5320 results about "Heating power" patented technology

The invention discloses a hydrogen fuel cell vehicle ultralow temperature cold start thermal management system, and relates to the technical field of hydrogen fuel cell vehicle thermal management, the hydrogen fuel cell vehicle ultralow temperature cold start thermal management system comprises a thermal management platform, the thermal management platform is in communication connection with the following modules: a temperature monitoring and predicting module, and the temperature prediction module is used for monitoring the temperature of key parts in the hydrogen fuel cell by using the temperature sensor and predicting the change trend of the temperature in the fuel cell in combination with the temperature prediction model. By utilizing the high-precision temperature sensor and the long-short-term memory network model, the internal temperature change trend of the fuel cell is pre-judged in advance, the heating power is dynamically adjusted, it is ensured that the temperature of the cell is rapidly increased to the proper working temperature in the ultralow-temperature environment, starting difficulty caused by local supercooling is avoided, the low-temperature starting time is remarkably shortened, and the starting efficiency is improved. The reliability and the stability of the system in an extreme environment are improved, and the normal operation of the hydrogen fuel cell vehicle in a cold region is guaranteed.

The invention relates to the technical field of drying process control, in particular to a medicinal material drying process control method and system for traditional Chinese medicinal material processing, and the method comprises the following steps: calling a temperature set value, a humidity discharge rate and heating power based on the operation condition of a drying chamber, and analyzing the difference value between the medicinal material moisture content and the target moisture content; combining the medicinal material density with the water content change rate, matching the coupling relationship between the humidity discharge rate and the heating power, and establishing an initial drying process control value; according to the method, through collaborative analysis of the temperature set value, the humidity discharge rate and the heating power, accurate matching of initial drying conditions and material characteristics is achieved, excessive drying is avoided, linkage of the air speed and the humidity discharge rate is optimized, humidity distribution uniformity and environmental stability are improved, the evaporation rate is dynamically matched, and the drying efficiency is improved. Drying uniformity and quality stability are improved, termination control parameters are optimized in real time, drying time is shortened, energy consumption is reduced, and active ingredients and physical characteristics of medicinal materials are reserved to the maximum extent.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention discloses a precise control system and method for an intelligent heating digestion furnace, relates to the field of intelligent control, and adopts an artificial intelligence technology based on deep learning to carry out real-time monitoring and data analysis on in-furnace temperature and heating power of the digestion furnace. According to the method, the time sequence change trend of the offset of the in-furnace temperature value relative to the set temperature value and the time sequence change trend of the heating power are extracted, and then the dynamic response mode of the in-furnace temperature relative to the heating power is excavated by performing fine-grained time sequence interaction analysis on the in-furnace temperature value and the time sequence change trend, so that intelligent regulation and control on the heating power are realized. Through the mode, the temperature control stability of the heating digestion furnace can be effectively improved, the energy consumption is reduced, and the efficiency and the reliability of the digestion process are improved.

The invention relates to the technical field of self-adaptive control, and discloses a self-adaptive temperature control method and system for an automobile heater, and the method comprises the steps: calculating the real-time temperature deviation and temperature change rate of an actuator according to multi-dimensional temperature data; thermal inertia dynamic analysis is carried out on the historical heating power and temperature response relation through a preset thermal inertia compensation function, and a thermal inertia index of the actuator is obtained; the real-time temperature deviation, the temperature change rate and the thermal inertia index are jointly input into a preset fuzzy PID controller, and a preliminary power adjustment instruction of an actuator is output; analyzing the temperature change trend of the actuator in the time period, and performing prospective correction on the initial power adjustment instruction based on the temperature change trend; and controlling an actuator to execute heating operation according to the temperature control instruction, collecting real-time feedback temperature after the heating operation is executed, and performing joint online correction on parameters of the thermal inertia compensation function and a prediction coefficient of the time sequence model according to the real-time feedback temperature. According to the invention, the accuracy of self-adaptive temperature control of the heater can be improved.

The invention relates to the technical field of non-electrical variable control, in particular to a digital visual control system and method for a glass bottle production line. The method comprises the following steps: acquiring digital image data of molten glass; molten glass bubble distribution is identified based on the molten glass digital image data, the quality state of the molten glass is evaluated, and micro-defect distribution map data is generated; collecting the internal temperature of the smelting furnace through a temperature sensor array, and constructing a three-dimensional temperature field distribution model of the smelting furnace; and predictively optimizing and adjusting the heating control parameters of the smelting furnace based on the three-dimensional temperature field distribution model of the smelting furnace, and generating a regional heating power distribution strategy. According to the invention, through fusion of a plurality of advanced technologies, accurate sensing and whole-process linkage control of the molten glass state are realized, intelligence, quality uniformity and process visualization level of glass bottle production are significantly improved, and glass manufacturing is promoted to develop towards the intelligent and efficient direction.

The invention provides a multi-mode control method and system for intelligent variable-frequency cooking equipment, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining a food material image and weight data through an image collection device, extracting feature information through a deep learning processor based on a pre-trained food material feature recognition model, and generating a feature data packet containing food material type, weight and volume information. The controller matches initial cooking mode parameters according to the data packet, collects real-time operation data such as temperature, pressure, heating power and stirring motor rotating speed in the cavity, and calculates power-temperature and motor rotating speed-torque response curves. And the controller generates a frequency conversion control instruction according to the response curve and a preset multi-mode switching judgment rule, and controls the heating unit and the stirring motor to perform cooperative adjustment. In the cooking process, real-time operation data are continuously collected, multi-mode self-adaptive switching is carried out, and the frequency conversion control instruction is optimized until cooking is completed.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention discloses a control system and method of an intelligent screw grouting extruder, and belongs to the technical field of wire and cable production, and the control system comprises the following steps: a flow control module monitors the material flow in real time, and adjusts the feeding amount through fluctuation detection and frequency analysis; the temperature control module monitors and predicts temperature deviation through a temperature sensor and adjusts heating power; the rotating speed control module initializes the rotating speed of the screw according to material characteristics and monitors the load of the screw through the torque sensor so as to dynamically adjust the rotating speed of the screw; the pressure adjusting module monitors and adjusts the pressure of the inner cavity of the extruder in real time, and adjusts the distance between the die orifice and the screw rod by calculating the pressure deviation; the quality detection module detects the physical and visual quality of the product on line through a laser range finder and an image recognition technology, and carries out comprehensive quality evaluation; according to the invention, the problem of how to dynamically adjust the running state of the extruder according to real-time parameters such as temperature, flow, pressure and rotating speed is solved, so that the consistency of the thickness of a cable sheath layer and the product quality is ensured.

The invention provides a disinfection equipment production and processing quality control method and system based on intelligent analysis. Precise control of the drying process is achieved through three-stage collaborative optimization, firstly, hot air distribution parameters and historical energy consumption data of the drying process are integrated, a hot air uniformity and energy efficiency collaborative optimization model is constructed, and a dynamic target parameter system is established. Secondly, temperature field distribution data are collected in real time in the equipment operation process, multi-dimensional dynamic correlation analysis is conducted on the temperature field distribution data and energy consumption data of the heat supply device, heat field abnormal fluctuation and energy consumption correlation characteristics are recognized through a machine learning algorithm, and finally a heating power dynamic adjusting instruction is generated based on an analysis result; and by adopting the synergistic effect of a differential control algorithm and a frequency conversion regulation technology, process requirements are accurately converted into power supply frequency regulation and control signals, and millisecond-level dynamic response and self-adaptive regulation of the heating power are realized. According to the technical scheme provided by the invention, the production and processing quality control efficiency and accuracy of the disinfection equipment can be improved.

The invention discloses an electric heating cooker uniform temperature control method and system for gradient temperature difference regulation and control, and relates to the technical field of temperature control, the method comprises the following steps: dividing an internal heating area of a target electric heating cooker, arranging temperature sensors, and acquiring meal temperature data of N subareas in real time through a subarea temperature sensor array; constructing a gradient temperature difference model based on meal heat conduction information and food material characteristic information, and performing deviation calculation on the meal temperature data of the N sub-regions based on the gradient temperature difference model to obtain temperature deviation data of the N sub-regions; and a PID controller is adopted to perform heating power analysis on the temperature deviation data of the N sub-regions, and the heating power adjustment amount of the N sub-regions is determined to control the target electric heating cooker to perform uniform-temperature heating control. The technical effects of realizing uniform temperature control of the electric heating cooker, effectively solving the problem of local overheating or half-cooking caused by a traditional heating mode, ensuring uniform heating of meals and improving the heating quality are achieved.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention relates to the technical field of production process control, in particular to a PVC developing functional granule production control method and system, and the method comprises the steps: calling a spectrometer to capture spectral data emitted from PVC granules in real time based on the starting of a production line, analyzing the intensity and wavelength distribution in the spectral data in real time, and generating spectral data characteristic values; and comparing and analyzing the deviation of historical data and real-time data through the spectral data characteristic value, and generating a deviation dynamic prediction analysis result. According to the invention, by monitoring and analyzing the spectral data of the PVC granules in real time, the response speed and precision of a production line are improved, and the spectral data are compared in real time, so that production deviations such as light source intensity and reaction temperature can be quickly found and adjusted, material waste is reduced, the output quality is improved, and continuous data analysis helps to adjust chemical agent feeding and heating power; product consistency is guaranteed, automatic fault diagnosis is achieved, production parameters are adjusted in time, the service life of equipment is effectively prolonged, shutdown is reduced, and cost benefits are increased.

The invention discloses a nodular cast iron pipe continuous annealing furnace temperature distribution optimization control system and method, and belongs to the technical field of temperature control, and the system comprises a temperature detection module, a data processing module, a control module and a fault diagnosis module; the temperature detection module is used for collecting temperature data in the nodular cast iron pipe annealing furnace in real time; the data processing module is used for preprocessing and analyzing the collected temperature data and constructing a temperature distribution model; the control module comprises a temperature optimization unit and an energy optimization unit; the temperature optimization unit is used for calculating the temperature adjustment amount according to the real-time temperature distribution condition in the furnace in the length direction, the radial direction and the circumferential direction, and formulating a partition heating and local compensation strategy; the energy optimization unit is used for dynamically adjusting the heating power according to the change of the production takt, and generating a dynamic heating strategy; the fault diagnosis module is used for monitoring the operation state of the nodular cast iron pipe annealing furnace in real time and conducting fault detection.

The invention provides a range hood control method and system based on the Internet of Things, and relates to the technical field of artificial intelligence and the Internet of Things, and the method comprises the steps: obtaining multi-source layered environment characteristic data of heating power, oil smoke and airflow through constructing a layered data collection network, and carrying out the processing of edge calculation and a cloud server, drawing force required by each area is calculated, a self-adaptive fan control curve is generated, and then an initial control parameter set containing a main fan rotating speed value, an auxiliary fan rotating speed value and an air deflector angle value is generated; the method comprises the following steps: constructing a vortex flow prediction model based on a vortex tensor and a velocity divergence by using a cloud edge collaborative architecture, predicting an airflow movement track, calculating a real-time airflow deviation degree, dynamically adjusting an initial control parameter group to generate a target control parameter group, and finally continuously optimizing the prediction model through a deep reinforcement learning network.

The invention relates to the field of intelligent control, and provides an automatic control method and system of a fly ash melting processing system, which adopts an image analysis and data processing algorithm based on artificial intelligence to carry out state analysis of a molten liquid state image and time sequence association of melting temperature data in a melting furnace. Therefore, the heating power recommendation decoding value of the next time point is intelligently obtained according to the prompt enhancement joint representation between the molten liquid state characteristic and the melting temperature time sequence correlation characteristic. Thus, compared with a traditional fixed temperature set value, the system can analyze the state of the melting process in real time, dynamically and accurately adjust the heating power and ensure that the temperature is always kept within the optimal range, so that temperature fluctuation is reduced, the stability of the melting process is kept, and intelligent monitoring and control over the fly ash melting process are achieved.

The invention relates to the technical field of alloy die-casting, and particularly discloses an alloy die-casting temperature field control method and system capable of reducing hot cracks, temperature gradients of different areas of a die are adjusted in real time according to flowing and solidification characteristics of alloy liquid, so that the surface temperature distribution of the die is matched with the solidification rate of the alloy liquid, and the alloy die-casting temperature field is controlled. Utilizing a machine learning algorithm to predict a risk area where hot cracks are generated on the basis of historical data and real-time monitoring data, and dynamically adjusting the flow velocity of a cooling system, the heating power of a die or die-casting process parameters according to a prediction result; through dynamic temperature gradient control, intelligent prediction and feedback control, a cooling strategy, a stress release mechanism and a real-time monitoring and optimizing process, thermal stress concentration can be effectively reduced, the possibility of generation of thermal cracks is reduced, the yield of castings is improved, the cooling time of the castings can be shortened through the cooling strategy, and therefore the production efficiency is improved, and the production cost is reduced. And reworking and scrapping of castings are reduced.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

The invention provides an immersed liquid cooling device, a control method and a system, and relates to the technical field of heat dissipation, the temperature of an inlet and an outlet of a chip-level evaporator at a chip and the highest temperature of the chip are obtained in real time through a preset sensor, and the corresponding relation between the inlet and outlet temperature difference and a preset threshold value is determined; if the outlet temperature difference is smaller than the preset threshold value, the valve opening degree of a pipeline valve corresponding to the outlet temperature difference is controlled to be reduced along with increasing of the outlet temperature difference according to a preset adjusting method, and the valve opening degree is determined in real time; when the valve opening degree reaches the minimum value and the outlet temperature is still smaller than the preset threshold value, the target fan rotating speed is determined through a first preset formula according to the chip highest temperature and the chip heating power; and if the outlet temperature difference is equal to the preset threshold value, the target fan rotating speed is determined according to the chip highest temperature and the chip heating power. According to the invention, the problem that an immersed liquid cooling device, a control method and a system which are controllable in cooling object and cooling effect and wide in application range are lacked in the prior art is solved.

The invention provides a cracking equipment data processing method combined with digital twinning, which comprises the following steps: acquiring initial state data and environmental data of a carbon fiber composite material in a cracking furnace, and performing time synchronization processing to obtain a time consistency data set; extracting pyrolysis characteristic parameters of interaction between the carbon fiber bundle and the matrix from the spatial registration data set, and calculating a reaction rate and pyrolysis behavior change of a microscopic scale of a fiber matrix interface to obtain a pyrolysis characteristic parameter set; if a deviation exists between the prediction result and a preset target carbon powder quality index, determining a heating power distribution adjustment amount and a change trend of an air inlet valve opening degree based on a deviation degree, and obtaining a control parameter adjustment set of the cracking furnace; and carrying out constraint and smoothing processing on the control parameter adjustment set to obtain a control set value, formatting the control set value into a closed-loop feedback instruction, and updating the closed-loop feedback instruction to a cracking furnace control system to obtain adjusted pyrolysis operation state data.

The invention discloses a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things, particularly relates to the technical field of hydrogen fuel engine control, and is used for solving the problems of cooling response lag and action conflict caused by disconnection of control logic and a real-time network state of an existing temperature control system in a dynamic network environment. Electronic thermostat temperature data and network state data are collected in real time to generate a dynamic synchronization coefficient, network topology toughness evaluation and causal influence analysis are combined to predict instruction conflict areas and divide priorities, and gradient correction is performed on heating power and valve opening based on a target deviation compensation value. And a correction instruction is issued through the distributed control unit and a communication reliability weight is synchronously updated, so that dynamic coordination of a network state and a temperature control parameter is realized, and efficient and stable operation of the hydrogen fuel engine under a complex working condition is ensured.

The invention relates to the technical field of intelligent control, and discloses a self-adaptive temperature curve control system for high-temperature alloy vacuum casting, which comprises a sensing module, a control module and a control module, and is characterized in that the sensing module acquires multi-point temperature information and preprocesses the information to obtain temperature distribution in a furnace; the acquisition module obtains vacuum environment parameters. The simulation module establishes a three-dimensional dynamic coupling model based on heat conduction, radiation, convection and phase change heat effect, and obtains a real-time temperature evolution prediction result. The generation module obtains a target temperature curve of the corresponding stage. The prediction control module collects a real-time temperature evolution prediction result and a target temperature curve, deviation calculation and trend analysis are carried out, and a heating power and cooling flow control instruction is generated. And collecting product feedback errors and carrying out self-learning correction on prediction model parameters to obtain an optimized control output result. The control and optimization of the high-temperature alloy vacuum casting temperature field are realized, and the structure uniformity of castings and the quality stability of finished products are improved.

The invention relates to the technical field of deep learning, and discloses an earphone mold injection molding parameter optimization method, device and equipment, and the method comprises the steps: collecting temperature distribution data and mold cavity pressure data in an earphone mold cavity, and carrying out earphone shell injection molding dynamic analysis to obtain an earphone shell material flow curve; carrying out topological characteristic analysis on the earphone shell material flow curve, and constructing an earphone shell injection molding process parameter model; based on the earphone shell injection molding process parameter model, the mold cavity temperature and the mold cavity pressure serve as state variables, the heating power and the injection pressure serve as control variables, and an earphone shell injection molding optimization controller is constructed; the earphone shell injection molding optimization controller is connected with the injection molding machine control system, the heating power and the injection pressure of the earphone mold are adjusted in real time, the optimal technological parameters of the earphone mold are solved, a real-time feedback and parameter fine adjustment mechanism is adopted, the injection molding machine control system can quickly respond to technological fluctuation, and the consistency of product quality is guaranteed.

The invention relates to the field of energy allocation, and discloses a regional building group multi-energy complementary cooperative regulation and control method, which comprises the following steps of: acquisition processing: acquiring multi-energy data in a regional building group by utilizing multiple sensors, and preprocessing to obtain processed data; load prediction: inputting the processed data into an LSTM-Attention mixed deep learning model, performing fusion calculation in combination with a grey prediction model, and outputting a cold and heat power ultra-short-term load prediction result; and cooperative regulation and control: based on the load prediction result and the real-time operation parameters of the building air conditioning system, the electric vehicle and the multi-element energy storage system of the building group. Multi-energy data in a regional building group are collected and preprocessed through multiple sensors, an LSTM-Attention mixed deep learning model and a grey prediction model are fused to calculate a load prediction result, an interactive regulation and control model is constructed based on load prediction and real-time equipment parameters, and a collaborative regulation and control strategy is formulated and executed.

The invention provides a high-voltage switch cabinet anti-condensation method based on simulation optimization and a storage medium. A three-dimensional physical structure model of the high-voltage switch cabinet is established in simulation software, material attributes and boundary conditions of all parts are set, steady-state or transient simulation calculation is carried out, and distribution of a temperature field, a humidity field and a flow velocity field in the cabinet is obtained. And on the basis of a simulation result, constructing a dew point distribution matrix, identifying condensation risk areas, and extracting geometric center, volume and temperature and humidity characteristics of each risk area. And the arrangement point and the power level of the heater are determined by calculating the heating power required by each condensation risk area, so that the anti-condensation design of the high-voltage switch cabinet is realized. According to the method, the condensation risk area in the cabinet can be accurately identified, the configuration of the heating power is optimized, and the balance and high efficiency of the anti-condensation effect are ensured. According to the invention, the precision and reliability of the anti-condensation design are improved, the problem of excessive heating or insufficient heating is avoided, and the safe and stable operation of the high-voltage switch cabinet in a complex environment is ensured.

The invention provides a cooking utensil superheated steam system and a temperature control method thereof, and relates to the technical field of steam cooking. The superheated steam system of the cooking utensil comprises the cooking utensil, a steam module, an ultrasonic heating module, a temperature monitoring module, a cooking data acquisition module, a data analysis module, a model prediction module, a display module, a dynamic adjustment heating strategy, a waste heat recovery module and a preheating box. Ultrasonic vibration is used for assisting heating, water molecules rapidly vibrate in a microwave field to generate heat, the waste heat recovery module captures heat generated in the cooking process through a heat exchanger and is used for preheating the next batch of food, the energy utilization efficiency is remarkably improved, energy waste and carbon emission are reduced, the heating strategy is dynamically adjusted, and the energy utilization rate is increased. According to the real-time sensor data and the prediction model, the heating power, the steam flow and the heating time parameters are automatically optimized, unnecessary energy consumption is avoided, and accurate temperature control and energy consumption optimization are achieved.

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

The invention relates to the technical field of automobile cup holder temperature control methods, and discloses an automobile cup holder temperature regulation control method based on a microcontroller. According to the method, real-time temperature data and environment parameters of a target container are collected, and an initial temperature parameter set containing a container surface temperature distribution curve and an environment heat exchange coefficient sequence is generated; then, establishing a first adjusting model containing a mapping relation between surface temperature change and refrigeration power according to the container surface temperature distribution curve, and establishing a second adjusting model containing environment heat interference and a heating power compensation rule according to the environment heat exchange coefficient sequence; during cup stand temperature control, based on real-time temperature difference fluctuation and container material heat conduction characteristic data, a target model is activated to generate a dynamic power instruction, finally, the instruction is input into a semiconductor temperature control unit of the cup stand, and the current gradient of a refrigeration sheet or the voltage distribution of a heating film is adjusted. The temperature of the automobile cup holder can be accurately and intelligently adjusted, and the user experience and the comfort level of an automobile cabin are improved.

The invention discloses a temperature and humidity adaptive control system and method for drying flower traditional Chinese medicinal materials, and the system comprises a humidity gradient monitoring module which is used for deploying a 12-channel capacitive humidity sensor spiral array and a laser radar, generating a three-dimensional humidity field thermodynamic diagram in combination with a Kriging interpolation method, calculating a humidity gradient change rate in real time, and carrying out the early warning of local abnormality; the heat-humidity ratio regulation and control module is used for integrating humidity gradient monitoring data and a real-time result of the evaporation latent heat calculation module based on a fuzzy PID algorithm, dynamically adjusting the heating power and the rotating speed of a dehumidification fan, and meanwhile, integrating a PVDF piezoelectric film sensor to monitor petal stress; the evaporation latent heat calculation module is used for predicting a future evaporation rate by combining an LSTM neural network based on an edge calculation unit and providing key parameters for the energy supply matching module; the energy supply matching module is used for outputting a combined control instruction of the heating power and the air door opening degree; and the air volume balance optimization module is used for dynamically adjusting air volume distribution according to the humidity gradient monitoring data.

The present disclosure provides an energy storage energy system, which converts energy into cold power and / or heat power by utilizing an energy storage mode and stores the cold power and / or heat quantity. The stored cold power is used for a technology and a cold source of an existing freezing or air conditioner refrigeration industry; the stored heat power is used for a technology and heat source of an existing heating or heat supply industry. The energy storage energy system of the present disclosure adopts a large amount of wind power and photovoltaic power generation electric energy. The heat storage is applied to a current heating or heat supply market for sales which is mainly coal-fired. Cold storage subverts the existing refrigerant compression cycle technology, replaces refrigeration compressor freezing and air conditioning refrigeration systems; heat storage subverts boiler heat supply technology, replaces fossil fuel boiler heating and heat supply systems.