A method for calculating the damage range of ground-impact nuclear explosion

Abstract

This invention proposes a method for calculating the damage range of a ground-impact nuclear explosion. The method includes decomposing the nuclear explosion energy into four main components: shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux. A heterogeneous medium model is constructed, incorporating atmospheric and geological models. Topographic and geological data are integrated to generate a joint map describing the spatial correlation between topography and strata. Nonlinear wave equations and a dynamic atmospheric transmittance model are used to calculate shock wave and thermal radiation propagation, and to assess the impact of seismic waves and electromagnetic pulses. A fuzzy logic algorithm is used to establish a damage probability mapping relationship, plotting a nuclear explosion damage range map, and dynamically updating it through a Bayesian learning module. Through multi-physics field coupling analysis and a dynamic learning mechanism, the invention improves the accuracy of nuclear explosion damage assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear explosion damage, and in particular to a method for calculating the damage range of a ground-impact nuclear explosion. Background Art

[0002] Calculating the damage range of a nuclear explosion is a core technology for assessing the effects of nuclear weapons and predicting battlefield situations. Its accuracy directly impacts strategic decision-making and tactical planning. Traditional methods rely on a combination of theoretical models and empirical formulas to estimate the damage range. However, this approach often struggles to accurately reflect the actual damage range of a ground-impact nuclear explosion when operating under complex terrain and geological conditions.

[0003] Some existing methods, such as the TNT equivalent method (which equates nuclear explosion energy to a TNT explosion) or empirical scaling models (e.g., the Brode formula), often employ one- or two-dimensional homogeneous medium models. These methods ignore the coupled effects of surface atmospheric turbulence, subsurface geological stratification, and topographical undulation. For example, the energy focusing effect caused by terrain shielding in Mach reflection zones in mountainous environments, or the secondary damage amplification effect caused by building resonance in urban areas, cannot be accurately described using homogeneous models. In practical applications, these methods lead to inaccurate predictions of the damage range of a ground-impact nuclear explosion, thus impacting tactical decision-making and battlefield situation prediction. Therefore, the present invention proposes a method for calculating the damage range of a ground-impact nuclear explosion. This method comprehensively considers multiple factors, including topographical undulation, stratum structure, atmospheric turbulence, and surface feature types, enabling a more accurate assessment of the damage range of a ground-impact nuclear explosion. Summary of the Invention

[0004] The present invention aims to at least solve the technical problem of low accuracy in evaluating the damage range of a ground-touching nuclear explosion in the prior art, and in particular innovatively proposes a method for calculating the damage range of a ground-touching nuclear explosion.

[0005] To achieve the above-mentioned object of the present invention, the present invention provides a method for calculating the damage range of a ground-impact nuclear explosion, the method comprising:

[0006] S1. Decompose the total energy of a nuclear explosion into four independent energy components: shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux;

[0007] S2. Construct a heterogeneous medium model that includes a surface atmospheric turbulence model and an underground geological stratification model. In this model, a digital elevation model and geological exploration data are integrated to generate a joint topographic-geological map. This joint topographic-geological map is then used to describe the spatial correlation between topographic relief and stratigraphic structure.

[0008] S3. Based on the shock wave energy flow, the nonlinear wave equation is used to describe the shock wave propagation process and calculate the range of the Mach reflection zone;

[0009] S4. Based on the dynamic model of atmospheric transmittance and the surface albedo field, the thermal radiation propagation path is corrected to distinguish the thermal radiation reflection characteristics of different types of land features;

[0010] S5. Construct a finite element model of underground stress wave propagation based on seismic wave energy flow, equating the explosion to a transient point source excitation, and couple it with a database of vibration response thresholds of surface buildings to assess secondary damage effects.

[0011] S6. Construct an electromagnetic pulse propagation path loss prediction model based on the electromagnetic pulse energy flow, and calculate the electromagnetic pulse impact range in combination with the electromagnetic environment parameters of the target area;

[0012] S7. Based on the terrain-geology joint map, a fuzzy logic fusion algorithm is used to establish a nonlinear mapping relationship between the damage probability of a single energy component and the combined damage probability under the coordinated action of multiple energy components;

[0013] S8. Based on the Mach reflection zone range, the thermal radiation reflection characteristics of different ground features, the secondary damage effects, and the nonlinear mapping relationship, and according to the joint damage probability, a map of the ground impact nuclear explosion damage range is drawn;

[0014] S9. Dynamically update the ground-impact nuclear explosion damage range map through the Bayesian online learning module.

[0015] The beneficial effects of the present invention are as follows: By constructing a heterogeneous medium model, implementing multi-physics field coupling analysis, introducing a dynamic Bayesian learning mechanism, developing a nuclear-driven bidirectional reflectance distribution function model, and establishing a multi-source verification system, the present invention significantly improves the accuracy of the assessment of the damage range of ground-impact nuclear explosions. Specifically, the introduction of a joint terrain-geology map breaks through the traditional homogeneous medium assumption and can accurately describe the coupling effects of surface atmospheric turbulence, underground geological stratification, and terrain undulations; the fuzzy logic fusion algorithm effectively captures the nonlinear synergy between multiple energy components such as shock waves, thermal radiation, seismic waves, and electromagnetic pulses, avoiding the error accumulation of traditional decoupling calculations; the Bayesian online learning module integrates the measured damage data after the explosion in real time, giving the assessment results the ability to be dynamically corrected and improving the timeliness to the minute level; the nuclear-driven BRDF model distinguishes the thermal radiation reflection characteristics of different types of terrain objects, solving the problem of inaccurate reflection characteristics caused by the simplification of terrain types in traditional methods; the multi-source verification system combines historical data with measured feedback, significantly enhancing the model's generalization ability in new battlefield environments.

[0016] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0018] Figure 1 The present invention is a flow chart of a method for calculating the damage range of a ground-impact nuclear explosion. DETAILED DESCRIPTION

[0019] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0020] like Figure 1 As shown, a method for calculating the damage range of a ground-impact nuclear explosion comprises:

[0021] S1. Decompose the total energy of a nuclear explosion into four independent energy components: shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux;

[0022] It should be noted that in step S1, the decomposition of the total nuclear explosion energy into four independent energy components—shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux—is based on the physical properties and energy release mechanisms of nuclear explosions. Nuclear explosions produce a variety of physical effects, including shock waves, thermal radiation, seismic waves, and electromagnetic pulses, each of which has different energy distributions and propagation characteristics. Therefore, decomposing the total nuclear explosion energy into these four independent energy components can more accurately describe and analyze the effects of a nuclear explosion on different targets. When decomposing the total nuclear explosion energy, it is also necessary to consider the interactions and synergistic effects between the various energy components. Although each has independent propagation mechanisms and destructive characteristics, during an actual nuclear explosion, these energy components can interfere with each other, superimpose, and even produce synergistic enhancement effects in some cases. Therefore, when decomposing the total nuclear explosion energy, it is necessary to employ physical models and numerical calculation methods to accurately describe the interactions between the various energy components. In this embodiment, these physical models and numerical calculation methods specifically employ three-dimensional dynamic fluid dynamics models, radiation transmission models, finite element analysis methods, and electromagnetic field calculation models. These models and methods can fully consider the impact of various factors such as terrain, geology, and meteorology on the energy propagation and destructive effects of nuclear explosions, thereby achieving accurate calculation of the damage range of nuclear explosions.

[0023] S2. Construct a heterogeneous medium model that includes a surface atmospheric turbulence model and an underground geological stratification model. In this model, a digital elevation model and geological exploration data are integrated to generate a joint topographic-geological map. This joint topographic-geological map is then used to describe the spatial correlation between topographic relief and stratigraphic structure.

[0024] It should be noted that in step S2, a heterogeneous medium model, comprising a surface atmospheric turbulence model and a subsurface geological stratification model, is constructed to accurately simulate the propagation of nuclear explosion energy both on the surface and underground. The surface atmospheric turbulence model describes the impact of atmospheric turbulence on the propagation of nuclear explosion shock waves and thermal radiation, while the subsurface geological stratification model reflects the influence of different strata on the propagation characteristics of seismic waves. By integrating the digital elevation model with geological exploration data, the resulting joint topographic-geological map accurately describes the spatial correlation between topographic relief and stratigraphic structure, providing critical topographic and geological information for subsequent calculations of nuclear explosion damage ranges.

[0025] S3. Based on the shock wave energy flow, the nonlinear wave equation is used to describe the shock wave propagation process and calculate the range of the Mach reflection zone;

[0026] It should be noted that in step S3, the use of nonlinear wave equations based on shock wave energy flux to describe the shock wave propagation process is intended to accurately capture the propagation characteristics of shock waves in surface and underground media. Nonlinear wave equations can account for nonlinear effects during shock wave propagation, such as the interaction between the shock wave and the medium, shock wave attenuation, and reflection. By solving the nonlinear wave equations, important information such as the shock wave propagation path, intensity distribution, and the extent of the Mach reflection zone can be obtained. The Mach reflection zone is a special region formed during shock wave propagation where the shock wave reflects and produces a strong compression effect, causing severe damage to the surrounding environment and targets. Therefore, calculating the extent of the Mach reflection zone is of great significance for assessing the destructive effects of nuclear explosions. When solving the nonlinear wave equations, advanced numerical calculation methods, such as the finite difference method, the finite element method, or the spectral method, are required to ensure the accuracy and reliability of the calculation results. Furthermore, it is necessary to consider the impact of the heterogeneity of the surface and underground media on shock wave propagation, as well as the interaction between the shock wave and the interfaces between different media.

[0027] S4. Based on the dynamic model of atmospheric transmittance and the surface albedo field, the thermal radiation propagation path is corrected to distinguish the thermal radiation reflection characteristics of different types of land features;

[0028] It should be noted that in step S4, the thermal radiation propagation path is corrected based on the thermal radiation energy flow through the atmospheric transmittance dynamic model and the surface albedo field to accurately simulate the propagation of thermal radiation on the surface and in the atmosphere. The atmospheric transmittance dynamic model can account for the effects of atmospheric absorption, scattering, and emission on thermal radiation propagation, while the surface albedo field reflects the reflection and absorption characteristics of thermal radiation from different surface types. By combining these two models, the thermal radiation propagation path can be corrected, allowing for more accurate calculation of the distribution and intensity of thermal radiation on different surface types. Furthermore, it is necessary to distinguish the thermal radiation reflection characteristics of different surface features. Different surface features, such as soil, vegetation, and buildings, have different thermal radiation reflection characteristics, which affect the propagation and distribution of thermal radiation on the surface. Therefore, when calculating the thermal radiation damage range, it is necessary to consider the reflection characteristics of different surface features to obtain more accurate damage assessment results. To achieve this goal, a kernel-driven bidirectional reflectance distribution function (BRDF) model can be used to describe the thermal radiation reflection characteristics of different surface features. The model can take into account the influence of factors such as surface roughness, incident angle and azimuth on the reflection characteristics, thereby more accurately simulating the reflection process of thermal radiation on the surface.

[0029] S5. Construct a finite element model of underground stress wave propagation based on seismic wave energy flow, equating the explosion to a transient point source excitation, and couple it with a database of vibration response thresholds of surface buildings to assess secondary damage effects.

[0030] It should be noted that in step S5, the underground stress wave propagation finite element model is constructed based on the seismic wave energy flow to accurately simulate the propagation and attenuation of seismic waves in the underground medium. The finite element model can account for the heterogeneity and complexity of the underground medium, such as stratum structure, rock type, and geological structure, thereby more accurately describing the propagation characteristics of seismic waves. Equating the nuclear explosion to a transient point source excitation simplifies the problem and facilitates calculations. By concentrating the explosive energy at a single point and using it as the excitation source, the propagation of seismic waves from the explosion point to the surrounding medium can be simulated. Step S5 utilizes advanced finite element analysis software and numerical calculation methods to ensure the accuracy and reliability of the calculation results. Furthermore, to assess secondary damage effects, a database of vibration response thresholds for surface buildings is also required. This database contains the vibration response thresholds for different types of buildings under earthquakes—that is, the vibration intensity at which a building begins to suffer damage or collapse. By comparing the output of the finite element model with the vibration response thresholds in the database, the vibration response of different surface buildings under earthquake waves can be assessed and the extent and scope of secondary damage effects determined. Step S5 comprehensively considers factors such as the building type, structure, height and foundation conditions to ensure the accuracy and reliability of the assessment results.

[0031] S6. Construct an electromagnetic pulse propagation path loss prediction model based on the electromagnetic pulse energy flow, and calculate the electromagnetic pulse impact range in combination with the electromagnetic environment parameters of the target area;

[0032] It should be noted that in step S6, the electromagnetic pulse propagation path loss prediction model is constructed based on the electromagnetic pulse energy flow in order to accurately predict the energy attenuation of the electromagnetic pulse during propagation. The electromagnetic pulse propagation path loss prediction model can account for various factors affecting electromagnetic waves during propagation, such as atmospheric absorption, ground reflection, and ionospheric disturbances. These factors can cause energy attenuation and changes in the propagation characteristics of the electromagnetic pulse during propagation. By constructing the prediction model, the electromagnetic pulse loss along different propagation paths can be calculated, thereby determining the electromagnetic pulse's impact range. When constructing the electromagnetic pulse propagation path loss prediction model, it is necessary to consider the electromagnetic environment parameters of the target area. These parameters include atmospheric density, ionospheric electron density, and ground conductivity, which have a significant impact on the propagation characteristics of the electromagnetic pulse. By incorporating these parameters into the prediction model, a more accurate description of the electromagnetic pulse's propagation process within the target area can be achieved, resulting in a more precise estimation of the electromagnetic pulse's impact range.

[0033] S7. Based on the terrain-geology joint map, a fuzzy logic fusion algorithm is used to establish a nonlinear mapping relationship between the damage probability of a single energy component and the combined damage probability under the coordinated action of multiple energy components;

[0034] It should be noted that the fuzzy logic fusion algorithm employed in step S7, based on the combined topographic and geological map, is designed to comprehensively consider the complex impact of various energy components on the damage range of a ground-to-ground nuclear explosion under varying topographic and geological conditions. Fuzzy logic fusion algorithms are capable of processing uncertain information and fuzzy data. By defining fuzzy sets and fuzzy rules, they quantify the damage probabilities of various energy components and the interactions between them. By establishing this nonlinear mapping relationship in step S7, a more comprehensive assessment of the damage effects of a ground-to-ground nuclear explosion under varying topographic and geological conditions can be achieved.

[0035] S8. Based on the Mach reflection zone range, the thermal radiation reflection characteristics of different ground features, the secondary damage effects, and the nonlinear mapping relationship, and according to the joint damage probability, a map of the ground impact nuclear explosion damage range is drawn;

[0036] It should be noted that in step S8, the combined damage probability is presented in an intuitive graphical format using advanced visualization technology and a geographic information system (GIS) based on the extent of the Mach reflection zone, the thermal radiation reflection characteristics of different terrain types, the secondary damage effects, and nonlinear mapping relationships. This step not only considers the damage effects of each energy component under independent action, but also fully accounts for the synergistic effects between them, as well as the complex influence of topographic and geological conditions on the damage range. By plotting the damage range map for a ground-impact nuclear explosion, the extent and scope of damage in different areas can be clearly visualized. When plotting the damage range map, the accuracy and reliability of the data, as well as the clarity and readability of the graphics, must also be considered to ensure the practicality and effectiveness of the final results.

[0037] S9. Dynamically update the ground-impact nuclear explosion damage range map through the Bayesian online learning module.

[0038] It should be noted that in step S9, the Bayesian online learning module dynamically updates the touchdown nuclear explosion damage map, enabling real-time monitoring and accurate assessment of the damage situation. This module utilizes newly acquired live damage image data to continuously adjust and optimize the touchdown nuclear explosion damage map, making it more realistic. This dynamic update mechanism ensures the timeliness and accuracy of the damage map. In practical applications, decision makers can use the updated damage map to promptly adjust rescue and response strategies, minimizing losses caused by the nuclear explosion.

[0039] The principle behind the method for calculating the damage range of a ground-impact nuclear explosion in this embodiment is as follows: First, the total energy of the nuclear explosion is decomposed into four independent energy components: shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux. These four components represent different physical effects produced by the nuclear explosion. Next, a heterogeneous medium model is constructed, which incorporates a surface atmospheric turbulence model and a subsurface geological layering model to accurately simulate the propagation of nuclear explosion energy both on the surface and underground. Next, a nonlinear wave equation is used to describe the propagation of the shock wave and calculate the extent of the Mach reflection zone. Simultaneously, the propagation path of thermal radiation is corrected using a dynamic atmospheric transmittance model and the surface albedo field to distinguish the thermal radiation reflection characteristics of different ground features. Furthermore, a finite element model of underground stress wave propagation is constructed to assess secondary damage effects, and an electromagnetic pulse propagation path loss prediction model is constructed to calculate the impact range of the electromagnetic pulse. After obtaining the damage probability of each energy component, a fuzzy logic fusion algorithm is used to establish a nonlinear mapping relationship between the damage probability of a single energy component and the combined damage probability of multiple energy components acting in concert. Finally, based on this information and data, a map of the damage range of a ground-impact nuclear explosion was constructed. This map was dynamically updated using a Bayesian online learning module, enabling real-time monitoring and accurate assessment of damage. This method not only improves the accuracy of damage range calculations for ground-impact nuclear explosions but also provides an important scientific basis for nuclear explosion emergency response and rescue efforts.

[0040] As an optional embodiment of the present invention, optionally, generating a topographic-geological joint map in step S2 includes:

[0041] S201. Collect meteorological data and geological exploration data;

[0042] It should be noted that in step S201, meteorological data and geological exploration data are collected using professional meteorological observation equipment and geological exploration instruments. Meteorological data includes parameters such as temperature, humidity, wind speed, and wind direction, which can reflect the state and changes of atmospheric turbulence. Geological exploration data covers detailed information such as the distribution of underground rock strata, rock types, and geological structures. This data is the basis for constructing an underground geological layering model. High-precision and high-frequency data acquisition ensures the accuracy and reliability of the combined topographic and geological maps. During data acquisition, attention must be paid to data integrity and consistency to ensure smooth subsequent data processing and model building. The collected meteorological and geological exploration data are then preprocessed, which includes operations such as data cleaning, format conversion, and missing value processing. Data cleaning primarily removes noise and outliers from the data to ensure data accuracy and reliability. Format conversion converts the data into a format suitable for subsequent analysis and model building. Missing value processing fills or deletes missing values ​​in the data to prevent adverse effects on subsequent analysis. During the preprocessing process, data quality control and verification are also required to ensure data integrity and consistency. The preprocessed meteorological data and geological exploration data can provide an accurate and reliable data basis for the subsequent construction of heterogeneous medium models and topographic-geological joint maps.

[0043] S202. Use meteorological data to establish a surface atmospheric turbulence model with Coriolis effect;

[0044] It should be noted that in step S202, by considering the influence of the Coriolis effect on atmospheric turbulent motion in the surface atmospheric turbulence model, the turbulent motion state in the atmosphere can be more accurately simulated. The Coriolis force is an inertial force generated by the rotation of the Earth. It affects the motion of fluids in the atmosphere, resulting in changes in the turbulent motion state. By establishing a surface atmospheric turbulence model that includes the Coriolis effect, the impact of atmospheric turbulence on the propagation of nuclear explosion shock waves and thermal radiation can be more accurately described. When constructing the model, it is necessary to comprehensively consider the spatiotemporal distribution and variation characteristics of meteorological data, as well as the specific impact mechanism of the Coriolis effect on atmospheric turbulence, to ensure the accuracy and reliability of the model. At the same time, the model needs to be solved and verified to further improve the model's predictive ability and application effect.

[0045] S203, establishing an underground geological layering model using geological exploration data;

[0046] It should be noted that, in step S203, the underground geological layering model is established by selecting high-precision geological exploration data and advanced modeling technology, such as geological radar, three-dimensional seismic exploration and geological profile measurement, which can accurately reflect the key information such as the distribution of underground rock strata, rock type, geological structure, etc. Step S203 first needs to carefully analyze and process the geological exploration data to extract the geometric form and physical properties of the underground rock strata. Then, this information is used to build the underground geological layering model, which can describe the spatial distribution and physical properties of the underground medium in detail. When building the model, the heterogeneity and complexity of the stratum, such as the diversity of rock types, the complexity of geological structure, etc., also need to be considered to ensure the accuracy and reliability of the model.

[0047] S204, spatially matching and fusing the surface atmospheric turbulence model and the underground geological layering model to form a heterogeneous medium model containing topographic and geological information;

[0048] It should be noted that in step S204, the atmospheric turbulence parameters in the surface atmospheric turbulence model need to be accurately spatially matched and fused with the geological parameters in the underground geological layering model. Through spatial matching technology and data fusion algorithm, the two models can be seamlessly connected in three-dimensional space, thereby forming a complete heterogeneous medium model containing topographic and geological information. This model can accurately describe the turbulent state of the surface atmosphere and the layered structure of the underground medium, as well as the interaction between them. When constructing a heterogeneous medium model, the resolution and accuracy of the model also need to be considered to ensure the accuracy and reliability of the simulation results. At the same time, the model also needs to be verified and calibrated.

[0049] S205, embedding a digital elevation model in the heterogeneous medium model, using the elevation information of the digital elevation model to describe the terrain, and combining it with the stratum distribution information in the geological exploration data to generate a topographic-geological joint map that can accurately reflect the spatial correlation between the terrain undulation and the stratum structure;

[0050] It should be noted that embedding the digital elevation model within the heterogeneous medium model in step S205 can further enhance the model's accuracy and practicality. The digital elevation model, using high-precision terrain data, provides a detailed description of the surface's undulations and morphology. By combining the digital elevation model with the heterogeneous medium model, the impact of terrain undulations on the propagation paths of shock waves and thermal radiation can be fully considered, resulting in a more accurate prediction of damage range. Furthermore, combined with stratigraphic distribution information from geological exploration data, the spatial correlation between terrain undulations and stratigraphic structure can be further revealed.

[0051] S206. Optimize the topographic-geological joint map through a genetic algorithm.

[0052] It should be noted that the optimization of the topographic-geological joint map using a genetic algorithm in step S206 is intended to further enhance the accuracy and practicality of the map. As a global optimization search method, the genetic algorithm can find the optimal solution in a complex problem space by simulating natural selection and genetic mechanisms. During optimization, the topographic-geological joint map must first be represented as individuals in the genetic algorithm, each of which contains a series of parameters describing the topographic and geological characteristics. The quality of each individual is then evaluated by defining a fitness function. The fitness function can be designed based on factors such as the accuracy and resolution of the map and the degree of match with actual observation data. Next, genetic operations such as selection, crossover, and mutation are used to iteratively optimize the individuals, gradually approaching the optimal solution. In each generation, individuals with higher fitness are selected as parents, a crossover operation is performed to produce new offspring individuals, and new genetic information is introduced through mutation operations to increase population diversity. Through multiple generations of genetic evolution, a set of optimized topographic-geological joint maps can ultimately be obtained, which can more accurately reflect the actual topographic and geological characteristics.

[0053] As an optional embodiment of the present invention, optionally, the expression for calculating the range of the Mach reflection zone in step S3 is:

[0054]

[0055] in, Represents the spatiotemporal distribution function of the Mach reflection zone, and outputs the Mach reflection energy density per unit volume (J / m³). Indicates the horizontal coordinate; Indicates vertical height; Indicates time; represents the control volume; represents the velocity potential function, which is used to describe the potential energy field distribution of shock wave propagation; Represents the delayed time coordinate, which is used to correct the time by taking into account the Doppler effect of the medium motion. represents the local speed of sound; Indicates the air compressibility correction factor; Indicates the air density; Laplace operator representing the velocity potential function; represents the nonlinear effect coefficient; represents the gradient of the velocity potential function; Represents the Heaviside step function, which is used when the incident angle Exceeding the critical angle The Mach reflex mechanism is activated. Represents the terrain correction factor.

[0056] As an optional embodiment of the present invention, optionally, distinguishing the thermal radiation reflection characteristics of different ground object types in step S4 includes:

[0057] S401. Establishing an atmospheric transmittance dynamic model and a surface albedo field based on the thermal radiation energy flow, and correcting the thermal radiation propagation path using the surface albedo field;

[0058] It should be noted that the atmospheric transmittance dynamic model established in step S401 based on the thermal radiation energy flow can account for the effects of factors such as atmospheric composition, temperature, and humidity on thermal radiation propagation, thereby more accurately describing the attenuation of thermal radiation in the atmosphere. The surface albedo field reflects the reflective and absorptive capabilities of different surface features for thermal radiation. By modifying the thermal radiation propagation path using the surface albedo field, the propagation effects of thermal radiation on different surface features can be more accurately calculated.

[0059] S402: Establish a kernel-driven bidirectional reflectance distribution function model for each land feature type, introduce a terrain slope correction term, and then couple the kernel-driven bidirectional reflectance distribution function model with a joint topographic-geological map;

[0060] It should be noted that in step S402, when establishing a kernel-driven BRDF model for each feature type, detailed field investigations and laboratory analysis are first required for each feature type to obtain its reflection characteristic parameters, such as reflectivity and reflection angle distribution. These parameters form the basis for constructing the kernel-driven BRDF model. During the investigation and analysis process, it is necessary to fully consider the diversity of feature types, such as vegetation, soil, rock, and buildings, as well as the variations in their reflection characteristics under different conditions, such as humidity, temperature, and surface roughness. Through precise measurement and data analysis, the reflection characteristic curve and distribution function for each feature type can be obtained. The obtained reflection characteristic parameters and distribution function are then introduced into the kernel-driven BRDF model to construct reflection models specific to each feature type. These models can describe in detail the reflection process of thermal radiation from different feature types, including variations in reflectivity and the distribution of reflection directions. Furthermore, the model is modified by introducing a terrain slope correction term to account for the influence of terrain slope on reflection characteristics. Terrain slope affects the incident and reflection angles of thermal radiation, thereby affecting the calculation of reflection characteristics. By introducing a terrain slope correction term, we can more accurately describe the effect of topographic relief on thermal radiation reflection characteristics. Finally, we couple the modified kernel-driven bidirectional reflectance distribution function model with a joint topographic-geological atlas. The joint topographic-geological atlas incorporates information about topographic relief and geological structure. By coupling the reflectance model with the atlas, we can accurately describe and calculate the thermal radiation reflection characteristics of different landform types. During the coupling process, we must fully consider the influence of topographic and geological factors on reflection characteristics, as well as the spatial correspondence between the reflectance model and the atlas, to ensure the accuracy and reliability of the coupling results.

[0061] S403. Calculate the atmospheric transmittance and surface reflection contribution of each thermal radiation propagation path based on the atmospheric transmittance dynamic model and the kernel-driven bidirectional reflectance distribution function model to obtain the actual intensity of the thermal radiation energy flow on each type of land feature;

[0062] It should be noted that in step S403, based on the atmospheric transmittance dynamic model and the kernel-driven bidirectional reflectance distribution function model, the attenuation of thermal radiation during propagation and the reflection contribution of different surface features to thermal radiation can be calculated. The atmospheric transmittance dynamic model can take into account the influence of complex factors such as atmospheric composition, temperature, and humidity on the propagation of thermal radiation, thereby providing a more accurate attenuation coefficient. The kernel-driven bidirectional reflectance distribution function model can describe in detail the reflection characteristics of each feature type to thermal radiation, including reflectivity, reflection direction, etc. By combining these two models, the atmospheric transmittance and surface reflection contribution on each thermal radiation propagation path can be calculated, and the actual intensity of the thermal radiation energy flow on each feature type can be obtained.

[0063] S404. Distinguish the thermal radiation reflection characteristics of different types of land features based on the actual intensity of the thermal radiation energy flow.

[0064] It should be noted that in step S404, by comprehensively considering the actual intensity of thermal radiation energy flux on different landform types, their thermal radiation reflection characteristics can be accurately distinguished. In practice, based on the actual intensity data of thermal radiation energy flux, combined with the topographic-geological joint map and the kernel-driven bidirectional reflectance distribution function model, it is necessary to conduct in-depth analysis and comparison of the reflection characteristics of different landform types. By comparing parameters such as reflectivity and reflection direction of different landform types, their different response mechanisms to thermal radiation propagation can be revealed, providing a more accurate and reliable basis for subsequent damage assessment and prediction.

[0065] As an optional embodiment of the present invention, optionally, in step S403, the expression for calculating the atmospheric transmittance and the surface reflection contribution of each thermal radiation propagation path is:

[0066]

[0067]

[0068] in, Indicates the distance from the heat radiation source to the The surface unit The total transmittance-reflection contribution of each wavelength channel is used to reflect the combined effect of atmospheric attenuation and surface reflection; Represents the total length of the heat radiation propagation path; represents the dynamic correction term of atmospheric transmittance; Represents the spatial coordinates along the heat radiation propagation path; represents the surface bidirectional reflectance distribution function; Indicates the wavelength of thermal radiation; represents the exponential function; represents the total absorption coefficient; represents the path tilt factor; represents the integral variable along the heat radiation propagation path; represents the path zenith angle dynamic function; Indicates the initial zenith angle of the thermal radiation propagation path; represents the projected area correction term; represents the isotropic component weight; represents the weight of the volume scattering component; represents the RossThick kernel function; represents the angle of incidence; represents the reflection angle; Indicates relative azimuth; represents the geometrical optics component weight; Represents the LiSparseR kernel function.

[0069] As an optional embodiment of the present invention, optionally, evaluating the secondary damage effect in step S5 includes:

[0070] S501, using a wavelet packet transform method to decompose the seismic wave energy flow into multiple frequency bands, wherein each frequency band corresponds to seismic waves of different frequency components;

[0071] It should be noted that the wavelet packet transform method used in step S501 can perform a detailed decomposition of the seismic wave energy flow in the time and frequency domains, thereby obtaining seismic wave signals in different frequency bands. These seismic wave signals in different frequency bands correspond to different frequency components and can more comprehensively reflect the propagation characteristics and energy distribution of seismic waves. By analyzing the seismic wave signals in each frequency band, we can gain a deeper understanding of the generation mechanism and impact range of secondary damage effects. Furthermore, the wavelet packet transform method also has the capability of multi-resolution analysis, allowing the appropriate number of decomposition levels and frequency band range to be selected as needed.

[0072] S502. For each frequency band of seismic waves, construct a corresponding underground stress wave propagation finite element model;

[0073] It should be noted that in step S502, since seismic wave propagation is affected by the complex properties of the underground medium, such as stratum distribution, rock type, and porosity, these factors must be fully considered when constructing the finite element model. Specifically, geological exploration data is first used to obtain information on the structure and physical properties of the underground medium, including stratum distribution, rock type, density, elastic modulus, Poisson's ratio, and so on. This information is then used to construct geometric and physical models of the underground medium. The geometric model accurately describes the distribution and morphology of the strata, as well as the geometric characteristics of the underground structure. The physical model then requires setting appropriate material parameters and boundary conditions based on the physical properties of the underground medium. The geometric and physical models are then imported into finite element software for meshing and finite element analysis. During meshing, the appropriate mesh type and size must be selected based on the complexity of the underground medium and the required computational accuracy. Finite element analysis involves two steps: seismic wave propagation simulation and stress field calculation. By simulating the propagation of seismic waves in the underground medium, parameters such as the propagation path, amplitude, and frequency of seismic waves of different frequency bands can be determined. At the same time, combined with stress field calculation, the stress and strain effects of seismic waves on underground media can be further analyzed.

[0074] S503, equating the nuclear explosion to a transient point source excitation, and applying the transient point source excitation to the center of the underground stress wave propagation finite element model to simulate the seismic wave propagation process generated by the nuclear explosion in different frequency bands;

[0075] It should be noted that in step S503, the nuclear explosion is equated with a transient point source excitation in order to simplify the model and highlight the key features of the nuclear explosion. By applying a transient point source excitation to the center of the underground stress wave propagation finite element model, the propagation of seismic waves generated by the nuclear explosion in the underground medium can be simulated. Due to the enormous energy released by a nuclear explosion, the seismic waves it produces have characteristics such as broadband and high amplitude. Therefore, the impact of these factors needs to be fully considered during the simulation process. Furthermore, because seismic waves in different frequency bands have different propagation characteristics and energy distributions, it is necessary to simulate the propagation process of seismic waves generated by nuclear explosions in different frequency bands separately to gain a deeper understanding of the generation mechanism and impact range of secondary damage effects. During the simulation process, it is necessary to record parameters such as the propagation path, amplitude, and frequency of the seismic waves and, combined with stress field calculations, analyze the stress and strain effects of the seismic waves on the underground medium.

[0076] S504. Utilize the surface building complex vibration response threshold database and, based on the output results of the underground stress wave propagation finite element model, evaluate the vibration response of different surface building complexes under the action of seismic waves and determine the extent and scope of secondary damage effects.

[0077] It should be noted that, in step S504, the vibration response thresholds of different building complexes under seismic waves can be obtained using the surface building complex vibration response threshold database. These thresholds reflect the maximum vibration intensity that a building complex can withstand under seismic waves; exceeding this intensity may result in structural damage or casualties. By comparing the output of the underground stress wave propagation finite element model with the surface building complex vibration response threshold database, the vibration response of different surface building complexes under seismic waves can be evaluated. Specifically, the degree and scope of the building complex's vibration response under seismic waves can be comprehensively determined based on parameters such as the amplitude and frequency of the seismic waves, combined with factors such as the building complex's structural type, height, and foundation form. Furthermore, the degree and scope of secondary damage effects can be determined based on the degree and scope of the vibration response. Secondary damage effects include structural damage, collapse, and casualties in the building complex, and their degree and scope are closely related to the intensity and propagation characteristics of the seismic waves, as well as the structural characteristics of the building complex.

[0078] As an optional embodiment of the present invention, optionally, the expression for calculating the electromagnetic pulse influence range in step S6 is:

[0079]

[0080] in, Indicates the radius of the electromagnetic pulse's impact range, specifically the maximum effective distance when the electromagnetic pulse's energy density drops to the threshold.

[0081] Indicates the center frequency of the electromagnetic pulse, which is determined by the nuclear explosion equivalent and ranges from 10MHz to 1GHz;

[0082] Represents the equivalent radiation power of the electromagnetic pulse, including the electromagnetic energy flow component of the nuclear explosion Equivalent conversion parameters to antenna gain;

[0083] represents the equivalent gain of the transmitting antenna, which is an equivalent gain model considering the ionospheric reflection effect of the explosion fireball;

[0084] Indicates the polarization matching gain of the receiving antenna, which depends on the angle between the polarization direction of the target device antenna and the polarization direction of the EMP. ;

[0085] represents the pulse duration correction factor;

[0086] represents the atmospheric propagation efficiency;

[0087] represents the ground reflection coefficient;

[0088] Indicates the electromagnetic pulse damage threshold of the target device, ranging from 10-6 to 10²W / m² (depending on the device type, such as 10-3 W / m² for C4ISR systems);

[0089] represents the ionospheric disturbance coefficient, which is 0.8 for nighttime explosions and 1.2 for daytime explosions, taking into account the effect of solar radiation on the D-layer ionization;

[0090] The standard deviation of the ionospheric electron density disturbance is corrected by real-time ionospheric TEC data and has a value range of 1010 to 1012 electrons / m³.

[0091] As an optional embodiment of the present invention, optionally, establishing a nonlinear mapping relationship between the damage probability of a single energy component and the joint damage probability under the coordinated action of multiple energy components in step S7 includes:

[0092] S701. Using the combined topographic and geological map as the reference coordinate system, an iterative closest point algorithm is used to achieve spatial registration of the damage probability field for the Mach reflection zone range, thermal radiation reflection characteristics, secondary damage effects, and electromagnetic pulse impact range.

[0093] It should be noted that the use of the combined topographic and geological map as the reference coordinate system in step S701 ensures the accuracy of the spatial registration of the damage probability field. The combined topographic and geological map contains rich topographic and geological information, reflecting the geographical characteristics and material composition of different regions, thus providing a reliable reference for the spatial registration of the damage probability field. Using an iterative closest point algorithm, the optimal spatial registration result can be gradually approached, allowing the damage probability fields for Mach reflection zone ranges, thermal radiation reflection characteristics, secondary damage effects, and electromagnetic pulse impact ranges to be compared and analyzed in a unified coordinate system. Spatial registration provides a more intuitive understanding of the spatial distribution and interrelationships of different damage factors.

[0094] S702, using a fuzzy logic fusion algorithm to fuse the registered damage probability field, and synthesize the interaction between different energy components during the ground impact nuclear explosion damage process;

[0095] It should be noted that in step S702, the fuzzy logic fusion algorithm fully accounts for the complexity and uncertainty of different energy components in the damage process of a touchdown nuclear explosion. This algorithm defines a series of fuzzy rules and membership functions to fuse the damage probability fields of different energy components, thereby generating a comprehensive damage probability field. This comprehensive damage probability field can more comprehensively reflect the extent and scope of damage caused by a touchdown nuclear explosion in different regions. In practice, appropriate fuzzy rules and membership functions must be selected based on actual conditions to ensure the accuracy and reliability of the fusion results. Through the fuzzy logic fusion algorithm, the interaction mechanism between different energy components in the damage process of a touchdown nuclear explosion can be further revealed.

[0096] S703. Based on the fused damage probability field, a nonlinear mapping relationship is established between the damage probability of a single energy component and the combined damage probability under the synergistic effect of multiple energy components. This nonlinear mapping relationship is used to reflect the contribution of each energy component to the damage range of a ground-impact nuclear explosion and the synergistic effect under different terrain and geological conditions.

[0097] It should be noted that the nonlinear mapping relationship established in step S703 based on the fused damage probability field can more accurately describe the complexity and diversity of the damage range of a touchdown nuclear explosion. This nonlinear mapping relationship not only considers the damage probability of each energy component acting alone, but also integrates the synergistic effects between them, thereby more comprehensively reflecting the degree and range of damage caused by a touchdown nuclear explosion under different terrain and geological conditions. Specifically, in practical applications, this nonlinear mapping relationship can be used to predict and assess the damage range based on the specific touchdown nuclear explosion scenario and terrain and geological conditions. By comprehensively analyzing the damage probabilities of different energy components, the potential threat level of a touchdown nuclear explosion to different regions can be more accurately determined.

[0098] S704: Verify and calibrate the established nonlinear mapping relationship.

[0099] It should be noted that the purpose of verifying and calibrating the established nonlinear mapping relationship in step S704 is to ensure its accuracy and reliability. The verification process can be achieved by comparing the predicted results with actual ground-impact nuclear explosion test data. By comparing the differences between the predicted results and the actual situation, the accuracy and applicability of the nonlinear mapping relationship can be evaluated. If there is a large deviation between the predicted results and the actual situation, the nonlinear mapping relationship needs to be adjusted and optimized. The calibration process includes correcting and improving the parameters in the nonlinear mapping relationship to improve its prediction accuracy and generalization ability. The specific calibration method can be selected according to the actual situation, such as using a machine learning algorithm to train and optimize the nonlinear mapping relationship, or improving the construction of the nonlinear mapping relationship by introducing new test data and topographic and geological information. Through verification and calibration, the accuracy and reliability of the nonlinear mapping relationship can be continuously improved.

[0100] As an optional embodiment of the present invention, optionally, in step S8, drawing a ground impact nuclear explosion damage range map based on the Mach reflection zone range, thermal radiation reflection characteristics of different ground object types, secondary damage effects, and nonlinear mapping relationships, and according to the combined damage probability includes:

[0101] S801. Perform data fusion processing on the range of the Mach reflection zone, the thermal radiation reflection characteristics of different ground features, the secondary damage effects, and the nonlinear mapping relationship to generate a preliminary map of the damage range of the ground impact nuclear explosion.

[0102] It should be noted that the data fusion processing performed in step S801 on the Mach reflection zone range, thermal radiation reflection characteristics of different terrain types, secondary damage effects, and nonlinear mapping relationships is intended to comprehensively consider the impact of various damage factors on a touchdown nuclear explosion. Data fusion can fully leverage the complementarity and correlation between this information to generate a more comprehensive and accurate preliminary map of the damage range of a touchdown nuclear explosion. In practice, a data fusion algorithm is required to ensure the accuracy and reliability of the fusion results. Through data fusion processing, the spatial distribution and interrelationships of different damage factors can be intuitively displayed.

[0103] S802. Calculate the spatial distribution of the joint damage probability in the preliminary map;

[0104] It should be noted that the purpose of calculating the spatial distribution of the joint damage probability in the preliminary map in step S802 is to clarify the specific location and extent of the damage range of a ground-impact nuclear explosion. By employing appropriate calculation methods, such as interpolation or spatial statistics, the joint damage probability can be estimated for each location in the preliminary map. This yields a map containing the spatial distribution of the joint damage probability, which can intuitively demonstrate the potential threat level of a ground-impact nuclear explosion to different regions. In practical applications, this map can be used to accurately delineate and assess the damage range.

[0105] S803. Color-code the preliminary map according to the joint damage probability, with different colors representing different degrees of damage;

[0106] It should be noted that the color coding of the preliminary map in step S803 is intended to more intuitively demonstrate the varying degrees and regions of damage from a touchdown nuclear explosion. By selecting an appropriate color scheme, such as using gradients or segmented colors to represent varying degrees of damage, the map can be made easier to understand and analyze. In practice, the color coding rules and standards need to be determined based on the magnitude and range of the combined damage probability. Through color coding, it is clear which areas have suffered severe damage and which areas have suffered relatively minor damage. Furthermore, color coding can also be used to create thematic maps of the damage range of a touchdown nuclear explosion, facilitating review and analysis by relevant departments and personnel.

[0107] S804. Using a geographic information system platform, the color-coded map is superimposed with the geographic information to obtain a map of the damage range of the ground-impact nuclear explosion.

[0108] It should be noted that in step S804, using a geographic information system (GIS) platform, the color-coded map can be overlaid with actual geographic information (such as topographic maps, administrative division maps, etc.) to generate a touchdown nuclear explosion damage map. The GIS platform provides powerful spatial data processing and analysis capabilities, supporting large-scale spatial data management and visualization. By overlaying the color-coded map with geographic information, the distribution of touchdown nuclear explosion damage across different geographic regions can be intuitively visualized. This not only helps relevant departments and personnel quickly understand the specific location and extent of the damage, but also provides an important reference for subsequent emergency response, rescue operations, and disaster assessments. After the touchdown nuclear explosion damage map is generated, it can be further edited and modified as needed, such as by adding annotations, legends, and scale information, to facilitate better display and analysis.

[0109] As an optional embodiment of the present invention, optionally, in step S9, dynamically updating the ground-impact nuclear explosion damage range map by using the Bayesian online learning module includes:

[0110] S901. Collecting live damage images at different time phases after the explosion, and preprocessing the live damage images;

[0111] It should be noted that the live damage images captured in step S901 contain various noise and interference information, such as cloud obstruction and lighting variations. Therefore, these images require preprocessing to improve the accuracy and reliability of subsequent analysis. This preprocessing process includes steps such as image enhancement, denoising, and registration to ensure that the quality of the live damage images meets the requirements of subsequent analysis. This preprocessing allows for the extraction of useful information from the live damage images.

[0112] S902: Establish a Bayesian online learning module, comprising a data input unit, a model updating unit, and a result output unit. The data input unit is configured to receive live damage images and a nuclear touchdown damage range map. The model updating unit updates the nuclear touchdown damage range map online based on the received live damage images. The result output unit outputs the updated nuclear touchdown damage range map.

[0113] It should be noted that the Bayesian online learning module established in step S902 enables dynamic updating of the touchdown nuclear blast damage map. The data input unit, serving as the entry point for the entire module, is responsible for receiving live damage images and existing touchdown nuclear blast damage maps from various channels. These images and maps contain rich damage information. The model update unit, the core component of the Bayesian online learning module, uses a Bayesian online learning algorithm to update the touchdown nuclear blast damage map in real time based on the received live damage images. The Bayesian online learning algorithm leverages existing prior knowledge and new observational data to continuously optimize the damage map through iterative updates. During the update process, the model considers new information from the live damage images, such as the expansion of the damage range and the emergence of new damage areas, thereby revising and improving the touchdown nuclear blast damage map. The result output unit is responsible for outputting the updated touchdown nuclear blast damage map to the user. Dynamic updates of the Bayesian online learning module ensure the accuracy and timeliness of ground-impact nuclear explosion damage maps, providing strong support for emergency response, rescue operations, and disaster assessment. During implementation, appropriate Bayesian online learning algorithms and parameter settings can be selected based on actual circumstances to optimize update performance and improve prediction accuracy. Furthermore, regular verification and calibration of the Bayesian online learning module are required to ensure its long-term stability and reliability.

[0114] S903. Use the updated touchdown nuclear explosion damage range map to monitor and evaluate the actual damage situation in real time, and adjust the parameters of the Bayesian online learning module based on the monitoring and evaluation results to improve the accuracy of the touchdown nuclear explosion damage range prediction.

[0115] It should be noted that the updated touchdown nuclear explosion damage map in step S903 can be used to monitor the actual damage after the explosion in real time, including the expansion of the damage range, the emergence of new damaged areas, and changes in the damage severity. Comprehensive analysis of this information can assess the damage severity and potential threat posed by the touchdown nuclear explosion to different regions. Simultaneously, based on the monitoring and assessment results, the parameters of the Bayesian online learning module can be adjusted and optimized. This adjustment can be based on the new live damage imagery and damage map, and through iterative updates, make the Bayesian online learning module more adaptable to the actual damage situation, thereby improving the accuracy of the touchdown nuclear explosion damage range prediction. In specific implementation, a series of evaluation metrics, such as prediction accuracy and update speed, can be set to quantify the performance of the Bayesian online learning module. Based on the feedback from these metrics, the module parameters can be fine-tuned to achieve optimal prediction results. Through this real-time monitoring and assessment, as well as parameter adjustment, the accuracy and timeliness of the touchdown nuclear explosion damage range map can be ensured.

[0116] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and alterations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for calculating the damage range of a ground-impact nuclear explosion, characterized in that: The method comprises: S1. Decompose the total energy of a nuclear explosion into four independent energy components: shock wave energy flux, thermal radiation energy flux, seismic wave energy flux, and electromagnetic pulse energy flux; S2. Construct a heterogeneous medium model that includes a surface atmospheric turbulence model and an underground geological stratification model. In this model, a digital elevation model and geological exploration data are integrated to generate a joint topographic-geological map. This joint topographic-geological map is then used to describe the spatial correlation between topographic relief and stratigraphic structure. S3. Based on the shock wave energy flow, the nonlinear wave equation is used to describe the shock wave propagation process and calculate the range of the Mach reflection zone; S4. Based on the dynamic model of atmospheric transmittance and the surface albedo field, the thermal radiation propagation path is corrected to distinguish the thermal radiation reflection characteristics of different types of land features; S5. Construct a finite element model of underground stress wave propagation based on seismic wave energy flow, equating the explosion to a transient point source excitation, and couple it with a database of vibration response thresholds of surface buildings to assess secondary damage effects. S6. Construct an electromagnetic pulse propagation path loss prediction model based on the electromagnetic pulse energy flow, and calculate the electromagnetic pulse impact range in combination with the electromagnetic environment parameters of the target area; S7. Based on the terrain-geology joint map, a fuzzy logic fusion algorithm is used to establish a nonlinear mapping relationship between the damage probability of a single energy component and the combined damage probability under the coordinated action of multiple energy components; S8. Based on the Mach reflection zone range, the thermal radiation reflection characteristics of different ground features, the secondary damage effects, and the nonlinear mapping relationship, and according to the joint damage probability, a map of the ground impact nuclear explosion damage range is drawn; S9. Dynamically update the ground-impact nuclear explosion damage range map through the Bayesian online learning module.

2. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: Generating a topographic-geological joint map in step S2 includes: S201. Collect meteorological data and geological exploration data; S202. Use meteorological data to establish a surface atmospheric turbulence model with Coriolis effect; S203, establishing an underground geological layering model using geological exploration data; S204, spatially matching and fusing the surface atmospheric turbulence model and the underground geological layering model to form a heterogeneous medium model containing topographic and geological information; S205, embedding a digital elevation model in the heterogeneous medium model, using the elevation information of the digital elevation model to describe the terrain, and combining it with the stratum distribution information in the geological exploration data to generate a topographic-geological joint map that can accurately reflect the spatial correlation between the terrain undulation and the stratum structure; S206. Optimize the topographic-geological joint map through a genetic algorithm.

3. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: The expression for calculating the range of the Mach reflection zone in step S3 is: in, represents the space-time distribution function of the Mach reflection zone, represents the horizontal coordinate, Indicates the vertical height, Indicates time, represents the control volume, represents the velocity potential function, represents the delay time coordinate, represents the local speed of sound, represents the air compressibility correction factor, represents the air density, The Laplace operator representing the velocity potential function, represents the nonlinear effect coefficient, represents the gradient of the velocity potential function, represents the Heaviside step function, Represents the terrain correction factor.

4. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: In step S4, the thermal radiation reflection characteristics of different types of ground objects are distinguished as follows: S401. Establishing an atmospheric transmittance dynamic model and a surface albedo field based on the thermal radiation energy flow, and correcting the thermal radiation propagation path using the surface albedo field; S402: Establish a kernel-driven bidirectional reflectance distribution function model for each land feature type, introduce a terrain slope correction term, and then couple the kernel-driven bidirectional reflectance distribution function model with a joint topographic-geological map; S403. Calculate the atmospheric transmittance and surface reflection contribution of each thermal radiation propagation path based on the atmospheric transmittance dynamic model and the kernel-driven bidirectional reflectance distribution function model to obtain the actual intensity of the thermal radiation energy flow on each type of land feature; S404. Distinguish the thermal radiation reflection characteristics of different types of land features based on the actual intensity of the thermal radiation energy flow.

5. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 4, wherein: In step S403, the expression for calculating the atmospheric transmittance and surface reflection contribution of each thermal radiation propagation path is: in, Indicates the distance from the heat radiation source to the The surface unit The total transmittance-reflection contribution of each wavelength channel, represents the total length of the heat radiation propagation path, represents the dynamic correction term of atmospheric transmittance, represents the spatial coordinates along the heat radiation propagation path, represents the surface bidirectional reflectance distribution function, represents the wavelength of thermal radiation, represents the exponential function, represents the total absorption coefficient, represents the path tilt factor, represents the integral variable along the heat radiation propagation path, represents the path zenith angle dynamic function, represents the initial zenith angle of the thermal radiation propagation path, represents the projected area correction term, represents the isotropic component weight, represents the volume scattering component weight, represents the RossThick kernel function, represents the angle of incidence, represents the reflection angle, represents the relative azimuth, represents the geometrical optics component weight, Represents the LiSparseR kernel function.

6. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: The evaluation of secondary damage effects in step S5 includes: S501, using a wavelet packet transform method to decompose the seismic wave energy flow into multiple frequency bands, wherein each frequency band corresponds to seismic waves of different frequency components; S502. For each frequency band of seismic waves, construct a corresponding underground stress wave propagation finite element model; S503, equating the nuclear explosion to a transient point source excitation, and applying the transient point source excitation to the center of the underground stress wave propagation finite element model to simulate the seismic wave propagation process generated by the nuclear explosion in different frequency bands; S504. Utilize the surface building complex vibration response threshold database and, based on the output results of the underground stress wave propagation finite element model, evaluate the vibration response of different surface building complexes under the action of seismic waves and determine the extent and scope of secondary damage effects.

7. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: The expression for calculating the electromagnetic pulse influence range in step S6 is: in, Indicates the radius of the electromagnetic pulse impact range, represents the center frequency of the electromagnetic pulse, represents the equivalent radiated power of electromagnetic pulse, represents the equivalent gain of the transmitting antenna, represents the receiving antenna polarization matching gain, represents the pulse duration correction factor, represents the atmospheric propagation efficiency, represents the ground reflection coefficient, Indicates the electromagnetic pulse damage threshold of the target device. represents the ionospheric disturbance coefficient, represents the standard deviation of the ionospheric electron density disturbance.

8. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: Establishing the nonlinear mapping relationship between the damage probability of a single energy component and the joint damage probability under the coordinated action of multiple energy components in step S7 includes: S701. Using the combined topographic and geological map as the reference coordinate system, an iterative closest point algorithm is used to achieve spatial registration of the damage probability field for the Mach reflection zone range, thermal radiation reflection characteristics, secondary damage effects, and electromagnetic pulse impact range. S702, using a fuzzy logic fusion algorithm to fuse the registered damage probability field, and synthesize the interaction between different energy components during the ground impact nuclear explosion damage process; S703. Based on the fused damage probability field, a nonlinear mapping relationship is established between the damage probability of a single energy component and the combined damage probability under the synergistic effect of multiple energy components. This nonlinear mapping relationship is used to reflect the contribution of each energy component to the damage range of a ground-impact nuclear explosion and the synergistic effect under different terrain and geological conditions. S704: Verify and calibrate the established nonlinear mapping relationship.

9. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: In step S8, based on the Mach reflection zone range, the thermal radiation reflection characteristics of different ground object types, the secondary damage effect and the nonlinear mapping relationship, and according to the combined damage probability, a ground impact nuclear explosion damage range map is drawn, which includes: S801. Perform data fusion processing on the range of the Mach reflection zone, the thermal radiation reflection characteristics of different ground features, the secondary damage effects, and the nonlinear mapping relationship to generate a preliminary map of the damage range of the ground impact nuclear explosion. S802. Calculate the spatial distribution of the joint damage probability in the preliminary map; S803. Color-code the preliminary map according to the joint damage probability, with different colors representing different degrees of damage; S804. Using a geographic information system platform, the color-coded map is superimposed with the geographic information to obtain a map of the damage range of the ground-impact nuclear explosion.

10. The method for calculating the damage range of a ground-impact nuclear explosion according to claim 1, wherein: In step S9, dynamically updating the ground-impact nuclear explosion damage range map using the Bayesian online learning module includes: S901. Collecting live damage images at different time phases after the explosion, and preprocessing the live damage images; S902: Establish a Bayesian online learning module, comprising a data input unit, a model updating unit, and a result output unit. The data input unit is configured to receive live damage images and a nuclear touchdown damage range map. The model updating unit updates the nuclear touchdown damage range map online based on the received live damage images. The result output unit outputs the updated nuclear touchdown damage range map. S903. Use the updated touchdown nuclear explosion damage range map to monitor and evaluate the actual damage situation in real time, and adjust the parameters of the Bayesian online learning module based on the monitoring and evaluation results to improve the accuracy of the touchdown nuclear explosion damage range prediction.

Images