Simulation experiment coupling method and device, electronic equipment and storage medium

Abstract

The invention discloses a simulation experiment coupling method and device, electronic equipment and a storage medium. An online coupling operation model and a simulation method are gradually improved through comprehensive measurement and analysis of a condition and state continuous change experiment. The method comprises the following steps: measuring a predetermined experimental system to obtain a measurement result; wherein the experiment system is composed of at least one experiment device; simulating the experimental system by using a predetermined simulation system to obtain a simulation result; wherein the simulation system is composed of at least one model or simulation method; the experimental device, the simulation instance, the model and the simulation method are in a many-to-many mapping relationship; and according to the comparison condition of the measurement result and the simulation result, adjusting the parameters of the simulation system to obtain an optimal simulation system. According to the technical scheme, the simulation system of the multi-parameter complex industrial process and equipment can be optimized, and the optimization efficiency is high.

Description

Technical Field

[0001] The present invention relates to the field of computer simulation technology, and in particular to a simulation experiment coupling method, device, electronic equipment and storage medium. Background Art

[0002] Computer simulation is an important means of studying complex systems. In recent years, with the rapid development of computer hardware and software technology, the spatial and temporal scales of processes or equipment that can be simulated within an acceptable time scale are getting larger and larger. However, for current complex industrial processes and equipment such as chemical reactors, computer simulation still faces huge challenges in accuracy, scale and speed, and it is difficult to adapt to the requirements of accurate optimal design and operation control.

[0003] The reason is that, as far as the model itself is concerned, although the control equations such as conservation of mass, energy and momentum are strictly established, the simulation of physical and chemical processes involves a large number of constitutive relations, such as state equations, physical property relations and rate equations, which are all subject to great uncertainty and often rely on empirical correlations. They often contain undetermined coefficients, and even the function form has various choices or cannot be determined. At the same time, the numerical calculation process will introduce a large number of adjustable or optional parameters in terms of numerical methods and formats, grid or unit shapes and scales. It is an extremely difficult task to determine these parameters or hyperparameters to establish the most accurate and effective simulation method. At the same time, due to many limitations of experimental and measurement accuracy and information volume, it is difficult to fully and deeply test and evaluate the simulation results, which makes the improvement of simulation and algorithms lack reliable standards and basis. In recent years, the rise of technologies such as artificial intelligence has provided new possibilities for the improvement of the above models and algorithms and their co-evolution with experimental measurements.

[0004] There are methods for optimizing simulations through comparative experiments in many fields. The current practice is mostly to first select simulation parameters, and after the simulation results are obtained, compare the measurement results of the experiment using the same operating parameters, and then adjust the simulation parameters according to the comparison results, re-enter the next round of simulation calculations, and iterate until the simulation results are consistent with the experimental measurement results or the error is small enough. The patents that have been disclosed to use this method include CN110826159A, CN113074459A, CN113990412A, CN110816885A (EP3845465A1; EP3845465A4; EP3845465B1; US11161629B2; US2021245898A1; WO2021088097A1), CN105 677983A, CN1942878A (EP1721281A2; JP2007534318A; JP4852430B2; US2005187745A1; US8554486B2; WO2005083618A2; WO2005083618A3), WO0157740A2 (WO0157740A3), EP3789904A1, etc. The methods proposed in the above patents are effective, but they also have some shortcomings. Especially for complex industrial processes and equipment, there are many parameters including hyperparameters, slow simulation speed, huge data volume and other problems. The effect of current method improvements and the scope of application are still very limited. Furthermore, the implementation process of these methods is mostly open-loop, and a lot of manual intervention is required during the implementation process. Finally, these methods mostly use the results of only one experimental condition to optimize the simulation parameters each time, which is very inefficient for simulation systems with long simulation time and the need to optimize more internal parameters at the same time. Summary of the invention

[0005] The present invention provides a simulation experiment coupling method, device, electronic equipment and storage medium, which can optimize the simulation system of complex industrial processes and equipment with multiple parameters and has high optimization efficiency.

[0006] According to one aspect of the present invention, a simulation experiment coupling method is provided, the method comprising:

[0007] Measuring a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device;

[0008] The experimental system is simulated based on a predetermined simulation system to obtain a simulation result; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, the simulation instance, the model and the simulation method;

[0009] According to the comparison between the measurement result and the simulation result, the parameters of the simulation system are adjusted to obtain the optimal simulation system.

[0010] According to another aspect of the present invention, a simulation experiment coupling device is provided, the device comprising:

[0011] A measurement result acquisition module, used to measure a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device;

[0012] A simulation result acquisition module, used to simulate the experimental system based on a predetermined simulation system to obtain simulation results; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, simulation instance, model and simulation method;

[0013] The parameter optimization module is used to adjust the parameters of the simulation system according to the comparison between the measurement results and the simulation results to obtain the optimal simulation system.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform a simulation experiment coupling method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a simulation experiment coupling method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention measures a predetermined experimental system to obtain a measurement result, simulates the experimental system based on a predetermined simulation system to obtain a simulation result, and then adjusts the parameters of the simulation system according to the comparison between the measurement result and the simulation result to obtain an optimal simulation system. The technical solution of the present invention can optimize the simulation system of complex industrial processes and equipment with multiple parameters, and the optimization efficiency is high.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of a simulation experiment coupling method provided according to the first embodiment of the present invention;

[0021] Figure 2 It is the overall framework of simulation experiment coupling optimization provided in Example 1 of the present application;

[0022] Figure 3 is a schematic diagram of the mapping relationship provided in Example 1 of the present application;

[0023] Figure 4 It is a schematic diagram of iterative optimization of a model of a multi-level neural network of a coupling mechanism model provided in Example 1 of the present application;

[0024] Figure 5 is a schematic diagram of a gas-solid fluidized bed experimental simulation fusion system provided in Example 2 of the present application;

[0025] Figure 6 is a schematic diagram of a gas-solid fluidized bed instantaneous dynamic data enhancement experimental system provided in Example 2 of the present application;

[0026] Figure 7 Schematic diagram of a fixed bed experimental simulation fusion system for gas-solid catalytic reaction provided in Example 3 of the present application;

[0027] Figure 8 is a schematic diagram of a roller equipment experimental simulation fusion system provided in Example 4 of the present application;

[0028] Fig. 9 is a schematic diagram of a roller device instantaneous dynamic data enhancement experimental system provided in Example 4 of the present application;

[0029] Fig.10 A schematic diagram of the structure of a simulation experiment coupling device provided in Embodiment 5 of the present invention;

[0030] Fig.11 It is a structural schematic diagram of an electronic device for implementing a simulation experiment coupling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "optimal" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 This is a flow chart of a simulation experiment coupling method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of optimizing the simulation system of complex industry and engineering. The method can be executed by a simulation experiment coupling device. The simulation experiment coupling device can be implemented in the form of hardware and / or software. The simulation experiment coupling device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110, measuring a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device.

[0036] In this scheme, for complex industrial processes and equipment, such as reactors, although the control equations such as conservation of mass, energy and momentum are strictly established, the simulation of physical and chemical processes involves a large number of constitutive relations, such as state equations, physical property relations and rate equations, which are all subject to great uncertainty and often contain undetermined coefficients. Even the function form has various choices or cannot be determined. At the same time, the numerical calculation process will introduce a large number of adjustable or optional parameters in terms of numerical methods and formats, grid or unit shapes and scales. At the same time, due to the limitations of experimental and measurement accuracy and the amount of information, it is difficult to fully and deeply test and evaluate the simulation results, which makes the improvement of models and simulation methods in the simulation system lack reliable standards and component basis. Therefore, by constructing a simulation experiment coupling system, simulation, experiment, measurement and parameter optimization can be integrated to form a closed loop, and multiple sets of real-time experimental data can be used for modeling to achieve the optimization of complex industrial process and equipment simulation systems, so as to achieve the simulation of complex industrial processes and equipment production processes.

[0037] In this embodiment, Figure 2 This is the overall framework of the simulation experiment coupling optimization provided in Example 1 of the present application, such as Figure 2 As shown, the experimental system is composed of multiple experimental devices. The measurement results can be obtained through continuous dynamic changes of the experimental system and corresponding continuous measurements, that is, sufficient measurement data samples can be obtained.

[0038] Optionally, the controllable conditions of the experimental system include at least one of operating conditions, physical property conditions, boundary conditions and variable structural parameters;

[0039] The operating conditions change according to any time series; the change forms of the operating conditions include monotonic, periodic, step, according to a specific function and random change;

[0040] The boundary conditions vary according to arbitrary spatial distribution and time series; the boundary conditions can be located at different boundary positions in space, including exits or entrances; the temporal variation of the boundary conditions includes monotonic, periodic, step, specific function and random variation of a single or multiple parameters.

[0041] In this embodiment, if Figure 2 As shown, the conditions of the experimental system include operating conditions, physical property conditions, boundary conditions and variable structural parameters. The operating conditions can change in any time series, including but not limited to monotonic, periodic, step, specific function and random changes. The boundary conditions can change in any spatial distribution and time series, including but not limited to monotonic, periodic, step, specific function and random changes of a single parameter or multiple parameters at different boundary parts, exits or entrances.

[0042] Optionally, the experimental system includes parts or components with controllable motion; wherein the motion forms of the parts or components with controllable motion include monotonic, periodic, step-wise, specific function and random motion of the whole or its parts.

[0043] In this embodiment, the experimental system includes components or members with controllable motion, and the motion forms thereof include but are not limited to monotonic, periodic, stepwise, specific functional, and random motion of the whole or its parts.

[0044] Optionally, the experimental system may be composed of one or more devices of different scales, structures and functions, and each experimental device may be coupled and operated or operated independently.

[0045] In this solution, the implementation system can be composed of one or more experimental devices of different scales, structures and functions, and each experimental device can be coupled and run independently, that is, each experimental device can be combined and run or run independently.

[0046] In this embodiment, the measurement location includes but is not limited to the boundary, exit or entrance, single point, multiple points or continuous area of ​​the experimental system. The measurement can be performed offline or online, intermittently or continuously. The measurement can obtain the state of the experimental system at a specific location or time, or can obtain the spatiotemporal distribution of the state of the experimental system in a specific area and time period. The data obtained by the measurement can be temporarily stored in the measuring device and then transmitted to the simulation system in batches, or transmitted to the simulation system in real time, or transmitted to the simulation system after preliminary processing.

[0047] S120. Use a predetermined simulation system to simulate the experimental system to obtain simulation results; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, simulation instance, model and simulation method.

[0048] In this embodiment, Figure 3 is a schematic diagram of the mapping relationship provided in Example 1 of the present application, such as Figure 3 As shown, the experimental system includes one or more experimental devices, and the simulation system applies one or more models or simulation methods to run one or more simulation instances; there is a many-to-many mapping relationship between the experimental devices, models or simulation methods, and simulation instances.

[0049] Further, such as Figure 2 As shown, simulation results, that is, simulation data, can be obtained by processing simulation instances in industrial processes and equipment production processes based on the simulation system.

[0050] Optionally, the parameters of the simulation system are scalars, vectors, matrices, tensors, known functions or unknown functions.

[0051] In this embodiment, the simulation system includes one or more parameters to be processed, and the parameters to be processed are scalars, vectors, matrices, tensors, known functions or unknown functions.

[0052] Optionally, adjusting the parameters of the simulation system according to the comparison between the measurement result and the simulation result to obtain the optimal simulation system includes:

[0053] The experiment, measurement, and simulation form a closed loop. Based on the comparison between the measurement results and the simulation results, at least one parameter of the model or simulation method in the simulation system is adjusted to obtain an optimal simulation system.

[0054] In this embodiment, the simulation system is composed of models or simulation methods, that is, the simulation system is composed of one or more models and one or more numerical simulation methods, and different models or simulation methods can be applied to the same scale or different scales. By adjusting the parameters of the model or simulation method, the simulation system can be optimized.

[0055] In this solution, the objects of simulation include not only the experimental system, but also the measurement and analysis system and the environment of the experimental, measurement and simulation system. The result data obtained from the experiment, measurement and analysis can be used to improve the model and simulation method. The improvement of the model and simulation method is carried out in real time in synchronization with the experiment, measurement and simulation.

[0056] S130. According to the comparison between the measurement result and the simulation result, the parameters of the simulation system are adjusted to obtain an optimal simulation system.

[0057] In this scheme, if Figure 2 As shown, the measurement results and the simulation results can be compared, and the parameters of the simulation system can be adjusted according to the difference between the measurement results and the simulation results to obtain the optimal simulation system.

[0058] Optionally, the method further includes:

[0059] The measurement results are obtained through continuous dynamic changes and continuous measurements of the experimental system;

[0060] By running the simulation system, the simulation results of the simulation instance are obtained;

[0061] According to the comparison between the measurement results and the simulation results, the parameters, structure and form of the model and the simulation method are improved; wherein the comparison between the measurement results and the simulation results includes the comparison between corresponding moments and positions, and between different moments and positions.

[0062] In this scheme, if Figure 2As shown in the figure, under the common controllable parameter set including equipment and material structure, operating status and physical properties, sufficient measurement results are accumulated through continuous dynamic changes of the experimental system and corresponding continuous online measurements; at the same time, the simulation system is run to obtain the simulation results of the corresponding simulation instance. The comparison and analysis between simulation and experimental data not only involves the corresponding time and position, but also involves the correlation between samples at different times and positions. The number of samples based on which the model and simulation method are improved is increased through the number of combinations generated thereby, so as to propose improvement plans for the model and method, including the parameters of the model or method and the structure and form of the model and method, so as to improve the effect and versatility of the improvement.

[0063] Optionally, the simulation also includes the observation object in the experiment, the structure of the experimental device, the structure that affects the observation object, the structure of the feeding device, and the measuring and analyzing instruments.

[0064] Furthermore, in order to eliminate or reduce the interference of changes in the experimental device itself or measurement errors on the optimization process, the simulation is not only aimed at the observation object in the experiment, but also includes the structure of the experimental device itself and the structure that has a significant impact on the observation object, auxiliary structures such as feeding devices, and the measuring and analysis instruments themselves.

[0065] Optionally, the method further includes:

[0066] The measured results are compared with the simulation results based on the average value of the samples repeatedly run under the same conditions; and the distribution of the gaps between different samples and the average value is introduced into the optimization iteration process.

[0067] In this embodiment, in order to take into account the inevitable uncertainties in experiments and simulations, such as individual differences in materials, background noise, etc., the comparison between the simulation results and the measurement results can be based on the average value of samples repeatedly run under the same conditions, that is, the ensemble average. At the same time, the distribution of the distances of different samples from the average value under the same ensemble is analyzed, and the distribution is introduced into the optimization iteration process. While obtaining the optimization results, the distribution and credibility of the errors are estimated.

[0068] Optionally, according to the comparison between the measurement result and the simulation result, adjusting the parameters of the simulation system to obtain the optimal simulation system further includes:

[0069] Based on a predetermined machine learning algorithm or artificial intelligence algorithm, and according to the comparison between the measurement results and the simulation results, the parameters of the simulation system are adjusted to obtain an optimal simulation system; wherein the machine learning algorithm or artificial intelligence algorithm includes evolutionary algorithm, Bayesian optimization, artificial neural network, deep learning and reinforcement learning, etc.

[0070] Specifically, machine learning algorithms or artificial intelligence algorithms are used to process the measurement results and simulation results. That is, based on evolutionary algorithms, Bayesian optimization, artificial neural networks, deep learning, and reinforcement learning, in the establishment of high-dimensional and hyperparameter spaces, an iterative relationship is established between the change in the parameters of the model and simulation method and the difference between the simulation results and the measurement results, thereby obtaining the optimal simulation system.

[0071] Optionally, the method further includes:

[0072] Decompose the experimental system and simulation system into various subsystems;

[0073] The parameters of the model and simulation method are optimized through each subsystem, and the remaining parameters of the model and simulation method are optimized by jointly operating the subsystems.

[0074] In this embodiment, Figure 4 is a schematic diagram of iterative optimization of a multi-level neural network model of a coupling mechanism model provided in Example 1 of the present application, such as Figure 4 As shown in the figure, by analyzing the physical and chemical mechanism of the process, the experimental system and the simulation system are decomposed into as many independent subsystems as possible. Each subsystem can independently optimize a part of the simulation (including model and simulation method) parameters, and then the remaining parameters are established in the joint operation of different subsystems or other more complex operations, thereby effectively reducing the dimension of the entire optimization process. The above process can be nested to form a multi-level iterative optimization network.

[0075] The technical solution of the embodiment of the present invention measures industrial and engineering production processes based on a predetermined experimental system to obtain measurement results, and processes simulation instances in industrial and engineering production processes based on a predetermined simulation system to obtain simulation results, and then adjusts the parameters of the simulation system based on the comparison between the measurement results and the simulation results to obtain the optimal simulation system. By executing this technical solution, the simulation system of complex industrial processes and equipment with multiple parameters can be optimized with high optimization efficiency.

[0076] Embodiment 2

[0077] In this scheme, the simulation experiment coupling system may include a gas-solid fluidized bed experiment simulation fusion system.

[0078] Specifically, the gas-solid fluidized bed experimental simulation fusion system automatically constructs a high-precision prediction model of the dynamic process of gas-solid two-phase flow, and the flow domain covers bubbling, turbulence and rapid fluidization. Figure 5 Schematic diagram of the gas-solid fluidized bed experimental simulation fusion system provided in Example 2 of the present application. Figure 5As shown, the time and space distribution of gas and particle movement in the gas-solid fluidized bed are obtained through experiments, including but not limited to the velocity and pressure field of the gas, the velocity and concentration field of the particles. This information is the transient spatial distribution, which establishes a basic database for the verification, correction and optimization of gas-solid two-phase flow simulation.

[0079] The specific implementation process includes:

[0080] The design of the overall structure of the fluidized bed includes size, particle feeding method, particle discharging method, inlet gas flow rate, particle feeding rate, particle inventory, operating pressure, etc.

[0081] Determination of the basic physical properties of fluidized bed operation. These parameters are used to determine which flow field the experimental system belongs to, including: particle properties, gas properties, environmental variables, and methods for measuring gas and particle movement. Among them, particle properties include particle density, particle size distribution, sphericity, recovery coefficient, Young's modulus, shear modulus, Poisson's ratio, friction coefficient, etc.; gas properties include gas density, viscosity, etc.; environmental variables include temperature and humidity of gas-solid fluidized bed operation; methods for measuring gas and particle movement include but are not limited to PIV (particle image velocimetry), PTV (particle tracking velocimetry), ECT (capacitance tomography), etc. The movement of gas and particles includes but is not limited to particle concentration, particle velocity, fluid velocity, pressure distribution, etc. PIV experiments can measure the concentration and velocity distribution of particles and fluids. ECT experiments can measure the concentration distribution of particles, etc. Different measurement methods can be used independently or in combination. The details of gas and particle movement are obtained through different measurement methods.

[0082] Furthermore, Figure 6 Schematic diagram of the instantaneous dynamic data enhancement experimental system for a gas-solid fluidized bed provided in Example 2 of the present application. In order to make the experimental system cover a wider range of gas and particle motion states, thereby providing more comprehensive reference data for the correction and optimization of the simulation system, the following is established: Figure 6The instantaneous dynamic data enhancement experimental system of the gas-solid fluidized bed shown in the figure. The biggest difference between this system and the general gas-solid fluidized bed experimental system is that by introducing the adjustment of the equipment structure and dynamic operating conditions, the movement of gas and particles in the fluidized bed is in a continuous changing process, the gas-solid two-phase flow structure is increased, and the sample space of the experimental measurement is improved. The specific implementation may include but is not limited to the dynamic adjustment of the gas inlet structure, intake speed, intake pulsation, etc.; the dynamic adjustment of the size and arrangement of internal components; the dynamic adjustment of the particle feeding structure, feeding method and feeding frequency, etc.; the diversified adjustment of the particle outlet structure. The simulation system adopts a discrete particle simulation method that can track the movement of particles, that is, the movement of gas is solved by the continuous medium model, and the movement of particles is solved by the discrete element method. Based on the above experimental system structure, operating conditions and measurement results, a discrete particle simulation method is automatically and efficiently established, including: automatically constructing the position and velocity of particles in the fluidized bed according to the particle concentration and velocity distribution obtained from experimental measurements; inputting the particle size distribution, automatically setting the grid size for fluid calculation, and determining the grid size with better calculation amount and accuracy through simulation test of grid size independence; automatically adjusting the drag model according to the instantaneous state of gas-solid two-phase flow. First, the Gidaspow drag model is used to calculate the gas-solid two-phase flow, and the drag coefficient is automatically corrected by examining the difference in instantaneous particle distribution and velocity in different local areas of the simulated and experimental bed layers. In this process, the influence of different mesoscale structures in the flow basin is considered. Not only does this paper use the existing mesoscale structure theory of gas-solid two-phase flow, but it also constructs a multi-factor prediction model of gas-solid drag through artificial intelligence methods such as Convolutional Neural Networks (CNN), Graphic Neural Network (GNN), NeuroFluid, Physics-informed Neural Network (PINN), Long Short-term Memory Network (LSTM), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Diffusion Model and Artificial Neural Network (ANN). The drag model between gas and particles is corrected according to the experimental mean, simulated instantaneous value, and transient values ​​within a period of time before the simulation and experiment, so as to improve the accuracy of simulating and predicting the instantaneous state of gas-solid motion.

[0083] Embodiment 3

[0084] In this scheme, the simulation experiment coupling system can also include a gas-solid catalytic reaction fixed bed experimental simulation fusion system.

[0085] Specifically, the model of the fixed-bed experimental simulation fusion system for gas-solid catalytic reaction covers multiple time and space scales from reaction molecules to reaction devices. Through experimental measurement and characterization, the changes in catalyst particle properties, structure, reactor structure, operating conditions, etc. with time and space are obtained, and a high-precision prediction model of the gas-solid two-phase catalytic reaction-flow coupling process in the fixed-bed reactor is automatically constructed.

[0086] Among them, the experimental measurement implementation process includes determining the physical properties of catalyst particles, particle pore structure, gas physical properties, environmental variables, gas concentration distribution and velocity distribution, gas component composition, and particle accumulation. Among them, the physical properties of catalyst particles include density, particle size distribution, sphericity, recovery coefficient, Young's modulus, shear modulus, Poisson's ratio, friction coefficient, etc.; the particle pore structure includes porosity, specific surface area, pore size distribution, content and distribution of functional sites, etc.; the physical properties of gas include type, density, viscosity, etc.; environmental variables include temperature, humidity, etc.; the methods that can be used for gas concentration distribution and velocity distribution include but are not limited to PIV, PTV, ECT, etc. The obtained gas movement includes but is not limited to fluid velocity, pressure distribution, etc. The PIV experiment can measure the concentration and velocity distribution of the fluid. Different measurement methods can be used independently or in combination. The details of gas movement are obtained by different measurement methods. The gas component composition can be detected by gas chromatography-mass spectrometry (GC-MS) experiment; particle accumulation can be measured by ETC experiment to measure the bed thickness of particle accumulation, the volume fraction distribution of accumulated particles, etc.

[0087] Furthermore, the experimental measurement implementation process also requires the determination of other operating conditions, such as inlet gas flow rate, inlet gas component concentration, bed axial pressure distribution, outlet gas component concentration, outlet gas flow rate, etc. The sample space for experimental measurement is increased by diversified adjustment of equipment structures and operating conditions such as inlet, internal components, outlet structure, inlet concentration, inlet velocity and pulsation. Thus, the temporal and spatial distribution of the concentration of each gas component in the gas-solid catalytic reaction bed can be obtained through experimental methods, including the gas velocity and bed pressure drop, providing basic data for the verification and correction of the gas-solid catalytic reaction-flow coupling model and simulation method.

[0088] Furthermore, for the gas-solid catalytic reaction simulation system, three-scale models of pore, single particle and reactor are constructed to describe the complex process. The pore scale model can obtain the diffusion coefficient and reaction kinetics data of each component in the pore under a given concentration distribution; the continuous model is used at the reactor scale to solve the flow field and concentration distribution of each component in the fixed bed layer; and the single particle scale model uses the macroscopic reaction-diffusion equation to describe the diffusion and reaction process of the internal components, and dynamically couples the solutions of the pore scale and reactor scale through the particle outer surface.

[0089] In this embodiment, according to the experimentally measured particle properties and pore structure parameters, including particle shape, size, porosity, specific surface area, pore topology, pore size distribution, content and distribution of functional sites, etc., and according to the chemical reaction process analysis, the parameters such as elementary reactions, reaction networks and reaction energy barriers are input, and a pre-trained particle pore scale reaction kinetics AI (Artificial Intelligence) prediction model is automatically selected. The AI ​​prediction model is pre-trained based on existing simulations and some experimental data;

[0090] Furthermore, the particle size distribution is input to automatically set the grid size for fluid calculation in the reactor. The independence of the grid size is tested through simulation to determine the grid size with better calculation amount and accuracy. According to parameters such as the particle size of a single particle, the pore-scale reaction rate constant, the multi-component diffusion coefficient in the pore, and the time step for reactor fluid calculation, the size and time step of the single particle calculation grid are automatically set. Combined with the pore-scale reaction kinetics AI prediction model, the concentration distribution of each component in a single particle and the overall concentration change of the entire particle are solved by methods such as FD (Finite Difference) when the instantaneous concentration distribution on the outer surface of a given particle is given.

[0091] According to the structural parameters of the fixed bed reactor, the physical properties of the gas and the operating conditions, a gas reaction-flow coupling model is constructed. The gas flow process in the reactor at each time step is solved by the CFD-DEM (Computational Fluid Dynamics-Discrete Element Method) method, and the calculated concentration distribution of each component is provided to the outer surface of each particle. Each particle will obtain the overall reaction rate and the concentration change of each component of the single particle according to the concentration distribution of each component at the outer surface and the calculation process, and feed it back to the CFD-DEM flow calculation process of the reactor. The flow calculation process updates the concentration distribution of the outer surface of the particle and performs the calculation of the next time step. In this way, the reaction-flow coupling calculation is promoted cyclically, and a high-precision multi-dimensional parameter AI model of gas-solid catalytic reaction-flow coupling is established.

[0092] Furthermore, Figure 7 Schematic diagram of a gas-solid catalytic reaction fixed bed experimental simulation fusion system provided in Example 3 of the present application, such as Figure 7 As shown in the figure, the temporal and spatial instantaneous values ​​of physical quantities and the average values ​​over a period of time (such as the concentration distribution and velocity distribution of each component gas at the reactor inlet, the axial pressure distribution of the bed, the inlet gas flow rate, etc.) are obtained according to the experimental measurements, and they are used as input to generate simulation data through the above continuous calculation, and the simulation results are compared with the experimental results (such as the concentration distribution and velocity distribution of each component gas at the reactor outlet, the axial pressure distribution of the bed, the outlet gas flow rate, conversion rate, selectivity, etc.). Combined with the reaction, diffusion and flow mechanism models, the reaction path network, pore structure and pore size distribution, distribution of functional sites and drag model are automatically adjusted according to the deviation, and through convolutional neural networks (CNN), graph neural networks (GNN), NeuroFluid, physics-informed neural networks (PINN), long short-term memory neural networks (LSTM), recurrent neural networks (RNN), generative adversarial networks (GAN), diffusion models ( A multidimensional parameter prediction model for gas-solid catalytic reaction is constructed by artificial intelligence methods such as artificial neural network (ANN) and artificial neural network (ANN). The multidimensional parameter prediction model is continuously trained and corrected according to the experimental instantaneous value, simulated instantaneous value, experimental mean value and simulated mean value over a period of time, so as to improve the simulation accuracy of catalytic reaction-flow coupling.

[0093] Embodiment 4

[0094] In this solution, the simulation experiment coupling system can also include a roller equipment experiment simulation fusion system.

[0095] Specifically, the drum equipment experimental simulation fusion system aims to automatically build a high-precision prediction model of particle movement in the drum. Figure 8 Schematic diagram of the drum device experimental simulation fusion system provided in Example 4 of the present application. Figure 8As shown in the figure, the time and space distribution of particle movement in the drum are obtained by experimental methods, including but not limited to the position, speed, repose angle (static and dynamic), particle collision contact number and particle mixing index of the particles, so as to establish a basic database for the verification, correction and optimization of the simulation method of particle movement in the drum. The specific implementation includes: the design of the overall structure of the drum, the determination of the basic physical properties of the drum operation, the selection of particles, the determination of the drum material, the determination of the liquid properties, the measurement of environmental variables, the determination of other physical properties, and the determination of the measurement method of particle movement. Among them, the design of the overall structure of the drum includes the overall size, the applicable particle size range, the size and arrangement of the internal components, the speed range of the drum, the particle filling amount, the liquid content, etc. The determination of the basic physical properties of the drum operation includes selecting the appropriate particle size distribution according to the design value of the drum size and filling rate, and selecting particles within a certain particle size range through equipment such as a vibrating screen; the particle properties include density, particle size distribution, sphericity, restitution coefficient, Young's modulus, shear modulus, Poisson's ratio, sliding friction coefficient, etc.; the drum material includes density, restitution coefficient, Young's modulus, shear modulus, Poisson's ratio, sliding friction coefficient; liquid properties include density, viscosity, surface tension coefficient, etc.; the measurement of environmental variables includes the temperature and humidity of the drum operation; other physical properties include viscosity coefficient, rolling friction coefficient, etc. These parameters need to be estimated in advance and then optimized through experimental simulation and fusion system; the methods used to measure particle motion include but are not limited to high-speed photography, PIV, PTV, CT, etc. The particle motion information obtained includes but is not limited to the position of the particles, particle velocity, stacking state, etc., as well as the statistical characteristics of the particles such as contact state, repose angle, mixing index, etc. analyzed based on these measurement information. Both direct measurement data and statistical analysis data can be used to correct and optimize the simulation method. High-speed photography can capture the position distribution of particles, PIV experimental measurement can measure the concentration and velocity distribution of particles, PTV can track the movement of tracer particles, and CT measurement can obtain the position distribution and accumulation state of particles. Different measurement methods can be used independently or in combination to obtain the details of the movement of particles in the drum in all directions.

[0096] Furthermore, Fig. 9 Schematic diagram of the instantaneous dynamic data enhancement experimental system of the drum device provided in the fourth embodiment of the present application. In order to make the experimental system cover a wider range of particle motion states, thereby providing more comprehensive reference data for the correction and optimization of the simulation method, the following is established: Fig. 9The instantaneous dynamic data enhancement experimental system of the drum device shown in the figure. The biggest difference between this system and the general drum device is that by introducing the adjustment of the equipment structure and the dynamic operating conditions, for example, the continuous dynamic change of the drum speed makes the movement of the particles in the drum in a continuous changing process, increases the change of the movement structure of the particles in the drum, and improves the sample space of the experimental measurement. The specific implementation may include but is not limited to the following methods: the adjustment of the particle conditions such as the type of particle material, particle size distribution, and filling rate; the adjustment of the internal components of the drum, the dynamic adjustment of the drum operating conditions; the adjustment of the liquid content to realize the sticky and wet particle system.

[0097] In this embodiment, the simulation method uses the Discrete Element Method (DEM) that can track the movement of particles. The key to the accuracy of this method is the inter-particle collision model. Some parameters can be obtained through precise experimental measurements, such as the density of particles, but due to the complexity of granular materials, some parameters are difficult to obtain through experimental measurements, or even if they can be obtained through experimental measurements, due to the limitations of computational complexity and amount of calculation, the simulation method cannot use fully accurate experimental measurement results, such as Young's modulus and particle size distribution. Therefore, based on the above-mentioned experimental system architecture, operating conditions and measurement results, it is the goal of the drum equipment simulation experiment fusion system to automatically and efficiently establish model parameters with appropriate computational complexity and amount of calculation, and to accurately predict the characteristic motion behavior of particles in the drum.

[0098] Furthermore, according to the particle size distribution obtained from the experimental measurement and different drum filling rates, the particle model in the drum is automatically constructed. In essence, the particle size of the particles is approximately continuous distribution, but numerical modeling cannot accurately model all particle sizes, and there must be differences in the particle size distribution of different batches of granular materials. What kind of particle size distribution can represent the actual particulate matter can be optimized by selecting a typical particle size distribution and the packing density of the particles. This step realizes the input of particle size distribution and particle mass, and automatically constructs a particle size distribution that can obtain the experimental packing density, as well as the initial state of the particles required for drum simulation. The movement behavior of particles in the drum is determined by the collision model between particles, the correlation between the collision effect and the particle deformation, and the model parameters of the collision effect, including but not limited to sphericity, Young's modulus, restitution coefficient, Poisson's ratio, sliding friction coefficient, rolling friction coefficient, viscosity coefficient, liquid viscosity, surface tension coefficient, wetting angle, etc. The verification, correction and optimization of parameters are based on the instantaneous state of particle movement in the drum. A multi-factor prediction model of particle movement in the drum is constructed through artificial intelligence methods such as convolutional neural networks (CNN), graph neural networks (GNN), physics-informed neural networks (PINN), diffusion models (Diffusion Model) and artificial neural networks (ANN). The particle collision model is corrected according to the experimental mean, simulated instantaneous value, and transient values ​​in the simulation and before the experiment to improve the accuracy of simulation prediction of the instantaneous state of particle movement in the drum.

[0099] By coupling the above-mentioned experiments and simulations of particle movement in the drum device, a computational model that accurately predicts the movement behavior of particles in the drum is realized, which provides support for the design and optimization of drum equipment for granular materials and provides a verification tool for building complex models of granular materials.

[0100] Embodiment 5

[0101] Fig.10 This is a schematic diagram of the structure of a simulation experiment coupling device provided in Embodiment 5 of the present invention. Fig.10 As shown, the device comprises:

[0102] The measurement result acquisition module 1010 is used to measure a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device;

[0103] The simulation result acquisition module 1020 is used to simulate the experimental system using a predetermined simulation system to obtain a simulation result; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, simulation instance, model and simulation method;

[0104] The parameter optimization module 1030 is used to adjust the parameters of the simulation system according to the comparison between the measurement results and the simulation results to obtain the optimal simulation system.

[0105] Optionally, the controllable conditions of the experimental system include at least one of operating conditions, physical property conditions, boundary conditions and variable structural parameters;

[0106] The operating conditions change according to any time series; the change forms of the operating conditions include monotonic, periodic, step, according to a specific function and random change;

[0107] The boundary conditions change according to arbitrary spatial distribution and time series; the boundary conditions can be located at different boundary positions in space, including exits or entrances; the boundary conditions can change in time in the form of monotonic, periodic, step, according to a specific function, and random changes.

[0108] Optionally, the measurement result acquisition module 1010 is specifically configured to:

[0109] The measurement locations include the boundary, exit or entrance of the experimental system, a single point, multiple points or continuous areas inside;

[0110] The measurement may be performed offline or online, and may be performed intermittently or continuously;

[0111] The measurement obtains the state of the experimental system at a specific location or time, or obtains the spatiotemporal distribution of the state of the experimental system in a specific area and time period;

[0112] The data obtained by the measurement is temporarily stored in the measuring device and then transmitted to the simulation system in batches, or transmitted to the simulation system in real time, or transmitted to the simulation system after preliminary processing.

[0113] Optionally, the experimental system includes parts or components with controllable motion; wherein the motion forms of the parts or components with controllable motion include monotonic, periodic, step-wise, specific function and random motion of the whole or its parts.

[0114] Optionally, the experimental system is composed of one or more devices of different scales, structures and functions, and each experimental device operates in a coupled manner or independently.

[0115] Optionally, the parameters of the simulation system are scalars, vectors, matrices, tensors, known functions or unknown functions.

[0116] Optionally, the model and simulation method are composed of one or more models and one or more numerical simulation algorithms or analytical methods, and different models or simulation methods are applied to the same scale or different scales.

[0117] Optionally, the parameter optimization module 1030 is specifically used for:

[0118] The experiment, measurement, and simulation form a closed loop. Based on the comparison between the measurement results and the simulation results, at least one parameter of the model or simulation method in the simulation system is adjusted to obtain an optimal simulation system.

[0119] Optionally, the parameter optimization module 1030 is further used to:

[0120] Based on a predetermined machine learning algorithm or artificial intelligence algorithm, and according to the comparison between the measurement results and the simulation results, the parameters of the simulation system are adjusted to obtain an optimal simulation system; wherein the machine learning algorithm or artificial intelligence algorithm includes evolutionary algorithm, Bayesian optimization, artificial neural network, deep learning and reinforcement learning, etc.

[0121] Optionally, the device further comprises:

[0122] The comparison between the measurement result and the simulation result includes the comparison between corresponding time and position, and between different time and position.

[0123] Optionally, the device further comprises:

[0124] Subsystem decomposition module, used to decompose the experimental system and simulation system into various subsystems;

[0125] The optimization integration module is used to optimize the parameters of the model and simulation method through each subsystem, and to jointly operate each subsystem to optimize the remaining parameters of the model and simulation method.

[0126] Optionally, the simulation also includes the observation object in the experiment, the structure of the experimental device, the structure that affects the observation object, the structure of the feeding device, and the measuring and analyzing instruments.

[0127] Optionally, the device further comprises:

[0128] The sample processing module is used to compare the measurement results and simulation results based on the average value of the samples repeatedly run under the same conditions; and to introduce the distribution of the gaps between different samples and the average value into the optimization iteration process.

[0129] A simulation experiment coupling device provided in an embodiment of the present invention can execute a simulation experiment coupling method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0130] Embodiment 6

[0131] Fig.11 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0132] like Fig.11 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a simulation experiment coupling method.

[0135] In some embodiments, a simulation experiment coupling method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a simulation experiment coupling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a simulation experiment coupling method in any other appropriate manner (e.g., by means of firmware).

[0136] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0138] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0139] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0141] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0142] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0143] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A simulation experiment coupling method, which gradually improves the model and simulation method of online coupling operation through comprehensive measurement and analysis of experiments with continuous changes in conditions and states, characterized in that: include: Measuring a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device; The experimental system is simulated using a predetermined simulation system to obtain a simulation result; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, the simulation instance, the model and the simulation method; According to the comparison between the measurement result and the simulation result, the parameters of the simulation system are adjusted to obtain the optimal simulation system.

2. The method according to claim 1, characterized in that The controllable conditions of the experimental system include at least one of operating conditions, physical property conditions, boundary conditions and variable structural parameters; The operating conditions change according to any time series; the change forms of the operating conditions include monotonic, periodic, step, according to a specific function and random change; The boundary conditions vary according to arbitrary spatial distribution and time series; the boundary conditions can be located at different boundary positions in space, including exits or entrances; the temporal variation of the boundary conditions includes monotonic, periodic, step, specific function and random variation of a single or multiple parameters.

3. The method according to claim 1, characterized in that The measurements of the experimental system include: The measurement locations include the boundary, exit or entrance of the experimental system, a single point, multiple points or continuous areas inside; The measurement may be performed offline or online, and may be performed intermittently or continuously; The measurement obtains the state of the experimental system at a specific location or time, or obtains the spatiotemporal distribution of the state of the experimental system in a specific area and time period; The data obtained by the measurement is temporarily stored in the measuring device and then transmitted to the simulation system in batches, or transmitted to the simulation system in real time, or transmitted to the simulation system after preliminary processing.

4. The method according to claim 1, characterized in that: The experimental system includes parts or components with controllable motion; wherein the motion forms of the parts or components with controllable motion include monotonic, periodic, step-wise, specific function and random motion of the whole or each part.

5. The method according to claim 1, characterized in that The experimental system is composed of one or more devices of different scales, structures and functions, and each experimental device operates in a coupled manner or independently.

6. The method according to claim 1, characterized in that The parameters of the simulation system are scalars, vectors, matrices, tensors, known functions or unknown functions.

7. The method according to claim 1, characterized in that The model and simulation method are composed of one or more models and one or more numerical simulation algorithms or analytical methods, and different models or simulation methods are applied to the same scale or different scales.

8. The method according to claim 1, characterized in that The simulation objects of the model and simulation method include experimental system, measurement and analysis system and the environment of the experimental, measurement and analysis system.

9. The method according to claim 1, characterized in that: According to the comparison between the measurement result and the simulation result, the parameters of the simulation system are adjusted to obtain the optimal simulation system, including: The experiment, measurement, and simulation form a closed loop. Based on the comparison between the measurement results and the simulation results, at least one parameter of the model or simulation method in the simulation system is synchronously adjusted to obtain an optimal simulation system.

10. The method according to claim 1, characterized in that According to the comparison between the measurement result and the simulation result, the parameters of the simulation system are adjusted to obtain the optimal simulation system, and the method further includes: Based on a predetermined machine learning algorithm or artificial intelligence algorithm, and according to the comparison between the measurement results and the simulation results, the parameters of the simulation system are adjusted to obtain an optimal simulation system; wherein the machine learning algorithm or artificial intelligence algorithm includes evolutionary algorithm, Bayesian optimization, artificial neural network, deep learning and reinforcement learning, etc.

11. The method according to claim 1, characterized in that The method further comprises: The measurement results are obtained through continuous dynamic changes and continuous measurements of the experimental system; By running the simulation system, the simulation results of the simulation instance are obtained; According to the comparison between the measurement results and the simulation results, the parameters, structure and form of the model and the simulation method are improved; wherein the comparison between the measurement results and the simulation results includes the comparison between corresponding moments and positions, and between different moments and positions.

12. The method according to claim 1, characterized in that The method further comprises: Decompose the experimental system and simulation system into various subsystems; The parameters of the model and simulation method are optimized through each subsystem, and the remaining parameters of the model and simulation method are optimized by jointly operating the subsystems.

13. The method according to claim 1, characterized in that The simulation also includes the observation object in the experiment, the structure of the experimental device, the structure that affects the observation object, the structure of the feeding device, and the measuring and analyzing instruments.

14. The method according to claim 1, characterized in that The method further comprises: The measured results are compared with the simulation results based on the average value of the samples repeatedly run under the same conditions; and the distribution of the gaps between different samples and the average value is introduced into the optimization iteration process.

15. A simulation experiment coupling device, characterized in that: include: A measurement result acquisition module, used to measure a predetermined experimental system to obtain a measurement result; wherein the experimental system is composed of at least one experimental device; A simulation result acquisition module, used to simulate the experimental system using a predetermined simulation system to obtain simulation results; wherein the simulation system is composed of at least one model or simulation method; and there is a many-to-many mapping relationship between the experimental device, simulation instance, model and simulation method; The parameter optimization module is used to adjust the parameters of the simulation system according to the comparison between the measurement results and the simulation results to obtain the optimal simulation system.

16. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform a simulation experiment coupling method as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a simulation experiment coupling method according to any one of claims 1 to 14 when executed.

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