508 results about "Thermal model" patented technology

The invention relates to the technical field of numerical control machine tools, in particular to a five-axis machining center real-time thermal error compensation system based on digital twinning and a medium. Firstly, a data acquisition unit is used for acquiring physical actual measurement data in real time; then, the digital twin processing unit constructs a model set used for representing the thermal dynamic behavior of the five-axis machining center, and the model set is composed of a plurality of virtual thermal model members; thirdly, fusing physical measured data into the model set through a data assimilation algorithm to dynamically correct the state of each model member and predict the thermal error of the machine tool; the prospective compensation decision-making unit receives the thermal error prediction result output by the digital twinning processing unit and generates a prospective compensation instruction; and finally, the compensation execution unit issues the prospective compensation instruction to a numerical control system of the five-axis machining center for real-time adjustment. The control precision of real-time thermal error compensation of the five-axis machining center can be improved.

The invention relates to the technical field of air conditioner cooling load prediction, and discloses an air conditioner cooling load prediction method and system. The method comprises the following steps: acquiring temperature, humidity and CO2 data through an environment sensor network and carrying out standardization processing; establishing a time slice matrix based on the environmental parameters and the personnel density, and obtaining user behavior characteristics; constructing a wall heat storage model by combining the environmental parameters, and calculating instantaneous heat flow; establishing a spatial downscaling model according to the heat flow and meteorological data; time sequence features are extracted based on local weather and heat flow data, and the cooling load is predicted; and a time-phased refrigeration control instruction is generated accordingly. By establishing a data processing and analysis process, factors such as building microclimate characteristics, user behavior modes and wall dynamic thermal characteristics are comprehensively considered, and more accurate prediction of the cooling load of the air conditioner is realized.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses an interactive simulation method for calculating aging parameters of a superconducting cable and a superconducting cable system.The system comprises the steps that a cable body electromagnetic transient model, a cooling system transient thermal model and a three-dimensional multi-field finite element model are built, and cable material characteristics, environment parameters and operation condition data are input; a model interaction interface is constructed through a simulation platform, data interaction and coupling among multiple models are realized, simulation parameters are dynamically adjusted, accurate matching of model step length and dynamic characteristics of the system is ensured, and aging parameters of the superconducting cable system under complex working conditions are calculated. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

A temperature control method for a lithium ion power battery of a new energy automobile comprises the steps that a lumped parameter thermal model of the lithium ion power battery is established, parameter identification is conducted on the lumped parameter thermal model through an eagle and African vulture mixed optimization algorithm, and model parameters are obtained; constructing a maximum correlation entropy tracking extended Kalman filter, and inputting the model parameters into the maximum correlation entropy tracking extended Kalman filter; a multi-target grey wolf algorithm improved based on a deep reinforcement learning algorithm is introduced to optimize the state noise covariance and the observation noise covariance of the maximum correlation entropy strong tracking extended Kalman filter, so that the internal temperature of the battery is estimated; and establishing a model prediction controller based on the estimated internal temperature of the battery, improving the model prediction controller, and controlling the internal temperature of the battery through the improved model prediction controller. According to the invention, accurate online estimation and efficient control can be carried out on the internal temperature of the battery.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

A wellbore temperature optimization and predication method integrating numerical models and machine learning includes the following steps: establishing a wellbore-formation transient heat transfer model, obtaining an initial data set composed of relevant parameters, normalizing the initial data set, training a wellbore temperature prediction model by using a random forest algorithm, then optimizing the hyperparameters of the random forest algorithm by using a genetic algorithm, performing global optimization by using an annealing algorithm to obtain the optimized wellbore temperature and related parameters, calculating the wellbore temperature by substituting the optimized parameters into the wellbore-formation transient heat transfer model, and performing comparative verification on the optimized wellbore temperature and the calculated wellbore temperature.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention provides an electric energy metering box temperature field simulation analysis method based on finite elements, and relates to the technical field of electrical equipment fault diagnosis. The method comprises the following steps: constructing a three-dimensional structure model considering asymmetric layout and material attributes of internal elements; identifying key heating elements and establishing a dynamic heat source; constructing an unsteady state heat conduction model by combining a Fourier law and an energy conservation equation; setting environment related boundary conditions; and outputting a temperature distribution diagram and an abnormal hot spot early warning result, thereby realizing accurate modeling and risk pre-judgment of the thermal behavior of the electric energy metering box.

The invention discloses a battery health state and residual life combined determination method, electronic equipment and a medium, and relates to the technical field of battery management. The method comprises the following steps: acquiring battery operation parameters at the current moment; constructing a hybrid model based on a physical model and a data driving model, and controlling the hybrid model through an adaptive gating mechanism to determine a current battery health state based on the battery operation parameters; the physical model is realized by introducing a thermal model on the basis of an equivalent circuit model, and the data driving model is realized by introducing a lightweight neural network on the basis of a neural network model; the current battery remaining life is determined based on the current battery health state through a life prediction model and an optimization algorithm, the life prediction model is achieved based on at least one of degradation curve extrapolation and multi-step rolling prediction, and the optimization algorithm is achieved based on Bayesian optimization or reinforcement learning. According to the scheme, the accuracy of determining the battery health state and the residual life can be improved.

A power tool system includes a power tool, a sensor, and an electronic controller of the power tool. The power tool includes a power tool housing, a motor housed within the power tool housing, and a pack interface coupled to the power tool housing. The pack interface receives a power tool battery pack having a corresponding tool interface. The sensor senses an electrical parameter corresponding to a power tool battery pack coupled to the pack interface. The electronic controller of the power tool controls the power tool battery pack to discharge current; receives an output of the sensor when the power tool battery pack is controlled to discharge current; generates a temperature estimate by processing the output of the sensor using a thermal model stored on the memory; and generates a temperature warning signal in response to the temperature estimate exceeding a temperature threshold.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.

The invention discloses an energy storage battery thermal runaway damage diffusion early warning method based on a dynamic thermal model, and relates to the technical field of battery safety monitoring, and the early warning method comprises the specific steps: multi-source parameter collection: arranging a plurality of sensors to collect data, and preprocessing and storing the data; thermal stress modeling is conducted, units are divided through a finite element method to calculate thermal stress, and structural damage is judged in combination with a damage threshold value; multi-physics field cooperative calibration is carried out, and characteristic quantity is acquired and substituted into a formula to correct thermal stress; dynamic thermal model construction: introducing thermal stress influence to construct a model containing a damage diffusion term; thermal runaway early warning judgment is carried out, a multi-dimensional index system is constructed, a risk value is calculated, and early warning is triggered; through a multi-source parameter acquisition technology, in combination with temperature, current, voltage, ultrasonic wave and broadband impedance multi-dimensional sensor data, and assisted by external condition monitoring of environment temperature and humidity, heat dissipation system flow and rotation speed, comprehensive perception of the working state of the battery is realized, and richness and accuracy of data acquisition are improved.

The invention relates to the field of heat supply network analogue simulation, and particularly discloses a regional heat supply network dynamic simulation method and system, a terminal and a medium, the method comprises the following steps: constructing a hydraulic model semi-analytical expression and a thermal model semi-analytical expression of a pipeline in a regional heat supply network, and according to the pipeline momentum conservation and the node mass conservation, calculating the thermal model semi-analytical expression of the pipeline in the regional heat supply network; constructing semi-analytic coefficient recursive relation linear equations, and respectively calculating semi-analytic coefficients of a hydraulic model semi-analytic expression and a thermal model semi-analytic expression through the two semi-analytic coefficient recursive relation linear equations according to initial conditions and boundary conditions of the pipe section; the calculated semi-analytic coefficient meets the condition that the truncation error is not greater than a truncation error threshold value; and substituting the semi-analytic coefficient into the semi-analytic expression to obtain a semi-analytic calculation result of each pipe section of each pipeline under all time steps. According to the method, non-iterative calculation of the PDAEs is realized, the calculation complexity is reduced, the simulation efficiency is improved, and the solution continuity can be ensured.

The invention relates to the technical field of box transformer substation control, and particularly discloses an intelligent charging box transformer substation overload protection threshold value dynamic calculation method and related equipment, and the method comprises the steps: continuously obtaining the load information, the in-box temperature information and the environment temperature information of an intelligent charging box transformer substation; identifying and acquiring a parameter set of the dynamic thermodynamic model to determine the dynamic thermodynamic model; the identified dynamic thermodynamic model is utilized to predict the temperature in the box at the next moment based on the load information and the environment temperature information; based on the predicted temperature in the box and a preset safe temperature threshold value, a temperature margin is obtained through calculation; adjusting an overload protection threshold according to the temperature margin; according to the method, the self-adaptive adjustment of the overload protection threshold is realized, so that the overload protection strategy can dynamically change according to the actual operation state of the box-type transformer substation, the safe operation of the box-type transformer substation under the working conditions of slow heat dissipation and load fluctuation is ensured, the overload capacity of the box-type transformer substation is fully utilized, and the continuity of the charging service is improved.

The invention discloses a computer chip cooling heat dissipation control method and system, and relates to the technical field of computer heat dissipation. The method comprises the following steps: acquiring multi-modal detection data of a target computer chip; constructing a self-adaptive thermal model; the adaptive thermal model is constructed based on a dynamic thermal resistance network, a depth Q network algorithm and a fuzzy PID algorithm; the dynamic thermal resistance network is used for describing thermal resistance parameters of the chip; inputting the multi-modal detection data into the self-adaptive thermal model, and performing dynamic hierarchical regulation and control by taking heat dissipation energy consumption, temperature fluctuation and chip life as optimization targets to obtain a hierarchical self-adaptive regulation and control strategy; the hierarchical adaptive regulation and control strategy comprises three levels of responses and corresponding control thresholds; the control threshold value is dynamically adjusted according to the performance attenuation trend of the heat dissipation system. The cooling and heat dissipation efficiency and effect of the computer chip can be improved, and the service life of the computer chip is further prolonged.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention provides a rotary table bearing-torque motor thermal vibration coupling modeling method, and relates to the technical field of machine tool reliability analysis. Comprising the steps of calculating bearing restoring force, torque and elastic rigidity; giving a dynamic balance equation of the main shaft system; unbalanced magnetic pulling force and unbalanced mass excitation are calculated; a displacement field is solved, and load changes caused by the displacement field are transmitted to the thermal model; the contact force borne by the bearing is updated by considering the load effect related to displacement, and the friction heat generation of the bearing and the heat generation of each part of the motor are calculated; calculating thermal deformation and thermal displacement of the bearing; thermal resistance among nodes is calculated, and a temperature field equilibrium equation is given through a thermal resistance network to achieve rapid thermal-mechanical coupling analysis; the thermal equilibrium equation is solved to calculate temperature response; importing the latest temperature into the kinetic model; lubricating grease viscosity and bearing thermal deformation are updated; and repeating the steps to finally obtain the main shaft displacement field and temperature field response. The method is high in accuracy, high in universality, low in test cost and high in test efficiency.

The invention discloses a PINN-based transformer convective heat transfer coefficient acquisition method, system and device and a medium, and relates to the technical field of transformers, and the method comprises the steps: building a three-dimensional thermal field simulation model based on transformer structure parameters, obtaining temperature field data through multi-physics field coupling simulation, building a sample set, training a physical information neural network model, and obtaining the convective heat transfer coefficient of a transformer. A loss function is defined, through weight adjustment and optimization algorithm training, loss change is dynamically monitored, convective heat transfer coefficient distribution is inversed, an inversion coefficient is substituted into a thermal model for forward calculation, iterative optimization is performed through comparison with independent measurement data, and accuracy and applicability are verified through actually measured temperature data. According to the method, the dependence on experimental data is reduced, the high-precision and high-efficiency acquisition of the convective heat transfer coefficient of the spatial change of the transformer is realized, and a reliable technical means is provided for thermal design and state evaluation of the transformer.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention discloses a rapid thermal simulation method and system for a semiconductor power assembly, and relates to the technical field of semiconductors, and the method comprises the steps: collecting a voltage V (t) signal and a current I (t) signal of the semiconductor power assembly in real time through a voltage and current sampling circuit, constructing a power pulse input sequence A, and carrying out the hot carrier accumulation analysis according to the power fluctuation change amplitude; introducing a transient thermal resistance function Zth (t), fitting the transient thermal resistance function Zth (t) with a hot carrier pulse response index Mcx after numerical integration to analyze junction temperature change caused by power pulse, and evaluating an equivalent junction temperature dynamic change rate; when the analysis result is that the thermal drift out-of-control trend exists, thermal risk analysis is carried out, and thermal risk state assessment is generated; the simulation model optimizes thermal model parameters and outputs thermal control suggestions through a rollback correction path, and the working state of the semiconductor power assembly is automatically adjusted. According to the method, real-time monitoring and effective prevention and control of thermal runaway can be realized, and the reliability and safety of the semiconductor power assembly are improved.

The invention relates to the technical field of heat supply control, in particular to a heat supply secondary network branch line intelligent hydraulic equilibrium method and device, a medium and a product. The method comprises the following steps: constructing a branch topological graph of a heat supply secondary network in a target heat supply area; the environment temperature is obtained in real time, the heat supply station is controlled to supply heat to each heating single body in the target heat supply area based on the dynamic flow control strategy, and the node flow and the node temperature of each node in the branch topological graph are collected; constructing a branch heat supply loss model of the branch topological graph based on the environment temperature, the node flow and the node temperature; the monomer temperature and heat supply backflow data of each heating monomer are obtained, and a monomer heating model is constructed for each heating monomer based on the environment temperature, the monomer temperature and the heat supply backflow data; and determining a flow regulation and control strategy based on the monomer heating model and the branch line heat supply loss model, and adjusting the monomer heat supply flow of the heat supply secondary network based on the flow regulation and control strategy. According to the method, heat supply hydraulic balance can be achieved through precise flow regulation and control precision.

A method for controlling HVAC equipment includes using a dynamic thermal model that describes heat transfer characteristics of a building to predict a temperature of the building as a function of control inputs to HVAC equipment that operate to affect the temperature of the building. The method includes obtaining time-varying utility prices and a predicted heating or cooling load for the building and performing a predictive control process using the dynamic thermal model, the time-varying utility prices, and the predicted heating or cooling load to generate the control inputs to the HVAC equipment subject to constraints on the temperature of the building.

The invention relates to a power battery performance degradation prediction method and system based on an electrochemical-thermal-mechanical-neural network model, and belongs to the technical field of battery management. According to the method, an enhanced single particle model considering liquid phase diffusion, an integrated thermal model, a particle mechanical stress model caused by concentration gradient and an aging mechanism model considering SEI growth, lithium precipitation and active material loss are coupled to establish ETMD, and based on the ETMD, the identification efficiency of aging model parameters is low aiming at the problem that a PSO algorithm is low. Under a circulating aging experiment of a limited path, the characteristic parameters of the aging model are obtained by using the feedforward neural network, and the parameters are substituted into the ETMD model again to predict the capacity, the power attenuation curve and the corresponding main attenuation mechanism under the current path, so that the prediction precision and the interpretability of the performance attenuation of the battery under the full path are improved. According to the method, the modeling capability of battery performance degradation under different working conditions can be effectively improved, and a theoretical basis and technical support are provided for residual life prediction and safety management of the power battery.

The invention discloses a power consumption prediction and optimization method in chip design, and relates to the technical field of chip design and energy efficiency optimizing.The method comprises the steps that a circuit netlist is analyzed, functional blocks are divided, and an association diagram is constructed; generating a typical working load containing a transient peak value and a steady-state interval based on actual application; calculating dynamic and static power consumption, and considering temperature sensitive leakage current; constructing a three-dimensional thermal model, gridding a chip, solving a heat conduction equation, and outputting temperature field distribution; three-level optimization of clock gating, DVFS and data flow reconstruction is carried out on the overtemperature area; the optimized parameters are imported into a simulation tool to verify the effect, and robustness is confirmed through Monte Carlo simulation. According to the method, the power consumption prediction precision and optimization pertinence are improved, the temperature distribution uniformity is improved, the power consumption and performance are balanced, the number of design iterations is reduced, and the chip reliability and energy efficiency ratio are enhanced.

The invention relates to the technical field of battery temperature control, in particular to a temperature control method and device for a capacity grading system, a medium and equipment, and the method comprises the steps: S1, obtaining real-time data in the charging and discharging process of a battery module; s2, constructing a dynamic thermal model based on a neural network, preprocessing the real-time data, taking the preprocessed real-time data as input, and predicting a temperature prediction value after the next three seconds through the dynamic thermal model; s3, the current working mode of the formation and capacity grading system is judged based on the temperature predicted value, a PID control signal is generated, and meanwhile an optimal air volume scheme is calculated through an alternating direction multiplier method; and S4, the PID control signal is output to a thermoelectric refrigerating unit for segmented adjustment of the refrigerating power, meanwhile, the rotating speed of a plurality of independent fans is adjusted according to the optimal air volume scheme, and the temperature fluctuation range is reduced while the dynamic response speed and the energy efficiency ratio are increased.

Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore set-points. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

The invention belongs to the technical field of simulation analysis, and particularly relates to an electric spindle multi-physics field coupling simulation method. The motorized spindle multi-physics field coupling simulation method comprises the following steps that S100, a multi-physics field coupling model is established through finite element software, wherein the steps of geometric model importing, material setting, rotor dynamics module setting, solid heat transfer module setting and grid division are included; and S200, solving through the finite element software: carrying out multi-physics field coupling on the bearing nonlinear restoring force model, the motor unbalanced magnetic pulling force model and the main shaft thermal model with the motorized main shaft rotor model, and solving to obtain the dynamic performance. By using the simulation model for motorized spindle dynamic characteristic research in the technical scheme, the dynamic characteristic research requirement of the motorized spindle under the actual complex working condition can be met.

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 discloses a construction method of an underground heat exchange model of a U-shaped middle-deep layer buried pipe. The construction method comprises the steps of collecting monitoring data of underground heat exchange of a preset buried pipe, preprocessing the monitoring data, and constructing a physical model of the underground heat exchange of the U-shaped middle-deep layer buried pipe based on the monitoring data; soil temperature distribution and wall temperature are obtained based on the physical model according to the heat transfer process outside the drill hole, and the water temperature at the next moment is obtained based on an energy equation of circulating fluid according to the heat transfer process inside the drill hole; performing underground temperature field evolution simulation analysis on the basis of the mathematical model of the soil temperature distribution, the wall temperature and the water temperature at the next moment to obtain simulation data, and constructing a U-shaped medium-deep buried pipe underground heat exchange model on the basis of underground temperature field evolution simulation; time sequence deviation analysis is conducted on the simulation data and the monitoring data to obtain time deviation data, the U-shaped middle-deep layer buried pipe underground heat exchange model is optimized according to the time deviation data, and a target model is output.