An artificial hail prevention operation area identification method, system, device and medium

Abstract

The present invention provides a method for identifying an artificial hail prevention operation area, comprising the following steps: determining an initial artificial hail prevention operation area based on relevant information; performing grid division on the initial artificial hail prevention operation area at a preset resolution, and respectively calculating the altitude corresponding to 0°C, -10°C, and -20°C above each grid point by using a difference algorithm according to meteorological sounding second-level data, and constructing a 0°C layer, a -10°C layer, and a -20°C layer; combining dual-polarization radar parameters at different radar scanning elevation angles to identify the artificial hail prevention operation area; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area, and a hail wet growth area; wherein, the hail embryo formation area includes: a frozen-drop type hail embryo area and a graupel type hail embryo area. This method can not only improve the accuracy of operation area identification, but also effectively cope with changing weather conditions, thereby providing more scientific and reliable guidance for artificial hail prevention operations.

Description

Technical Field

[0001] The present invention relates to the field of artificial hail prevention operations, and more particularly to a method, system, device and medium for identifying an artificial hail prevention operation area. Background Art

[0002] In the field of artificial hail prevention operations, traditional methods mainly rely on indicators such as radar reflectivity and vertically integrated liquid water content provided by the new generation weather radar to identify hail. Although these indicators can provide information about the liquid water content and reflection intensity in the cloud, they are insufficient in describing the microphysical characteristics related to hail in the cloud, especially the limited ability to identify in the initial stage of hail formation.

[0003] For example, the VIL threshold and its jump phenomenon can be used to indicate the area where hail may form, but these methods often can only provide effective signals when hail has already formed or is about to form, missing the best opportunity for artificial intervention.

[0004] In recent years, with the development of dual-polarization radar technology, researchers have begun to explore using dual-polarization radar parameters to improve hail identification methods. These studies mainly focus on the identification after hail formation, usually using methods based on fuzzy logic or other algorithms to analyze various parameters in the radar echo, such as horizontal reflectivity factor, differential reflectivity, correlation coefficient, etc., to identify the presence of hail. Although these methods have made significant progress in hail identification, they mainly focus on the formed hail, rather than the embryo stage before hail formation or the wet growth stage of hail.

[0005] Traditional hail identification indicators can only identify the areas where hail has formed, and cannot effectively identify the hail embryo formation area, that is, the area in the cloud where hail has not yet formed but has the potential to form hail. At the same time, during the process of hail growing from a small ice nucleus to a large hail, there is a critical wet growth stage, which is crucial for the final size of hail, but the existing technology fails to clearly identify this area, resulting in the inability to carry out effective artificial intervention at this stage.

[0006] In addition, traditional hail prevention operation methods usually rely on a single data source, such as radar data or weather forecast data, lacking the ability to comprehensively analyze multi-source data such as weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information. This leads to inaccurate identification of the operation area and also fails to fully consider the actual feasibility and pertinence of the operation.

[0007] Moreover, when determining the altitude corresponding to the key temperature layers (such as the 0°C, -10°C, and -20°C layers), relatively rough methods are usually adopted, lacking the ability to accurately locate the temperature layers. This makes the understanding of hail formation conditions inaccurate and reduces the effectiveness of the operation.

[0008] Therefore, how to design a method for identifying artificial hail prevention operation areas to make up for the deficiencies in aspects such as comprehensive information analysis, temperature layer positioning, radar parameter analysis ability, and three-dimensional space analysis in the existing technology, and improve the accuracy and effectiveness of operations is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, the present invention provides a method for identifying artificial hail prevention operation areas, which combines weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information, uses a difference algorithm to calculate the altitude corresponding to a specific temperature layer, and combines dual-polarization radar parameters to identify artificial hail prevention operation areas.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] In the first aspect, the present invention provides a method for identifying artificial hail prevention operation areas, including the following steps:

[0012] S1. Determine the initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information;

[0013] S2. Divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use a difference algorithm to calculate the altitude corresponding to 0°C, -10°C, and -20°C above each grid point respectively according to meteorological sounding second-level data, and construct a 0°C layer, a -10°C layer, and a -20°C layer;

[0014] S3. Combine dual-polarization radar parameters at different radar scanning elevation angles to identify artificial hail prevention operation areas; the artificial hail prevention operation areas include: strong updraft areas, hail embryo formation areas, and hail wet growth areas; among them, the hail embryo formation areas include: frozen drop type hail embryo areas and graupel type hail embryo areas.

[0015] Preferably, in S1, determining the initial artificial hail prevention operation area based on relevant information includes:

[0016] Define the hail occurrence probability at time t and position (x, y) as P(t, x, y) based on weather forecast data; define the historical hail disaster days or times record at position (x, y) as H(x, y) based on artificial hail prevention protection area information; define the artificial hail prevention protection economic crop demand level at position (x, y) as S(x, y) based on artificial hail prevention protection area information; define the traffic accessibility T(x, y) at position (x, y) based on artificial hail prevention operation point information, where T(x, y) = e -αd , d represents the distance to the nearest transportation infrastructure, and α represents the decline rate of traffic accessibility when the distance increases;

[0017] Determine the initial artificial hail prevention operation area through the risk assessment model R(t, x, y); where R(t, x, y) = β 1 P(t, x, y) + β 2 H(x, y) + β 3 S(x, y) + β 4 T(x, y), β 1 、β 2 、β 3 and β 4 are weight coefficients.

[0018] Preferably, in S2, use the difference algorithm to calculate the altitude corresponding to 0°C, -10°C, and -20°C above each grid point respectively, including:

[0019] For each grid point, according to the two height points Z 1 and Z 2 in the meteorological sounding second-level data that are closest to the target temperature T 1 and T 2 , use the interpolation algorithm to calculate the height Z corresponding to the target temperature T;

[0020]

[0021] Calculate the altitude Z corresponding to 0°C, -10°C, and -20°C above each grid point through the interpolation algorithm 0 、Z -10 and Z -20 .

[0022] Preferably, in S3, the identification of the strong updraft area includes:

[0023] According to the dual-polarization radar parameters at different radar scanning elevation angles, identify the regional layers in each layer from the lowest layer upwards; the regional layer is the area where the Z DR value is greater than 2 dB and the area is greater than 1 square kilometer;

[0024] Combine the altitude and the longitude and latitude coordinates to merge all the regional layers and construct a three-dimensional image;

[0025] Based on the three-dimensional image, identify the area that continuously extends upwards from the lowest layer elevation angle to more than 1 kilometer above the 0°C layer;

[0026] Judge whether the identified area forms a continuous columnar area; if so, then the columnar area is the strong updraft area.

[0027] Preferably, in S3, the identification of the frozen-droplet type hail embryo area includes:

[0028] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the 0°C layer, identify the horizontal reflectivity factor Z H Greater than 45 dBz and the differential reflectivity Z DR Less than or equal to 0 dB region;

[0029] Determine whether the identified area is less than 5 kilometers away from the top edge of the strong updraft area; if so, proceed to the next step;

[0030] Determine whether the length, width, and height of the identified area all exceed 500 meters; if so, this area is the frozen-drop type hail embryo area.

[0031] Preferably, in S3, the identification of the graupel type hail embryo area includes:

[0032] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the -10°C layer, identify the horizontal reflectivity factor Z H Greater than 35 dBz, the differential reflectivity Z DR Less than 1 dB and the correlation coefficient CC greater than 0.99 region;

[0033] Determine whether the length, width, and height of the identified area all exceed 500 meters; if so, this area is the graupel type hail embryo area.

[0034] Preferably, in S3, the identification of the hail wet growth area includes:

[0035] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the -10°C layer and less than the -20°C layer, identify the correlation coefficient CC greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50 dBz, the differential reflectivity Z DR Less than 0 dB and the specific differential phase K DP Missing data region;

[0036] Determine whether the length, width, and height of the identified area are all greater than 500 meters; if so, this area is the hail wet growth area.

[0037] In a second aspect, the present invention provides an artificial hail prevention operation area identification system, including:

[0038] Initial artificial hail prevention operation area determination module: used to determine the initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information;

[0039] Temperature layer construction module: used to divide the initial artificial hail prevention operation area into grid points at a preset resolution, and calculate the corresponding altitude of 0°C, -10°C, and -20°C above each grid point respectively according to the meteorological sounding second-level data, and construct the 0°C layer, -10°C layer, and -20°C layer;

[0040] Artificial hail prevention operation area identification module: used to identify the artificial hail prevention operation area by combining dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area, and hail wet growth area; among them, the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area.

[0041] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned artificial hail prevention operation area identification method is implemented.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned artificial hail prevention operation area identification method is implemented.

[0043] The descriptions of the second to fourth aspects in the present invention can refer to the detailed description of the first aspect; and, for the beneficial effects of the descriptions of the second to fourth aspects, reference can be made to the analysis of the beneficial effects of the first aspect, which will not be elaborated here.

[0044] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This method integrates multiple data sources such as weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information, and determines the initial operation area by establishing a risk assessment model. This not only improves the accuracy of identification, but also ensures that the selected operation area is more in line with actual needs, and can better guide the implementation of hail prevention operations.

[0046] 2. By using the difference algorithm to calculate the altitude corresponding to specific temperature layers (0°C, -10°C, and -20°C), this method can accurately locate the key layers for hail formation, providing an accurate vertical position reference for subsequent operation area identification, thereby enhancing the ability to identify hail formation conditions.

[0047] 3. By constructing a three-dimensional image and combining data at different radar scanning elevation angles, this method can realize the three-dimensional analysis of the operation area. Especially for the identification of strong updraft areas, it can ensure that the identified areas are not only reasonably distributed on the horizontal plane, but also meet specific conditions in the vertical direction, providing more comprehensive information support for hail prevention operations.

[0048] 4. By using different combinations of dual-polarization radar parameters, this method can efficiently identify key regions such as strong updraft regions, hail embryo regions of frozen-drop type, hail embryo regions of graupel type, and hail wet growth regions. These precise identifications contribute to early warning and effective intervention in the formation and development process of hail. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0050] Figure 1 It is a flowchart of the method for identifying an artificial hail prevention operation area provided by an embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of the identification process of a strong updraft region provided by an embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of the identification process of a hail embryo region of frozen-drop type provided by an embodiment of the present invention;

[0053] Figure 4 It is a schematic diagram of the identification process of a hail embryo region of graupel type provided by an embodiment of the present invention;

[0054] Figure 5 It is a schematic diagram of the identification process of a hail wet growth region provided by an embodiment of the present invention;

[0055] Figure 6 It is a schematic diagram of the structure of an artificial hail prevention operation area identification system provided by an embodiment of the present invention;

[0056] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0058] The recognition method provided by the embodiments of the present application can be applied to a recognition server. The above recognition server can be hardware or software. When the recognition server is hardware, it can be implemented as a distributed server cluster providing recognition services or as a single server. When the recognition server is software, it can be installed in the above-listed servers. It can be implemented as multiple software or software modules, or as a single software or software module, and no specific limitation is made here.

[0059] Embodiment 1:

[0060] As Figure 1 shown, the present invention provides a method for identifying an artificial hail prevention operation area, including the following steps:

[0061] S1. Determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information;

[0062] S2. Perform grid division on the initial artificial hail prevention operation area at a preset resolution, and calculate the altitude corresponding to 0°C, -10°C, and -20°C above each grid point respectively using a difference algorithm according to meteorological sounding second-level data, and construct a 0°C layer, a -10°C layer, and a -20°C layer;

[0063] S3. Combine dual-polarization radar parameters at different radar scanning elevation angles to identify the artificial hail prevention operation area; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area, and a hail wet growth area; among them, the hail embryo formation area includes: a frozen-drop type hail embryo area and a graupel type hail embryo area.

[0064] The above method for identifying an artificial hail prevention operation area realizes the accurate identification of the key areas for hail formation by comprehensively using weather forecast data, artificial hail prevention protection area information, and operation point information, combined with a risk assessment model and dual-polarization radar parameters. It not only improves the accuracy and practicability of operation area selection, but also can effectively intervene before hail formation or at the hail embryo stage, thus significantly enhancing the effect and pertinence of artificial hail prevention operations.

[0065] The following further elaborates on the above steps and related features in detail:

[0066] In step S1 of this embodiment, determining the initial artificial hail prevention operation area based on relevant information includes:

[0067] Define the hail occurrence probability at time t and position (x, y) based on weather forecast data as P(t, x, y), and this parameter is used to predict the possibility of hail occurrence at a specific time and location.

[0068] Based on the information of artificial hail protection areas, the historical number or frequency of hail disasters recorded at location (x, y) is denoted as H(x, y). This parameter reflects the situation of a certain area suffering from hail disasters in the past period and can be used to evaluate the hail risk level of this area.

[0069] Based on the information of artificial hail protection areas, the demand level of economic crops for artificial hail protection at location (x, y) is defined as S(x, y); this parameter takes into account the needs of local agricultural production, especially the demand degree for crops vulnerable to hail.

[0070] Based on the information of artificial hail operation points, the traffic accessibility T(x, y) at location (x, y) is defined, where T(x, y) = e -αd , d represents the distance to the nearest traffic infrastructure, and α represents the rate of decrease in traffic accessibility as the distance increases; this parameter mainly considers the convenience and efficiency of implementing artificial hail operations, that is, whether it can reach and start work quickly.

[0071] Taking into account the influence of the above four factors comprehensively to determine the areas that most need artificial hail operations. The initial artificial hail operation area is determined through the risk assessment model R(t, x, y); where R(t, x, y) = β 1 P(t, x, y) + β 2 H(x, y) + β 3 S(x, y) + β 4 T(x, y), β 1 、β 2 、β 3 and β 4 are weight coefficients.

[0072] In this embodiment, for the weight coefficients β 1 、β 2 、β 3 and β 4 are optimized, including:

[0073] Collect a historical dataset including weather forecast data, historical hail disaster records, and traffic accessibility; preprocess the data to extract features related to the risk assessment model; use machine learning methods such as support vector machine (SVM) or random forest (RandomForest) to build a model, and train the model through cross-validation; find the optimal weight combination through methods such as grid search or random search to optimize the risk assessment model; use an independent test set to verify the performance of the optimized model and apply the optimal weights to the risk assessment model R(t, x, y).

[0074] It utilizes information such as real-time meteorological data, historical disaster records, agricultural production requirements, and traffic accessibility, and through corresponding mathematical models and statistical analyses, generates a comprehensive and accurate initial artificial hail suppression operation area.

[0075] In step S2 of this embodiment, grid division is performed on the initial artificial hail suppression operation area at a preset resolution, and according to the meteorological sounding second-level data, the altitude corresponding to 0°C, -10°C, and -20°C is calculated respectively for each grid point using the interpolation algorithm, and the 0°C layer, -10°C layer, and -20°C layer are constructed.

[0076] In this step, first, a suitable grid point resolution is selected according to the requirements of hail suppression operations and the accuracy of available data. For example, each grid point covers an area of 1 km × 1 km. At the same time, considering the requirements of computing resources, the requirements of computing efficiency and accuracy are balanced. Then, grid division is performed on the initial artificial hail suppression operation area to ensure that the geographical space range covered by each grid point is consistent, and the grid point size is dynamically adjusted according to the terrain complexity and the change of meteorological conditions, and smaller grid points are used in areas with large terrain undulations or drastic changes in meteorological conditions. Finally, the meteorological sounding second-level data is matched with the grid coordinates, and for the meteorological sounding data that spans multiple grid points, it is integrated by the average value or other statistical methods to ensure that the data of each grid point represents the situation within the area.

[0077] For each grid point, according to the two height points Z 1 and Z 2 in the meteorological sounding second-level data that are closest to the target temperature T 1 and their corresponding temperatures T 2 , the meteorological sounding second-level data contains temperature measurement values at different heights.

[0078] The interpolation algorithm is used to calculate the height Z corresponding to the target temperature T;

[0079]

[0080] The altitude Z 0 、Z -10 and Z -20 corresponding to 0°C, -10°C, and -20°C above each grid point is calculated through the interpolation algorithm.

[0081] In step S3 of this embodiment, the dual-polarization radar parameters at different radar scanning elevation angles are combined to identify the artificial hail suppression operation area;

[0082] In this step, the radar dual-polarization parameters provide important information about the shape and structure of precipitation particles, which is crucial for identifying strong updraft regions, hail embryo formation regions, and hail wet growth regions in artificial hail suppression operation areas. By analyzing these parameters, the key regions for hail formation and development can be determined more accurately, thus providing scientific basis and support for artificial hail suppression operations.

[0083] Specifically, it includes:

[0084] Horizontal reflectivity factor Z H (Unit: dBz): An index used to quantify the radar echo intensity, which represents the radar reflectivity factor in the horizontal polarization direction. The larger its value, the stronger the echo, usually indicating the presence of more precipitation particles.

[0085] Differential reflectivity Z DR (Unit: dB): The logarithmic form of the ratio of the radar reflectivity factors in the vertical polarization direction to the horizontal polarization direction. Its positive value usually indicates that the reflectivity factor in the vertical polarization direction is higher than that in the horizontal polarization direction, which is caused by the non-spherical shape of precipitation particles.

[0086] Specific differential phase K DP (Unit: ° / km): The rate of change of the phase difference between the vertical polarization and horizontal polarization signals. It reflects the non-spherical degree of precipitation particles and the complexity of the internal structure of precipitation particles.

[0087] Correlation coefficient CC (unit: dimensionless): The degree of correlation between the vertical polarization and horizontal polarization signals. A value close to 1 indicates a strong correlation between the two polarization signals, meaning that the shapes of precipitation particles are relatively consistent.

[0088] Such as Figure 2 As shown, the identification of strong updraft regions includes:

[0089] According to the dual-polarization radar parameters at different radar scanning elevation angles, identify the regional layers in each layer from the lowest layer upwards; the regional layer is the area where the Z DR value is greater than 2 dB and the area is greater than 1 square kilometer;

[0090] Combined with the altitude and longitude and latitude coordinates, merge all the regional layers to construct a three-dimensional image;

[0091] Based on the three-dimensional image, identify the region that continuously extends upwards from the lowest layer elevation angle to 1 kilometer above the 0 °C layer altitude;

[0092] Judge whether the identified region forms a continuous columnar region; if so, this columnar region is the strong updraft region.

[0093] Such as Figure 3 As shown, the identification of frozen-drop type hail embryo regions includes:

[0094] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the 0°C layer, identify the horizontal reflectivity factor Z H greater than 45 dBz and the differential reflectivity Z DR less than or equal to 0 dB region;

[0095] Judge whether the identified area is less than 5 kilometers away from the top edge of the strong updraft area; if so, proceed to the next step;

[0096] Judge whether the length, width, and height of the identified area all exceed 500 meters; if so, this area is the frozen-drop type hail embryo area.

[0097] As Figure 4 shown, the identification of the graupel type hail embryo area includes:

[0098] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the -10°C layer, identify the horizontal reflectivity factor Z H greater than 35 dBz, the differential reflectivity Z DR less than 1 dB and the correlation coefficient CC greater than 0.99 region;

[0099] Judge whether the length, width, and height of the identified area all exceed 500 meters; if so, this area is the graupel type hail embryo area.

[0100] As Figure 5 shown, the identification of the hail wet growth area includes:

[0101] Based on the dual-polarization radar parameters at different radar scanning elevation angles, in the area where the altitude is greater than the -10°C layer and less than the -20°C layer, identify the correlation coefficient CC greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H greater than 50 dBz, the differential reflectivity Z DR less than 0 dB and the specific differential phase K DP missing measurement region;

[0102] Judge whether the length, width, and height of the identified area are all greater than 500 meters; if so, this area is the hail wet growth area.

[0103] In this step, by analyzing the dual-polarization radar parameters at different radar scanning elevation angles, key areas such as the strong updraft area, frozen-drop type hail embryo area, graupel type hail embryo area, and hail wet growth area are efficiently identified.

[0104] Specifically, a strong updraft area is determined by constructing a three-dimensional image recognition of a columnar area continuously extending from the lowest elevation angle to 1 kilometer above the 0°C layer altitude; in the area where the altitude is greater than the 0°C layer, an area with a horizontal reflectivity factor greater than 45 dBz and a differential reflectivity less than or equal to 0 dB is identified as the frozen-drop type hail embryo area; in the area where the altitude is greater than the -10°C layer, an area with a horizontal reflectivity factor greater than 35 dBz, a differential reflectivity less than 1 dB, and a correlation coefficient greater than 0.99 is identified as the graupel type hail embryo area; in the area where the altitude is greater than the -10°C layer and less than the -20°C layer, an area with a correlation coefficient between 0.85 and 0.95, a horizontal reflectivity factor greater than 50 dBz, a differential reflectivity less than 0 dB, and a missing specific differential phase KDP is identified as the hail wet growth area. This not only improves the accuracy of recognition but also helps to give early warnings and effectively intervene in the formation and development process of hail.

[0105] Furthermore, after identifying the above key areas such as the strong updraft area, the frozen-drop type hail embryo area, the graupel type hail embryo area, and the hail wet growth area, the specific process of hail prevention operations includes:

[0106] According to the identified key areas, formulate a detailed operation plan, including operation time, location, type of catalyst used, etc. Prepare operation tools such as rockets or anti-aircraft guns equipped with cold cloud catalysts and ensure that the equipment is in good condition.

[0107] After identifying the strong updraft area, use a rocket or anti-aircraft gun to send the catalyst into this area. The updraft will carry the catalyst to higher places in the cloud, especially ensuring that the catalyst reaches the hail embryo area or the wet growth area to achieve the effect of inhibiting the formation and growth of hail.

[0108] Operation in the frozen-drop type hail embryo area: Once the frozen-drop type hail embryo area is identified, immediately start the operation procedure. Spray the catalyst into this area to prevent or slow down the formation of hail, and intervene as much as possible at the hail embryo stage to prevent the further development of hail.

[0109] Operation in the graupel type hail embryo area: After identifying the graupel type hail embryo area, take prompt action and drop the catalyst into this area. Change the characteristics of the graupel through catalytic action to prevent or slow down the formation of hail.

[0110] Operation in the hail wet growth area: After identifying the hail wet growth area, take prompt operation measures. Prevent or slow down the formation of large hailstones (with a diameter of more than 2 cm) by operating in this area, especially ensuring that the catalyst can effectively act on the wet growth stage of hail.

[0111] In addition, continuously monitor radar data and other meteorological parameters during the operation process to evaluate the operation effect. And according to the monitoring results, adjust the operation strategy in a timely manner to ensure the best operation effect.

[0112] Through the above operation process, this embodiment can not only accurately identify the key areas, but also take effective measures before or at the initial stage of hail formation, thereby significantly improving the effect and pertinence of artificial hail prevention operations.

[0113] Embodiment 2;

[0114] As Figure 6 shown, this embodiment provides an artificial hail prevention operation area identification system, including:

[0115] Initial artificial hail prevention operation area determination module: used to determine the initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information;

[0116] Temperature layer construction module: used to divide the grid points of the initial artificial hail prevention operation area at a preset resolution, and calculate the altitude corresponding to 0°C, -10°C, and -20°C above each grid point respectively according to the meteorological sounding second-level data, and construct the 0°C layer, -10°C layer, and -20°C layer;

[0117] Artificial hail prevention operation area identification module: used to combine the dual-polarization radar parameters at different radar scanning elevation angles to identify the artificial hail prevention operation area; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area, and hail wet growth area; among them, the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area.

[0118] This system includes three main modules: an initial artificial hail prevention operation area determination module, which is used to determine the initial operation area based on weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information; a temperature layer construction module, which is responsible for dividing the grid points of the initial operation area and calculating the altitude corresponding to 0°C, -10°C, and -20°C above each grid point by using the difference algorithm to construct the temperature layer; an artificial hail prevention operation area identification module, which identifies key areas such as strong updraft areas, frozen drop type hail embryo areas, graupel type hail embryo areas, and hail wet growth areas by analyzing the dual-polarization radar parameters at different radar scanning elevation angles. This system can efficiently and accurately identify the key areas of hail formation and provide important decision-making support for artificial hail prevention operations.

[0119] Embodiment 3;

[0120] As Figure 7 shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the above artificial hail prevention operation area identification method.

[0121] Example 4

[0122] This embodiment provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above-described method for identifying an artificial hail prevention operation area.

[0123] In the above embodiments provided by the present application, it should be understood that the disclosed methods, systems, devices, and media can be implemented in other ways. The method, system, device, and media embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. Each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0124] The units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0125] Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying an artificial hail prevention operation area, characterized in that: The following steps are involved: S1. Determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information; S2. Divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use the interpolation algorithm to calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer; S3. Combining the dual-polarization radar parameters at different radar scanning elevation angles, identifying the artificial hail prevention operation area; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area and a hail wet growth area; wherein the hail embryo formation area includes: a frozen drop type hail embryo area and a graupel type hail embryo area; the identification of the hail wet growth area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, in the area with an altitude greater than -10℃ layer and less than -20℃ layer, the identification correlation coefficient CC is greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50dBz, differential reflectivity Z DR Less than 0dB and better than differential phase K DP Areas that are missing; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a hail wet growth area.

2. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S1, determining the initial artificial hail prevention operation area based on relevant information includes: The probability of hail occurrence at time t and location (x, y) is defined as P(t, x, y) based on weather forecast data; the number of days or times of historical hail disasters at location (x, y) is defined as H(x, y) based on the artificial hail protection protection area information; the demand level of economic crops protected by artificial hail protection at location (x, y) is defined as S(x, y) based on the artificial hail protection protection area information; the traffic accessibility T(x, y) at location (x, y) is defined based on the artificial hail protection operation point information, where T(x, y) = e -αd , d represents the distance to the nearest transportation infrastructure, and α represents the rate of decrease of transportation accessibility when the distance increases; The initial artificial hail prevention operation area is determined by the risk assessment model R(t,x,y); where R(t,x,y)=β1P(t,x,y)+β2H(x,y)+β3S(x,y)+β4T(x,y), and β1, β2, β3 and β4 are weight coefficients.

3. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S2, the interpolation algorithm is used to calculate the altitudes corresponding to 0°C, -10°C and -20°C above each grid point, including: For each grid point, the height Z corresponding to the target temperature T is calculated using the interpolation algorithm based on the two height points Z1 and Z2 closest to the target temperature T in the second-level meteorological sounding data and their corresponding temperatures T1 and T2; The interpolation algorithm is used to calculate the altitudes Z0, Z2 corresponding to 0℃, -10℃ and -20℃ above each grid point. -10 and Z -20 .

4. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, the identification of the strong updraft area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, the regional layers in each layer are identified layer by layer from the lowest layer upwards; the regional layers are differential reflectivity Z DR Areas with a noise level greater than 2 dB and an area greater than 1 square kilometer; Combine the altitude and longitude and latitude coordinates to merge all the area layers and construct a three-dimensional image; Based on the three-dimensional image, identifying an area extending continuously upward from the lowest elevation layer to an altitude of more than 1 kilometer above the 0°C layer; Determine whether the identified area forms a continuous columnar area; if so, the columnar area is a strong updraft area.

5. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, the identification of frozen drop type hail embryo area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than 0℃ layer. H Greater than 45dBz and differential reflectivity Z DR Areas less than or equal to 0dB; Determine whether the identified area is less than 5 kilometers from the top edge of the strong updraft area; if so, proceed to the next step; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a frozen drop type hail embryo area.

6. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, identification of hail embryo areas of graupel types includes: Based on the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than -10℃. H Greater than 35dBz, differential reflectivity Z DR The area with a correlation coefficient CC greater than 0.99 is less than 1dB; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a sleet-type hail embryo area.

7. An artificial hail prevention operation area identification system, characterized in that: include: An initial artificial hail prevention operation area determination module is used to determine an initial artificial hail prevention operation area based on relevant information; The relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information; Temperature layer construction module: It is used to divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use the interpolation algorithm to calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer; Artificial hail prevention operation area identification module: used to identify the artificial hail prevention operation area in combination with dual polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area and hail wet growth area; wherein the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area; the hail wet growth area identification includes: According to the dual polarization radar parameters at different radar scanning elevation angles, in the area with an altitude greater than -10℃ layer and less than -20℃ layer, the identification correlation coefficient CC is greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50dBz, differential reflectivity Z DR Less than 0dB and better than differential phase K DP Areas that are missing; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a hail wet growth area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for identifying the artificial hail prevention operation area according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying an artificial hail prevention operation area according to any one of claims 1 to 6 is implemented.

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