679 results about "Thermal inertia" patented technology

The invention belongs to the field of equipment coupling monitoring, and particularly relates to a dynamic coupling compensation method for thermal expansion and axial displacement, which comprises the following steps of: calculating a first characteristic value for representing reference dynamic offset of a sensor based on a three-dimensional thermal-structure coupling model by acquiring a thermal expansion parameter and axial displacement parameter sequence of a casing; calculating a second characteristic value representing the real position change of the rotor in combination with a rotor-casing axial thermodynamic model, establishing a piecewise coupling function considering a nonlinear effect, working condition self-adaption and cross interference, and eliminating the mechanical thermal inertia and measurement system lag influence through a dynamic delay compensation mechanism; an accurate axial displacement measurement total error compensation index is generated, and grading compensation actions are intelligently triggered according to error grades; the problem of measurement distortion caused by dynamic coupling of thermal expansion and axial displacement is effectively solved, and the accuracy and reliability of state monitoring of the rotating machine are remarkably improved.

The invention discloses a digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably 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 belongs to the technical field of intelligent control, and particularly relates to an intelligent regulation and control method for an operating environment in an SF6 ring main unit. The method comprises the following steps: firstly, acquiring temperature, gas pressure and humidity data of a plurality of sampling points in the ring main unit, and constructing a time sequence environment data set through time synchronization and missing interpolation processing; analyzing the temperature data, extracting fluctuation characteristics, determining an environment disturbance factor in combination with an electric arc trigger intensity index, and obtaining a cabinet body temperature control safety factor in combination with a dynamically adjusted temperature rise threshold and a gas insulation state; then constructing an environment equivalent thermal inertia model and a hot-pressure covariant model based on historical data, and calculating a thermal stability index of the cabinet body; and finally, whether adjustment is triggered or not is judged according to the thermal stability index and the temperature control safety factor, if yes, a temperature abnormal area is positioned through a convolutional neural network and a clustering method, the temperature control response level is calculated, a temperature control strategy library is called to be matched with adjustment parameters, and accurate control is achieved.

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

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

The invention discloses a cable optical fiber temperature measurement monitoring system based on big data AI fusion, particularly relates to the technical field of cable optical fiber temperature measurement monitoring, and aims to realize thermal-electric dynamic coupling monitoring through high-density distributed optical fiber temperature measurement and big data AI fusion modeling and introduction of current load synchronous acquisition, and realize high-density distributed optical fiber temperature measurement and big data AI fusion by means of time synchronization and space relocation processing. The method comprises the following steps: identifying a weak response signal of a thermal inertia hysteresis characteristic and an initial temperature rise stage, constructing a cross-scale time sequence recurrent neural network through multi-dimensional enhancement and vector fusion, outputting a continuous scoring sequence reflecting abnormal credibility, and constructing a risk factor group reflecting a fault evolution path in combination with a scoring fluctuation amplitude, a continuous period and a response delay characteristic, and carrying out positioning offset correction through correlation mapping of risk factors and along-cable temperature distribution and by utilizing a topological relation between optical fiber path coordinates and a cable space, and realizing space reverse projection of an abnormal peak value, thereby recovering an accurate position of a fault heat source and a latent hidden danger section.

The invention relates to the technical field of turret heat management, in particular to a power turret heat management optimization method and system based on intelligent cooling, and the method comprises the steps: obtaining the temperature distribution data and related working condition parameters of a power turret in real time, and building a heat state feature vector representing the dynamic relation between the heat generation and heat dissipation capability of the turret; constructing a cooling decision model, inputting the state vector into the cooling decision model, and generating an initial control strategy; the initial control strategy is simulated and executed in the digital twin environment, and a strategy reconstruction mechanism is triggered when the predicted temperature suppression rate is lower than a set threshold value; and performing real-time dynamic optimization on the verified initial control strategy according to the processing quality constraint and the equipment safety threshold, and superposing the thermal inertia delay compensation amount to generate a final control strategy. According to the method and the device, the problem of temperature control failure caused by thermal inertia time lag is effectively solved, accurate space-time matching of the cooling instruction and the thermal load peak value is realized, and thermally induced deformation is remarkably inhibited.

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 invention relates to the technical field of temperature control, in particular to an enzyme reaction kettle self-adaptive temperature control system and method, and the system comprises a temperature detection module, a temperature control module, a heat conduction oil flow control module and an automatic regulation and control module; temperature data of a reaction kettle are collected in real time through a temperature detection module, a temperature control module adjusts parameters based on a PID algorithm and fuzzy logic to generate a control instruction, and a heat conduction oil flow control module adjusts flow by combining double-closed-loop cascade control with a feedforward compensation algorithm, so that thermal inertia lag is shortened. And the automatic regulation and control module generates optimized process parameters based on a reaction kinetic model and a genetic algorithm, and dynamically adjusts a temperature set value. The control method comprises the steps of temperature data acquisition, PID control instruction generation, conduction oil flow coordinated regulation, process parameter optimization and the like. The problems that in the prior art, temperature control precision is insufficient, conduction oil flow control is not accurate enough, and technological parameters cannot be automatically regulated and controlled are solved.

The invention relates to the technical field of intelligent buildings and energy management, and particularly discloses an intelligent building heating intelligent optimization operation method and system based on deep learning, and the method comprises the steps: collecting building environment parameters and equipment operation data in real time through a distributed optical fiber sensing network and an infrared thermal imaging system; extracting minute-level fluid transmission and distribution parameters and hour-level building thermal inertia characteristics by adopting wavelet packet transformation and a graph neural network; then, constructing a neural differential equation prediction model fused with physical constraints, and outputting high-precision thermal load demand prediction through a differentiable heat conduction operator coupling multi-time scale feature; then, a mixed integer optimization model considering equipment life loss is established, and boiler start-stop combination and pipe network flow distribution are synchronously optimized by adopting a hierarchical decision-making mechanism; and finally, closed-loop control is realized through a multi-mode actuator network, and model parameters are dynamically adjusted in combination with an online learning mechanism.

The invention relates to the technical field of thermal equipment operation state dynamic test and compensation control, in particular to an AI-based boiler air pre-heater temperature uniformity regulation and control system, which comprises a collaborative closed-loop control network of a data acquisition module, an AI prediction module, a heat storage system control module, a temperature compensation control module and a thermal stress analysis module. The data acquisition module acquires flue gas temperature, coal quality parameters and load instructions in real time, the flue gas temperature, the coal quality parameters and the load instructions are transmitted to the AI prediction module through an industrial bus, XGBoost, LightGBM and a deep neural network model are used for carrying out spatial-temporal feature extraction on the evolution trend of a temperature field, and the heat storage system control module dynamically switches heat storage and release modes according to the load state. The heat distribution proportion among the multiple units is adjusted by combining the three-dimensional stress field distribution data of the thermal stress analysis module; the bottleneck of lack of thermal inertia regulation of an existing system is broken through, dynamic balance of a temperature field under the condition that thermal stress of a heat transfer element is controllable is achieved, and the heat exchange efficiency and operation stability of the air pre-heater are improved.

The invention relates to the technical field of heat pump systems, in particular to a heat pump system temperature sensor fault diagnosis and fault-tolerant control method, which comprises the following steps that: a system calculates a temperature change rate based on a current temperature average value and a temperature average value in the previous second, and performs working condition identification in combination with the running state of a compressor; generating a working condition state label according to the working condition identification result; determining a current behavior template curve based on the working condition state label and the temperature change rate, extracting a temperature sliding sequence of the sensor in a second before the current moment, and generating a comprehensive behavior score by combining the current behavior template curve, the temperature sliding sequence and a sensor behavior deviation item; combining the comprehensive behavior score, the score stability adjustment item, the trend score value and the sensor behavior deviation item to generate a health score of the sensor; and the system performs normalization processing on the health score to generate a fusion weight, and the system outputs a temperature value in combination with the fusion weight and the thermal inertia offset compensation term.

The invention discloses a temperature measurement sensor data processing method and system in a chemical synthesis environment. The method comprises the steps of original data acquisition and preprocessing, sensor drift compensation, dynamic response optimization and anomaly detection and adaptive calibration. The system comprises an original data acquisition and preprocessing module, a sensor drift compensation module, a dynamic response optimization module and an anomaly detection and adaptive calibration module. Through multi-module deep fusion of LSTM drift compensation and adaptive Kalman filtering, + / -0.5 DEG C measurement precision is realized in a range of 80-300 DEG C; the MPC predictive control compresses the thermal inertia lag of the sensor from 500ms to less than or equal to 80ms, so that the sudden rising / dropping temperature in the synthesis reaction can be effectively captured, and the product decomposition caused by local overheating is avoided; isolated forest anomaly detection and least square calibration are combined, so that the maintenance period of the sensor in a strong corrosion environment is prolonged from 7 days to 90 days, and the annual maintenance cost is reduced by 65%.

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

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

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

The invention relates to the technical field of intelligent carbon management, in particular to a building carbon emission monitoring system based on BIM (Building Information Modeling), which comprises a model construction module, a thermal inertia analysis module, a carbon list generation module, a carbon emission monitoring module and an energy consumption parameter optimization module. According to the method, the carbon emission parameter set is generated in combination with the IFC data structure through standardized analysis of the material type, the size and the engineering quantity of the component, so that the carbon emission factor has the targeted adjustment capability. An actual measurement environment temperature change sequence is introduced into thermal inertia calculation, response difference correction is carried out on the thermal capacity and the heat conduction behavior of the component, and the dynamic thermal performance of the material in different environments is accurately reflected. In stage carbon emission measurement and calculation, component thermal inertia response and functional time periods are fused for refined staging, and functional frequency and time occupancy ratio are superposed in carbon emission calculation, so that dual sensitive adjustment of carbon emission on time and component function dimensions is realized.

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 a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention provides an LED lamp adaptive speed regulation heat dissipation method and system based on real-time junction temperature monitoring. The method comprises the following steps: S100, acquiring a PWM signal or an analog signal of an LED driving command signal in real time; s200, on the basis of the change of the driving instruction signal, feedforward prediction is carried out by using a dynamic thermal inertia mapping model, and a feedforward fan control quantity is generated; s300, LED junction temperature is monitored in real time through a forward voltage method, and feedback fan control quantity is generated through a feedback controller according to the deviation between the real-time junction temperature and the target junction temperature; and S400, synthesizing the feedforward control quantity and the feedback control quantity to generate a fan PWM control signal, outputting the fan PWM control signal to a fan driving circuit, and adjusting the rotating speed of the cooling fan. According to the method, driving signals and junction temperature information are processed in parallel, and feedforward and feedback control is combined, so that self-adaptive accurate regulation and control are realized, thermal load change is quickly responded, system lag is effectively inhibited, and LED heat dissipation performance is optimized.

The invention discloses an optimal scheduling method for a ground source heat pump system, and relates to the technical field of new energy utilization and intelligent control of a heat supply system.The method comprises the steps that the ground source heat pump system is divided into a tail end heat exchange layer, a water pump transmission and distribution layer and a source side heat pump coupling layer on the basis of a heat energy conversion and transmission path; constructing a response coupling relation and a mapping model; operating data of three layers of equipment are collected, a time sequence of each layer is established, a dynamic delay structure matching coefficient is calculated, and a multi-layer linkage response lag risk is identified based on a first threshold value; further monitoring a second derivative of the source side temperature change, calculating a thermal inertia interference index, and judging that the thermal response of the source side is abnormal in combination with a second threshold value; and the water supply flow of each branch is extracted, the target water supply value and the total flow of the system are combined, a flow deviation factor is calculated, flow distribution deviation is recognized according to a third threshold value, accurate strategy generation and intelligent scheduling control based on multi-parameter dynamic characteristics are achieved, and the response coordination and operation stability of the system are improved.

The invention relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, and the method comprises the following steps: constructing a ratio group of a trigger incremental sequence quantity and a frequency spectrum cluster set based on a partial discharge pulse interval, deducing impedance frequency boundary migration, analyzing a conductor heating and cooling rate difference, and forming thermal response lag information. According to the method, frequency spectrum energy evolution characteristics are extracted through partial discharge pulse interval dynamic behaviors, a frequency migration path is constructed to reflect an impedance boundary fluctuation process, hysteresis effect characteristics are extracted in combination with thermal inertia response differences in a temperature rise stage, and the impedance boundary fluctuation is determined. And analyzing a voltage and current phase fluctuation rule and deducing a change trend of a combined feature, establishing a layer-by-layer progressive judgment logic between multi-stage dynamic coupling features, realizing evolutionary process tracking from local disturbance to a global abnormal state, and improving a fine-grained identification level of an abnormal mechanism in an electrical equipment operation behavior.

The invention provides a drain valve leakage online monitoring method and system based on infrared thermal imaging and deep learning. Comprising the following steps: step 1, collecting a thermal image sequence of a drain valve area in real time through an infrared thermal imager, and simultaneously obtaining a drain valve start-stop signal; step 2, based on a steam trap start-stop signal, synchronously segmenting the thermal image sequence into a working state sequence and a shutdown state sequence according to a working period, and performing dynamic background difference processing on each frame of image to eliminate thermal inertia interference of equipment; 3, calculating a temperature gradient map of each frame of image, generating a temperature gradient mask, and highlighting temperature mutation characteristics of the leakage area; 4, constructing a two-channel time sequence compression network model; 5, evaluating the environmental interference degree based on the dynamic interference index, and dynamically adjusting a leakage judgment threshold value; and step 6, in combination with the leakage region area change rate, the temperature gradient change rate and the sensor data trend, carrying out graded judgment on leakage events, and distinguishing real leakage from background thermal interference through texture feature matching.

The invention discloses a new energy building integration energy-saving design analysis method, and relates to the technical field of new energy building integration, and the method comprises the following steps: carrying out the time sequence alignment and unit normalization processing of collected heat flow data, temperature rise data and power output data; according to the heat conduction characteristic of the heat conduction structure and the thermal inertia parameters of the building enclosure structure, a heat flux track and a thermal response profile with a unified thermal resistance reference axis are constructed; and based on the heat flux track and the thermal response profile, extracting a reverse heat flux strength parameter and a heat flux continuous influence duration parameter, and combining the parameters with a temperature control target and a heat conduction structure attribute set in the building to construct a reverse heat conduction interference atlas for judging the heat conduction interference degree of the cold load in the building. The problem of cold load misjudgment caused by the fact that photovoltaic heat is transmitted through a high-heat-conduction structure is solved, and cold and heat conduction accurate recognition and energy consumption dynamic regulation and control are achieved.

The invention relates to the technical field of control and adjustment, in particular to an intelligent control system and method for an induction cooker, and the method comprises the steps: collecting the material and temperature data of cookware through a multi-source sensing module; the collaborative prediction module dynamically adjusts the window size of the time convolution network and generates a power compensation parameter and a frequency tuning parameter; the simulation execution module executes triple calculation of particle swarm optimization, thermal inertia calibration and nonlinear constraint verification, and outputs optimization control parameters; and the optimization feedback module updates the weight of the thermal inertia model in real time through a double-loop feedback mechanism and calibrates prediction parameters. Perceptual data synchronous processing is achieved through edge calculation, knowledge graph driving load sudden change rapid decision is achieved, the simulation efficiency is improved through dynamic step length control, and material parameter mapping is optimized through historical deviation statistics. The problem of multi-link cooperative response mismatch under the load sudden change working condition is solved, the power regulation response time delay is shortened, and the heating efficiency stability and the safety protection precision are remarkably improved.

The invention provides a multi-area temperature collaborative management method and system for a high-low temperature all-in-one machine. According to the method, the temperature gradient data of each temperature region and the thermal imaging data of the surface of the non-uniform material are collected in real time, and the recognition of the abnormal region of the heat conduction path is combined, so that the heat transfer lag characteristics of the material are extracted, and the region thermal inertia compensation coefficient map is generated. An infrared reflectivity distribution matrix is further constructed according to the dynamic change trend of the atlas, and the infrared reflectivity of each area is adjusted to optimize the distribution parameters of the heat absorptivity. And finally, carrying out spatial correlation on the thermal response delay evolution path and the thermal absorption rate parameter to generate a multi-region collaborative temperature regulation and control instruction. Through thermal inertia compensation and thermal absorption rate dynamic adjustment, accurate matching and dynamic balance control of a multi-region temperature field of the non-uniform material are realized. The efficiency and precision of multi-area temperature collaborative management of the high and low temperature all-in-one machine can be improved.

The invention provides a temperature sensor data calibration method and system, and relates to the technical field of sensor calibration, and the method comprises the steps: collecting a time sequence voltage sequence of a to-be-calibrated temperature sensor under the excitation of a temperature step; calculating a gradient change rate of the time sequence voltage sequence to obtain a dynamic response characteristic of the to-be-calibrated temperature sensor; based on the gradient change rate, the dynamic response asymmetry degree of the temperature sensor to be calibrated under temperature step excitation is calculated; establishing a thermal inertia evolution equation of the temperature sensor to be calibrated according to the dynamic response asymmetry degree; the theoretical lag amplitude of the temperature sensor to be calibrated is corrected with the trend of the thermal inertia evolution equation to be stable as the target, and the actual lag amplitude is obtained; obtaining the original measurement temperature of the target temperature by the temperature sensor to be calibrated; and correcting the original measurement temperature in combination with the actual lag amplitude and the dynamic response asymmetry degree to complete data calibration of the temperature sensor to be calibrated. And the measurement precision of the temperature sensor in a complex temperature change scene is improved.