250 results about "Heat load" patented technology

The invention discloses a copious cooling rectification real-time monitoring and correction system based on digital twinning, and the system comprises a data processing module which is used for collecting and preprocessing data of a sensor; the graph structure construction module is used for constructing a twin graph structure and generating a topological adjacency matrix; the feature extraction module is used for executing graph embedding operation and extracting virtual twin features; the feature decoupling module is used for carrying out dynamic decoupling on the virtual twin tensor and constructing a feature sequence; the twinning correction module is used for inputting the state mapping vector into a twinning correction network and correcting a virtual twinning tensor; the variational inference module is used for inferring an output confidence interval and generating a reliability mask; and the parameter optimization module is used for adjusting the reflux ratio, the tower top temperature and the reboiling heat load in the credible area to realize real-time monitoring and correction. According to the invention, the intelligent monitoring capability and the self-adaptive control level of the cryogenic distillation process are obviously improved, and the system robustness, the energy-saving property and the industrial applicability are higher.

The invention provides a spindle constant-temperature AI closed-loop heat engine system, and relates to the technical field of numerical control machine tool control and heat management. The invention aims to solve the problems of response lag, uncontrollable thermal stress, single heating strategy and the like of the traditional spindle heat engine method. The system comprises a multi-dimensional thermal state sensing module used for constructing a multi-node discrete temperature field representing the overall thermal distribution of the main shaft; the real-time heat balance feedback module is used for judging the cold / hot starting state of the main shaft according to the initial temperature difference and executing a differentiated feedback control strategy; and an AI prediction forward compensation module that predicts a future cutting thermal load by pre-reading a machining instruction and generates a feedforward thermal compensation instruction. According to the method, the thermal stress is controlled by introducing the radial thermal gradient index as the safety constraint, negative precooling or positive preheating is achieved in combination with the AI predictive capacity, a feedback and feedforward composite control mode is formed, and the dynamic thermal stability and the machining precision of the main shaft under the complex working condition are improved.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention discloses an air conditioning system control method and system based on reinforcement learning and a twinborn model strategy. The method comprises the steps that S1, a data-driven twinborn model of a central air conditioning system is constructed; s2, performing offline pre-training based on a virtual environment formed by a twin model; s3, performing online integration on a plurality of pre-trained reinforcement learning algorithms and the twin model to form a reinforcement learning strategy integration framework; and S4, the final control action is issued to execution equipment of the central air conditioning system. According to the invention, by constructing the serial data-driven twinborn model, the running state, energy consumption and cooling and heating load supply-demand relationship of the central air-conditioning system are accurately simulated, a high-fidelity virtual environment is provided for various reinforcement learning algorithms, offline pre-training is realized, an initial control strategy is generated, and risks caused by blind exploration in a real system are avoided. Continuous self-adaptive optimization is achieved, and the stability and the energy efficiency level of intelligent control of the air conditioning system are remarkably improved.

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention relates to the technical field of machine room environment regulation and control, in particular to a communication base station intelligent energy-saving window control design method and system, and the method comprises the steps: obtaining and classifying base station source load data, setting energy-saving window parameters and an off-network strategy, monitoring a power supply state, and cooperatively controlling temperature control equipment in combination with electric quantity temperature difference. According to the method, the thermodynamic state is digitalized by calculating the outdoor cold source potential energy gradient and the indoor thermal load cumulative acceleration and combining the enthalpy difference allowance so as to accurately evaluate the natural cold source potential; the optimal ventilation opening degree and the fan rotating speed are matched through multi-dimensional feature clustering, so that the condensation risk is strictly restrained according to dew point approximation deviation while the heat dissipation efficiency is guaranteed, and in addition, a dual-target evaluation function in an off-grid scene is constructed to solve a non-dominated solution set of endurance time and thermal safety. A dynamic protection threshold value is generated; and when the commercial power is interrupted, a control strategy is adaptively adjusted according to the real-time load and the environment temperature, so that the off-network life cycle of the base station is maximized, and core equipment is ensured to always run within a safe temperature boundary.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

The invention provides a centralized heating optimal scheduling method and system based on multi-heat-source dynamic matching, and the method comprises the steps: constructing a thermal load prediction model, and fusing various types of data to generate a thermal load demand dynamic curve in the next 24 hours; establishing a dynamic priority ranking model, and generating a heat source priority sequence; generating a multi-heat-source cooperative operation strategy; constructing a multi-heat-source dynamic matching optimization model to obtain an optimal scheduling strategy; heat sources are adjusted in real time through the central control system, and dynamic correction is conducted by combining pipe network feedback data; and periodically updating the thermal load prediction and dynamic priority ranking model. According to the method, a bidirectional LSTM neural network is fused with data to construct a high-precision thermal load prediction model, and a curve is generated in combination with a feature pyramid network; meanwhile, related data are periodically combined, the weight of the LSTM neural network is updated through an incremental learning algorithm, the weight of the priority ranking model is adjusted through a gradient descent method, the adaptability of the prediction model is continuously optimized, and the prediction precision is improved.

The invention discloses an intelligent liquid feeding control method and system for a single-phase immersion liquid cooling data center cabinet, and relates to the technical field of cabinet liquid cooling. The intelligent liquid feeding control method and system for the single-phase immersion liquid cooling data center cabinet comprise the following steps: S1, collecting cabinet liquid cooling telemetry data, and preprocessing the cabinet liquid cooling telemetry data; s2, evaluating a liquid cooling supply and demand matching degree of the cabinet, judging a cooling supply and demand state, generating a flow regulation instruction, and triggering cooling resource redistribution when a cooling insufficiency state and a heat imbalance condition are met; s3, analyzing the flow regulation instruction, executing pump valve regulation of a liquid feeding branch, collecting feedback data for deviation evaluation after execution, and performing compensation regulation when an evaluation result does not meet the expectation; and S4, after the fault event is triggered, evaluating the overall liquid cooling operation risk. The problems that the temperature rise of a high-load cabinet exceeds the limit and liquid flow in a low-load area is wasted due to the fact that liquid cooling supply cannot be matched in real time due to severe thermal load change between the cabinets are solved.

The invention discloses a cooling control method and device for immersed liquid cooling energy storage equipment and electronic equipment. The method comprises the following steps: acquiring battery pack temperature information and cooling liquid temperature information of the immersed liquid-cooled energy storage equipment, and determining a thermal load value of the immersed liquid-cooled energy storage equipment according to the battery pack temperature information and the cooling liquid temperature information; establishing a temperature prediction model according to the thermal load value, and determining a temperature change trend of a battery pack in the immersed liquid cooling energy storage equipment in a preset time period through the temperature prediction model; and adjusting cooling control parameters of the circulating cooling system by adopting a self-adaptive segmented control strategy according to the temperature change trend, and cooling the battery pack according to the cooling control parameters. The technical problems that the circulating cooling system of the immersed liquid cooling energy storage equipment in the related technology cannot effectively predict the temperature change trend of the battery pack and dynamically adjust the cooling control parameters, the system energy consumption is high, and it is difficult to ensure that the battery pack runs within the safe working range are solved.

The invention relates to the technical field of building energy collaborative management and renewable energy grid-connected regulation and control, in particular to a ground source heat pump and solar photovoltaic integrated management system based on multi-energy complementation, which comprises a data acquisition module for acquiring a data output real-time operation data set. The multi-time scale prediction module generates a prediction curve and calculates a time sequence matching degree index. And the energy coordinated optimization module constructs a decision model by taking maximization of renewable energy local consumption as a target, and generates a storage battery charging and discharging instruction and a heat pump adjusting instruction according to a matching degree index. The execution control module converts the instruction into an equipment-level operation signal, controls the operation of the photovoltaic inverter, the heat pump frequency converter and the storage battery management system, and collects equipment state data and feeds the data back to the data collection module to form closed-loop control. Through multi-time scale prediction and real-time optimization scheduling, the problem that photovoltaic power generation intermittency is not matched with a building thermal load time sequence is effectively solved, and the energy utilization efficiency is improved.

The invention provides a liquid cooling heat dissipation power intelligent control method fusing multiple parameters of a machine room, and relates to the technical field of data centers, and the method comprises the steps: building an environment state matrix through obtaining multi-parameter data representing the environment thermal degree of the machine room in real time; a liquid cooling heat dissipation control model based on a reinforcement learning algorithm is constructed, a strategy function takes the environment state matrix as input and takes the heat dissipation power adjusting quantity of the liquid cooling system as action output, and a reward function taking a thermal load distribution uniformity index, a thermal load change trend index and a system energy efficiency ratio index as optimization targets is constructed; parameters of the strategy function are iteratively updated through a reinforcement learning algorithm, so that the strategy function outputs a heat dissipation power adjusting quantity of a maximized reward function output value under a given environment state matrix; and inputting the environment state matrix constructed in real time into the strategy function, and outputting an optimal liquid cooling heat dissipation power control instruction. The problem that in the prior art, the cooling efficiency, the energy efficiency and the safety are difficult to collaboratively optimize is solved.

The utility model provides a fog-dispersal water-saving ventilation tower, and relates to the technical field of ventilation cooling towers. The fog-dispersal water-saving ventilation tower comprises a tower body, a plurality of heat exchange modules are arranged in the tower body, a plurality of heat exchange assemblies are arranged in each heat exchange module, each heat exchange assembly comprises fins and a heat conduction pipe, an installation plate is connected between the fins, and a fixing plate is arranged at the lower end of each heat exchange module. According to the fog-dispersal water-saving ventilation tower, the heat exchange module is arranged in the tower body, circulating hot water enters the heat exchange assembly to absorb the cooling capacity of external cold air, so that water vapor is condensed into water drops to drop into the tower body again, the evaporation heat load of the circulating water is remarkably reduced, the evaporation capacity is remarkably reduced, and the purposes of saving water and dispersal fog are achieved; the space in the tower can be more effectively utilized, so that the heat exchange process is more compact, the heat exchange efficiency is improved, the temperature and humidity distribution in the tower can be better controlled by the internal heat exchange module, and the fog dispersal effect can be enhanced.

The application discloses a kind of central air conditioning system outlet water temperature and cold hot water pump frequency coupling operation method, including S1, collection basic data;S2, based on the basic data collected analysis central air conditioning cold and heat load influencing factor;S3, based on the cold and heat load influencing factor analyzed, constructs decision regression tree model;S4, according to the decision regression tree model constructed, central air conditioning system is carried out cooling and heating condition division;S5, according to the condition category divided to central air conditioning operation data is screened, determines the optimal host outlet water temperature and cold hot water pump frequency under each condition category as corresponding condition optimal setting parameter, realizes coupling operation.(1) the operation control method provided by the application simultaneously to host outlet water temperature and cold hot water pump frequency optimization, improve the energy-saving potential of central air conditioning system;The method of the application starts from actual engineering data, optimizes water temperature and water pump frequency while ensuring indoor comfort requirements, ensure the feasibility of the method.

The application discloses a kind of air-cooled refrigerator CAN bus multi-node instruction scheduling method and system, it is related to refrigerator control technical field, it is solved in the air-cooled refrigerator multi-node CAN bus control scene, when sudden heat load of freezing chamber, emergency refrigeration switching instruction is easily crowded by ordinary periodic message and leads to the problem of execution lag, first build cabinet temperature acquisition result, message category and periodic occupation relationship, then according to temperature deviation, temperature rise rate, door opening disturbance and forecast overrun result generate cabinet emergency degree, and form emergency refrigeration instruction set and ordinary message degradation set, then according to basic cycle division synchronous reference window, emergency exclusive window, key feedback window and ordinary arbitration window, make emergency refrigeration instruction priority transmission and priority confirmation, finally according to key message response delay, cabinet recovery state and ordinary message backlog situation iteration correction next round of scheduling parameters, to improve the timeliness and stability of multi-node collaborative control under sudden load scenario.

The application discloses a kind of sample storage equipment automation storage control method and system, it is related to storage equipment control technical field, method includes: obtaining sample access request, residual task quantity and mechanical arm heat load value;Task density analysis is carried out to sample access request, and current task mode is identified;If current task mode is high-density task mode, then according to residual task quantity, queue length influence coefficient, mechanical arm heat load value and heat load influence coefficient, buffer interval time is calculated;According to buffer interval time, sample access request is batched processing;In batch processing, high-density operation mode is executed;If current task mode is low-density task mode, then execute routine operation mode.The application can combine residual task quantity and mechanical arm heat load value to calculate buffer interval time, and sample access request is batched processing, to realize storage control, improve accuracy and reliability.

The application discloses an intelligent cold and heat source adjusting control system, which comprises an operation data acquisition module, an adjusting basic target function construction module, a network constraint module, a cold and heat source adjusting limit construction module, a cold and heat source adjusting network training module and a real-time adjusting control module. The application belongs to the field of intelligent control and specifically discloses an intelligent cold and heat source adjusting control system. The application constructs a joint constraint target function, combines network constraints and prevents the repeated jumping of an actuator. A positive and negative deviation buffer margin is introduced to ensure that the fluctuation range of a control instruction is limited within a safety boundary. A bidirectional independent buffer margin mechanism is introduced to limit the fluctuation amplitude of different operation states in layers. A working condition smoothing loss function is reconstructed and a load gradient sensing coefficient is introduced to realize the real-time sensing of a cold and heat load change rate, automatically saturate and inhibit an ultra-large deviation, avoid the loss of control caused by extreme abnormal values and improve the cold and heat source adjusting control effect.

The application discloses a load prediction and adaptive adjustment method of a district heating system, and belongs to the field of intelligent heating and energy management automation. The method constructs a graph model representing the physical topology structure of the heating pipe network, and quantifies the heat transfer attenuation by giving dynamic connection weights to the connection between nodes based on the pipe physical parameters. After obtaining the thermal data and outdoor environment data, the historical data and the graph model are input into the load prediction model to extract the spatial coupling characteristics between each heat exchange station, and the time domain coding characteristics are extracted based on the outdoor environment data. A feedforward compensation mechanism for the thermal inertia of building envelopes is introduced in the process to correct the temperature response lag. The above-mentioned characteristics are fused to generate a heat load prediction value, and the circulating pump frequency or the primary side electric regulating valve opening degree of each heat exchange station is adaptively adjusted accordingly. The application solves the problems that the existing method cannot fuse the spatial coupling relationship of the pipe network and lacks thermal inertia compensation, and improves the prediction accuracy and system energy efficiency.

The invention discloses a flow boiling radiator fin structure and a design method thereof.The flow boiling radiator fin structure comprises a heat dissipation shell and profiling dune sets, the heat dissipation shell is provided with an inlet and an outlet, and the multiple profiling dune sets are fixed in the heat dissipation shell and arranged between the inlet and the outlet in parallel at equal intervals; each profiling sand dune group is formed by arranging a plurality of crescent-imitated sand dune fins at equal intervals, the arrangement direction of the crescent-imitated sand dune fins is perpendicular to the flowing direction from the inlet to the outlet, the protruding end of each crescent-imitated sand dune fin faces the inlet, and the concave end of each crescent-imitated sand dune fin faces the outlet; according to the structure, the flow boiling heat exchange efficiency can be improved, meanwhile, steam bubbles can be effectively guided to be separated from a path, the flow resistance is reduced, the performance adjusting requirements under different heat load and flow conditions are met, the more efficient and more reliable two-phase cooling system structure configuration can be easily achieved, and the heat dissipation requirement of high-heat-flux application is met.

The present application relates to building energy saving and energy consumption data analysis technical field, disclose a kind of based on multi-source data's existing building energy consumption evaluation system and method, system includes: data acquisition module obtains multi-source data sequence and solves power factor;Steady-state slice module removes transient power sequence to generate quasi-steady data set;Load decoupling module extracts and deducts static baseline and dynamic activity load;Efficiency compensation module constructs compensation factor according to power factor variance, exports pure heat load power sequence;Hysteresis diagnosis module decomposes high and low frequency components, extracts shallow and deep thermal inertia delay parameters;Defect evaluation module calculates equivalent comprehensive heat transfer coefficient, and it is combined with two delay parameters and compared with standard benchmark, judge the defect condition of envelope structure.The present application excludes personnel activity interference, equipment transient fluctuation and unit energy efficiency deviation, obtains energy consumption data reflecting the actual heat transfer characteristics of building envelope structure, realizes the determination of building physical defect type.

The application relates to the technical field of data processing, and provides a multi-modal water-cooling heat dissipation system and method. A heat load urgency is generated by real-time acquisition of temperature change rates and thermal power densities of components, the running state of the components is monitored, a user scene identifier is output, a temperature difference of cooling liquid at the inlet and outlet of a radiator and a cooling liquid flow are compared to estimate instantaneous heat dissipation, a temperature change trajectory is predicted based on the heat load urgency and the user scene identifier, the temperature change trajectory is calibrated in combination with the instantaneous heat dissipation, a candidate mode set and an optimization weight are determined through the user scene identifier, a target heat dissipation mode is selected, the temperature distribution of different regions of the radiator is acquired according to the target heat dissipation mode, a fan target rotating speed is distributed, local high heat loads are responded to, the topology structure of a cooling liquid flow path is dynamically adjusted through a multi-way valve, and the cooling liquid flow is distributed, so that the fan and the cooling liquid are deeply cooperated.

The application provides a rare earth light emitting diode lighting device and a cooling control method. The lighting device is used to determine a heat load temperature threshold of the lighting device in a current working state through cumulative aging characteristics of each LED chip in the lighting device and historical time sequence characteristics of a heat load temperature. When a real-time junction temperature of the lighting device is greater than the heat load temperature threshold, the cumulative effect value of the thermal fatigue temperature in each LED chip of the lighting device is calculated based on the junction temperature information of the lighting device, the heat accumulation characteristics of the thermal fatigue temperature in the lighting device are determined through all the cumulative effect values, and the thermal fatigue state value of the lighting device after a specified time period is predicted according to the heat accumulation characteristics and the temperature gradient of the shell temperature information of the lighting device. The rare earth light emitting diode in the lighting device is actively and preventively cooled through the temperature difference between the thermal fatigue state value and the real-time junction temperature. The active cooling control of the lighting device based on the dynamic temperature threshold and the fatigue accumulation is realized.

The application discloses a winter daily maximum load prediction method and system based on load-sensitive air temperature distribution, which comprises the following steps: determining the negative growth change rule of statutory holiday load by adopting the year-by-year statistical method; taking months as units, using the observation analysis method, the variation coefficient and other methods to statistically calculate the dispersion degree of daily maximum load, and determining the analysis period of winter heating load; constructing a trend change model; comprehensively considering the influence of multiple factors, setting the load-sensitive air temperature isotherm distribution range of winter heating load; determining the specific range of load-sensitive air temperature isotherm distribution in the calculation and analysis period; constructing a correlation model of heating load and load-sensitive air temperature isotherm distribution range; and realizing daily maximum load prediction. The method can realize short-term quantitative prediction of daily maximum load in a simple and fast table lookup mode, has strong regional expansibility, and has certain guiding significance for regional short-term winter daily maximum load prediction.

The invention discloses a battery thermal management assembly reliability verification method and system. The method comprises the following steps: acquiring real vehicle operation data; based on real vehicle operation data, identifying and clustering abuse behavior modes, and constructing a corresponding thermal load time sequence spectrum; establishing a battery internal short circuit evolution model based on electrochemical-thermal-mechanical coupling, and dynamically generating the position of a thermal runaway initial trigger point, the trigger time, the energy release rate and a local temperature rise curve; based on thermal runaway triggering conditions generated by the internal short circuit evolution model, a thermal propagation induction experiment is implemented, the response capability of the thermal management assembly in the thermal runaway propagation process is monitored in real time, and a verification platform is built; installing the thermal management assembly to be verified on the verification platform, executing an accelerated verification experiment, recording a degradation track, and judging the reliability requirement of the thermal management assembly; the integral and high-fidelity evaluation of the reliability of the battery thermal management assembly is realized, and the safety and reliability confidence level are remarkably improved.

The utility model relates to sealing connecting device technical field especially relates to a kind of vacuum sintering furnace sealing connecting device.Its technical scheme includes furnace body, rotates and installs on the cover of furnace body, further include cooling cavity being located on the cover, sealing body being fixedly installed in cooling cavity, the flow-through groove being equipped with in sealing body and cooling cavity intercommunication, input pipe and output pipe being fixedly installed on the cover and with cooling cavity intercommunication, hydraulic balance component being installed on the cover, the hydraulic balance component includes the detection cylinder being fixedly installed on the cover and with cooling cavity intercommunication, detection plate being slidably installed in the detection cylinder and moving according to the hydraulic pressure size inside cooling cavity.The utility model is directly contacted with the design of sealing body by water cooling device, cooling liquid and sealing body high-efficiency heat exchange, rapidly export high-temperature heat load, strictly control sealing interface temperature in material tolerance threshold, completely avoid rubber carbonization, metal creep and other thermal failure.

The invention provides a composite target material for producing astatine-211 through a cyclotron and an active cooling system of the composite target material, and relates to the technical field of radionuclide preparation. The composite target material comprises a working part and a connecting part which are integrally formed, the upper surface of the working part is provided with an inclined surface or a concave curved surface used for depositing a bismuth layer, and a plurality of liquid cooling runners communicated with one another are integrated in the connecting part; the active cooling system further comprises a supporting cooling base detachably connected with the composite target material, and the supporting cooling base and the composite target material jointly form a closed circulating cooling loop. The thermal load distribution can be optimized by changing the geometric configuration of the working surface, and the integrated liquid cooling and modular assembly and disassembly design is combined, so that efficient heat dissipation is realized, and meanwhile, rapid replacement of the target material is supported. According to the system, the anti-meltdown capability and the operation stability of the target material are remarkably improved, and the system is suitable for large-scale high-yield preparation of astatine-211.

The utility model relates to the technical field of solar storage battery temperature control, in particular to a solar storage battery sensing temperature control self-heating system which comprises a protection box, a heating load module is arranged in the protection box, a storage battery is placed in the heating load module, and the storage battery is arranged in the protection box. A pulse generation and control module electrically connected with the heating load module is arranged at one end of the outer side wall of the protection box, and a temperature collection and feedback module is arranged in the middle of the side wall of the heating load module. The pulse generation and control module is combined with the PWM controller, so that the dynamic adjustment of the pulse frequency and the duty ratio is realized, the heating power can be accurately controlled according to the real-time temperature feedback, the energy consumption is obviously reduced, and the system adaptability is improved; a grid frame structure consisting of transverse and longitudinal PTC heating sheets is adopted, and a silica gel heat diffusion layer (the heat conductivity coefficient is greater than or equal to 3W / m.K) is combined, so that rapid and uniform transfer of heat is ensured.

The invention discloses a multi-channel heating control device and a control method thereof, and belongs to the field of medical instruments, the heating control device comprises a plurality of heating channels, a heating load in each channel is connected with a heating unit, the heating units are connected with a data processing unit, and a data source of the data processing unit is from a temperature acquisition unit. A data source collected by the temperature collecting unit is from the heating unit, a heating threshold value is set in the data processing unit, heating is started when the temperature reaches the lower limit of the heating threshold value, heating is stopped when the temperature reaches the upper limit of the heating threshold value, the same heating mode is adopted in the multiple channels for heating in turn, and heating equipment operates for heating in a low-power state. The heating device can reduce sudden change of current of equipment caused by starting of a power supply and influence of electromagnetic radiation of the equipment on other equipment in the same environment, and can ensure stable heating and stable operation of the equipment.

The invention discloses a PU value online monitoring and intelligent adjusting system in a wine sterilization process, and the system comprises an identification loading module which is used for reading container identification information before a container to be sterilized enters a sterilization device, and loading a product thermosensitive parameter set corresponding to the container according to the container identification information; the multi-source acquisition module is used for acquiring the temperature and the conveying speed of a spraying medium according to spraying partitions in the sterilization process, and synchronously acquiring the temperature of the outer surface of the container to form a continuous temperature sequence; and the reference temperature measurement module is used for selecting an accompanying reference container and acquiring an internal temperature sequence of the accompanying reference container for constructing an internal temperature calibration constraint. According to the invention, through combination of cold point sensing, PU conservative calculation and target adaptive adjustment, precise control of sterilization intensity and thermal load minimization are realized.

This application discloses a structural adhesive and its preparation method, belonging to the field of battery technology. The structural adhesive includes phase change microcapsules and flame-retardant microcapsules. The phase change core material of the phase change microcapsules undergoes a solid-liquid phase change when the battery temperature reaches its phase change point, absorbing a large amount of heat and providing instantaneous "peak shaving and valley filling" capabilities that surpass traditional thermal conductive materials. This effectively copes with the battery's peak heat load and effectively reduces the temperature of local hot spots. When the temperature further increases, the flame-retardant microcapsules rupture, precisely and rapidly releasing a high concentration of flame-retardant core material, i.e., flame retardant, to the heat source point. This achieves on-demand release of the flame retardant, preventing premature failure of the flame retardant under normal operating conditions, thereby greatly improving flame-retardant efficiency and ultimately enhancing the safety of the entire vehicle.