Ground feature influence area identification method, device and equipment based on radar historical observation data

Abstract

The invention discloses a method, a device and equipment for identifying a ground object influence area based on radar historical observation data, and relates to the technical field of radar rainfall detection. According to the method, firstly, the historical rainfall data set detected by the radar is constructed, preprocessing and screening are carried out, then the evaluation data are generated through point-to-point accumulation processing, the influence area of the terrain and the building is accurately recognized from the evaluation data, the ground feature influence in the radar observation data can be recognized, and the quality of the observation data is improved.

Description

Technical Field

[0001] The present application relates to the field of radar precipitation detection technology, and in particular to a method, device and equipment for identifying a terrain impact zone based on historical radar observation data. Background Art

[0002] Dual polarization radar simultaneously transmits horizontal and vertical polarization waves to detect precipitation particles, except for the reflectivity factor (Z H ), radial velocity (V), and other factors such as differential reflectivity factor (Z DR ), differential propagation phase shift (Φ DP ), differential propagation phase shift rate (K DP ) and correlation coefficient (ρ HV ) and other dual polarization quantities related to the size, phase and concentration of precipitation particles are of great significance for the monitoring and early warning of severe convective weather. However, good quality is the basis for the application of the above dual polarization quantities, and data quality assessment is a necessary link before business application.

[0003] In response to the impact of ground objects (such as mountains, buildings, etc.) on radar observation data, the existing measure is to set an adaptive Gaussian frequency domain (GMAP) filter in the weather radar hardware to filter out clutter and restore the remaining weather echoes through Gaussian fitting. Although the GMAP filter can remove ground objects in the reflectivity factor to a certain extent and restore precipitation information, there are still certain limitations. It is difficult for the GMAP filter to completely remove strong ground object signals in the reflectivity factor, and the beam obstruction caused by the ground objects cannot be judged. For dual polarization quantities with smaller magnitudes and higher consistency requirements for the two channels, the GMAP filter is currently unable to restore precipitation information well. Therefore, how to accurately identify the impact of ground objects in radar observation data has become a key technical challenge to improve data accuracy and quality. Summary of the invention

[0004] The purpose of this application is to provide a method, device and equipment for identifying the influence area of ​​ground objects based on historical radar observation data, so as to realize the identification of the influence of ground objects in radar observation data and improve the quality of observation data.

[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 a ground object influence zone based on historical radar observation data, comprising:

[0007] Construct a historical precipitation dataset using radar detection;

[0008] Preprocessing and screening the historical precipitation data sets respectively to obtain screened historical precipitation data sets;

[0009] The observation data of the same observation point at different historical moments in the screened historical precipitation data set are accumulated to obtain the evaluation data of each observation point within the radar radiation range;

[0010] The impact area of ​​the ground object is identified based on the evaluation data of each observation point, and the identification result of the impact area of ​​the ground object of each observation point is obtained.

[0011] In a second aspect, the present application provides a device for identifying a terrain influence zone based on historical radar observation data. The device for identifying a terrain influence zone based on historical radar observation data applies the above-mentioned method for identifying a terrain influence zone based on historical radar observation data. The device for identifying a terrain influence zone based on historical radar observation data includes:

[0012] The historical precipitation dataset construction module is used to construct the historical precipitation dataset detected by radar;

[0013] A preprocessing and screening module, used to preprocess and screen the historical precipitation data set to obtain a screened historical precipitation data set;

[0014] The accumulation module is used to accumulate the observation data of the same observation point at different historical moments in the filtered historical precipitation data set to obtain the evaluation data of each observation point within the radar radiation range;

[0015] The ground object influence area identification module is used to identify the ground object influence area based on the evaluation data of each observation point and obtain the ground object influence area identification result of each observation point.

[0016] In a third 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 the above-mentioned method for identifying terrain influence zones based on historical radar observation data.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects.

[0018] The present application provides a method, device and equipment for identifying the impact area of ​​land objects based on historical radar observation data. The present application first constructs a historical precipitation data set detected by radar, and performs preprocessing and screening. Then, evaluation data is generated through point-to-point accumulation processing, and the impact area of ​​terrain and buildings is accurately identified from the evaluation data. The present application can realize the identification of the impact of land objects in radar observation data and improve the quality of observation data.

[0019] This application also subdivides the evaluation data into five parts: beam complete obstruction area, beam partial obstruction area, dual polarization main lobe contamination area, dual polarization side lobe contamination area, and no impact area. According to the different degrees of influence of weather radar observation data in different terrain and building influence areas, a graded quality control plan was established to retain the usable weather radar observation data to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0021] Figure 1 A flowchart of a method for identifying a terrain influence zone based on historical radar observation data is provided in accordance with an embodiment of the present application.

[0022] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] In order to make the above-mentioned objects, 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.

[0025] This application aims to solve the data quality problems caused by the influence of ground objects in current weather radar observation data without relying on high-precision digital elevation models (DEMs).

[0026] In an exemplary embodiment, Figure 1 As shown, a method for identifying a terrain influence zone based on historical radar observation data is provided, including the following steps 101 to 104.

[0027] Step 101: construct a historical precipitation dataset detected by radar.

[0028] Step 102: preprocess and filter the historical precipitation dataset to obtain a filtered historical precipitation dataset.

[0029] Step 103, accumulating the observation data of the same observation point at different historical moments in the filtered historical precipitation data set to obtain evaluation data of each observation point within the radar radiation range.

[0030] Step 104 , identifying the land object influence area based on the evaluation data of each observation point, and obtaining the land object influence area identification result of each observation point.

[0031] By implementing the above steps 101 to 104 , it is possible to identify the influence of ground objects in radar observation data, thereby improving the quality of the observation data.

[0032] In another exemplary embodiment of the present application, in the above step 101, for any S / C / X band dual polarization weather radar (hereinafter referred to as radar), data with large-area precipitation characteristics are screened out from its observation data of more than 1 year. Specifically, the horizontal structure (PPI) of the 2.4° elevation angle in the polar coordinate volume scan data at a certain moment is used to calculate the first layer elevation reflectivity factor (Z H )>15dBZ and the correlation coefficient (ρhv)>0.95 account for more than 15% of the cases are recorded as large-area precipitation cases and stored in the historical precipitation data set of the radar, which specifically includes the following steps 201-step 202.

[0033] Step 201, obtaining observation data of the radar at each historical moment in a historical time period.

[0034] Step 202, selecting the observation data of each historical moment in the historical time period that meets the precipitation area condition to form a historical precipitation data set of the radar; the precipitation area condition is: the observation data with the first layer elevation reflectivity factor greater than 15dBZ and the correlation coefficient>0.95 is not less than 15% of the total observation data.

[0035] Step 202 is specifically implemented using the following steps 301 to 304.

[0036] Step 301, initialize the value of historical time t to 1.

[0037] Step 302, determine whether the observation data of the radar at the historical time t meets the precipitation area condition, and obtain a judgment result.

[0038] Step 303: if the judgment result is yes, the observation data of the radar at the historical time t is added to the historical precipitation data set; otherwise, the observation data of the radar at the historical time t is not added to the historical precipitation data set.

[0039] Step 304, increase the value of t by 1, and return to the step of "determining whether the observation data of the radar at the historical moment t meets the precipitation area condition and obtaining the judgment result" until all the historical moments in the historical time period are traversed.

[0040] In another exemplary embodiment, in the above step 102, each data in the historical precipitation data set is processed, and the differential propagation phase shift (Φ DP ) is collapsed, the initial value is determined, and then the standard deviation SD (Φ DP ). For S-band weather radar, its observation results are basically not attenuated and no further screening is required, while C and X-band weather radars are susceptible to attenuation. DP <10° is used as a threshold to filter out observation data that is substantially unaffected by attenuation point by point, specifically including the following steps 401 to 403.

[0041] Step 401, obtaining the differential propagation phase shift of the observation data at each historical moment in the historical precipitation data set.

[0042] Step 402, according to the differential propagation phase shift of the observation data at each historical moment in the historical precipitation dataset, the observation data at each historical moment in the historical precipitation dataset are subjected to radially-by-radial differential propagation phase shift defolding processing to obtain a preprocessed historical precipitation dataset.

[0043] Step 403: Filter out the pre-processed historical precipitation data sets that meet the condition Φ DP The observation data with a temperature of <10° constitute the filtered historical precipitation data set; among them, Φ DP is the differential propagation phase shift of the observed data.

[0044] In another exemplary embodiment, in the above step 103, considering the fact that terrain and building echoes vary little with space and time, the historical precipitation data set after being screened by each weather radar is accumulated point by point to obtain the reflectivity factor (Z) before radar hardware object suppression. T ), reflectivity factor after ground object suppression (Z H ), differential reflectivity factor (Z DR ), correlation coefficient (ρ hv ), differential propagation phase shift standard deviation (SD(Φ DP ) evaluation data. In the accumulation process, two schemes were used according to different influencing factors:

[0045] Solution A, in order to solve the beam blocking problem, uses the minimum detectable sensitivity of the radar at 100 km as Z H Threshold, ensuring that the sample is not affected by different distance sensitivities.

[0046] Solution B, to solve the pollution problem of dual polarization, uses weak precipitation (Z H Located at 25-35dBZ) as Z H Threshold value ensures that the sample is not affected by hail and non-meteorological echoes.

[0047] Exemplarily, the above-mentioned evaluation data includes first evaluation data and second evaluation data; the above-mentioned step 103 can be replaced by the following steps 501 to 504.

[0048] Step 501, obtaining observation data in the filtered historical precipitation data set whose reflectivity factor after ground object suppression is not less than a first reflectivity factor threshold, to form a first precipitation data set to be accumulated; the first reflectivity factor threshold is the reflectivity factor after ground object suppression corresponding to the minimum measurable sensitivity of the radar at 100 km.

[0049] Step 502, obtaining observation data in the filtered historical precipitation data set whose reflectivity factor after ground object suppression is not less than a second reflectivity factor threshold, to form a second precipitation data set to be accumulated; the second reflectivity factor threshold is the reflectivity factor after ground object suppression within the interval of 25dBZ-35dBZ.

[0050] Step 503: accumulate the observation data of the same observation point at different historical moments in the first precipitation data set to be accumulated to obtain first evaluation data of each observation point.

[0051] Step 504: accumulate the observation data of the same observation point at different historical moments in the second precipitation data set to be accumulated to obtain second evaluation data of each observation point.

[0052] If the radar historical precipitation dataset contains observation data with multiple scanning modes and different temporal and spatial resolutions, the data with the same scanning mode are selected for separate processing during accumulation to obtain the evaluation data under different scanning modes at the same observation point.

[0053] Exemplarily, the above-mentioned accumulation method can be any data statistical method, such as average value calculation, variance calculation, standard deviation calculation, maximum and minimum value determination, etc. The embodiment of the present application preferably uses the average value calculation method. Other methods can also be selected in some specific implementation processes, which are not limited here.

[0054] In another exemplary embodiment, in the above step 104, the evaluation data accumulated at different observation points are analyzed and identified point by point to determine the different types of terrain and building impact areas corresponding to each observation point, as follows:

[0055] A. Complete beam obstruction area. In the evaluation data obtained by accumulating scheme A, the area without valid observation data is considered to be caused by terrain or buildings near the radar blocking beam propagation, and is defined as the beam obstruction area.

[0056] B. Partial beam obstruction area. In the evaluation data obtained by the cumulative scheme A, the ZH of a certain radial direction is significantly lower than the average value of the evaluation data. It is considered that the terrain or buildings near the radar partially block the beam propagation, which is defined as the partial beam obstruction area.

[0057] C. Dual polarization main lobe contamination area. In the evaluation data obtained by accumulating scheme B, ρ HV <0.9、SD(Φ DP )>5° dual polarization abnormal region and Z T With Z H The difference (ΔZ H )>5dB, it is considered that the ground objects or buildings appear near the main lobe of the weather radar, which is defined as the dual-polarization main lobe pollution area.

[0058] D. Dual polarization sidelobe contamination area. HV <0.9、SD(Φ DP )>5°, and Z T With Z H The difference (ΔZ H )<5dB, it is considered to be caused by the electromagnetic waves emitted by the weather radar side lobes contacting the ground or buildings, and is defined as the dual-polarization side lobe contamination area.

[0059] E. Unaffected area. All the accumulated points in the assessment data that do not belong to the above characteristics are not affected by terrain and buildings and are defined as unaffected areas by terrain and buildings.

[0060] Exemplarily, the above step 104 may be replaced by the following steps 601 to 605.

[0061] Step 601, determine whether the first evaluation data of the j-th observation point meets the beam blocking condition. If so, determine that the observation point belongs to the beam blocking area; j=1, 2, ..., J, J is the number of observation points within the radar radiation range, and the beam blocking condition is: the observation data of the j-th observation point are all invalid.

[0062] Step 602, determine whether the first evaluation data of the j-th observation point meets the partial beam blocking condition. If so, determine that the j-th observation point belongs to the partial beam blocking area; the partial beam blocking condition is: the minimum value of the difference between the reflectivity factor of the observation point after the ground object is suppressed in each radial direction and the average value of the reflectivity factor of the observation point after the ground object is suppressed in all radial directions is less than the deviation threshold.

[0063] Step 603, determining whether the second observation data of the j-th observation point meets the dual-polarization main lobe pollution condition, and if so, determining that the j-th observation point belongs to the dual-polarization main lobe pollution area; the dual-polarization main lobe pollution condition is: HV <0.9 and SD(Φ DP )>5° and ΔZ H >5dB, where ρ HV is the correlation coefficient, SD(Φ DP ) is the standard deviation of the differential propagation phase shift, ΔZ H It is the difference between the reflectivity factor before and after ground object suppression.

[0064] Step 604, determining whether the second observation data of the j-th observation point meets the dual polarization side lobe contamination condition, and if so, determining that the j-th observation point belongs to the dual polarization side lobe contamination area; the dual polarization side lobe contamination condition is ρ HV <0.9 and SD(Φ DP )>5° and ΔZ H ≤5dB.

[0065] Step 605: If the first evaluation data of the j-th observation point does not meet the partial beam blocking condition, and the first evaluation data of the j-th observation point does not meet the partial beam blocking condition, and the second evaluation data of the j-th observation point does not meet the dual polarization main lobe contamination condition, and the second evaluation data of the j-th observation point does not meet the dual polarization side lobe contamination condition, then it is determined that the j-th observation point belongs to the no-impact zone.

[0066] In an exemplary embodiment, the above step 104 further includes the following step 105 .

[0067] Step 105: Perform quality control on the observation data to be processed at each observation point according to the identification result of the ground object influence area at each observation point. The observation data to be processed may be the observation data of the observation point in the radar historical or future precipitation data set.

[0068] In step 105, different quality control schemes are adopted according to the identification results of the evaluation data and taking into account the different sensitivities and influences of different observation quantities of the weather radar. The specific schemes are as follows:

[0069] A. Beam completely blocked area. There is no observation data in this area and no quality control is required.

[0070] B. Partial beam obstruction area. The observation data in this area is partially obstructed by ground objects. H , Z DR , HV The observation quantity is assigned invalid value removal, Φ DP Observations are retained.

[0071] C. Dual polarization main lobe contamination area. This area is close to terrain and buildings. H , Z DR , HV The observation quantity is assigned invalid value removal, Φ DP The observed value is assigned a filling mark value, and after removing the value, the radial front and rear unaffected area data is interpolated and filled to calculate K DP .

[0072] D. Dual polarization sidelobe pollution area, this area is far away from the terrain and buildings, Z H Observation retention, Z DR , HV The observation quantity is assigned invalid value removal, Φ DP The observed value is assigned a filling mark value, and after removing the value, the radial front and rear unaffected area data is interpolated and filled to calculate K DP .

[0073] Exemplarily, the above step 105 specifically includes the following steps 701 to 703.

[0074] Step 701, removing the reflectivity factor, differential reflectivity factor and correlation coefficient after ground object suppression in the observation data to be processed of each observation point belonging to the beam partial occlusion area;

[0075] Step 702, removes the reflectivity factor, differential reflectivity factor and correlation coefficient after ground object suppression in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area, assigns a first differential propagation phase shift filling mark value to the differential propagation phase shift in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area, and assigns a first differential propagation phase shift rate filling mark value to the differential propagation phase shift rate in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area; the first differential propagation phase shift filling mark value is obtained by interpolating the differential propagation phase shift of the observation points in the unaffected area radially adjacent to the dual-polarization main lobe contamination area in front and behind; the first differential propagation phase shift rate filling mark value is calculated based on the first differential propagation phase shift filling mark value.

[0076] Step 703, remove the differential reflectivity factor and correlation coefficient in the observation data to be processed of each observation point belonging to the dual polarization sidelobe contamination area, assign a second differential propagation phase shift filling mark value to the differential propagation phase shift in the observation data to be processed of each observation point belonging to the dual polarization sidelobe contamination area, and assign a second differential propagation phase shift rate filling mark value to the differential propagation phase shift rate in the observation data to be processed of each observation point belonging to the dual polarization main lobe contamination area; the second differential propagation phase shift filling mark value is obtained by: interpolating the differential propagation phase shift of the unaffected area radially adjacent to the dual polarization sidelobe contamination area in front and behind; the second differential propagation phase shift rate filling mark value is calculated based on the second differential propagation phase shift filling mark value.

[0077] Based on the same inventive concept, the embodiment of the present application also provides a device for identifying the area of ​​influence of objects based on radar historical observation data, which is used to implement the method for identifying the area of ​​influence of objects based on radar historical observation data. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for identifying the area of ​​influence of objects based on radar historical observation data provided below can refer to the limitations of the method for identifying the area of ​​influence of objects based on radar historical observation data above, and will not be repeated here.

[0078] In an exemplary embodiment, a device for identifying a terrain influence zone based on historical radar observation data is provided, comprising the following modules.

[0079] The historical precipitation dataset construction module is used to construct the historical precipitation dataset detected by radar.

[0080] The preprocessing and screening module is used to preprocess and screen the historical precipitation data set to obtain a screened historical precipitation data set.

[0081] The accumulation module is used to accumulate the observation data of the same observation point at different historical moments in the filtered historical precipitation data set to obtain the evaluation data of each observation point within the radar radiation range.

[0082] The ground object influence area identification module is used to identify the ground object influence area based on the evaluation data of each observation point and obtain the ground object influence area identification result of each observation point.

[0083] 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 2 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the 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 the 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 a ground object influence area based on historical radar observation data is implemented.

[0084] Those skilled in the art will understand that Figure 2 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 those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0085] 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.

[0086] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can 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).

[0087] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0088] The technical features of the above embodiments may 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.

[0089] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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 method for identifying the influence area of ​​ground objects based on historical radar observation data, characterized in that: include: Construct a historical precipitation dataset detected by radar; Preprocessing and screening the historical precipitation data sets respectively to obtain screened historical precipitation data sets; The observation data of the same observation point at different historical moments in the screened historical precipitation data set are accumulated to obtain the evaluation data of each observation point within the radar radiation range; The impact area of ​​the ground object is identified based on the evaluation data of each observation point, and the identification result of the impact area of ​​the ground object of each observation point is obtained.

2. The method for identifying the terrain influence area based on historical radar observation data according to claim 1, characterized in that: Construct a historical precipitation dataset detected by radar, including: Obtain the radar's observation data at each historical moment in the historical time period; The observation data at each historical moment in the historical time period that meets the precipitation area condition are selected to form the historical precipitation data set of the radar; the precipitation area condition is: the observation data with the first layer elevation reflectivity factor greater than 15dBZ and the correlation coefficient>0.95 is not less than 15% of the total observation data.

3. The method for identifying the terrain influence area based on historical radar observation data according to claim 2 is characterized in that: The observation data at each historical moment in the historical time period that meets the precipitation area conditions are selected to form the radar historical precipitation data set, which specifically includes: Initialize the value of historical moment t to 1; Determine whether the radar observation data at the historical time t meets the precipitation area condition and obtain the judgment result; If the judgment result is yes, the observation data of the radar at the historical time t is added to the historical precipitation data set, otherwise, the observation data of the radar at the historical time t is not added to the historical precipitation data set; Let the value of t increase by 1, and return to the step of "determining whether the observation data of the radar at the historical moment t meets the precipitation area condition and obtaining the judgment result" until all historical moments in the historical time period are traversed.

4. The method for identifying the terrain influence area based on historical radar observation data according to claim 1, characterized in that: The historical precipitation data set is preprocessed and screened respectively to obtain a screened historical precipitation data set, specifically including: Obtain the differential propagation phase shift of the observation data at each historical moment in the historical precipitation dataset; According to the differential propagation phase shift of the observation data at each historical moment in the historical precipitation data set, the observation data at each historical moment in the historical precipitation data set are subjected to radially-by-radial de-folding of the differential propagation phase shift to obtain a pre-processed historical precipitation data set; Filter out the preprocessed historical precipitation data sets that meet the condition Φ DP The observation data with a temperature of <10° constitute the filtered historical precipitation data set; among them, Φ DP is the differential propagation phase shift of the observed data.

5. The method for identifying the terrain influence area based on historical radar observation data according to claim 1, characterized in that: The evaluation data includes first evaluation data and second evaluation data; The observation data of the same observation point at different historical moments in the filtered historical precipitation data set are accumulated to obtain the evaluation data of each observation point within the radar radiation range, specifically including: Obtain observation data whose reflectivity factor after ground object suppression is not less than a first reflectivity factor threshold value from the filtered historical precipitation data set to form a first precipitation data set to be accumulated; the first reflectivity factor threshold value is the reflectivity factor after ground object suppression corresponding to the minimum measurable sensitivity of the radar at 100 km; Obtain observation data whose reflectivity factor after ground object suppression is not less than a second reflectivity factor threshold value from the filtered historical precipitation data set to form a second precipitation data set to be accumulated; the second reflectivity factor threshold value is the reflectivity factor after ground object suppression within the interval of 25 dBZ-35 dBZ; Accumulating the observation data of the same observation point at different historical moments in the first precipitation data set to be accumulated to obtain first evaluation data of each observation point; The observation data of the same observation point at different historical moments in the second precipitation data set to be accumulated are accumulated to obtain second evaluation data of each observation point.

6. The method for identifying the terrain influence area based on historical radar observation data according to claim 5, characterized in that: Based on the evaluation data of each observation point, the ground object influence area is identified to obtain the ground object influence area identification results of each observation point, including: Determine whether the first evaluation data of the j-th observation point meets the beam blocking condition. If so, determine that the observation point belongs to the beam blocking area; j=1, 2, ..., J, J is the number of observation points within the radar radiation range, and the beam blocking condition is that the observation data of the j-th observation point are all invalid; Determine whether the first evaluation data of the j-th observation point meets the beam partial obstruction condition, and if so, determine that the j-th observation point belongs to the beam partial obstruction area; the beam partial obstruction condition is: the minimum value of the difference between the reflectivity factor after the ground object suppression in each radial direction of the observation point and the average value of the reflectivity factor after the ground object suppression in all radial directions of the observation point is less than the deviation threshold; Determine whether the second observation data of the j-th observation point meets the dual-polarization main lobe pollution condition. If so, determine that the j-th observation point belongs to the dual-polarization main lobe pollution area; the dual-polarization main lobe pollution condition is: HV <0.9 and SD(Φ DP )>5° and ΔZ H >5dB, where ρ HV is the correlation coefficient, SD(Φ DP ) is the standard deviation of differential propagation phase shift, ΔZ H It is the difference between the reflectivity factor before and after the object suppression; Determine whether the second observation data of the j-th observation point meets the dual polarization side lobe contamination condition. If so, determine that the j-th observation point belongs to the dual polarization side lobe contamination area; the dual polarization side lobe contamination condition is ρ HV <0.9 and SD(Φ DP )>5° and ΔZ H ≤5dB; If the first evaluation data of the jth observation point does not meet the partial beam blockage condition, and the first evaluation data of the jth observation point does not meet the partial beam blockage condition, and the second evaluation data of the jth observation point does not meet the dual polarization main lobe contamination condition, and the second evaluation data of the jth observation point does not meet the dual polarization side lobe contamination condition, then it is determined that the jth observation point belongs to the non-influenced area.

7. The method for identifying the terrain influence area based on historical radar observation data according to claim 1, characterized in that: Based on the evaluation data of each observation point, the influence area of ​​the ground object is identified, and the identification results of the influence area of ​​each observation point are obtained, and then the following are included: According to the identification results of the land object influence area at each observation point, quality control is performed on the observation data to be processed at each observation point.

8. The method for identifying the terrain influence area based on historical radar observation data according to claim 7, characterized in that: According to the identification results of the ground object influence area of ​​each observation point, the quality control of the observation data to be processed at each observation point is carried out, including: Removing the reflectivity factor, differential reflectivity factor and correlation coefficient after ground object suppression in the observation data to be processed for each observation point belonging to the partially blocked area of ​​the beam; The reflectivity factor, differential reflectivity factor and correlation coefficient after ground object suppression in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area are removed, and the differential propagation phase shift in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area is assigned a first differential propagation phase shift filling mark value, and the differential propagation phase shift rate in the observation data to be processed of each observation point belonging to the dual-polarization main lobe contamination area is assigned a first differential propagation phase shift rate filling mark value; the first differential propagation phase shift filling mark value is obtained by interpolating the differential propagation phase shift of the observation points in the unaffected area radially adjacent to the dual-polarization main lobe contamination area in front and behind; the first differential propagation phase shift rate filling mark value is calculated based on the first differential propagation phase shift filling mark value. The differential reflectivity factor and correlation coefficient in the observation data to be processed of each observation point belonging to the dual polarization sidelobe contamination area are removed, and the differential propagation phase shift in the observation data to be processed of each observation point belonging to the dual polarization sidelobe contamination area is assigned a second differential propagation phase shift filling mark value, and the differential propagation phase shift rate in the observation data to be processed of each observation point belonging to the dual polarization main lobe contamination area is assigned a second differential propagation phase shift rate filling mark value; the second differential propagation phase shift filling mark value is obtained by: interpolating the differential propagation phase shift of the unaffected area radially adjacent to the dual polarization sidelobe contamination area in front and behind; the second differential propagation phase shift rate filling mark value is calculated based on the second differential propagation phase shift filling mark value.

9. A device for identifying the influence area of ​​ground objects based on historical radar observation data, characterized in that: The device for identifying the terrain influence zone based on historical radar observation data applies the method for identifying the terrain influence zone based on historical radar observation data according to any one of claims 1 to 8, and the device for identifying the terrain influence zone based on historical radar observation data comprises: The historical precipitation dataset construction module is used to construct the historical precipitation dataset detected by radar; A preprocessing and screening module, used to preprocess and screen the historical precipitation data set to obtain a screened historical precipitation data set; The accumulation module is used to accumulate the observation data of the same observation point at different historical moments in the filtered historical precipitation data set to obtain the evaluation data of each observation point within the radar radiation range; The ground object influence area identification module is used to identify the ground object influence area based on the evaluation data of each observation point and obtain the ground object influence area identification results of each observation point.

10. 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 the method for identifying the terrain influence zone based on radar historical observation data as described in any one of claims 1 to 8.

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