590 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 relates to the technical field of power adapter temperature management, and discloses a gallium nitride power adapter temperature management system comprising a dynamic thermal model module used for accurately predicting the temperature change trend of each key component; the multi-level temperature monitoring network module is used for forming a complete temperature distribution diagram; the multi-device collaborative sensing module is used for generating a sensing result of the whole heat dissipation environment; the self-adaptive air duct structure module is used for optimizing an air flow path; the heat dissipation path module based on swarm intelligence is used for calculating and generating an optimal multi-device collaborative heat dissipation path by applying a swarm intelligence algorithm; the self-adaptive heat dissipation control strategy module is used for realizing multi-target balance optimization of temperature, noise and energy consumption; the problem that heat dissipation is difficult when multiple adapters are used in a centralized mode is solved, heat dissipation efficiency is improved, energy consumption is reduced, and noise is reduced.

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 provides a heat exchanger intelligent design method based on deep reinforcement learning, and relates to the technical field of data processing. Establishing a flow model for simulating the flow path, speed distribution and pressure change of the fluid in the heat exchanger; according to the geometrical shape of the heat exchanger, the physical property of a heat exchanger material and fluid flowing in the heat exchanger, a heat exchange model is established by combining the thermodynamic principle, and the heat exchange efficiency under different fluid flowing conditions is calculated; the flow model and the heat exchange model are coupled, so that a coupling performance evaluation model for evaluating the overall performance of the heat exchanger under the specific working condition is obtained; and determining a state space, an action space and a reward function according to the flow heat exchange intensity coupling performance evaluation model. The design efficiency is improved.

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 bottom heat flow and deep layer temperature inversion method based on an underground medium heat transfer model, and relates to the field of deep stratum temperature field prediction. The underground medium heat transfer model-based bottom heat flow and deep temperature inversion method mainly comprises the following steps of: constructing an electromagnetic data three-dimensional axial anisotropy inversion parameter target functional to carry out fine inversion on an electromagnetic data electrical structure of a target area to obtain an electrical structure model and an underground heat storage stratum structure model of the target area; a deep stratum heat transfer model is obtained in combination with regional geothermal geological information; and utilizing a Newton-Raphson optimization algorithm to perform inversion on the bottom prior heat flow value distribution boundary condition to obtain temperature field distribution of the deep stratum heat transfer model. According to the bottom heat flow and deep layer temperature inversion method based on the underground medium heat transfer model, the electrical characteristics of the underground medium can be converted into temperature field distribution based on a small number of borehole logging constraints, the prediction precision is high, and the prediction range is wide.

The invention relates to the technical field of digital pump operation control, in particular to a digital pump operation method and system, and provides the following scheme that winding temperature gradient distribution, axial strain and cooling liquid dielectric property parameters are collected and input into a preset finite element thermal model, boundary conditions are corrected by adopting an adjoint matrix method, and the temperature gradient distribution, the axial strain and the cooling liquid dielectric property parameters are obtained. And reconstructing a three-dimensional transient heat flux density field to realize precise positioning of a local overheating area. Contribution of a heat source item to a temperature field is calculated based on sensitivity analysis, a heat flow density field is optimized in combination with the Fourier law, and calculation precision is improved. The device is suitable for the fields of urban water supply, petrochemical engineering, aviation fuel pumps and the like, the heat management precision of equipment can be improved, the service life is prolonged, and the operation efficiency is improved.

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 provides a thermal runaway prediction method and device, equipment and a storage medium. The method comprises the following steps: respectively constructing an electrochemical model, a thermal model, a mechanical model and a physical information neural network; converting the electrochemical model, the thermal model and the mechanical model into a loss function of the physical information neural network; training the physical information neural network to be trained according to a thermal runaway training data set to reduce the loss of the loss function; if the loss of the loss function is smaller than a preset loss threshold value, obtaining the trained physical information neural network; and inputting internal parameters of the battery into the trained physical information neural network for prediction to obtain a thermal runaway prediction result. By applying the technical scheme of the invention, the problem of relatively low solving efficiency caused by large calculation amount of a finite element method in solving the thermal runaway process mechanism model of the battery in the prior art can be solved.

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 discloses a medium-deep buried pipe heat pump system capacity configuration design method, which comprises the following steps of: acquiring short-term heat exchange test data and heat exchange theoretical data of a medium-deep buried pipe, and performing data inversion to obtain comprehensive thermophysical parameters of a rock-soil body; building a building heat load prediction model according to the historical heat load, the historical state data and the building information to obtain a predicted heat load, determining a heat supply mode according to the predicted heat load, and performing dynamic heat supply simulation by adopting a long-term heat exchange model of the medium-deep buried pipe heat pump system to obtain configuration operation data, and optimizing the medium-deep layer buried pipe heat pump system long-term heat exchange model according to the configuration operation data, and outputting project system capacity configuration by the optimized medium-deep layer buried pipe heat pump system long-term heat exchange model. According to the method, the precision and efficiency of capacity configuration of the medium-deep buried pipe heat pump system can be improved, meanwhile, good interpretability is achieved, and the method has great significance in reducing the construction cost, saving energy and reducing emission.

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 intelligent cooling capacity distribution method and system for a data center refrigeration system, and relates to the technical field of data center refrigeration. Through the thermal model based on data driving, airflow distribution and temperature field change are accurately simulated, thermal load prediction errors are effectively reduced, and high-precision data support is provided for cold distribution, so that the energy utilization efficiency and the refrigeration efficiency are improved, and energy efficiency improvement and reliable operation of a data center refrigeration system are achieved. And meanwhile, the interaction among heat transfer, power consumption and a control strategy is comprehensively considered, the overall efficiency loss caused by local optimization is avoided, and the comprehensive energy efficiency of the refrigerating system is improved. Besides, the cooling capacity distribution optimization model is constructed based on the power-cooling capacity characteristic curve, so that the optimal balance between the cooling power and the cooling index is realized, and the PUE is effectively reduced.

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.

Monitoring and control system and method for additive manufacturing an in-situ macro-field thermography that uses an IR camera or near-IR camera to prioritize the data captured from an entire print layer, processes data streams layer-by-layer, and provides thermal history of previously printed layers as feedback to the control system. The system leverages one or more different mathematical thermophysical-based (MTB) models to compute a thermally related specification in order to optimize the printing sequence of islands or stripes layer-by-layer. The thermal feedback from the thermography system calibrates or updates the thermal model layer-by-layer. The system and / or method may provide real-time optimization of scanning topology in chessboard and stripe printing strategies. The method may be implemented by one or more computer programs controlling one or more computer controllers of an additive manufacturing system, such as a 3D printer implementing a metal powder bed fusion additive manufacturing process.

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.

A method and system for performing analysis and calculation of multi-energy flow for integrated energy system (IES), comprising establishing electrical power flow model, hydraulic model and thermal model, to form preliminary model of IES; constructing DHL-LSTM neural network for nonlinear regression of electrical power flow model, construct SHL-LSTM neural network for nonlinear regression of hydraulic model, and finding optimal parameters of two neural networks; training the two models, and adding error compensations into the two models; simplifying the thermal model, to obtain mechanism-driven linear thermal mode; and embedding mechanism-driven linear thermal model into error-compensated hydraulic model and error-compensated electrical power flow model respectively, to form and use final model of the IES to perform analysis and calculation of multi-energy flow. The present invention fully considers the coupling property inside the system, and avoids the situation that the convergence speed is slowed down when solving high nonlinear problems.

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.