6002results about "Temperatue control" patented technology

The invention relates to a temperature control method, device and equipment of semiconductor equipment and a storage medium. The method comprises the following steps: performing finite element mesh division and modal coordinate transformation on a plurality of temperature control areas of semiconductor equipment to obtain a temperature control area dynamic model; collecting measurement data of a temperature sensor and performing time-frequency domain transformation to generate a temperature prediction data sequence; gaussian mixture modeling is carried out to obtain prior distribution, and temperature parameter probability distribution is obtained; performing sub-domain division on the temperature control range to obtain a temperature inter-domain feature mapping matrix; and a temperature interval thermal coupling coefficient matrix is calculated based on the temperature control area dynamic model, a temperature control objective function is constructed in combination with the temperature prediction data sequence and the temperature inter-domain feature mapping matrix, and a temperature control quantity sequence is solved by adopting a model prediction control algorithm. According to the invention, the accuracy and dynamic response speed of temperature control are improved, and the stability of temperature control and the energy utilization efficiency are ensured.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention relates to the technical field of temperature equalization control, and discloses a temperature equalization control method and device for a liquid cooling over-charging system. The method comprises the following steps: carrying out grid division on the liquid cooling overcharge system, inputting thermophysical parameters, and obtaining a temperature field calculation matrix through finite difference time domain analysis; establishing a two-layer distributed control network of a station-level control layer and a pile-level control layer based on the temperature field calculation matrix; acquiring real-time temperature data of the two layers of distributed control networks, and performing temperature field mapping to obtain temperature field distribution data; performing linear prediction and nonlinear prediction calculation on the temperature field distribution data to obtain a temperature change trend and a temperature extreme point; the target temperature is calculated according to the temperature change trend and the temperature extreme point, the cascade PID controller is adopted to adjust the rotating speed of the cooling liquid pump, and the temperature balance control parameters are obtained, station-level and pile-level cooperative control is achieved, cooling resources among the multiple charging piles are reasonably distributed, and the overall cooling efficiency of the system is improved.

The invention relates to the technical field of industrial automation control, and discloses a high-precision and quick-response industrial thermostat temperature regulation system which comprises a temperature acquisition module, a fluctuation analysis module, a dynamic calibration module, a gradient optimization module, a threshold management module and a regulation execution module. The temperature acquisition module acquires real-time temperature and environment thermal inertia parameters, and sets a temperature adjustment interval; the fluctuation analysis module divides temperature nodes and generates fluctuation feature vectors; the dynamic calibration module extracts a temperature deviation index and establishes an adjustment compensation rule; the gradient optimization module identifies a temperature change grade and calculates a fluctuation density difference value; the threshold value management module deduces an optimal adjustment threshold value and generates a threshold value deviation sequence; and the adjustment execution module is integrated to form a temperature adjustment execution scheme. The system realizes deep analysis and dynamic compensation of temperature fluctuation through cooperation of multiple modules, improves the adjustment precision and the response speed, and is suitable for high-precision temperature control industrial scenes such as chemical engineering and mechanical manufacturing.

The invention relates to the technical field of industrial control systems, in particular to a PET extruder multi-zone intelligent temperature regulation and control system, which comprises a temperature field parameter acquisition module for acquiring the input power, the real-time temperature value, the temperature change rate value and the temperature difference value between adjacent temperature zones of an extruder to obtain a temperature zone associated state set; and summarizing and mapping the temperature zone association state set, and establishing temperature field response characteristics. According to the method, through correlation analysis of the temperature states of the multiple areas, independent adjustment of a single area is upgraded to dynamic optimization based on the thermodynamic coupling relation, and the coordination of temperature adjustment of the multiple areas is improved. Dynamic parameters required by temperature regulation and control can be calculated in real time in combination with the heat transfer rate, the temperature response delay time and the temperature response gain coefficient of the temperature interval instead of depending on a fixed set value, different operation working conditions are adapted, and the regulation and control flexibility is improved.

The invention discloses a pepper freezing temperature gradient dynamic adjusting method and system, particularly relates to the technical field of industrial freezing temperature control, and is used for solving the problem of temperature gradient misalignment caused by the fact that a static model does not sense dynamic changes of material thermodynamic parameters in real time in an existing freezing control method. Temperature distribution and gas flow rate data in the freezing process are collected in real time, a high-confidence-coefficient data subset of a low-flow-rate area is screened based on spatial correlation, the dynamic heat conduction coefficient of the material is identified online through a recursive algorithm, and the dynamic heat conduction coefficient of the material is identified through a double-threshold-value judgment mechanism of the energy release rate and the phase lag angle. Performing segmentation fitting to generate a stable parameter sequence; further dynamically adjusting a preset temperature gradient model through a nonlinear correction strategy to generate a self-adaptive gradient control curve, and finally driving heat exchange rate adjustment to realize closed-loop tracking of the actual temperature gradient on a target curve; the freezing uniformity and energy efficiency of the high-moisture material are remarkably improved, and the control precision caused by thermal response mismatch is improved.

The invention relates to the technical field of processing temperature control, and discloses a temperature control method and system for a hot processing process and a storage medium. The method comprises the following steps: collecting hot working whole process temperature data through multiple sensors, calculating a temperature change rate, an extreme point and a uniformity index, and obtaining key characteristic parameters; establishing a quantitative relation matrix of the process parameters and the temperature field by using an orthogonal test; calculating an optimal process parameter combination based on the matrix by using a rolling time domain optimization algorithm to generate a control instruction; and performing execution opportunity and duration prediction and correction on the instruction to form a precise target control instruction. The temperature control precision and stability in the hot working process of the large alloy steel component are improved, so that the product quality is improved, the energy consumption is reduced, and the production period is shortened.

The invention discloses a temperature control management method for an electric oven, and relates to the technical field of intelligent household appliance control, and the method comprises the steps: carrying out the offset correction of a pre-stored mode mapping table based on a calibration matrix, loading a target temperature interval and a three-dimensional temperature field partition parameter according to a cooking mode selected by a user, and triggering a dynamic learning mark; according to the dynamic learning mark, the furnace chamber temperature and the environment temperature change rate are collected in real time, the power compensation amount is calculated through a thermal inertia compensation model, and a partition power control matrix is generated in combination with the three-dimensional temperature field partition parameters; and distributing the power compensation amount and the partition power control matrix to the heating pipe according to a preset topology, dynamically adjusting power output, and implementing temperature early warning and no-load identification based on an acoustic emission spectrum entropy value. The temperature gradient change rate is calculated through the Fourier heat conduction law, the three-dimensional temperature field partition parameters are dynamically generated, and accurate control over the temperature field in the complex three-dimensional space is ensured so as to cope with environment changes and user demand changes.

The invention discloses a temperature control method and system for Chinese food cooking based on multi-modal representation recognition, and relates to the technical field of temperature control for Chinese food cooking, and the method comprises the steps: carrying out the data collection through a sensor, and carrying out the preprocessing; analyzing the food state change, monitoring the food state change in real time, and performing multi-dimensional feature fusion; capturing texture dynamic characteristics, monitoring a cooking image, introducing a feedback variable, and controlling and adjusting the temperature; environment variables are monitored, and preference setting is carried out; analyzing the big data and user historical data; according to the method, comprehensive monitoring of multi-dimensional data such as temperature, humidity and color is realized through fusion processing of a multi-modal sensor, and accuracy and real-time performance of cooking data are ensured; by combining multiple technologies such as multi-modal sensor data fusion, big data analysis, time and space attenuation modeling and user preference setting, the food state is dynamically analyzed, the cooking stage is accurately judged, the temperature control strategy is optimized, and the extensive control problem of a traditional method is avoided.

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 temperature measurement, in particular to a multi-zone temperature distribution real-time calibration method based on a heating body, which comprises the following steps of: 1, etching on the surface of a metal alloy heating body of which the resistance temperature coefficient is greater than a preset material threshold value to form N discrete series resistance heating units which are electrically connected in series but thermally discrete; 2, establishing a resistance-temperature dynamic relation library of each discrete series resistance heating unit; 3, synchronously measuring the real-time resistance value of each unit when the heating body works; 4, reversely deducing the temperature of a unit center point through a unit exclusive relational expression according to the real-time resistance value, and reconstructing a continuous temperature distribution matrix on the surface of the heating body in combination with a heat conduction equation; and 5, comparing the target temperature field with the real-time temperature field matrix, and adjusting the driving power of the corresponding unit in the out-of-tolerance region. Through continuous calibration and temperature adjustment, accurate control of a temperature field can be maintained in different use environments.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously 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 discloses a hydration heat monitoring and dynamic regulation and control method and system in a mass concrete pouring process, and relates to the technical field of concrete pouring, and the method comprises the steps: pre-burying a plurality of dual-mode temperature measurement probes in concrete, collecting dual-mode temperature field data in the concrete in real time, and synchronously collecting environmental parameters; carrying out preprocessing, dynamic weight distribution and data fusion on the obtained dual-mode temperature field data; according to the unsteady state heat transfer equation and the hydration heat model, a finite element heat conduction model is constructed, and the total hydration heat and the temperature difference trend are inverted through actually measured temperature field data; predicting a concrete core and surface temperature difference and a surface and environment temperature difference in a future set time period by combining an LSTM neural network model; and according to the predicted temperature difference result, the cooling water flow rate and the cooling water temperature are subjected to closed-loop regulation. Through the combination of dual-mode sensor temperature measurement and heat inversion, the hydration heat temperature difference in the concrete can be predicted in advance, a cooling scheme is regulated and controlled in real time in a linkage mode, and temperature stress cracks are avoided.

The invention relates to the technical field of process optimization, and discloses a hot work die steel heat treatment process optimization method and system.The method comprises the steps that hot work die steel samples are collected under multiple sets of different process conditions, and a performance basic data set is obtained; constructing a multi-target coordination optimization model based on the performance basic data set; performing phase change detection on the hot work die steel sample to obtain phase change monitoring data; performing prediction in combination with the phase change monitoring data to obtain a performance prediction result; and solving an optimal process parameter combination based on the performance prediction result and the multi-target coordinated optimization model, and realizing simultaneous optimization and coordinated balance of a plurality of performance indexes in the heat treatment process of the hot work die steel by making full use of associated information among different performance indexes.

The invention discloses a temperature monitoring and control system in a goat breeding house. The system comprises an environmental data sensing unit, an edge data preprocessing unit, an optimized long-short term memory neural network processing unit, an air internal and external circulation regulation and control strategy generation unit, an equipment linkage execution unit and a system operation state monitoring unit. Environmental data in a breeding house is collected in a self-adaptive mode through multiple types of high-precision sensors, after edge processing, temperature spatial-temporal characteristics are extracted deeply through an optimized long-short-term memory neural network, an air circulation regulation and control strategy is generated in combination with a multi-target optimization model, equipment is driven to execute precisely, and system dynamic optimization is achieved through closed-loop feedback. The system overcomes the defects of insufficient monitoring coverage and extensive regulation and control in the prior art, considers the coupling relationship between temperature and air components, realizes multi-factor collaborative accurate regulation and control, effectively reduces energy consumption, creates a stable and comfortable growth environment for goats, and improves the breeding benefit and intelligent management level.

The invention discloses a control method for a cooling circulation system of a multi-core processor, and relates to the technical field of computer hardware heat dissipation, and the control method comprises the following steps: the multi-core processor is responsible for executing a calculation task; the sensor module monitors the temperature of each core; the cooling system control module is used for receiving data from the temperature sensor and the load monitoring module, calculating the cooling requirement of each core according to the data and commanding a cooling unit to adjust; the cooling unit is used for adjusting cooling equipment to reduce the temperature of the processor according to an instruction of the control unit; and the debugging interface is used for monitoring the state of the system and displaying the temperature, the load and the cooling resource distribution condition of each core. The method has the beneficial effects that the rotating speed of the fan, the liquid cooling flow and the working state of the heat pipe system can be accurately controlled by combining the accurate analysis of the load change and the temperature data, the heat dissipation efficiency of the processor is improved, and the energy waste is reduced.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention discloses a cooling cooperative control method, and the method comprises the steps: obtaining a temperature parameter, a load state parameter and a cooling system parameter of a transformer, and forming a parameter set; constructing a gating circulation unit model, and predicting thermal load distribution of each region of the transformer by adopting the parameter set through the gating circulation unit model; and performing real-time distribution on cooling resources by adopting a dynamic region distribution algorithm. According to the cooling cooperative control method, temperature and load changes of all areas of the transformer are predicted in real time through a gating circulation unit model; intelligent distribution of cooling resources is achieved through a dynamic area distribution algorithm, the cooling cooperative control method can dynamically adjust the flow speed of a cooling medium and the rotating speed of a fan according to real-time temperature and load changes, it is ensured that all areas of a transformer obtain appropriate cooling resources, and extra cooling is preferentially provided for areas with the temperature abnormally increased; and a cooling strategy is optimized through multiple iterations in combination with historical operation data, so that the response speed and accuracy of the cooling system are remarkably improved.

The invention relates to the technical field of chip temperature control, and discloses a chip temperature regulation and control system and method, and the method comprises the steps: building a thermal field dynamic model, integrating real-time power consumption data and environment heat dissipation parameters, and generating a thermal resistance adjustment coefficient and a power consumption correction coefficient; establishing a temperature prediction network to perform multi-source temperature field prediction; a dynamic regulation and control strategy is set according to a prediction result, and heat dissipation control parameters are optimized to achieve thermal field balance control; and executing feedback calibration and model iteration. The system comprises a thermal field dynamic modeling module, a multi-source temperature field prediction module, a dynamic regulation and control strategy execution module, a feedback calibration module and an iteration module. The method can accurately predict the heat distribution of the chip, realizes real-time and intelligent temperature regulation and control, improves the performance and reliability of the chip, reduces the energy consumption, and has good adaptability and universality.

The invention discloses an agricultural product cold chain carriage temperature control method based on space equalization, and relates to the technical field of intelligent cold chain logistics, and the method comprises the steps: collecting carriage three-dimensional point cloud data and agricultural product electronic tag information, building a carriage loading three-dimensional model, carrying out the space grid division, and generating a partition topological feature vector; collecting temperature, humidity and carbon dioxide concentration data, calculating average temperature and average gas concentration observation values, and generating a breathing heat estimation value in combination with the partition topological feature vector; inputting the breathing heat estimation value into a dynamic prediction model, generating a subarea cold load curve and a priority weight by combining the subarea temperature deviation, and outputting a subarea cold load sequence; and according to the partition cold load sequence, generating a local execution proposal, constructing a global optimization problem taking the space temperature balance degree as an optimization target, and generating a global control plan. According to the method, global temperature equalization and energy consumption collaborative optimization are realized through network reasoning and dynamic optimization of the meta-reinforcement learning strategy.

The invention relates to the technical field of chip stacking, and discloses an intelligent temperature control method and device of a multi-chip stacking structure and computer equipment. The method comprises the following steps: acquiring target temperature data of each layer of chip in a multi-chip stacking structure, and establishing a nonlinear temperature response model; constructing a hierarchical control framework, and inputting the target temperature data into an agent auxiliary temperature control algorithm for processing to obtain temperature distribution characteristic data; performing optimal sampling analysis to obtain local temperature regulation and control parameters; constructing a temperature-flow multi-stage coordination control model, and generating a cooling liquid flow distribution scheme and a flow direction control instruction; the flow and temperature of cooling liquid are controlled, gradient cooling treatment is conducted on the temperature abnormal area, and temperature adjusting process data are obtained; and carrying out multistable distribution analysis to generate a temperature control optimization strategy. According to the invention, the problem of temperature balance in a multi-chip stacking structure is effectively solved.

The present application belongs to the technical field of material processing, and relates to a mold-free mirror image filament laying method and a mold-free mirror image filament laying system. The method lays a filament ribbon in a mirror image symmetry, without a mold, thereby improving production flexibility and efficiency. The method acquires the temperature and pressure of the filament ribbon in real time, calculates the output power of a heater and the modification value of a filament laying end motion mechanism path by using temperature and pressure differences, and optimizes controller parameters through reinforcement learning, so as to realize stable control of the temperature and pressure. The system comprises a hardware part and a control system. The hardware part includes pressure and heat source components. The control system adjusts through real-time feedback to ensure the quality of filament laying. The present application solves the problem that the temperature and pressure are difficult to accurately control in the process of mold-free mirror image filament laying, and improves the performance of composite products.

The invention discloses a CNNLSTM-based energy storage equipment temperature control method and system, and the method comprises the steps: collecting the load temperature of energy storage equipment through a temperature sensor, inputting the load temperature into a trained CNNLSTM model for temperature prediction, and taking an error between the predicted temperature and the target temperature of the energy storage equipment as feedforward input data of a PID control unit, and the PID control unit controls the input data to a temperature control system of the energy storage equipment according to the error feedback so as to adjust the load temperature. According to the CNNLSTM model, the CNNLSTM model combines the CNN and the LSTM to process and predict the load temperature of the energy storage equipment, has strong capabilities of processing time sequence data, extracting features and capturing a long-term dependency relationship, and shows remarkable advantages in the fields of energy storage equipment load temperature prediction, equipment state monitoring and the like.

The invention relates to the technical field of electric digital data processing, in particular to a display process temperature management optimization system which comprises a data acquisition module, a dynamic modeling module, a cooperative compensation module and an early warning module. The data acquisition module acquires temperature data of a heating area through a multi-type temperature sensor array, and generates a temperature field matrix with a confidence label through multi-source verification; the dynamic modeling module receives the matrix, operates a plurality of machine learning algorithms in parallel to generate a thermal coupling strength distribution diagram, and outputs a region coupling coefficient and a confidence coefficient thermodynamic diagram; the cooperative compensation module calls knowledge base strategies in a layered mode according to the thermodynamic diagram and feeds back early warning signals when abnormity is detected. According to the system, a cross-module closed-loop control mechanism is formed through confidence label driving algorithm weight distribution, confidence thermodynamic diagram control strategy priority, abnormal signal triggering model reconstruction and knowledge base updating, and millisecond-level cooperative suppression of the thermal crosstalk effect of multiple heating areas is achieved.

The invention relates to the technical field of data processing, and discloses a temperature monitoring method and system for a motor controller. The method comprises the steps of obtaining a three-dimensional temperature data flow in real time through a temperature sensor array, performing temperature analysis and hot spot detection, constructing an adaptive protection strategy, dynamically adjusting PWM parameters, and optimizing the operation safety and performance of a motor. According to the invention, the technical problems that the coverage of temperature monitoring points is not comprehensive, and the protection strategy lacks pertinence and adaptability are solved, through multi-point temperature collection and intelligent analysis processing, the accuracy of temperature monitoring of the motor controller and the flexibility of the protection strategy are improved, and it is ensured that the motor is safe and reliable and keeps the optimal performance under different working conditions.

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 a multi-point temperature control method and system based on intelligent temperature equalization control, and the method comprises the steps: collecting the temperature data of each region in frequency converter equipment in real time, and enabling the regions to comprise but not limited to a power module, a power module and a heat dissipation region; based on the temperature data, a deviation value between the temperature of each region and a preset target temperature is calculated through a preset analysis algorithm, and a temperature adjustment requirement of each region is generated based on the deviation value; based on the temperature adjusting requirements of all the areas, adjusting parameters of the cooling equipment are calculated through an optimization algorithm, and a corresponding control instruction is generated in combination with the load state of the frequency converter equipment; and the control instruction is transmitted to the cooling equipment, and corresponding parameters of the cooling equipment are adjusted according to the control instruction. The method has the effect of improving the temperature control precision.

The invention relates to the field of intelligent temperature control of industrial kilns, and discloses a graphitization furnace temperature uniformity optimization processing system which comprises a temperature monitoring module, a dynamic matrix control module, a material-control coupling module, a lattice feedback module and a multi-objective optimization module. The method comprises the following steps: constructing an equivalent temperature field through multi-source temperature data fusion; predicting a graphitization trend and generating a dynamic matrix control instruction in combination with a multi-physical field model; dynamically adjusting a thermal conductivity matching temperature gradient of the guard plate; online correcting a material constant based on lattice feedback; closed-loop control is formed, and the process stability and energy efficiency of the graphitization furnace are improved. Through multi-source fusion monitoring, multi-physical field coupling modeling, dynamic thermal conductivity regulation and control, lattice feedback online correction and reinforcement learning multi-objective optimization, temperature uniformity control and energy efficiency improvement of the graphitization furnace are realized, and material performance and process economy are guaranteed.