391 results about "Thermal model" patented technology

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.

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 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 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.

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 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.

The invention discloses a power management system in an embedded network terminal and a control method thereof, and relates to the technical field of micro-grids. The system is deployed in a building micro-grid and can communicate with a micro-grid central control unit, an adjacent terminal or an edge server. The processing unit receives the day-ahead moving trajectory as a rolling optimization initial solution, identifies building thermal model parameters based on local observation data and carries out linearization to obtain a local optimization model; local short-term prediction and edge server correction information are fused, an optimization sub-problem is constructed, distributed solution is carried out through an alternating direction multiplier method, and continuous variable candidate solutions are obtained in a calculation quota; aiming at a binary switch decision query strategy mapping table, when solving is not converged, adopting the table to output or finite depth search results; and when the communication is interrupted or the solving is failed, executing the rollback control to guarantee the indoor comfort and the energy storage safety. The method is used for improving scheduling real-time performance and communication efficiency under the condition of limited calculation and bandwidth, so that indoor comfort and energy storage safety are guaranteed.

The invention discloses an automatic shell-making wire environment simulation system. The system comprises an environmental parameter testing unit, a heat and mass transfer model establishing unit and an optimization processing unit. The environmental parameter testing unit is used for testing environmental parameters influencing the drying process of the shell-making drying cabin through testing equipment, evaluating the uniformity of airflow in the drying cabin and the change condition of the environmental parameters, and sending testing and evaluating results to the heat and mass transfer model establishing unit; the environmental parameters at least comprise wind speed. And the heat and mass transfer model building unit is used for building a shell drying heat transfer model and a shell drying mass transfer model, evaluating the shell drying heat transfer model and the shell drying mass transfer model based on the model expression indexes after building is completed, and analyzing the influence of the environmental parameters on shell heat transfer and mass transfer based on the information transmitted by the environmental parameter testing unit. And the optimization processing unit is used for introducing the influence of load, airflow in the drying cabin and air into the established shell drying heat transfer model and shell drying mass transfer model for simulation and optimization.

The invention discloses a power battery liquid cooling system flow adjusting method based on working condition prediction, and relates to the technical field of battery cooling control, and the method comprises the steps that a controller collects the module temperature, the cooling liquid inlet and outlet temperature, the current charge state and the vehicle working condition, and predicts the temperature and temperature rise trend based on a lightweight thermal model; and calculating a thermal margin, dividing an urgent area and a non-urgent area, and outputting a temperature upper limit constraint, a temperature difference constraint, a pump speed boundary and a hot spot risk weight. Entering a calibration window when the thermal margin is sufficient and the temperature rise trend is smooth, applying limited disturbance to the rotating speed of the water pump and the opening degree of the branch valve, updating the equivalent resistance parameter and the heat exchange marginal slope according to disturbance-response, and giving credibility and an abnormal mark. And then valve position distribution is carried out according to hot spot temperature changes corresponding to water pump work, the pump speed is determined in a water pump efficient working area, and conservative control is returned if abnormity occurs, so that pump energy consumption is reduced, and adaptability to assembly difference and aging drift is improved.

The invention discloses a lithium ion battery electric-thermal-aging coupling model construction method based on an EIS, and the method comprises the steps: firstly, determining the number of elements of a fractional order equivalent circuit model through relaxation time distribution based on the actual measurement electrochemical impedance spectroscopy data of a battery, completing the parameter identification through a particle swarm optimization algorithm, and improving the model precision; secondly, in combination with geometric parameters and thermophysical property data of the battery, constructing a homogenized thermal model, calculating heat production, heat transfer and heat dissipation processes of each unit through grid division, and generating a three-dimensional temperature profile map; in addition, a semi-experience aging model is introduced, a dynamic association equation of the health state and the heat transfer coefficient is established, and real-time updating of aging parameters is achieved; through a closed-loop coupling mechanism driven by a time step length, electrical parameters, temperature distribution and an aging state are dynamically adjusted, so that the problem that a traditional single model cannot adapt to changes of internal and external environments is solved.

An intelligent method for optimizing scan sequences in additive manufacturing processes is disclosed. The method integrates thermomechanical modeling to optimally select scan paths that minimize part deformation, residual stresses or other thermomechanical metrics of printed parts. Unlike prior approaches that rely solely on thermal models, the disclosed method incorporates both thermal indicators, e.g., derived from simulated or sensor-based temperature distributions, and mechanical indicators, e.g., those capturing deformation or stress via finite element analysis. These indicators are combined into an objective function, which is minimized using an optimization strategy executable within the interlayer time of the additive manufacturing process. The method is compatible with laser powder bed fusion (LPBF), electron beam powder bed fusion (EB-PBF), directed energy deposition (DED). The method is broadly applicable to any process involving localized heating and material deposition.

The invention provides a method and device for increasing the rated current breaking frequency of a direct-current solid-state circuit breaker and application of the method and device, and aims to solve the problem that the breaking frequency of an existing direct-current solid-state circuit breaker is limited due to over-temperature damage caused by performance degradation after multiple current impacts of a piezoresistor and difficult measurement of the junction temperature of a power device. The system comprises a junction temperature prediction module based on a physical information neural network, an auxiliary energy absorption module and a power device voltage clamping module. The junction temperature prediction module is used for accurately predicting the junction temperature of the power device in real time by fusing a physical thermal model and a neural network; the voltage clamping module suppresses turn-off overvoltage; and the auxiliary energy absorption module is started when the junction temperature is close to the limit to absorb residual energy. According to the design, turn-off energy is reasonably distributed, the impact frequency and strength of the piezoresistor are reduced, the service life of the piezoresistor is remarkably prolonged (for example, the service life is prolonged by about 10 times under the impact of 200A), then the rated breaking frequency of the solid-state circuit breaker is greatly increased, and the solid-state circuit breaker is low in cost, high in reliability and suitable for scenes such as a direct-current power grid.

The invention discloses a lithium battery internal temperature estimation method based on thermal parameter identification of AWPSO and IKF, and aims to solve the problems that thermal parameter identification is susceptible to noise interference and the temperature estimation precision is insufficient in the prior art, the method comprises the following steps: firstly, collecting battery voltage, current and surface temperature data, and constructing a lumped parameter thermal model; an adaptive weighted particle swarm optimization (AWPSO) algorithm is adopted to dynamically adjust the inertia weight, and thermal resistance and thermal capacity parameters are identified by taking the error minimization of the model calculation temperature and the actually measured temperature as a target; secondly, based on a thermal model of identification parameters, the internal temperature is estimated in real time through an incremental Kalman filter (IKF) algorithm, a residual feedback mechanism is introduced into the algorithm to dynamically update noise covariance, and the state prediction and measurement updating process is optimized. According to the method, the robustness of thermal parameter identification and the accuracy of internal temperature estimation are remarkably improved, and an effective technical support is provided for safety monitoring and service life management of the lithium battery.

The invention relates to a multi-split air conditioner regulation and control method, device and equipment, a storage medium and a product. The method comprises the steps that reference temperature data of a target building where the multi-split air conditioner is located and reference power data of the multi-split air conditioner within a preset reference time period are obtained, and a target thermal parameter value of the target building is obtained through a preset building thermal model, the reference temperature data and the reference power data, the building thermal model is used for calculating heat change data and heat exchange data of the target building, and the target thermal parameter value is a value of a thermodynamic parameter of the target building under the condition that the heat change data and the heat exchange data meet a preset heat balance relationship; and the actual load-shedding power amount of the multi-split air conditioner in the current regulation and control time period is determined, so that the multi-split air conditioner is regulated and controlled according to the load-shedding power amount. By adopting the method, the dynamic thermodynamic characteristics of the building under the actual operation working condition can be determined, the adjustable potential of the air conditioning system can be accurately quantified, and the regulation and control accuracy can be improved.

The invention is suitable for the technical field of power electronics, and provides an intelligent management method and system for a power amplifier, and the method comprises the steps: obtaining the output voltage and current of the power amplifier, and determining the real-time load impedance; predicting the junction temperature of the power amplifier according to a coupling thermal model associating the effective thermal resistance with the load mismatch degree; based on the predicted junction temperature and real-time load impedance, stability compensation parameters for maintaining operational stability and pre-distortion parameters for compensating for non-linear distortion are cooperatively determined and adjusted. Through cooperative intelligent management, safety and reliability can be improved, performance maximization is realized, and high fidelity of all working conditions is ensured.

The invention discloses a control method, and particularly relates to an energy storage power converter self-adaptive control method based on digital twinning, which comprises the following steps: step 1, digital twinning model synchronization and state sensing; step 2, multi-target collaborative optimization based on prediction; and step 3, optimizing instruction issuing and physical system execution. Through real-time prediction and optimization of the digital twinborn model, it is ensured that the power switch tube always works in a zero-voltage switching state, switching loss is effectively reduced, and overall efficiency is improved. Dynamic thermal balance management is realized by combining thermal model prediction, local overheating is avoided, the service life of devices is prolonged, and the reliability of long-term operation of the system is enhanced.

The invention provides a magnetic suspension turbo molecular pump rotor temperature estimation method, and belongs to the technical field of semiconductor molecular pump temperature control. According to the method, on the basis of construction of the permanent magnet thermal model, key variables influencing the rotor temperature are obtained, determination of input variables is achieved, and the method plays an important role in accuracy and high efficiency of model training and application. Meanwhile, a deep learning technology is introduced, a model structure Bi-LSTM-CNN suitable for magnetic suspension turbo molecular pump rotor temperature estimation is designed, and the model structure comprises a Bi-LSTM module, a multi-head attention mechanism module, a CNN module and a full connection layer. The temperature of the rotor can be accurately estimated according to the real-time change of the working state of the rotor and the environment condition, so that intelligent management and maintenance of the pump system are realized. Meanwhile, the initial weight of the convolutional neural network model is trained by using the genetic algorithm, so that the probability of obtaining the global optimal value is relatively high, the model is effectively trained, and the generalization performance is improved.

The invention relates to the technical field of medical equipment manufacturing. More specifically, the invention relates to an ultra-short wave therapeutic apparatus production consistency control method and system, and the method comprises the steps: building a virtual digital twin model according to the design parameters of an ultra-short wave therapeutic apparatus, and the model comprises a circuit model, an electromagnetic field model and a thermal model; various parameters of production equipment and products are collected in real time, and the collected parameters are compared with theoretical parameters in the digital twinborn model to determine the difference between the parameters; adjusting the parameters of the production equipment according to the difference so as to compensate the parameter drift of the component; and receiving radio frequency power data of the directional coupler, and calling an iterative learning control algorithm to optimize the driving voltage curve so as to realize dynamic adjustment of the radio frequency power. According to the scheme, the consistency and reliability of products are effectively improved, the production process is optimized, and quality tracing is achieved.