1997results about "Computational theoretical chemistry" patented technology

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention is applicable to the technical field of coal bed gas development and geological sequestration, and provides a THMC multi-field coupling simulation method and system for a process of displacing CH4 by coal bed CO2, and the method comprises the following steps: collecting multi-source parameter information of a target coal bed; according to the multi-source parameter information, constructing a coal seam structure model comprising a matrix and a crack system; constructing a control equation system based on the coal seam structure model, and introducing a multi-field coupling mechanism to obtain a fully-coupled mathematical model; performing numerical solution on the fully-coupled mathematical model to obtain a spatio-temporal evolution result of the key field variables; and according to the spatio-temporal evolution result of the key field variable, evaluating the index of the target coal seam. According to the invention, a heat-force-gas-chemical multi-physical field full-coupling simulation system is constructed, so that the CH4 recovery rate and the CO2 storage stability can be accurately predicted. The method is suitable for gas injection-storage coupling optimization of a multi-coal-rank and low-permeability coal seam, and has high precision, high adaptability and remarkable engineering guidance value.

The invention provides a lithium ion battery core temperature prediction method based on a finite element model, and the method comprises the following steps: 1, designing an electrochemical test experiment, and constructing a one-dimensional electrochemical model containing a lithium ion battery electrochemical heat production mechanism; 2, designing a lithium ion battery temperature test experiment, obtaining electrochemical parameters, heat transfer characteristic parameters, internal thermophysical parameters and environmental parameters of the battery under different working conditions, and obtaining a temperature change curve of the center position and the surface of the battery; and step 3, based on the heat balance equation, establishing a three-dimensional heat transfer model having the same geometric characteristics as the battery used in the experiment. According to the experimental result in the step 2, the three-dimensional heat transfer model is subjected to non-uniform region division, each region corresponds to one one-dimensional electrochemical model, and the heat production rate per unit volume is calculated by the one-dimensional electrochemical model in the step 1; introducing the heat production rate of each area into a three-dimensional heat transfer model to calculate the temperature distribution of the battery, and designing an experiment to correct a one-dimensional electrochemical model; 4, modifying the operation conditions and heat dissipation conditions of the battery, and calculating and analyzing the temperature difference delta T between the center position and the surface position of the battery under different operation conditions and heat dissipation conditions by using the three-dimensional heat transfer model in the step 3; and 5, measuring the surface temperature of the battery, and calculating the temperature of the center of the battery according to the working condition of the battery in the step 4 and the delta T corresponding to the environment temperature, thereby realizing monitoring of the temperature of the center position of the battery. According to the method, a high-precision algorithm model is constructed and verified by utilizing data accumulated in an earlier-stage experiment, the central point temperature which is difficult to directly measure in the battery is predicted only through real-time and easily-acquired battery surface temperature information, operation condition parameters and heat dissipation conditions, key thermal state information is provided for a battery management system, and the battery management efficiency is improved. The method is used for real-time safety monitoring and thermal management optimization.

The invention relates to the technical field of water conservancy projects, and discloses a water conservancy project water quality detection method and system based on artificial intelligence, and the method comprises the steps: collecting first multi-source data for a water conservancy project, carrying out the alignment of water quality parameter data, remote sensing image data and meteorological and hydrological data in the first multi-source data based on a timestamp, and obtaining second multi-source data; performing data preprocessing and feature extraction processing on the second multi-source data to obtain target feature data; designing a multi-task space-time diagram convolutional network, performing parameter optimization by adopting a genetic algorithm, and constructing a water quality detection model; inputting the target characteristic data into a water quality detection model, and outputting a water quality detection result through the water quality detection model; when the water quality detection result determines that the water quality is abnormal, generating a pollution diffusion simulation map based on a space-time diagram structure and a water quality diffusion rule, and providing emergency decision suggestions according to a water conservancy project scheduling scheme; the accuracy and functional integrity of water quality detection are improved.

The invention discloses an intelligent marinating regulation and control method for leisure old marinated claw snacks based on multi-source sensing, which comprises the following steps: acquiring environmental parameters and material parameters in a marinating process through a multi-source sensor, and analyzing texture change of old marinated claws by utilizing a collagen triple-helix structure denaturation kinetic model; a dynamic time warping smell recognition algorithm is adopted to recognize a smell change stage, a color change rule is analyzed through a Maillard reaction kinetics coupling model, multiple parameters are fused to establish an incidence matrix, and the heating power of marinating equipment, the marinating liquid circulation rate and the ventilation quantity are regulated and controlled according to the matrix. According to the method, multi-parameter collaborative monitoring and deep analysis are realized, the regulation and control accuracy and the automation level are improved, the defects that a traditional method is insufficient in key component change analysis and poor in smell recognition and multi-parameter fusion collaboration are overcome, the product quality stability is guaranteed, and the standardized production requirement is met.

The invention discloses a numerical simulation method and system for corrosion expansion cracking of reinforced concrete based on near-field dynamics, and relates to the technical field of numerical simulation of civil engineering materials. Discretizing a concrete calculation area into a near-field dynamic material point model to form a discrete model; applying boundary conditions to the discrete model; establishing a mapping relation between the material point damage degree and the erosion coefficient; acquiring chloride ion concentration distribution and oxygen concentration distribution of the discrete model at the current time step, and judging whether the chloride ion concentration of the surface of the steel bar reaches a blunt removal threshold value or not; if the reinforcing steel bar is blunt, obtaining a displacement field of the model at the current time step and the damage degree of each material point, and updating the erosion coefficient of each material point in the model; according to the method, a crack path does not need to be preset, initiation and expansion of complex cracks can be naturally described, the problem of singularity of a crack tip and the problem of convergence do not exist, damage behaviors such as brittle fracture and peeling of concrete can be simulated, and extra constitutive adjustment and model adjustment are not needed.

The invention belongs to the technical field of energy storage system control, and particularly relates to an SOC offset optimization control method and system for a 5G base station energy storage system, and the method comprises the steps: obtaining the operation data of an energy storage battery; updating an SOC offset optimization interval through a dynamic threshold adjustment algorithm, performing prediction in combination with a base station load prediction model to obtain a prediction result, and dynamically selecting a target strategy in a charging mode and a discharging mode according to the prediction result and a battery state to perform charging or discharging; based on the target strategy, optimizing the output power of the bidirectional converter by adopting a model prediction control algorithm, and correcting the SOC offset in real time in combination with a dynamic loss model; and the corrected SOC offset, temperature and load fluctuation parameters are transmitted to a cloud monitoring platform, multi-level data fusion analysis is carried out, meanwhile, self-inspection is carried out on the health state of the battery regularly, and three-dimensional alarm information containing a fault position probability cloud picture is fed back. Therefore, the problems of poor adaptability, insufficient estimation precision and the like in the prior art are solved.

The invention discloses a roadbed and pavement compactness detection and regulation method for road engineering. The method comprises the following steps: deploying an intrusive sensor array to monitor an internal physical state and water-heat-force coupling phase change data; synchronously acquiring operation data of intelligent compaction equipment and scanning data of nondestructive detection equipment, and identifying a phase change critical point of the material; a disturbance propagation coefficient is calculated through a mechanical transmission chain model after data fusion, a target point is determined, and automatic micro sampling is carried out; and fusing all data to establish a macro-medium-micro three-scale fused compaction prediction model, generating a real-time regulation and control instruction to realize closed-loop control, and finally generating a three-dimensional compaction degree distribution digital twin model. According to the method, multi-source data are integrated, material phase change and mechanical transfer rules are associated, and the stability and the intelligent level of compaction quality are improved.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses a lithium battery dynamic rapid charging track generation method based on multi-state estimation and risk criterion, belongs to the technical field of electric vehicle lithium ion battery charging, and aims to solve the problem that the charging speed, the energy efficiency and the battery life are difficult to consider in the rapid charging process of a lithium ion battery in a wide temperature range. And a relatively high lithium plating safety risk exists. The method comprises the following steps: firstly, constructing an electrochemical thermal aging coupling model, and establishing a lithium plating risk criterion and risk map based on the model; then, on-line multi-state estimation is realized based on an electrothermal coupling equivalent circuit model, and a multi-objective optimization function and a safety constraint system which comprehensively consider charging time, energy efficiency and life loss are constructed; and finally, a model prediction control framework is utilized, a state estimation result and a risk criterion are fused, and an optimal charging current track is generated in a real-time rolling manner.

The invention provides a dosing control method, system and equipment for a sewage treatment process and a medium, and the method comprises the following steps: obtaining multi-source heterogeneous data, and processing the multi-source heterogeneous data to construct a high-dimensional state vector; inputting the high-dimensional state vector into a drug demand dynamic prediction model to obtain a predicted concentration sequence; obtaining a target control instruction according to the current high-dimensional state vector and the predicted concentration sequence; the target control instruction is issued to a controller of the target dosing pump, and dosing control is carried out through the controller; the method solves the industrial problems of response lag, excessive or insufficient addition, incapability of coping with sudden change of water quality, high operation cost and the like existing in the traditional artificial experience control or simple PID (Proportion Integration Differentiation) control. According to the method, under the rigid constraint of absolutely ensuring that the effluent quality is stable and reaches the standard, the medicament consumption can be reduced to the greatest extent, the operation cost is reduced, the secondary pollution is avoided, and the automation and intelligence level of the operation of the whole sewage treatment system is improved.

The invention discloses a CFD calculation method and system based on a neural network and adaptive parameter optimization, and belongs to the technical field of computational fluid dynamics. Initializing a flow field by reading calculation parameters and a grid file; solving the N-S equation, recording solving parameters and outputting an intermediate flow field; inputting the intermediate flow field into a pre-trained physical constraint neural network, sequentially applying boundary condition hard constraint and soft constraint based on control equation residual error, and outputting a corrected flow field; based on the convergence dynamic characteristics and the solving parameters, outputting a parameter adjustment amount through a strategy network of reinforcement learning training, and dynamically optimizing the solving parameters; and taking the corrected flow field as an initial flow field of the next iteration step, updating solving parameters, and circularly executing until convergence. According to the method, the physical prior is embedded into the neural network, intelligent self-adaptive regulation and control of solving parameters are realized through reinforcement learning, and the prediction precision and convergence efficiency of complex flow simulation are remarkably improved.

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention discloses a mouse spinal cord flexible electrode and a stimulation parameter optimization method, and relates to the technical field of parameter optimization. The mouse spinal cord flexible electrode comprises a soluble insertion layer, a gradient substrate layer, spinal cord anastomosis ribs, a conductive snakelike net, a hemispherical touch dot matrix, a microfluidic layer, an optical waveguide layer and a top packaging layer; the soluble insertion layer is photo-crosslinked PEG-DMA with the thickness of 5-25 m, the Young modulus is kept within the range of 0.8-1.5 GPa before implantation, and the soluble insertion layer is hydrolyzed in cerebrospinal fluid within 0.5-3 hours after implantation and completely falls off; the spinal cord anastomosis rib comprises an expandable S-shaped folding structure arranged on the upper surface of the gradient substrate layer, the distance between every two adjacent ribs ranges from 80 m to 200 m, the height of the folded state is not larger than 15 m, the height of the unfolded state ranges from 15 m to 40 m, and the spinal cord anastomosis rib is used for forming a cerebro-spinalfluid bypass groove extending in the longitudinal direction of the spinal cord on the surface of the dura mater. According to the invention, the positioning precision, safety and long-term efficiency of mouse spinal cord electrical stimulation can be effectively improved.

The invention discloses an administration agent in-vivo release and absorption visualization method based on a CFD-PBM coupling model. The method comprises the following steps: constructing a CFD model of a physiological environment of an injection site; establishing a population balance model (PBM) of the drug particles; the PBM is embedded into a CFD model solver, multi-scale coupling simulation is carried out, and a CFD-PBM coupling model is obtained; the three-dimensional visualization engine dynamically displays drug concentration distribution and particle behaviors; reversely adjusting preparation prescription parameters by using a multi-parameter optimization algorithm; and calibrating model parameters, and generating a key data report. According to the method, the prediction precision and the research and development efficiency can be remarkably improved, and the method is suitable for development of long-acting preparations such as microspheres and implants.

The invention discloses a molecular simulation method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring molecular system information of a current molecular configuration; calculating the first energy and the first gradient of the key atom region by adopting a variable component sub-feature solicitation solver algorithm, and calculating the second energy and the second gradient of the non-key atom region; wherein the first gradient is used for expressing the stress condition of each atom in the key atom area; the second gradient is used for expressing the stress condition of each atom in the non-key atom area; and updating the current molecular configuration according to the first energy, the first gradient, the second energy and the second gradient to obtain a target molecular configuration. According to the method and the device, the technical problem that the accuracy of the simulation result cannot be ensured under the condition that the computing resources are limited due to excessive computing resources consumed by the quantum chemical computing mode when the molecules are simulated by adopting the quantum chemical computing mode in the related technology is solved.

The invention provides a method for identifying a microscopic association body structure in a heavy oil molecule simulation trajectory, which comprises the following steps of: analyzing a molecular dynamics trajectory, identifying an aromatic ring structure in a heavy oil molecule component, calculating a geometric center of the aromatic ring structure, combining a periodic boundary condition, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of an association body; and performing post-processing optimization on the clustering result, merging the shared clusters to generate a final association body set, and analyzing the number, size distribution, morphological characteristics and dynamic evolution information of the association bodies based on the structural characteristics of the final association body set. The method can accurately capture pi-pi accumulation behaviors of heavy oil molecules, further reveals morphological characteristics and dynamic evolution laws of heavy oil microassociates and influences of the heavy oil microassociates on the apparent viscosity of heavy oil, provides theoretical support for heavy oil viscosity-causing mechanism analysis and viscosity reducer design, can complement experimental research, and has a wide application prospect. And efficient development and green utilization of a complex thickened oil system are promoted.

The invention provides an organoid drug reaction whole-course monitoring system, and relates to the technical field of data monitoring, and the system comprises an acquisition module which is used for acquiring a three-dimensional image sequence of an organoid at a set time interval, and monitoring the concentration data of metabolites in a culture solution to obtain multi-source monitoring data; the processing module is used for performing voxelization processing on the three-dimensional image sequence to form a time sequence three-dimensional voxel data set; and the calculation module is used for calculating barycentric coordinates of the organ-like structure based on the three-dimensional space distribution characteristics of the organ-like structure in the time sequence three-dimensional voxel data set, and establishing two mutually orthogonal space analysis axes in the maximum cross section of the organ-like structure by taking the barycentric coordinates as the center. According to the method, comprehensive and dynamic monitoring and evaluation of the organoid drug reaction process are realized through multi-source data collaborative acquisition, standardized spatial analysis, accurate form dynamic extraction, form and metabolism time sequence correlation modeling and real-time time sequence registration evaluation.

The invention relates to the technical field of microcosmic acoustic nonlinear effect simulation, and particularly discloses a method for analyzing the influence of pearlite spheroidization on ultrasonic harmonics based on molecular dynamics, which comprises the following steps: firstly, constructing a pearlite undamaged model meeting a Bagaryatskii orientation relationship and a plurality of spheroidization damage models representing different spheroidization damage degrees; carrying out molecular dynamics simulation on the eight constructed models, and carrying out energy minimization and relaxation treatment to obtain a spheroidized model with a stable structure; applying ultrasonic excitation signals V1 and V2 with opposite phases to each spheroidizing damage model to obtain displacement signals after ultrasonic propagation in different areas; and finally, processing the displacement signal by adopting a phase inversion method, extracting second harmonics, analyzing the change rule of the amplitude of the second harmonics, and establishing an incidence relation between the pearlite spheroidizing damage and the ultrasonic nonlinear effect. Through molecular dynamics simulation and a phase reversal method, the influence of pearlite spheroidization on ultrasonic harmonic waves is disclosed, and accurate theoretical support and method reference are provided for nondestructive testing of the pearlite spheroidization.

The invention discloses a carbon dioxide seepage dominant channel judgment and permeation enhancement effect evaluation method and system, and the method comprises the following steps: obtaining three-dimensional image data through the three-dimensional CT scanning of a rock sample before and after a CO2 mineralization storage experiment reaction, extracting a crack network as a topological structure center line skeleton, calculating the topological indexes of a network structure, and calculating the permeability enhancement effect of the network structure; a fracture network topological structure model constructed based on a graph theory is coupled with a discrete fracture network model for CO2 flow simulation, an equivalent permeability tensor is obtained, an improved Dijkstra shortest path algorithm is adopted, spatial distribution of CO2 seepage dominant channels is obtained, dominant channel characteristics before and after transformation are compared with the equivalent permeability tensor, the seepage enhancement effect is quantitatively evaluated, and the CO2 seepage enhancement effect is obtained. And changing injection conditions to perform multi-group simulation under different working conditions, calculating the seepage enhancement effect and the mineralization reaction rate under different injection conditions, establishing a quantitative evaluation model of the mineralization sealing permeation enhancement effect, and realizing sealing effect prediction and seepage enhancement effect evaluation.

The invention discloses a construction method and system for volatile organic compound release behavior characterization, and belongs to the technical field of air analysis and risk assessment. According to the method, feature analysis is carried out based on multi-source information such as a molecular structure, a release carrier and environmental conditions, modeling is carried out on a release process by utilizing a mapping operator, and a three-dimensional representation vector V = [I, S, E] reflecting release intensity, environmental sensitivity and time evolution is constructed. The representation is output in a behavior vector or digital label mode, and unified quantitative benchmarking of different substances in a cross-scene mode is achieved. According to the method, depth characteristics such as molecular polarity and desorption activation energy are introduced, so that the dependence on long-period environmental chamber experiments is reduced, the generalization prediction capability on unknown pollutants is improved, the logic connection problem between a bottom-layer physical mechanism and an upper-layer intelligent management and control system is solved, and the reliability of the system is improved. And standardized and explainable behavior input is provided for prospective risk research and judgment.

The invention relates to the technical field of molecular simulation, and provides a method for constructing a heavy component average molecular structure of thickened oil, which comprises the following steps: inputting parameters, generating a core aromatic ring unit, constructing an inner-layer aliphatic ring unit, constructing an outer-layer aliphatic ring unit, generating a side chain unit, and storing and outputting structural information. A plurality of molecular structures meeting experimental data are generated at a time through a traversal algorithm thought, composition characteristics of a heavy oil complex mixture are met, more available molecular structures are provided for molecular simulation, the accuracy and efficiency of molecular modeling are improved, the molecular structures completely meeting the experimental data can be rapidly generated, and the method is suitable for large-scale popularization and application. And only the mass spectrometer, the elemental analyzer, the nuclear magnetic resonance hydrogen spectrum and the infrared spectrum data need to be input, so that the experiment cost is reduced.

The invention discloses an energy storage battery thermal diffusion modeling method based on P2D-solid heat transfer coupling, and relates to the technical field of energy storage battery thermal management. The method comprises the following steps: constructing a pseudo two-dimensional (P2D) electrochemical model and a three-dimensional heat conduction model of a single battery, and calculating lithium ion concentration, potential and corresponding heat source distribution; mapping the obtained heat source as a volume heat source to a thermal model and realizing bidirectional feedback; setting a thermal runaway triggering condition to simulate temperature rise abrupt change; battery SOH state modeling is introduced, and the aging process is dynamically corrected and reflected through material parameters; finally, time domain simulation is carried out, and temperature field evolution and safety behavior prediction in the whole life cycle are obtained. According to the method, the coupling process of the microelectrochemical reaction and the macroscopic temperature change of the battery can be captured at the same time, the thermal diffusion characteristics of different decline stages are accurately described, and visual modeling of thermal runaway behaviors is supported. The method has the remarkable advantages of high precision, high applicability, perfect safety assessment capability and the like.

The invention belongs to the technical field of polymer composite materials, and discloses a carbon fiber interface modifier aided design method and system based on collaborative optimization, and the method is used for designing a carbon fiber interface modifier based on collaborative optimization of machine learning and molecular dynamics simulation. By constructing a closed loop iteration process of generation-prediction-verification-learning, interfacial modifier molecules meeting multi-target performance requirements are quickly and accurately obtained under the condition that a large amount of early-stage chemical synthesis and experimental screening are not needed. According to the method, through deep combination of high-throughput prediction of machine learning and virtual experiment verification of molecular dynamics, efficient and automatic design of the interface modifier molecules is achieved, experimental synthesis and trial and error links are greatly reduced, the research and development efficiency is improved, and the method is widely suitable for design and optimization of the interface modifier molecules of the carbon fiber / resin-based composite material.

The invention discloses a cultural relic restoration decision-making method and system based on computer three-dimensional modeling, and relates to the technical field of cultural relic restoration, and the method comprises the following steps: building a unified time baseline, generating a phase reference field, introducing a double-frequency comb interference structure to reconstruct a humidity refraction field, locking a zero-phase anchor point, and forming an initial constraint condition of a scanning path; under an initial constraint condition, an airflow phase gradient is obtained by adopting a Schlenn pilot frequency chromatography method, a nonlinear diffraction nucleus is calculated, and a refraction residual spectrum is output for modeling correction. According to the method, humidity disturbance is recognized through interference measurement and chromatography, real-time compensation is achieved in combination with phase sheath and humidity control, the crack and material characteristics are fused through neural field modeling, the solidification process is played back in combination with a mineralization mechanism, and the three-dimensional modeling precision and the restoration decision scientificity are improved.

The invention discloses a multi-scale simulation method for a thermal compression deformation behavior of TC4 titanium alloy, and belongs to the field of metal material processing simulation. Comprising the following steps: 1, carrying out thermal compression experiments at different temperatures to obtain a deformed sample; 2, analyzing the grain orientation, orientation difference angle and texture evolution of the sample by using an electron back scattering diffraction technology; 3, constructing a crystal plasticity finite element model, and simulating a polycrystalline deformation process; 4, constructing a molecular dynamics model, and simulating phase change and dislocation evolution in thermal compression and cooling processes; and 5, based on a multi-scale simulation result, hot working parameters are optimized, dynamic recrystallization and a beta-to-alpha phase change path are regulated and controlled, and grain refinement and performance homogenization are achieved. The invention discloses a correlation mechanism of the microstructure and the mechanical property in TC4 titanium alloy thermal compression deformation, and provides a theoretical basis for optimization of a thermal processing technology of a high-performance titanium alloy component in the aerospace field.