A high-speed aircraft electromagnetic calculation method and system based on a quantum classification algorithm

Abstract

The application relates to the technical field of high-speed aircraft ground simulation and electromagnetic characteristic calculation, and provides a high-speed aircraft electromagnetic calculation method and system based on a quantum classification algorithm. A variational quantum classifier is used as an intelligent scheduling core to perform feature identification and region division on a full flow field, and a transmission matrix method and a WKB approximation method are adaptively called for hybrid calculation according to the division result, so that the technical problem that a traditional single calculation strategy is difficult to cooperatively optimize in terms of precision and efficiency is solved, and the method has the advantages that global calculation resources can be allocated on demand, the overall solving speed is greatly improved while the high simulation precision of key regions is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-speed aircraft ground simulation and electromagnetic characteristic calculation, in particular to a high-speed aircraft electromagnetic calculation method and system based on a quantum classification algorithm. BACKGROUND

[0002] The electromagnetic characteristic calculation of a high-speed aircraft is a key link in ground simulation simulation, and the rapidity and accuracy of its results directly affect the reliability of radar target feature recognition. In the twenty-first century, with the rapid development of high-speed aircraft technology by various space powers, the flow field space faced by ground simulation is increasingly complex, and the calculation domain is rapidly expanding, which poses a dual challenge to the efficiency and accuracy of existing electromagnetic calculation methods. Especially when dealing with targets with complex aerodynamic shape, extremely fast flight speed and vast flow field area, how to achieve high-precision and fast calculation of the whole space under limited computing power has become a core bottleneck restricting the improvement of ground simulation capability.

[0003] In the current environment of rapid development of high-speed aircraft, ground electromagnetic characteristic simulation and calculation systems are facing increasingly severe computing pressure. The existing classical calculation paradigm usually uses the transmission matrix method to model the electromagnetic characteristics of the aircraft flow field by discretizing the non-uniform flow field into a large number of uniform thin layers and calculating the propagation and interface effects of electromagnetic waves layer by layer. Although this method can ensure high calculation accuracy in principle, it has the following limitations in terms of calculation efficiency and resource allocation rationality when dealing with large-scale practical engineering problems:

[0004] (1) Low calculation efficiency and long solution time. The calculation complexity of the transmission matrix method is closely related to the number of layers of flow field discretization. In order to accurately simulate the region where physical quantities such as shock waves change dramatically, a very high spatial resolution must be used, resulting in a sharp increase in the total number of layers. The core recursive process of this method is essentially serial, i.e. the calculation result of the nth layer strictly depends on the result of the (n-1)th layer, and this inherent sequential dependency seriously hinders the full play of the advantages of large-scale parallel computing.

[0005] (2) Rigid utilization of computational resources. Existing methods employ a globally uniform discretization strategy and a single algorithm, neglecting the highly non-uniform distribution of the physical characteristics of the flow field itself. For example, in narrow regions with extremely large gradients, such as shock waves and boundary layers, physical parameters change drastically, requiring meticulous modeling to ensure accuracy; while in the vast far-field region, physical parameters change gradually, and approximation methods can provide sufficiently reliable results. Using a globally high-precision algorithm (such as the full-region fine-grained layered transfer matrix method) would over-decompose the far-field region, resulting in a huge waste of computational resources; conversely, if a globally approximation algorithm (such as the full-region WKB method) is used, unacceptable errors will be introduced in critical high-gradient regions due to method failure. This one-size-fits-all strategy cannot achieve dynamic optimization of computational resources based on regional importance.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of this application is to provide a high-speed aircraft electromagnetic calculation method and system based on quantum classification algorithm. It has the advantages of achieving adaptive and accurate division of the calculation region by recognizing flow field characteristics through quantum intelligence, and adopting a hybrid calculation strategy based on the division results, thereby significantly improving the efficiency of global simulation while ensuring high calculation accuracy in key areas.

[0008] In a first aspect, this application provides a high-speed aircraft electromagnetic calculation method based on a quantum classification algorithm, including:

[0009] Classical calculations and feature extractions are performed on the entire flow field of a high-speed aircraft to obtain the physical parameters and gradient information of multiple grid points in the flow field, and to construct the local feature vector of each grid point.

[0010] The local feature vector is input into a pre-trained variable quantum classifier, which outputs the probability that each grid point belongs to a preset category. Based on this probability, a region labeling map is generated to divide the entire flow field into a shock wave critical region and a smooth region. The physical parameter gradient of the shock wave critical region is higher than a preset threshold, while the physical parameter gradient of the smooth region is lower than the preset threshold.

[0011] Based on the region marking map, a first electromagnetic calculation method is used for the critical shock wave region, and a second electromagnetic calculation method is used for the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method.

[0012] By integrating the calculation results of the critical shock wave region and the smooth region, the electromagnetic characteristic data of the entire flow field are obtained and visualized.

[0013] Furthermore, in this application, the classical calculation and feature extraction of the entire flow field includes:

[0014] The NS equations were discretized on a spatial grid using the finite element method and solved iteratively to obtain flow field data including temperature, pressure, density, and electron density.

[0015] The flow field data is reconstructed to obtain at least one electromagnetic parameter among plasma frequency, collision frequency, dielectric constant, and conductivity.

[0016] For each grid point, the maximum and average values ​​of the gradient magnitude of the electromagnetic parameter within a preset local neighborhood window centered on that grid point are calculated to construct a multidimensional local feature vector for that grid point.

[0017] Furthermore, in this application, the variable quantum classifier is pre-trained in the following manner:

[0018] Obtain a training sample set; each sample in the training sample set includes a local feature vector extracted from historical flow field data, and a category label annotated by experts, which is used to indicate whether the corresponding grid point belongs to a critical shock wave region or a flat region.

[0019] The design includes a variable quantum circuit comprising an angle encoding layer for encoding the local feature vector into the quantum bit state space, a variational evolution layer consisting of a parameterized rotation gate and a fixed entanglement gate, and a measurement layer for performing Pauli Z measurements on a specified quantum bit.

[0020] The training loop is executed on the quantum simulator. The gradient of the loss function with respect to the line parameters is calculated using the parameter shifting rule, and the parameters are iteratively updated using the classical optimizer until the model converges.

[0021] Furthermore, in this application, the preset category is a shock wave critical region category;

[0022] The output calculates the probability that each grid point belongs to a preset category, and generates a region marking map based on this probability to divide the entire flow field into a shock wave critical region and a smooth region, including:

[0023] A classification threshold is set. For each grid point, if the probability value of its belonging to the critical shock region is greater than or equal to the classification threshold, the grid point is marked as the critical shock region; otherwise, it is marked as a flat region.

[0024] Furthermore, in this application, the first electromagnetic calculation method is the transfer matrix method, and the second electromagnetic calculation method is the WKB approximation method;

[0025] When using the transfer matrix method for the critical region of the shock wave, the non-uniform medium in the critical region of the shock wave is finely divided into a first number of uniform thin layers along the electromagnetic wave propagation direction, and the scattering field of the region is calculated recursively by the transfer matrix.

[0026] When applying the WKB approximation method to the smooth region, the medium within the smooth region is divided into a second number of layers along the electromagnetic wave propagation direction, and the approximate scattering field of the region is obtained by solving the process function equation and the transmission equation; wherein, the first number is greater than the second number.

[0027] Furthermore, in this application, the electromagnetic characteristic data of the entire flow field includes the total scattered field of the entire flow field; the process of fusing the calculation results of the critical shock wave region and the smooth region to obtain the electromagnetic characteristic data of the entire flow field includes:

[0028] The total scattered field of the entire flow field is synthesized according to the following formula:

[0029] E total (r)=E TMM (r) I shock (r)+E WKB (r) (1-I shock (r));

[0030] Among them, E total (r) represents the total scattered field at position r; E TMM (r) represents the scattered field calculated using the transfer matrix method; E WKB (r) represents the scattered field calculated using the WKB approximation method; I shock (r) is the indicator function of the region marker map, which takes a value of 1 in the critical shock region and a value of 0 in the flat region.

[0031] Furthermore, in this application, the visualization output includes at least one of radar cross section distribution map, electromagnetic field intensity spatial cloud map, and transmission attenuation curve.

[0032] Secondly, this application also proposes a high-speed aircraft electromagnetic computing system based on a quantum classification algorithm, comprising:

[0033] The flow field preprocessing module performs classical calculations and feature extraction on the entire flow field of the high-speed aircraft, obtains the physical parameters and gradient information of multiple grid points in the flow field, and constructs the local feature vector of each grid point.

[0034] The quantum classification module inputs the local feature vector into a pre-trained variable quantum classifier, outputs the probability that each grid point belongs to a preset category, and generates a region labeling map based on the probability to divide the entire flow field into a shock wave critical region and a smooth region; wherein, the physical parameter gradient of the shock wave critical region is higher than a preset threshold, and the physical parameter gradient of the smooth region is lower than the preset threshold.

[0035] The electromagnetic calculation module, based on the region marking map, applies a first electromagnetic calculation method to the critical region of the shock wave and a second electromagnetic calculation method to the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method.

[0036] The fusion output module fuses the calculation results of the key shock wave region and the smooth region to obtain the electromagnetic characteristic data of the entire flow field, and then outputs the visualization data.

[0037] Thirdly, this application also proposes an electronic device comprising: one or more processors, and a memory for storing one or more computer programs; the computer programs are configured to be executed by the one or more processors, and the programs include steps for performing the high-speed aircraft electromagnetic calculation method based on the quantum classification algorithm described in the first aspect.

[0038] Fourthly, this application also proposes a storage medium storing a computer program; the program is loaded and executed by a processor to implement the steps of the high-speed aircraft electromagnetic calculation method based on the quantum classification algorithm as described in the first aspect.

[0039] As described above, the electromagnetic computation method and system for high-speed aircraft based on quantum classification algorithms provided in this application first performs classical calculations and feature extraction on high-speed flow field data to obtain the spatial distribution and gradient information of key physical parameters. Then, the extracted gradient feature vectors are input into a pre-trained variable quantum classifier. This classifier uses its quantum circuitry to process and intelligently judge the input features in parallel, quickly and automatically classifying the entire computational domain into critical shock wave regions and smooth regions. Based on this intelligent labeling map, a hybrid electromagnetic computation strategy is implemented. In the labeled critical shock wave regions, a high-precision but computationally intensive transfer matrix method with dense grid layering is used; in the smooth regions, a high-efficiency WKB approximation method with sparse grid layering is used. Finally, the computation results from the two regions are fused to obtain the high-precision electromagnetic characteristics of the entire flow field. This method replaces the traditional experience-based manual division with quantum intelligent recognition, achieving adaptive optimal allocation of computational resources in space, significantly improving overall computational efficiency while ensuring the accuracy of critical regions. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm disclosed in an embodiment of the present invention.

[0042] Figure 2 This is a flowchart illustrating the classical calculation and feature extraction steps for the entire flow field as disclosed in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the electromagnetic computing system structure of a high-speed aircraft based on a quantum classification algorithm disclosed in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the operation flow of another embodiment of the electromagnetic computing system for high-speed aircraft based on quantum classification algorithm disclosed in this invention.

[0045] Figure 5 This is a schematic diagram of the operation flow of another embodiment of the electromagnetic computing system for high-speed aircraft based on quantum classification algorithm disclosed in this invention. Detailed Implementation

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments belong; the terminology used herein and in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit these embodiments; the terms "comprising" and "having," and any variations thereof, in the specification of these embodiments and the foregoing drawings, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification of these embodiments and the foregoing drawings are used to distinguish different objects, not to describe a particular order.

[0047] The implementation details of the technical solution in this embodiment are described in detail below:

[0048] This application proposes a high-speed aircraft electromagnetic calculation method based on a quantum classification algorithm, such as... Figure 1 As shown, the method includes:

[0049] S101 performs classical calculations and feature extraction on the entire flow field of a high-speed aircraft, obtains the physical parameters and gradient information of multiple grid points in the flow field, and constructs the local feature vector of each grid point.

[0050] Furthermore, in this application, the classical calculation and feature extraction of the entire flow field, such as... Figure 2 As shown, it includes:

[0051] S1001 uses the finite element method to discretize and iteratively solve the NS equation on a spatial grid to obtain flow field data including temperature, pressure, density and electron density;

[0052] Specifically, in this embodiment, flow field calculation is performed first. The flow field calculation module uses the finite element method to discretize and solve the Navier-Stokes equations on a spatial grid, calculating physical quantities such as temperature and pressure in the flow field. In three-dimensional Cartesian coordinates, the three-dimensional conserved form of the compressible Navier-Stokes equations can be expressed as:

[0053]

[0054] in Q For conservation of quantity, E, F, G for x, y, z Diffusion term in direction, E V 、F V 、G V For convection terms in three directions, S The source vector is expressed as follows:

[0055]

[0056] In the above formula, u, v, w They are respectively x, y, z velocity in the direction, p p , p S These represent the gas pressure, density, density of component S, and mass fraction, respectively. N S 、n S These represent the density of component s and the total number of components, respectively. N A is Avogadro's constant. E, E V 、e vs These represent the total energy of the gas per unit volume, the total vibrational energy component s, and the vibrational energy per unit mass, respectively. D S 、H S These are the component diffusion coefficient and enthalpy per unit mass, respectively. W S The density generation rate of components caused by chemical reactions. W v This represents the vibration energy term. During the calculation, the calculation module iteratively solves the problem based on the boundary conditions until the physical quantities converge. This step reveals the flow characteristics of the aircraft surface, such as the location of shock waves and the distribution of the boundary layer.

[0057] S1002, The flow field data is reconstructed and calculated to obtain at least one electromagnetic parameter among plasma frequency, collision frequency, dielectric constant and conductivity.

[0058] Specifically, in this embodiment, the module incorporates a physical algorithm to reconstruct the flow field output data. For example, it derives electron density from temperature and pressure based on chemical reaction concentration.

[0059] Using the formula:

[0060]

[0061] Derive the plasma frequency, where n e where e is the electron density, e is the electron charge, and m is the electron density. e Where ε is the electron mass, and ε0 is the dielectric constant;

[0062] Using an empirical formula based on gas density and temperature:

[0063]

[0064] Calculate the electron-neutral particle collision frequency, where For collision frequency, The total number of all particles;

[0065] Using a collision-based plasma Drude model Used to calculate the relative permittivity; , used to calculate electrical conductivity.

[0066] in, and These are the real and imaginary parts of the relative permittivity, respectively. The incident wave frequency, For collision frequency, and These represent the real and imaginary parts of the relative conductivity, respectively. The reconstructed parameters include plasma frequency, collision frequency, dielectric constant, and conductivity.

[0067] S1003, For each grid point, calculate the maximum and average values ​​of the gradient magnitude of the electromagnetic parameter within a preset local neighborhood window centered on that grid point, in order to construct a multidimensional local feature vector for that grid point.

[0068] Specifically, in this embodiment, the core of this step lies in transforming the continuous, physically spatially distributed flow field information into a set of discrete feature vectors that can be effectively processed by the subsequent quantum classifier and highly characterize the intensity of local physical changes. Spatial gradient calculation is performed on the key parameter field obtained in S1002 to calculate the electron density gradient field ▽N. e (r), pressure gradient field ▽P(r). For each grid point r in the flow field, define a local cubic window (e.g., a 3×3×3 grid) centered on it, and construct a local feature vector Xr for that grid point:

[0069]

[0070] Here, represents the value taken within a defined local window w, and max and mean calculate the maximum and average values ​​of the gradient magnitude of the physical quantity within that window, respectively. This 4-dimensional vector simultaneously captures the "peak" and "average" intensity of local changes, directly quantifying the drastic degree of change of physical parameters at various points in space. It is an intrinsic indicator for identifying high-gradient physical structures such as shock waves and boundary layers.

[0071] In addition, a variable quantum classifier needs to be trained before step 102.

[0072] Furthermore, in this application, the variable quantum classifier is pre-trained in the following manner:

[0073] Obtain a training sample set; each sample in the training sample set includes a local feature vector extracted from historical flow field data, and a category label annotated by experts, which is used to indicate whether the corresponding grid point belongs to a critical shock region or a flat region.

[0074] The design includes a variable quantum circuit comprising an angle encoding layer for encoding the local feature vector into the quantum bit state space, a variational evolution layer consisting of a parameterized rotation gate and a fixed entanglement gate, and a measurement layer for performing Pauli Z measurements on a specified quantum bit.

[0075] The training loop is executed on the quantum simulator. The gradient of the loss function with respect to the line parameters is calculated using the parameter shifting rule, and the parameters are iteratively updated using the classical optimizer until the model converges.

[0076] Specifically, in this embodiment, this step is the offline stage of building an intelligent classifier, with the aim of obtaining a trained quantum model with fixed parameters. Based on historical simulation data or high-precision CFD results from typical operating conditions, the following sub-steps are performed:

[0077] A. Feature Vector Generation: For each grid point of known category in the flow field, calculate its local feature vector X according to the method described in S1.3. k =[g1,g2,g3,g4], where g1 and g2 are the maximum and mean values ​​of the electron density gradient mode within the window, and g3 and g4 are the maximum and mean values ​​of the pressure gradient mode within the window.

[0078] B. Expert Labeling: Representative data are selected from the flow field calculation results under typical operating conditions. Domain experts manually label each sample point with the true category label y using a high-precision shock wave detection algorithm. k :

[0079]

[0080] C. Dataset Partitioning: Divide the feature vector X k Its corresponding label y k Pairing them together forms a supervised learning training sample set {(X k ,y k )} is used for model training, parameter tuning, and final performance evaluation.

[0081] In this embodiment, the variable quantum classifier is implemented using parameterized circuitry on a quantum simulator, and its mathematical representation is a unitary transformation. U(X, θ) Specifically, it includes the following steps:

[0082] A. Data Encoding: Angle encoding is used to map the 4-dimensional classical feature vector X to the state space of 2 qubits. For example, for the i-th qubit (i=0,1), R is applied sequentially. Y (π x2i+1 ) and R Z (π x 2i+2 Revolving door operation: injecting classical information into a quantum state. .

[0083] B. Variational Evolution: Consists of L (e.g., L=3) identical entangled modules cascaded together, which is the trainable part of the model. Each module contains: (1) Parameterized Rotation Layer: Applying RY(θ) in parallel to all qubits. l,j (1) Gate, where l is the layer index and j is the qubit index. (2) Entangled layer: Apply a fixed two-bit entanglement gate, such as a CNOT gate, to connect adjacent qubits (Qubit0 to Qubit1). This layer is used to create quantum entanglement to express complex correlations between features. The overall operation of this layer is as follows:

[0084]

[0085] C. Measurement Layer: A Z-direction measurement is performed on the first qubit (Qubit0) under the computational basis. The measurement yields |1 Expected value of the state Z0 Mapped to classification probabilities via linear transformation:

[0086]

[0087] This probability value represents the confidence level of the model in predicting that the current input point belongs to the "critical shock region".

[0088] Furthermore, in this embodiment, the training process of the variable quantum classifier includes:

[0089] A. Initialization: In the initialization of the quantum bit |0 Apply parameterized gate (The unitary operator is a unitary operator that maintains a modulus of 1), which will convert the original classical data points... Encoded as a quantum state | This quantum state can be processed by a quantum simulator.

[0090] B. Transformation and Measurement: Transforming the input state | Through parameters Parameterized circuit ( Transform the output state to obtain the output state. = ( )| And the quantum state after processing by the quantum neural network | Measurements and post-processing are performed to obtain the predicted label X. k .

[0091] C. Calculate the loss function: The core of classification is a decision-making process, that is, determining a data point X. k Which label should it be assigned? k Therefore, by optimizing the predicted label X k With actual label The loss function is defined by the cumulative distance between them. ( For example, in this binary classification task, a quadratic loss function can be used: ; Calculate the quadratic loss function for this batch of samples.

[0092] D. Adjusting the θ value: Continuously adjust the parameters using optimization algorithms such as gradient descent. The value of θ is used to minimize the loss function, and the loss function is calculated for each parameter θ. i partial derivatives L / θ i The rule requires rerunning the quantum circuit and calculating the loss after shifting the parameters by ±π / 2, as shown in the formula:

[0093]

[0094] E. Classical Parameter Update: The classical optimizer receives gradients. θL, and update the parameters according to its algorithm rules. .

[0095] F. Iteration and Convergence: Repeat step BF until the loss function no longer decreases significantly on the validation set, or the preset number of iterations is reached. Save the parameters that maximize the accuracy on the validation set. Finally, save the optimal parameters. This yields a usable "shock region identification quantum model".

[0096] S102, the local feature vector is input to a pre-trained variable quantum classifier, the probability of each grid point belonging to a preset category is output, and a region labeling map is generated based on the probability to divide the entire flow field into a shock wave critical region and a smooth region; wherein, the physical parameter gradient of the shock wave critical region is higher than a preset threshold, and the physical parameter gradient of the smooth region is lower than the preset threshold.

[0097] Furthermore, in this application, the preset category is a shock wave critical region category; the step of outputting the probability of each grid point belonging to the preset category and generating a region marking map based on the probability to divide the entire flow field into a shock wave critical region and a smooth region includes: setting a classification threshold; for each grid point, if its probability value of belonging to the shock wave critical region is greater than or equal to the classification threshold, then the grid point is marked as a shock wave critical region; otherwise, it is marked as a smooth region.

[0098] Specifically, in this embodiment, quantum intelligent region identification is the online application stage of the present invention, executed by the classical-quantum collaborative computing module. It utilizes a trained quantum classifier to perform rapid and automated region division of new flow field data. This includes the following sub-steps:

[0099] A. New Flow Field Feature Extraction: For a new high-speed aircraft flow field example, repeat the flow field calculation steps and data reconstruction steps. That is, first perform CFD calculations to obtain flow field data, and then extract the flow field features for each grid point r in the entire three-dimensional space where electromagnetic calculations are required. j Extract its local feature vector X j .

[0100] B. Quantum Classification: The feature vector X of each grid point... j Input the trained model parameters θ into the quantum simulator. For each point, perform the following operations:

[0101] (1) Initialize the quantum register to |00 state.

[0102] (2) Load data: Execute encoding line U enc (X new ), encoding classical features into quantum states |ψ enc .

[0103] (3) Execute the trained model: apply the variational evolution circuit U with fixed parameters var (θ), to obtain the final quantum state |ψ out =U var (θ)|ψ enc .

[0104] (4) Measurement: for |ψ out The first qubit in the array is measured multiple times (e.g., 1024 times). The number of times a "1" is measured is N1. The predicted probability is:

[0105]

[0106] This probability value directly reflects the likelihood that the grid point belongs to the critical region of the shock wave.

[0107] C. Generate region marker map: Obtain P for each grid point. shock Then, the system uses judgment logic to generate a region label map, first setting a configurable threshold η∈(0,1). For each point, its region label is determined by the following formula:

[0108]

[0109] Finally, a binary image (matrix) that is completely isomorphic to the CFD computation grid is generated. The area marked as 1 in the image is the "critical shock region" intelligently identified by the system, and the area marked as 0 is the "flat region".

[0110] S103, based on the region marking map, a first electromagnetic calculation method is applied to the critical region of the shock wave, and a second electromagnetic calculation method is applied to the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method.

[0111] Furthermore, in this application, the first electromagnetic calculation method is the transfer matrix method, and the second electromagnetic calculation method is the WKB approximation method; wherein, when the transfer matrix method is used for the critical shock wave region, the non-uniform medium within the critical shock wave region is finely divided into a first number of uniform thin layers along the electromagnetic wave propagation direction, and the scattered field of the region is calculated recursively using the transfer matrix; when the WKB approximation method is used for the smooth region, the medium within the smooth region is divided into a second number of layers along the electromagnetic wave propagation direction, and the approximate scattered field of the region is obtained by solving the equation of process function and the transfer equation; wherein, the first number is greater than the second number.

[0112] Specifically, in this embodiment, hybrid electromagnetic computing is the target implementation stage of the present invention, executed by the classical electromagnetic computing module. Based on the intelligently divided regions, differentiated high-precision and high-efficiency algorithms are used for collaborative computation. This includes the following sub-steps:

[0113] Partitioned electromagnetic transport calculation: Based on the generated region marking map, the entire computational domain is divided into two subdomains: the shock critical region Ω. shock and gentle area Ω smooth .

[0114] In Ω shock Region: Due to the extremely large plasma parameter gradient here, a high-precision transfer matrix method is employed. The non-uniform plasma within the region is finely divided into M sections along the electromagnetic wave propagation direction. dense Layer (M) dense (The electromagnetic field is relatively large), and the parameters within each layer are approximately uniform. Using Maxwell's equations, the reflection and transmission of electromagnetic waves at each layer interface are calculated, and the overall scattered field E of the region is obtained recursively through the transmission matrix. TMM .

[0115] In Ωs mooth Region: Due to the slow parameter changes in this area, the geometrical optics approximation conditions are met, and the highly efficient WKB approximation method is employed. This region can use a much smaller number of layers M compared to the shock region. sparse (M) sparse M dense The WKB method can even directly perform integration to solve for the approximate scattering field E in the region by solving the process function equation and the transport equation. WKB .

[0116] S104, the calculation results of the key shock wave region and the smooth region are fused to obtain the electromagnetic characteristic data of the entire flow field, and then visualized and output.

[0117] Furthermore, in this application, the electromagnetic characteristic data of the entire flow field includes the total scattered field of the entire flow field; the process of fusing the calculation results of the critical shock wave region and the smooth region to obtain the electromagnetic characteristic data of the entire flow field includes:

[0118] The total scattered field of the entire flow field is synthesized according to the following formula:

[0119] E total (r)=E TMM (r) I shock (r)+E WKB (r) (1-I shock (r));

[0120] Among them, E total (r) represents the total scattered field at position r; E TMM (r) represents the scattered field calculated using the transfer matrix method; E WKB (r) represents the scattered field calculated using the WKB approximation method; I shock (r) is the indicator function of the region marker map, which takes a value of 1 in the critical shock wave region and a value of 0 in the gentle region. This ultimately outputs a high-fidelity, continuous total electromagnetic scattering field distribution E across the entire flow field. total .

[0121] Furthermore, in this application, the visualization output includes at least one of radar cross section distribution map, electromagnetic field intensity spatial cloud map, and transmission attenuation curve.

[0122] In this embodiment, electromagnetic wave calculation results are received, and the binary dataset is visualized through a graphical interface and data reports to support user analysis and decision-making. The visualized content includes radar cross-section distribution, transmission attenuation curves, and electromagnetic transmission intensity distribution.

[0123] Compared with existing technologies, the electromagnetic computing method and system for high-speed aircraft based on quantum classification algorithms provided by this invention brings significant technological progress and synergistic optimization effects by introducing a quantum-classical hybrid intelligent computing architecture, specifically reflected in the following aspects:

[0124] 1. Achieved a synergistic leap in global computational accuracy and efficiency. Traditional methods are limited by the dilemma of low efficiency in globally high-precision algorithms and insufficient accuracy in globally high-efficiency algorithms. This invention intelligently identifies the physical characteristics of the flow field through a variable quantum classifier, constructing a hybrid computational strategy that precisely calculates local data while efficiently approximating the global data. The computational domain is precisely divided into a high-gradient shock wave region and a low-gradient flat region. In the shock wave region, the transfer matrix method is used to ensure high fidelity of the core physical processes, while in the flat region, the WKB approximation method is used to achieve fast computation. This strategy of partitioning and hybrid computation can improve the overall computational efficiency by several times, achieving a balance between accuracy and efficiency.

[0125] 2. Intelligent and accurate identification of flow field feature regions is achieved. This invention utilizes a variable quantum classification algorithm to replace the traditional segmentation method that relies on human experience criteria. This quantum classifier, through parallel processing and pattern learning of the multi-dimensional gradient feature vectors of the flow field, can automatically and accurately identify high-gradient key regions (such as shock layers) with drastic changes in physical parameters and low-gradient regions with gradual changes from complex flow fields. This method has high accuracy in identifying key regions such as shock waves and can adjust parameters for different aircraft shapes and incoming flow conditions, eliminating reliance on manual intervention and achieving objectivity, automation, and high precision in the segmentation process.

[0126] Secondly, this embodiment also proposes a high-speed aircraft electromagnetic computing system based on a quantum classification algorithm, such as... Figure 3 As shown, it includes:

[0127] The flow field preprocessing module 301 performs classical calculations and feature extraction on the entire flow field of the high-speed aircraft, obtains the physical parameters and gradient information of multiple grid points in the flow field, and constructs the local feature vector of each grid point.

[0128] The quantum classification module 302 inputs the local feature vector into a pre-trained variable quantum classifier, outputs the probability that each grid point belongs to a preset category, and generates a region labeling map based on the probability to divide the entire flow field into a shock wave critical region and a smooth region; wherein, the physical parameter gradient of the shock wave critical region is higher than a preset threshold, and the physical parameter gradient of the smooth region is lower than the preset threshold.

[0129] The electromagnetic calculation module 303, based on the region marking map, applies a first electromagnetic calculation method to the critical region of the shock wave and a second electromagnetic calculation method to the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method.

[0130] The fusion output module 304 fuses the calculation results of the key shock wave region and the smooth region to obtain the electromagnetic characteristic data of the entire flow field and outputs it in a visual form.

[0131] This system can be used to execute the electromagnetic calculation method for high-speed aircraft based on the quantum classification algorithm described in the first aspect, which will not be elaborated further here.

[0132] Furthermore, this embodiment also provides an implementation method for the electromagnetic computing system of high-speed aircraft based on quantum classification algorithms. For example... Figure 4 The diagram shown is an operational flowchart of another embodiment of the electromagnetic computing system for high-speed aircraft based on quantum classification algorithm in this example.

[0133] 1. Flow field pretreatment subsystem.

[0134] This system is deployed on high-performance CPU computing nodes and is responsible for performing computational fluid dynamics simulations and preparing feature data for subsequent quantum classification. It mainly includes the following functional modules:

[0135] (1) Flow field calculation module: This module is responsible for performing high-fidelity computational fluid dynamics simulations, accurately solving the flow field around the aircraft, and providing physical field data for subsequent feature extraction. It mainly includes the following functions: a) Physical field solution: Using the finite element method, the NS governing equations are discretized and iteratively solved on a three-dimensional spatial grid until the physical quantities of the flow field (temperature, pressure, density, velocity) converge. b) Chemical field solution: When the incoming flow conditions trigger a chemical reaction, this module solves the chemical component transport equations and reaction kinetic equations in parallel, and calculates key chemical parameters such as electron density and concentration of each component.

[0136] (2) Data Reconstruction Module: This module is responsible for converting flow field data into input parameters that can be directly used for electromagnetic calculations. It relies on the system's central processing unit to call built-in physical algorithms to perform a series of conversion calculations on the collected flow field data. For example, it calculates electron density using the concentration of chemical reaction components combined with temperature and pressure; and it uses the formula relating electron density to plasma frequency:

[0137]

[0138] The plasma frequency was derived; the collision frequency was derived using the gas density and temperature formulas, etc. At the same time, the reconstructed electromagnetic parameters (dielectric constant, conductivity, etc.) were logically correlated with the original flow field data to form a complete multiphysics dataset.

[0139] (3) Feature Extraction Module: This module is responsible for preparing input features for the quantum classifier. Its core is to transform the continuous parameter field into a discrete feature vector that characterizes the degree of local change. This module scans the three-dimensional electromagnetic parameter field (such as the electron density field) output by the data reconstruction module. For each grid point in the field, the following operations are performed: a local neighborhood window (such as 3×3×3) is defined with the point as the center, and the statistical characteristics (such as the maximum value and the average value) of the parameter gradient magnitude within the window are calculated. A fixed-dimensional feature vector X=[gmax,gmean,...] is generated for each grid point. This vector quantifies the physical gradient intensity at that location.

[0140] 2. Region identification and classification subsystem.

[0141] This system is primarily responsible for performing offline training and classification of the quantum model, and providing a reference for subsequent hybrid electromagnetic computation. It mainly includes the following functional modules:

[0142] (1) Quantum Classification Model Training Module: This module is the core of the system's offline learning, responsible for generating usable shock region identification quantum models, running on a server connected to a classical quantum simulator. It mainly includes the following functions: a) Training Sample Preparation Unit: Manages historical high-precision simulation data or typical working condition data, organizes expert annotations, and generates a supervised learning dataset containing feature vectors and real region labels. b) Variable Quantum Circuit Design Unit: Provides parameterized quantum circuit templates, including an angle encoding layer for encoding classical feature vectors, a variable layer composed of adjustable parameter rotation gates and fixed entanglement gates (such as CNOT gates), and a measurement layer specifying the qubits. c) Model Training and Optimization Unit: Executes training loops on quantum hardware or a simulator. The quantum states encoded from the sample data are input into the parameterized circuit, the output is measured, and the prediction loss is calculated; classical optimizers (such as gradient descent) combined with parameter shift rules are used to update the circuit parameters, iteratively optimizing until the model reaches a predetermined accuracy on the validation set, and finally saving the optimal parameter set θ. .

[0143] (2) Quantum Algorithm Classification Module: This module uses a trained quantum model to perform real-time and automatic region division for new flow field examples. It mainly includes the following functions: a) Quantum State Preparation: Receives the flow field grid point feature vector sequence from the feature extraction module, and through quantum circuit encoding operations, converts each classical feature vector X... j Transform into the corresponding initial quantum state |ψ enc b) Quantum computing: encoding the quantum state |ψ enc Input to loaded fixed optimal parameters θ Variable quantum circuit U var (θ Perform quantum evolution to obtain the output quantum state |ψout c) Quantum measurement: Perform multiple measurements on the target qubit in the output quantum state, and statistically obtain the probability P that the grid point belongs to the "critical shock region". shock Based on a preset or adaptive classification threshold η, the point is determined as "1" (critical area) or "0" (flat area), ultimately generating a binary region labeling map covering the entire computational domain.

[0144] 3. Hybrid electromagnetic computing subsystem.

[0145] This system is the executor of a hybrid computing strategy, invoking different algorithms based on the region labeling map. It mainly includes the following functional modules:

[0146] (1) WKB approximation calculation module: This module is activated for the flat region marked "0". It takes advantage of the slow change of physical parameters in this region and uses the direct integration method to quickly solve the process function equation and transmission equation under the geometric optics approximation, and efficiently obtain the approximate scattering field E in this region. WKB .

[0147] (2) Transmission Matrix Method Calculation Module: This module is specifically responsible for performing high-precision electromagnetic calculations on the "critical shock wave region" marked on the regional map. This module uses the transmission matrix method. Its main function is to finely stratify the non-uniform plasma within the region along the electromagnetic wave propagation direction, apply Maxwell's equations to solve for interface effects within each layer, and accurately calculate the electromagnetic wave scattering and transmission field E of the region through recursive transfer matrix calculation. TMM .

[0148] (3) Field Distribution Synthesis Module: This module is responsible for merging the partitioned calculation results into a unified global electromagnetic field solution. This module reads the region marking map and the output field E of the two calculation channels. TMM and E WKB Based on the spatial correspondence, perform intelligent fusion computing: E total (r)=E TMM (r) I shock (r)+E WKB (r) (1-I shock (r)), where I shock (r) is the indicator function for the region marker map. The final output is a high-fidelity, continuous total electromagnetic scattering field distribution E across the entire flow field. total .

[0149] (4) Visualization Output Module: This module serves as the interface between the system and the user, responsible for converting the binary calculation results into intuitive graphics and reports. This module receives the E output from the field distribution synthesis module. totalThe data is used to generate visualizations such as radar cross section (RCS) patterns, electromagnetic field intensity spatial cloud maps, and transmission attenuation curves. It also supports overlaying intelligent region segmentation maps to intuitively demonstrate quantum classification effects, and allows export of all results and analysis reports.

[0150] To enable those skilled in the art to better understand and implement this invention, a detailed description is provided below with reference to a specific embodiment. This embodiment uses the calculation of the electromagnetic scattering characteristics of the plasma sheath of a classic hypersonic target—an AHV vehicle—at a flight altitude of 70 km and a flight speed of Mach 25 as an example to fully demonstrate the implementation process of this solution. The software program of this solution is deployed on a hybrid computing platform composed of a classical high-performance computing cluster and a high-performance quantum simulator interconnected by a network. The classical computing nodes are equipped with multi-core central processing units, large-capacity memory, high-speed solid-state drives, and professional graphics processing units; quantum computing resources are accessed through a dedicated API. Figure 5 As shown, the specific implementation steps are as follows:

[0151] Step S501: Parameter input and flow field initialization;

[0152] 1. User Operation: Users can upload the 3D geometric model and computational mesh file of the AHV aircraft through the system's graphical interface, and set the flight conditions in the parameter interface: flight altitude 70km, flight speed Mach 25 (approximately 8500m / s), incoming atmospheric composition is standard air, and specify high-precision electromagnetic scattering analysis.

[0153] 2. System execution - flow field calculation:

[0154] a) The flow field calculation module is started. Based on the input extreme hypersonic conditions, it automatically selects and solves the three-dimensional compressible Navier-Stokes equations and the 19-component air chemical reaction model, which include thermochemical nonequilibrium effects.

[0155] b) The module solves in parallel iteratively on the computing cluster until the physical quantities of the flow field (temperature, pressure, density) and chemical composition (including electron density n) are obtained. e The residuals converged to 10. -6 The flow field data was obtained at a magnitude of approximately 9500 K. In this embodiment, the stagnation temperature of the flow field was calculated to be approximately 9500 K, and the peak electron density n... e Approximately 5×10 20 m -3 .

[0156] Step S502: Electromagnetic parameter reconstruction and feature extraction;

[0157] 1. System Execution - Data Reconstruction: a) The data reconstruction module reads the converged flow field data. b) The module calls the built-in model and calculates the plasma frequency ω point by point. p(Formula: ω) p =sqrt((n e e 2 ) / (ε0m e and electron-neutral particle collision frequency ν c Key electromagnetic parameters are used to generate a three-dimensional complex permittivity distribution field ε. r (r).

[0158] 2. System Execution - Feature Extraction: a) The feature extraction module analyzes the dielectric constant field. For each grid point r in the computational domain... j a) Take a 3×3×3 local neighborhood around it. b) Calculate the statistical characteristics of the gradient magnitude of the complex permittivity within this neighborhood, such as the maximum value g. max and average value g mean Construct the 4-dimensional feature vector X of this point. j =[g max (Re(ε)),g mean (Re(ε)),g max (Im(ε)),g mean (Im(ε))]. The set of eigenvectors of all points {X} j The data is sent to the region identification and classification subsystem.

[0159] Step S503: Quantum classification model training;

[0160] This step is an offline preparation before system deployment, using historical data to train the core classification model. 。

[0161] 1. System Execution - Model Training: a) The quantum classification model training module loads the pre-prepared labeled dataset. This dataset contains flow field grid point data (labeled as "1-shock layer / sheath core region" or "0-smooth far-field region") under various operating conditions, jointly labeled by a high-precision shock wave capture algorithm and expert knowledge. b) The module is trained on a quantum simulator using the variable quantum circuit described in the main text. Gradients are calculated using parameter shift rules, and the Adam optimizer is used to minimize the cross-entropy loss function. c) After tens of thousands of iterations, the model achieves a classification accuracy of over 98.5% on the independent validation set. The optimal parameters θ obtained during training are... It is saved to the central log and model library for use during online recognition.

[0162] Step S504: Quantum intelligent region division;

[0163] 1. System Execution - Quantum Classification:

[0164] a) The quantum algorithm classification module loads the trained model parameters θ from the library. and receive the feature vector set {X} from the feature extraction module. j}

[0165] module b) encodes feature vectors in batches into quantum states and loads θ... Forward reasoning is performed using a fixed quantum circuit. For each point, the probability P that the target qubit belongs to the critical region is obtained by measuring the target qubit. shock .

[0166] c) Set the classification threshold η = 0.75. The system automatically determines: if P shock If the gradient is greater than or equal to 0.75, the point is marked as "Class 1 region" (high gradient region, requiring precise calculation); otherwise, it is marked as "Class 0 region" (low gradient region, which can be approximated).

[0167] 2. Generate a marker map: The system outputs a binary region marker map that perfectly corresponds to the flow field mesh. The results show that the electron density gradients in regions such as the nose cone shock layer and wing leading edge are severe and are successfully identified as "Type 1 regions"; while most of the far-field region is identified as "Type 0 regions". The "Type 1 regions" account for about 15% of the total computational domain, but contain the core physical processes that determine the electromagnetic scattering characteristics.

[0168] Step S505: Partitioned hybrid electromagnetic calculation;

[0169] 1. System execution - dual-channel parallel computing:

[0170] a) The transmission matrix calculation module (high-precision channel) is activated, specifically for processing the "Class 1 region" in the marked map. This module meticulously divides this type of region into 500 layers along the radar wave incident direction (0° head-on direction in this example), and uses the transmission matrix method to rigorously solve Maxwell's equations layer by layer to calculate the precise scattered field E of this region. TMM .

[0171] b) The WKB calculation module (high-efficiency channel) is also activated to process the "Class 0 region" in the marked map. This module uses only 50 layers of coarse subdivision for this region and uses WKB to approximate and quickly solve for the phase accumulation and attenuation of electromagnetic waves to obtain the approximate scattered field E. WKB .

[0172] c) The two computing tasks are executed in parallel on different nodes of the computing cluster, making full use of system resources.

[0173] 2. System Execution - Field Distribution Synthesis: a) The field distribution synthesis module synchronously receives the calculation results and region labeling map from both channels. b) Based on the labeling map as a weight template, E... TMM and E WKB The fusion process generates a globally continuous, high-fidelity total scattered field E. totalThis step ensures the physical consistency between the high-precision solution within the shock layer and the efficient solution in the far field.

[0174] Step S506: Result visualization and performance analysis;

[0175] 1. System Execution - Visualization Output: a) The visualization output module reads the synthesized total field data E total b) The module automatically generates and renders a series of professional charts, including: i. a three-dimensional distribution cloud map of the radar cross section (RCS) of the AHV aircraft in the X-band (10GHz). ii. a curve showing the variation of RCS in the nose cone direction with frequency (1-18GHz). iii. a superimposed display of the quantum intelligent region division effect (overlaying the binary marker map with the flow field density cloud map to intuitively display the classification boundary).

[0176] Through steps S501-S506 above, this embodiment fully demonstrates how the system of the present invention starts with flow field solving, achieves precise partitioning of the computational region through quantum intelligent recognition, drives heterogeneous algorithms for collaborative computation, and ultimately obtains complex electromagnetic property results efficiently and with high precision. The entire process fully reflects the synergistic leap in accuracy and efficiency brought about by the deep integration of quantum intelligent scheduling and classical heterogeneous computing.

[0177] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A high-speed aircraft electromagnetic calculation method based on quantum classification algorithm, characterized in that, include: Classical calculations and feature extractions are performed on the entire flow field of a high-speed aircraft to obtain the physical parameters and gradient information of multiple grid points in the flow field, and to construct the local feature vector of each grid point. The local feature vector is input into a pre-trained variable quantum classifier, which outputs the probability that each grid point belongs to a preset category. Based on this probability, a region labeling map is generated to divide the entire flow field into a shock wave critical region and a smooth region. The physical parameter gradient of the shock wave critical region is higher than a preset threshold, while the physical parameter gradient of the smooth region is lower than the preset threshold. Based on the region marking map, a first electromagnetic calculation method is used for the critical shock wave region, and a second electromagnetic calculation method is used for the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method. By integrating the calculation results of the critical shock wave region and the smooth region, the electromagnetic characteristic data of the entire flow field are obtained and visualized. Wherein, the first electromagnetic calculation method is the transfer matrix method, and the second electromagnetic calculation method is the WKB approximation method; when the transfer matrix method is used for the critical region of the shock wave, the non-uniform medium in the critical region of the shock wave is finely divided into a first number of uniform thin layers along the electromagnetic wave propagation direction, and the scattered field of the critical region of the shock wave is calculated recursively through the transfer matrix; when the WKB approximation method is used for the smooth region, the medium in the smooth region is divided into a second number of layers along the electromagnetic wave propagation direction, and the approximate scattered field of the smooth region is obtained by solving the equation of process function and the transfer equation; wherein, the first number is greater than the second number.

2. The electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm according to claim 1, characterized in that, The classical calculation and feature extraction of the entire flow field of the high-speed aircraft includes: The NS equations were discretized on a spatial grid using the finite element method and solved iteratively to obtain flow field data including temperature, pressure, density, and electron density. The flow field data is reconstructed to obtain at least one electromagnetic parameter among plasma frequency, collision frequency, dielectric constant, and conductivity. For each grid point, the maximum and average values ​​of the gradient magnitude of the electromagnetic parameter within a preset local neighborhood window centered on that grid point are calculated to construct a multidimensional local feature vector for that grid point.

3. The electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm according to claim 1 or 2, characterized in that, The variable quantum classifier is pre-trained in the following manner: Obtain a training sample set; each sample in the training sample set includes a local feature vector extracted from historical flow field data, and a category label annotated by experts, which is used to indicate whether the corresponding grid point belongs to a critical shock wave region or a flat region. The design includes a variable quantum circuit comprising an angle encoding layer for encoding the local feature vector into the quantum bit state space, a variational evolution layer consisting of a parameterized rotation gate and a fixed entanglement gate, and a measurement layer for performing Pauli Z measurements on a specified quantum bit. The training loop is executed on the quantum simulator. The gradient of the line parameters is adjusted by calculating the loss function through the parameter shifting rule, and the parameters are iteratively updated using the classical optimizer until the model converges.

4. The electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm according to claim 1, characterized in that, The preset category is the shock wave critical region category; The output determines the probability that each grid point belongs to a preset category, and a region marking map is generated based on this probability to divide the entire flow field into a shock wave critical region and a gentle region, including: A classification threshold is set. For each grid point, if the probability value of its belonging to the critical shock region is greater than or equal to the classification threshold, the grid point is marked as the critical shock region; otherwise, it is marked as a flat region.

5. The electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm according to claim 1, characterized in that, The electromagnetic properties data of the entire flow field include the total scattered field of the entire flow field; The total scattered field of the entire flow field is synthesized according to the following formula: E total (r)=E TMM (r) I shock (r)+E WKB (r) (1-I shock (r)); Among them, E total (r) represents the total scattered field at position r; E TMM (r) represents the scattered field calculated using the transfer matrix method; E WKB (r) represents the scattered field calculated using the WKB approximation method; I shock (r) is the indicator function of the region marker map, which takes a value of 1 in the critical shock region and a value of 0 in the flat region.

6. The electromagnetic calculation method for high-speed aircraft based on quantum classification algorithm according to claim 5, characterized in that, The visualization output includes at least one of the following: radar cross section distribution map, electromagnetic field intensity spatial cloud map, and transmission attenuation curve.

7. A high-speed aircraft electromagnetic computing system based on a quantum classification algorithm, characterized in that, include: The flow field preprocessing module performs classical calculations and feature extraction on the entire flow field of the high-speed aircraft, obtains the physical parameters and gradient information of multiple grid points in the flow field, and constructs the local feature vector of each grid point. The quantum classification module inputs the local feature vector into a pre-trained variable quantum classifier, outputs the probability that each grid point belongs to a preset category, and generates a region labeling map based on the probability to divide the entire flow field into a shock wave critical region and a smooth region; wherein, the physical parameter gradient of the shock wave critical region is higher than a preset threshold, and the physical parameter gradient of the smooth region is lower than the preset threshold. The electromagnetic calculation module, based on the region marking map, applies a first electromagnetic calculation method to the critical region of the shock wave and a second electromagnetic calculation method to the gentle region; the calculation accuracy of the first electromagnetic calculation method is higher than that of the second electromagnetic calculation method. The fusion output module fuses the calculation results of the key shock wave region and the smooth region to obtain the electromagnetic characteristic data of the entire flow field and outputs it in a visual form. Wherein, the first electromagnetic calculation method is the transfer matrix method, and the second electromagnetic calculation method is the WKB approximation method; when the transfer matrix method is used for the critical region of the shock wave, the non-uniform medium in the critical region of the shock wave is finely divided into a first number of uniform thin layers along the electromagnetic wave propagation direction, and the scattered field of the critical region of the shock wave is calculated recursively through the transfer matrix; when the WKB approximation method is used for the smooth region, the medium in the smooth region is divided into a second number of layers along the electromagnetic wave propagation direction, and the approximate scattered field of the smooth region is obtained by solving the equation of process function and the transfer equation; wherein, the first number is greater than the second number.

8. An electronic device, the electronic device comprising: One or more processors, a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, the programs including steps for performing the electromagnetic calculation method for high-speed aircraft based on a quantum classification algorithm as described in any one of claims 1-5.

9. A storage medium storing a computer program; characterized in that, The program is loaded and executed by a processor to implement the steps of the high-speed aircraft electromagnetic calculation method based on quantum classification algorithm as described in any one of claims 1-5.

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