498 results about "Thermal network" patented technology

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention provides a rotor temperature estimation method and device of a motor, a vehicle and a storage medium, and the method is applied to the field of motor temperature monitoring, and comprises the steps: obtaining the current environment temperature, the current shutdown time, the stator loss and the rotor loss of the motor, determining the initial rotor temperature of the motor according to the current environment temperature and the current shutdown duration, and obtaining the temperature rise of the motor by using a preset thermal network model and a preset double-Kalman filter based on the current environment temperature, the stator loss and the rotor loss; and obtaining the actual rotor temperature of the motor according to the initial rotor temperature and the temperature rise. According to the method, the high-precision estimation of the rotor temperature of the motor is realized by establishing the thermal network model and combining the double Kalman filters, the dependence on a hardware sensor is not needed, the cost is reduced, and the reliability and robustness of the system are improved.

The invention discloses an IGBT junction temperature real-time decoupling modeling method and system based on dynamic electric heating compensation, and relates to the technical field of state monitoring of power electronic devices. At present, junction temperature estimation is not accurate due to IGBT on-state voltage drop measurement distortion and chip internal thermal gradient in a high-voltage environment. The method comprises the following steps: in an offline stage, constructing an ideal electrothermal characteristic database of an IGBT, a dynamic temperature gradient compensation database in a chip and Foster thermal network model parameters; in the on-line stage, synchronous sampling and preprocessing are carried out on IGBT module operation parameters; combining online real-time parameters with an offline database by using an adaptive Kalman filter, and iteratively and cooperatively estimating the real-time junction temperature and loss of the IGBT; and finally, outputting and applying the estimated accurate junction temperature and loss data. According to the technical scheme, the hot spot effect in the chip is accurately decoupled, the junction temperature estimation precision and the robustness of online monitoring are improved, and core data support is provided for fine thermal management and early fault early warning of the flexible direct current converter valve.

The invention discloses an axial flux motor temperature prediction method based on equivalent thermal circuit method calculation, and the method comprises the following steps: 1, motor thermal circuit unit division: decomposing a motor into a stator unit, a rotor unit, an air gap unit and a housing unit, and constructing an equivalent thermal network through a node-branch method; 2, calculating a multi-physics coupling heat source; 3, thermal resistance parameter dynamic calculation and CFD coupling optimization: calculating conduction thermal resistance, convection thermal resistance and contact thermal resistance; and 4, parameter self-calibration based on machine learning: constructing a PINN model according to a physical law and known physical parameters, introducing a Bayesian optimization algorithm on the basis of the PINN model, adjusting and optimizing the parameters of the PINN model by using Bayesian optimization, designing a reinforcement learning algorithm, and carrying out self-calibration on the parameters of the PINN model. Enabling the reinforcement learning algorithm to dynamically adjust parameters according to the real-time operation data of the system and the prediction result of the PINN model; 5, constructing and solving a heat balance equation; and step 6, visualizing and verifying the temperature field.

The invention provides a temperature prediction method and device for a high-frequency transformer, and belongs to the field of transformers. The method comprises the following steps: acquiring a temperature field parameter of a target high-frequency transformer; calculating initial magnetic core loss and initial winding loss according to the temperature field parameters; inputting the initial magnetic core loss and the initial winding loss into a pre-constructed thermal network model to obtain the temperature of each node in the pre-constructed thermal network model; based on the initial temperature of each node, correcting the initial magnetic core loss and the initial winding loss, and inputting the corrected initial magnetic core loss and initial winding loss into a pre-constructed thermal network model to recalculate the temperature of each node; and when the temperature of each node satisfies a preset condition, obtaining a temperature prediction value of each electromagnetic component of the target high-frequency transformer. According to the method, the problem that the high-frequency transformer is inaccurate due to inaccurate modeling and excessive conditions in a traditional method can be solved, and the accurate and effective temperature prediction method for the high-frequency transformer is provided.

The invention provides an IGBT junction temperature prediction method and system based on dynamic thermal impedance correction. The method comprises the following steps: acquiring temperature data in real time, reversely deducing IGBT loss in combination with a pre-constructed thermal network model, and storing the IGBT loss in historical data; obtaining an IGBT loss average value under the same working condition, and calculating a difference value between the IGBT loss average value and the IGBT loss and a specific value of the IGBT loss average value to obtain a loss offset coefficient; judging whether the loss deviation coefficient is continuously greater than a preset value in a continuous period, if so, judging that the heat-conducting silicone grease is aged, calculating an aging coefficient through loss data and temperature data in the continuous period, and correcting the thermal network model by adopting the aging coefficient; and obtaining the corrected internal transient thermal impedance of the IGBT and the loss of the IGBT, and further predicting the junction temperature of the IGBT. According to the method, the thermal network model is adopted to reversely deduce the real-time loss power of the IGBT, and electric parameter drift errors are avoided; and a dynamic thermal impedance correction mechanism is introduced to ensure long-term accuracy of junction temperature prediction.

The invention relates to the technical field of graphics card performance monitoring, and discloses a graphics card component performance monitoring method, device and equipment, and the method comprises the steps: setting a temperature sensor array for a GPU chip, a video memory, a power management module and a radiator of a graphics card, and carrying out the temperature data collection and preprocessing, and obtaining a temperature distribution matrix; establishing a thermal network topological structure and performing finite element discretization calculation to obtain a thermal network state space equation; partial least square regression analysis is carried out to obtain m temperature control areas; the temperature state vector and the load state vector of the temperature control area are input into a mixed Q network structure, an epsilon-greedy strategy and a Q learning updating rule are executed, and a heat dissipation control parameter sequence is obtained.By means of the method, accurate calculation of a temperature coupling coefficient and design of a temperature decoupling controller are achieved, the problem of temperature coupling between assemblies is effectively solved, and the reliability of heat dissipation control is improved. And the heat dissipation control strategy can be adaptively adjusted to adapt to different working load conditions.

The invention relates to the technical field of solar thermal power generation and energy storage, in particular to a multi-temperature-zone coupled staged fused salt heat storage system and a control method thereof.The system comprises a multi-stage heat storage module, a multi-stage heat storage module and a multi-stage heat storage module, the multi-stage heat storage module comprises a high-temperature storage tank, a medium-temperature storage tank, a low-temperature storage tank and a buffer storage tank, and the storage tanks are dynamically partitioned through adjustable partition plates; the self-adaptive heat exchange network comprises a plate heat exchanger and a double-pipe heat exchanger which are combined in series-parallel connection, the plate heat exchanger is used for a high-temperature section, the double-pipe heat exchanger is used for a medium-low-temperature section, and an electric three-way valve is arranged between the heat exchangers to automatically switch connection modes according to the temperature of a heat source; and the intelligent control module integrates a temperature gradient sensor and a fused salt flow regulating valve, and optimizes the heat storage priority and the heat release rate in real time. The heat storage density is increased by 20%-30%, and the system circulation efficiency reaches 85% or above; multi-heat-source input and multi-grade heat energy output are supported, and complex working conditions are adapted; and through the modular design, the construction cost is reduced by 30%, and the service life is prolonged to 25 years.

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 permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention discloses a real-time detection method for abnormal pressure data of a heat supply network pipeline, and the method comprises the steps: building a full-life-cycle management system of the heat supply network pipeline, carrying out the risk recognition and evaluation, and analyzing a key position where the risk of the heat supply network pipeline is liable to occur after a risk evaluation report of the whole heat supply network pipeline is formed; according to the difference between the real-time pressure data and the simulated pressure data of the key positions and the important nodes and the difference between the real-time pressure data and the pressure data predicted value, a pressure data comprehensive deviation value is obtained, and meanwhile the pressure data abnormal threshold range of each key position and each important node is configured; and the heat supply network pipeline carries out early warning based on the pressure data comprehensive deviation value and the configured pressure data abnormal threshold range, the abnormal condition of the heat supply network pipeline is displayed, and pressure abnormal positions and nodes are identified.

The invention discloses a converter state monitoring method based on IGBT (Insulated Gate Bipolar Translator) loss, which relates to the technical field of power electronics, and comprises the following steps: S1, acquiring direct current side input voltage, direct current side input current, alternating current side output voltage and alternating current side output current of a converter, and calculating IGBT power loss and diode power loss; s2, constructing an IGBT module thermal network model, and calculating IGBT junction temperature and IGBT conduction voltage drop; s3, detecting the state of the IGBT by combining the three-phase current output by the AC side of the IGBT and the parameter value of the IGBT element, and carrying out VSC fault positioning; real-time state monitoring is carried out on the converter based on the change of IGBT power loss, power testing is carried out on the IGBT without damaging packaging and stopping, the aging state of the IGBT is monitored by utilizing electrical parameters before and after current conversion, meanwhile, PQ control is introduced, the stability of output power and current is improved, and the reliability of the converter is improved. And a replacement plan of the aged power module is better arranged before the service life of the IGBT is ended.

The invention relates to the technical field of antennas, in particular to a multi-level intelligent overheating protection system and method based on a gallium nitride module antenna. According to the method, a thermal load optimal migration path is analyzed based on a virtual thermal capacity network model, and an input impedance matching network of a target unit is synchronously tuned according to the thermal load optimal migration path to compensate beam phase distortion caused by thermal migration; and according to the boundary constraint condition of the real-time threat scene feature injection virtual thermal capacity network model, dynamically solving a game equilibrium solution of a thermal load migration path and a beam parameter tuning amount, and generating a thermal-radio frequency combined control instruction set. According to the method, full-process closed-loop control from thermal inertia characteristic analysis, thermal network modeling to thermal-radio frequency collaborative optimization is realized, so that the gallium nitride antenna system can intelligently plan an optimal thermal migration path and compensate thermally induced performance distortion in real time in a complex thermal environment and a threat scene, and finally dual optimization of heat dissipation efficiency and radio frequency indexes is achieved.

The invention discloses a transformer noise suppression method, system, equipment and medium, and the method comprises the steps: collecting the winding temperature, iron core temperature, cooling system working condition, load current and other operation parameters of a transformer in real time, constructing a multi-physical field coupling model covering electromagnetic, thermal and mechanical characteristics, and providing basic data for noise analysis; based on a heat conduction equation and a vibration acoustics theory, a mapping relation between a heat dissipation state and a transformer noise source is established, and dynamic influences of heat dissipation on noise amplitude and frequency are quantified; through combination of finite element simulation and a thermal network model, winding / iron core deformation caused by temperature change is analyzed, then a modulation rule of deformation on noise radiation characteristics is derived, and a heat dissipation state-physical deformation-noise characteristic transfer function is constructed; and based on the transfer function, dynamically adjusting operation parameters of the transformer by adopting a model predictive control and adaptive filtering technology, and minimizing noise output on the premise of ensuring heat dissipation efficiency.

An intelligent room temperature regulation and control method for a heat supply building belongs to the field of building heat supply, and comprises the following steps: constructing a heat supply building pipe network hydraulic and thermal model, and forming a hydraulic and thermal coupling model; the hydraulic and thermal coupling model comprises a pipe network hydraulic model, a regulating valve model, a heat exchanger model and a building thermal network model; constructing a multi-agent deep reinforcement learning model by using the hydraulic and thermal coupling model, and defining that each hot area of the heat supply building corresponds to one agent; and the multi-agent deep reinforcement learning model is used for solving the valve opening degree of each hot area, intelligent valve regulation and control are carried out according to the indoor temperature set value of the user, and the indoor temperature regulation and control of the heat user of the heat supply building are achieved. According to the method, based on the idea of reinforcement learning, the heat supply operation data of the building is fully utilized, the environment model adaptive to reinforcement learning is constructed based on the simulation model, and technical support is provided for development of intelligent heat supply.

The invention relates to the technical field of motor control, and discloses a bus capacitor temperature online estimation method and device of a motor controller, and the method comprises the steps: building a thermal network model, and determining a temperature collection point and a current collection point according to the thermal network model; capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are calculated according to the current acquisition data, and a rotating speed-torque-voltage three-dimensional loss table is made; calculating a thermal resistance parameter and a thermal capacity parameter in the thermal network model; according to the current rotating speed, torque and voltage, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are inquired from a three-dimensional loss table; and establishing a thermal network state equation, and calculating the current capacitance temperature. Thermal resistance parameters and thermal capacitance parameters are obtained through experimental test calculation, capacitor direct current side copper bar loss, capacitor loss and capacitor IGBT side copper bar loss are obtained through off-line calculation, the precision of thermal network model parameters and loss is improved, and then the estimation precision of the capacitor temperature is improved.

The invention provides a method and a system for calculating carbon emission in a building operation stage, and relates to the field of carbon emission metering. The method comprises the following steps: acquiring model driving data in real time, wherein the model driving data comprises meteorological data in a building operation stage, heating system operation data, building use state data and power grid carbon emission intensity data; the model driving data are input into a third-order RC model to predict the dynamic energy consumption in the building operation stage and obtain energy consumption prediction result data, and the third-order RC model is a multi-region coupled model established according to the partition characteristics of the building structure and comprises indoor environment nodes, enclosure structure nodes, indoor air nodes and internal heat quality nodes; constructing a dynamic differential equation set according to a thermal network theory; and according to the energy consumption prediction result data and the dynamically updated carbon emission factor database, calculating to obtain corresponding carbon emission data. According to the technical scheme, accurate and real-time calculation of the carbon emission in the operation stage of the building can be achieved with low cost.

The invention discloses a flexible DC converter valve DC support capacitor temperature on-line monitoring method and system. The method comprises the steps of obtaining operation data of a thermistor arranged on a target capacitor wiring terminal; processing the operation data to obtain external temperature data of the target capacitor; constructing a thermal network model of the target capacitor based on the structure data and the material data of the target capacitor; performing optimization processing on the thermal network model through internal temperature data of the target capacitor acquired by the thermocouple to obtain an equivalent thermal resistance and thermal capacitance network model; inputting the obtained real-time current data of the target capacitor into the equivalent thermal resistance and thermal capacitance network model for processing to obtain internal temperature rise data of the target capacitor; and fusing the external temperature data and the internal temperature rise data to realize real-time monitoring of the temperature of the target capacitor according to a fused result. According to the method provided by the invention, real-time monitoring and accurate evaluation of the temperature of the capacitor are realized.

The invention provides a thermodynamic equilibrium simulation analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, building a steam thermodynamic equilibrium model based on the technological parameters of a thermodynamic network; 2, analyzing a thermal distribution state through a steam thermal equilibrium model, identifying an unbalance factor in a process chain, and establishing a four-vertex geometric topological relation among a heat exchanger inlet / outlet, a pressure reduction module and a drain valve process node according to the physical layout of a thermal network; and step 3, based on the dynamic proportion characteristic of the four-vertex geometric topological relation, calculating a thermal correction factor, and adjusting the heat transfer coefficient, the steam pressure and the condensate water temperature parameter of the steam thermal equilibrium model according to the thermal correction factor to obtain the corrected steam thermal equilibrium model parameter. According to the method, through dynamic correction of model parameters and simulation optimization, accurate identification and dynamic regulation and control of thermal unbalance are realized, a closed loop for optimized feedback is formed, and the operation efficiency and stability of a thermal system are improved.

The invention provides a self-adaptive optimization method and system for motor multi-working-condition temperature rise prediction, and relates to the technical field of motor temperature calculation and prediction. According to the method, the motor thermal network model is constructed through refined node division, the correction coefficient and thermal resistance sensitivity analysis are introduced, and the thermal resistance is scientifically graded, so that the model accuracy is improved. Furthermore, according to the method, the adaptive genetic algorithm is adopted to carry out sectional optimization on the thermal resistance parameters, and rapid and accurate calculation of the temperatures of the motor under various working conditions is realized. In addition, the invention further provides a system based on a computer readable storage medium, and the system is used for achieving the construction and optimization process of the model. According to the method, the genetic algorithm and thermal resistance sensitivity analysis are combined, rapid parameter optimization of the thermal network model is achieved, the temperature calculation precision of the model under different motor working conditions is remarkably improved, and the method has wide application prospects.

The invention discloses a quick response flexible temperature sensor with low thermal crosstalk and a preparation method thereof. The sensor comprises a contact layer, a sensing layer, an electrode layer and a substrate layer, wherein the contact layer comprises a heat conduction layer and a heat insulation network; the heat-conducting layer is made of a heat-conducting composite material and comprises polydimethylsiloxane and flake carbon powder; the heat insulation network and the substrate layer are made of a heat insulation composite material which comprises polydimethylsiloxane and hollow glass microspheres; the sensing layer comprises a plurality of sensing units arranged in an array, the sensing units are connected with the electrode layer and output resistance signals, the heat conduction layer and the substrate layer are arranged over and under the sensing units respectively, and the heat conduction and heat insulation synergistic structure can enhance the quick response of the sensing units to the temperature; a thermal insulation network is embedded in the contact layer to block thermal crosstalk between adjacent sensing units. The temperature sensor has the advantages of quick response, low thermal crosstalk and high reliability in a wide range, and large-scale production and preparation can be realized.

The invention relates to a preparation method of a magnetic field-electric field synergistically driven Fe3O4-loaded graphene three-dimensional interlocking network / metal composite material, and belongs to the field of electronic packaging materials. An alternating magnetic field and a direct-current electric field are applied in the solidification process of metal melt, Lorentz force is used for driving graphene nanosheets loaded with Fe3O4 to rotate directionally and be arranged vertically, meanwhile, the electric field induces dissociation of graphene edge functional groups, chemical bonding with a metal matrix is enhanced, and a space interlocking heat conduction network is formed. The graphene reinforced metal composite material is obviously superior to a traditional graphene reinforced metal composite material and is suitable for a heat dissipation substrate of aerospace electronic equipment.

The invention discloses a synchronous reluctance motor magnetothermal coupling rapid calculation method based on a dynamic thermal network, and belongs to the technical field of motors. Comprising the following steps: calculating motor loss by adopting a finite element electromagnetic field model, and calculating a convective heat transfer coefficient by adopting a finite element fluid field model; constructing a three-dimensional thermal network comprising conduction, contact, convection thermal resistance, thermal capacity and a heat source, and solving to obtain an average temperature and a boundary temperature of component nodes; constructing a sub-domain temperature field calculation model of the heat source component, and calculating temperature distribution in combination with boundary conditions; introducing average temperature compensation thermal resistance and carrying out dynamic correction; carrying out magnetothermal coupling iteration, and updating the loss and the temperature until the precision is met; and introducing maximum temperature compensation thermal resistance into the thermal network, and solving to obtain an average temperature and a maximum temperature transient temperature rise curve of each component. Based on the technical means of combining electromagnetic field and fluid field finite element simulation with thermal network and subdomain temperature field calculation, efficient calculation of magnetothermal coupling of the synchronous reluctance motor is achieved.

A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.

The invention discloses a control method for defrosting of a parallel flow evaporator, which comprises the following steps of: constructing a multi-dimensional state sensing network, constructing a three-dimensional state sensing matrix by utilizing a distributed temperature sensor array on the surface of an evaporator coil, an air inlet humidity sensor and an inlet and outlet differential pressure type pressure sensor, and acquiring information such as temperature distribution, environment humidity and air pressure difference; through dynamic threshold self-adaptive judgment, based on an environment temperature field, the historical temperature of an evaporator and an icing critical temperature field calculated by a time sequence prediction model, the defrosting triggering condition is judged by combining humidity and an air pressure difference change gradient field; gradient type energy injection defrosting is adopted, in the first-stage defrosting process, the refrigerant flow is adjusted through a proportional electromagnetic valve to conduct directional defrosting, and in the second-stage defrosting process, a compressor is closed, a fin gap shape memory alloy heating network is activated, and latent heat of a phase change material is released to conduct uniform defrosting. And performing multi-physical field cooperative regulation and control, constructing a coupling model, and adjusting the heating power and pulse modulation duty ratio sequence.

The invention discloses a temperature control system and method of a micro-fluidic chip, and relates to the technical field of micro-fluidic temperature control, and the method comprises the following steps: in the manufacturing process of the micro-fluidic chip, embedding topological insulator nanowires into a substrate in a directional arrangement mode to form a pre-locking array, and meanwhile, generating an anisotropic heat conduction network under the continuous action of a sound-magnetic field, a topological vortex invariant is calculated, and a heat conduction weight coefficient is generated; arranging a fluorescent layer on the surface of a micro-reaction cavity of the anisotropic heat conduction network, collecting a fluorescence lifetime attenuation curve in real time, and calculating a transient temperature value and a temperature change rate according to a heat conduction constraint variation model and a heat conduction weight system; nonlinear feed-forward compensation power is calculated according to the transient temperature value and the temperature change rate, feedback compensation power is calculated in combination with a deviation field of the target temperature and the real-time temperature, and a compensation power instruction is generated; according to the method, the thermal pilot frequency domain filtering function is constructed through the compensation power instruction, so that the uniformity and response speed of thermal response are improved.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.

The invention provides a high-speed oil-cooled flat wire motor stator oil path structure design method, which comprises the following steps of: sequentially establishing a structure model, a loss model and a thermal analysis model of a high-speed flat wire motor based on the thoughts of a thermal network method and a finite element method; determining an optimal design scheme of the motor oil cooling system by gradually simulating and calculating temperature rise conditions of important positions in the motor when different spraying ring structures and iron core oil path layout are selected, and finally constructing a test motor entity according to the optimal design scheme determined by simulation and carrying out a bench test to verify the reasonability of the scheme. Therefore, an accurate and efficient oil cooling system design theory suitable for the flat wire winding permanent magnet driving motor is formed.

The invention discloses a multi-energy micro-grid control method and system in extreme weather, and the method comprises the steps: constructing a multi-agent federated migration reinforcement learning framework of a multi-energy micro-grid energy management system in extreme weather, and selecting a state variable, an action variable and a reward function which represent the multi-energy management system; through interaction with real-time data, the system can adapt to continuously changing environmental conditions, deal with uncertainty of renewable energy sources, an energy storage system, a heat power network supply end and a power load side, and realize optimization of an energy management strategy. The application fields of the method cover multiple fields of scheduling and control of a multi-energy system, multi-agent reinforcement learning, federated learning, transfer learning and the like, and economic operation and toughness improvement of the multi-energy micro-grid in extreme weather can be realized.

The invention discloses a multi-time-scale life evaluation method for a SiC power module of an aviation converter. The method comprises the following steps: establishing a semi-physical simulation platform; establishing a finite element model of the S < iC > power module to perform electric heating multi-physics field coupling simulation; constructing a thermal network model of the S C power module; obtaining junction temperature data of the SiC power module in a whole year; and calculating low-frequency damage consumption and fundamental frequency damage consumption for evaluating the service life of the SiC power module. According to the method, the preparation process of SiC power module packaging can be quantitatively simulated through mathematical modeling based on the finite element theory, the complex working condition environment can be analyzed and calculated, and the cost of products in the aspects of development and research, data acquisition, experimental verification and the like is greatly reduced. The internal packaging of the SiC power module is relatively complicated, so that the influence of the actual physical structure and the material attribute deviation of a research object on the accuracy of a simulation model can be better considered by adopting the multi-physics-field coupled three-dimensional modeling, and the deviation of a thermal parameter result in an actual operation environment is reduced to a certain extent.