interval can be used for clear sky monitoring. b) Beam angle and frequency: According to different targets (such as precipitation, meteorological phenomena, wind field, etc.), set the beam angle (horizontal, pitch angle) and frequency of the weather radar to optimize the accuracy and range of weather radar detection. c) Transmit power: According to the observation distance and precipitation intensity, adjust the transmit power of the weather radar to ensure the reliability of the data. Generally, the transmit power of the weather radar may need to be adjusted in long-distance or heavy precipitation environments.

[0031] (2) Adjustment of working parameters:

[0032] Different weather and environmental conditions have different effects on the quality of weather radar base data. The weather radar configuration module needs to adjust the working parameters according to different weather and environmental conditions: a) Clear sky mode: When the weather radar is used for clear sky monitoring, it is necessary to reduce the interference with ground objects, select the appropriate scanning mode and beam frequency, and ensure that only valid meteorological data is collected. b) Precipitation mode: When the weather radar is used for precipitation monitoring, the weather radar configuration module may automatically select appropriate beam angles, transmission power and other parameters according to the precipitation intensity and radar detection requirements to ensure balanced detection of weak and strong precipitation.

[0033] It can be understood that the data collection module plays a vital role in the weather radar base data collection and classification subsystem. It is mainly responsible for collecting weather radar base data of various meteorological conditions from the weather radar in real time and classifying and managing these data. Specifically, it involves the collection of weather radar base data under different precipitation processes, the establishment of data sets, the classification of base data, and the logical implementation of switching based on different observation modes.

[0034] The main functions of the data collection module are:

[0035] (1) Collection of weather radar data under different precipitation processes

[0036] The data collection module can select the appropriate scanning mode according to the actual situation through real-time judgment of the weather radar scanning mode. By analyzing the radar echo intensity, beam position and scanning interval, the module can identify the current weather type and switch to the appropriate observation mode. For example, if a large area of ​​precipitation is currently observed, the system will automatically switch to the severe convection observation mode.

[0037] The data collection module needs to judge the current weather conditions based on the observation mode of the weather radar (such as VCP31, VCP21, VCP11, etc.) and dynamically switch to the appropriate mode. VCP31 mode: This mode is usually used for clear sky or light precipitation monitoring. The weather radar adopts a more relaxed scanning strategy, a wider observation range, and focuses on monitoring the meteorological background. VCP21 mode: This mode is suitable for large-scale precipitation monitoring. The weather radar scans more intensively and covers a larger area. The goal is to capture a wide range of precipitation areas. VCP11 mode: Suitable for monitoring severe convective weather. The weather radar will perform intensive scanning, focusing on strong echoes and high reflectivity areas, and quickly capture dangerous meteorological phenomena such as thunderstorms or tornadoes.

[0038] (2) Dataset establishment

[0039] The data collection module also needs to establish data sets for different precipitation processes. These data sets include not only basic data such as precipitation intensity and echo intensity, but may also include meteorological variables such as wind speed and wind direction. Data sets for each precipitation type need to be labeled and grouped differently for subsequent analysis and evaluation. a) Structure of the data set: Usually the data set will contain data in multiple dimensions, such as time, space (such as observation location), radar scanning mode, echo intensity, radar signal quality, etc. In addition, the data set may also include labels to distinguish different precipitation processes or weather phenomena. b) Data preprocessing: The collected raw data may contain noise or incomplete information. The data collection module needs to perform necessary data cleaning, such as removing invalid data and filling missing data.

[0040] (3) Basic data classification

[0041] Another key function of the data collection module is to classify the collected base data. Base data classification is to classify base data into different categories according to multiple dimensions such as weather radar observation mode, precipitation process, echo characteristics, etc. According to different meteorological processes, the logic of observation mode switching in ROSE is used to set specific thresholds for parameters such as radar echo intensity (Z), differential reflectivity (ZDR) and radial velocity (VR), and combine the actual observation values ​​of these parameters to judge the weather conditions. For example, when the echo intensity (Z) is lower than 20dBZ, it is considered to be clear sky; when the Z value is greater than 40dBZ and the ZDR is greater than 3dB, it is considered to be strong convection; if Z is between 20dBZ and 40dBZ, and the ZDR is between 0.5dB and 3dB, and the radial velocity changes little, it is considered to be large-scale precipitation. The base data can be classified as follows: a) Clear sky data: This refers to weather radar base data when there is no significant precipitation or atmospheric disturbance. Clear sky data usually has a lower echo intensity and is mainly used for the establishment of benchmark data. b) Large-area precipitation data: This type of data refers to precipitation processes covering a wide range, with relatively high and uniform echo intensity, usually accompanied by large-scale precipitation areas. c) Severe convection data: This type of data usually contains rapidly changing and strong echoes, with very high echo intensity and rapid spatial changes, which may represent convective weather phenomena such as thunderstorms and tornadoes.

[0042] (4) Output base data classification record file

[0043] In order to facilitate the subsequent quality analysis of the base data, the data collection module will output a record file of the base data classification. The record file usually contains the following content:

[0044] Data name: record the name of the data set to facilitate identification of the data source or type.

[0045] Echo area: records the spatial distribution area of ​​echo signals to help determine the scope and intensity of precipitation.

[0046] Maximum echo intensity: records the value of the maximum echo intensity in the data set, which is usually used to identify areas of heavy precipitation or convective weather.

[0047] Precipitation type: According to the different precipitation processes (such as clear sky, large-area precipitation, strong convection, etc.), record the precipitation type to which the data belongs.

[0048] Observation Mode: Records the radar scanning mode used to collect the data, such as VCP31, VCP21, VCP11, etc.

[0049] In a certain embodiment, the weather radar base data quality analysis subsystem includes an isolated noise point removal effect analysis module, and the isolated noise point removal effect analysis module includes an isolated noise point removal module and an isolated noise point removal effect score mapping module;

[0050] The isolated noise point removal module is used to remove isolated noise points in the weather radar echo data that are less than the echo intensity threshold; wherein the echo intensity threshold is determined by meteorological conditions;

[0051] It is understandable that the intensity of radar echoes varies greatly depending on meteorological conditions. The present invention dynamically adjusts the echo intensity threshold according to the two meteorological conditions of clear sky and precipitation to achieve the best noise removal effect.

[0052] Specifically: When the sky is clear, the radar echo signal is weak. At this time, a higher echo strength threshold (such as 30dBZ) is set to avoid mistakenly rejecting weak real echoes. When it is raining, the radar echo is strong, especially the precipitation echo. At this time, setting a lower echo strength threshold (such as 10dBZ) can effectively reject low-intensity noise points while retaining effective precipitation echoes.

[0053] The isolated noise point removal module is also used to analyze the spatial distribution of weather radar echo data, identify isolated noise points using a spatial clustering algorithm, and remove the identified isolated noise points;

[0054] It can be understood that after the initial screening of the echo intensity threshold, the present invention further identifies isolated noise points through a spatial cluster analysis method. The core idea of ​​spatial cluster analysis is that the distance between echo points and the echo intensity should be consistent. If the spatial position of some echo points is far away from other echo points and its intensity is much lower than that of the surrounding area, it can be determined as an isolated noise point.

[0055] The commonly used spatial clustering method is the density-based spatial clustering algorithm (such as DBSCAN). DBSCAN can automatically identify isolated noise points based on the spatial position and density information of the echo points. If the echo point is too far away from other echoes around it and its intensity is weak, it will be judged as an isolated noise point. The spatial clustering algorithm divides all echo points into different categories, where echo points clustered into one category are considered valid echoes, and isolated echo points will be marked as isolated noise points and removed.

[0056] The isolated noise point elimination effect score mapping module is used to compare the weather radar echo data after eliminating isolated noise points with the echo data of the standard radar, calculate the number of isolated noise points that have not been eliminated included in the weather radar echo data after eliminating isolated noise points, and map the number of isolated noise points that have not been eliminated to the corresponding isolated noise point elimination effect score.

[0057] It can be understood that: the isolated noise point removal effect analysis module is used to evaluate the isolated noise point removal effect of the weather radar under clear sky and precipitation conditions.

[0058] The echo data from the standard radar has been rigorously validated to represent real meteorological phenomena.

[0059] Specifically: if the number of isolated noise points not removed is 0 (i.e. the weather radar echo data after removing the isolated noise points is completely consistent with the echo data of the standard radar), it is mapped to 100 points;

[0060] If the number of isolated noise points not eliminated is 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, they are mapped to 90 points, 80 points, 70 points, 60 points, 50 points, 40 points, 30 points, 20 points, 10 points, and 0 points respectively;

[0061] If the number of isolated noise points that were not eliminated was greater than 10, it was mapped to 0 points;

[0062] In addition, the present invention also determines whether there is a problem of false elimination (i.e., effective echoes are eliminated) during the elimination process, and adjusts the echo intensity threshold, the parameters of the spatial clustering algorithm, and the overall noise point elimination strategy according to the existing problem (there are isolated noise points that are not eliminated or effective echo points are eliminated), which can further improve the accuracy of the elimination effect.

[0063] Finally, the present invention also outputs a rejection result report.

[0064] The elimination result report includes the following:

[0065] Echo area: the spatial coverage of radar echo after removing isolated noise points.

[0066] Maximum echo intensity: The maximum echo intensity in the weather radar echo data after removing isolated noise points.

[0067] Echo distribution: The spatial distribution of echo signals after removing isolated noise points can effectively reflect meteorological characteristics.

[0068] Classification of meteorological conditions: Determine the current meteorological conditions, such as clear sky, precipitation, etc., based on the characteristics of the echo data (such as intensity distribution, shape, etc.).

[0069] In a certain embodiment, the weather radar base data quality analysis subsystem further includes a ground object echo suppression effect analysis module, and the ground object echo suppression effect analysis module includes a ground object echo suppression module and a ground object echo suppression effect score mapping module;

[0070] The ground object echo suppression module includes a radial alignment unit, a fuzzy processing unit, a weighted summation unit and a ground object echo rejection unit;

[0071] The radial alignment unit is used to align the weather radar echo radial data to a uniform azimuth distribution;

[0072] It is understandable that weather radar echo radial data are usually recorded in polar coordinates, that is, distributed along different azimuths and range gates. However, in actual observations, the radar azimuth distribution may not be strictly uniform, so it is necessary to align the weather radar echo radial data to a uniform azimuth distribution (such as 0°-360°, one point per degree). This step helps improve the accuracy of data processing and simplifies subsequent analysis.

[0073] To achieve alignment, the present invention adopts an algorithm based on linear interpolation, the core idea of ​​which is to remap the radial data of the echoes with the original azimuth distribution onto a uniform azimuth grid through an interpolation function.

[0074] The specific steps are as follows:

[0075] a) Generate a uniform azimuth array.

[0076] Specifically: Use NumPy's linspace function to generate a uniform array from 0° to 360°, where each point corresponds to an interpolated azimuth.

[0077] b) Construct an interpolation function.

[0078] The interpld function provided by SciPy is used to construct a mapping function through linear interpolation (kind='linear'), which can map the original echo data to the new azimuth.

[0079] c) Data interpolation and alignment.

[0080] The original echo radial data are input into the interpolation function and mapped to a uniform azimuth distribution to obtain aligned echo radial data.

[0081] The fuzzy processing unit is used to calculate the fuzzy membership of reflectivity, radial velocity, spectrum width and differential phase in the weather radar base data;

[0082] It is understandable that:

[0083] 1) The fuzzy membership of the reflectivity is calculated using the fuzzy membership function sigmf, i.e., the Sigmoid Membership Function;

[0084] The formula for the sigmoid function is as follows:

[0085]

[0086] Wherein, f(x) represents the fuzzy membership; b represents the center value of the S-type function, b=30 means that the fuzzy membership is 0.5 when the reflectivity is 30 dBZ; c is the slope of the S-type function, which represents the steepness of the transition from low reflectivity to high reflectivity, c=1;

[0087] If the reflectivity is greater than 30dBZ, it is more likely to be ground clutter. If the reflectivity is less than 30dBZ, it is likely to be a meteorological target.

[0088] 2) The fuzzy membership of the radial velocity is calculated using the fuzzy membership function zmf, namely the Z-Shaped Membership Function;

[0089] The formula for the Z-type function is as follows:

[0090]

[0091] Where a and b represent the two thresholds of the Z-type function, specifically a=-2, b=2, which means that when the radial velocity is in the range of [-2,2], it is more likely to be ground clutter. Ground objects are usually stationary or move very slowly, so the radial velocity is close to 0. Areas greater than 2m / s or less than -2m / s are more likely to be weather targets.

[0092] 3) The fuzzy membership of the spectrum width is also calculated using the fuzzy membership function: zmf, i.e., the Z-type function. Among them, a=2, b=3 are set: indicating that the spectrum width between 2-3m / s is more likely to be ground clutter. Ground objects usually have narrow spectrum widths because the reflection signals from ground objects are stable and consistent. Larger spectrum widths usually indicate weather targets, such as wind shear or turbulence.

[0093] 4) The fuzzy membership of the differential phase is also calculated using the membership function: zmf, Z-type function. In which, a=-50, b=50 are set: when the differential phase changes less in the range of [-50°, 50°], it is more likely to be ground clutter. Ground echoes usually have a gentler differential phase change, while precipitation echoes have a larger phase change.

[0094] The weighted summation unit is used to perform weighted summation on the fuzzy membership of reflectivity, radial velocity, spectral width, and differential phase to obtain a weighted summation result;

[0095] The weighted summation result is obtained based on the following formula:

[0096] clutter score = w refl *reflectivity fuzzy +w vel * velocity fuzzy +w sw *spectrumwidth fuzzy +wphi *phidp fuzzy

[0097] Among them, clutter score represents the weighted summation result; reflectivity fuzzy Represents the fuzzy membership of reflectivity, w refl Reflectivity fuzzy The weight of velocity fuzzy represents the fuzzy membership of radial velocity; w vel Indicates velocity fuzzy The weight of spectrum width fuzzy The fuzzy membership degree of the spectrum width, w sw Indicates spectrumwidth fuzzy The weight of phidp fuzzy represents the fuzzy membership of the differential phase, w phi represents phidp fuzzy Specifically, the present invention will w refl Set to 0.4; w vel Set to 0.2; w sw Set to 0.2; w phi Set to 0.2.

[0098] The ground object echo rejection unit is used to reject ground object echoes whose weighted summation result is greater than a first threshold;

[0099] It can be understood that: the present invention sets the first threshold to 0.7 (the first threshold can be adjusted according to actual needs to adapt to different radar detection scenarios); if the clutter score is greater than 0.7, the corresponding echo point is marked as ground clutter; if the chutter score is less than or equal to 0.7, the corresponding echo point is considered to be a weather echo.

[0100] The ground object echo suppression effect score mapping module is used to calculate the intensity difference between the ground object echo intensity after the ground object echo is removed and the ground object echo intensity of the standard radar, and map the intensity difference to a corresponding ground object echo suppression effect score.

[0101] It is understandable that ground object echoes, such as mountains or buildings, may interfere with radar echoes and affect the accuracy of meteorological data. The ground object echo suppression effect analysis module is used to check whether the weather radar can effectively suppress the influence of ground object echoes under clear sky conditions.

[0102] Specifically, if the intensity difference is equal to 0dB, it is mapped to 100 points; when the intensity difference is (0-5dB), 5 points will be deducted for every 1dB increase; when the intensity difference is [5-10dB], 8 points will be deducted for every 1dB increase; when the difference exceeds 10dB, 10 points will be deducted for every 1dB increase, and the lowest score is 0 points.

[0103] In addition, the present invention also compares the spatial distribution to check whether there are problems of false rejection (valid echoes are rejected) or missed rejection (ground object echoes are not rejected), especially those areas that may be ground object echoes. And according to the problems of false rejection (valid echoes are rejected) or missed rejection (ground object echoes are not rejected), the following adjustments are made:

[0104] Intensity threshold (i.e., first threshold) adjustment: adjust the intensity threshold (i.e., first threshold) of ground object echo to avoid mistakenly rejecting valid echoes.

[0105] Spatial range adjustment: adjust the spatial elimination range of ground object echoes according to the comparison results to ensure that valid echoes are not mistakenly eliminated.

[0106] In a certain embodiment, the weather radar-based data quality analysis subsystem further includes a minimum measurable echo intensity analysis module;

[0107] The minimum measurable echo intensity analysis module is used to calculate the echo intensity measured by the weather radar at a specific distance, and map the measured echo intensity to a corresponding minimum measurable echo intensity score using a second threshold.

[0108] It can be understood that: the minimum measurable echo intensity analysis module is used to evaluate the detection capability of the weather radar for weak echoes at a specific distance.

[0109] The specific distance is 50 km; the second threshold is -4.5dBZ;

[0110] Specifically: when the weather radar measures the echo intensity at 50 kilometers below -4.5dBZ, it is mapped as 100 points; for every additional 1dB increase in the difference between the measured echo intensity and the second threshold, 10 points are deducted, and the minimum score is 0 points.

[0111] The second threshold is obtained based on the following formula:

[0112]

[0113] in, Indicates the minimum detectable signal power; noise bandwidth B n =Signal bandwidth; B n =1 / τ; Noise factor F 0 =1+T / 290; C represents the radar constant, in dB; λ represents the radar operating wavelength (specifically 10 cm), in cm; Pt represents the transmitted pulse power, in kW; G represents the antenna gain, in dB; τ represents the pulse width, in us; θ represents the horizontal beam width, in degrees; Indicates the vertical beam width, unit: °; L Σ It represents the total system loss = matching filter loss + transmission branch loss + receiving branch loss + pulse compression loss (pulse compression radar), in dB; Z represents the echo intensity; L at represents atmospheric loss, which is 0.011 for S-band, 0.019 for C-band and 0.025 for X-band; R represents echo distance, in m, corresponding to the specific distance of 50 kilometers in the present invention.

[0114] In a certain embodiment, the weather radar-based data quality analysis subsystem further includes a co-located radar echo consistency analysis module;

[0115] The co-located radar echo consistency analysis module is used to screen the basic data whose body scanning time difference between the weather radar and the standard radar is less than a preset time difference threshold, and obtain a first group of basic data; wherein the weather radar is the marked radar;

[0116] The co-located radar echo consistency analysis module is used to filter out basic data whose elevation angle difference between weather radar and standard radar is less than a preset elevation angle difference threshold and basic data whose azimuth angle difference between body scan is less than a preset azimuth angle difference threshold from the first group of basic data, to obtain a second group of basic data;

[0117] The co-located radar echo consistency analysis module is used to select basic data with a position difference less than a preset position difference threshold from the second group of basic data to obtain a third group of basic data;

[0118] The co-located radar echo consistency analysis module is used to filter out meteorological echo data from the third group of basic data;

[0119] The co-located radar echo consistency analysis module is used to convert the observation coordinates of the weather radar and the standard radar to obtain the geodetic coordinates of the weather radar and the geodetic coordinates of the standard radar; and use the distance library matching algorithm, the geodetic coordinates of the weather radar and the geodetic coordinates of the standard radar in the geodetic coordinate system to determine whether the distance difference between the weather radar and the standard radar is less than a preset distance difference threshold. If it is less than the preset distance difference threshold, the difference between various echo products of the weather radar and various echo products of the standard radar in the matching distance library is calculated, and the difference is mapped to a corresponding echo consistency effect score; wherein, various echo products of the weather radar and various echo products of the standard radar are all selected from the meteorological echo data.

[0120] In a certain embodiment: various types of echo products include echo intensity, radial velocity, differential reflectivity, spectral width, correlation coefficient, differential phase shift, and differential phase shift rate;

[0121] The corresponding differences include the difference between the echo intensity of the weather radar and the echo intensity of the standard radar, the difference between the differential reflectivity of the weather radar and the differential reflectivity of the standard radar; the difference between the radial velocity of the weather radar and the radial velocity of the standard radar, etc.;

[0122] In this embodiment:

[0123] First, the basic data with a time difference of less than 30 seconds between weather radar and standard radar are selected to obtain the first set of basic data;

[0124] Then, basic data with a difference of less than 0.1° in elevation angle between the weather radar and the standard radar and basic data with a difference of less than 0.1° in azimuth angle between the weather radar and the standard radar are selected from the first group of basic data to obtain a second group of basic data;

[0125] Next, select the second set of base data with a position difference less than The third set of basic data is obtained; among them, Δr max It is the maximum value of the distance resolution of weather radar and standard radar; weather radar and standard radar are radars of the same band.

[0126] Next, the meteorological echo data is selected from the third group of basic data; the thresholds are as follows: (1) the distance range corresponding to the selected echo data is 50-230 kilometers to exclude interference from ground objects; (2) the correlation coefficient is between 0.97-1; (3) the differential reflectivity is between -2-2dB; (4) the signal-to-noise ratio is between 15-100dB; (5) the radial velocity range is between -1-1m / s; (6) the spectrum width is between 0-3m / s; (7) the reflectivity factor before ground object echo suppression is less than 50dBT. When comparing the basic reflectivity factor, the condition that the echo intensity comparison range is 15-45dBZ is added.

[0127] The distance library matching algorithm is specifically:

[0128] Assume that R1 is a weather radar (or standard radar) with large range resolution, and R2 is a standard radar (or weather radar) with small range resolution. Divide each range bin of radars R1 and R2 into half (subtract 1 / 2 of the first range bin from each range bin, M i =N i -Δr / 2,i=1,2,…,n)N is the initial distance library, M is the calculated distance library, Δr is the distance resolution, and n is the number of distance libraries; after updating the distance libraries of the two radars, it is determined whether the distance library difference between the two radars is within the set threshold ( I,j=1,2,…,n), the threshold set in this paper is half of the small distance resolution of the two radars, and finally the distance library with the same or similar distance of the two radars is found.

[0129] like Figure 2 As shown, the process of range library matching of two radars with resolutions of 0.25km and 0.15km is demonstrated;

[0130] The co-located radar echo consistency analysis module is used to evaluate the consistency of various echo products of weather radar and standard radar; the present invention sets corresponding deduction rules for the difference between various echo products;

[0131] Take the echo intensity of various echo products as an example: when the difference in echo intensity is within 2dB, no points will be deducted; 10 points will be deducted for every 1dB deviation, and the lowest mapped score is 0 points and the highest is 100 points;

[0132] The final echo consistency effect score is:

[0133] Where N represents the number of various echo products, M i represents the score deducted from the i-th echo product; F represents the final echo consistency effect score.

[0134] The co-located radar echo consistency analysis module can help determine the stability and consistency of radar output data, ensuring that the radar output will not affect meteorological analysis due to abnormal fluctuations.

[0135] In a certain embodiment, the weather radar base data quality analysis subsystem further includes a differential reflectivity system deviation analysis module, and the differential reflectivity system deviation analysis module includes a data acquisition module, a first data screening module, a second data screening module, a third data screening module, a median extraction module, and a differential reflectivity factor system deviation score mapping module;

[0136] The data acquisition module is used to read the differential reflectivity factor, correlation coefficient, signal-to-noise ratio, reflectivity factor before ground object echo suppression, radial velocity, and echo distance in the weather radar base data;

[0137] The first data screening module is used to screen the differential reflectivity factor value of the required distance azimuth according to the preset distance threshold;

[0138] The second data screening module is used to remove non-precipitation echoes according to preset screening conditions to obtain the differential reflectivity factor value after the second screening; wherein the preset screening conditions are 0.95≤correlation coefficient≤1,15dB≤signal-to-noise ratio≤100dB,5dB≤reflectivity factor≤30dB,-0.5m / s≤radial velocity≤0.5m / s;

[0139] The third data screening module is used to remove invalid data and null values ​​to obtain the final differential reflectivity factor series;

[0140] The median extraction module is used to extract the median of the differential reflectivity factor numerical deviation from the final differential reflectivity factor sequence;

[0141] The differential reflectivity factor system deviation score mapping module is used to obtain the system deviation of the differential reflectivity factor, and use the system deviation of the differential reflectivity factor to map to the corresponding differential reflectivity factor system deviation score; wherein the system deviation of the differential reflectivity factor is the median.

[0142] It can be understood that the differential reflectivity (ZDR) is an important parameter for distinguishing different precipitation types, especially the reflectivity under light rain conditions.

[0143] Specific:

[0144] When the system deviation is less than 0.2dB, the score is 100 points; for every 0.05dB increase in the system deviation, 10 points will be deducted, with the minimum score being 0.

[0145] In addition, the present invention also provides a probability density diagram of the differential reflectivity system deviation (eg Figure 3 The distribution of the differential reflectivity system deviation can be more intuitively seen by looking at the differential reflectivity system deviation probability histogram.

[0146] In a certain embodiment, the weather radar base data quality analysis subsystem further includes a radial velocity standard deviation analysis module, which includes a velocity fuzzy processing module, a distance folding data removal module, a window definition and standard deviation calculation module, a percentile statistics module, a radial velocity standard deviation output module, and a radial velocity standard deviation score mapping module;

[0147] The velocity blur processing module is used to restore the blurred radial velocity to the real radial velocity;

[0148] The distance folding data elimination module is used to eliminate the distance folding data in the weather radar echo data;

[0149] The window definition and standard deviation calculation module is used to define a grid window for each radar echo data point, and traverse and calculate the standard deviation value of each valid radial velocity data in each grid window; wherein, if the grid window includes less than 9 valid radial velocity data, the grid window is skipped; the grid window size is 7*9, that is, 7 points are arranged along the azimuth direction, and 9 points are arranged along the distance direction;

[0150] The percentile statistics module is used to sort all calculated standard deviation values ​​and filter out the standard deviation values ​​ranked at 75%;

[0151] The radial velocity standard deviation output module is used to output the standard deviation value at the 75% position;

[0152] The radial velocity standard deviation score mapping module is used to map the standard deviation value at the 75% position to the corresponding radial velocity standard deviation score.

[0153] It is understandable that in weather radar, radial velocity measurement is to determine the target's speed along the radar beam direction through the Doppler effect. However, radial velocity ambiguity and range folding are common problems in radar measurement. Radial velocity ambiguity refers to the phenomenon that when the target speed exceeds the maximum speed range detectable by the radar system, the radar echo velocity will periodically return, which is the so-called "blurring" phenomenon. Range folding means that due to the observation distance limitation of the radar system, the echo beyond a certain distance will be mistakenly judged as a closer target.

[0154] In this case, velocity deblurring refers to using the radar's system characteristics and related algorithms to restore blurred velocity information, while eliminating distance-folded data refers to removing erroneous data caused by radar system problems within a certain distance, thereby improving the accuracy of radial velocity.

[0155] The radial velocity standard deviation analysis of the present invention is based on a 7×9 grid window to calculate the radial velocity standard deviation within the window, and finally performs percentile statistics on these standard deviations to calculate the upper quartile (75% quantile) as the radial velocity standard deviation of the time.

[0156] Specific:

[0157] Velocity ambiguity processing: Preliminary preprocessing of radar echo data, first check whether the radial velocity of each echo exceeds the effective measurement range of the radar system. When the radial velocity exceeds this range, the radar will be "blurred", that is, the radial velocity of the target is incorrectly reflected. To solve this problem, it is necessary to apply the ambiguity theorem to recover these ambiguous radial velocities. The ambiguity recovery process is based on the frequency offset of the radar echo and the known radar transmission frequency to infer the true target radial velocity. This process calculates the frequency shift of the target and combines the parameters of the radar system to reversely infer the true velocity, reducing the data error caused by ambiguity.

[0158] Eliminate distance folded data: In the preprocessing of radar data, in addition to fuzzy processing, it is also necessary to eliminate "distance folded" data. The measurement range of the radar is limited. When the echo signal reflection exceeds the detection range of the radar system, the system cannot accurately locate the true position of the echo, resulting in distance folding. To avoid this problem, first determine whether it exceeds the effective detection range of the system based on the distance information of the echo. If the distance of the echo is greater than the maximum measurement range of the radar, the data is marked as invalid data and filtered out from the data set to ensure that the final data set accurately reflects the actual detection capability of the radar.

[0159] Window definition and standard deviation calculation: After data preprocessing, the next step is to perform window processing on each radar data point. Define a 7×9 grid window centered on the current data point, with 7 points arranged in the azimuth direction and 9 points arranged in the range direction. The size of this window is designed to capture the adjacent radar measurement data around the point, ensuring that the calculation of the standard deviation covers sufficient spatial and temporal ranges. The standard deviation of all valid radial velocity data within each window is calculated using the following formula:

[0160]

[0161] Among them, v i is the radial velocity of the i-th valid data point in the window, is the average value of all valid data in the window, and N is the number of valid data points in the window. The standard deviation measures the discreteness of the radial velocity in the window and is an important indicator for evaluating the radar measurement accuracy.

[0162] Valid data point screening: To ensure the accuracy of standard deviation calculation, the number of valid data points in each 7×9 window must reach at least 9 valid values. If the number of valid data points in the window is less than 9, it is considered that the data in the window is insufficient to calculate the standard deviation, so the window is skipped. This step ensures that the calculation of the standard deviation will not be affected by sparse or poor data quality, thereby improving the reliability of the evaluation results.

[0163] Percentile statistics: Each data point in the radar data set is processed, and the radial velocity standard deviation in the 7×9 window where each data point is located is calculated. The smaller the standard deviation value, the smaller the change in the radial velocity value in the window, and the higher the accuracy of the radar measurement. During the calculation process, the entire data set will be traversed, and the corresponding standard deviation will be calculated at the position of each data point to obtain the local accuracy evaluation of each point. After calculating the standard deviation values ​​of all windows, all these standard deviation values ​​are sorted and percentile statistical analysis is performed. A commonly used statistical method is to calculate the upper quartile (75% quantile), that is, to arrange all standard deviation values ​​in order of size and find the standard deviation value ranked at the 75% position. The upper quartile reflects the deviation of most data and can help evaluate the measurement accuracy of the radar in most time periods and regions. The calculation of this quantile can effectively suppress the influence of extreme values ​​and provide a stable accuracy evaluation.

[0164] Output radial velocity standard deviation: Finally, the upper quartile calculated by percentile statistics is the radial velocity standard deviation of that time. This value is used as the main indicator to evaluate the radial velocity measurement accuracy of the radar system during that period. A lower standard deviation indicates that the radar system has a higher consistency and better accuracy in the measurement results during that period; a higher standard deviation indicates that the radar system's measurement results have greater fluctuations and may require further optimization or adjustment.

[0165] The radial velocity standard deviation analysis module evaluates the deviation between the radar radial velocity data and the standard value

[0166] Specific:

[0167] When the standard deviation value at the 75% position is less than 1m / s, the score is 100 points; for every additional 1m / s of deviation, 10 points will be deducted, with the minimum score being 0.

[0168] In a certain embodiment, the weather radar base data quality analysis subsystem further includes an antenna pointing accuracy analysis module, which includes a fixed ground object target selection module, a radar data acquisition module, a valid data screening module, an antenna pointing error calculation module, a statistical error analysis module, an error distribution and visualization module, and an antenna pointing error score mapping module;

[0169] The fixed ground object target selection module is used to select a ground object target that is stable and has a known position;

[0170] The radar data acquisition module is used to collect the measured azimuth and measured elevation angles returned by the weather radar after scanning the ground object target;

[0171] The effective data screening module is used to screen the effective echo data returned by the weather radar after scanning the ground object target

[0172] The antenna pointing error calculation module is used to calculate the azimuth error and the elevation error of the effective echo data; wherein the azimuth error is equal to the measured azimuth minus the expected azimuth; the elevation error is equal to the measured elevation minus the expected elevation;

[0173] The statistical error analysis module is used to calculate the mean and standard deviation of each azimuth error, as well as the mean and standard deviation of each elevation error;

[0174] The error distribution and visualization module is used to display the azimuth error and elevation error of the effective echo data;

[0175] The antenna pointing error score mapping module is used to map the azimuth error and elevation angle error of the effective echo data to the corresponding antenna pointing error score.

[0176] It is understandable that:

[0177] Fixed ground object selection: Before performing antenna pointing accuracy analysis, it is necessary to first select some stable ground objects with known positions as references. The selection of ground objects is very critical. Usually, targets such as towers, mountains, bridges, etc. with strong reflection signals and within the radar scanning area are selected. The selected targets should have sufficient echo signals to ensure the accuracy of the measurement results. In addition, the actual positions of these ground objects should be calibrated in a known coordinate system to ensure that the analysis process has reference value. The selection of ground objects should cover multiple azimuths and elevations of the radar beam to ensure the comprehensiveness of the evaluation.

[0178] Radar data collection: Weather radar scans these fixed ground objects and records the echo signals of each target. During the collection process, the radar system will obtain two important parameters of the echo signal: azimuth and elevation. The azimuth is the horizontal angle between the radar antenna and the target, while the elevation is the vertical angle. These two angles determine the direction of the radar beam. The radar scans fixed ground objects at different times and locations by continuously rotating the antenna to obtain a series of echo data.

[0179] Antenna pointing error calculation: After obtaining the radar echo data, the next step is to calculate the antenna pointing error. Assume that the actual position of each selected fixed object is known (for example, through GPS positioning), and the expected azimuth of the object target in the radar coordinate system can be calculated. and the expected pitch angle Expected azimuth and the expected pitch angle It is calculated based on the geometric relationship between the ground object and the radar, and is usually converted into an angle in the radar coordinate system using the geographical coordinates of the ground object. Specifically, the expected azimuth and elevation angles can be calculated using the straight-line distance, relative position, and trigonometric functions between the radar and the ground object. For each fixed ground object, the echo signal measured by the radar will provide the actual measured azimuth and measure the pitch angle After comparing these measured values ​​with the expected values, the error value of each echo signal is calculated. The error is the difference between the expected value and the measured value. The calculation formulas for azimuth error and elevation error are as follows:

[0180]

[0181]

[0182] Among them, Δθ az and Δθ el are the error values ​​of azimuth and elevation respectively.

[0183] Screening valid data: In practical applications, some abnormal data or noise interference may appear in the echo data measured by the radar. Therefore, it is necessary to screen the collected echo data to ensure that only valid echo data is used when calculating the error. The standard for valid data can be screened according to the size of the error. For example, error calculation can only be performed when the number of echo data points of each ground object target is greater than or equal to 10 valid values. In order to eliminate outliers, the present invention also sets an error threshold. If the azimuth or pitch angle error of a certain echo data exceeds the set maximum error range (for example, 10°), the data is considered to be an outlier and is eliminated.

[0184] Statistical error analysis: For each ground object, after calculating its azimuth error and elevation error, it is necessary to perform statistical analysis on these error values. Common statistical methods include calculating the mean value and standard deviation of the error. First, calculate the mean value μ of the azimuth error az and the mean value of the pitch angle error μ el :

[0185]

[0186] Where N represents the number of valid echo data points for each feature. and are the azimuth error and elevation error of the i-th valid data point respectively.

[0187] Next, calculate the standard deviation σ of the azimuth error az and the standard deviation of the pitch angle error σ el :

[0188]

[0189] The standard deviation reflects the degree of dispersion of the error. A smaller standard deviation indicates that the radar's pointing accuracy is higher and the error is more stable.

[0190] Error distribution and visualization: After the error calculation is completed and statistical analysis is performed, the results can be displayed graphically. Common visualization methods include error histograms, scatter plots, and error heat maps. The error histogram can show the distribution of azimuth and elevation errors, reflecting the concentration of errors. The scatter plot can display the measurement error of each ground object and intuitively show the pointing accuracy of the radar system at different azimuths and elevations. The error heat map can analyze the error distribution of the radar system in different areas, help find out whether there are large deviations in certain areas, and provide a basis for subsequent optimization.

[0191] In one embodiment, the visual business warning subsystem includes a comprehensive scoring module, a warning information identification module, and a warning information solution suggestion generation module;

[0192] The comprehensive scoring module is used to perform standardization and weighted processing (weighted summation) on the isolated noise point removal effect score, the ground object echo suppression effect score, the minimum measurable echo intensity score, the echo consistency effect score, the differential reflectivity factor system deviation score, the radial velocity standard deviation score, and the antenna pointing error score to obtain a comprehensive score;

[0193] The warning information identification module is used to automatically identify data exceeding the standard and abnormal conditions according to the analysis results of the weather radar-based data quality analysis subsystem to obtain the warning information.

[0194] The warning information solution suggestion generation module is used to provide corresponding solution suggestions for the data exceeding the standard and abnormal situations.

[0195] In one embodiment, the weights of the isolated noise point elimination effect score after standardization, the ground object echo suppression effect score, the minimum measurable echo intensity score, the echo consistency effect score, the differential reflectivity factor system deviation score, the radial velocity standard deviation score, and the antenna pointing error score are 15%, 20%, 30%, 25%, 20%, 15%, and 10%, respectively;

[0196] The invention generates a detailed evaluation report, which includes:

[0197] 1) The standardized scores of various analysis indicators (i.e., the scores of isolated noise point elimination effect, ground object echo suppression effect, minimum measurable echo intensity, echo consistency effect, differential reflectivity factor system deviation, radial velocity standard deviation, and antenna pointing error), weighted scores, and the impact of various analysis indicators in the comprehensive score;

[0198] 2) Comprehensive score: displays the comprehensive score of the radar system, reflecting its overall performance;

[0199] 3) Performance analysis and optimization suggestions: Based on the analysis results, the system will give specific performance analysis and optimization suggestions for each indicator. If the score of some indicators is low, the system will recommend adjustment measures, such as calibration, optimization algorithm, etc.

[0200] In one embodiment, the visual business warning subsystem includes a visualization module and a business warning module.

[0201] 1) Visualization module

[0202] The visualization module is an important part of the visualization business warning subsystem. It is mainly responsible for presenting complex radar-based data and analysis results to users in an intuitive and easy-to-understand way. This module can not only enhance the operability of the system, but also help technicians quickly understand and analyze radar performance and provide decision support. The main functions of the visualization module include data interaction, data transmission, and display of system analysis results.

[0203] (1)Data interaction.

[0204] Data interaction is a core function in the visualization module. It is mainly responsible for interacting user operations with the system and providing flexible data display and analysis methods. Through data interaction, users can choose different views and data display methods according to their needs, so as to deeply analyze and understand the analysis results.

[0205] Interactive interface: Users can interact with the system through a graphical interface, such as selecting analysis data at different times, locations or radar configurations. The system supports multiple interactive methods, such as clicking, zooming, dragging, etc., allowing users to freely select data range, accuracy and display mode.

[0206] Dynamic display: Users can view the changes and analysis results of radar base data in real time, and support dynamic updates. For example, the system can display the changes of current echo intensity, radial velocity and other indicators based on the real-time collected radar base data, helping users to quickly evaluate the real-time performance of the radar.

[0207] Data screening and filtering: Users can set screening conditions, select specific time periods, precipitation types, radar parameters, etc. The system will automatically filter out basic data that meets the conditions according to the settings and display them on the interface, facilitating more detailed analysis.

[0208] (2) Data transmission

[0209] The data transmission function refers to how the system efficiently transmits the collected radar-based data and analysis results to the user end or other analysis systems to ensure the timeliness, accuracy and completeness of the data.

[0210] Real-time data transmission: The system can transmit the basic data collected by the radar to the visual interface in real time, and display the real-time updated analysis results on the interface. In this way, users can quickly obtain the latest data of the current radar performance analysis, helping decision makers to grasp the radar status in real time.

[0211] Data upload and download: Users can not only view real-time base data, but also download historical base data or import external base data for analysis. The system supports uploading and downloading of multiple data formats (such as CSV, JSON, XML, etc.) to ensure data compatibility.

[0212] Data synchronization and distribution: In order to support multi-user operation and remote collaboration, the system can synchronize analysis results to multiple terminals, allowing different users to view and operate base data in different locations. Through cloud or LAN transmission, base data can be updated and distributed to various work sites in a timely manner.

[0213] (3) Interface display of system analysis results

[0214] The display of system analysis results is the final output of the visualization module. Its purpose is to transform complex radar-based data and analysis results into easy-to-understand visualization graphs or tables to help users quickly obtain information and make decisions.

[0215] Analysis result chart display: Through graphical means (such as bar charts, line charts, radar charts, etc.), the analysis results of various analysis indicators can be intuitively displayed. For example, echo intensity analysis can use line charts to show the change of echo intensity at different times and configurations, and radial velocity analysis can use heat maps to show the velocity distribution in different areas.

[0216] Heatmap and vector map display: Radar-based data often involves the display of spatial distribution, so the visualization module supports the display of heatmaps and vector maps. For example, the echo intensity can be displayed in heatmaps of different shades, and the radial velocity can be represented by the size and direction of the arrows in the vector map. In this way, users can quickly identify outliers or hot spots in a certain area.

[0217] Multi-dimensional display and comprehensive analysis: In order to help users comprehensively evaluate the performance of the radar system, the interface can display multiple analysis indicators at the same time. For example, in the same view, multiple analysis indicators such as echo intensity, radial velocity and antenna pointing accuracy can be displayed at the same time to facilitate comprehensive performance analysis. It can also be combined with time series analysis to display the trend of indicators over time, helping technicians analyze the dynamic changes of radar performance.

[0218] Analysis result scores and ratings: For each analysis indicator, the system will calculate a comprehensive score based on the scoring rules and weights and display it on the interface. The analysis results can be annotated by color, level, etc., so that users can quickly understand the results. For example, a radar system with a high comprehensive score may be displayed in green, indicating good performance; while a low-scoring system may be displayed in red, indicating that adjustments or optimizations are needed.

[0219] (4) Characteristics of visual design

[0220] Intuitive: Through graphical display, users can easily understand complex radar performance analysis results. Different graphics and colors can intuitively express the changes and trends of basic data.

[0221] Interactivity: Users can freely choose display content, adjust views, and screen and filter basic data according to their needs, which greatly enhances the user's operating experience.

[0222] Real-time: The system can display basic data and analysis results in real time, helping users make decisions quickly and take necessary optimization measures.

[0223] Flexibility and customizability: The visualization module supports a variety of display styles and views, which can be customized according to the needs of different users to meet different evaluation needs and scenarios.

[0224] 2) Business warning module

[0225] The business warning module is one of the core functions of the visual business warning subsystem. It is mainly used to monitor the performance indicators of the radar in real time, detect abnormalities in time and issue warnings. It can automatically trigger an alarm when the radar indicators exceed the preset threshold, prompt the operator where the problem is, and provide corresponding calibration suggestions based on the current analysis results to help technicians quickly take corresponding measures to correct it.

[0226] (1) Warning trigger mechanism. The service warning module monitors multiple analysis indicators (such as echo intensity, radial velocity, antenna accuracy, etc.) and detects in real time whether these indicators exceed the normal range. The system sets a threshold for each indicator and determines whether it exceeds the preset threshold through continuous real-time data analysis. Once an indicator exceeds the standard, the system will immediately trigger a warning and display the abnormal information in a prominent color (such as red or orange) in the visual interface.

[0227] (2) Early warning. The module will classify the warning level according to the degree of exceeding the standard, which is divided into three levels: "warning", "serious" and "critical". For indicators that are slightly exceeded, the system will prompt "warning" and recommend further inspection; for more serious exceeding of the standard, the system will give a "serious" warning and recommend calibration correction; and at the "critical" level, the system will recommend immediate measures to avoid large-scale failure of radar performance.

[0228] (3) Calibration suggestions and corrective measures. When the system finds an excess, in addition to triggering an alert, the business alert module will automatically generate calibration suggestions based on the type of anomaly. For example, if the echo intensity is too low, the system may prompt "It is recommended to adjust the radar antenna gain or transmit power"; if the radial velocity fluctuates greatly, it is recommended to "recalibrate the radar frequency." These calibration suggestions help technicians quickly understand the problem and deal with it.

[0229] The generation of calibration suggestions is not only based on real-time analysis results, but also combines historical data and experience to give the corrected value. For example, when there is a large deviation in radial velocity, the system may give a suggestion of "adjusting the frequency calibration error by ±0.2m / s" based on historical records to help users make more accurate adjustments.

[0230] In addition, the business warning module will record each warning record in detail to form a warning log, which is convenient for technical personnel to track and trace the problem. Each warning log will contain information such as the triggering time, indicator name, exceeded value, analysis results and related suggestions, providing data support for subsequent analysis and optimization.

[0231] (4) Warning record and tracking. After the problem is solved, the module will follow up on the calibration measures to ensure that the corrective measures are appropriate and effectively restore the normal value of the indicator. If the calibration adjustment fails to effectively solve the problem, the system will issue a second warning or further remind the technician to perform more inspections or adjustments.

[0232] Finally, the business warning module can be linked with other system modules such as the weather radar base data collection and classification subsystem, the weather radar base data quality analysis subsystem and the visualization module to form a complete closed loop. For example, the base data transmitted in real time by the weather radar base data collection and classification subsystem will automatically trigger the weather radar base data quality analysis subsystem for analysis. When the analysis results exceed the standard, the warning information will be immediately displayed to the visualization module, making it easier for operators to take measures.

[0233] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0234] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A weather radar data quality analysis and over-standard early warning system, characterized in that: It includes weather radar base data collection and classification subsystem, weather radar base data quality analysis subsystem and visual business warning subsystem; The weather radar base data collection and classification subsystem is used to obtain weather radar base data from the real-time operating weather radar and classify it; The weather radar base data quality analysis subsystem is used to perform quality analysis on weather radar base data; wherein the quality analysis includes analysis of isolated noise point removal effect, ground object echo suppression effect, minimum measurable echo intensity, co-location radar echo consistency, differential reflectivity system deviation, radial velocity standard deviation, and antenna pointing accuracy. The visual business warning subsystem is used to display the weather radar base data and its classification results, and the analysis results of the weather radar base data quality analysis subsystem; The visual business warning subsystem is also used to calculate the comprehensive score, identify warning information and generate solution suggestions based on the analysis results of the weather radar base data quality analysis subsystem; The visual business warning subsystem is also used to display the comprehensive score, the warning information and the solution suggestions.

2. A weather radar data quality analysis and over-standard early warning system according to claim 1, characterized in that: The weather radar-based data collection and classification subsystem includes a weather radar configuration module and a data collection module; The weather radar configuration module is used to manage and adjust the working parameters of the weather radar; The data collection module is used to collect weather radar basic data of various meteorological conditions in real time and classify and manage them.

3. A weather radar data quality analysis and over-standard early warning system according to claim 1, characterized in that: The weather radar base data quality analysis subsystem includes an isolated noise point removal effect analysis module, and the isolated noise point removal effect analysis module includes an isolated noise point removal module and an isolated noise point removal effect score mapping module; The isolated noise point removal module is used to remove isolated noise points in the weather radar echo data that are less than the echo intensity threshold; wherein the echo intensity threshold is determined by meteorological conditions; The isolated noise point removal module is also used to analyze the spatial distribution of weather radar echo data, identify isolated noise points using a spatial clustering algorithm, and remove the identified isolated noise points; The isolated noise point elimination effect score mapping module is used to compare the weather radar echo data after eliminating isolated noise points with the echo data of the standard radar, calculate the number of isolated noise points that have not been eliminated included in the weather radar echo data after eliminating isolated noise points, and map the number of isolated noise points that have not been eliminated to the corresponding isolated noise point elimination effect score.

4. A weather radar data quality analysis and over-standard early warning system according to claim 3, characterized in that: The weather radar base data quality analysis subsystem also includes a ground object echo suppression effect analysis module, and the ground object echo suppression effect analysis module includes a ground object echo suppression module and a ground object echo suppression effect score mapping module; The ground object echo suppression module includes a radial alignment unit, a fuzzy processing unit, a weighted summation unit and a ground object echo rejection unit; The radial alignment unit is used to align the weather radar echo radial data to a uniform azimuth distribution; The fuzzy processing unit is used to calculate the fuzzy membership of reflectivity, radial velocity, spectrum width and differential phase in the weather radar base data; The weighted summation unit is used to perform weighted summation on the fuzzy membership of reflectivity, radial velocity, spectral width, and differential phase to obtain a weighted summation result; The ground object echo rejection unit is used to reject ground object echoes whose weighted summation result is greater than a first threshold; The ground object echo suppression effect score mapping module is used to calculate the intensity difference between the ground object echo intensity after the ground object echo is removed and the ground object echo intensity of the standard radar, and map the intensity difference to a corresponding ground object echo suppression effect score.

5. A weather radar data quality analysis and over-standard early warning system according to claim 4, characterized in that: The weather radar base data quality analysis subsystem also includes a minimum measurable echo intensity analysis module; The minimum measurable echo intensity analysis module is used to calculate the echo intensity measured by the weather radar at a specific distance, and map the measured echo intensity to a corresponding minimum measurable echo intensity score using a second threshold.

6. A weather radar data quality analysis and over-standard early warning system according to claim 5, characterized in that: The weather radar base data quality analysis subsystem also includes a co-located radar echo consistency analysis module; The co-located radar echo consistency analysis module is used to screen the basic data whose body scanning time difference between the weather radar and the standard radar is less than a preset time difference threshold, and obtain a first group of basic data; wherein the weather radar is the marked radar; The co-located radar echo consistency analysis module is used to filter out basic data whose elevation angle difference between weather radar and standard radar is less than a preset elevation angle difference threshold and basic data whose azimuth angle difference between body scan is less than a preset azimuth angle difference threshold from the first group of basic data, to obtain a second group of basic data; The co-located radar echo consistency analysis module is used to select basic data with a position difference less than a preset position difference threshold from the second group of basic data to obtain a third group of basic data; The co-located radar echo consistency analysis module is used to filter out meteorological echo data from the third group of basic data; The co-located radar echo consistency analysis module is used to convert the observation coordinates of the weather radar and the standard radar to obtain the geodetic coordinates of the weather radar and the geodetic coordinates of the standard radar; and use the distance library matching algorithm, the geodetic coordinates of the weather radar and the geodetic coordinates of the standard radar in the geodetic coordinate system to determine whether the distance difference between the weather radar and the standard radar is less than a preset distance difference threshold. If it is less than the preset distance difference threshold, the difference between various echo products of the weather radar and various echo products of the standard radar in the matching distance library is calculated, and the difference is mapped to a corresponding echo consistency effect score; wherein, various echo products of the weather radar and various echo products of the standard radar are all selected from the meteorological echo data.

7. A weather radar data quality analysis and over-standard early warning system according to claim 6, characterized in that: The weather radar base data quality analysis subsystem also includes a differential reflectivity system deviation analysis module, which includes a data acquisition module, a first data screening module, a second data screening module, a third data screening module, a median extraction module, and a differential reflectivity factor system deviation score mapping module; The data acquisition module is used to read the differential reflectivity factor, correlation coefficient, signal-to-noise ratio, reflectivity factor before ground object echo suppression, radial velocity, and echo distance in the weather radar base data; The first data screening module is used to screen the differential reflectivity factor value of the required distance azimuth according to the preset distance threshold; The second data screening module is used to remove non-precipitation echoes according to preset screening conditions to obtain a differential reflectivity factor value after the second screening; The third data screening module is used to remove invalid data and null values ​​to obtain the final differential reflectivity factor series; The median extraction module is used to extract the median of the differential reflectivity factor numerical deviation from the final differential reflectivity factor sequence; The differential reflectivity factor system deviation score mapping module is used to obtain the system deviation of the differential reflectivity factor, and use the system deviation of the differential reflectivity factor to map to the corresponding differential reflectivity factor system deviation score; wherein the system deviation of the differential reflectivity factor is the median.

8. A weather radar data quality analysis and over-standard early warning system according to claim 7, characterized in that: The weather radar base data quality analysis subsystem also includes a radial velocity standard deviation analysis module, which includes a velocity fuzzy processing module, a distance folding data removal module, a window definition and standard deviation calculation module, a percentile statistics module, a radial velocity standard deviation output module and a radial velocity standard deviation score mapping module; The velocity blur processing module is used to restore the blurred radial velocity to the real radial velocity; The distance folding data elimination module is used to eliminate the distance folding data in the weather radar echo data; The window definition and standard deviation calculation module is used to define a grid window for each radar echo data point, and traverse and calculate the standard deviation value of each valid radial velocity data in each grid window; wherein, if the grid window includes less than 9 valid radial velocity data, the grid window is skipped; the grid window size is 7*9, that is, 7 points are arranged along the azimuth direction, and 9 points are arranged along the distance direction; The percentile statistics module is used to sort all calculated standard deviation values ​​and filter out the standard deviation values ​​ranked at 75%; The radial velocity standard deviation output module is used to output the standard deviation value at the 75% position; The radial velocity standard deviation score mapping module is used to map the standard deviation value at the 75% position to the corresponding radial velocity standard deviation score.

9. A weather radar data quality analysis and over-standard early warning system according to claim 8, characterized in that: The weather radar base data quality analysis subsystem also includes an antenna pointing accuracy analysis module, which includes a fixed ground object target selection module, a radar data acquisition module, a valid data screening module, an antenna pointing error calculation module, a statistical error analysis module, an error distribution and visualization module, and an antenna pointing error score mapping module; The fixed ground object target selection module is used to select a ground object target that is stable and has a known position; The radar data acquisition module is used to collect the measured azimuth and measured elevation angles returned by the weather radar after scanning the ground object target; The effective data screening module is used to screen the effective echo data returned by the weather radar after scanning the ground object target; The antenna pointing error calculation module is used to calculate the azimuth error and the elevation error of the effective echo data; wherein the azimuth error is equal to the measured azimuth minus the expected azimuth; the elevation error is equal to the measured elevation minus the expected elevation; The statistical error analysis module is used to calculate the mean and standard deviation of each azimuth error, as well as the mean and standard deviation of each elevation error; The error distribution and visualization module is used to display the azimuth error and elevation error of the effective echo data; The antenna pointing error score mapping module is used to map the azimuth error and elevation angle error of the effective echo data to the corresponding antenna pointing error score.

10. A weather radar data quality analysis and over-standard early warning system according to claim 9, characterized in that: The visual business warning subsystem includes a comprehensive scoring module, a warning information identification module and a warning information solution suggestion generation module; The comprehensive scoring module is used to perform standardization and weighting on the isolated noise point elimination effect score, the ground object echo suppression effect score, the minimum measurable echo intensity score, the echo consistency effect score, the differential reflectivity factor system deviation score, the radial velocity standard deviation score, and the antenna pointing error score to obtain a comprehensive score; The warning information identification module is used to automatically identify data exceeding the standard and abnormal conditions according to the analysis results of the weather radar base data quality analysis subsystem; The warning information solution suggestion generation module is used to provide corresponding solution suggestions for the data exceeding the standard and abnormal situations.

Images