Methods, devices and electronic equipment for predicting high-altitude electromagnetic pulse waveforms

Abstract

This invention provides a method, apparatus, and electronic device for predicting high-altitude electromagnetic pulse (HEP) waveforms. The method first acquires characteristic parameters of the HEP at a target location, including peak value, rise time, and full width at half maximum (FWHM). Then, a predicted waveform of the HEP is generated based on these characteristic parameters. This prediction method improves the accuracy of HEP waveform prediction by reconstructing the waveform based on the characteristic parameters of the target location. Furthermore, this method only requires the characteristic parameters of the target location, effectively reducing the sample size and computational load compared to existing solutions, thus improving the efficiency of HEP waveform prediction.

Description

Technical Field

[0001] This invention relates to the field of high-altitude electromagnetic pulse technology, and in particular to a method, apparatus, and electronic device for predicting high-altitude electromagnetic pulse waveforms. Background Technology

[0002] In practical applications, the High Altitude Electromagnetic Pulse (HEMP) waveform reflects the temporal and spatial intensity changes of the electromagnetic pulse generated by an upper-altitude nuclear explosion. It is a crucial basis for assessing the electromagnetic damage effects of HEMP on electronic equipment and systems. Therefore, accurately predicting HEMP waveforms helps improve the scientific rigor and effectiveness of protection design and is an important prerequisite for ensuring the security of critical information infrastructure.

[0003] Current HEMP waveform prediction methods mainly utilize two approaches: data simulation and raw waveform learning. In the data simulation method, input parameters include the gamma time spectrum, explosion height, and photon equivalent. A spatial grid is constructed, and a numerical electromagnetic field solver is used for physical simulation to obtain the time-domain waveform at each grid point. However, this method is time-consuming. Furthermore, in the raw waveform learning method, each waveform simulated by the data simulation method is normalized, truncated, or resampled to a fixed length. Then, a neural network model is used to learn the overall temporal characteristics of the waveform to obtain the complete waveform. However, each waveform contains thousands of time steps, resulting in a high input dimensionality for the neural network model. Simultaneously, to capture subtle rising and decay characteristics, tens of thousands to hundreds of thousands of waveform samples are required, leading to a significant increase in model training resource consumption, thus reducing the prediction efficiency and accuracy of HEMP waveforms. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus and electronic device for predicting high-altitude electromagnetic pulse waveforms, so as to alleviate the above problems and improve the prediction efficiency and accuracy of high-altitude electromagnetic pulse waveforms.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting high-altitude electromagnetic pulse waveforms, the method comprising: acquiring characteristic parameters of a high-altitude electromagnetic pulse at a target location; wherein the characteristic parameters include: peak value, rise time, and full width at half maximum (FWHM); and generating a predicted waveform of the high-altitude electromagnetic pulse based on the characteristic parameters.

[0006] Preferably, the step of generating a predicted waveform of an upper-level electromagnetic pulse based on characteristic parameters includes: determining a target waveform function based on the rise time and half-width at half-maximum (WHM); wherein the target waveform function includes a double exponential waveform function and a double Gaussian waveform function; and generating a predicted waveform based on the characteristic parameters and the target waveform function.

[0007] Preferably, the step of determining the target waveform function based on the rise time and half-width at half-maximum (WHM) includes: calculating the ratio of WHM to rise time; and determining the target waveform function based on the ratio and a preset threshold.

[0008] Preferably, the step of determining the target waveform function based on the ratio and a preset threshold includes: if the ratio is greater than the preset threshold, then the target waveform function is determined to be a bi-exponential waveform function.

[0009] Preferably, the step of determining the target waveform function based on the ratio and a preset threshold includes: if the ratio is not greater than the preset threshold, then the target waveform function is determined to be a double Gaussian waveform function.

[0010] Preferably, the step of obtaining the characteristic parameters of the high-altitude electromagnetic pulse at the target location includes: obtaining the target location parameters and the basic parameters of the high-altitude electromagnetic pulse; wherein, the basic parameters include: photon equivalent, explosion height and gamma-ray time spectrum; and inputting the target location parameters and the basic parameters into a pre-trained calculation model so that the calculation model outputs the characteristic parameters.

[0011] Preferably, the method further includes: acquiring a training sample set; wherein the training sample set includes: position training parameters for multiple spatial locations, and multiple basic training parameters for high-altitude electromagnetic pulses, each basic training parameter including: photon equivalent training value, explosion height training value, and gamma-ray time spectrum training value; training the neural network model based on the training sample set to obtain a trained computational model.

[0012] Secondly, embodiments of the present invention also provide a high-altitude electromagnetic pulse waveform prediction device, the device comprising:

[0013] The parameter acquisition module is used to acquire the characteristic parameters of the high-altitude electromagnetic pulse at the target location; among which, the characteristic parameters include: peak value, rise time, and full width at half maximum (FWHM).

[0014] The waveform generation module is used to generate predicted waveforms of high-altitude electromagnetic pulses based on characteristic parameters.

[0015] Thirdly, embodiments of the present invention also provide 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 computer program to implement the steps of the method described in the first aspect.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in the first aspect.

[0017] The embodiments of the present invention bring the following beneficial effects:

[0018] This invention provides a method, apparatus, and electronic device for predicting high-altitude electromagnetic pulse (HEP) waveforms. First, characteristic parameters of the HEP at a target location are obtained; these characteristic parameters include peak value, rise time, and full width at half maximum (FWHM). Then, a predicted waveform of the HEP is generated based on these characteristic parameters. This prediction method improves the waveform prediction accuracy of HEPs by predicting and reconstructing the waveform based on the characteristic parameters of the target location. Furthermore, this method only requires the characteristic parameters of the target location, effectively reducing the sample size and computational load compared to existing solutions, thereby improving the waveform prediction efficiency of HEPs.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A flowchart of a high-altitude electromagnetic pulse waveform prediction method provided in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a high-altitude electromagnetic pulse waveform predicted and restored based on a double exponential waveform function is provided as an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of a high-altitude electromagnetic pulse waveform prediction device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0028] Example 1

[0029] This invention provides a method for predicting high-altitude electromagnetic pulse waveforms, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Obtain the characteristic parameters of the high-altitude electromagnetic pulse at the target location.

[0031] The characteristic parameters include: peak value, rise time, and full width at half maximum (FWHM); the peak value E0 can also be referred to as the maximum electric field intensity of the high-altitude electromagnetic pulse waveform at the target location, and the rise time t... r Also known as rise time, full width at half maximum (FWHM) t FWHM It refers to the width at half the peak value of the signal. In practical applications, pulse width refers to the time interval from the start to the end of a pulse signal. Although pulse width can provide information about the duration of the pulse signal, it does not take into account the actual changes in the pulse signal, thus failing to reflect the waveform characteristics. The half-width at half-maximum (WHM) is... FWHM This can reflect the waveform characteristics of the pulse signal. Therefore, by using a full width at half maximum (FWHM) t FWHM Peak value E0 and rise time t r As a characteristic parameter for predicting and reconstructing high-altitude electromagnetic pulse waveforms, it further improves the prediction accuracy of high-altitude electromagnetic pulse waveforms.

[0032] Step S104: Generate the predicted waveform of the high-altitude electromagnetic pulse based on the characteristic parameters.

[0033] The aforementioned method for predicting high-altitude electromagnetic pulse (HEP) waveforms first obtains the characteristic parameters of the HEP at the target location, including peak value, rise time, and full width at half maximum (FWHM). Then, it generates a predicted waveform of the HEP based on these characteristic parameters. Therefore, this prediction method improves the waveform prediction accuracy by reconstructing the HEP waveform based on the characteristic parameters of the target location. Furthermore, this method only requires the characteristic parameters of the target location, effectively reducing the sample size and computational load compared to existing schemes, thus improving the waveform prediction efficiency of HEPs.

[0034] In one embodiment, the step of generating a predicted waveform of an upper-level electromagnetic pulse based on characteristic parameters includes: determining a target waveform function based on the rise time and full width at half maximum (FWHM); wherein the target waveform function includes a double exponential waveform function and a double Gaussian waveform function; and generating a predicted waveform based on the characteristic parameters and the target waveform function.

[0035] Specifically, the ratio of half-width at half-maximum (FWHM) to rise time is calculated, and the target waveform function is determined based on this ratio and a preset threshold. In practical applications, for the characteristic parameters of the target position, the ratio t of FWHM to rise time is first calculated. FWHM / t r Then, if the ratio t FWHM / t r If the ratio t is greater than a preset threshold, the target waveform function is determined to be a double exponential waveform function; otherwise, if the ratio t is less than a preset threshold, the target waveform function is determined to be a double exponential waveform function. FWHM / t r If the value is not greater than a preset threshold, the target waveform function is determined to be a double Gaussian waveform function. The preset threshold is preferably 4.3, but can be adjusted adaptively according to actual conditions.

[0036] The expression for the double exponential waveform function is as follows:

[0037] E(t) = E0 × K × (e -αt -e -βt (1)

[0038] The expression for the double Gaussian waveform function is as follows:

[0039]

[0040] In the above formulas (1) and (2), E(t) represents the high-altitude electromagnetic pulse field strength, E0 represents the peak value, K represents the normalization factor, t represents time, and α and β represent the fall time parameter and rise time parameter, respectively, which are used to determine the decay rate and rise rate of the waveform.

[0041] Therefore, when predicting and reconstructing the waveform of a high-altitude electromagnetic pulse based on characteristic parameters, the target waveform function is first determined based on the rise time and full width at half maximum (FWHM), and the time range and time resolution corresponding to the target waveform function are intelligently set. Simultaneously, the two parameters α and β controlling the waveform shape are initially estimated. Then, the difference between the currently generated waveform and the characteristic parameters is quantified using an error function. The expression for the error function `loss` is as follows:

[0042]

[0043] Among them, t r,fit t represents the rise time of the currently generated waveform. r w represents the rise time of the characteristic parameter. trIndicates the rise time weight, t FWHM,fit t represents the full width at half maximum (FWHM) of the currently generated waveform. FWHM w represents the full width and height of the feature parameters. FWHM This indicates the weight of the half-height width.

[0044] The aforementioned error function considers both rise time and full width at half maximum (FWHM) for restoration accuracy, automatically assigning weights based on their ratio to balance restoration performance under different waveform characteristics. Furthermore, by continuously adjusting the waveform control parameters α and β until the error function value is minimized or the error is less than a preset error (e.g., 5%), the optimal parameter combination is obtained, thereby improving the prediction and restoration accuracy of high-altitude electromagnetic pulse waveforms. For example, when the target waveform function is a double exponential waveform function, the predicted and restored high-altitude electromagnetic pulse waveform is as follows: Figure 2 As shown, the horizontal axis represents time t, the vertical axis represents E(t), and the curve is the predicted and restored waveform of the high-altitude electromagnetic pulse.

[0045] Therefore, the high-altitude electromagnetic pulse waveform prediction method provided in this embodiment of the invention can generate the predicted waveform of the high-altitude electromagnetic pulse based on the characteristic parameters of the target location without user intervention. It not only maintains physical rationality but also greatly improves the waveform prediction accuracy and efficiency of the high-altitude electromagnetic pulse. It is also applicable to scenarios such as electromagnetic pulse modeling, simulation test waveform synthesis, and device response analysis, and has good engineering application value.

[0046] In one embodiment, the step of obtaining characteristic parameters of an upper-air electromagnetic pulse at a target location includes: obtaining target position parameters and basic parameters of the upper-air electromagnetic pulse; wherein the basic parameters include: photon equivalent, explosion height, and gamma-ray time spectrum; and inputting the target position parameters and basic parameters into a pre-trained computational model so that the computational model outputs characteristic parameters.

[0047] The above-mentioned computational model is based on a neural network model trained. The specific training process is as follows: First, a training sample set is obtained. The training sample set includes: position training parameters for multiple spatial locations, and multiple basic training parameters for high-altitude electromagnetic pulses. Each basic training parameter includes: photon equivalent training value, explosion height training value, and gamma-ray time spectrum training value. Then, the neural network model is trained based on the training sample set to obtain the trained computational model.

[0048] In practical applications, for multiple basic training parameters, such as training values ​​for various gamma-ray time spectra, multiple explosion heights (e.g., 60km–300km), and multiple photon equivalents, a spatial grid (2000km × 2000km, 1km interval) is constructed. Then, the peak value E0 and rise time t of the time-domain waveform at each grid point are extracted. r Half-height and width tFWHM Three key features constitute the feature parameter training samples. Then, position training parameters from multiple spatial locations and multiple fundamental training parameters from high-altitude electromagnetic pulses are used as input parameters, and the feature parameter training samples are used as output parameters to train a neural network model such as an MLP (Multilayer Perceptron). The number of grid layers, nodes, and learning rate are determined through grid search and cross-validation until a well-trained computational model is obtained. It should be noted that the above-mentioned grid search and cross-validation processes can refer to existing technologies, and will not be described in detail here.

[0049] Therefore, for any target location, the characteristic parameters of the target location can be quickly obtained through the above calculation model and the target location parameters and the basic parameters of the high-altitude electromagnetic pulse, so as to generate the predicted waveform of the high-altitude electromagnetic pulse based on the characteristic parameters. Compared with the existing original waveform sequence method, it not only effectively reduces the sample size and computation, but also improves the waveform prediction efficiency and prediction accuracy of the high-altitude electromagnetic pulse.

[0050] It should be noted that for any photon equivalent, explosion height, gamma-ray time spectrum, and position parameters, the corresponding characteristic parameters can be determined through the calculation model, and waveform prediction and reconstruction can be performed based on the characteristic parameters. In addition, the total predicted waveform of the high-altitude electromagnetic pulse can be generated by combining the predicted waveforms corresponding to multiple target positions, thus realizing the prediction and reconstruction of the complete time-domain waveform.

[0051] Example 2

[0052] Corresponding to the above method embodiments, this invention also provides a high-altitude electromagnetic pulse waveform prediction device, such as... Figure 3 As shown, the device includes a parameter acquisition module 31 and a waveform generation module 32; the functions of each module are as follows:

[0053] The parameter acquisition module 31 is used to acquire the characteristic parameters of the high-altitude electromagnetic pulse at the target position; wherein, the characteristic parameters include: peak value, rise time and half width at half maximum (WHM);

[0054] Waveform generation module 32 is used to generate a predicted waveform of high-altitude electromagnetic pulse based on characteristic parameters.

[0055] The aforementioned high-altitude electromagnetic pulse waveform prediction device first acquires the characteristic parameters of the high-altitude electromagnetic pulse at the target location; these characteristic parameters include peak value, rise time, and full width at half maximum (FWHM). Then, it generates a predicted waveform of the high-altitude electromagnetic pulse based on these characteristic parameters. Therefore, this prediction method improves the waveform prediction accuracy by predicting and reconstructing the high-altitude electromagnetic pulse waveform based on the characteristic parameters of the target location. Furthermore, this method only requires the characteristic parameters of the target location, effectively reducing the sample size and computational load compared to existing solutions, thereby improving the waveform prediction efficiency of high-altitude electromagnetic pulses.

[0056] Optionally, the waveform generation module 32 is further configured to: determine a target waveform function based on the rise time and half-width at half-maximum; wherein the target waveform function includes a double exponential waveform function and a double Gaussian waveform function; and generate a predicted waveform based on the characteristic parameters and the target waveform function.

[0057] Optionally, determining the target waveform function based on the rise time and half-width at half-maximum (WHM) includes: calculating the ratio of WHM to rise time; and determining the target waveform function based on the ratio and a preset threshold.

[0058] Optionally, determining the target waveform function based on the ratio and a preset threshold includes: if the ratio is greater than the preset threshold, then the target waveform function is determined to be a bi-exponential waveform function.

[0059] Optionally, determining the target waveform function based on the ratio and a preset threshold includes: if the ratio is not greater than the preset threshold, then the target waveform function is determined to be a double Gaussian waveform function.

[0060] Optionally, the parameter acquisition module 31 is also used to: acquire the target position parameters of the target location and the basic parameters of the high-altitude electromagnetic pulse; wherein the basic parameters include: photon equivalent, explosion height and gamma-ray time spectrum; and input the target position parameters and basic parameters into a pre-trained calculation model so that the calculation model outputs feature parameters.

[0061] Optionally, the device further includes: acquiring a training sample set; wherein the training sample set includes: position training parameters for multiple spatial locations, and multiple basic training parameters for high-altitude electromagnetic pulses, each basic training parameter including: photon equivalent training value, explosion height training value, and gamma-ray time spectrum training value; training the neural network model based on the training sample set to obtain a trained computational model.

[0062] The high-altitude electromagnetic pulse waveform prediction device provided in this embodiment of the invention has the same technical features as the high-altitude electromagnetic pulse waveform prediction method provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0063] This invention also provides an electronic device, including a processor and a memory. The memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the above-described high-altitude electromagnetic pulse waveform prediction method.

[0064] See Figure 4 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores machine-executable instructions that can be executed by the processor 100. The processor 100 executes the machine-executable instructions to implement the above-described high-altitude electromagnetic pulse waveform prediction method.

[0065] Furthermore, Figure 4 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 100, the communication interface 103 and the memory 101 connected via the bus 102.

[0066] The memory 101 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 103 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network. The bus 102 may be an ISA (Industrial Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Enhanced Industry Standard Architecture) bus. These buses can be categorized as address buses, data buses, and control buses. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0067] Processor 100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 100 or by instructions in software form. Processor 100 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 101, and the processor 100 reads the information from memory 101 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0068] This embodiment also provides a machine-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to implement the above-described high-altitude electromagnetic pulse waveform prediction method.

[0069] The computer program products of the high-altitude electromagnetic pulse waveform prediction method, apparatus and electronic equipment provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0071] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0072] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0074] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting high-altitude electromagnetic pulse waveforms, characterized in that, The method includes: The method involves obtaining characteristic parameters of the high-altitude electromagnetic pulse at any target location in the complete time domain; wherein, the method obtains target location parameters and basic parameters of the high-altitude electromagnetic pulse; the basic parameters include: photon equivalent, explosion height, and gamma-ray time spectrum; the method inputs the target location parameters and the basic parameters into a pre-trained computational model, so that the computational model outputs the characteristic parameters; the characteristic parameters include: peak value, rise time, and full width at half maximum (FWHM). The predicted waveform of the high-altitude electromagnetic pulse is generated based on the characteristic parameters; wherein, a target waveform function is determined based on the rise time and the full width at half maximum (FWHM); wherein, the target waveform function includes a double exponential waveform function and a double Gaussian waveform function; the predicted waveform is generated based on the characteristic parameters and the target waveform function. Obtain a training sample set; wherein the training sample set includes: position training parameters for multiple spatial locations, and multiple basic training parameters for the high-altitude electromagnetic pulse, each of the basic training parameters including: photon equivalent training value, explosion height training value, and gamma ray time spectrum training value; train the neural network model based on the training sample set to obtain the trained computational model.

2. The method according to claim 1, characterized in that, The step of determining the target waveform function based on the rise time and the half-width at half-maximum includes: Calculate the ratio of the half-width at half-maximum (FWHM) to the rise time; The target waveform function is determined based on the ratio and the preset threshold.

3. The method according to claim 2, characterized in that, The step of determining the target waveform function based on the ratio and a preset threshold includes: If the ratio is greater than the preset threshold, then the target waveform function is determined to be the biexponential waveform function.

4. The method according to claim 2, characterized in that, The step of determining the target waveform function based on the ratio and a preset threshold includes: If the ratio is not greater than the preset threshold, then the target waveform function is determined to be the double Gaussian waveform function.

5. A high-altitude electromagnetic pulse waveform prediction device, characterized in that, The device includes: A parameter acquisition module is used to acquire characteristic parameters of the high-altitude electromagnetic pulse at any target location in the complete time domain; wherein, the target location parameters and the basic parameters of the high-altitude electromagnetic pulse are acquired; the basic parameters include: photon equivalent, explosion height, and gamma-ray time spectrum; the target location parameters and the basic parameters are input into a pre-trained calculation model so that the calculation model outputs the characteristic parameters; the characteristic parameters include: peak value, rise time, and full width at half maximum (FWHM). A waveform generation module is used to generate a predicted waveform of the high-altitude electromagnetic pulse based on the characteristic parameters; wherein, a target waveform function is determined based on the rise time and the full width at half maximum (FWHM); wherein, the target waveform function includes a double exponential waveform function and a double Gaussian waveform function; and the predicted waveform is generated based on the characteristic parameters and the target waveform function. Obtain a training sample set; wherein the training sample set includes: position training parameters for multiple spatial locations, and multiple basic training parameters for the high-altitude electromagnetic pulse, each of the basic training parameters including: photon equivalent training value, explosion height training value, and gamma ray time spectrum training value; train the neural network model based on the training sample set to obtain the trained computational model.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the method described in any one of claims 1-4.

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