A method and system for modified modeling of lightning leader incidence angle distribution

Abstract

This invention provides a method and system for correcting the modeling of lightning incidence angle distribution, comprising: constructing a lightning incidence angle probability density model based on the lightning incidence angle; constructing multiple candidate sets of model parameters based on the parameters of the lightning incidence angle probability density model; calculating the normalization constant of each candidate set of model parameters; calculating the goodness of fit and binning error corresponding to each candidate set of model parameters based on the lightning incidence angle probability density model; and determining the optimal parameters by comprehensively comparing the goodness of fit and binning error. This improves the fitting accuracy and stability of the incident angle statistical distribution to measured data.

Description

Technical Field

[0001] This invention relates to the field of lightning protection for transmission lines, and specifically to a method and system for correcting the modeling of lightning incidence angle distribution. Background Technology

[0002] Lightning protection and lightning strike risk assessment for transmission lines are crucial components of power system reliability design. With the increasing scale of ultra-high voltage / extra-high voltage lines, inter-regional interconnection lines, and complex terrain corridors (mountainous areas, coastal areas, windy zones), the sensitivity of these lines to lightning activity has further increased. In engineering practice, shielding protection is typically achieved through methods such as installing ground wires, optimizing the geometric arrangement of phase and ground wires, and configuring insulation and protective gaps. During the design phase, probability distribution models and electrical geometric models are used to calculate and verify the risks of transmission line shielding failure, backflashover, and backflashover.

[0003] A key issue in the above assessment is that lightning leaders do not always approach the ground or power line in a strictly vertical direction, but may approach the power line at a certain angle of incidence. This angle of incidence affects the spatial relative position of the lightning leader and the ground / phase wire, thereby changing the phase wire exposure range and the equivalent attraction range, and affecting the probability of the final attachment point. Especially in scenarios with partial shielding or near-critical boundary conditions, the angle of incidence distribution will significantly affect the calculation results of the shielding failure probability. Therefore, establishing a reasonable statistical distribution model of the angle of incidence is a fundamental step in line lightning protection calculations that is as important as geometric layout and strike distance parameters.

[0004] In existing technologies, leader angle distribution often adopts a simplified parameterized form, typically by using the incident angle of the lightning leader. Defined as the angle relative to the vertical direction, and in Within the range, it is assumed that its probability density is the same as Proportional, that is, assuming the angle of incidence exist The interval follows a probability density function : in: : The angle between the downward leader of lightning and the vertical direction (in radians).

[0005] m: power exponent, which controls the shape of the distribution.

[0006] : Normalization constant, making That is, the total probability is 1. As m takes different values, The values ​​are different.

[0007] The list shows the values ​​of m from 1 to 5. and :

[0008] Accordingly, lightning... The probability of incident within the angular interval is: This traditional model uses a single shape parameter *m* to characterize the degree to which the lightning precursor deviates from verticality. As *m* increases, the probability density becomes more concentrated in... As m decreases, the distribution becomes smoother and allows for larger oblique incidence angles. This model is simple in form, easy to normalize, and convenient for multi-integral calculations embedded in line shielding, and is therefore widely used.

[0009] However, the above single parameter The model has certain limitations in practical applications: its distribution shape is mainly controlled by m alone, and the probability density curve often struggles to simultaneously account for the independent changes in the "concentration in the central region (corresponding to small angles)" and the "weight in the shoulder region (for medium angles)." When measured data shows that small angles are relatively concentrated while medium angles still have relatively significant probability weights, relying solely on adjusting m can easily lead to a situation where one aspect is overlooked, resulting in biased probability estimation of angle intervals, which in turn affects key calculation results such as shielding failure probability and landing point probability allocation. Especially when it is necessary to adapt to the statistical characteristics of the incident angle under different route corridors and different meteorological and topographical conditions, the fitting ability and interpretability of the existing model still have room for further improvement.

[0010] Therefore, while maintaining the normalizability and embeddability of the model in engineering calculations, it is necessary to introduce a more expressive incident angle distribution modeling method with clear parameter meanings to better fit the statistical characteristics of measured angle samples in different angle ranges, thereby improving the accuracy and stability of lightning protection assessment for transmission lines. Summary of the Invention

[0011] In the existing lightning protection assessment of transmission lines, the lightning first incidence angle distribution model is used. To address the problem of insufficient expressive power leading to deviations in the estimation of incident probability within the angle range and further affecting the calculation results of shielding failure probability and impact point probability, this invention provides a corrective modeling method and system for the lightning incident angle distribution. While maintaining the model's normalizability and embedding in engineering integral calculations, it improves the fitting accuracy and stability of the incident angle statistical distribution to measured data. The method includes: Based on the lightning incidence angle, construct the probability density model of the lightning incidence angle; Receive raw data of the lightning incident angle; Based on the parameters of the lightning initial incidence angle probability density model, multiple sets of model parameter candidate sets are constructed; Calculate the normalization constant for each set of candidate model parameters; Based on the lightning incidence probability density model, the goodness of fit and binning error of each set of model parameter candidates are calculated. The optimal parameters are determined by comprehensively comparing the goodness of fit and binning error.

[0012] Furthermore, the lightning is first introduced into the incidence angle probability density model, specifically as follows: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The initial lightning strike probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the curve of the lightning first-injection angle probability density model; The integer value is used to adjust the weight of medium or large angles in the curve of the lightning incidence probability density model.

[0013] Furthermore, after the step of receiving the raw data of the lightning incidence angle, the process also includes: The original data of the lightning incident angle is validated for legality. The raw data of the lightning incident angle is converted from angle data to radians.

[0014] Furthermore, based on the parameters of the lightning initial incidence angle probability density model, multiple sets of model parameter candidate sets are constructed, including: Generate an integer such that m is a positive integer. A set of multiple candidate model parameters, which are integers.

[0015] Furthermore, the normalization constant for each set of candidate model parameters is calculated, including: For each set of candidate model parameters (m, Calculate the normalization constant K: .

[0016] Furthermore, based on the lightning incidence probability density model, the goodness of fit and binning error corresponding to each set of model parameter candidates are calculated. The optimal parameters are determined by comprehensively comparing the goodness of fit and binning error, including: Calculate the candidate set of model parameters for each group (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

[0017] This invention also provides a correction modeling system for the lightning advance angle distribution, comprising: The model building module is used to construct the lightning incidence angle probability density model based on the lightning incidence angle. The data receiving module is used to receive the raw data of the lightning incident angle; The candidate set construction module is used to construct multiple sets of model parameter candidate sets based on the parameters of the lightning first-injection angle probability density model; The normalization constant calculation module is used to calculate the normalization constant for each set of candidate model parameters; The optimal parameter determination module is used to calculate the goodness of fit and binning error corresponding to each set of model parameter candidates based on the lightning first-introduction angle probability density model, and determine the optimal parameters by comprehensively comparing the goodness of fit and binning error.

[0018] Furthermore, the lightning is first introduced into the incidence angle probability density model, specifically as follows: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The initial lightning strike probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the curve of the lightning first-injection angle probability density model; The integer value is used to adjust the weight of medium or large angles in the curve of the lightning incidence probability density model.

[0019] Furthermore, it also includes: The legality verification module is used to verify the legality of the original data of the lightning incident angle; The data conversion module is used to convert the raw data of the lightning incident angle from angle data into radians.

[0020] Furthermore, the candidate set building module includes: The candidate set generation submodule is used to generate candidate sets that satisfy m as a positive integer. A set of multiple candidate model parameters, which are integers.

[0021] Furthermore, the normalization constant calculation module includes: The normalization constant calculation submodule is used to calculate the normalization constant for each set of candidate model parameters (m, Calculate the normalization constant K: .

[0022] Furthermore, the optimal parameter determination module includes: The comprehensive comparison submodule is used to calculate the candidate set of model parameters (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

[0023] The present invention also provides a computer-readable storage medium storing a computer program for performing the method described in any one of the preceding claims.

[0024] The present invention also provides an electronic device, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of the preceding claims.

[0025] This invention provides a method and system for correcting the incident angle distribution of lightning, and proposes a method for correcting the incident angle distribution. Furthermore, by using a parameter selection method under discrete constraints to determine m, β, and K, the accuracy of the incident angle distribution model fitting and the accuracy of interval probability estimation can be improved while maintaining normalizability and engineering integration. This reduces the error propagation to lightning protection calculation results such as shielding failure probability and improves the fitting accuracy and stability of the incident angle statistical distribution to the measured data. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for correcting the modeling of lightning incidence angle distribution according to an embodiment of the present invention. Figure 2 This refers to the classical model probability density curve cluster involved in the embodiments of the present invention (taking m=1-5 as an example). Figure 3This refers to the cluster of probability density curves of the modified model involved in the embodiments of the present invention (taking m=2 and β values ​​(-5,-3,-1,0,1,3,5) as an example). Figure 4 This is a flowchart of the incident angle distribution modeling and output scheme involved in the embodiments of the present invention; Figure 5 This is a schematic diagram of the structure of a correction modeling system for lightning incidence angle distribution provided in an embodiment of the present invention. Detailed Implementation

[0027] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0028] This invention addresses the problem of "difficulty in accurately representing the incident angle distribution in lightning protection assessment of transmission lines, leading to discrepancies in the calculation of interval probability and shielding failure" by proposing a normalizable, engineering-embeddable, parameter-interpretable, and data-adaptable incident angle distribution correction model. The paper presents a method for determining parameters under discrete constraints and a scheme for lightning protection calculation.

[0029] This method constructs a system containing an exponential correction term based on the probability characteristics of measured incident angle samples in different angle intervals (including the central small angle region and the medium angle region). The probability density function of the incident angle : Based on the interval probability and overall distribution characteristics of the samples, the parameters m and are obtained by parameter selection under discrete constraints. And the normalization constant K, and then controllably characterize the "component dominated by the central tendency" and the "correction component caused by the moderate angle weight deviation" in the incident angle distribution, to obtain an incident angle distribution model that is more in line with the measured statistical law, for use in lightning protection calculations such as shielding failure probability.

[0030] like Figure 1 As shown, the present invention provides a method for correcting the modeling of the lightning advance angle distribution, comprising the following steps: Step S101: Construct the lightning incidence angle probability density model based on the lightning incidence angle.

[0031] Step S102: Receive the raw data of the lightning incident angle.

[0032] Step S103: Based on the parameters of the lightning initial incidence angle probability density model, construct multiple sets of model parameter candidate sets.

[0033] Step S104: Calculate the normalization constant for each set of candidate model parameters.

[0034] Step S105: Based on the lightning initial incidence angle probability density model, calculate the goodness of fit and binning error corresponding to each set of model parameter candidates, and determine the optimal parameters by comprehensively comparing the goodness of fit and binning error.

[0035] Specifically, the lightning is first introduced into the incidence angle probability density model, as follows: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The initial lightning strike probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the lightning incidence angle probability density model curve; m can determine the degree of concentration at the center of the curve and the degree of suppression of extreme large-angle oblique incidence, and also determine the intensity of the overall contraction from large angle to small angle.

[0036] This is an integer used to adjust the weights of medium- or large angles in the lightning incidence probability density model curve, enhancing / suppressing the probability at medium- or large angles. That is... The probability mass is mainly redistributed in the transition zone from the center to the shoulder, and the adjustment is based on the shoulder thickness / flatness rather than the boundary order.

[0037] when The weights at large angles on both sides of the probability density curve are amplified, the center is compressed, and the tails become thicker. This makes the distribution more "flat" and more "outward-expanding." When the angle is large enough, a bimodal pattern can appear, and the incident angle is no longer concentrated solely around 0°, indicating that the model prefers a leading angle that is "incident at a more inclined angle".

[0038] when : Degenerate to the original model .

[0039] when : The smaller angles on both sides strongly suppress large angles, making the incident angle more concentrated near 0 degrees, resulting in a sharper and higher peak.

[0040] Furthermore, after the step of receiving the raw data of the lightning incidence angle, the following steps are also included: The original data of the lightning incident angle is validated for legality. The raw data of the lightning incident angle is converted from angle data to radians.

[0041] Furthermore, based on the parameters of the lightning initial incidence angle probability density model, multiple sets of model parameter candidate sets are constructed, including: Generate an integer such that m is a positive integer. A set of multiple candidate model parameters, which are integers.

[0042] Furthermore, the normalization constants for each set of candidate model parameters are calculated, including: For each set of candidate model parameters (m, Calculate the normalization constant K: .

[0043] Furthermore, based on the lightning incidence probability density model, the goodness of fit and binning error corresponding to each set of model parameter candidates are calculated. The optimal parameters are determined by comprehensively comparing the goodness of fit and binning error, including: Calculate the candidate set of model parameters for each group (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

[0044] like Figure 4 As shown, the incident angle distribution correction model is applied as follows: (1) Data acquisition: Receive the raw data of lightning incident angle. Data sources can include measuring devices, lightning location systems, historical databases, or manually entered files. Data input in degrees (°) is supported.

[0045] (2) Data preprocessing: Next, the input angles are validated for legality. And perform unit conversion from degrees to radians.

[0046] (3) Generation of candidate set of model parameters: Generate a set of parameters that satisfies the condition that m is a positive integer. The candidate set of integers, for example , .

[0047] (4) Calculation of normalization constant: For each group (m, Calculate the normalization constant K: (5) Model comparison and parameter selection: Calculate the parameters for each group (m, The corresponding goodness-of-fit R² and binning error RMSE are compared and the optimal parameters (m0, m0, m2, m3, m4) are determined. ).

[0048] (6) Output Results: Output the optimal parameters (m0, The incident angle probability distribution model can be used to display the incident angle distribution and calculate the probability of incident at a certain angle range, for use by shielding failure probability, landing point probability allocation or other transmission line lightning protection calculation modules.

[0049] Taking the lightning protection assessment of a certain transmission line as an example, the lightning angle sample data of the area is first imported from the lightning monitoring / location system or historical database of the line corridor. When calculating the probability of shielding failure / the probability of landing point, the incident angle distribution model or interval probability calculation result output by this invention is called and embedded as an angle weight term into the existing geometric and electrical parameter calculation process, so as to obtain an assessment result that is more consistent with the statistical characteristics of the measured incident angle, which can be used to guide the optimization of ground wire layout, insulation coordination verification and lightning protection measure configuration decisions.

[0050] Based on the same inventive concept, this invention also provides a correction modeling system 500 for the distribution of lightning advance incidence angle, such as... Figure 5 As shown, it includes: The model building module 510 is used to build the lightning incidence angle probability density model based on the lightning incidence angle. The data receiving module 520 is used to receive the raw data of the lightning incident angle; The candidate set construction module 530 is used to construct multiple sets of model parameter candidate sets based on the parameters of the lightning first-injection angle probability density model; The normalization constant calculation module 540 is used to calculate the normalization constant for each set of candidate model parameters; The optimal parameter determination module 550 is used to calculate the goodness of fit and binning error corresponding to each set of model parameter candidate sets based on the lightning first-introduction angle probability density model, and determine the optimal parameters by comprehensively comparing the goodness of fit and binning error.

[0051] Furthermore, the lightning is first introduced into the incidence angle probability density model, specifically as follows: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The initial lightning strike probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the curve of the lightning first-injection angle probability density model; The integer value is used to adjust the weight of medium or large angles in the curve of the lightning incidence probability density model.

[0052] Furthermore, it also includes: The legality verification module is used to verify the legality of the original data of the lightning incident angle; The data conversion module is used to convert the raw data of the lightning incident angle from angle data into radians.

[0053] Furthermore, the candidate set building module includes: The candidate set generation submodule is used to generate candidate sets that satisfy m as a positive integer. A set of multiple candidate model parameters, which are integers.

[0054] Furthermore, the normalization constant calculation module includes: The normalization constant calculation submodule is used to calculate the normalization constant for each set of candidate model parameters (m, Calculate the normalization constant K: .

[0055] Furthermore, the optimal parameter determination module includes: The comprehensive comparison submodule is used to calculate the candidate set of model parameters (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

[0056] Existing technologies often employ A single-parameter model can only adjust the "centrality" and "intermediate angular probability level" simultaneously via m, such as... Figure 2 As shown, the system is not well adapted to situations that often occur in measured data, such as "the center is relatively sharp, but there is still a significant probability weight at medium angles".

[0057] This invention is in Introducing an exponential correction term based on A method for correcting the incident angle distribution modeling was proposed. Furthermore, by determining m, β, and K through a parameter selection method under discrete constraints, the accuracy of the incident angle distribution model fitting and the accuracy of interval probability estimation can be improved while maintaining normalizability and engineering integration. This reduces the error propagation to lightning protection calculation results such as shielding failure probability. The results are as follows: Figure 3 As shown. Compared to existing single-parameter models It has the following effects and advantages: (1) More flexible shape control and stronger engineering adaptability m mainly controls proximity The intensity of inhibition in the region and the degree of hard constraint on the overall convergence to 0°; The main adjustment is to the weight distribution of the "medium angle area (curved shoulder) relative to the center area" to achieve controllable correction of the thickness of the distributed shoulder, thereby solving the problem that the traditional model cannot simultaneously take into account "center concentration" and "shoulder probability" by relying solely on m.

[0058] (2) It is simple to implement and easy to deploy in engineering. Only incident angle sample data is required. The parameters are limited to integers and normalization can be stably achieved through numerical integration. This facilitates parameter selection in engineering systems using discrete search / table lookup methods, improving repeatability and versatility, and making it easy to embed into existing lightning protection assessment software and calculation processes.

[0059] (3) Improve fitting accuracy and reduce calculation error It can improve the fitting accuracy of the incident angle distribution model and the accuracy of interval probability estimation, and reduce the error propagation of lightning protection calculation results such as shielding failure probability.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A modified modeling method for the lightning advance angle distribution, characterized in that, include: Based on the lightning incidence angle, construct the probability density model of the lightning incidence angle; Receive raw data of the lightning incident angle; Based on the parameters of the lightning initial incidence angle probability density model, multiple sets of model parameter candidate sets are constructed; Calculate the normalization constant for each set of candidate model parameters; Based on the lightning first-injection angle probability density model, the goodness of fit and binning error corresponding to each set of model parameter candidates are calculated. By comprehensively comparing the goodness of fit and binning error, the optimal parameters are determined.

2. The method according to claim 1, characterized in that, The lightning strike is first introduced into the incidence angle probability density model, specifically: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The lightning incidence angle probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the curve of the lightning first-injection angle probability density model; The integer value is used to adjust the weight of medium or large angles in the curve of the lightning incidence probability density model.

3. The method according to claim 1, characterized in that, Following the step of receiving the raw data of the lightning incidence angle, the following steps are also included: The original data of the lightning incident angle is validated for legality. The raw data of the lightning incident angle is converted from angle data to radians.

4. The method according to claim 1, characterized in that, Based on the parameters of the lightning incidence probability density model, multiple candidate sets of model parameters are constructed, including: Generate an integer such that m is a positive integer. A set of multiple candidate model parameters, which are integers.

5. The method according to claim 1, characterized in that, Calculate the normalization constant for each candidate set of model parameters, including: For each set of candidate model parameters (m, Calculate the normalization constant K: 。 6. The method according to claim 1, characterized in that, Based on the lightning incidence angle probability density model, the goodness of fit and binning error of each set of model parameter candidates are calculated. The optimal parameters are determined by comprehensively comparing the goodness of fit and binning error, including: Calculate the candidate set of model parameters for each group (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

7. A correction modeling system for the lightning advance angle distribution, characterized in that, include: The model building module is used to construct the lightning incidence angle probability density model based on the lightning incidence angle. The data receiving module is used to receive the raw data of the lightning incident angle; The candidate set construction module is used to construct multiple sets of model parameter candidate sets based on the parameters of the lightning first-injection angle probability density model; The normalization constant calculation module is used to calculate the normalization constant for each set of candidate model parameters; The optimal parameter determination module is used to calculate the goodness of fit and binning error corresponding to each set of model parameter candidates based on the lightning first-introduction angle probability density model, and determine the optimal parameters by comprehensively comparing the goodness of fit and binning error.

8. The system according to claim 7, characterized in that, The lightning strike is first introduced into the incidence angle probability density model, specifically: Let the initial angle of lightning be... , The angle is relative to the vertical direction, and its range is [range missing]. The lightning incidence angle probability density model is as follows: in, The normalization constant makes That is, the total probability is 1; as m and Values, The specific expressions are as follows, depending on the value taken: m is a positive integer used to control the overall concentration and boundary attenuation order of the curve of the lightning first-injection angle probability density model; The integer value is used to adjust the weight of medium or large angles in the curve of the lightning incidence probability density model.

9. The system according to claim 7, characterized in that, Also includes: The legality verification module is used to verify the legality of the original data of the lightning incident angle; The data conversion module is used to convert the raw data of the lightning incident angle from angle data into radians.

10. The system according to claim 7, characterized in that, The candidate set construction module includes: The candidate set generation submodule is used to generate candidate sets that satisfy m as a positive integer. A set of multiple candidate model parameters, which are integers.

11. The system according to claim 7, characterized in that, The normalization constant calculation module includes: The normalization constant calculation submodule is used to calculate the normalization constant for each set of candidate model parameters (m, Calculate the normalization constant K: 。 12. The system according to claim 7, characterized in that, The optimal parameter determination module includes: The comprehensive comparison submodule is used to calculate the candidate set of model parameters (m, The corresponding goodness-of-fit R² and binning error RMSE are used to determine the optimal parameters (m0, m0, m2, m3) by comprehensively comparing the goodness-of-fit and binning error. ).

13. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the method described in claims 1-6.

14. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in claims 1-6.

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