Ground feature filtering capability evaluation method, device, equipment and storage medium

Abstract

The embodiment of the invention provides a ground object filtering capability evaluation method and device, equipment and a storage medium, and is applied to the technical field of meteorological detection. The method comprises the following steps: acquiring a radar-based data sample acquired under a clear sky condition and a rainfall condition; the method comprises the following steps: analyzing a radar base data sample collected under a clear sky condition, calculating a surface feature suppression ratio, and taking the surface feature suppression ratio as a surface feature filtering capability statistical analysis index; the method comprises the following steps: analyzing a radar base data sample collected under a rainfall condition, calculating an influence value of ground object suppression on rainfall echoes, and taking the influence value as a statistical analysis index of the influence of ground object filtering on the rainfall echoes; and comprehensively evaluating the ground object filtering capability according to the indexes. In this way, not only is the ground feature filtering capacity considered, but also the influence of the ground feature filtering capacity on the rainfall echoes is considered, comprehensive evaluation and quantification of the filtering capacity can be achieved, then mistaken filtering of the real rainfall echoes is effectively avoided, and the risks of false rainfall, rainfall missing report and the like are remarkably reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of meteorological detection, and in particular to a ground object filtering capability evaluation method and device, equipment and a storage medium. BACKGROUND

[0002] In weather radar observation and quantitative precipitation estimation (QPE) and other applications, ground object filtering has always been a key link to improve data quality and quantitative product accuracy. So-called ground object filtering refers to identifying and removing the "clutter" signals caused by terrain, buildings, towers and other non-meteorological targets in radar echoes through algorithmic means, and only retaining true meteorological echo information. This process is crucial in the actual business of Doppler weather radar, because ground object clutter often covers a wide range of low-elevation ranging data, interfering with the detection of weak echo weather systems and quantitative precipitation estimation.

[0003] Firstly, the pros and cons of ground object filtering capability directly affect the accuracy of precipitation identification and quantitative estimation. When ground object clutter is not effectively removed, it will "pollute" the reflectivity data, misjudging the clutter as precipitation, leading to the emergence of false precipitation, affecting the credibility of rainfall prediction and flood warning. On the contrary, if the ground object filtering is excessive or inaccurate, it may remove the weak precipitation at the edge or the real echo near the terrain, causing precipitation to be missed. Such errors are particularly prominent in mesoscale severe convective systems and complex terrain areas, posing a hidden danger to weather forecasting and disaster warning. Therefore, scientific and systematic evaluation and analysis of the ground object filtering capability are needed to ensure that the filtering algorithm applied in the business system still maintains a high recognition rate and low false positive rate in complex environments.

[0004] Secondly, scientific ground object filtering capability evaluation algorithms are constantly improving. By quantitatively detecting and comparing the filtering effects under different observation modes, the shortcomings of current ground object filtering algorithms in actual business can be found, and the filtering accuracy can be continuously optimized and improved, providing a solid foundation for new generation radar products, intelligent precipitation identification systems, automatic station / radar fusion and other high-level meteorological applications. In addition, good ground object filtering capability also helps the effective implementation of radar quantitative calibration, wind field inversion and other diversified meteorological analysis products.

[0005] In summary, ground object filtering capability evaluation is a prerequisite for ensuring the accuracy of radar quantitative precipitation and encryption, and can be applied to existing radars and multi-band multi-system radars to be calibrated, and is a basic link to promote the intelligent and refined development of weather forecasting, playing an irreplaceable important role in ensuring disaster prevention and mitigation and improving radar application level.

[0006] At present, the existing ground object filtering capability evaluation schemes generally use a fixed threshold, a fixed region or a simple statistical feature method to analyze the ground object filtering capability, and do not analyze the influence of ground object filtering on precipitation echo, so that the effective precipitation echo is easily mis-filtered, and the risks of false precipitation and precipitation missing report are increased. SUMMARY

[0007] In a first aspect, the embodiments of the present disclosure provide a ground object filtering capability evaluation method, which comprises: acquiring radar-based data samples collected under clear sky conditions and precipitation conditions; analyzing the radar-based data samples collected under the clear sky conditions, calculating a ground object suppression ratio, and taking the ground object suppression ratio as a ground object filtering capability statistical analysis index; analyzing the radar-based data samples collected under the precipitation conditions, calculating a ground object suppression influence on precipitation echo, and taking the ground object suppression influence on precipitation echo as a ground object filtering influence on precipitation echo statistical analysis index; comprehensively evaluating the ground object filtering capability according to the ground object filtering capability statistical analysis index and the ground object filtering influence on precipitation echo statistical analysis index.

[0008] In some implementable manners of the first aspect, the method further comprises: under the clear sky conditions, setting a ground object filtering algorithm as an adaptive frequency domain filtering algorithm, closing a CCOR / CSR threshold control, and collecting the radar-based data samples.

[0009] In some implementable manners of the first aspect, the method further comprises: under the precipitation conditions, setting the ground object filtering algorithm as the adaptive frequency domain filtering algorithm, closing the CCOR / CSR threshold control and a ground object recognition algorithm function, and collecting the radar-based data samples.

[0010] In some implementable manners of the first aspect, the analyzing the radar-based data samples collected under the clear sky conditions, calculating the ground object suppression ratio, comprises: selecting radar-based data samples in the radar-based data samples collected under the clear sky conditions at the lowest two elevation angles and within a preset distance, and according to a reflectivity factor T before filtering and a reflectivity factor Z after filtering in the selected radar-based data samples, screening out a strong ground object echo distance library, and then screening out a distance library with a radial velocity of 0 based on the strong ground object echo distance library, and then calculating a difference value of the reflectivity factor T before filtering and the reflectivity factor Z after filtering, and statistically analyzing the calculated difference value to obtain an average value, and finally taking the average value as the ground object suppression ratio.

[0011] In some possible implementation manners of the first aspect, when the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z is calculated, if the post-filter reflectivity factor Z is a valid value, T-Z is calculated and taken as the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z; if the post-filter reflectivity factor Z is an invalid value, the pre-filter reflectivity factor T is directly taken as the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z.

[0012] In some possible implementation manners of the first aspect, the radar-based data samples collected under the precipitation condition are analyzed, and the influence value of the ground object suppression on the precipitation echo is calculated, including: The radar-based data samples at a high elevation angle and above a preset distance in the radar-based data samples collected under the precipitation condition are selected, and pure precipitation echo data is screened out according to the pre-filter reflectivity factor T and the post-filter reflectivity factor Z in the selected radar-based data samples. Then, the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z is calculated based on the screened pure precipitation echo data, and statistical analysis is performed on the calculated difference to obtain an average value. The probabilities of the difference being distributed in 0-0.5 dB, 0.5-1 dB, 1-2 dB, and above 2 dB are respectively calculated, and finally the average value and the probability distribution are taken as the influence value of the ground object suppression on the precipitation echo.

[0013] In some possible implementation manners of the first aspect, when the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z is calculated, |T-Z| is calculated and taken as the difference between the pre-filter reflectivity factor T and the post-filter reflectivity factor Z.

[0014] In the second aspect, the embodiments of the present disclosure provide a ground object filtering capability evaluation device, which includes: The acquisition module is configured to acquire radar-based data samples collected under a clear-sky condition and a precipitation condition. The ground object filtering capability statistical analysis module is configured to analyze the radar-based data samples collected under the clear-sky condition, calculate a ground object suppression ratio, and take the ground object suppression ratio as a ground object filtering capability statistical analysis index. The ground object filtering influence on precipitation echo statistical analysis module is further configured to analyze the radar-based data samples collected under the precipitation condition, calculate an influence value of the ground object suppression on the precipitation echo, and take the influence value of the ground object suppression on the precipitation echo as a ground object filtering influence on precipitation echo statistical analysis index. The evaluation module is configured to comprehensively evaluate the ground object filtering capability according to the ground object filtering capability statistical analysis index and the ground object filtering influence on precipitation echo statistical analysis index.

[0015] In a third aspect, an electronic device is provided, which includes at least one processor, and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0016] In a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, and the computer instructions are used to enable a computer to perform the method as described above.

[0017] In the embodiments of the present disclosure, not only the filtering capability of ground objects is focused on, but also the influence of the filtering capability on precipitation echoes is considered, so that comprehensive evaluation and quantification of the filtering capability can be achieved, and false filtering of real precipitation echoes can be effectively avoided, and the risks of false precipitation and precipitation missing report can be significantly reduced.

[0018] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description when taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the present disclosure. The drawings provided are for illustrative purposes only and, therefore, should not be considered to be limiting of the present disclosure. In the drawings: Figure 1 A flowchart of a ground object filtering capability evaluation method provided by the embodiments of the present disclosure is shown; Figure 2 A structural diagram of a ground object filtering capability evaluation device provided by the embodiments of the present disclosure is shown; Figure 3 A structural diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present disclosure.

[0021] In addition, the term "and / or" in this document merely describes an association relationship of associated objects, and indicates that three relationships can exist, for example, A and / or B can represent three cases of A existing alone, A and B existing together, and B existing alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects.

[0022] To solve the problems in the background art, the embodiment of the present disclosure provides a ground object filtering capability evaluation method, device, equipment and storage medium. Specifically, radar-based data samples collected under clear sky conditions and precipitation conditions can be obtained; the radar-based data samples collected under clear sky conditions are analyzed, the ground object suppression ratio is calculated, and the ground object suppression ratio is taken as a ground object filtering capability statistical analysis index; the radar-based data samples collected under precipitation conditions are analyzed, the ground object suppression influence on precipitation echo is calculated, and the ground object filtering influence on precipitation echo is taken as a ground object filtering influence on precipitation echo statistical analysis index; and the ground object filtering capability is comprehensively evaluated according to the above indexes.

[0023] In this way, not only the ground object filtering capability itself is concerned, but also the influence of the ground object filtering capability on the precipitation echo is considered, so that comprehensive evaluation and quantification of the filtering capability can be realized, and false filtering of real precipitation echo can be effectively avoided, and the risk of false precipitation and precipitation omission can be significantly reduced.

[0024] The embodiment of the present disclosure provides a ground object filtering capability evaluation method, device, equipment and storage medium, which will be described in detail below in combination with the accompanying drawings and specific embodiments.

[0025] Figure 1 A flowchart of a ground object filtering capability evaluation method provided by the embodiment of the present disclosure is shown, as shown in Figure 1 The method 100 can include the following steps: S110, radar-based data samples collected under clear sky conditions and precipitation conditions are obtained.

[0026] In some embodiments, under clear sky conditions, the ground object filtering algorithm can be set to an adaptive frequency domain filtering algorithm, the CCOR / CSR threshold control can be closed, and the radar-based data samples can be collected. In addition, under precipitation conditions (for example, stratiform or convective precipitation conditions), the ground object filtering algorithm can be set to an adaptive frequency domain filtering algorithm, the CCOR / CSR threshold control and the ground object recognition algorithm function can be closed, and the radar-based data samples can be collected.

[0027] S120, the radar-based data samples collected under clear sky conditions are analyzed, the ground object suppression ratio is calculated, and the ground object suppression ratio is taken as a ground object filtering capability statistical analysis index.

[0028] In some embodiments, radar-based data samples at the lowest two elevation angles and within a preset distance (e.g., 50 km) in a radar-based data sample collected under clear sky conditions can be selected, and based on the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z in the selected radar-based data samples, a strong ground object (T>50 dBZ and Z is a valid value) echo distance library (i.e., a pure ground object distance library) can be screened out, and then based on the strong ground object echo distance library, a distance library with a radial velocity of 0 can be screened out, and then based on this, a difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z is calculated, and statistical analysis is performed on the calculated difference to obtain an average value, which is finally taken as a ground object suppression ratio.

[0029] For example, when calculating the difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z, if the post-filtered reflectivity factor Z is a valid value, T-Z is calculated and taken as the difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z; if the post-filtered reflectivity factor Z is an invalid value, the pre-filtered reflectivity factor T is directly taken as the difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z.

[0030] S130, analyzing the radar-based data sample collected under precipitation conditions, calculating the ground object suppression effect on precipitation echo, and taking the ground object suppression effect on precipitation echo as a ground object filtering effect on precipitation echo statistical analysis index.

[0031] In some embodiments, radar-based data samples at a high elevation angle and above a preset distance (e.g., 4 km) in a radar-based data sample collected under precipitation conditions can be selected, and based on the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z in the selected radar-based data samples, pure precipitation echo data is screened out, and then based on the screened pure precipitation echo data, a difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z is calculated, and statistical analysis is performed on the calculated difference to obtain an average value, and the probability distribution of the difference value distributed in 0-0.5 dB, 0.5-1 dB, 1-2 dB and above 2 dB is respectively calculated, and finally the average value and the probability distribution are taken as the ground object suppression effect on precipitation echo. It is worth noting that the smaller the difference value, the greater the probability distribution, indicating that the ground object filtering has less effect on precipitation echo, otherwise it is greater.

[0032] For example, when calculating the difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z, |T-Z| is calculated and taken as the difference between the pre-filtered reflectivity factor T and the post-filtered reflectivity factor Z.

[0033] S140, comprehensively evaluating the ground object filtering capability according to the ground object filtering capability statistical analysis index and the ground object filtering effect on precipitation echo statistical analysis index.

[0034] In summary, the embodiments of the present disclosure provide scientific and comprehensive basis for the development and application of product ground object filtering algorithm by systematically and quantitatively evaluating the ground object filtering capability and its influence on precipitation echo, and significantly improve the competitiveness of the product in the industry. Compared with the prior art, the embodiments of the present disclosure have at least the following technical effects: (1) Quantitative evaluation of filtering capability is realized to support efficient optimization iteration of algorithm.

[0035] Based on clear sky conditions and quantitative indicators (ground object suppression ratio), the embodiments of the present disclosure systematically quantify the actual suppression effect of the ground object filtering algorithm. The data and results obtained through standardized processes provide a data basis for accurately tracking algorithm performance evolution and targeting key issues, and promote the continuous progress of product ground clutter identification and suppression capability. This quantifiable and traceable technical evaluation method is superior to the traditional method relying on artificial experience or qualitative evaluation, and greatly improves the technical research and development level and engineering reliability of the product.

[0036] (2) Both ground object filtering and precipitation signal integrity are considered to ensure business precision and safety.

[0037] The embodiments of the present disclosure not only focus on the effective suppression of ground clutter, but also specially investigate the influence of the filtering algorithm on precipitation echo, fully evaluate and quantify the protection capability of the algorithm on actual meteorological signals. Through the probability distribution analysis of various difference intervals of precipitation signals, the false filtering phenomenon of effective precipitation echo is effectively avoided, and the risk of false precipitation and precipitation missing report is significantly reduced. This method of evaluating algorithm performance with double indicators not only improves the applicability of the product in radar precipitation quantitative estimation, strong weather monitoring and other businesses, but also provides higher reliability and precision protection for users.

[0038] (3) Process specification is unified to adapt to the promotion of multiple types of radars and multiple business scenarios.

[0039] The embodiments of the present disclosure follow strict process specifications and quantitative index system, and can be flexibly applied in different radar sites, different climate zones and business scenarios without significant manual parameter adjustment, which greatly reduces the difficulty of business promotion and large-scale deployment. Compared with some existing products relying on local experience or manual adaptation, the present product can significantly improve the efficiency of standardized management and regional quality monitoring, and meet the future development needs of radar networking and automation.

[0040] (4) Improve the credibility of the product and enhance the market recognition.

[0041] Through the full-link, data-driven filtering capability evaluation and result traceability, the embodiments of the present disclosure not only help to obtain authoritative recognition in product industry application, but also form a benign closed loop for continuous optimization of products. The geophysical filtering index system relying on this will become an important support for products to establish a competitive barrier and continuously expand market share in the field of meteorological monitoring.

[0042] Therefore, the embodiments of the present disclosure effectively promote the transformation and upgrading of radar geophysical filtering technology from qualitative to quantitative and from experience to data-driven, greatly improve the technical level and market performance of products in core links such as geophysical clutter suppression and precipitation signal protection, and bring significant application value and competitive advantage to related fields such as meteorological observation and strong weather monitoring.

[0043] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0044] The above is the introduction of the method embodiment, and the following will further illustrate the scheme of the present disclosure through the device embodiment.

[0045] Figure 2 The structure diagram of a geophysical filtering capability evaluation device provided by the embodiments of the present disclosure is shown in FIG. 2. Figure 2 As shown in FIG. 2, the device 200 can include: The acquisition module 210 is configured to acquire radar-based data samples collected under clear-sky conditions and precipitation conditions.

[0046] The geophysical filtering capability statistical analysis module 220 is configured to analyze the radar-based data samples collected under clear-sky conditions, calculate a geophysical suppression ratio value, and take the geophysical suppression ratio value as a geophysical filtering capability statistical analysis index.

[0047] The geophysical filtering influence on precipitation echo statistical analysis module 230 is further configured to analyze the radar-based data samples collected under precipitation conditions, calculate a geophysical suppression influence on precipitation echo value, and take the geophysical suppression influence on precipitation echo value as a geophysical filtering influence on precipitation echo statistical analysis index.

[0048] The evaluation module 240 is configured to comprehensively evaluate the geophysical filtering capability according to the geophysical filtering capability statistical analysis index and the geophysical filtering influence on precipitation echo statistical analysis index.

[0049] It can be understood that, Figure 2The various modules / units in the apparatus 200 shown have the functions of implementing Figure 1 The functions of the various steps in the method 100 shown and the corresponding technical effects can be achieved, and for brevity, will not be repeated here.

[0050] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. The electronic device 300 is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 300 can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0051] As shown, Figure 3 The electronic device 300 can include a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded into a random access memory (RAM) 303 from a storage unit 308. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0052] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, a speaker, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0053] The computing unit 301 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer program product, including a computer program tangibly embodied in a computer readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded onto the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the method 100 by any other appropriate means, such as by means of firmware.

[0054] The various implementations described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0055] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0056] In the context of this disclosure, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0057] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by executing the method in the embodiments of this disclosure. For the sake of brevity, they will not be described in detail here.

[0058] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.

[0059] To provide interaction with a user, the embodiments described above can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0060] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0061] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0062] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0063] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for evaluating the filtering capability of ground features, characterized in that, The method includes: Acquire radar base data samples collected under clear sky and precipitation conditions; Analyze radar base data samples collected under clear sky conditions, calculate the ground object suppression ratio, and use the ground object suppression ratio as a statistical analysis index of ground object filtering capability; The radar base data samples collected under precipitation conditions were analyzed to calculate the impact of ground object suppression on precipitation echoes, and the impact of ground object suppression on precipitation echoes was used as a statistical analysis index of the impact of ground object filtering on precipitation echoes. The ground feature filtering capability is comprehensively evaluated based on the statistical analysis indicators of the ground feature filtering capability and the statistical analysis indicators of the impact of ground feature filtering on precipitation echo.

2. The method according to claim 1, characterized in that, The method further includes: Under clear sky conditions, the ground object filtering algorithm is set to an adaptive frequency domain filtering algorithm, CCOR / CSR threshold control is turned off, and radar base data samples are collected.

3. The method according to claim 1, characterized in that, The method further includes: Under precipitation conditions, the ground feature filtering algorithm is set to an adaptive frequency domain filtering algorithm, and the CCOR / CSR threshold control and ground feature identification algorithm functions are turned off, and radar base data samples are collected.

4. The method according to claim 1, characterized in that, The analysis of radar base data samples collected under clear-sky conditions, and the calculation of ground cover suppression ratio, includes: Radar base data samples collected under clear sky conditions are selected from those at the two lowest elevation angles and within a preset distance. Based on the reflectivity factor T before filtering and the reflectivity factor Z after filtering in the selected radar base data samples, a strong ground object echo range library is screened. Then, a range library with a radial velocity of 0 is screened based on the strong ground object echo range library. The difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering is calculated, and the calculated difference is statistically analyzed to obtain the average value. Finally, this value is used as the ground object suppression ratio.

5. The method according to claim 4, characterized in that, When calculating the difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering, if the reflectivity factor Z after filtering is an effective value, then TZ is calculated and used as the difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering; if the reflectivity factor Z after filtering is an invalid value, then the reflectivity factor T before filtering is directly used as the difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering.

6. The method according to claim 1, characterized in that, The analysis of radar-based data samples collected under precipitation conditions, and the calculation of the impact of ground cover suppression on precipitation echoes, includes: Radar base data samples collected under precipitation conditions at high elevation angles and above a preset distance were selected. Pure precipitation echo data were screened based on the unfiltered reflectivity factor T and the filtered reflectivity factor Z in the selected radar base data samples. The difference between the unfiltered reflectivity factor T and the filtered reflectivity factor Z was then calculated based on the screened pure precipitation echo data. The calculated difference was statistically analyzed, the average value was obtained, and the probability distributions of the difference distributions at 0-0.5dB, 0.5-1dB, 1-2dB, and above 2dB were statistically analyzed. Finally, the average value and probability distributions were used as the influence values ​​of ground object suppression on precipitation echoes.

7. The method according to claim 6, characterized in that, When calculating the difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering, |TZ| is calculated and used as the difference between the reflectivity factor T before filtering and the reflectivity factor Z after filtering.

8. A ground feature filtering capability assessment device, characterized in that, The device includes: The acquisition module is used to acquire radar base data samples collected under clear sky and precipitation conditions. The ground feature filtering capability statistical analysis module is used to analyze radar base data samples collected under clear sky conditions, calculate the ground feature suppression ratio, and use the ground feature suppression ratio as a ground feature filtering capability statistical analysis index. The statistical analysis module for the impact of ground object filtering on precipitation echoes is also used to analyze radar base data samples collected under precipitation conditions, calculate the impact value of ground object suppression on precipitation echoes, and use the impact value of ground object suppression on precipitation echoes as a statistical analysis index of the impact of ground object filtering on precipitation echoes. The evaluation module is used to comprehensively evaluate the ground feature filtering capability based on the statistical analysis indicators of the ground feature filtering capability and the statistical analysis indicators of the impact of ground feature filtering on precipitation echo.

9. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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