Lightning positioning method, device and system

Abstract

The application relates to a lightning positioning method, device and system, and relates to the technical field of lightning positioning. The lightning positioning method comprises the following steps: in response to detecting that lightning occurs in a target area, obtaining monitoring data of the target area, wherein the monitoring data comprises atmospheric electric field intensity corresponding to a plurality of monitoring points in the target area; obtaining thunderstorm cloud height information of the target area; determining longitude and latitude coordinates of lightning through a pre-constructed lightning positioning model according to the monitoring data and the thunderstorm cloud height information; and determining position information of the lightning in the target area according to the longitude and latitude coordinates of the lightning. The technical scheme can improve lightning positioning precision on the basis of reducing hardware cost.

Description

Technical Field

[0001] This application relates to the field of lightning location technology, specifically to a lightning location method, device, and system. Background Technology

[0002] Lightning activity in nature, especially ground lightning, poses a significant threat to human life and property. Lightning disasters, as one of the most important natural disasters affecting human activities, have been listed as one of the "ten most serious natural disasters." Therefore, high-precision lightning location is crucial for lightning protection, disaster reduction, and emergency response.

[0003] Currently used lightning positioning technologies, such as the time difference method and the time difference of arrival method, are limited by terrain obstruction and electromagnetic interference in actual deployment, which leads to increased time synchronization errors and decreased positioning accuracy. In addition, they require dense deployment of measurement stations, resulting in high hardware costs. Summary of the Invention

[0004] The purpose of this application is to provide a lightning positioning method, apparatus, and system that can improve lightning positioning accuracy while reducing hardware costs.

[0005] To achieve the above objectives, in a first aspect, this application provides a lightning location method, comprising: in response to detecting lightning in a target area, acquiring monitoring data of the target area, the monitoring data including: atmospheric electric field intensity corresponding to multiple monitoring points in the target area; acquiring thunderstorm cloud height information of the target area; determining the latitude and longitude coordinates of the lightning based on the monitoring data and the thunderstorm cloud height information using a pre-constructed lightning location model; and determining the location information of the lightning in the target area based on the latitude and longitude coordinates of the lightning.

[0006] Optionally, acquiring the monitoring data of the target area includes: acquiring atmospheric electric field intensity data monitored by multiple atmospheric electric field meters in the target area; wherein each monitoring point is equipped with an atmospheric electric field meter, and the multiple atmospheric electric field meters are arranged in a star shape in the target area, and the technical parameters of each atmospheric electric field meter meet preset constraints, the preset constraints including at least one of the following constraints: electric field measurement range, electric field measurement distance, electric field measurement accuracy, electric field measurement resolution, response time, monitoring elements, and support for timestamp synchronization; and determining the monitoring data of the target area based on the atmospheric electric field intensity data monitored by the multiple atmospheric electric field meters.

[0007] Optionally, obtaining the thunderstorm cloud height information of the target area includes: obtaining the ambient temperature of the target area; obtaining the dew point temperature of the target area; determining the cloud base height based on the ambient temperature and the dew point temperature; and determining the thunderstorm cloud height information of the target area based on the cloud base height.

[0008] Optionally, the process of constructing the lightning location model includes: acquiring a training dataset, which includes multiple training samples, each training sample including: atmospheric electric field intensity samples, thunderstorm cloud height samples, and sample labels, wherein the sample labels are the latitude and longitude coordinates of the lightning corresponding to the atmospheric electric field intensity samples and the thunderstorm cloud height samples; and training the lightning location model to be trained based on the training dataset to obtain the pre-constructed lightning location model.

[0009] Optionally, obtaining the training dataset includes: constructing an atmospheric electric field intensity change rate model, which characterizes the relationship between the change rate of atmospheric electric field intensity, thunderstorm cloud height, and distance information, wherein the distance information is the distance between a monitoring point in the target area and the lightning location; dividing the target area into multiple grid points and generating the latitude and longitude coordinates of each grid point; simulating the situation when lightning occurs at each grid point using the atmospheric electric field intensity change rate model to obtain simulated monitoring data of the target area and simulated thunderstorm cloud height information when lightning occurs at each grid point, wherein the simulated monitoring data includes the simulated atmospheric electric field intensity corresponding to multiple monitoring points in the target area; and generating the training dataset based on the latitude and longitude coordinates of each grid point, the simulated monitoring data of the target area when lightning occurs at each grid point, and the simulated thunderstorm cloud height information.

[0010] Optionally, the step of constructing the atmospheric electric field intensity change rate model includes: acquiring historical monitoring data of the target area, wherein the historical monitoring data includes: historical atmospheric electric field intensity corresponding to multiple monitoring points in the target area; acquiring historical thunderstorm cloud height information corresponding to the historical monitoring data; acquiring historical lightning location information corresponding to the historical monitoring data; and constructing the atmospheric electric field intensity change rate model based on the historical monitoring data, the historical thunderstorm cloud height information, and the historical lightning location information.

[0011] Optionally, determining the latitude and longitude coordinates of lightning using a pre-built lightning location model based on the monitoring data and the thunderstorm cloud height information includes: filtering target monitoring data from the monitoring data whose time interval between the occurrence time of lightning is less than a preset time interval; generating a feature vector based on the target monitoring data and the thunderstorm cloud height information; and inputting the feature vector into the pre-built lightning location model to obtain the latitude and longitude coordinates of lightning output by the pre-built lightning location model.

[0012] Optionally, the lightning location method further includes: determining target devices in the target area that pose a safety risk based on the location information of the lightning in the target area and the information of each device in the target area, wherein the information of each device includes: device type and device location; and sending safety risk warning information to the monitoring terminal of the target device.

[0013] Secondly, this application provides a lightning location device, comprising: an acquisition module, configured to acquire monitoring data of the target area in response to detecting lightning in the target area, the monitoring data including atmospheric electric field intensity corresponding to multiple monitoring points in the target area; the acquisition module is further configured to acquire thunderstorm cloud height information of the target area; a location module, configured to determine the latitude and longitude coordinates of the lightning based on the monitoring data and the thunderstorm cloud height information using a pre-constructed lightning location model; the location module is further configured to determine the location information of the lightning in the target area based on the latitude and longitude coordinates of the lightning.

[0014] Thirdly, this application provides a lightning location system, comprising: multiple atmospheric electric field meters, which are respectively deployed at different monitoring points in a target area, each atmospheric electric field meter being used to monitor the atmospheric electric field intensity at each monitoring point; and a location device, which is respectively connected to the multiple atmospheric electric field meters and is used to execute the lightning location method described in the first aspect.

[0015] The above technical solution involves setting up multiple monitoring points in the target area and detecting the atmospheric electric field intensity corresponding to each point as monitoring data, as well as the thunderstorm cloud height information of the target area. Based on the monitoring data and thunderstorm cloud height information, a pre-built lightning location model is used to achieve lightning location. On the one hand, atmospheric electric field intensity is relatively easy to monitor, resulting in low hardware costs; on the other hand, lightning location achieved through a pre-built lightning location model offers high accuracy. Therefore, this technical solution can improve the accuracy of lightning location while reducing hardware costs.

[0016] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the following detailed description to explain the present application, but do not constitute a limitation thereof. In the drawings:

[0018] Figure 1 This is a structural block diagram of a lightning positioning system according to an exemplary embodiment.

[0019] Figure 2 This is a flowchart illustrating a lightning location method according to an exemplary embodiment.

[0020] Figure 3 This is an example diagram illustrating a deployment method of an atmospheric electric field meter according to an exemplary embodiment.

[0021] Figure 4 This is a schematic diagram of a model architecture according to an exemplary embodiment.

[0022] Figure 5 This is a flowchart illustrating a lightning positioning scheme according to an exemplary embodiment.

[0023] Figure 6 This is a schematic diagram showing the location of lightning at different times according to an exemplary embodiment.

[0024] Figure 7 This is a structural block diagram of a lightning positioning device according to an exemplary embodiment.

[0025] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0026] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0027] As mentioned in the background section, current lightning positioning technology suffers from high hardware costs and difficulty in guaranteeing positioning accuracy.

[0028] Based on this, this application provides a technical solution in which multiple monitoring points are set up for the target area to be monitored, and the atmospheric electric field intensity corresponding to each monitoring point is detected as monitoring data, as well as the thunderstorm cloud height information of the target area; lightning location is achieved based on the monitoring data and thunderstorm cloud height information through a pre-constructed lightning location model. On the one hand, atmospheric electric field intensity is a relatively easy data to monitor, and its hardware cost is low; on the other hand, lightning location is achieved through a pre-constructed lightning location model, resulting in high positioning accuracy.

[0029] Therefore, this technical solution can improve the accuracy of lightning positioning while reducing hardware costs.

[0030] The technical solutions of this application can be applied to various application scenarios, such as lightning warning, forest fire prevention, power plant fire prevention, etc., and are not limited here.

[0031] Figure 1 This is a structural block diagram of a lightning positioning system according to an exemplary embodiment, such as... Figure 1 As shown, a lightning location system may include: a location device and multiple atmospheric electric field meters.

[0032] Multiple atmospheric electric field meters are connected to positioning devices, and the communication between the atmospheric electric field meters and positioning devices can be via Internet of Things (IoT) communication.

[0033] The multiple atmospheric electric field meters are deployed at different monitoring points in the target area, and each atmospheric electric field meter is used to monitor the atmospheric electric field intensity at each monitoring point.

[0034] The target area can be any area that needs lightning location monitoring, such as forest areas, power plant areas, or areas that need early warning, etc., without any restrictions.

[0035] It is understandable that each atmospheric electric field meter can synchronize the atmospheric electric field intensity monitoring data of each monitoring point to the positioning equipment, so that the positioning equipment can achieve lightning positioning based on the monitoring data.

[0036] The positioning device can be a host computer, a computer, or other device with data processing and storage functions; there are no restrictions on its capabilities.

[0037] The deployment method of multiple atmospheric electric field meters will be described in detail in subsequent embodiments.

[0038] Figure 2 This is a flowchart illustrating a lightning location method according to an exemplary embodiment, which can be applied to... Figure 1 Positioning devices in, such as Figure 2As shown, the lightning location method includes the following steps:

[0039] Step S21: In response to the detection of lightning in the target area, acquire monitoring data of the target area, including the atmospheric electric field intensity corresponding to multiple monitoring points in the target area.

[0040] Step S22: Obtain the height information of thunderstorm clouds in the target area.

[0041] Step S23: Using a pre-built lightning location model, determine the latitude and longitude coordinates of the lightning based on monitoring data and thunderstorm cloud height information.

[0042] Step S24: Determine the location information of the lightning in the target area based on the latitude and longitude coordinates of the lightning.

[0043] In step S21, lightning can be detected in the target area in various ways.

[0044] As one example, real-time meteorological data from a weather station can be used to determine whether lightning is expected in a target area. As another example, lightning monitoring devices installed in the target area, such as acoustic or optical monitors, can be used to determine whether lightning is expected.

[0045] Regarding multiple monitoring points in the target area, these could be locations where atmospheric electric field meters are deployed; for example, one atmospheric electric field meter could be deployed at each monitoring point.

[0046] When selecting multiple monitoring points for the target area, it is necessary to ensure that the monitoring range, when superimposed, covers the entire area, and the spacing between each monitoring point should be determined according to the size of the target area.

[0047] Therefore, the location of monitoring points in the target area can determine the deployment method of the atmospheric electric field instrument.

[0048] As an optional implementation method, each monitoring point is equipped with an atmospheric electric field meter, and multiple atmospheric electric field meters are arranged in a star shape in the target area. The technical parameters of each atmospheric electric field meter meet the preset constraints, which include at least one of the following constraints: electric field measurement range, electric field measurement distance, electric field measurement accuracy, electric field measurement resolution, response time, monitoring elements, and support for timestamp synchronization.

[0049] Figure 3 This is an example diagram illustrating a deployment method of an atmospheric electric field meter according to an exemplary embodiment, such as... Figure 3 As shown, an atmospheric electric field meter can be placed at the geometric center of the target area, and the remaining atmospheric electric field meters can be evenly distributed around the target area, forming a star-shaped arrangement.

[0050] In one implementation, the distance between adjacent peripheral atmospheric electric field meters and the central atmospheric electric field meter is determined based on the size of the target area. As an example, the distance can be between 5 and 15 km to ensure that there are no blind spots in the monitoring range.

[0051] In one implementation, the electric field measurement range can be -100kV / m to 100kV / m; the electric field measurement distance can be a radius R ≥ 20km; the electric field measurement accuracy can be ≤3%; the electric field measurement resolution can be 10V / m; and the response time can be 1s. Furthermore, the monitored elements can include the six meteorological elements, such as temperature, humidity, wind speed, wind direction, precipitation, and air pressure. Regarding timestamp synchronization, it can be understood that all atmospheric electric field meters support timestamp synchronization.

[0052] Based on the deployment method of the electric field meter, step S21 may include: acquiring atmospheric electric field intensity data monitored by multiple atmospheric electric field meters in the target area; and determining the monitoring data of the target area based on the atmospheric electric field intensity data monitored by multiple atmospheric electric field meters.

[0053] It is understandable that after obtaining atmospheric electric field strength data from multiple atmospheric electric field meters, these monitoring data can be preprocessed using methods such as timestamp synchronization processing, and then the preprocessed data can be integrated into monitoring data, which includes the atmospheric electric field strength of each monitoring point.

[0054] By unifying the timestamps, we can ensure that the electric field intensity data at the same moment correspond to the same lightning development stage (such as the leader stage and the return stroke stage), thus avoiding feature extraction errors caused by time deviations.

[0055] In step S22, the height information of thunderstorm clouds in the target area is obtained.

[0056] In one implementation, the height of thunderstorm clouds can be directly detected using devices such as laser astronomy meters.

[0057] In another implementation, to reduce hardware costs, the height of thunderstorm clouds can be determined by estimating the height of the thunderstorm clouds.

[0058] Therefore, as an optional implementation, step S22 includes: acquiring the ambient temperature of the target area; acquiring the dew point temperature of the target area; determining the cloud base height based on the ambient temperature and the dew point temperature; and determining the thunderstorm cloud height information of the target area based on the cloud base height.

[0059] In one implementation, the ambient temperature and dew point temperature can be the temperatures monitored by a weather station, which can be obtained directly.

[0060] In one implementation, the cloud base height can be calculated using ambient temperature and dew point temperature, and then used as an approximate thunderstorm cloud height.

[0061] In one implementation, the cloud base height H can be expressed as: Where T represents the ambient temperature, T d This represents the dew point temperature. This calculation formula is suitable for estimating the height of low-to-mid-tropospheric clouds, with an error of 200m, and meets the model input requirements.

[0062] In one implementation, the dew point temperature can also be calculated using the Magnus-Tetens approximation method, and the specific calculation formula is as follows: , Among them, ambient temperature T and dew point temperature T d The unit is ℃, the relative humidity RH is a percentage, and the constants a and b are: a=17.27; b=237.7℃.

[0063] Furthermore, the estimated cloud base height can be directly determined as the thunderstorm cloud height, or a certain error value can be added to or subtracted from the cloud base height to obtain the result as the thunderstorm cloud height.

[0064] In step S23, feature vectors can be generated through feature extraction, and then the feature vectors can be input into a pre-built lightning location model to obtain the latitude and longitude coordinates of the lightning output by the pre-built lightning location model.

[0065] Therefore, as an optional implementation, step S23 includes: filtering out target monitoring data from the monitoring data whose time interval between the occurrence time of lightning is less than a preset time interval; generating a feature vector based on the target monitoring data and thunderstorm cloud height information; and inputting the feature vector into a pre-built lightning location model to obtain the latitude and longitude coordinates of the lightning output by the pre-built lightning location model.

[0066] In one implementation, the target monitoring data, whose time interval between the occurrence of lightning and the occurrence of lightning is less than a preset time interval, can be the target monitoring data of the lead phase, which can be within 1 to 3 seconds after the lightning occurs.

[0067] In one implementation, electric field intensity features can be extracted first based on target monitoring data, and then integrated with thunderstorm cloud height information to form a feature vector.

[0068] Therefore, we can first draw an electric field intensity waveform based on the atmospheric electric field intensity data of each monitoring point, and then extract the electric field intensity characteristics of the leader stage. The leader stage is the stage of drastic changes in the electric field 1-3 seconds before the lightning occurs, and its characteristics are the most significant.

[0069] Regarding the characteristics of the electric field intensity, it can be the leading peak electric field intensity E peak This represents the maximum electric field value during the lead phase. Therefore, the resulting lightning characteristic vector can be: Where n is the number of monitoring points. Further, the lightning feature vector is integrated with the thunderstorm cloud height to obtain the feature vector of the input model: .

[0070] After inputting the feature vector into the pre-built lightning localization model, the pre-built lightning localization model can directly output the latitude and longitude coordinates of the lightning.

[0071] The internal processing logic of this lightning location model will be described in the subsequent model construction examples.

[0072] As an optional implementation method, the process of constructing a lightning location model includes: acquiring a training dataset, which includes multiple training samples, each training sample including: atmospheric electric field intensity samples, thunderstorm cloud height samples, and sample labels, the sample labels being the latitude and longitude coordinates of the lightning corresponding to the atmospheric electric field intensity samples and thunderstorm cloud height samples; and training the lightning location model to be trained based on the training dataset to obtain a pre-constructed lightning location model.

[0073] In one implementation, in addition to the training dataset, a test dataset can be constructed to test the pre-built lightning localization model, thereby continuously improving the model's accuracy and generalization ability.

[0074] In one implementation, an atmospheric electric field intensity change rate model can be pre-constructed, and a training dataset can be generated based on this model.

[0075] Therefore, in one implementation, obtaining the training dataset may include: constructing an atmospheric electric field intensity change rate model, which characterizes the relationship between the change rate of atmospheric electric field intensity, thunderstorm cloud height, and distance information, wherein the distance information is the distance between the monitoring point in the target area and the lightning location; dividing the target area into multiple grid points and generating the latitude and longitude coordinates of each grid point; simulating the situation when lightning occurs at each grid point using the atmospheric electric field intensity change rate model, obtaining simulated monitoring data of the target area when lightning occurs at each grid point and simulated thunderstorm cloud height information, wherein the simulated monitoring data includes the simulated atmospheric electric field intensity corresponding to multiple monitoring points in the target area; and generating a training dataset based on the latitude and longitude coordinates of each grid point, the simulated monitoring data of the target area when lightning occurs at each grid point, and the simulated thunderstorm cloud height information.

[0076] In one implementation, an atmospheric electric field intensity change rate model is used to characterize the change in atmospheric electric field intensity with respect to thunderstorm cloud height and distance information. Therefore, this change rate model can represent the relationship between the change rate of atmospheric electric field intensity, thunderstorm cloud height, and distance information. The distance information refers to the distance between the monitoring point in the target area and the lightning location.

[0077] In one implementation, the atmospheric electric field intensity change rate model can be constructed based on real historical data. Therefore, constructing the atmospheric electric field intensity change rate model may include: acquiring historical monitoring data for a target area, including historical atmospheric electric field intensities corresponding to multiple monitoring points in the target area; acquiring historical thunderstorm cloud height information corresponding to the historical monitoring data; acquiring historical lightning location information corresponding to the historical monitoring data; and constructing the atmospheric electric field intensity change rate model based on the historical monitoring data, historical thunderstorm cloud height information, and historical lightning location information.

[0078] It is understandable that historical monitoring data, historical thunderstorm cloud height information, and historical lightning location information need to be time-synchronized. That is, for the same time t, there need to be corresponding historical monitoring data, historical thunderstorm cloud height, and historical lightning location information.

[0079] Based on real historical data, we can analyze the rate of change of atmospheric electric field intensity as it changes with the height and distance of thunderstorm clouds.

[0080] Assuming the lightning location is Monitoring points are If the charge of a thunderstorm cloud is equivalent to a point charge Q, and the height of the thunderstorm cloud is H, then the distance information can be expressed as: Furthermore, the atmospheric electric field strength at the monitoring point satisfies the derived relationship of Coulomb's law: Where E is the atmospheric electric field strength, and k is the electrostatic constant. Therefore, the rate of change of electric field intensity with horizontal distance, γ, can be defined as: The physical meaning of this rate of change is: the closer to the lightning, the faster the electric field strength decays with distance (the larger the absolute value of γ), which is consistent with the actual propagation law of lightning electric fields.

[0081] Therefore, by using historical lightning event data (lightning location, atmospheric electric field intensity E at monitoring points, and thunderstorm cloud height) of the target area, the least squares method is used to fit γ with E, H, and The relationship can be used to calibrate the model and obtain the γ model parameters adapted to the target region.

[0082] Regarding the grid division of the target area, the target area is divided into one grid for every 0.001° of latitude and longitude. Since 1° of longitude corresponds to approximately 111.32km, and 0.001° is approximately 111m, each grid size can be approximately 111m × 111m (an approximation, balancing computational accuracy and efficiency). Each grid point is numbered (e.g., Gxy, where x is the latitude direction number and y is the longitude direction number), and the latitude and longitude coordinates (x, y, y) of each grid point are recorded. j ,y j (), as a "label benchmark" for subsequent positioning.

[0083] Furthermore, for each grid point (x) in the division j ,y j Assuming the lightning occurred at that point, the electric field strength at each monitoring point is calculated using the following parameters to generate the dataset required for model training:

[0084] Atmospheric electric field strength E: range of 1000-50000V / m, step (value interval) of 100V / m (covering all stages of the thunderstorm life cycle).

[0085] Thunderstorm cloud height H: range of 500-4000m, in 100m increments (covering thunderstorm clouds at different heights).

[0086] Simulation calculation process: for each grid point By iterating through the atmospheric electric field intensity E and the thunderstorm cloud height H, and substituting them into the γ model, the values ​​at each monitoring point are calculated in conjunction with H. electric field strength This forms the "input features" of each sample. ) and "output labels" In the "Input Features" field, n represents the number of atmospheric electric field meters. For example, for grid points (30.004°, 120.004°), with E = 25000 V / m and H = 1000 m, the distance to monitoring point A is... Substituting the values ​​into the γ model, the electric field intensity monitored at point A can be calculated. The final dataset size needs to be ≥10^6 to ensure sufficient training capacity for the model.

[0087] Figure 4 This is a schematic diagram of a model architecture according to an exemplary embodiment, such as... Figure 4 As shown, the lightning localization model includes an input layer, an attention layer, a hidden layer, and an output layer. The following section describes the construction process of the lightning localization model based on this architecture.

[0088] The construction of a lightning location model can be understood as constructing a fully connected neural network model, that is, constructing a fully connected neural network with an attention mechanism to establish a mapping relationship between "electric field intensity characteristics - thunderstorm cloud height - lightning location".

[0089] Regarding the input layer, the number of neurons is n+1 (n is the number of atmospheric electric field meters, corresponding to the electric field strength E1-E at each monitoring point). n One neuron corresponds to the height H of a thunderstorm cloud. The input features can be Min-Max normalized (normalized to [0,1]), as shown in the formula: Where X is the input feature before normalization, X norm X represents the normalized feature. min and X max This represents the normalized parameters. Normalization eliminates the impact of dimensional differences (atmospheric electric field intensity E is V / m, thunderstorm cloud height H is m) on model training.

[0090] Furthermore, during model application, the feature vectors input to the model are also normalized. Regarding the attention mechanism layer, it applies local activation units to the electric field intensity features at each monitoring point and calculates the correlation weight between each feature and the lightning location. This achieves "weighting of important features and deweighting of secondary features," with the specific calculation formula as follows: ,in, (Weight matrix) and (Bias vector) is a learnable parameter, and the Softmax function ensures that all The sum is 1. For example, monitoring points closer to lightning. The changes are more significant. Larger features allow the model to focus more on high-contribution features.

[0091] Regarding the hidden layers, there are two fully connected hidden layers: the first layer has 64 neurons, and the second layer has 128 neurons. Both layers use the ReLU activation function, introducing nonlinear feature mapping capabilities to fit the complex nonlinear relationship between electric field intensity and lightning location. The formulas for calculating the output h1 of the first layer and the output h2 of the second layer are as follows: ; ;in, The attention-weighted feature vector ( W1, b1, W2, and b2 are the hidden layer parameters.

[0092] Between the two hidden layers, a regularization layer can be added with a regularization rate of 0.5. This randomly discards some neurons to prevent overfitting and improve generalization ability. The output layer consists of two neurons, corresponding to the longitude (x) and latitude (y) of the lightning location. A linear activation function is used to directly output the latitude and longitude coordinates (in degrees).

[0093] During model training, a loss function and an optimizer can be introduced. The mean squared error can be used as the loss function to measure the prediction error of the regression task, as shown in the formula: Where m is the number of samples, and k represents any sample. Predict coordinates for the model. These are the actual coordinates of the grid points. The optimizer can use Adam (adaptive learning rate optimizer) with an initial learning rate of 0.001, which adaptively adjusts the learning rate to accelerate training convergence.

[0094] Model training and evaluation can include: dataset partitioning, model training, and model evaluation.

[0095] Regarding dataset partitioning, the constructed dataset can be divided into training, validation, and test sets in a 7:2:1 ratio to ensure that the grid points in each set are evenly distributed (covering different locations in the target area) and to avoid data bias.

[0096] For model training, batch gradient descent (batch size=32) is used with 100 iterations. After each training round, the model performance is evaluated using a validation set. If the validation set loss does not decrease for 5 consecutive rounds, an early stopping mechanism is triggered to prevent overfitting.

[0097] Regarding model evaluation, mean absolute error and root mean square error can be used as evaluation metrics, with the following formula: , Where MAE stands for Mean Absolute Error and RMSE stands for Root Mean Square Error. When MAE < 0.0005° (approximately 55m) and RMSE < 0.0008° (approximately 88m) on the test set, the model meets the accuracy requirements. The optimal model parameters are saved to obtain the final lightning positioning model.

[0098] Furthermore, based on the constructed lightning location model, actual lightning location can be performed according to the lightning location method described in the aforementioned embodiments, including: input feature processing, model processing, and output lightning location results.

[0099] Figure 5 This is a flowchart illustrating a lightning positioning scheme according to an exemplary embodiment, such as... Figure 5 As shown, the technical solution can be divided into two parts: lightning location model construction and actual lightning location.

[0100] The process of building a lightning location model involves: deploying an atmospheric electric field meter, dividing the target area into grids, building a model of the rate of change of atmospheric electric field intensity, building a dataset, building a fully connected neural network model, and training and evaluating the model.

[0101] The actual lightning location process involves: lightning feature extraction, input feature processing (lightning features and thunderstorm cloud height), model processing, and output lightning location results.

[0102] In one implementation, the latitude and longitude coordinates of lightning output by the lightning location model can be directly used as the location information of the lightning.

[0103] In another implementation, the lightning's latitude and longitude coordinates can be converted into actual geographical location, and combined with the grid division results, the specific location of the lightning within the target area can be determined, with a positioning accuracy of up to 111m within the grid.

[0104] It is understandable that the location information of lightning can also be applied in different application scenarios.

[0105] Therefore, as an optional implementation, the method further includes: determining the target devices in the target area that pose a safety risk based on the location information of lightning in the target area and the information of each device in the target area, wherein the information of each device includes: device type and device location; and sending safety risk warning information to the monitoring terminal of the target device.

[0106] This application method is suitable for scenarios where the target area involves multiple devices that may be affected by lightning. Therefore, by combining the location information of lightning in the target area with the information of each device in the target area, the devices with safety risks can be assessed, and then safety risk warning information can be sent to the monitoring terminal of the target device, so that users at the monitoring terminal can pay attention to the status of the target device in a timely manner.

[0107] Equipment that can be affected by lightning, such as electrical equipment and power line equipment, is not specified here.

[0108] In one embodiment, the type of equipment and its location can jointly determine whether there is a safety risk. For example, the closer the equipment is to the lightning strike location, the higher the safety risk.

[0109] In addition to this scenario, in some forest fire prevention and fire early warning scenarios, the risk of fire can be assessed and fire early warnings can be issued based on the situation of the target area and the location of lightning.

[0110] The technical solutions of the embodiments of this application can achieve the following technical effects:

[0111] High positioning accuracy: Through latitude and longitude grid division of 0.001° (111m×111m), regional adaptation γ model and fully connected neural network model, the positioning accuracy can reach 111m grid. By increasing the latitude and longitude division density of the target area, it can also meet the needs of higher precision scenarios.

[0112] High real-time performance: Fully connected neural networks have fast inference speed (single sample inference time <0.001s), no need for complex time difference calculations in data processing, and rapid output response.

[0113] Low deployment cost: Only a small number of atmospheric electric field meters are needed to meet the lightning location requirements, and the hardware cost is significantly lower than that of deploying traditional lightning location meters. In addition, the already deployed atmospheric electric field meters can be effectively utilized.

[0114] To verify the effectiveness of the technical solutions in the embodiments of this application, the following detailed explanation is provided in conjunction with specific experimental cases.

[0115] Experimental Area and Equipment Deployment: The target area was selected as XX city, with latitude and longitude ranging from 30.475°N to 30.845°N and 103.858°E to 104.328°E. Seven atmospheric electric field meters were arranged in a star configuration (refer to...). Figure 3 ):

[0116] Central monitoring point - Atmospheric electric field meter G (30.657762°, 104.065946°); peripheral monitoring points - Atmospheric electric field meter A (30.739637°, 104.190855°), peripheral monitoring point - Atmospheric electric field meter B (30.617619°, 104.20926°), peripheral monitoring point - Atmospheric electric field meter C (30.571998°, 104.069757°), peripheral monitoring point - Atmospheric electric field meter D (30.674516°, 103.937104°), peripheral monitoring point - Atmospheric electric field meter E (30.758981°, 103.958416°), peripheral monitoring point - Atmospheric electric field meter F (30.789554°, 104.109565°).

[0117] The monitoring points are roughly evenly distributed in a star shape with the central monitoring point - atmospheric electric field meter G as the center, with a spacing of 12-14km. The electric field measurement range is -100kV / m to 100kV / m, and the time synchronization error is <0.5s.

[0118] Grid generation and construction of the atmospheric electric field intensity change rate model (γ): The target area was divided into 173,900 grid points at 0.001° intervals, with each grid approximately 111m × 111m. Based on lightning calibration data from June to August 2024 (lightning locations were provided by the local lightning location network, with an accuracy of <50m), an atmospheric electric field intensity change rate model (γ) was fitted, with a goodness of fit R² = 0.93, which closely reflects the electric field change pattern in the region.

[0119] Dataset and Model Training: Traversing all grid points, 36 H values ​​(500-4000m, 100m step), and 491 E values ​​(1000-50000V / m, 100V / m step), 173900×36×491=316400400 samples were generated. The model was trained using the PyTorch framework. After 100 training rounds, the test set MAE=0.00042 and RMSE=0.00068, meeting the accuracy requirements.

[0120] Actual location test: Taking three thunderstorm weather periods on May 4, 2025, from 23:00:00 to 23:30:00, on May 7, 2025, from 20:03:58 to 20:33:59, and on May 7, 2025, from 21:01:17 to 21:39:33, the error range between the model's located lightning position and the actual lightning position is 0.02-3.8km.

[0121] Regarding the range of error in lightning location Figure 6 This is a schematic diagram illustrating the location of lightning at different times according to an exemplary embodiment. Figure 6 In the diagram, the horizontal and vertical axes represent latitude and longitude, respectively, and adjacent points represent the lightning location located by the model and the actual lightning location, respectively. It can be seen that the error range between the lightning location located by the model and the actual lightning location meets the requirements.

[0122] Figure 7 This is a structural block diagram of a lightning positioning device 700 according to an exemplary embodiment, such as... Figure 7 As shown, the lightning positioning device 700 includes:

[0123] The acquisition module 701 is used to acquire monitoring data of the target area in response to the detection of lightning in the target area. The monitoring data includes the atmospheric electric field intensity corresponding to multiple monitoring points in the target area.

[0124] The acquisition module 701 is also used to: acquire the height information of thunderstorm clouds in the target area.

[0125] The positioning module 702 is used to: determine the latitude and longitude coordinates of lightning based on the monitoring data and the thunderstorm cloud height information using a pre-built lightning positioning model.

[0126] The positioning module 702 is also used to: determine the location information of the lightning in the target area based on the latitude and longitude coordinates of the lightning.

[0127] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0128] Figure 8 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example... Figure 8 As shown, the electronic device 800 may include a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0129] The processor 801 controls the overall operation of the electronic device 800 to complete all or part of the steps in the aforementioned lightning positioning method. The memory 802 stores various types of data to support the operation of the electronic device 800. This data may include, for example, instructions for any application or method operating on the electronic device 800, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0130] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the lightning positioning method described above.

[0131] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the lightning location method described above. For example, the computer-readable storage medium may be the memory 802 including program instructions described above, which may be executed by the processor 801 of the electronic device 800 to complete the lightning location method described above.

[0132] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a processor, which, when executed by the processor, implements the steps of the lightning positioning method described above.

[0133] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solution of this application, and these simple modifications all fall within the protection scope of this application.

[0134] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this application will not describe the various possible combinations separately.

[0135] Furthermore, various different implementations of this application can be combined in any way, as long as they do not violate the spirit of this application, they should also be regarded as the content disclosed in this application.

Claims

1. A lightning location method, characterized in that, include: In response to the detection of lightning in the target area, monitoring data of the target area is acquired, including the atmospheric electric field intensity corresponding to multiple monitoring points in the target area. Obtain thunderstorm cloud height information for the target area; The latitude and longitude coordinates of lightning are determined by using a pre-built lightning location model, based on the monitoring data and the thunderstorm cloud height information. Based on the latitude and longitude coordinates of the lightning, determine the location information of the lightning in the target area; The construction process of the lightning location model includes: An atmospheric electric field intensity change rate model is constructed. This model is used to characterize the relationship between the change rate of atmospheric electric field intensity, thunderstorm cloud height, and distance information. The distance information refers to the distance between the monitoring point in the target area and the lightning location. A training dataset is obtained using the atmospheric electric field intensity change rate model. The training dataset includes multiple training samples. Each training sample includes: atmospheric electric field intensity sample, thunderstorm cloud height sample, and sample label. The sample label is the latitude and longitude coordinates of the lightning corresponding to the atmospheric electric field intensity sample and the thunderstorm cloud height sample. Based on the training dataset, the lightning localization model to be trained is trained to obtain the pre-built lightning localization model.

2. The lightning location method according to claim 1, characterized in that, The acquisition of monitoring data for the target area includes: The atmospheric electric field intensity data monitored by multiple atmospheric electric field meters in the target area are obtained. Each monitoring point is equipped with an atmospheric electric field meter, and the multiple atmospheric electric field meters are arranged in a star shape in the target area. The technical parameters of each atmospheric electric field meter meet preset constraints, which include at least one of the following constraints: electric field measurement range, electric field measurement distance, electric field measurement accuracy, electric field measurement resolution, response time, monitoring elements, and support for timestamp synchronization. The monitoring data for the target area are determined based on the atmospheric electric field intensity data monitored by multiple atmospheric electric field meters.

3. The lightning location method according to claim 1, characterized in that, The acquisition of thunderstorm cloud height information for the target area includes: Obtain the ambient temperature of the target area; Obtain the dew point temperature of the target area; The cloud base height is determined based on the ambient temperature and the dew point temperature. Based on the cloud base height, determine the thunderstorm cloud height information for the target area.

4. The lightning location method according to claim 1, characterized in that, The process of obtaining the training dataset using the atmospheric electric field intensity change rate model includes: The target area is divided into multiple grid points, and the latitude and longitude coordinates of each grid point are generated; The atmospheric electric field intensity change rate model is used to simulate the situation when lightning occurs at each grid point, and the simulated monitoring data of the target area and the simulated thunderstorm cloud height information are obtained when lightning occurs at each grid point. The simulated monitoring data includes the simulated atmospheric electric field intensity corresponding to multiple monitoring points in the target area. The training dataset is generated based on the latitude and longitude coordinates of each grid point, the simulated monitoring data of the target area when lightning occurs at each grid point, and the simulated thunderstorm cloud height information.

5. The lightning location method according to claim 1, characterized in that, The construction of the atmospheric electric field intensity change rate model includes: Obtain historical monitoring data for the target area, including historical atmospheric electric field intensities corresponding to multiple monitoring points in the target area. Obtain historical thunderstorm cloud height information corresponding to the historical monitoring data; Obtain historical lightning location information corresponding to the historical monitoring data; Based on the historical monitoring data, the historical thunderstorm cloud height information, and the historical lightning location information, the atmospheric electric field intensity change rate model is constructed.

6. The lightning location method according to claim 1, characterized in that, The process of determining the latitude and longitude coordinates of lightning using a pre-built lightning location model, based on the monitoring data and the thunderstorm cloud height information, includes: From the monitoring data, target monitoring data with a time interval less than a preset time interval are selected; Based on the target monitoring data and the thunderstorm cloud height information, a feature vector is generated; The feature vector is input into the pre-built lightning location model to obtain the latitude and longitude coordinates of the lightning output by the pre-built lightning location model.

7. The lightning location method according to any one of claims 1 to 6, characterized in that, The lightning location method also includes: Based on the location information of the lightning in the target area and the information of each device in the target area, the target devices in the target area that pose a security risk are identified. The information of each device includes: device type and device location. Send security risk warning information to the monitoring terminal of the target device.

8. A lightning positioning device, characterized in that, include: The acquisition module is used to acquire monitoring data of the target area in response to the detection of lightning in the target area. The monitoring data includes the atmospheric electric field intensity corresponding to multiple monitoring points in the target area. The acquisition module is also used to: acquire thunderstorm cloud height information of the target area; The positioning module is used to: determine the latitude and longitude coordinates of lightning based on the monitoring data and the thunderstorm cloud height information using a pre-built lightning positioning model; The positioning module is further configured to: determine the location information of the lightning in the target area based on the latitude and longitude coordinates of the lightning; The construction module is used to: construct an atmospheric electric field intensity change rate model, which is used to characterize the relationship between the change rate of atmospheric electric field intensity, thunderstorm cloud height, and distance information, wherein the distance information is the distance between the monitoring point in the target area and the lightning location; A training dataset is obtained using the atmospheric electric field intensity change rate model. The training dataset includes multiple training samples. Each training sample includes: atmospheric electric field intensity sample, thunderstorm cloud height sample, and sample label. The sample label is the latitude and longitude coordinates of the lightning corresponding to the atmospheric electric field intensity sample and the thunderstorm cloud height sample. Based on the training dataset, the lightning localization model to be trained is trained to obtain the pre-built lightning localization model.

9. A lightning positioning system, characterized in that, include: Multiple atmospheric electric field meters are deployed at different monitoring points in the target area, and each atmospheric electric field meter is used to monitor the atmospheric electric field intensity at each monitoring point. The positioning device is connected to the plurality of atmospheric electric field meters respectively, and is used to perform the lightning positioning method according to any one of claims 1 to 7.

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