Regional lightning prediction method, device, equipment and medium

Abstract

The present invention discloses a regional lightning prediction method, device, equipment and medium, belonging to the field of lightning prediction technology. The method includes: collecting lightning strike point data and radar echo intensity data of a target area in a first time period; preprocessing the lightning strike point data and radar echo intensity data; obtaining gridded lightning data of the target area based on the preprocessed lightning strike point data; constructing a lightning threat probability score matrix of the target area based on the gridded lightning data of the target area; inputting the preprocessed radar echo intensity data and the lightning threat probability score matrix of the target area into a pre-trained regional lightning prediction model to obtain the lightning prediction result of the target area in a second time period. The regional lightning prediction method proposed in the present invention uses the lightning threat probability score and radar echo data as input, and achieves good regional lightning hazard prediction through a machine learning model.

Description

Technical Field

[0001] The present invention belongs to the technical field of lightning forecasting and relates to a method, device, equipment and medium for regional lightning prediction. Background Art

[0002] Lightning is a powerful electrical discharge in the atmosphere, typically occurring during severe convective weather. It is caused by the accumulation and release of charge between clouds or between clouds and the ground, and its main characteristics include intense currents, extremely high temperatures, and bright light. Lightning not only releases enormous amounts of energy in a short period of time but is also accompanied by a dramatic acoustic phenomenon, namely thunder. Lightning has significant impacts on the environment and human activities, and is a serious natural disaster that can cause fires, damage buildings and electronic equipment, and even cause casualties, threatening socioeconomic development and human safety.

[0003] Previous lightning prediction studies have primarily relied on detected scattered lightning strike point information to assess lightning risk. These traditional methods typically make predictions based on the distribution of lightning strike points. However, due to the complexity and randomness of lightning phenomena, when lightning strikes, its current and energy can span thousands of meters, and its impact range far exceeds a single strike point. Therefore, predicting lightning threats based solely on strike point information often fails to fully reflect the actual risk to surrounding areas. In addition, the data correlations between meteorological elements are extremely complex, and traditional meteorological prediction methods typically rely on linear models or empirical rules, which struggle to capture the potential nonlinear relationships and complex interactions in meteorological data. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a regional lightning prediction method, device, equipment and medium, which can accurately predict the probability and threat of regional lightning based on regional lightning threat probability scoring and machine learning models.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a regional lightning prediction method, comprising:

[0007] Collect lightning strike point data and radar echo intensity data in the target area during the first time period;

[0008] Preprocessing the lightning strike point data and radar echo intensity data;

[0009] Based on the pre-processed lightning strike data, the target area is gridded to obtain gridded lightning data of the target area;

[0010] Based on the gridded lightning data of the target area, the lightning threat probability score of each grid point in the target area is obtained, and the lightning threat probability score matrix of the target area is constructed;

[0011] The preprocessed radar echo intensity data and the lightning threat probability matrix of the target area are input into the pre-trained regional lightning forecast model to obtain the lightning prediction results of the target area in the second time period.

[0012] Furthermore, the lightning strike point data includes text data of the lightning strike point latitude and longitude, time, intensity and type.

[0013] Furthermore, the lightning strike point data is pre-processed, including: accumulating the lightning strike point data according to a preset time period;

[0014] The radar echo intensity data is preprocessed, including: processing abnormal values ​​and interpolating missing values ​​in the radar echo intensity data; and converting the processed radar echo intensity data into grayscale image data through a function mapping method.

[0015] Furthermore, the lightning threat probability score of each grid point in the target area is obtained, including:

[0016] ,

[0017] ,

[0018] in, Representing grid points Lightning threat probability score; Indicates the The location of the lightning event, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations of lightning events; Indicates the lightning threat attenuation parameter, whose value is set according to the lightning impact distance; Indicates the occurrence location and grid point The distance is less than or equal to The total number of lightning events; by lightning type, Representing grid points Cloud flash threat probability score, Representing grid points Ground-to-ground lightning threat probability score; represents the cloud-flash threat attenuation parameter, Indicates the ground-to-ground lightning threat attenuation parameter; Indicates the occurrence location and grid point The distance is less than or equal to The set of cloud flash event locations, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations where lightning ground events occur.

[0019] Furthermore, the processed radar echo intensity data is converted into grayscale image data through a function mapping method, including:

[0020] ,

[0021] in, is the grayscale value of the pixel after conversion, is the radar echo intensity value, is the maximum value in the radar echo intensity data; is the minimum value in the radar echo intensity data.

[0022] Furthermore, the pre-processed radar echo intensity data and the lightning threat probability score matrix of the target area are input into the pre-trained regional lightning forecast model to obtain the lightning forecast results of the target area in the second time period, including:

[0023] Inputting the preprocessed radar echo intensity data into a pre-trained first regional lightning prediction model to obtain a first feature matrix;

[0024] Inputting the lightning threat probability score matrix of the target area into a pre-trained second-area lightning forecast model to obtain a second feature matrix;

[0025] Concatenate the first feature matrix and the second feature matrix to obtain feature fusion data;

[0026] Inputting the feature fusion data into a linear convolution layer, and outputting a lightning prediction result for the target area in the second time period;

[0027] The lightning prediction result of the target area in the second time period includes the lightning threat probability of each grid point in the target area in the second time period.

[0028] Furthermore, the first regional lightning forecast model and the second regional lightning forecast model include 3D-Unet machine learning models.

[0029] In a second aspect, the present invention further provides a regional lightning prediction device, comprising:

[0030] A data acquisition module is used to collect lightning strike point data and radar echo intensity data in the target area during the first time period;

[0031] A data preprocessing module, used for preprocessing the lightning strike point data and radar echo intensity data;

[0032] A gridded lightning data acquisition module is used to grid the target area based on the pre-processed lightning strike data to obtain the gridded lightning data of the target area;

[0033] A lightning threat probability score matrix construction module is used to obtain the lightning threat probability score of each grid point in the target area based on the gridded lightning data of the target area, and to construct the lightning threat probability score matrix of the target area;

[0034] The lightning prediction result acquisition module is used to input the preprocessed radar echo intensity data and the lightning threat probability score matrix of the target area into the pre-trained regional lightning prediction model to obtain the lightning prediction result of the target area in the second time period.

[0035] In a third aspect, the present invention further provides a computer device, comprising:

[0036] memory for storing computer programs;

[0037] A processor is configured to execute the computer program to implement the steps of the above-mentioned regional lightning prediction method.

[0038] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the steps of the above-mentioned regional lightning prediction method.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The regional lightning prediction method provided by the present invention converts lightning strike data into lightning threat probability score data through a mathematical model, which can comprehensively assess the actual threat of lightning to a specific area and has practical value in meteorological warning, disaster prevention and mitigation. By comprehensively analyzing factors such as the spatial distribution of lightning events, energy release, and weather conditions, the lightning threat probability in a specific area can be effectively calculated. By comprehensively analyzing lightning strike data and radar echo data, changes in severe convective weather can be dynamically monitored. In combination with advanced machine learning models, the real-time update of computer-automated lightning forecasts is used to improve the accuracy and timeliness of lightning forecasts. The regional lightning forecast model can output the lightning threat probability prediction results for each grid point in the monitoring area, clarify the threat range of lightning, and facilitate users to intuitively understand the specific distribution of lightning risks, and then take targeted preventive and emergency measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic flow chart of a regional lightning prediction method provided by an embodiment of the present invention;

[0042] Figure 2This is a schematic diagram of gridded lightning data of a target area in an embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the principle of lightning threat probability score calculation in an embodiment of the present invention;

[0044] Figure 4 Schematic diagrams comparing gridded lightning data of a target area and lightning threat probability score data of the target area in an embodiment of the present invention, wherein (a) is a schematic diagram of gridded lightning data of a target area, and (b) is a schematic diagram of lightning threat probability score data of a target area;

[0045] Figure 5 Schematic diagram of the structure of a regional lightning forecast model in an embodiment of the present invention;

[0046] Figure 6 Schematic diagram of lightning prediction results at different classification thresholds and different prediction time periods of a regional lightning prediction method using the hit rate POD as an evaluation indicator in an embodiment of the present invention;

[0047] Figure 7 Schematic diagram of lightning prediction results at different classification thresholds and different prediction time periods using a regional lightning prediction method with the false alarm rate (FAR) as the evaluation indicator in an embodiment of the present invention;

[0048] Figure 8 A schematic structural diagram of a regional lightning prediction device provided by an embodiment of the present invention;

[0049] Figure 9 This is a diagram of the internal structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. The embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations of the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0051] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0052] Example 1:

[0053] like Figures 1 to 7 As shown, an embodiment of the present invention provides a regional lightning prediction method. Figure 1 The flowchart of the regional lightning prediction method is shown in FIG. This flowchart only shows the logical sequence of the method of this embodiment. In other possible embodiments of the present invention, different methods may be used without conflict. Figure 1 The steps shown or described are accomplished in the order shown.

[0054] The regional lightning prediction method provided in this embodiment can be applied to a terminal and can be executed by a regional lightning prediction device. The device can be implemented by software and / or hardware, and the device can be integrated into the terminal.

[0055] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps 1 to 4, wherein:

[0056] Step 1: Collect lightning strike point data and radar echo intensity data in the target area in the first time period.

[0057] Among them, the lightning landing point data can be obtained through a lightning detection station, and the data includes text data such as the latitude and longitude, time, intensity and type of the lightning landing point; the radar echo intensity data can be obtained through meteorological radar detection, and the data is in matrix form.

[0058] Meteorological radar is one of the most effective tools for monitoring severe convective weather systems. Weather systems with strong and active vertical convective motion often appear as high-reflectivity echo areas on radar echo maps. Lightning is closely related to severe convective motion. Locations with higher echo intensity in radar echoes can be used to correspond to locations with a high probability of lightning occurrence, thereby determining the probability distribution of lightning disasters in severe convective weather across the region.

[0059] In order to achieve lightning prediction, the first time period and the second time period of the present invention are usually a historical time period and a future time period based on the current time.

[0060] Step 2: Preprocess the lightning strike point data and radar echo intensity data.

[0061] Among them, the lightning strike point data is pre-processed, including: accumulating the lightning strike point data according to a preset time period, that is, accumulating lightning strike point data for a longer period of time in a single data frame, while the time difference between adjacent data frames is shorter. After processing, the lightning strike point data can reflect the overall change trend of regional lightning.

[0062] The calculation formula for accumulating lightning strike data by time period is:

[0063] ,

[0064] in, represents the total data set after processing; Indicates the number of time periods; Represents the cumulative results of a single time period; Represents raw lightning strike data.

[0065] The radar echo intensity data is preprocessed, including: using a linear interpolation method to process outliers and perform interpolation replacement on missing values ​​in the radar echo intensity data; and converting the processed radar echo intensity data into grayscale image data through a function mapping method to obtain radar grayscale image data.

[0066] Among them, the calculation formula for converting the processed radar echo intensity data into grayscale image value through the function mapping method is:

[0067] ,

[0068] in, is the grayscale value of the pixel after conversion, is the radar echo intensity value (in dBZ), is the maximum value in the radar echo intensity data; is the minimum value in the radar echo intensity data.

[0069] Step 3: Based on the preprocessed lightning strike data, the target area is gridded to obtain gridded lightning data of the target area.

[0070] Gridding refers to dividing the space into multiple small areas (grid points), each grid point represents a specific geographical location. Figure 2 As shown in the figure, based on the preprocessed lightning strike data, the target area is divided into grids, and the grid points are assigned values ​​according to the energy intensity of the lightning detected at the grid points. The values ​​are normalized to obtain the gridded lightning data of the target area, which is used for the subsequent calculation of the lightning threat probability score.

[0071] In this embodiment of the present invention, the area to be predicted is equivalent to the target area. Therefore, gridding the target area is equivalent to gridding the area to be predicted. In other embodiments, to comprehensively consider the lightning threat probability score at the boundary of the area to be predicted, the target area can be expanded so that the area to be predicted is located at the center of the target area. In this case, the target area and the area to be predicted are gridded separately, and lightning prediction for the area to be predicted is performed using the gridded lightning data of the target area.

[0072] Step 4: Based on the gridded lightning data of the target area, obtain the lightning threat probability score of each grid point in the target area and construct the lightning threat probability score matrix of the target area.

[0073] The calculation formula for the lightning threat probability score of each grid point in the target area is:

[0074] ,

[0075] ,

[0076] in, Representing grid points Lightning Threat Probability Score; Indicates the The location of the lightning event, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations of lightning events; Indicates the lightning threat attenuation parameter, whose value is set according to the lightning impact distance; Indicates the occurrence location and grid point The distance is less than or equal to The total number of lightning events. According to the location of lightning, lightning is divided into cloud-to-ground lightning and ground-to-cloud lightning. Representing grid points Cloud flash threat probability score, Representing grid points Ground-to-ground lightning threat probability score; represents the cloud-flash threat attenuation parameter, Indicates the ground-to-ground lightning threat attenuation parameter; Indicates the occurrence location and grid point The distance is less than or equal to The set of cloud flash event locations, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations where lightning ground events occur.

[0077] like Figure 3 As shown, the present invention designs a calculation formula for the lightning threat probability score of each grid point based on the idea that the closer the lightning is, the greater the threat level. The threat level of each lightning event near this grid point to this grid point is calculated in sequence, and the accumulated results are used as the lightning threat probability score of this grid point.

[0078] According to the lightning threat probability score of each grid point in the target area, a lightning threat probability score matrix of the target area is constructed.

[0079] Figure 4A schematic diagram comparing the gridded lightning data of the target area and the lightning threat probability score data of the target area is shown. It can be seen that a small number of scattered lightning flashes correspond to low threat probability values, while areas with dense lightning flashes correspond to high threat probability values. It can be seen that the lightning threat probability score calculation method designed by the present invention is reasonable and effective.

[0080] Step 5: Input the preprocessed radar echo intensity data and the lightning threat probability score matrix of the target area into the pre-trained regional lightning forecast model to obtain the lightning forecast results of the target area in the second time period.

[0081] Step 5 specifically includes:

[0082] Inputting the preprocessed radar echo intensity data, i.e., the radar grayscale image data, into a pre-trained first regional lightning prediction model to obtain a first feature matrix;

[0083] Inputting the lightning threat probability score matrix data of the target area into the pre-trained second-area lightning forecast model to obtain a second feature matrix;

[0084] The first feature matrix and the second feature matrix are spliced ​​to obtain feature fusion data;

[0085] The feature fusion data is input into the linear convolution layer to output the lightning prediction result of the target area in the second time period;

[0086] The lightning prediction result of the target area in the second time period includes the lightning threat probability of each grid point in the target area in the second time period.

[0087] In an embodiment of the present invention, both the first-region lightning forecast model and the second-region lightning forecast model use the 3D-Unet machine learning model. The network structure of 3D-Unet is an extension of the traditional U-Net to process three-dimensional data, and mainly includes an encoder, a bottleneck layer, a decoder, and an output layer. The encoder part consists of multiple 3D convolution blocks and maximum pooling layers, which are used to extract spatial features and gradually reduce the spatial size of the feature map; the bottleneck layer is located between the encoder and the decoder, and further extracts features through deep 3D convolution; the decoder part uses 3D transposed convolution for upsampling, and splices the feature map in the encoder with the feature map in the decoder through jump connections to restore detailed information; the output layer maps the feature map of the decoder to the target space to generate a prediction result.

[0088] The 3D-Unet structure can effectively capture the spatial and temporal features of three-dimensional data. Figure 5As shown, the first and second regional lightning forecast models of the present invention use the 3D-Unet model with the last two convolutional layers removed, a network structure model named the 3D-Unet Core module. The radar grayscale image and the lightning threat probability score matrix are input to the 3D-Unet Core module, which outputs multiple feature maps from the intermediate stage. The feature maps of the two channels are spliced ​​together in the sequence dimension, using a method similar to the feature skipping method in Unet.

[0089] The feature fusion data is linearly combined using a 1×1×1 3D convolution kernel to achieve information interaction and perform a second feature extraction. Finally, the feature map is output through the Relu linear activation function to achieve dimensionality reduction and obtain the final two-dimensional predicted image.

[0090] In this embodiment of the present invention, the first and second regional lightning forecast models are pre-trained, validated, and evaluated using the target region's historical lightning threat probability score data and historical radar echo image data from the past two years. The historical lightning threat probability score data and historical radar echo image data are divided into training and test sets in a 4:1 ratio. It was found that lightning data accounted for a relatively small proportion of the collected historical data, with lightning events accounting for approximately 4% and non-lightning events accounting for approximately 96%. To address the extreme imbalance in the dataset, data sampling was performed using data from time periods close to those in which lightning events occurred, thereby increasing the proportion of lightning events in the overall data.

[0091] like Figure 6 and Figure 7 As shown, the hit rate POD and false alarm rate FAR are used as evaluation indicators to verify the effect of the regional lightning prediction method of the present invention.

[0092] Lightning prediction is performed by setting the second time period to 30 minutes, 1 hour, 1 hour and 30 minutes, and 2 hours in the future, respectively, based on the current time. As can be seen from the lightning prediction result evaluation chart, the method performance does not significantly decrease as the prediction time increases. Furthermore, lowering the classification threshold can improve the prediction hit rate, while raising the classification threshold can reduce the false alarm rate. The present invention can adjust the threshold to obtain corresponding model performance for different requirements.

[0093] Example 2:

[0094] Based on the same inventive concept as Example 1, this embodiment of the present invention also provides a regional lightning prediction device for implementing the aforementioned regional lightning prediction method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the regional lightning prediction device embodiment provided below can be found in the aforementioned limitations of the regional lightning prediction method and will not be further elaborated here.

[0095] like Figure 8 As shown, an embodiment of the present invention provides a regional lightning prediction device, comprising:

[0096] A data acquisition module is used to collect lightning strike point data and radar echo intensity data in the target area during the first time period;

[0097] A data preprocessing module, used for preprocessing the lightning strike point data and radar echo intensity data;

[0098] A gridded lightning data acquisition module is used to grid the target area based on the pre-processed lightning strike data to obtain the gridded lightning data of the target area;

[0099] A lightning threat probability score matrix construction module is used to obtain the lightning threat probability score of each grid point in the target area based on the gridded lightning data of the target area, and to construct the lightning threat probability score matrix of the target area;

[0100] The lightning prediction result acquisition module is used to input the preprocessed radar echo intensity data and the lightning threat probability score matrix of the target area into the pre-trained regional lightning prediction model to obtain the lightning prediction result of the target area in the second time period.

[0101] Example 3:

[0102] The embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The I / O 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 via a network connection. When the computer program is executed by the processor, it implements the regional lightning prediction method in the aforementioned embodiment.

[0103] Those skilled in the art will understand that Figure 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0104] Example 4:

[0105] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the following method:

[0106] Collect lightning strike point data and radar echo intensity data in the target area during the first time period;

[0107] Preprocessing the lightning strike point data and radar echo intensity data;

[0108] Based on the pre-processed lightning strike data, the target area is gridded to obtain gridded lightning data of the target area;

[0109] Based on the gridded lightning data of the target area, the lightning threat probability score of each grid point in the target area is obtained, and the lightning threat probability score matrix of the target area is constructed;

[0110] The preprocessed radar echo intensity data and the lightning threat probability matrix of the target area are input into the pre-trained regional lightning forecast model to obtain the lightning prediction results of the target area in the second time period.

[0111] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A regional lightning prediction method, characterized in that: include: Collect lightning strike point data and radar echo intensity data in the target area during the first time period; Preprocessing the lightning strike point data and radar echo intensity data; Based on the pre-processed lightning strike data, the target area is gridded to obtain gridded lightning data of the target area; Based on the gridded lightning data of the target area, the lightning threat probability score of each grid point in the target area is obtained, and the lightning threat probability score matrix of the target area is constructed; The pre-processed radar echo intensity data and the lightning threat probability score matrix of the target area are input into the pre-trained regional lightning forecast model to obtain the lightning forecast results of the target area in the second time period; The lightning threat probability score for each grid point in the target area is obtained, including: ， ， in, Representing grid points Lightning Threat Probability Score; Indicates the The location of the lightning event, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations of lightning events; Indicates the lightning threat attenuation parameter, whose value is set according to the lightning impact distance; Indicates the occurrence location and grid point The distance is less than or equal to The total number of lightning events; by lightning type, Representing grid points Cloud flash threat probability score, Representing grid points Ground-to-ground lightning threat probability score; represents the cloud-flash threat attenuation parameter, Indicates the ground-to-ground lightning threat attenuation parameter; Indicates the occurrence location and grid point The distance is less than or equal to The set of cloud flash event locations, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations where lightning ground events occur.

2. The regional lightning prediction method according to claim 1, characterized in that: The lightning strike point data includes text data of the lightning strike point latitude and longitude, time, intensity and type.

3. The regional lightning prediction method according to claim 2, characterized in that: Preprocessing the lightning strike data includes: accumulating the lightning strike data according to a preset time period; The radar echo intensity data is preprocessed, including: processing abnormal values ​​and interpolating missing values ​​in the radar echo intensity data; and converting the processed radar echo intensity data into grayscale image data through a function mapping method.

4. The regional lightning prediction method according to claim 3, characterized in that: The processed radar echo intensity data is converted into grayscale image data through the function mapping method, including: ， in, is the grayscale value of the pixel after conversion, is the radar echo intensity value, is the maximum value in the radar echo intensity data; is the minimum value in the radar echo intensity data.

5. The regional lightning prediction method according to claim 4, characterized in that: The pre-processed radar echo intensity data and the lightning threat probability score matrix of the target area are input into the pre-trained regional lightning forecast model to obtain the lightning forecast results for the target area in the second time period, including: Inputting the preprocessed radar echo intensity data into a pre-trained first regional lightning prediction model to obtain a first feature matrix; Inputting the lightning threat probability score matrix of the target area into a pre-trained second-area lightning forecast model to obtain a second feature matrix; Concatenate the first feature matrix and the second feature matrix to obtain feature fusion data; Inputting the feature fusion data into a linear convolution layer, and outputting a lightning prediction result for the target area in the second time period; The lightning prediction result of the target area in the second time period includes the lightning threat probability of each grid point in the target area in the second time period.

6. The regional lightning prediction method according to claim 5, characterized in that: The first regional lightning forecast model and the second regional lightning forecast model include 3D-Unet machine learning models.

7. A regional lightning prediction device, characterized in that: include: A data acquisition module is used to collect lightning strike point data and radar echo intensity data in the target area during the first time period; A data preprocessing module, used for preprocessing the lightning strike point data and radar echo intensity data; A gridded lightning data acquisition module is used to grid the target area based on the pre-processed lightning strike data to obtain the gridded lightning data of the target area; A lightning threat probability score matrix construction module is used to obtain the lightning threat probability score of each grid point in the target area based on the gridded lightning data of the target area, and to construct the lightning threat probability score matrix of the target area; The lightning threat probability score for each grid point in the target area is obtained, including: ， ， in, Representing grid points Lightning Threat Probability Score; Indicates the The location of the lightning event, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations of lightning events; Indicates the lightning threat attenuation parameter, whose value is set according to the lightning impact distance; Indicates the occurrence location and grid point The distance is less than or equal to The total number of lightning events; by lightning type, Representing grid points Cloud flash threat probability score, Representing grid points Ground-to-ground lightning threat probability score; represents the cloud-flash threat attenuation parameter, Indicates the ground-to-ground lightning threat attenuation parameter; Indicates the occurrence location and grid point The distance is less than or equal to The set of cloud flash event locations, Indicates the occurrence location and grid point The distance is less than or equal to The set of locations of ground-to-ground lightning events; The lightning prediction result acquisition module is used to input the preprocessed radar echo intensity data and the lightning threat probability score matrix of the target area into the pre-trained regional lightning prediction model to obtain the lightning prediction result of the target area in the second time period.

8. A computer device, characterized in that: include: memory for storing computer programs; A processor, configured to execute the computer program to implement the steps of the regional lightning prediction method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the regional lightning prediction method according to any one of claims 1 to 6 are implemented.

Images