Quantum current sensor electric-thermal-magnetic-force multi-physical field simulation method, device, equipment, storage medium and program product

Abstract

The application relates to a quantum current sensor electro-thermal-magnetic-force multi-physical field simulation method and device, computer equipment, readable storage medium and program product, and relates to the technical field of physical simulation. The application can simultaneously consider simulation calculation amount and simulation precision. The method comprises the following steps: for a quantum current sensor, an atomic-level molecular dynamics modeling, a device-level model reduction finite element modeling and a system-level time domain finite difference modeling are performed to construct a cross-scale simulation model of an atomic level-device level-system level; a unified cross-scale coupling interface is constructed in the cross-scale simulation model; based on the cross-scale simulation model, electro-thermal-magnetic-force four physical field collaborative iterative solving is performed until the whole process residual satisfies the preset convergence criterion, and the next global time step is entered; after all global time steps are deduced, the dynamic evolution curve of the quantum current sensor in the service environment containing three reliability parameters of electromagnetic compatibility margin, thermal stress distribution and packaging leakage rate is output.

Description

Technical Field

[0001] This application relates to the field of physical simulation technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for simulating the electro-thermal-magnetic-force multiphysics fields of a quantum current sensor. Background Technology

[0002] With the rapid development of new power systems, smart grids, and high-end equipment manufacturing, traditional electromagnetic current transformers are no longer sufficient to meet the precise measurement requirements of high-voltage, high-current scenarios in terms of dynamic range, anti-saturation capability, and insulation volume. Consequently, quantum current sensors based on diamond nitrogen-vacancy color centers have emerged. Leveraging the inherent advantages of quantum coherent measurement principles, such as high sensitivity, wide bandwidth, electromagnetic interference resistance, and intrinsic insulation, they are widely regarded by the industry as a core candidate solution for next-generation current metering devices.

[0003] However, the overall size of a quantum current sensor is on the order of centimeters, while the characteristic size of nitrogen-vacancy color center lattice defects is on the order of angstroms, a difference of approximately eight orders of magnitude. If a unified, fine-mesh model is used for direct modeling, the degrees of freedom in a single simulation can exceed 10. 8 The sheer scale of the computation is beyond the reach of conventional computing resources; if coarse mesh simplification is used, key physical information at the nitrogen-vacancy color center level will be lost, resulting in low accuracy of the simulation results and thus losing their engineering reference value. This leads to the current simulation technology's inability to simultaneously balance computational load and accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for simulating the electro-thermal-magnetic-force multiphysics fields of a quantum current sensor, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a multiphysics simulation method for quantum current sensors, including:

[0006] For quantum current sensors, a cross-scale simulation model is constructed by using atomic-level molecular dynamics modeling, device-level model reduction to finite element modeling, and system-level time-domain finite difference modeling.

[0007] A unified cross-scale coupling interface is constructed in the cross-scale simulation model, and bidirectional data transfer between models at different levels in the cross-scale simulation model is carried out through the mapping operator in the cross-scale coupling interface.

[0008] Based on the aforementioned cross-scale simulation model, the four physical fields of electricity, heat, magnetism and force are solved in a coordinated iterative manner until the residuals throughout the process meet the preset convergence criteria, and then the next global time step is entered.

[0009] After all global time steps are simulated, the dynamic evolution curves of the quantum current sensor under service conditions, including three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate, are output.

[0010] In one embodiment, the execution of the electro-thermal-magnetic-mechanical four-field coordinated iterative solution includes:

[0011] Initialize the material parameters and boundary conditions of each level of the model and set the global time step; within the global time step, sequentially complete the system-level electromagnetic field solution, device-level thermo-mechanical response solution, and atomic-level lattice and spin behavior solution to obtain the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function; based on the equivalent dielectric constant correction, the thermal expansion coefficient correction, and the spin response function, iteratively update the material parameters and boundary conditions through the cross-scale coupling interface.

[0012] In one embodiment, the atomic-level molecular dynamics modeling includes:

[0013] Using molecular dynamics methods, based on a hybrid interatomic interaction potential employing Tersoff and Stillinger-Weber potentials, a refined model of the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior in the diamond nitrogen-vacancy color center lattice defect region is obtained, yielding an atomic-level model of the diamond nitrogen-vacancy color center lattice defect region. The local stress tensor and defect mobility are then simulated and output using this atomic-level model as constitutive correction inputs for device-level simulations.

[0014] In one embodiment, the device-level model is reduced to finite element modeling, including:

[0015] A model-reduced finite element method is used to perform a reduced-order modeling of the thermo-mechanical-electrical response of the MEMS sensing element based on the local stress tensor and the defect mobility, resulting in a device-level model of the MEMS sensing element. Based on the device-level model, a thermo-mechanical coupling response equation is established, with the electromagnetic loss heat source transmitted at the system level as input, and the temperature field and stress field as output. The temperature field and stress field serve as the thermal vibration and stress boundary conditions for atomic-level simulation, and as temperature correction terms for the material dielectric constant and conductivity in system-level simulation.

[0016] In one embodiment, the system-level time-domain finite-difference modeling includes:

[0017] The finite-difference time-domain method is used to simulate the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the overall package of the quantum current sensor, resulting in a system-level model of the quantum current sensor. Based on the measured current waveform and external electromagnetic interference excitation, calculations are performed using the system-level model to obtain the electromagnetic field distribution and electromagnetic loss heat source output by the system-level model, which serve as key inputs for the device-level simulation and the atomic-level simulation.

[0018] In one embodiment, the mapping operator includes a geometric mapping operator, a time mapping operator, and a physical quantity mapping operator, and the method further includes:

[0019] The geometric mapping operator employs trilinear interpolation for the downlink from coarse to fine scale and volume weighted average for the uplink from fine to coarse scale. The time mapping operator uses quasi-static extrapolation to coordinate the time step differences between sub-time steps. The physical quantity mapping operator performs conversions of different physical quantities in terms of unit system and tensor order.

[0020] Secondly, this application also provides a quantum current sensor electro-thermal-magnetic-force multiphysics field simulation device, comprising:

[0021] The model building module is used to construct cross-scale simulation models of quantum current sensors by means of atomic-level molecular dynamics modeling, device-level model reduction to finite element modeling, and system-level time-domain finite difference modeling.

[0022] The interface coupling module is used to construct a unified cross-scale coupling interface in the cross-scale simulation model, and to perform bidirectional data transfer between models at different levels in the cross-scale simulation model through the mapping operator in the cross-scale coupling interface.

[0023] The collaborative solution module is used to perform collaborative iterative solution of the four physical fields of electricity, heat, magnetism and force based on the cross-scale simulation model until the residuals throughout the process meet the preset convergence criteria and enter the next global time step;

[0024] The results output module is used to output the dynamic evolution curves of the quantum current sensor under service environment, including three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate, after all global time step simulations are completed.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] For quantum current sensors, a cross-scale simulation model is constructed at the atomic, device, and system levels through atomic-level molecular dynamics modeling, device-level model reduction to finite element modeling, and system-level finite-difference time-domain modeling. A unified cross-scale coupling interface is built within this model, enabling bidirectional data transfer between different levels of the simulation model via mapping operators. Based on this model, a collaborative iterative solution is performed for the four physical fields of electro-thermal-magnetic-mechanical, until the residuals satisfy a preset convergence criterion, at which point the model proceeds to the next global time step. After all global time steps are completed, the dynamic evolution curves of the quantum current sensor under service conditions, including three major reliability parameters—electromagnetic compatibility margin, thermal stress distribution, and packaging leakage rate, are output.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0028] For quantum current sensors, a cross-scale simulation model is constructed at the atomic, device, and system levels through atomic-level molecular dynamics modeling, device-level model reduction to finite element modeling, and system-level finite-difference time-domain modeling. A unified cross-scale coupling interface is built within this model, enabling bidirectional data transfer between different levels of the simulation model via mapping operators. Based on this model, a collaborative iterative solution is performed for the four physical fields of electro-thermal-magnetic-mechanical, until the residuals satisfy a preset convergence criterion, at which point the model proceeds to the next global time step. After all global time steps are completed, the dynamic evolution curves of the quantum current sensor under service conditions, including three major reliability parameters—electromagnetic compatibility margin, thermal stress distribution, and packaging leakage rate, are output.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0030] For quantum current sensors, a cross-scale simulation model is constructed at the atomic, device, and system levels through atomic-level molecular dynamics modeling, device-level model reduction to finite element modeling, and system-level finite-difference time-domain modeling. A unified cross-scale coupling interface is built within this model, enabling bidirectional data transfer between different levels of the simulation model via mapping operators. Based on this model, a collaborative iterative solution is performed for the four physical fields of electro-thermal-magnetic-mechanical, until the residuals satisfy a preset convergence criterion, at which point the model proceeds to the next global time step. After all global time steps are completed, the dynamic evolution curves of the quantum current sensor under service conditions, including three major reliability parameters—electromagnetic compatibility margin, thermal stress distribution, and packaging leakage rate, are output.

[0031] The aforementioned multi-physics simulation method, device, computer equipment, computer-readable storage medium, and computer program product for quantum current sensors integrates three simulation methods—molecular dynamics, model-reduced finite element method, and finite-difference time-domain method—which belong to different spatial scales, into a unified solution framework through a unified cross-scale coupling interface. This achieves synchronous simulation across eight orders of magnitude, from nitrogen-vacancy color center lattice defects at 0.1 nm to the entire sensor package at 10 cm. Compared to existing single-scale or local dual-scale schemes, this scheme can also completely restore the complete causal chain of "atomic-level defect evolution—device-level response drift—system-level failure behavior," organically connecting previously isolated simulation units into a unified reliability assessment network, thus balancing computational load and simulation accuracy. Furthermore, the four-field collaborative iterative solution process of electro-thermal-magnetic-mechanical fields constructed in this scheme incorporates the strong coupling interactions between the electromagnetic, temperature, magnetic, and mechanical fields within the sensor into a unified solution framework at once, avoiding the physical information loss problem caused by traditional pairwise coupling schemes. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a diagram illustrating the application environment of a multiphysics simulation method for quantum current sensors based on electro-thermal-magnetic-force fields in one embodiment.

[0034] Figure 2 This is a schematic diagram of the overall framework of the electro-thermal-magnetic-force multiphysics field coupling simulation technology for a quantum current sensor in one embodiment;

[0035] Figure 3 This is a flowchart illustrating the multiphysics simulation method for a quantum current sensor based on electrothermal-magnetic-force fields in one embodiment.

[0036] Figure 4 This is a hierarchical architecture diagram of a cross-scale simulation model at the atomic, device, and system levels in one embodiment;

[0037] Figure 5 This is a schematic diagram of the process for the coordinated iterative solution of the four physical fields of electricity, heat, magnetism and force in one embodiment;

[0038] Figure 6This is a mapping diagram of cross-scale coupling interface data transmission in one embodiment;

[0039] Figure 7 This is a timing diagram showing the time step coordination of the FDTD / MOR-FEM / MD three-scale solver in one embodiment;

[0040] Figure 8 This is a comparison curve of the reliability parameter prediction accuracy between the present application and the traditional single-scale scheme in one embodiment;

[0041] Figure 9 This is a flowchart illustrating a multiphysics simulation method for a quantum current sensor based on electrothermal-magnetic-force fields, as shown in a specific embodiment.

[0042] Figure 10 This is a structural block diagram of a quantum current sensor electro-thermal-magnetic-force multiphysics simulation device in one embodiment;

[0043] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] The multiphysics simulation method for quantum current sensors based on electrothermal-magnetic-force provided in this application can be applied to, for example... Figure 1 The application environment shown illustrates this. In this environment, the terminal can communicate with the server via a network. The data storage system can store the data that the server needs to process. The data storage system can be integrated onto the server or located on the cloud or other network servers. In situations such as... Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0046] This application proposes a multi-physics simulation method for quantum current sensors, encompassing electro-thermal-magnetic-force fields. The overall technical framework of this method is shown in the attached figure. Figure 2 As shown, this framework uses a three-level cross-scale model of "atomic level - device level - system level" as its backbone and the collaborative solution of four physical fields of "electric field - thermal field - magnetic field - force field" as its core. Through a unified cross-scale coupling interface and model reduction algorithm, it realizes the simultaneous solution and dynamic evolution analysis of three major reliability parameters of quantum current sensors: electromagnetic compatibility margin, thermal stress distribution and packaging leakage rate under wide temperature range, high voltage and strong electromagnetic field environment.

[0047] In one embodiment, such as Figure 3 As shown, a multiphysics simulation method for quantum current sensors, encompassing electro-thermal-magnetic-force fields, is presented. This method can be applied to… Figure 1 In the terminal, the method may include the following steps:

[0048] Step S301: For the quantum current sensor, a cross-scale simulation model is constructed by atomic-level molecular dynamics modeling, device-level model reduction finite element modeling, and system-level time-domain finite difference modeling.

[0049] Specifically, the cross-scale simulation model in this embodiment can be composed of a three-level solver and a cross-scale coupled scheduling kernel, as shown in the attached figure. Figure 4 As shown:

[0050] Atomic-scale: Using molecular dynamics (MD) methods, the local stress, thermal vibration, defect migration and spin-lattice coupling behavior of the nitrogen-vacancy color center lattice defect region in diamond are finely modeled with a feature scale of 0.1~10nm and a time step on the femtosecond (fs) scale.

[0051] Device-scale: The Model Reduction Finite Element (MOR-FEM) method is used to perform reduced-order modeling of the thermal-mechanical-electric response of MEMS sensitive elements. The feature scale is 1μm~1mm and the time step is on the order of microseconds (μs).

[0052] System-scale: The finite-difference time-domain (FDTD) method is used to simulate the electromagnetic field distribution, electromagnetic compatibility margin and high-frequency interference response within the quantum current sensor package. The characteristic scale is 1mm to 10cm, and the time step is on the picosecond (ps) scale.

[0053] Step S302: Construct a unified cross-scale coupling interface in the cross-scale simulation model, and use the mapping operator in the cross-scale coupling interface to perform bidirectional data transfer between models at different levels in the cross-scale simulation model.

[0054] Specifically, the terminal constructs a unified cross-scale coupling interface in the cross-scale simulation model. Data is transferred between the three levels through the cross-scale coupling interface (CSCI). The overall solution process is iteratively advanced under the constraint of the four-physics field collaborative convergence criterion until the residuals of the entire field meet the preset convergence threshold.

[0055] Step S303: Based on the cross-scale simulation model, perform a coordinated iterative solution of the four physical fields of electricity, heat, magnetism and force until the residuals throughout the process meet the preset convergence criteria, and then proceed to the next global time step.

[0056] Specifically, the terminal first initializes the material parameters and boundary conditions of each level of the cross-scale simulation model and sets the global time step. Within the global time step, it sequentially completes the system-level electromagnetic field solution, the device-level thermo-mechanical response solution, and the atomic-level lattice and spin behavior solution to obtain the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function. Finally, based on the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function, iteratively updates the material parameters and boundary conditions through the cross-scale coupling interface until the residuals throughout the process meet the preset convergence criteria, and then enters the next global time step.

[0057] Step S304: After all global time steps are completed, output the dynamic evolution curve of the quantum current sensor under service environment, including the three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate.

[0058] Specifically, after the terminal completes the simulation of all global time steps, it outputs the dynamic evolution curve of the quantum current sensor under the service environment, including the three major reliability parameters of electromagnetic compatibility margin, thermal stress distribution and packaging leakage rate, through a cross-scale simulation model based on the simulation results.

[0059] In this embodiment, by integrating three independent solvers—FDTD, MOR-FEM, and MD—into a unified cross-scale simulation model, and combining them with a four-physics-physics-physical-physical-mechanical-physical-mechanical convergence process and a POD-DEIM (Proper Orthogonal Decomposition with Discrete Empirical Interpolation Method) order reduction algorithm, the system achieves for the first time an integrated digital reliability assessment capability for quantum current sensors, from atomic-level defects to system-level packaging. After dual verification through numerical validation and prototype comparison, this solution controls the prediction errors of the three major reliability indicators—electromagnetic compatibility margin, thermal stress distribution, and packaging leakage rate—to within 5%, providing reliable digital technical support for the engineering design and lifetime prediction of quantum current sensors.

[0060] In one embodiment, step S301 above, atomic-level molecular dynamics modeling, may include the following steps:

[0061] Using molecular dynamics methods, based on a hybrid interatomic interaction potential employing Tersoff and Stillinger-Weber potentials, a refined model of the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior in the diamond nitrogen-vacancy color center lattice defect region is obtained, yielding an atomic-level model of the diamond nitrogen-vacancy color center lattice defect region. Through the atomic-level model, the simulation outputs the local stress tensor and defect mobility, which serve as constitutive correction inputs for device-level simulations.

[0062] Among them, the Tersoff potential and the Stillinger-Weber potential are two potential energy functions commonly used to simulate semiconductors and other crystalline materials.

[0063] Specifically, regarding the microscopic behavior of the nitrogen-vacancy color center lattice defect region in diamond, this embodiment employs classical molecular dynamics methods. The interatomic interaction potential adopts a hybrid form of the Tersoff and Stillinger-Weber potentials to account for both covalent bond energy and many-body effects in the defect region. The total Hamiltonian of the atomic-level model... It can be represented as:

[0064] (1)

[0065] In the above formula, p i Let m be the momentum of the i-th atom. i V is the atomic mass. ij V ijk These are the potential energy terms for the interaction between two and three bodies, respectively. and Interatomic spacing, The bond angle is formed by three atoms. This indicates that all atomic pairs are traversed. This represents traversing all atomic triples. The spin Hamiltonian of the nitrogen-vacancy color center under the influence of an applied magnetic field and stress is expressed as:

[0066] (2)

[0067] In the above equation, D is the zero-field splitting parameter, E is the stress-induced transverse splitting, S is the spin operator, and Π ij σ is the stress coupling coefficient. ij This is the local stress tensor. MD simulation directly outputs the local stress tensor σ. ijMD Defect mobility is used as a constitutive correction input for device-level simulation.

[0068] In one embodiment, step S301 above, reducing the device-level model to finite element modeling, may include the following steps:

[0069] The model-reduced finite element method is adopted to perform reduced-order modeling of the thermo-mechanical-electric response of MEMS (Micro-Electro-Mechanical System) sensing element based on the local stress tensor and defect mobility, thus obtaining the device-level model of the MEMS sensing element. Based on the device-level model, the thermo-mechanical coupling response equation is established, with the electromagnetic loss heat source transmitted at the system level as input, and the temperature field and stress field as output. The temperature field and stress field are used as the thermal vibration and stress boundary conditions for atomic-level simulation, and as temperature correction terms for the material dielectric constant and conductivity for system-level simulation.

[0070] Specifically, to address the issues of excessive freedom and low solution efficiency in device-level finite element models, this embodiment introduces a model order reduction algorithm based on Krylov subspaces. The original finite element discrete equations are as follows:

[0071] (3)

[0072] In the above formula, M, C, and K are the mass, damping, and stiffness matrices, respectively; B is the input distribution matrix; L is the output selection matrix; x(t) is the nodal displacement vector; and the dimension n is typically located at... Order of magnitude. By constructing a q-order... Krylov subspace projection matrix V∈R n×q :

[0073] (4)

[0074] in Let x(t) ≈ Vx(t), and using the Galerkin projection method, we can obtain the reduced-order system:

[0075] (5)

[0076] Where M=V T MV, C=V T CV, K=V T KV. Actual measurements have verified that when q is on the order of 503.

[0077] The thermo-mechanical coupling response equation is:

[0078] (6)

[0079] (7)

[0080] In the above formula, Q em The electromagnetic loss heat source (transferred from the system-level FDTD), Q vis As a viscous heat dissipation source, α kl C is the thermal expansion coefficient tensor. ijklThis is the elastic stiffness tensor. The temperature field T(x,t) and stress field σ output by MOR-FEM are... ij(x,t) On the one hand, it is transmitted down to the atomic-level MD as thermal vibration and stress boundary conditions, and on the other hand, it is transmitted up to the system-level FDTD as temperature correction terms for the material's dielectric constant and conductivity.

[0081] In one embodiment, step S301 above, the system-level time-domain finite-difference modeling, may include the following steps:

[0082] The finite-difference time-domain method is used to simulate the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the quantum current sensor package, resulting in a system-level model of the quantum current sensor. Based on the measured current waveform and external electromagnetic interference excitation, the electromagnetic field distribution and electromagnetic loss heat source output by the system-level model are calculated and used as key inputs for device-level and atomic-level simulations.

[0083] Specifically, the system-level simulation uses the three-dimensional FDTD method to solve the Maxwell equations:

[0084] (8)

[0085] (9)

[0086] In the above formula, The electric field intensity vector, The magnetic field strength vector. Permeability, Where is the dielectric constant. For electrical conductivity, For equivalent permeability loss, This represents the density of the applied current source.

[0087] After discretization using the Yee grid, the electric and magnetic field components are updated alternately in time and space:

[0088] (10)

[0089] In the above formula, For the next moment At spatial grid points The electric field component in the x-direction at that location, For the current moment The electric field component in the x-direction at the same grid point and For half a time step The magnetic field components, and The spatial grid step size is in the y and z directions. and The coefficient is determined by material parameters (dielectric constant, conductivity, and time step).

[0090] The time step must satisfy the Courant stability condition:

[0091] (11)

[0092] In the above formula, For time step, At the speed of light, , and The spatial grid step size is denoted by x, y, and z.

[0093] By setting a perfectly matched layer (PML) absorbing boundary around the sensor's overall model, the interference of truncated reflections on the electromagnetic compatibility assessment results can be effectively eliminated. Electromagnetic field distribution at system-level output. (x,t) (x,t) and electromagnetic loss heat source Q em =σ∣ | / 2 serves as a key input for both device-level and atomic-level simulations.

[0094] In one embodiment, step S303 above, based on the cross-scale simulation model, performs a cooperative iterative solution of the four physical fields of electricity, heat, magnetism, and force, which may include the following steps:

[0095] Initialize the material parameters and boundary conditions of each level of the multi-scale simulation model and set the global time step. Within the global time step, sequentially complete the system-level electromagnetic field solution, device-level thermo-mechanical response solution, and atomic-level lattice and spin behavior solution to obtain the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function. Based on the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function, iteratively update the material parameters and boundary conditions through the multi-scale coupling interface.

[0096] Specifically, the four-physics collaborative solution process constructed in this embodiment is as follows: Figure 5 As shown, the overall strategy adopts "field-by-field solution, cross-field iteration, and unified convergence," and the specific execution steps are as follows:

[0097] Step 1: Initialize the material parameters and boundary conditions of each level of the model, and set the global time step Δt. g With each scale sub-time step Δt MD Δt FEM Δt FDTD The three are coordinated through a multi-time-step algorithm.

[0098] Step 2: The system-level FDTD solver takes the measured current waveform I(t) and external electromagnetic interference excitation as input and calculates the electromagnetic field distribution inside the sensor. , and electromagnetic loss heat source Q em .

[0099] Step 3: Transfer Q em and The temperature field T and stress field σ of the MEMS sensing element are calculated by passing the data to the device-level MOR-FEM solver via a cross-scale coupling interface. ij .

[0100] Step 4: Output the local temperature T at the device level loc With stress σ ijloc As the initial heat bath temperature and applied stress boundary conditions for atomic-level MD simulation, the micro-stress distribution, defect migration law, and spin Hamiltonian modulation in the nitrogen-vacancy color center lattice defect region are calculated.

[0101] Step 5: Correct the equivalent dielectric constant Δε, thermal expansion coefficient Δα, and spin response function χ of the MD output. spin The data is fed back to the device-level and system-level solvers to update the material parameters.

[0102] Step 6: Check the full convergence criterion:

[0103] (12)

[0104] In the above formula, Let be the electric field distribution vectors for the k-th and k+1-th iterations. The temperature field distribution for the k-th and k+1-th iterations is shown. Let be the magnetic field distribution vectors for the k-th and k+1-th iterations. The stress field distributions for the k-th and k+1-th iterations are shown. This is the preset convergence tolerance.

[0105] If the convergence criterion is not met, return to step 2 to continue iterating; if it is met, proceed to the next global time step.

[0106] Step 7: After all time steps are completed, output the reliability parameter evolution curves, including the electromagnetic compatibility margin M. EMC(t) Thermal stress distribution σ th and package leakage rate L pkg(t) .

[0107] In one embodiment, the mapping operator includes a geometric mapping operator, a time mapping operator, and a physical quantity mapping operator. The method of this application further includes the following steps:

[0108] The geometric mapping operator employs trilinear interpolation for the downpropagation process from coarse to fine scale and volume weighted average for the uppropagation process from fine to coarse scale. The time mapping operator uses quasi-static extrapolation to coordinate the time step differences between sub-time steps. The physical quantity mapping operator performs conversions of different physical quantities in terms of unit system and tensor order.

[0109] Specifically, the cross-scale coupling interface (CSCI) constructed in this embodiment is the key link connecting the three scale solvers, and its structure is as follows: Figure 6 As shown. CSCI mainly includes three types of mapping operators:

[0110] Geometric mapping operator G: Used to perform spatial interpolation of physical quantities between meshes of different scales. For the downpropagation process from coarse to fine scale, a trilinear interpolation method is used; for the uppropagation process from fine to coarse scale, a volume-weighted average method is used.

[0111] (13)

[0112] In the above formula, For coarse-scale units, fine-scale unit physical quantity, fine-scale unit volume, This represents the total volume of the coarse-scale unit. It is the set of all fine-scale units contained within a coarse-scale unit.

[0113] The time mapping operator T is used to coordinate time steps of different scales. Considering that the MD step size is smaller in practice but the total simulation time is limited, time extrapolation is required, and a quasi-static extrapolation method is adopted:

[0114] (14)

[0115] In the above formula, Let be the value of the physical field at the current time t. It is the first time derivative (rate of change) of the physical field. This is the second time derivative (acceleration) of the physical field. This is the global time step.

[0116] Physical quantity mapping operator P: Used to convert different physical quantities in terms of unit system and tensor order. For example, converting the atomic-level stress output by MD into a continuous medium stress tensor:

[0117] (15)

[0118] In the above formula, These are the components of the stress tensor in a continuous medium. The volume of the atomic set. For atoms quality and For atoms exist and The velocity component in the direction, For atoms and Position vector components between For atoms and The interaction force components between them.

[0119] In one embodiment, the steps for extracting and evaluating the three major reliability parameters are as follows:

[0120] Electromagnetic compatibility margin M EMC Defined as the margin of the sensor's output signal-to-noise ratio relative to the interference threshold.

[0121] (16)

[0122] In the above formula, V interf V is directly given by FDTD simulation. noise It comprises two parts: nitrogen-vacancy color center shot noise and electronic readout noise. The voltage amplitude of the target signal.

[0123] Thermal stress distribution σ th The key performance indicators (KPIs) are the nitrogen-vacancy color center implantation region and the encapsulation interface, as output by MOR-FEM, and are characterized using von Mises equivalent stress.

[0124] (17)

[0125] In the above formula, These are components of the stress tensor. The maximum von Mises equivalent stress over the entire device domain. This indicates taking the maximum value over the entire solution domain.

[0126] Package leakage rate L pkg The results are provided by co-simulation at the device and system levels, following the diffusion-penetration coupling equation:

[0127] (18)

[0128] In the above formula, P is the partial pressure of the external gas, A is the permeation cross-sectional area, and D... e E is the effective diffusion coefficient, l is the permeation path length, and E is the effective diffusion coefficient. aTo activate energy, Boltzmann's constant, This refers to absolute temperature.

[0129] To further explain, to improve simulation efficiency, this embodiment introduces a nonlinear order reduction strategy based on intrinsic orthogonal decomposition (POD) and discrete empirical interpolation (DEIM) on top of the traditional Krylov subspace order reduction. The snapshot matrix X=[x1,x2,…,x] during the simulation process is used... Ns Perform singular value decomposition:

[0130] (19)

[0131] Construct the POD basis U using the first r principal singular vectors. r The energy cutoff error is satisfied as follows:

[0132] (20)

[0133] In the above formula, These are the singular values ​​obtained from the singular value decomposition of the snapshot matrix. The total order of the snapshot matrix. This is the preset energy cutoff error threshold.

[0134] By employing the POD-DEIM combination strategy, this application reduces the simulation time for a single four-field coupling operation from approximately 72 hours to approximately 3 hours under typical operating conditions of a quantum current sensor, achieving a speedup of over 20 times. The overall timing coordination mechanism of the simulation process is as follows: Figure 7 As shown.

[0135] The key technical parameters of the simulation method in this application are shown in Table 1.

[0136] Table 1. Core Technical Parameters for Multiphysics Coupling Simulation of Quantum Current Sensor

[0137]

[0138] Visualization of simulation results, for example Figure 8 As shown, the prediction accuracy of this scheme in terms of the evolution trend of the three major reliability parameters is significantly improved compared with the traditional single-scale simulation scheme.

[0139] In one embodiment, such as Figure 9 As shown, a multiphysics simulation method for a quantum current sensor, encompassing electro-thermal-magnetic-force fields, is provided in a specific embodiment, including the following steps:

[0140] Step S901: Using molecular dynamics, based on a hybrid interatomic interaction potential employing Tersoff and Stillinger-Weber potentials, a fine model is constructed of the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior of the diamond nitrogen-vacancy color center lattice defect region, resulting in an atomic-level model of the diamond nitrogen-vacancy color center lattice defect region. Through the atomic-level model, the simulation outputs the local stress tensor and defect mobility as constitutive correction inputs for device-level simulation.

[0141] Step S902: Using the model reduction finite element method, the thermo-mechanical-electric response of the MEMS sensing element is modeled in a reduced order based on the local stress tensor and defect mobility to obtain the device-level model of the MEMS sensing element; based on the device-level model, the thermo-mechanical coupling response equation is established, the electromagnetic loss heat source transmitted at the system level is input, and the temperature field and stress field are output; the temperature field and stress field are used as the thermal vibration and stress boundary conditions for the atomic-level simulation, and as the temperature correction terms for the material dielectric constant and conductivity of the system-level simulation.

[0142] Step S903: Using the finite-difference time-domain method, the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the quantum current sensor's overall package are simulated to obtain a system-level model of the quantum current sensor. Based on the measured current waveform and external electromagnetic interference excitation, calculations are performed through the system-level model to obtain the electromagnetic field distribution and electromagnetic loss heat source output by the system-level model, which serve as key inputs for device-level and atomic-level simulations.

[0143] Step S904: For the quantum current sensor, a cross-scale simulation model is constructed at the atomic level, device level, and system level by using atomic-level molecular dynamics modeling, device-level model reduction finite element modeling, and system-level time-domain finite difference modeling. A unified cross-scale coupling interface is constructed in the cross-scale simulation model, and bidirectional data transfer between models at different levels in the cross-scale simulation model is carried out through the mapping operator in the cross-scale coupling interface.

[0144] Step S905: Initialize the material parameters and boundary conditions of each level of the cross-scale simulation model and set the global time step. Within the global time step, sequentially complete the system-level electromagnetic field solution, device-level thermo-mechanical response solution, and atomic-level lattice and spin behavior solution to obtain the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function. Based on the equivalent dielectric constant correction, thermal expansion coefficient correction, and spin response function, iteratively update the material parameters and boundary conditions through the cross-scale coupling interface until the residuals throughout the process meet the preset convergence criteria, and then proceed to the next global time step. After all global time steps are completed, output the dynamic evolution curves of the quantum current sensor under service environment, including the three major reliability parameters of electromagnetic compatibility margin, thermal stress distribution, and package leakage rate.

[0145] The beneficial effects of the above embodiments are as follows:

[0146] (1) Substantial breakthrough in cross-scale modeling capability: By integrating three simulation methods belonging to different spatial scales—molecular dynamics, model-reduced finite element method, and time-domain finite difference method—into the same solution framework through a unified cross-scale coupling interface, this application achieves for the first time a full-scale synchronous simulation capability spanning eight orders of magnitude, from 0.1 nm NV color center lattice defects to 10 cm sensor assembly. Compared to existing single-scale or local dual-scale schemes, this application can completely restore the complete causal chain of "atomic-level defect evolution—device-level response drift—system-level failure behavior," organically connecting existing isolated simulation units into a unified reliability assessment network.

[0147] (2) The four-physics field collaborative solution significantly improves simulation accuracy: The four-field collaborative convergence process of electro-thermal-magnetic-mechanical fields constructed in this application incorporates the strong coupling interactions between the electromagnetic field, temperature field, magnetic field and mechanical field inside the sensor into a unified solution framework at once, avoiding the problem of physical information loss caused by the traditional pairwise coupling scheme. Simulation results show that the prediction error of the three major reliability parameters of the quantum current sensor under wide temperature range, high voltage and strong electromagnetic field environment in this application is reduced from 15%~30% of the traditional method to less than 5%, which significantly reduces the trial and error cost in the prototype development stage and shortens the engineering iteration cycle by at least 40%.

[0148] (3) The order reduction algorithm greatly improves the practicality of simulation engineering: By combining the Krylov subspace order reduction and POD-DEIM strategy, this application reduces the degree of freedom of the device-level finite element model from the original 10 5 The simulation scale is compressed to 200 dimensions, while the total time for a single four-field coupling simulation is reduced from approximately 72 hours to approximately 3 hours, a speedup of over 20 times. This engineering-grade simulation efficiency allows designers to complete large-scale parameter scanning and sensitivity analysis on conventional workstations, thus transforming the concept of "reliability digital twin" into a practically deployable engineering tool, providing solid underlying support for the mass production and field deployment of quantum current sensors.

[0149] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0150] Based on the same inventive concept, this application also provides a quantum current sensor electro-thermal-magnetic-force multiphysics field simulation device for implementing the above-mentioned quantum current sensor electro-thermal-magnetic-force multiphysics field simulation method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more quantum current sensor electro-thermal-magnetic-force multiphysics field simulation device embodiments provided below can be found in the limitations of the quantum current sensor electro-thermal-magnetic-force multiphysics field simulation method described above, and will not be repeated here.

[0151] In one exemplary embodiment, such as Figure 10 As shown, a quantum current sensor electro-thermal-magnetic-force multiphysics simulation device is provided, which may include:

[0152] Model building module 1001 is used to construct a cross-scale simulation model of the quantum current sensor by means of atomic-level molecular dynamics modeling, device-level model reduction finite element modeling, and system-level time-domain finite difference modeling.

[0153] The interface coupling module 1002 is used to build a unified cross-scale coupling interface in the cross-scale simulation model and to perform bidirectional data transfer between models at different levels in the cross-scale simulation model through the mapping operator in the cross-scale coupling interface.

[0154] The collaborative solution module 1003 is used to perform collaborative iterative solution of the four physical fields of electricity, heat, magnetism and force based on the cross-scale simulation model until the residuals throughout the process meet the preset convergence criteria and enter the next global time step.

[0155] The result output module 1004 is used to output the dynamic evolution curves of the quantum current sensor under service environment, including three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate, after all global time step simulations are completed.

[0156] In one embodiment, the model building module 1001 is further used to employ molecular dynamics methods to perform fine modeling of the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior of the diamond nitrogen-vacancy color center lattice defect region based on a mixed interatomic interaction potential using Tersoff and Stillinger-Weber potentials, thereby obtaining an atomic-level model of the diamond nitrogen-vacancy color center lattice defect region; through the atomic-level model, the simulation outputs the local stress tensor and defect mobility as constitutive correction inputs for device-level simulation.

[0157] In one embodiment, the model building module 1001 is further configured to use the model reduction finite element method to perform reduced-order modeling of the thermo-mechanical-electric response of the MEMS sensitive element based on the local stress tensor and defect mobility, thereby obtaining a device-level model of the MEMS sensitive element; based on the device-level model, a thermo-mechanical coupling response equation is established, the electromagnetic loss heat source transmitted at the system level is input, and the temperature field and stress field are output; the temperature field and stress field serve as the thermal vibration and stress boundary conditions for the atomic-level simulation, and as the temperature correction terms for the material dielectric constant and conductivity for the system-level simulation.

[0158] In one embodiment, the model building module 1001 is also used to simulate the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the quantum current sensor package using the finite-difference time-domain method, thereby obtaining a system-level model of the quantum current sensor; and to calculate the electromagnetic field distribution and electromagnetic loss heat source output by the system-level model based on the measured current waveform and external electromagnetic interference excitation, which serve as key inputs for device-level and atomic-level simulations.

[0159] In one embodiment, the collaborative solver module 1003 is also used to initialize the material parameters and boundary conditions of each level of the model in the cross-scale simulation model, and set the global time step; within the global time step, the system-level electromagnetic field solution, the device-level thermo-mechanical response solution, and the atomic-level lattice and spin behavior solution are completed sequentially to obtain the equivalent dielectric constant correction, the thermal expansion coefficient correction, and the spin response function; the material parameters and boundary conditions are iteratively updated through the cross-scale coupling interface based on the equivalent dielectric constant correction, the thermal expansion coefficient correction, and the spin response function.

[0160] In one embodiment, the mapping operator includes a geometric mapping operator, a time mapping operator, and a physical quantity mapping operator. The device may further include: an operator application module, used to employ trilinear interpolation for the downlink process from coarse to fine scale and volume weighted average for the uplink process from fine to coarse scale using the geometric mapping operator; to coordinate the time step differences between sub-time steps using quasi-static extrapolation using the time mapping operator; and to perform conversions of different physical quantities in terms of unit system and tensor order using the physical quantity mapping operator.

[0161] The various modules in the aforementioned quantum current sensor electro-thermal-magnetic-force multiphysics simulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in a computer device, or stored in software within the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0162] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a multiphysics simulation method for a quantum current sensor, encompassing electro-thermal-magnetic-force fields. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0163] Those skilled in the art will understand that Figure 11The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0164] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0165] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0167] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0168] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multiphysics simulation method for quantum current sensors, characterized in that, The method includes: For quantum current sensors, a molecular dynamics approach is employed, based on a hybrid interatomic interaction potential combining Tersoff and Stillinger-Weber potentials, to finely model the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior of the diamond nitrogen-vacancy center lattice defect region, resulting in an atomic-level model of the region. Using this atomic-level model, the simulation outputs the local stress tensor and defect mobility as constitutive correction inputs for device-level simulations. A model-reduced finite element method is then used to perform reduced-order modeling of the thermo-mechanical-electrical response of the MEMS sensing element based on the local stress tensor and defect mobility, yielding a device-level model of the MEMS sensing element. Based on this device-level model, a quantum current sensor is then constructed. A thermo-mechanical coupling response equation is established, with the electromagnetic loss heat source transmitted at the system level as input, and the temperature field and stress field as output. The temperature field and stress field serve as the thermal vibration and stress boundary conditions for atomic-level simulation, and as temperature correction terms for the material dielectric constant and conductivity in system-level simulation. The finite-difference time-domain method is used to simulate the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the quantum current sensor's overall package, obtaining the system-level model of the quantum current sensor. Based on the measured current waveform and external electromagnetic interference excitation, calculations are performed through the system-level model to obtain the electromagnetic field distribution and electromagnetic loss heat source output by the system-level model, which serve as key input terms for the device-level simulation and the atomic-level simulation, constructing a cross-scale simulation model from atomic level to device level to system level. A unified cross-scale coupling interface is constructed in the cross-scale simulation model, and bidirectional data transfer between models at different levels in the cross-scale simulation model is carried out through the mapping operator in the cross-scale coupling interface. Based on the aforementioned cross-scale simulation model, the four physical fields of electricity, heat, magnetism and force are solved in a coordinated iterative manner until the residuals throughout the process meet the preset convergence criteria, and then the next global time step is entered. After all global time steps are simulated, the dynamic evolution curves of the quantum current sensor under service conditions, including three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate, are output.

2. The method according to claim 1, characterized in that, The method of performing a coordinated iterative solution of the four physical fields (electric, thermal, magnetic, and mechanical) based on the cross-scale simulation model includes: Initialize the material parameters and boundary conditions of each level of the model in the multi-scale simulation model, and set the global time step; Within the global time step, the system-level electromagnetic field solution, the device-level thermo-mechanical response solution, and the atomic-level lattice and spin behavior solution are sequentially completed to obtain the equivalent dielectric constant correction, the thermal expansion coefficient correction, and the spin response function. The material parameters and boundary conditions are iteratively updated through the cross-scale coupling interface based on the equivalent dielectric constant correction, the thermal expansion coefficient correction, and the spin response function.

3. The method according to any one of claims 1 to 2, characterized in that, The mapping operators include geometric mapping operators, time mapping operators, and physical quantity mapping operators; the method further includes: Using the geometric mapping operator, trilinear interpolation is used for the downlink process from coarse to fine scale, and volume weighted average is used for the uplink process from fine to coarse scale. The time step differences between sub-time steps are coordinated by using the time mapping operator and the quasi-static extrapolation method. The physical quantity mapping operator is used to convert different physical quantities in terms of unit system and tensor order.

4. A quantum current sensor electro-thermal-magnetic-force multiphysics field simulation device, characterized in that, The device includes: The model building module is used for quantum current sensors. Employing molecular dynamics methods and a hybrid interatomic interaction potential combining Tersoff and Stillinger-Weber potentials, it performs detailed modeling of the local stress, thermal vibration, defect migration, and spin-lattice coupling behavior in the diamond nitrogen-vacancy center lattice defect region, obtaining an atomic-level model of the defect region. Using this atomic-level model, the simulation outputs the local stress tensor and defect mobility as constitutive correction inputs for device-level simulation. A model-reduced finite element method is then used to perform reduced-order modeling of the thermo-mechanical-electrical response of the MEMS sensing element based on the local stress tensor and defect mobility, obtaining a device-level model of the MEMS sensing element. Based on this device... A system-level model is established, and a thermo-mechanical coupling response equation is constructed. The electromagnetic loss heat source transmitted at the system level is input, and the temperature and stress fields are output. These temperature and stress fields serve as the thermal vibration and stress boundary conditions for atomic-level simulation, and as temperature correction terms for the material dielectric constant and conductivity in system-level simulation. The finite-difference time-domain method is used to simulate the electromagnetic field distribution, electromagnetic compatibility margin, and high-frequency interference response within the quantum current sensor's package, yielding a system-level model of the quantum current sensor. Based on the measured current waveform and external electromagnetic interference excitation, calculations are performed using this system-level model to obtain the electromagnetic field distribution and electromagnetic loss heat source output by the model. These serve as key input terms for both device-level and atomic-level simulations, constructing a cross-scale simulation model from atomic to device to system levels. The interface coupling module is used to construct a unified cross-scale coupling interface in the cross-scale simulation model, and to perform bidirectional data transfer between models at different levels in the cross-scale simulation model through the mapping operator in the cross-scale coupling interface. The collaborative solution module is used to perform collaborative iterative solution of the four physical fields of electricity, heat, magnetism and force based on the cross-scale simulation model until the residuals throughout the process meet the preset convergence criteria and enter the next global time step; The results output module is used to output the dynamic evolution curves of the quantum current sensor under service environment, including three major reliability parameters: electromagnetic compatibility margin, thermal stress distribution, and package leakage rate, after all global time step simulations are completed.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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