339 results about "Heat capacity" patented technology

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention relates to the technical field of welding, in particular to a hot riveting welding head synchronous temperature control method and system based on the temperature change rate, and the method comprises the steps: executing full-power heating pulse triggering operation on a welding head, calculating the pulse duration according to a start timestamp and an end timestamp, calculating the temperature rise, and eliminating peak noise through a digital filtering algorithm; environment temperature and relative humidity are collected in real time, and equivalent heat capacity is calculated; the initial temperature difference between any welding heads is calculated, and a dynamic target temperature curve is constructed; and calculating an actual temperature change rate, synchronously extracting a target change rate of the dynamic target curve at a corresponding moment, calculating a change rate deviation between the two change rates, judging whether intervention adjustment is needed or not, calculating an output signal by using an incremental PID algorithm if intervention adjustment is needed, and adjusting the heating power according to the output signal. According to the scheme, the stability of the hot riveting welding quality is effectively improved by accurately quantifying the thermal characteristics of the heterogeneous welding head, dynamically planning a personalized temperature rising track and performing high-frequency closed-loop regulation and control.

The invention relates to the technical field of electrical performance testing, in particular to a method and system for measuring loss of an inductance winding, and the method comprises the following steps: applying a direct current to a measured inductance winding, synchronously monitoring the temperature, and multiplying a pre-measured direct current resistance value by a direct current value to obtain a direct current loss power value. The direct current is applied to the inductance winding, the temperature is monitored synchronously, the direct-current loss power is obtained by combining the direct-current resistance value and the current value, the temperature curve in the initial stage is extracted, the temperature rise rate is calculated, and therefore the winding heat capacity parameter is obtained, loss measurement can be closely associated with thermal response, and the measurement accuracy is improved. Introduction of a heat capacity factor is added in loss calculation, dynamic characteristics of a winding material on a thermodynamic level can be reflected, instantaneous current with load step change is applied after an inductor is cooled, an instantaneous loss power curve is generated by collecting a voltage sequence and a current sequence at the same time, and loss calculation is not limited to average value or steady state measurement any more.

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 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 temperature compensation error elimination method in a full-automatic transformer transformation ratio test, and belongs to the technical field of transformers. A heat conduction coefficient matrix, a convective heat transfer coefficient matrix and a radiation form factor matrix are utilized to construct a temperature field dynamic evolution equation set, and a neural network compensation model combining a multi-layer perceptron architecture and an attention mechanism is adopted. A hierarchical fusion weight is dynamically adjusted based on a thermal gain stability matrix eigenvalue, a temperature gradient vector modulus length and a thermal capacity matrix condition number through a gating weight function, compensation parameters are adjusted in advance by using a temperature field prediction algorithm, and continuous improvement of measurement precision is realized through a temperature compensation effect evaluation mechanism and a dynamic parameter optimization strategy. The technical problems that the measurement precision is reduced and accurate temperature compensation cannot be realized due to temperature change in the transformation ratio test process of the transformer are solved.

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 the technical field of energy management, in particular to a dynamic energy management method and system for a central air-conditioning system based on a large model, and the method specifically comprises the steps: outputting a short-term load demand prediction curve in a rolling manner through a time sequence depth prediction model; carrying out inversion calculation on equivalent heat capacity and equivalent heat resistance of the building material through a physical information neural network, and quantifying thermal inertia parameters of the building material in real time; evaluating a phase change delay time coefficient of the building material; obtaining a photovoltaic power generation power prediction curve of a power grid system and a power grid time-of-use electricity price signal, and determining an optimal energy charging and discharging time window of the building material; and adopting a multi-objective decision algorithm to decide and generate an optimal operation strategy of the central air-conditioning system. The problem that in the prior art, an energy management system matched with the thermal dynamic characteristics of a building is lacked is solved.

The invention relates to the field of brake early warning, in particular to a brake early warning method, device and equipment and a medium. According to the brake disc temperature, the brake disc thickness and the set temperature, the first residual heat capacity is determined, and the set temperature is the temperature for guaranteeing the braking efficiency; acquiring data corresponding to the unmanned aerial vehicle task, and calculating braking energy according to the data corresponding to the unmanned aerial vehicle task; brake early warning information is determined according to the first residual heat capacity and the brake energy, and the brake early warning information is used for conducting brake warning. The first residual heat capacity is determined according to the brake disc temperature, the brake disc thickness and the set temperature; according to the data corresponding to the unmanned aerial vehicle task, brake energy is calculated; brake early warning information is determined according to the first residual heat capacity and the brake energy; the multi-dimensional real-time evaluation of the brake disc state, the task parameters and the brake efficiency is integrated; the thermal risk is pre-judged through the task parameters, and accurate brake early warning information is determined.

The invention discloses a building energy consumption regulation control method and system based on energy-saving monitoring, and relates to the technical field of buildings, the energy-saving monitoring method of the invention significantly improves the precision of building energy efficiency regulation by accurately capturing temperature evolution sequences of different structural units and establishing a building response vector group. The indoor temperature can be dynamically predicted and adjusted by utilizing the heat capacity, the lag error and the cooling capacity release curve of each structural unit and combining accurate recognition of the cooling capacity release time window. Compared with a traditional heat supply control method based on fixed temperature control setting, the method has the advantages that thermal inertia and energy release of the building structure unit can be sensed in real time, excessive heat supply and energy waste are reduced, accurate control based on the structure unit characteristics and the time window feedback mechanism is beneficial to reduction of energy consumption of the building, and the energy consumption of the building is reduced. And meanwhile, the indoor temperature control comfort and the energy-saving effect are improved.

The invention belongs to the technical field of airborne environmental control / heat management, and particularly relates to a negative-pressure-controlled phase-change heat exchange system which comprises a phase-change evaporation device for storing a phase-change working medium, and the phase-change working medium cools a working medium in a heat exchange unit through phase-change heat absorption; the heat exchange unit and the heat dissipation equipment form a circulation loop, and the circulation loop transfers heat through a working medium; the anti-freezing heating unit is used for heating the phase change working medium in the phase change evaporation device and preventing the working medium from being frozen under the condition of too low temperature; the vacuum generator is used for applying negative pressure into the phase change evaporation device and reducing the saturation temperature of the phase change working medium; the control unit detects the temperature, the pressure and the liquid level in the phase change evaporation device through a sensor, adjusts the vacuum degree in the phase change evaporation device according to control requirements, and starts the anti-freezing heating unit before the freezing temperature is reached, and the system has the advantages of being large in adaptive heat capacity, capable of achieving heat dissipation heat sink self-realization, low in airborne energy consumption, high in expansibility and the like.

The invention discloses an IGBT module junction temperature prediction method and system based on an LSTM thermal neural network, and belongs to the technical field of IGBT application. Comprising the following steps: analyzing the unsteady state heat transfer characteristics of multiple layers of materials in the IGBT module, and constructing a discrete heat transfer (DHT) equation; a training data set containing temperature response data under different power loss conditions is generated through finite element FEM simulation; constructing an LSTM thermal neural network junction temperature prediction model embedded into a physical layer, wherein the physical layer comprises a sub-neural network to extract a mapping relation between input features and thermal resistance and thermal capacity; and training the model by using the training data set, and converting the DHT equation as a physical constraint into a model parameter dynamic constraint condition, thereby realizing prediction of the junction temperature of the IGBT module under different operation conditions. According to the method, the physical heat transfer process and the LSTM network are fused, limitation of an existing method is overcome, accurate and rapid prediction of the junction temperature of the IGBT module is achieved, and reliability and economical efficiency of thermal management of the IGBT module are effectively improved.

The invention provides a transformer heavy overload evaluation analysis method and device, and the method comprises the steps: building a thermal circuit model of load-induced heat point temperature rise of a transformer according to the heating power, equivalent heat capacity and equivalent heat resistance of an internal winding of the transformer, and load-induced heat top oil temperature rise of the transformer; converting a heat flow source in the heat circuit model into a hot pressure source based on the Thevenin theorem, constructing an equivalent heat circuit model of the heat circuit model, and calculating load heating hot spot temperature rise of the transformer under different load rates according to the equivalent heat circuit model; according to the historical environment heating temperatures of the transformer under different load rates, obtaining predicted environment heating temperatures of the transformer under different load rates; and obtaining winding hot-spot temperatures of the transformer under different load rates according to the load heating hot-spot temperature rise and the predicted environment heating temperature of the transformer under the same load rate. According to the invention, the thermal state sensing precision of the transformer under the dynamic load is improved.

According to the intelligent plunger pump temperature control method and system based on model predictive control, a thermodynamic model predictive control algorithm is introduced, key parameters such as the oil temperature of a plunger pump body, the water temperature of a water tank and the environment temperature are brought into a unified calculation model, and cooperative closed-loop control over a heating plate, a semiconductor chilling plate and a circulating pump is achieved. By constructing the heat balance equation of the lumped heat capacity system and solving the rolling optimization problem in the limited time domain, the heat load change of the plunger pump can be accurately predicted and adjusted in advance, the oil temperature is always stabilized in the preset target interval, and the temperature control precision is remarkably improved; meanwhile, mutual exclusion constraints of heating and refrigerating power are set, and weights are dynamically adjusted and optimized according to working conditions, so that meaningless consumption of energy is effectively avoided, and the operation cost of equipment is reduced; in addition, according to the control method, preheating, cooling and energy-saving modes can be automatically recognized, so that the plunger pump is rapidly started in the severe cold environment and stably operates in the high-temperature environment.

The invention discloses a real-time online superconducting accelerator low-temperature system construction method and system.The method comprises the steps that physical property calculation in a low-temperature system is simplified, and a simplified physical property function of a working medium in the low-temperature system is obtained; the key point temperature is simulated according to the combination of a one-dimensional pipeline model and a 0-dimensional tank sub-model based on an agent model technology, the one-dimensional pipeline model is used for simulating the delay of heat transfer, and the 0-dimensional tank sub-model is used for simulating heat capacity; and responding to the simplified physical property function and the key point temperature, and solving node parameters in the low-temperature system by adopting a large-scale nonlinear sparse matrix equation. According to the invention, the fast simulator which operates independently can be provided, and meanwhile, the fast simulator can be combined with a real low-temperature system to operate to provide analysis and prediction functions for the real low-temperature system.

The invention relates to a temperature control method of a temperature controller, which comprises the following steps: S1, collecting multi-source heterogeneous data, fusing the collected data through an adaptive weighted Kalman filtering algorithm, and outputting a temperature field model, S2, inputting the temperature field model into a double-layer LSTM neural network, calculating the importance weight of each region of the temperature field model, and outputting a real-time temperature field and a time derivative dT / dt thereof, s4, calculating a heat capacity parameter C and a heat resistance parameter R in real time, and adjusting a PID (Proportion Integration Differentiation) parameter through a fuzzy rule base; S5, switching to a pulse strong control mode when dT / dt is detected to be greater than or equal to 5 DEG C, and switching to a PID mode when delta T is less than or equal to 5 DEG C and is stable for 2 seconds; the system has the advantage of accurate control.

The invention belongs to the technical field of asphalt back-doping, and particularly relates to an old material and new material ratio optimization method and system for asphalt back-doping treatment, and the method comprises the steps: obtaining the performance parameters of old materials and new materials, the performance parameters comprising a fluidity parameter and a temperature parameter; determining a heating demand based on the temperature parameter; when the heating demand meets a preset heating capacity constraint, determining an optimal proportion between the old material and the new material based on the fluidity parameter, and generating at least one alternative proportion scheme according to a maximum allowable doping proportion of the old material, actual heat capacity of the old material, actual heat capacity of the new material and an allowable heating capacity upper limit; according to the method, the fluidity parameter and the temperature parameter are fused, and the proportion is dynamically corrected, so that the accurate determination of the proportion scheme is realized; through constraint verification of the heating process, effective activation of the performance of the old material is ensured; and by generating a substitution ratio scheme, the construction flexibility and reliability are enhanced, and the asphalt pavement quality and the engineering practicability are improved.

The invention provides a dynamic peak regulation method of a thermal power plant using high-temperature heat energy storage, and belongs to the technical field of dynamic peak regulation of thermal power plants. The current heat storage capacity and the residual heat storage capacity of a heat storage tank are calculated through an intelligent peak regulation decision maker, the optimal steam extraction proportion and the heat storage release strategy are determined based on a dynamic peak regulation game model, and intermediate-stage steam of a steam turbine is extracted to the heat storage tank for heat storage according to the optimal steam extraction proportion in the power grid load valley period. The heat storage tank heat release system is started in the peak load period of the power grid to increase the power generation power to achieve power compensation, the optimal steam extraction proportion is adjusted in real time through an intelligent peak regulation decision maker, and a heat storage efficiency monitoring system is established to monitor the operation state of the heat storage tank in real time and optimize operation parameters. The technical problem that the optimal steam extraction proportion cannot be determined in real time is solved.

The invention discloses a pollution emission monitoring and early warning method based on physical prior and information value driving, and relates to the technical field of environment monitoring and pollution control, and the method comprises the following steps: collecting production side certificate data, executing time alignment and self-calibration drift removal, and constructing a standardized multi-source database; judging whether a heat island effect occurs or not, and if yes, collecting volume heat capacity, solar irradiance and heat island intensity data of a single building; according to the method, the heat island effect is introduced to judge and generate a new time-space diagram network, real-time heat island intensity changes caused by building materials and solar irradiation can be dynamically captured, and building wind field and plume data are accurately corrected. The problem that a conventional method cannot adapt to irregular fluctuation of vertical wind shear caused by the real-time heat island effect is solved, the pollution plume low-altitude compression phenomenon caused by the heat island effect is effectively corrected, and the monitoring direction is always aligned with the real diffusion path of pollutants.

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 relates to the technical field of control of intelligent implanted medical instruments, and discloses an aneurysm neck sealing system and a control method. Digital subtraction angiography is adopted to measure the neck diameter in multiple directions, an average value is calculated, the target neck sealing radius is set by combining the size of an embolism device, and it is ensured that instrument type selection is scientific and reasonable. The triggering temperature and recovery kinetics of the shape memory polymer are determined through a constant-temperature experiment, an actual expansion radius model is established, and quantitative control over the heating process is ensured. A heat balance model is established by using the heat capacity and cooling curve of the embolism device, and temperature rise and heating power expressions are deduced, so that fine regulation and control of temperature and power are realized. And natural cooling is performed after heating to a set terminal point, the plugging effect is evaluated in real time through iconography measurement, and compensation heating or model replacement is performed according to actual differences to ensure the plugging effect.

The invention relates to the technical field of electric power energy storage control, and discloses a multi-dimensional state driven energy storage cold energy storage optimization control system and method, and the method comprises the steps: collecting the multi-dimensional state parameters of an energy storage system and the price of an electric power market, and judging a current operation time period mode; in the active cold storage mode, target cold storage capacity is determined according to the maximum allowable heat capacity buffer margin, and cold pre-storage is completed; in the cold capacity release mode, cold capacity-heat capacity collaborative multi-stage judgment is executed, the minimum critical mass flow rate meeting the heat exchange requirement is reversely solved, the minimum critical mass flow rate is mapped to a discrete control gear of the liquid cooling circulating pump to generate a final rotating speed instruction, and meanwhile closed-loop flow correction is executed; according to the method, the minimum critical mass flow rate is accurately solved, the rotating speed of the liquid cooling circulating pump is remarkably reduced while battery thermal inertia buffering is utilized, parasitic energy consumption is greatly reduced through the cubic relation between pumping power consumption and the rotating speed, and the problem that electric charge income is counteracted due to excessive pumping is solved.

The invention discloses a self-adaptive control method and device for the multi-source temperature of a case fan. The self-adaptive control method comprises the steps that the temperature value of each temperature monitoring point at the current moment is collected in real time based on temperature sensors which are arranged in a plurality of typical heat source distribution areas in a case in advance; fusing the temperature values of the temperature monitoring points to obtain a comprehensive temperature value of the temperature monitoring points after weighted fusion; predicting a predicted temperature at a future time point of the current moment according to the current heating load, the fan air volume and the case heat capacity; the deviation between the predicted temperature and the preset target temperature serves as input, and a control signal of the fan is calculated through a self-adaptive PID controller; correcting the control signal based on the real-time noise sensing signal of the case to obtain a corrected control signal; and according to the air flow topology matrix and the final control signal, the rotating speed of each fan in the case is optimally distributed through a fan rotating speed distribution formula.

The invention relates to the technical field of gas heat conductivity coefficient measurement, and provides a gas heat conductivity coefficient measurement data processing method, device and equipment and a medium, and the method comprises the following steps: obtaining hot-wire temperature rise experiment data of hot-wire temperature rise along with time change measured by a transient hot-wire method experiment; the hot line temperature rise experiment data are corrected through a theoretical correction model, corrected theoretical temperature rise data are obtained, and the theoretical correction model is derived based on a physical model considering the hot line limited heat capacity and the radiation heat exchange effect; and determining the heat conductivity coefficient of the detected gas based on the corrected theoretical temperature rise data. According to the technical scheme, a test data processing correction method is provided, and the accuracy of a test result is improved.

Method for storing and / or transporting temperature-sensitive materials. In one embodiment, the method involves predicting whether a given passive thermal shipping system will maintain a payload within a desired temperature range over its entire transport / delivery route. To this end, thermal capacitance data is compiled for the shipping system at a plurality of temperatures spanning a broad range of potential ambient temperatures to which the shipping system may be exposed. In addition, forecasted ambient temperature data is obtained for a plurality of time intervals spanning the transport / delivery route. An effective ambient temperature, based on the forecasted ambient temperature, as well as rolling and cumulative averages, is then determined at each of the various time intervals. Using the effective ambient temperature, the thermal capacitance is determined from the compiled data and is compared to the cumulative absorbed energy. The shipping system fails when the cumulative absorbed energy exceeds the thermal capacitance.