A lightning positioning method and device based on multiple corrections
By employing multiple correction methods, including time delay factor correction, uncertainty adjustment weights, and genetic algorithm solutions, the problem of low positioning accuracy in existing lightning location methods has been solved, achieving high-precision lightning location calculation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUBEI QIJIN ELECTRIC POWER TECH CO LTD
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-26
Smart Images
Figure CN122283591A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of lightning location technology, and in particular to a lightning location method and apparatus based on multiple corrections. Background Technology
[0002] Lightning disasters are characterized by their suddenness and destructiveness, and can cause significant losses to fields such as power, aviation, and rail transportation. Lightning location technology is a core means of preventing lightning disasters.
[0003] Existing lightning location methods do not perform bias corrections during the calculation of lightning arrival time, resulting in data bias; they do not consider the monitoring errors of monitoring stations; the solution for lightning location is prone to getting trapped in local optima, leading to inaccurate lightning location; and they do not correct for uncertainty and geometric quality of lightning location, resulting in low positioning accuracy and affecting the effectiveness of lightning disaster prevention. Summary of the Invention
[0004] To address the aforementioned problems, in a first aspect, the present invention provides a lightning location method based on multiple corrections, comprising: Lightning waveforms of the target lightning event are collected by all monitoring stations within the target area to obtain initial waveform data. The initial waveform data is then preprocessed to obtain optimized waveform data. Acquire historical lightning monitoring data for the target area, and calculate the lightning arrival time based on the historical lightning monitoring data and optimized waveform data to obtain the initial lightning arrival time. Set a time delay factor and correct the initial lightning arrival time to obtain the final lightning arrival time; A lightning analysis model is constructed. Optimized waveform data is input into the lightning analysis model to identify lightning events and calculate uncertainties, thereby obtaining the uncertainty of the monitoring station. Adjustment weights are then set based on the uncertainty. A lightning arrival time difference objective function is constructed based on the final lightning arrival time and adjustment weights. The lightning arrival time difference objective function is solved by a genetic algorithm to obtain the initial lightning location set. The final lightning locations are obtained by correcting and accumulating the initial lightning location set using uncertainty.
[0005] In one embodiment, the step of acquiring historical lightning monitoring data of the target area and calculating the lightning arrival time based on the historical lightning monitoring data and optimized waveform data to obtain the initial lightning arrival time includes: Historical waveform data is obtained from historical lightning monitoring data. The matching score for each moment is calculated based on the optimized waveform data and the historical waveform data. The moment with the highest matching score is taken as the initial lightning arrival time.
[0006] In one embodiment, setting a time delay factor to correct the initial lightning arrival time and obtain the final lightning arrival time includes: Historical lightning locations, arrival times, and three-dimensional coordinates of monitoring stations are obtained from historical lightning monitoring data. Based on the historical lightning locations, the three-dimensional coordinates of the monitoring station, and the historical lightning arrival times, a time delay factor is set, and the initial lightning arrival time is subtracted from the time delay factor to obtain the final lightning arrival time.
[0007] In one embodiment, the step of inputting optimized waveform data into a lightning analysis model for lightning event identification and uncertainty calculation to obtain the uncertainty of the monitoring station includes: A lightning analysis model is constructed based on a mask layer, encoder, decoder and fully connected layer. The optimized waveform data is passed through the mask layer, encoder and decoder in sequence to output lightning feature vectors. Lightning feature vectors are input into a fully connected layer for lightning event identification to obtain predicted lightning events. Obtain the predicted probability of a lightning event, the code of the predicted lightning event, and the code of the target lightning event; The uncertainty of the monitoring station is calculated based on the predicted probability of the lightning event, the code of the predicted lightning event, and the code of the target lightning event.
[0008] In one embodiment, constructing the objective function for the time difference of lightning arrival based on the final lightning arrival time and adjusted weights includes: The estimated lightning location and estimated lightning occurrence time are used as independent variables in the objective function of lightning arrival time difference. The objective function of lightning arrival time difference is constructed based on the estimated lightning location, estimated lightning occurrence time, final lightning arrival time, and adjustment weights.
[0009] In one embodiment, the step of solving the objective function of lightning arrival time difference using a genetic algorithm to obtain the initial lightning location set includes: All monitoring stations are grouped to obtain multiple monitoring station combinations; For a monitoring station ensemble, the estimated lightning location and the estimated lightning occurrence time are treated as individuals, and a population is constructed using these individuals; The individual population is input into the monitoring station combination corresponding to the lightning arrival time difference objective function, the lightning arrival time difference value of each individual is calculated, and the individual with the smallest lightning arrival time difference value is taken as the current optimal individual; The population is updated a preset number of times through crossover and mutation operations. The current best individual with the smallest difference in lightning arrival time is taken as the final best individual, and the estimated lightning location corresponding to the final best individual is taken as the initial lightning location corresponding to the monitoring station combination. The initial lightning location set is formed by combining the initial lightning locations corresponding to all monitoring stations.
[0010] In one embodiment, the step of correcting and accumulating the initial lightning location set based on uncertainty to obtain the final lightning location includes: Obtain the three-dimensional coordinates of each monitoring station within the monitoring station combination corresponding to each initial lightning location in the initial lightning location set, and calculate the geometric mass of each monitoring station combination based on the three-dimensional coordinates; The average uncertainty of each monitoring station combination is obtained based on the uncertainty calculation. Based on the geometric mass and average uncertainty, the correction parameters for each monitoring station combination are calculated. The corrected lightning position is obtained by multiplying the correction parameter by the corresponding initial lightning position, and the final lightning position is obtained by summing all the corrected lightning positions.
[0011] Secondly, the present invention provides a lightning location device based on multiple corrections, used to implement the lightning location method based on multiple corrections, the device comprising: The optimized waveform data acquisition module is used to collect lightning waveforms of the target lightning event through all monitoring stations in the target area, obtain initial waveform data, and preprocess the initial waveform data to obtain optimized waveform data. The initial lightning arrival time acquisition module is used to acquire historical lightning monitoring data of the target area, calculate the lightning arrival time based on the historical lightning monitoring data and optimized waveform data, and obtain the initial lightning arrival time. The final lightning arrival time acquisition module is used to set a delay factor and correct the initial lightning arrival time to obtain the final lightning arrival time. The adjustment weight acquisition module is used to construct a lightning analysis model. It inputs optimized waveform data into the lightning analysis model to identify lightning events and calculate uncertainties, obtains the uncertainty of the monitoring station, and sets adjustment weights based on the uncertainty. The initial lightning location set acquisition module is used to construct a lightning arrival time difference objective function based on the final lightning arrival time and adjustment weights, and solve the lightning arrival time difference objective function through a genetic algorithm to obtain the initial lightning location set. The final lightning location acquisition module is used to correct and accumulate the initial lightning location set through uncertainty to obtain the final lightning location.
[0012] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lightning location method based on multiple corrections.
[0013] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the lightning location method based on multiple corrections.
[0014] The present invention has the following beneficial effects: 1. A time delay factor correction is applied to the lightning arrival time. The difference between theoretical and measured propagation time delays is calculated using historical data to compensate for the systematic deviation of the initial arrival time, making the calculation of lightning arrival time more accurate and reducing the error in the positioning time dimension from the source. The monitoring station's monitoring error is reduced by adjusting the weights of the uncertainty settings. A genetic algorithm is used to solve the objective function of the lightning arrival time difference. Through crossover and mutation operations, the population is iteratively updated to find the globally optimal initial lightning location, avoiding the problem of traditional algorithms getting trapped in local optima and improving the accuracy of the location solution. The initial lightning location set is corrected for both uncertainty and geometric quality to avoid the local error of a single monitoring station combination, greatly improving the positioning accuracy of lightning locations.
[0015] 2. Construct a lightning analysis model based on a lightweight Transformer encoding and decoding architecture to achieve accurate identification of lightning events while quantitatively calculating the uncertainty of each monitoring station, intuitively reflecting the credibility of single-station data in identifying target lightning events; adjust the weights according to the uncertainty, so that the data of high-credibility monitoring stations have a higher weight in the TDOA objective function, and the interference of low-credibility is greatly reduced, thereby improving the accuracy of positioning calculation from the data weighting level.
[0016] 3. By incorporating geometric quality into the calculation of correction parameters, the spatial distribution quality of the monitoring station combination is quantified, ensuring that the correction parameters balance data reliability and geometric spatial constraints. All monitoring stations are grouped into multiple monitoring station combinations, and the initial lightning positions of each combination are solved separately before correction and accumulation. This avoids the impact of individual monitoring station failures or data anomalies on the overall positioning results, allowing the system to still output valid positioning results when some monitoring station data fails, significantly improving anti-interference and fault tolerance. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2This is a structural diagram of the device according to an embodiment of the present invention. Detailed Implementation
[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0020] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.
[0021] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.
[0022] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.
[0023] Reference Figure 1 This invention provides a lightning location method based on multiple corrections, comprising: The S100 collects lightning waveform data of the target lightning event through all monitoring stations within the target area, obtains initial waveform data, and preprocesses the initial waveform data to obtain optimized waveform data.
[0024] In some embodiments, all lightning monitoring stations deployed within the target area are equipped with high-precision lightning electromagnetic pulse sensors and high-speed digital acquisition units. Simultaneously, each monitoring station integrates a BeiDou satellite timing module to achieve time synchronization across the entire area. When a lightning event occurs, the sensors at each monitoring station capture the analog electromagnetic pulse signal generated by the lightning discharge in real time. The acquisition unit rapidly converts the analog signal into digital waveform data, forming the initial waveform data for each monitoring station. After the data collection is completed, each monitoring station transmits its initial waveform data to the regional lightning location center in real time via a dedicated wireless communication network, forming a global initial waveform dataset of the target lightning event.
[0025] The initial waveform data after aggregation undergoes refined processing including noise reduction, baseline correction, filtering, and normalization. The core of this process is based on an adaptive noise estimation algorithm to purify the basic signal. A series of auxiliary operations are then used to eliminate various interferences and hardware deviations. Finally, combined with validity verification, optimized waveform data is obtained. The expression is: Where i is the monitoring station number, and t is the sampling time. For initial waveform data, For adaptive noise estimation, This provides optimized waveform data for monitoring station i at time t.
[0026] The S200 acquires historical lightning monitoring data of the target area, calculates the lightning arrival time based on the historical lightning monitoring data and optimized waveform data, and obtains the initial lightning arrival time.
[0027] In some embodiments, acquiring historical lightning monitoring data of the target area, and calculating the lightning arrival time based on the historical lightning monitoring data and optimized waveform data to obtain the initial lightning arrival time includes: Historical waveform data is obtained from historical lightning monitoring data. The matching score for each moment is calculated based on the optimized waveform data and the historical waveform data. The moment with the highest matching score is taken as the initial lightning arrival time.
[0028] In some embodiments, a three-dimensional index is constructed in the historical lightning monitoring database built by the regional lightning location center, based on the unique monitoring station number + lightning event timestamp + lightning type, to store the preprocessed historical waveform data of all monitoring stations. Furthermore, the data format is consistent with the optimized waveform, and the expression for the matching score is: Where k is the number of the historical waveform data. for Match score at any given moment for The k-th historical waveform data at time point k.
[0029] Iterate through all time offsets Corresponding matching score The time offset corresponding to the maximum matching score is selected as the initial lightning arrival time of the monitoring station for the target lightning event. The initial lightning arrival time of monitoring station i is... .
[0030] The S300 sets a delay factor, which corrects the initial lightning arrival time to obtain the final lightning arrival time.
[0031] In some embodiments, setting a time delay factor to correct the initial lightning arrival time and obtain the final lightning arrival time includes: Historical lightning locations, arrival times, and three-dimensional coordinates of monitoring stations are obtained from historical lightning monitoring data. Based on the historical lightning locations, the three-dimensional coordinates of the monitoring station, and the historical lightning arrival times, a time delay factor is set, and the initial lightning arrival time is subtracted from the time delay factor to obtain the final lightning arrival time.
[0032] In some embodiments, the three types of core data required for calibration are first accurately retrieved from the historical lightning monitoring database, and the format and dimensions are standardized and aligned. Using the unique lightning event identifier and monitoring station number as an index, historical lightning locations and arrival times are retrieved, along with the three-dimensional coordinates of all valid monitoring stations within the target area. The delay factor is the difference between the theoretical propagation delay and the measured arrival delay calculated based on historical data. It is used to compensate for the systematic deviation of the initial lightning arrival time. The expression for the delay factor is:
[0033] Where p represents the historical lightning location, Let p be the time delay factor for the historical lightning location p to reach monitoring station i. Let p be the historical arrival time of lightning at monitoring station i, and c be the reference velocity. , Let be the three-dimensional coordinates of monitoring station i.
[0034] The difference between the initial lightning arrival time and the time delay factor is the final lightning arrival time. The final lightning arrival time for monitoring station i is... .
[0035] The S400 constructs a lightning analysis model, inputs optimized waveform data into the lightning analysis model to identify lightning events and calculate uncertainties, obtains the uncertainty of the monitoring station, and sets adjustment weights based on the uncertainty.
[0036] In some embodiments, the step of inputting optimized waveform data into a lightning analysis model for lightning event identification and uncertainty calculation to obtain the uncertainty of the monitoring station includes: A lightning analysis model is constructed based on a mask layer, encoder, decoder and fully connected layer. The optimized waveform data is passed through the mask layer, encoder and decoder in sequence to output lightning feature vectors. Lightning feature vectors are input into a fully connected layer for lightning event identification to obtain predicted lightning events. Obtain the predicted probability of a lightning event, the code of the predicted lightning event, and the code of the target lightning event; The uncertainty of the monitoring station is calculated based on the predicted probability of the lightning event, the code of the predicted lightning event, and the code of the target lightning event.
[0037] In some embodiments, the lightning analysis model adopts a lightweight Transformer encoding / decoding architecture, and the functions and parameter settings of each layer are as follows: The core function of the masking layer is to shield invalid segments in the optimized waveform data. A binary mask matrix is set, with valid sampling points marked as 1 and invalid segments marked as 0, allowing only valid data to enter subsequent layer calculations and avoiding interference from invalid information in feature extraction. The encoder uses a 2-layer Transformer encoder, and the decoder uses a matching 2-layer Transformer decoder. A cross-attention mechanism focuses on the core features output by the encoder, ultimately condensing the intermediate feature matrix into a fixed-length lightning feature vector. The fully connected layer is divided into two sub-layers to adapt to the requirements of lightning event classification. The first sub-layer performs dimensionality reduction on the feature vector without linear mapping; the second sub-layer uses Softmax activation and outputs the probability value for each event type.
[0038] The lightning analysis model employs self-supervised pre-training, which uses mask reconstruction loss, as shown in the formula: in, This is the loss value. For the masking operator of the masking layer, For encoder, For decoder, Let y be the lightning feature vector, and y be the lightning event label.
[0039] Optimized waveform data from a single monitoring station The waveform is normalized to the [-1,1] interval and converted into a one-dimensional tensor recognizable by the model. After masking, a mask matrix corresponding to the waveform is generated. After masking invalid segments, the masked waveform tensor is input into the encoder. The encoder captures the temporal correlation features of the waveform, the decoder extracts the core features, and finally outputs the lightning feature vector of the monitoring station. .
[0040] Lightning feature vector The input is a fully connected layer, the second fully connected layer is activated by Softmax, and the output is the probability distribution of M types of lightning events. The event type corresponding to the maximum probability is taken as the predicted lightning event. Its probability value is the predicted probability. .
[0041] Standardized lightning event codes are pre-established for target lightning events. The corresponding code is Predicting lightning events The corresponding code is .
[0042] Uncertainty The larger the coding difference and the lower the prediction probability, the higher the uncertainty. The calculation formula is: in, Let represent the uncertainty of monitoring station i.
[0043] The expression for adjusting the weights is: in, The adjustment weight for monitoring station i, , to prevent division by zero of constants.
[0044] The S500 constructs a lightning arrival time difference objective function based on the final lightning arrival time and adjustment weights. It then solves the lightning arrival time difference objective function using a genetic algorithm to obtain the initial lightning location set.
[0045] In some embodiments, constructing the objective function for the lightning arrival time difference based on the final lightning arrival time and adjusted weights includes: The estimated lightning location and estimated lightning occurrence time are used as independent variables in the objective function of lightning arrival time difference. The objective function of lightning arrival time difference is constructed based on the estimated lightning location, estimated lightning occurrence time, final lightning arrival time, and adjustment weights.
[0046] In some embodiments, the core function of the Lightning Time Difference of Arrival (TDOA) is to quantify the deviation between the theoretical arrival time and the measured final arrival time. The objective function of the Lightning Time Difference of Arrival is... The expression is: Where N is the number of monitoring stations, and p is the estimated location of the lightning strike. To estimate the time of lightning occurrence, For the final arrival time of lightning at monitoring station i, The adjustment weight for monitoring station i, Let be the three-dimensional coordinates of monitoring station i, and c be the reference velocity.
[0047] In some embodiments, the step of solving the objective function of lightning arrival time difference using a genetic algorithm to obtain the initial lightning location set includes: All monitoring stations are grouped to obtain multiple monitoring station combinations; For a monitoring station ensemble, the estimated lightning location and the estimated lightning occurrence time are treated as individuals, and a population is constructed using these individuals; The individual population is input into the monitoring station combination corresponding to the lightning arrival time difference objective function, the lightning arrival time difference value of each individual is calculated, and the individual with the smallest lightning arrival time difference value is taken as the current optimal individual; The population is updated a preset number of times through crossover and mutation operations. The current best individual with the smallest difference in lightning arrival time is taken as the final best individual, and the estimated lightning location corresponding to the final best individual is taken as the initial lightning location corresponding to the monitoring station combination. The initial lightning location set is formed by combining the initial lightning locations corresponding to all monitoring stations.
[0048] In some embodiments, a monitoring station group consists of at least two monitoring stations, and the intersection area is formed by the DOA (Direction of Arrival) of each monitoring station within the monitoring station group. Take the intersection area The center is used as the initial value of the objective function for the lightning arrival time difference.
[0049] Construct a list of monitoring station combinations ,in Representing the A combination of monitoring stations, for a single monitoring station combination The estimated lightning location and estimated lightning occurrence time are used as individuals to construct a population.
[0050] Input each individual in the population into the objective function of lightning arrival time difference corresponding to that combination, calculate the lightning arrival time difference value, i.e., the value of the objective function of lightning arrival time difference. The smaller the value, the better the individual. Iterate through all individuals in the population. .
[0051] Based on the crossover probability of the parent population Perform a real crossover operation to generate offspring individuals; then sort the offspring individuals according to their mutation probability. Perform real-number mutation to introduce random perturbation; replace inferior individuals in the parent population with the offspring resulting from crossover and mutation. After the iteration, the current best individual with the smallest lightning arrival time difference is selected. For monitoring station combination The ultimate optimal individual. For all Repeat the above steps for each monitoring station combination to obtain the initial lightning location corresponding to each monitoring station combination.
[0052] S600 corrects and accumulates the initial lightning location set by adjusting the uncertainty to obtain the final lightning location.
[0053] In some embodiments, the step of correcting and accumulating the initial lightning location set using uncertainty to obtain the final lightning location includes: Obtain the three-dimensional coordinates of each monitoring station within the monitoring station combination corresponding to each initial lightning location in the initial lightning location set, and calculate the geometric mass of each monitoring station combination based on the three-dimensional coordinates; The average uncertainty of each monitoring station combination is obtained based on the uncertainty calculation. Based on the geometric mass and average uncertainty, the correction parameters for each monitoring station combination are calculated. The corrected lightning position is obtained by multiplying the correction parameter by the corresponding initial lightning position, and the final lightning position is obtained by summing all the corrected lightning positions.
[0054] In some embodiments, the geometrical density (GDOP) of the monitoring station assembly is characterized by the geometrical density attenuation factor. The smaller the GDOP value, the more reasonable the spatial distribution of the monitoring stations within the assembly, the stronger the geometrical constraint on the location of lightning strikes, and the higher the geometrical density.
[0055] First, calculate the geometric center of the monitoring station cluster. Construct a relative position matrix for each monitoring station based on its geometric center. Solve for the covariance matrix of the relative position matrix. After inverting the covariance matrix, the trace is calculated, and the square root of the trace is the geometric mass of the monitoring station assembly. The formula is: .
[0056] The correction parameter characterizes the weight of the initial lightning location corresponding to each monitoring station combination in the final lightning location calculation. Better geometric quality and lower average uncertainty correspond to a larger correction parameter and a higher weight for the initial lightning location. The expression for the correction parameter is: in, The correction parameters for monitoring station combination j are: Let j be the geometric mass of the monitoring station combination. Let m be the average uncertainty of monitoring station combination j, and m be the number of monitoring station combinations. To prevent division by zero constants, This is the adjustment coefficient.
[0057] Final lightning location ,in, Let J be the initial lightning location for monitoring station combination j.
[0058] Reference Figure 2 This invention provides a lightning location device 20 based on multiple corrections, used to implement a lightning location method based on multiple corrections. The device includes: The optimized waveform data acquisition module 21 is used to collect lightning waveforms of the target lightning event through all monitoring stations in the target area, obtain initial waveform data, and preprocess the initial waveform data to obtain optimized waveform data. The initial lightning arrival time acquisition module 22 is used to acquire historical lightning monitoring data of the target area, calculate the lightning arrival time based on the historical lightning monitoring data and optimized waveform data, and obtain the initial lightning arrival time. The final lightning arrival time acquisition module 23 is used to set a delay factor and correct the initial lightning arrival time through the delay factor to obtain the final lightning arrival time. The adjustment weight acquisition module 24 is used to construct a lightning analysis model. It inputs optimized waveform data into the lightning analysis model to identify lightning events and calculate uncertainties, obtains the uncertainty of the monitoring station, and sets adjustment weights based on the uncertainty. The initial lightning location set acquisition module 25 is used to construct a lightning arrival time difference objective function based on the final lightning arrival time and adjustment weights, and solve the lightning arrival time difference objective function through a genetic algorithm to obtain the initial lightning location set. The final lightning location acquisition module 26 is used to correct and accumulate the initial lightning location set through uncertainty to obtain the final lightning location.
[0059] This application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the lightning location method based on multiple corrections according to any of the above schemes.
[0060] Specifically, the processor may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.
[0061] Memory can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and also random access memory (RAM) or flash memory; and / or wired / wireless communication links.
[0062] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the lightning location method based on multiple corrections as described above. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method as described in the embodiments of this application.
[0063] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.
[0064] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A lightning location method based on multiple corrections, characterized in that, include: Lightning waveforms of the target lightning event are collected by all monitoring stations within the target area to obtain initial waveform data. The initial waveform data is then preprocessed to obtain optimized waveform data. Acquire historical lightning monitoring data for the target area, and calculate the lightning arrival time based on the historical lightning monitoring data and optimized waveform data to obtain the initial lightning arrival time. Set a time delay factor and correct the initial lightning arrival time to obtain the final lightning arrival time; A lightning analysis model is constructed. Optimized waveform data is input into the lightning analysis model to identify lightning events and calculate uncertainties, thereby obtaining the uncertainty of the monitoring station. Adjustment weights are then set based on the uncertainty. A lightning arrival time difference objective function is constructed based on the final lightning arrival time and adjustment weights. The lightning arrival time difference objective function is solved by a genetic algorithm to obtain the initial lightning location set. The final lightning locations are obtained by correcting and accumulating the initial lightning location set using uncertainty.
2. The lightning location method based on multiple corrections according to claim 1, characterized in that, The process of acquiring historical lightning monitoring data of the target area, calculating the lightning arrival time based on the historical lightning monitoring data and optimized waveform data, and obtaining the initial lightning arrival time includes: Historical waveform data is obtained from historical lightning monitoring data. The matching score for each moment is calculated based on the optimized waveform data and the historical waveform data. The moment with the highest matching score is taken as the initial lightning arrival time.
3. The lightning location method based on multiple corrections according to claim 1, characterized in that, The setting of the time delay factor, which corrects the initial lightning arrival time to obtain the final lightning arrival time, includes: Historical lightning locations, arrival times, and three-dimensional coordinates of monitoring stations are obtained from historical lightning monitoring data. Based on the historical lightning locations, the three-dimensional coordinates of the monitoring station, and the historical lightning arrival times, a time delay factor is set, and the initial lightning arrival time is subtracted from the time delay factor to obtain the final lightning arrival time.
4. The lightning location method based on multiple corrections according to claim 1, characterized in that, The process of inputting optimized waveform data into a lightning analysis model for lightning event identification and uncertainty calculation to obtain the uncertainty of the monitoring station includes: A lightning analysis model is constructed based on a mask layer, encoder, decoder and fully connected layer. The optimized waveform data is passed through the mask layer, encoder and decoder in sequence to output lightning feature vectors. Lightning feature vectors are input into a fully connected layer for lightning event identification to obtain predicted lightning events. Obtain the predicted probability of a lightning event, the code of the predicted lightning event, and the code of the target lightning event; The uncertainty of the monitoring station is calculated based on the predicted probability of the lightning event, the code of the predicted lightning event, and the code of the target lightning event.
5. The lightning location method based on multiple corrections according to claim 1, characterized in that, The objective function for constructing the lightning arrival time difference based on the final lightning arrival time and adjusted weights includes: The estimated lightning location and estimated lightning occurrence time are used as independent variables in the objective function of lightning arrival time difference. The objective function of lightning arrival time difference is constructed based on the estimated lightning location, estimated lightning occurrence time, final lightning arrival time, and adjustment weights.
6. The lightning location method based on multiple corrections according to claim 5, characterized in that, The objective function of lightning arrival time difference is solved using a genetic algorithm to obtain an initial set of lightning locations, including: All monitoring stations are grouped to obtain multiple monitoring station combinations; For a monitoring station ensemble, the estimated lightning location and the estimated lightning occurrence time are treated as individuals, and a population is constructed using these individuals; The individual population is input into the monitoring station combination corresponding to the lightning arrival time difference objective function, the lightning arrival time difference value of each individual is calculated, and the individual with the smallest lightning arrival time difference value is taken as the current optimal individual; The population is updated a preset number of times through crossover and mutation operations. The current best individual with the smallest difference in lightning arrival time is taken as the final best individual, and the estimated lightning location corresponding to the final best individual is taken as the initial lightning location corresponding to the monitoring station combination. The initial lightning location set is formed by combining the initial lightning locations corresponding to all monitoring stations.
7. The lightning location method based on multiple corrections according to claim 1, characterized in that, The step of correcting and accumulating the initial lightning location set through uncertainty to obtain the final lightning location includes: Obtain the three-dimensional coordinates of each monitoring station within the monitoring station combination corresponding to each initial lightning location in the initial lightning location set, and calculate the geometric mass of each monitoring station combination based on the three-dimensional coordinates; The average uncertainty of each monitoring station combination is obtained based on the uncertainty calculation. Based on the geometric mass and average uncertainty, the correction parameters for each monitoring station combination are calculated. The corrected lightning position is obtained by multiplying the correction parameter by the corresponding initial lightning position, and the final lightning position is obtained by summing all the corrected lightning positions.
8. A lightning location device based on multiple corrections, used to implement the lightning location method based on multiple corrections as described in any one of claims 1 to 7, characterized in that, The device includes: The optimized waveform data acquisition module is used to collect lightning waveforms of the target lightning event through all monitoring stations in the target area, obtain initial waveform data, and preprocess the initial waveform data to obtain optimized waveform data. The initial lightning arrival time acquisition module is used to acquire historical lightning monitoring data of the target area, calculate the lightning arrival time based on the historical lightning monitoring data and optimized waveform data, and obtain the initial lightning arrival time. The final lightning arrival time acquisition module is used to set a delay factor and correct the initial lightning arrival time to obtain the final lightning arrival time. The adjustment weight acquisition module is used to construct a lightning analysis model. It inputs optimized waveform data into the lightning analysis model to identify lightning events and calculate uncertainties, obtains the uncertainty of the monitoring station, and sets adjustment weights based on the uncertainty. The initial lightning location set acquisition module is used to construct a lightning arrival time difference objective function based on the final lightning arrival time and adjustment weights, and solve the lightning arrival time difference objective function through a genetic algorithm to obtain the initial lightning location set. The final lightning location acquisition module is used to correct and accumulate the initial lightning location set through uncertainty to obtain the final lightning location.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the lightning location method based on multiple corrections as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lightning location method based on multiple corrections as described in any one of claims 1 to 7.