406 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 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 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 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.

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 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.

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 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 an elastic evaluation method for a comprehensive energy distribution network in the field of power systems and energy management, and the method comprises the following steps: S10, constructing a dynamic coupling model and a comprehensive evaluation index system of the comprehensive energy distribution network, the dynamic coupling model of the comprehensive energy distribution network comprises energy flow equations and equipment operation constraints of a power network, a natural gas network and a thermal network, and the comprehensive evaluation index system comprises an energy coupling index, a recovery capability index and an uncertainty index. According to the invention, through multi-energy coupling and uncertainty comprehensive evaluation, the coupling relationship of multiple energies and various uncertainty factors are comprehensively incorporated into an elastic evaluation system of the comprehensive energy distribution network, a set of complete comprehensive evaluation index system is established, and the limitation that a traditional evaluation method only pays attention to single energy or ignores uncertainty is broken through; and the actual elasticity condition of the system can be reflected more truly.

The invention provides a real-time temperature prediction method for a permanent magnet synchronous motor. The method comprises the following steps: acquiring real-time working condition data and temperature data of the permanent magnet synchronous motor; pre-dividing and defining motor hot nodes based on each physical component; constructing a lumped heat estimation optimization model composed of a heat conductivity estimation sub-network, a power loss estimation sub-network, a reverse heat capacity estimation sub-network and a lumped parameter heat network recursor; inputting the real-time working condition data and the temperature data into a lumped heat estimation optimization model to respectively obtain a heat guiding quantity, a power loss vector and a reverse heat capacity matrix; and inputting the parameters and the real-time temperature data into a lumped parameter thermal network recursion device, and outputting a temperature real-time predicted value. According to the temperature real-time prediction method of the permanent magnet synchronous motor, the precision and reliability of temperature prediction are further improved through combination of the lumped parameter thermal network and the neural sub-network.

The invention relates to an IGBT module junction temperature estimation method and system, and the method comprises the following steps: obtaining a performance curve set of an IGBT module under different working conditions, and carrying out the fitting optimization, and obtaining an optimal performance curve set; constructing a transient thermal resistance curve model according to a transient thermal resistance principle, and constructing a second-order Foster thermal network model based on the transient thermal resistance curve model; performing Laplace transformation and discretization processing based on a backward difference method on the second-order Foster thermal network model to obtain a junction temperature estimation model; collecting a phase current instantaneous value, and calculating the total power loss of the IGBT module in real time in combination with the optimal performance curve set; and finally, the junction temperature of the IGBT module is obtained in real time by using the total power loss of the IGBT module obtained through real-time calculation and the junction temperature estimation model, and junction temperature estimation is completed. According to the method, the effect of improving the power loss precision of the IGBT module is achieved by optimizing the performance curve, and then the junction temperature estimation precision is improved.

The invention belongs to the field of crimping IGBT junction temperature estimation, and particularly discloses a crimping IGBT half-bridge module junction temperature online estimation method and system, and the method comprises the steps: carrying out the decoupling of a double-side thermal network model based on a multi-heat-flow path of a crimping IGBT half-bridge module, and achieving the junction temperature estimation of the crimping IGBT half-bridge module; wherein the establishment method of the double-sided thermal network model comprises the step of constructing the double-sided thermal network model based on the double-sided junction-to-case thermal resistance, the water-cooled radiator thermal resistance, the circulating water equivalent thermal resistance and the coupling thermal resistance of the crimping IGBT half-bridge module by taking the water inlet temperature of the water-cooled radiator as the reference temperature and combining the power loss of each crimping IGBT in the crimping IGBT half-bridge module. The method has research value and application prospect for junction temperature estimation and reliability evaluation of the converter valve crimping IGBT.

The invention provides a thermal management system energy grade and energy conversion difference measurement method based on # imgabs0 # analysis, and the method comprises the steps: obtaining key parameters of all parts of a system in real time, carrying out # imgabs1 # analysis, and obtaining an energy quality coefficient; performing grade difference measurement and heat flow distribution to obtain grade grading and flow direction configuration of each component; constructing a heat energy network model, and optimizing a network loop to form a high and low temperature multi-loop heat energy network; measuring component energy conversion efficiency difference to obtain a difference analysis result; analyzing the corresponding speed of energy conversion and performing control medium classification to obtain corresponding speed classification and medium distribution suggestions; and constructing a heat flow prediction model, designing an algorithm reward function, and obtaining an optimization control strategy based on response difference measurement. The heat flow can be reasonably distributed and utilized step by step according to the grade, the energy utilization efficiency of the total system is improved, the optimal energy utilization terminal combination scheme can be designed, energy loss is reduced, and the overall performance of the system is improved.

The invention provides an IGBT junction temperature prediction model construction method and system, and relates to the technical field of power electronic device thermal management, and the method comprises the steps: obtaining an initial data set representing the transient thermal response relation between an IGBT chip and an NTC temperature measurement point in an IGBT power module where the IGBT chip is located; training a neural network model based on the initial data set, and learning and representing a nonlinear function relationship between time and thermal impedance; inputting a preset new time sequence into the trained neural network model, and generating a time-continuous and noise-suppressed optimized thermal impedance data set; and based on the data set, performing parameter fitting on a preset lumped parameter thermal network model to identify thermal resistance and thermal capacity parameters of the model so as to complete construction of the junction temperature prediction model. According to the method, the neural network is used as a data optimization tool in a physical model parameter identification process, and accurate and convenient online prediction of the IGBT junction temperature is realized only by using the temperature signal of the NTC temperature measurement point of the module.

The invention relates to the technical field of heat-conducting materials, in particular to a boron nitride heat-conducting film and a preparation method thereof.The boron nitride heat-conducting film is prepared through a coating technology and a roll forming technology, deionized water is selected as a solvent, and the boron nitride heat-conducting film is green and environmentally friendly and is combined with the coating technology and the roll forming technology; the preparation method is simple, the production speed is high, the efficiency is high, the cost is low, large-scale industrialization can be achieved, the boron nitride nanosheets in the slurry can be directionally arranged under the action of a coating process, so that a heat-conducting network is formed in the boron nitride heat-conducting film, the boron nitride heat-conducting film has excellent heat-conducting performance, and the service life of the boron nitride heat-conducting film is prolonged. And the in-plane thermal conductivity of the finally formed heat conduction film can reach 54.46 W / (m.K), so that the technical problems that in the preparation process of the heat conduction film in the prior art, the preparation method is complex, the environment is not friendly, and the production efficiency is low due to the adoption of sheet flat pressing molding are solved.

The invention relates to the technical field of intelligent heat supply management, in particular to an intelligent heat supply integrated management system based on multi-source data driving, which comprises a multi-source data acquisition module for acquiring multi-source data of each target heat supply area of a heat supply network; the key delay time extraction module is used for acquiring the adjustment condition of the heat supply control data to generate key delay time; the sensitive parameter identification module is used for determining a heat supply adjustment sensitive parameter and a heat supply response sensitive parameter based on the key delay time; the characteristic interval analysis module is used for determining a change trend characteristic interval based on a pre-change trend corresponding to the change characteristic interval; the pre-adjustment time determining module is used for determining a plurality of pre-adjustment times based on the change trend characteristic interval; and the pre-adjustment time calling module is used for acquiring the real-time multi-source data of the heat supply network to determine whether pre-adjustment is started or not, and determining the pre-adjustment time. The response speed and the adjusting precision of the heat supply system are improved.

The invention discloses a variable switching frequency-based power enhancement method for a network-constructing type converter under a dynamic state of a power grid, and relates to the field of network-constructing type converter control. According to the method, the power module loss and thermal network model is constructed, the junction temperature of the power device of the network-forming type converter is estimated to keep the junction temperature unchanged, the relation model of the power reference value and the switching frequency of the converter is established, and the power-switching frequency relation model is looked up after the power reference value is obtained through the network-forming algorithm during the dynamic period of the power grid. And obtaining the switching frequency corresponding to the current power reference value for underclocking operation, thereby reducing the switching loss of the power module, and ensuring that the maximum power output capability of the converter is maintained under the dynamic state of the power grid until the switching frequency is reduced to the lowest amplitude limit. The method for enhancing the power of the constructed network type current converter effectively improves the voltage and frequency supporting capability under the dynamic state of the power grid.

The invention discloses an intelligent charging management method based on a mobile charging device and heat supply network inspection robot dogs. The intelligent charging management method comprises the following steps: setting a plurality of inspection robot dogs to execute different types of heat supply network inspection tasks; the back-end service platform analyzes the real-time inspection power consumption of the inspection robot dogs according to the heat supply network inspection paths, the inspection tasks and the electric quantity attenuation mechanism of the inspection robot dogs, calculates the real-time residual electric quantity of each inspection robot dog, compares the real-time residual electric quantity with the inspection power consumption at the next moment, and sets the charging plan of each inspection robot dog; a plurality of new energy mobile charging devices are arranged to charge each inspection robot dog, and the back-end service platform predicts the power supply value of each new energy mobile charging device; the back-end service platform performs charging optimization distribution of the mobile charging device, and sets an optimal charging path of the mobile charging device and the inspection robot dog; and each mobile charging device and the inspection robot dog autonomously drive to a charging meeting point according to the optimal charging path, and charging is completed in a wireless charging mode.