2623 results about "Heating power" patented technology

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 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 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 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 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 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 invention provides a dynamic control method and system for hot-pressing plugging process parameters of a network cable end electronic wire, which realizes real-time optimization of the process parameters through a dynamic compensation technology, dynamically calculates an initial heating temperature and a riveting depth based on a wire diameter, an insulating layer thickness, a copper alloy hardness and an environment temperature, and realizes the hot-pressing plugging process parameters of the network cable end electronic wire. A compensation formula is combined with a conductor diameter compensation coefficient, a hardness compensation coefficient and an environment temperature compensation coefficient, heating temperature fluctuation and riveting pressure displacement deviation are monitored in real time, Z-axis displacement is adjusted by controlling a servo motor, a temperature-pressure coupling compensation model is established, and heating power and riveting pressure are dynamically corrected. And the online quality detection module corrects subsequent batch parameters in a self-adaptive manner at a learning rate eta = 0.2 through continuous five batches of data, so that the riveting depth precision and the connection strength are ensured to reach the standard, intelligent dynamic control of the hot-pressing plugging process is realized, and the product percent of pass and the production efficiency are improved.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The multi-energy complementary heat supply system comprises an external basic heat source, a middle-grade synergistic heat source, a high-grade energy storage heat source, an intelligent heating power mixing center and a central controller, and the intelligent heating power mixing center is used for heating heat source equipment of all levels and operation instructions of associated assemblies according to the operation instructions of the heat source equipment of all the levels and the operation instructions of the associated assemblies. Heat energy of different grades is mixed and allocated, and heat loads meeting the grade requirements of heat consumers are output to the heat consumers; the central controller is used for establishing a multi-target optimization model taking the minimum total operation cost of the system as a core target based on the electricity price prediction data and the different-grade thermal load prediction data, solving the multi-target optimization model and generating a system operation strategy under the condition that preset basic guarantee logic, economic operation logic and peak response and power grid interaction logic are met; and the generated system operation strategy is converted into an operation instruction for the heat source equipment of each level and the associated assembly, so that the reliability and the adaptability of operation control of the heat supply system are effectively improved.

The invention discloses an intelligent control system for a high-precision emulsifying pot, relates to the field of intelligent control, and is used for collecting multi-dimensional data such as temperature field distribution, material rheological characteristics, liquid drop behaviors and mechanical states in real time through distributed sensors and monitoring the emulsifying process in real time. The system evaluates the phase stability risk level by adopting a multi-source data fusion technology, and dynamically adjusts the process parameters such as the stirring rotating speed, the emulsifier flow and the heating power according to the risk level. For a high-risk state, by analyzing the correlation between a temperature field and torque fluctuation, an abnormal reason is accurately identified, and a targeted regulation and control measure is triggered. A liquid drop coalescence monitoring mechanism is introduced into the system, and when liquid drop coalescence exceeds the standard, temperature compensation is automatically enhanced. The system has a self-learning capability, dynamically optimizes a risk judgment threshold according to historical data, and improves the control precision.

The invention discloses a regulation and control system and method based on heat energy of an energy storage cabinet, and belongs to the technical field of intelligent heat management. Temperature data in the energy storage cabinet are collected, a temperature distribution matrix is constructed, and a thermal gradient vector and an overheating risk area are extracted based on a difference algorithm; further establishing a thermal power load mapping model, and dynamically generating an air circulation path optimization diagram; the opening angle of a flow deflector and the rotating speed of a fan are adjusted in combination with an optimized path, and self-adaptive regulation and control of a thermal field in the cabinet body are achieved; meanwhile, a machine learning model is trained by utilizing historical operation data, an optimal regulation and control strategy is output, and an execution layer is driven to realize collaborative response of a fan and a flow deflector; the method has the advantages of high regulation and control precision, high response speed, low energy consumption and the like, and is suitable for heat management requirements of various types of energy storage cabinets.

The invention relates to the technical field of thermal field modeling and control, and particularly provides a molecular beam epitaxy substrate temperature field control method and system based on artificial intelligence, and the method comprises the following steps: obtaining the all-region temperature data of the surface of a substrate, and the heating power and environment parameters of each region of a heating cavity; inputting the heating power and the environmental parameters into the temperature field model, and outputting a temperature distribution matrix and temperature gradient data of the surface of the substrate; predicting temperature prediction data according to the temperature distribution matrix, the heating power and the sequence data of the environmental parameters in the historical time period; and inputting a state vector formed by the temperature distribution matrix, the temperature gradient data, the heating power data and the temperature prediction data into a deep reinforcement learning model, and then outputting the heating power adjusting quantity of each area to control a temperature field. According to the invention, the problems of insufficient prediction precision and real-time performance of the substrate temperature in the prior art are solved, the influence of environmental factors and heating power on the substrate temperature is considered, and the temperature field control capability is improved.

The invention relates to the technical field of intelligent sintering, and discloses a rare earth magnet intelligent sintering production line and a control system, and the system comprises a temperature data collection module, a temperature gradient evaluation module, a temperature change rate fitting module, a heating power adjustment module, a directional heat energy compensation module and an optimized heating instruction generation module. Temperature distribution information in the sintering furnace is obtained; evaluating real-time temperature gradients of different positions in the sintering furnace, and comparing the real-time temperature gradients with a preset gradient threshold value to identify a temperature abnormal region in the sintering furnace; fitting the temperature change rate of the temperature abnormal area; the material phase change critical parameters of the rare earth magnet in the current sintering stage are combined to adjust the heating power of the temperature abnormal area; performing directional heat energy compensation on the temperature abnormal region to obtain a power compensation amount; superposing the power compensation amount and the reference power of the sintering furnace to generate an optimized heating instruction; the intelligent sintering control efficiency of the rare earth magnet can be improved.

The invention relates to the technical field of industrial production control, in particular to a polyurethane raw material preheating control method and system. The method comprises the following steps: acquiring tank body temperature, environment temperature, batch temperature rise, tank body structure and raw material liquid data, constructing a thermal resistance-thermal capacitance series-parallel model to represent system thermal inertia characteristics, then identifying an initial value drift resistance section, a main temperature rise section and a disturbance absorption section in a segmented manner, and implementing power back-stepping based on segmented data, so as to obtain the thermal inertia characteristic of the system. Dynamically generating a heating power track; and a heating track is replanned in combination with real-time change data, so that precise modeling and self-adaptive control of a temperature rise process are realized, and the temperature control precision and energy efficiency response capability of the preheating system under different raw material batches and different working conditions are effectively improved.

The invention discloses an intelligent marinating regulation and control method for leisure old marinated claw snacks based on multi-source sensing, which comprises the following steps: acquiring environmental parameters and material parameters in a marinating process through a multi-source sensor, and analyzing texture change of old marinated claws by utilizing a collagen triple-helix structure denaturation kinetic model; a dynamic time warping smell recognition algorithm is adopted to recognize a smell change stage, a color change rule is analyzed through a Maillard reaction kinetics coupling model, multiple parameters are fused to establish an incidence matrix, and the heating power of marinating equipment, the marinating liquid circulation rate and the ventilation quantity are regulated and controlled according to the matrix. According to the method, multi-parameter collaborative monitoring and deep analysis are realized, the regulation and control accuracy and the automation level are improved, the defects that a traditional method is insufficient in key component change analysis and poor in smell recognition and multi-parameter fusion collaboration are overcome, the product quality stability is guaranteed, and the standardized production requirement is met.

The invention provides a data center calculation, electricity and heat collaborative optimization scheduling method and system, and relates to the technical field of comprehensive energy scheduling. According to the scheduling method and system, a collaborative scheduling model of computing power, electric power and thermal power is constructed, and a user side thermal demand response mechanism is introduced, so that mismatching of waste heat supply of a data center and user thermal demand in time and space is dynamically relieved, and the waste heat utilization rate and the overall energy efficiency of the system are remarkably improved; by establishing a joint optimization framework, a complex coupling relationship among computing power, electric power and heating power is accurately described and coordinated, so that the comprehensive operation cost of the data center is minimized on the premise of ensuring the service quality of the workload of the data center; and a large language model-assisted deep learning algorithm is further adopted to solve the scheduling model so as to cope with multiple challenges of workload fluctuation, electricity price change and heat demand uncertainty, and a self-adaptive, intelligent and interpretable scheduling decision is realized.

The invention discloses an efficient anti-freezing and anti-blocking integrated induction heating system and method for an oil and gas pipeline, belongs to the technical field of anti-freezing and anti-blocking of oil and gas pipelines, and aims at solving the problems that in the prior art, heating efficiency is low, energy consumption is huge, heating uniformity and adaptability are poor, the system integration degree is low, and the intelligent degree is insufficient. Comprising the steps of S1, induction heating unit arrangement and power management; s2, real-time temperature monitoring and data acquisition; s3, intelligent data analysis and precise heat management; the electromagnetic induction heating principle is adopted, the induction coil directly heats the pipeline and the internal medium, heat conduction loss and heat energy radiation loss of heating modes such as traditional resistance wires and heat tracing bands are reduced, the intelligent power management module can optimize power supply and power adjustment, and the self-adaptive environment and load adjustment strategy is combined, so that the self-adaptive load adjustment is achieved. The heating power can be automatically adjusted according to the environment temperature and the pipeline load, the anti-freezing and anti-blocking effect is guaranteed, meanwhile, energy consumption is remarkably reduced, and the burden of an electric power supply system is relieved.

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 discloses an intelligent temperature control wind turbine generator blade electric heating deicing system and method, and belongs to the technical field of wind power generation. The system comprises an electric heating film arranged on a blade, a thermal infrared imager and a plurality of sensors, the electric heating film, the thermal infrared imager and the plurality of sensors are all connected with an intelligent control unit; the intelligent control unit is connected with an unmanned aerial vehicle carrying a laser radar and is used for regularly scanning ice layer distribution on the surface of the blade; and the electric heating film adopts zone control. The method comprises the following steps: acquiring environmental parameters and blade state monitoring data, and starting a deicing system; the method comprises the following steps: collecting deicing data from a deicing system in real time and preprocessing the deicing data to obtain preprocessed deicing data; establishing an intelligent temperature control model based on the environmental parameters and the blade state monitoring data; and inputting the pre-processed deicing data into an intelligent temperature control model, calculating to obtain dynamic adjustment data of the heating power, and transmitting the dynamic adjustment data to a deicing system for deicing.

The invention relates to the technical field of glass hot working control, and discloses a rapid prototyping control system and a rapid prototyping control method for tempered glass production. Comprising a thermal coupling module, a temperature control decision module, a flow field solving module, a photoelastic stress analysis module, a quantum annealing optimization module and a time domain synchronous control module. According to the system, a three-dimensional thermal-stress field is constructed on the basis of physical properties and thermal boundaries of glass, heating power is predicted through reinforcement learning, flow field simulation and stress image analysis are combined, control parameter self-adaptive adjustment is achieved through quantum annealing optimization, beats of all subsystems are coordinated through a synchronization module, and an efficient closed-loop control structure is formed. By introducing the quantum annealing optimization module, parameter adjustment in the control system is optimized, the technical effect of improving the precision of complex control decisions is achieved, and the optimization speed and the decision quality of the system are improved.

The invention relates to a copper cable insulation layer extrusion molding method and system based on dynamic temperature control, and belongs to the technical field of copper cable production. The method comprises the steps that the surface temperature of a copper core before the copper core enters an extrusion die head and the melt temperature of a plurality of independent temperature control sections divided by the extrusion die head in the material advancing direction are obtained in real time; meanwhile, feedback adjusting quantity is calculated based on the temperature difference between the melt temperature and the preset target melt temperature, the feed-forward compensation quantity and the feedback adjusting quantity are superposed to obtain comprehensive adjusting quantity of the heating power of each section, and real-time closed-loop adjustment is conducted on the heating power of each section of the extrusion die head according to the comprehensive adjusting quantity; and the flow and the temperature of the cooling medium are adaptively adjusted according to the temperature gradient of the surface temperature along the axial direction, and non-uniform gradient cooling is carried out on the insulating layer. The copper cable insulation layer extrusion molding method and system based on dynamic temperature control at least solve the problems of poor thickness uniformity of an insulation layer, large internal residual stress and unstable adhesive force with a copper core in the prior art.

The invention discloses a real-time regulation and control method for a pouring temperature field of a concrete panel during a construction period. The real-time regulation and control method comprises the following steps: step 1, establishing a temperature-crystallization-crack multi-physics field coupling model; 2, outputting a regulation and control instruction according to the coupling model, and adjusting the temperature field distribution; 3, optimizing a temperature regulation and control strategy based on model iteration; 4, outputting a temperature regulation and control result and generating an intensity state evaluation report; according to the method, the strength of the concrete panel is analyzed through the multi-physical coupling model, the distribution proportion of the cooling water flow and the heating power is dynamically adjusted, and real-time control over the pouring temperature field is achieved; a temperature gradient model is constructed through physical parameters of concrete, ambient temperature fluctuation and an exponential decay function, and the gradient temperature of each level of the concrete panel is predicted and calculated according to the surface temperature of the concrete panel, so that the temperature difference between the levels is controlled, cracks in the concrete panel are reduced, and the quality of the concrete panel is improved. And the strength of the concrete panel is effectively improved.

The invention provides an electric kettle heating control method and system based on intelligent temperature control, and relates to the technical field of household appliances. A multi-dimensional temperature measurement module is introduced, a plurality of high-precision temperature sensors are distributed at different positions in an electric kettle, and temperature distribution information of the different positions in the kettle can be obtained in real time; and local temperature misjudgment caused by a single sensor is effectively avoided. Meanwhile, the water level detection module can accurately measure the real-time water volume. On the basis, the system can dynamically distribute contribution indexes of all temperature measurement point positions according to the real-time water quantity, the heating power is adjusted in real time in combination with a PID algorithm, the temperature control precision and stability are remarkably improved, temperature misjudgment is reduced, accurate control over the heating process is achieved, and it is ensured that the water temperature can accurately reach the target temperature set by a user.

The invention discloses a super-large-scale silicon single crystal growth method based on a high-temperature superconducting magnetic control technology, and relates to the technical field of silicon single crystal growth, and the method comprises the following steps: setting and calibrating an initial magnetic field intensity, and combining a gradient graphite felt heat conductivity coefficient function to optimize temperature field uniformity; a dynamic magnetic field regulation and control mechanism is introduced in the melt stage, melt convection is inhibited through flow velocity-magnetic field joint control, and oxygen concentration monitoring is synchronized; a multi-physics coupling simulation and multi-objective optimization algorithm is adopted in the crystal growth stage, the magnetic field intensity, the heating power and the crystal pulling parameters are adjusted in real time, and the oxygen content and the growth rate are balanced; and the tail stress is relieved through a gradient field reduction technology, and finally, the low-defect and high-uniformity silicon single crystal is obtained. Melt flow and temperature field uniformity are accurately regulated and controlled through a high-temperature superconducting magnetic control technology, dynamic monitoring and a multi-target optimization algorithm are combined, the growth quality of silicon single crystals is remarkably improved, the defect rate and energy consumption are reduced, and meanwhile efficient and stable production of large-size crystals is achieved.

The invention provides a tobacco leaf curing process control method and device based on reinforcement learning, and relates to the field of tobacco leaf manufacturing. The method comprises the steps that environment parameters and tobacco leaf physical and chemical indexes in a baking room are obtained, the environment parameters comprise temperature field distribution data, humidity data and gas concentration data, and the tobacco leaf physical and chemical indexes comprise water content data, morphological data and physiological activity data of tobacco leaves; constructing a multi-dimensional state vector based on the environmental parameters and the physical and chemical indexes of the tobacco leaves; processing the multi-dimensional state vector through a pre-trained hierarchical reinforcement learning model to generate a dynamic control parameter; and the heating power, the ventilation rate and the humidity compensation amount of the baking equipment are adjusted according to the dynamic control parameters. Through the technical scheme provided by the invention, the problem of insufficient adaptability of a traditional PID control method for baking tobacco leaves is solved, the adaptive capacity of the tobacco leaf baking control process is improved, and the tobacco leaf baking quality is improved.

The invention discloses a rotating wheel dehumidification system operation control method and system. The dehumidification system comprises a dehumidification rotating wheel, a treatment air circulation unit arranged on the treatment side of the dehumidification rotating wheel and a regeneration air circulation unit arranged on the regeneration side of the dehumidification rotating wheel. The regenerated air circulating unit comprises an air heat exchanger, an electric heater and a heat pump heater; the heat pump heater comprises an evaporator, a condenser and a compressor; the control method comprises the steps that the temperature of regeneration air entering the dehumidification rotating wheel is detected to obtain the regeneration temperature; according to the regeneration temperature and a preset regeneration temperature threshold value, the heating power of the electric heater is dynamically adjusted, and the heating power is larger than or equal to zero and smaller than or equal to rated power. According to the scheme, regeneration exhaust heat can be recycled to the maximum extent, the energy efficiency ratio of the system is remarkably increased, dependence on a high-energy-consumption electric heater is reduced, the operation cost is saved, the regeneration temperature is dynamically controlled, and stable operation under different working conditions is ensured.

The application discloses a processing method of gel candies, and belongs to the technical field of gel candies. The method comprises a control unit with multiple independent pouring units, each pouring unit comprising a pouring head and a temperature control module, and the following steps are performed: S1: initial sensing and parameter setting; S2: a staged dynamic pouring step; S201: a starting stage, in which pouring is started at P0 and the F0, and lasts for a predetermined starting time Δt1; S202: a stable stage, in which after Δt1 ends, a control mode based on real-time viscosity feedback is switched to, the heating power is adjusted to maintain the real-time viscosity μ of the material in a target range [μ min , μ max ], and pouring is performed at a basic pump pressure F base . Through the staged dynamic control, the application realizes the precision and high efficiency of the gel candy pouring process.

The invention discloses a method for inhibiting temperature fluctuation of an oriented silicon steel BAF furnace, which comprises the following steps: adding 0.005-0.01 wt% of Nb, and forming a nanoscale Nb (C, N) precipitate; a three-dimensional heat conduction model is established in a mode of linkage of infrared thermal imaging and electromagnetic induction heating in combination with a finite element method, and real-time monitoring and accurate control of a temperature field in the furnace are achieved. On the basis of a fuzzy PID hybrid control strategy, the optimal heating power and scanning path are predicted in combination with a convolutional neural network, and closed-loop control of local temperature compensation is achieved; a random forest model with the temperature gradient delta T, the Als content and the Nb precipitated phase density as input parameters is constructed, technological parameters are iteratively optimized in combination with a genetic algorithm, and dynamic self-adaptive adjustment of an annealing temperature curve is achieved. According to the method, uniform growth of Gaussian grains in the secondary recrystallization process is achieved, and the effects of reducing annealing temperature fluctuation, improving secondary recrystallization uniformity and optimizing grain orientation distribution are achieved.

The invention relates to a vertical baking oven control system for biscuit production, which comprises an intelligent scheduling module, the output end of the intelligent scheduling module is electrically connected with a multi-mode sensing module, and the output end of the multi-mode sensing module is electrically connected with a self-adaptive wind field control module. And the output end of the self-adaptive wind field control module is electrically connected with a thermodynamic compensation module. According to the application, the baking tray three-dimensional space optimization layout is realized through the intelligent scheduling module, a precise thermal field distribution model is constructed in combination with multi-modal sensing data, and the system adopts a self-adaptive wind field control and thermodynamic compensation technology to dynamically adjust the heating power and the airflow distribution, so that the baking uniformity is ensured; the digital twin central control module optimizes a control strategy through virtual simulation to realize intelligent iteration of process parameters, the intelligent early warning system predicts the drying quality and the equipment state based on machine learning and establishes a grading alarm mechanism, and the energy optimization module reduces energy consumption through a load balancing strategy.