Clear sky echo identification and elimination method and device, medium and product
By obtaining the reflectivity factor graph of the radar echo graph, the sliding window calculates the volatility and uses the threshold to judge the clear sky echo, the problem of over-identification and error removal of clear sky echo in traditional methods is solved, and a higher precision clear sky echo recognition and removal is achieved.
Patent Information
- Application Number
- CN202510744476.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-05
- Publication Date
- 2025-08-15
AI Technical Summary
Traditional clear sky echo recognition and removal methods often have problems of over-recognition and error removal in low-altitude weak cloud weather scenarios.
By obtaining the original radar echo map, extracting the reflectance factor map, determining whether it is collected during rainfall, sliding the preset sliding window, calculating the volatility, and using the volatility threshold to judge the clear sky echo data, and echo data in the reflectance factor map are eliminated.
It improves the accuracy of clear sky echo recognition and culling, accurately recognizes and retains low-altitude weak cloud echo data, and reduces excessive culling.
Smart Images
Figure CN120491081A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of atmospheric detection technology, and in particular to a method, device, medium and product for identifying and eliminating clear-sky echoes. Background Art
[0002] Millimeter-wave cloud radar is a high-precision weather radar device that uses millimeter-wave frequencies (such as 35 GHz and 94 GHz) to detect clouds. Its strong penetration and high resolution enable precise measurement of key meteorological parameters such as cloud height, cloud thickness, and the phase and motion of cloud particles. Compared to lidar, millimeter-wave cloud radar exhibits greater adaptability in adverse weather conditions, leading to its widespread application in meteorological observation, aviation safety, and climate research.
[0003] Atmospheric motion within the atmospheric boundary layer typically exhibits significant turbulence, and meteorological elements exhibit strong diurnal variations. Even in clear skies without significant cloud cover or precipitation, millimeter-wave cloud radar can still detect weak signals, known as "clear-air echoes." In low-altitude, weakly cloudy weather scenarios, traditional clear-air echo identification and rejection methods based on reflectivity feature thresholds often suffer from over-identification and false rejection. Summary of the Invention
[0004] The purpose of this application is to provide a method, device, medium and product for identifying and rejecting clear-sky echoes to solve the problem of over-identification and erroneous rejection of clear-sky echoes.
[0005] To achieve the above objectives, this application provides the following solutions:
[0006] In a first aspect, the present application provides a method for identifying and removing clear-sky echoes, comprising:
[0007] Acquire an original radar echo map, and extract a reflectivity factor map from the original radar echo map; the original radar echo map includes radar echo data at multiple heights and multiple times, and the reflectivity factor map includes data points at multiple heights and multiple times;
[0008] Based on the reflectivity factor map, determining whether the original radar echo map was collected during rainfall to obtain a first determination result;
[0009] If the first judgment result is no, sliding the preset sliding window in the reflectivity factor map according to a preset step size;
[0010] During the sliding process, determining the fluctuation degree of the preset sliding windows at different positions based on all data points whose heights in the preset sliding windows at different positions are lower than a first preset height;
[0011] Based on the fluctuations and fluctuation thresholds of the preset sliding windows at different positions, determining whether the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data, to obtain a second judgment result;
[0012] If the second judgment result is yes, the corresponding reflectivity factor is removed from the reflectivity factor map to obtain a reflectivity factor map after removing the clear sky echo.
[0013] In one embodiment, after removing the corresponding reflectivity factor from the reflectivity factor map to obtain the reflectivity factor map after removing the clear sky echo, the method further includes:
[0014] Slide the preset sliding window in the reflectivity factor map after removing the clear sky echo according to the preset step size;
[0015] During the sliding process, any position is determined as the clutter recognition position;
[0016] Determine the number of data points in a preset sliding window at a clutter identification position as reflectivity factors to obtain a first number;
[0017] All data points in the preset sliding window at the clutter identification position are removed from the reflectivity factor map after clear sky echo removal to obtain the reflectivity factor map after clutter removal.
[0018] In one embodiment, the data point is a reflectivity factor or a null value;
[0019] Determining whether the original radar echo image is collected during rainfall based on the reflectivity factor image includes:
[0020] Determine the number of heights in the reflectivity factor map that are lower than a second preset height and whose reflectivity factors are greater than a preset reflectivity factor threshold, to obtain a second number;
[0021] determining that all data points in the reflectivity factor map are quantities of reflectivity factors to obtain a third quantity;
[0022] When the second number is greater than a second preset number threshold and the third number is greater than a third preset number threshold, the original radar echo image is collected during rainfall.
[0023] In one embodiment, during the sliding process, determining the fluctuation of the preset sliding windows at different positions based on all data points whose heights in the preset sliding windows at different positions are lower than a first preset height includes:
[0024] Sequentially determine any position during the sliding process as the current position;
[0025] Determine the number of data points in the preset sliding window at the current position whose reflectivity factor has a height lower than the first preset height to obtain a fourth number;
[0026] Determine whether the fourth quantity is greater than a fourth preset quantity threshold, and obtain a third judgment result; the fourth preset quantity threshold is n is the side length of the preset sliding window, n is 3 or 5;
[0027] If the third determination result is no, determining the position next to the current position as the current position, and returning to "determining the number of data points in the preset sliding window at the current position whose reflectivity factor height is lower than the first preset height, to obtain a fourth number";
[0028] If the third judgment result is yes, projecting each reflectivity factor in the preset sliding window at the current position whose height is lower than the first preset height into a three-dimensional Cartesian coordinate system to obtain the three-dimensional coordinates of the corresponding reflectivity factor;
[0029] Determining a fitting equation of a fitting plane corresponding to the preset sliding window at the current position based on the three-dimensional coordinates of each reflectivity factor having a height lower than a first preset height in the preset sliding window at the current position;
[0030] Obtaining the distance between each reflectivity factor and the fitting plane based on the three-dimensional coordinates of each reflectivity factor and the fitting equation;
[0031] The fluctuation degree of the preset sliding window at the current position is determined based on the distances between all reflectivity factors with heights lower than a first preset height in the preset sliding window at the current position and the fitting plane.
[0032] In one embodiment, the x-axis coordinate in the three-dimensional coordinate is the height corresponding to the reflectivity factor, the y-axis coordinate is the time corresponding to the reflectivity factor, and the z-axis coordinate is the reflectivity factor.
[0033] In one embodiment, the process of determining the volatility threshold includes:
[0034] Obtain multiple historical clear sky echo images;
[0035] Determine the volatility of each historical clear-air echo map;
[0036] Sort the volatility of each historical clear sky echo map from large to small to obtain a volatility sequence;
[0037] The volatility threshold is determined from the volatility sequence according to a coverage rate of 90%.
[0038] In one embodiment, based on the fluctuations and fluctuation thresholds of the preset sliding windows at different positions, determining whether radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data, and obtaining a second judgment result includes:
[0039] Determine any position as a position to be determined;
[0040] When the fluctuation of the preset sliding window at the position to be judged is greater than the fluctuation threshold, the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the position to be judged is clear sky echo data, and the second judgment result is yes.
[0041] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for identifying and eliminating clear sky echoes.
[0042] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for identifying and eliminating clear sky echoes.
[0043] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for identifying and eliminating clear sky echoes.
[0044] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0045] The present application discloses a method, device, medium and product for identifying and eliminating clear sky echoes. First, an original radar echo image is obtained, and a reflectivity factor map is extracted from the original radar echo image. Then, based on the reflectivity factor map, it is determined whether the original radar echo image was collected during rainfall. If not, a preset sliding window is slid in the reflectivity factor map according to a preset step size. Secondly, during the sliding process, the fluctuation of the preset sliding windows at different positions is determined based on all data points whose heights are lower than a first preset height in the preset sliding windows at different positions. Subsequently, based on the fluctuation and fluctuation threshold of the preset sliding windows at different positions, it is determined whether the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data. Finally, if so, the corresponding reflectivity factor is eliminated from the reflectivity factor map to obtain a reflectivity factor map after eliminating the clear sky echo. This application utilizes the reflectivity factor to judge rainfall, determine the volatility and volatility threshold, identify clear sky echo data, and eliminate clear sky echoes. By analyzing the volatility at different locations, clear sky echoes are identified and eliminated, thereby improving the accuracy of clear sky echo identification and elimination. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0047] Figure 1 A schematic flow chart of a method for identifying and eliminating clear-sky echoes provided in one embodiment of the present application;
[0048] Figure 2 This is a schematic diagram of the clear sky echo recognition and rejection architecture;
[0049] Figure 3 This is a schematic diagram of the clear sky echo data judgment process;
[0050] Figure 4 Reflectivity factor map collected by millimeter-wave cloud radar;
[0051] Figure 5 This is the reflectivity factor map obtained by the traditional method after removing the clear sky echo;
[0052] Figure 6 This is the reflectivity factor map obtained by the method of the present application after the clear sky echo is removed;
[0053] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0054] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0055] The purpose of this application is to provide a method, device, medium and product for clear sky echo identification and elimination, aiming to improve the accuracy of clear sky echo identification and elimination.
[0056] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0057] In an exemplary embodiment, Figure 1 and Figure 2As shown, a clear sky echo recognition and rejection method is provided, comprising:
[0058] Step 1: Obtain the original radar echo image and extract the reflectivity factor map from the original radar echo image.
[0059] The original radar echo map includes radar echo data at multiple heights and multiple times, and the reflectivity factor map includes data points at multiple heights and multiple times.
[0060] Specifically, millimeter-wave cloud radar is used to obtain the original radar echo image.
[0061] Step 2: Based on the reflectivity factor map, determine whether the original radar echo map was collected during rainfall, and obtain a first judgment result.
[0062] As an optional implementation, the data point is a reflectivity factor or a null value.
[0063] Step 2 includes:
[0064] Step 21: Determine the number of reflectivity factor maps whose heights are lower than a second preset height and whose reflectivity factors are greater than a preset reflectivity factor threshold, and obtain a second number.
[0065] Step 22: Determine the quantity of all data points in the reflectivity factor map as reflectivity factors to obtain a third quantity.
[0066] Step 23: When the second number is greater than the second preset number threshold and the third number is greater than the third preset number threshold, the original radar echo image is collected during rainfall.
[0067] Specifically, the second preset height is 1 km, the preset reflectivity factor threshold is -25 dBZ, the second preset quantity threshold is 20, and the third preset quantity threshold is 25.
[0068] Step 3: If the first judgment result is no, the preset sliding window is slid in the reflectivity factor map according to the preset step size.
[0069] Step 4: During the sliding process, the fluctuations of the preset sliding windows at different positions are determined based on all data points whose heights in the preset sliding windows at different positions are lower than the first preset height.
[0070] As an optional implementation, step 4 includes:
[0071] Step 41: sequentially determine any position during the sliding process as the current position.
[0072] Step 42: Determine the number of data points in the preset sliding window at the current position whose reflectivity factor height is lower than the first preset height to obtain a fourth number.
[0073] Specifically, the first preset altitude is 3 km. Clear-air echoes are mostly below 3 km. Due to the dramatic changes in meteorological elements and uneven distribution of water vapor in the atmospheric boundary layer, radar beams have strong energy at close range and are easily received at low elevation angles. Insects and other biological activities are more prevalent at this altitude. Therefore, this application processes data below 3 km.
[0074] Step 43: Determine whether the fourth quantity is greater than the fourth preset quantity threshold, and obtain a third judgment result; the fourth preset quantity threshold is n is the side length of the preset sliding window, and n is 3 or 5.
[0075] Step 44: If the third judgment result is no, the next position of the current position is determined as the current position, and the method of "determining the number of data points in the preset sliding window at the current position whose reflectivity factor height is lower than the first preset height to obtain a fourth number" is returned.
[0076] Step 45: If the third judgment result is yes, each reflectivity factor in the preset sliding window at the current position whose height is lower than the first preset height is projected into a three-dimensional Cartesian coordinate system to obtain the three-dimensional coordinates of the corresponding reflectivity factor.
[0077] As an optional implementation, in step 45, the x-axis coordinate in the three-dimensional coordinates is the height corresponding to the reflectivity factor, the y-axis coordinate is the time corresponding to the reflectivity factor, and the z-axis coordinate is the reflectivity factor.
[0078] Step 46: Determine a fitting equation of a fitting plane corresponding to the preset sliding window at the current position based on the three-dimensional coordinates of each reflectivity factor whose height is lower than the first preset height in the preset sliding window at the current position.
[0079] Specifically, the size of the preset sliding window with a side length of n is n×n. Assume that the number of data points whose reflectivity factor height is lower than the first preset height in the preset sliding window at the current position (the fourth number) is m, and the i-th data point whose reflectivity factor height is lower than the first preset height in the preset sliding window at the current position is recorded as (x i ,y i ,z i ).
[0080] The clear sky echo data judgment process is as follows Figure 3 As shown, the initial equation of the fitting plane is assumed to be z=ax+by+c, where a, b and c are coefficients to be determined.
[0081] According to the principle of least squares method, the sum of the squares of the distances from the three-dimensional coordinates of each reflectivity factor whose height in the preset sliding window at the current position is lower than the first preset height to the fitting plane needs to be minimized, that is:
[0082]
[0083] In order to minimize S, we take the partial derivatives of a, b, and c on both sides of equation (1) and set them to 0, and we can get:
[0084]
[0085] Rewrite equation (2) into matrix form:
[0086]
[0087] make Vector to be found Constant vector Then formula (3) can be expressed as AX=B.
[0088] By solving the linear equations, we can get the values of coefficients a, b and c. Generally, we can use the matrix inversion method to solve the unknown vector, that is, X = A -1 B, get the values of a, b and c, and thus determine the fitting equation of the fitting plane.
[0089] Step 47: Based on the three-dimensional coordinates of each reflectivity factor and the fitting equation, the distance between each reflectivity factor and the fitting plane is obtained.
[0090] Specifically, the distance of the point above the fitting plane is positive, the distance of the point below the fitting plane is negative, and the distance d between the i-th reflectivity factor and the fitting plane is i The calculation formula is:
[0091]
[0092] Step 48: Determine the fluctuation of the preset sliding window at the current position based on the distance between all reflectivity factors in the preset sliding window at the current position whose height is lower than the first preset height and the fitting plane.
[0093] Specifically, the calculation formula for the fluctuation F of the preset sliding window at any position is:
[0094] F=max(d)-min(d) (5)
[0095] Among them, max(d) is the maximum value of the distance between all reflectivity factors in the preset sliding window at the current position whose height is lower than the first preset height and the fitting plane; min(d) is the minimum value of the distance between all reflectivity factors in the preset sliding window at the current position whose height is lower than the first preset height and the fitting plane.
[0096] Step 5: Based on the fluctuation and fluctuation threshold of the preset sliding window at different positions, determine whether the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data, and obtain a second judgment result.
[0097] As an optional implementation, step 5 includes:
[0098] Step 511: Determine any position as a position to be determined.
[0099] Step 512: When the fluctuation of the preset sliding window at the position to be determined is greater than the fluctuation threshold, the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the position to be determined is clear sky echo data, and the second judgment result is yes.
[0100] As an optional implementation, in step 5, the process of determining the volatility threshold includes:
[0101] Step 521: Acquire multiple historical clear sky echo images.
[0102] Step 522: Determine the fluctuation of each historical clear-sky echo map.
[0103] Step 523: Sort the volatility of each historical clear-sky echo graph in descending order to obtain a volatility sequence.
[0104] Step 524: Determine the volatility threshold F from the volatility sequence according to the 90% coverage rate s .
[0105] Specifically, assuming that the total number of positions is k, the volatility sequence is F(1), F(3), F(3), …, F(k), where F(k) is the kth volatility in the volatility sequence, k1, 2, 3, …, K. F(0.9k) is determined as the volatility threshold.
[0106] Step 6: If the second judgment result is yes, the corresponding reflectivity factor is removed from the reflectivity factor map to obtain a reflectivity factor map after removing the clear sky echo.
[0107] As an optional implementation manner, after step 6, the method further includes:
[0108] The preset sliding window is slid in the reflectivity factor map after removing the clear sky echo according to the preset step size.
[0109] During the sliding process, any position is determined as a clutter recognition position.
[0110] The data points in the preset sliding window at the clutter identification position are determined to be the number of reflectivity factors to obtain a first number.
[0111] When the first number is less than a first preset number threshold, all radar echo data in the preset sliding window at the clutter identification position are removed from the reflectivity factor map after clear sky echoes are removed to obtain a reflectivity factor map after clutter removal.
[0112] Specifically, the first preset quantity threshold is
[0113] Furthermore, the method of the present application was also used for actual operation. Figure 4 Reflectivity factor map collected by millimeter-wave cloud radar; Figure 5 This is the reflectivity factor map obtained by the traditional method after removing the clear sky echo; Figure 6 This is the reflectivity factor diagram after the clear sky echo is removed using the method of this application. By comparison, Figure 4 The traditional method fails to accurately identify the low-altitude weak cloud area marked by the middle circle, and there is an over-elimination phenomenon; however, the method of this application can accurately identify the low-altitude weak clouds in this area and effectively retain the relevant cloud echo data. It can be seen that this application distinguishes and identifies low-altitude weak clouds and clear sky echo signals based on fluctuation, which can effectively improve the recognition accuracy of low-altitude clear sky echoes and low-altitude weak cloud echoes.
[0114] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a clear sky echo identification and rejection method.
[0115] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for identifying and rejecting clear-sky echoes is implemented.
[0116] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which implements a clear-air echo identification and rejection method when executed by a processor.
[0117] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for identifying and eliminating clear sky echoes is implemented.
[0118] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.
[0119] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0120] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.
[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0123] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A clear sky echo identification and elimination method, characterized in that: The clear sky echo identification and elimination method includes: Acquire an original radar echo map, and extract a reflectivity factor map from the original radar echo map; the original radar echo map includes radar echo data at multiple heights and multiple times, and the reflectivity factor map includes data points at multiple heights and multiple times; Based on the reflectivity factor map, determining whether the original radar echo map was collected during rainfall to obtain a first determination result; If the first judgment result is no, sliding the preset sliding window in the reflectivity factor map according to a preset step size; During the sliding process, determining the fluctuation degree of the preset sliding windows at different positions based on all data points whose heights in the preset sliding windows at different positions are lower than a first preset height; Based on the fluctuations and fluctuation thresholds of the preset sliding windows at different positions, determining whether the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data, to obtain a second judgment result; If the second judgment result is yes, the corresponding reflectivity factor is removed from the reflectivity factor map to obtain a reflectivity factor map after removing the clear sky echo.
2. The clear sky echo identification and elimination method according to claim 1, characterized in that: After removing the corresponding reflectivity factor from the reflectivity factor map to obtain the reflectivity factor map after removing the clear sky echo, the method further includes: Slide the preset sliding window in the reflectivity factor map after removing the clear sky echo according to the preset step size; During the sliding process, any position is determined as the clutter recognition position; Determine the number of data points in a preset sliding window at a clutter identification position as reflectivity factors to obtain a first number; All data points in the preset sliding window at the clutter identification position are removed from the reflectivity factor map after clear sky echo removal to obtain the reflectivity factor map after clutter removal.
3. The clear sky echo identification and elimination method according to claim 1, characterized in that: The data point is a reflectivity factor or a null value; Determining whether the original radar echo image is collected during rainfall based on the reflectivity factor image includes: Determine the number of heights in the reflectivity factor map that are lower than a second preset height and whose reflectivity factors are greater than a preset reflectivity factor threshold, to obtain a second number; determining that all data points in the reflectivity factor map are quantities of reflectivity factors to obtain a third quantity; When the second number is greater than a second preset number threshold and the third number is greater than a third preset number threshold, the original radar echo image is collected during rainfall.
4. The clear sky echo identification and elimination method according to claim 1, characterized in that: During the sliding process, determining the fluctuation of the preset sliding windows at different positions based on all data points whose heights in the preset sliding windows at different positions are lower than a first preset height in sequence includes: Sequentially determine any position during the sliding process as the current position; Determine the number of data points in the preset sliding window at the current position whose reflectivity factor has a height lower than the first preset height to obtain a fourth number; Determine whether the fourth quantity is greater than a fourth preset quantity threshold, and obtain a third judgment result; the fourth preset quantity threshold is n is the side length of the preset sliding window, n is 3 or 5; If the third determination result is no, the position next to the current position is determined as the current position, and the method returns to "determining the number of data points in the preset sliding window at the current position whose reflectivity factor height is lower than the first preset height, to obtain a fourth number"; If the third judgment result is yes, projecting each reflectivity factor in the preset sliding window at the current position whose height is lower than the first preset height into a three-dimensional Cartesian coordinate system to obtain the three-dimensional coordinates of the corresponding reflectivity factor; Determining a fitting equation of a fitting plane corresponding to the preset sliding window at the current position based on the three-dimensional coordinates of each reflectivity factor having a height lower than a first preset height in the preset sliding window at the current position; Obtaining the distance between each reflectivity factor and the fitting plane based on the three-dimensional coordinates of each reflectivity factor and the fitting equation; The fluctuation degree of the preset sliding window at the current position is determined based on the distances between all reflectivity factors with heights lower than a first preset height in the preset sliding window at the current position and the fitting plane.
5. The clear sky echo identification and elimination method according to claim 4, characterized in that: The x-axis coordinate in the three-dimensional coordinate is the height corresponding to the reflectivity factor, the y-axis coordinate is the time corresponding to the reflectivity factor, and the z-axis coordinate is the reflectivity factor.
6. The clear sky echo identification and elimination method according to claim 1, characterized in that: The process of determining the volatility threshold includes: Obtain multiple historical clear sky echo images; Determine the volatility of each historical clear-air echo map; Sort the volatility of each historical clear sky echo map from large to small to obtain a volatility sequence; The volatility threshold is determined from the volatility sequence according to a coverage rate of 90%.
7. The clear sky echo identification and elimination method according to claim 1, characterized in that: Based on the fluctuations and fluctuation thresholds of the preset sliding windows at different positions, respectively, determining whether the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the corresponding position is clear sky echo data, obtaining a second judgment result, including: Determine any position as a position to be determined; When the fluctuation of the preset sliding window at the position to be judged is greater than the fluctuation threshold, the radar echo data corresponding to the reflectivity factor of the center of the preset sliding window at the position to be judged is clear sky echo data, and the second judgment result is yes.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the clear sky echo identification and elimination method described in any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the clear sky echo identification and elimination method described in any one of claims 1 to 7 is implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the clear sky echo identification and elimination method described in any one of claims 1 to 7 is implemented.