327 results about "Thermal network" patented technology

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 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 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 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 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 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 hot spot and average temperature estimation method for a permanent magnet synchronous motor based on a thermal neural network, and belongs to the field of motor state monitoring. Predicting and outputting a thermal parameter correction coefficient of the current time step by using the trained thermal parameter correction neural network; based on the reference thermal parameter, the thermal parameter correction coefficient of the current time step, the motor state data of the current time step and the node average temperature prediction value of the previous time step, predicting and outputting the average temperature of each node of the permanent magnet synchronous motor of the current time step by using a lumped parameter thermal network model; and based on the motor state data of the current time step and the average temperature prediction value of each node of the permanent magnet synchronous motor, predicting and outputting the hot-spot temperature of the permanent magnet synchronous motor of the current time step by using the trained hot-spot temperature prediction neural network. The method is supported by the Zhejiang Scientific and Technology Project (2025C01199 (SD2)) of Zhejiang Province 'Scientific and Technical Project' of 'Scientific and Technical Project

The invention relates to the technical field of integrated energy systems, and particularly provides a multi-energy-flow scheduling method and system for a regional integrated energy system, and the method comprises the steps: S1, carrying out the unified modeling of a DC micro-grid, a heat supply network and a natural gas network based on a circuit comparison method, and obtaining a network model of each energy network; s2, constructing a direct-current micro-grid island hierarchical control strategy; and S3, according to the state parameters of the regional integrated energy system, performing dynamic load flow calculation on the direct-current microgrid under the island hierarchical control strategy based on a decomposition load flow method and the network model of each energy network, performing steady-state load flow calculation on the heat supply network and the natural gas network, and generating a scheduling instruction, so that multi-energy-flow coupling response can be accurately captured, and the scheduling efficiency is improved. According to the method, the model inconsistency error is reduced, the unit calculation burden is reduced, the expandability and fault tolerance of the system are improved, and high-precision support is provided for optimal scheduling and long-term planning.

The invention discloses a temperature prediction method and device, and a storage medium. The temperature prediction method comprises the following steps: constructing a two-node thermal network model of a motor; wherein the two-node thermal network model comprises a stator node and a rotor node, and the two-node thermal network model takes the temperature of a cooling medium in a motor cooling loop as a temperature reference point; predicting a first temperature of the target node at the next moment based on the temperature of the target node at the current moment by using a two-node thermal network model; wherein the target node comprises at least one of a stator node and a rotor node. In this way, the efficiency of predicting the temperature of the target node can be improved.

Disclosed is a method for constructing an optimal scheduling model of an integrated energy system considering virtual thermal storage, which relates to the technical field of electric power systems. The method includes: establishing an operation model of an apparatus, a demand response model of an energy load, a charging model of an electric vehicle, and an energy flow model of a heat supply network of the virtual thermal storage in the integrated energy system; processing, according to a second-order cone relaxation method, a direct current power flow model of a power distribution network, and constructing the optimal scheduling model of the integrated energy system by taking minimization of total cost of an energy operator as an objective, thereby coordinately and optimally scheduling a plurality of energy sources.

The invention discloses a temperature field estimation method for a liquid cooling-phase change hybrid cooling structure of a permanent magnet motor. The method comprises the steps that a motor cooling structure is divided into independent areas, and a three-dimensional heat conduction model is constructed under overload working condition operation; setting temperature nodes in the independent areas of the stator and the phase change material along the circumferential direction, taking the other independent areas as the temperature nodes, and taking motor loss as a heat source, so as to construct a thermal network topological structure; a convective heat transfer coefficient is obtained, the heat capacity of cooling oil in the circumferential direction and the equivalent specific heat capacity of a phase change material are applied to temperature nodes, motor loss is applied to a heat source, and an equivalent transient heat network model is constructed; and obtaining the temperature rise of each temperature node until the overload condition is finished, and obtaining an estimated temperature field. The phase-change material effectively inhibits the instantaneous large temperature rise of the permanent magnet under the overload working condition; the model can completely represent the circumferential temperature gradient distribution, reflect the temperature space distribution difference of the stator winding under the overload condition, and improve the estimation speed of the temperature field.

The invention discloses a silicon carbide power device junction temperature online accurate extraction method based on a double-sensor Kalman filtering weighted average algorithm, and the method comprises the following steps: 1, enabling a loss model of a power device to receive the phase current, the DC bus voltage, the duty ratio, the phase current and the DC bus voltage from an inverter; the on resistance of the SIC MOSFET, the reference direct current bus voltage, the on loss energy and the off loss energy under the on current and the on threshold voltage of the Schottky diode are obtained from a data manual of the chip, and the gate driving resistor is used for extracting the junction temperature and monitoring operation in real time; and step 2, obtaining an estimated junction temperature through loss calculation of a power device and a thermal network model, and weighting the junction temperatures estimated at different measurement points through a Kalman filtering weighted average algorithm to obtain a more accurate junction temperature for next iterative calculation. According to the method, the limitation of a traditional single sensor in a dynamic working environment can be effectively compensated, and the accuracy and the real-time performance of junction temperature monitoring are improved.

The invention relates to a heat exchange network transformation method and device. The method comprises the steps that a heat exchange network topological structure is constructed; continuously updating model parameters of the basic model of each heat exchange device based on the time sequence data to obtain updated model parameters of each time period in the plurality of time periods; calculating evaluation indexes of the heat exchange devices according to the updated model parameters, and comprehensively sorting all the heat exchange devices based on the evaluation indexes to obtain a sorting result; target time periods in typical working conditions are determined from the multiple time periods, and heat exchange equipment to be transformed is screened out from all the heat exchange equipment on the basis of the sorting result of the target time periods; solving the constructed optimization problem to obtain a transformation scheme of the heat exchange equipment to be transformed; the optimization problem takes the increase amplitude of the heat exchange area of the heat exchange equipment to be transformed as an optimization variable, and takes the minimum total annual cost as an optimization target, so that the dependence on artificial experience can be reduced, and the transformation scheme generation efficiency is improved.

The invention discloses an electricity-gas-hydrogen-heat comprehensive energy system toughness planning method combining self-adaption and distributed robust optimization. The method comprises the following steps: 1) establishing an electricity-gas-hydrogen-heat comprehensive energy system deterministic planning model; 2) establishing a fuzzy set and an uncertainty set of an electricity-gas-heat network and equipment fault of the integrated energy system, and an uncertainty set of electricity-gas-heat load fluctuation; 3) based on the deterministic model constructed in the step 1) and the fuzzy set and the uncertainty set constructed in the step 2), establishing an electricity-gas-hydrogen-heat comprehensive energy system toughness planning model combining self-adaption and distributed robust optimization; and 4) solving the electricity-gas-hydrogen-heat comprehensive energy system toughness planning model combined with adaptive and distributed robust optimization to obtain a comprehensive energy system toughness planning scheme.

The application discloses a driving motor temperature estimation method based on machine learning and double Kalman filtering and relates to the technical field of motor temperature monitoring and control. The application comprises the following steps: constructing a finite element simulation model, collecting motor loss data and temperature response data under multiple working conditions through finite element simulation, and constructing an offline parameter database through a physical information machine learning method; based on the offline parameter database, a second-order lumped parameter thermal network model is constructed, and the second-order lumped parameter thermal network model is converted into a linear parameter variation state space model. The application switches parameters in real time through an LPV model, dynamically corrects model errors in combination with a double Kalman filter, overcomes parameter drift caused by temperature and rotating speed, and solves the problem that the precision of an offline modeling open-loop estimator is insufficient and accurate real-time estimation under all working conditions cannot be realized due to modeling errors, time-varying thermal parameters and other reasons in the traditional rotor temperature estimation method under wide-range complex operating conditions of an electric vehicle.

The invention relates to the technical field of high-power high-voltage frequency converters, and discloses a high-voltage frequency converter IGBT module junction temperature monitoring method, which comprises the steps of constructing an electrothermal coupling model by adopting a Foster type equivalent thermal network based on electrical characteristics and structural parameters of an IGBT module; based on the electrothermal coupling model, simulation is carried out under different operation conditions, junction temperature data of multiple groups of IGBT modules and corresponding electrical data are obtained, and a training sample set is constructed; constructing a long-short-term memory recurrent neural network based on a macro-micro attention mechanism, and training by using the training sample set to obtain an IGBT module junction temperature monitoring model; and inputting real-time electrical data by using the trained IGBT module junction temperature monitoring model, and outputting an IGBT junction temperature monitoring value. According to the method, the generalization and engineering applicability of the model are improved while the junction temperature monitoring precision is ensured.

Disclosed in the present invention is a method for implementing temperature monitoring on power devices by means of a thermal observer. The method comprises: mounting sensors on the upper surface of a radiator and at an NTC in a power module, so as to construct a sensor network to implement information collection with respect to heating situations of different power devices; fitting a thermal network on the basis of a thermal response curve, and extracting thermal resistance and thermal capacitance from a model to establish a thermal impedance matrix, and forming a state-space equation for a thermal sensor network; establishing a thermal observer by using a Gillijn De Moor filter algorithm, so as to estimate losses of the power devices, and realize thermal information observation with respect to operations of the devices; and fitting a thermal network model of the devices and the environment by means of the thermal network, so as to realize real-time tracking of junction temperatures of the power devices. The present invention can be flexibly expanded on the basis of the number of devices and a module structure, thereby realizing device temperature monitoring with high safety, good real-time performance and high accuracy.

The present invention relates to an energy system (1) and a method for operating the energy system (1), which comprises a hydrogen production unit (2) with at least one electrolyzer (3) for water electrolysis thermally coupled to a cooling water circuit (4), and a heat pump (5) whose heat source side (6) is thermally coupled to the cooling water circuit (4) and whose heat sink side (7) is connected to a district or commercial heating circuit (10) via a heat exchanger (8). According to the invention, a heat storage device (9) is provided in the district or commercial heating circuit (10), whose return line (11) is connected after the heat exchanger (8) and whose feed line (12) is connected before the heat exchanger (8) of the heat pump (5), so that the return temperature of the district or commercial heating circuit (10) is increased by heat from the heat storage device (9).

The application provides a kind of IGBT junction temperature prediction model construction method and system, it is related to power electronic device thermal management technical field, method includes: obtaining the initial data set of the transient thermal response relationship between the IGBT chip and the NTC temperature measuring point in the IGBT power module;Based on the initial data set, the neural network model is trained, and the nonlinear function relationship between time and thermal impedance is learned and characterized;The preset new time series is input into the trained neural network model, and the optimized thermal impedance data set with continuous time and noise suppression is generated;Based on the data set, the lumped parameter thermal network model is parameter fitted, to identify its thermal resistance and heat capacity parameters, complete the construction of junction temperature prediction model.The application uses neural network as a data optimization tool in the parameter identification process of physical model, only uses the temperature signal of NTC temperature measuring point provided by the module, realizes the accurate, convenient online prediction of IGBT junction temperature.

The invention provides a variable reluctance motor temperature distribution prediction method and prediction system. The method comprises the following steps: dividing and modeling network nodes based on the structure and network precision requirements of a variable reluctance motor; calculating conduction thermal resistance and thermal capacity according to geometric parameters and material parameters of the motor; calculating air gap convection thermal resistance according to the motor working condition and the air gap viscosity coefficient; considering the relative position relationship between the armature winding and the excitation winding and establishing a complete variable reluctance motor thermal network model; calculating iron loss and copper loss by using an empirical formula based on state variables of actual operation of the motor, and taking the iron loss and the copper loss as input sources of the variable reluctance motor thermal network model; and iteratively solving the thermal network model of the variable reluctance motor by using a thermal transient equation to obtain predicted temperature distribution of the variable reluctance motor. The invention provides a complete modeling and prediction method suitable for temperature distribution of the variable reluctance motor, the calculation efficiency and precision are both considered, and powerful support is provided for multi-physics field joint optimization.

The utility model provides a heat exchange pipe, a condensation heat exchanger and a gas water heater, and belongs to the technical field of hot water equipment. The heat exchange tube comprises at least two layers of heat exchange tube sets, and each layer of heat exchange tube set is composed of a plurality of heat exchange tubes. One ends of two adjacent heat exchange tubes in each layer of heat exchange tube group are connected through a horizontal connecting tube; and the other ends of the two vertically adjacent heat exchange tubes in the two adjacent layers of heat exchange tube groups are connected through an upper connecting tube and a lower connecting tube. The interior of each layer of heat exchange pipe set and different layers of heat exchange pipe sets are connected through horizontal connecting pipes, upper connecting pipes and lower connecting pipes, and a complex three-dimensional heat exchange network is formed. And the total heat exchange area is increased, so that the heat exchange efficiency is improved.

The invention provides a wind driven generator fault diagnosis method, device and equipment and a storage medium, and relates to the technical field of intelligent operation and maintenance of wind power generation equipment. The method comprises the following steps: constructing a thermal network model of the wind driven generator, and obtaining a transient heat transfer equation and a first thermal conductance matrix and a first heat source intensity vector of the wind driven generator in a healthy state based on the thermal network model; according to the first thermal conductance matrix, the first heat source intensity vector and the current temperature of each temperature measurement point of the wind driven generator, performing inversion based on a transient heat transfer equation to obtain a second thermal conductance matrix and a second heat source intensity vector; obtaining a first fault feature matrix according to the second thermal conductivity matrix and the first thermal conductivity matrix, and obtaining a second fault feature matrix according to the second heat source intensity vector and the first heat source intensity vector; and obtaining a fault diagnosis result of the wind driven generator according to the first fault feature matrix, the second fault feature matrix and a preset fault diagnosis model. According to the invention, high-precision fault diagnosis of the wind driven generator can be realized.