95 results about "Thermal state" patented technology

This invention discloses a method for predicting and protecting the overheating risk of asynchronous motors, specifically relating to the field of motor control and protection technology. Based on an intelligent fusion model, it estimates temperature and thermal stress field, calculates the rate of change of thermal stress, non-uniformity, and hotspot trends, and calculates a dynamic health index using historical data. These parameters are then input into a multi-objective reinforcement learning controller to optimize long-term health and short-term performance, generating a thermal shaping control vector to regulate the motor. This invention combines physical mechanisms with data-driven approaches through an intelligent fusion model, utilizing a graph neural network to learn the structure of the heat conduction graph and verify physical laws, thereby improving the accuracy of thermal state estimation. It achieves a multi-dimensional risk characterization combining transient impact and cumulative effects through the rate of change of thermal stress, non-uniformity, hotspot trends, and dynamic health index. By optimizing long-term health and short-term performance losses, a thermal shaping control vector is generated to achieve regulation from passive protection to active prevention, extending the motor's service life.

This invention discloses an adaptive charging method for nickel-metal hydride batteries based on pulse modulation. By simultaneously sampling the temperature at multiple characteristic locations on the battery surface and calculating the average temperature, thermal field standard deviation, and maximum battery temperature, a comprehensive quantitative perception of the overall and local thermal state of the battery is achieved, overcoming the limitations of single-point temperature measurement and accurately identifying local thermal risks. By comparing the thermal field standard deviation with a thermal field imbalance threshold and switching the charging control mode based on the comparison result, combined with a differentiated duty cycle adjustment strategy matched to the charging control mode, adaptive control of charging parameters is achieved, balancing charging efficiency when the thermal field is uniform and suppressing local hot spots when the thermal field is unbalanced. By simultaneously monitoring the real-time battery voltage, maximum battery temperature, and cumulative charging time, and immediately stopping pulse charging when a set threshold is reached, a triple safety protection mechanism is constructed, mitigating overcharging and overheating risks from multiple dimensions and significantly improving the safety and reliability of the charging process.

The application relates to the technical field of polymer material processing and molding, and discloses a temperature gradient control method for melting and plasticizing of food packaging PET bottle blanks. The method aims to solve the problems of static temperature control, poor melt homogeneity and weak process adaptability in the traditional plasticizing process. The method comprises the following steps: constructing an axial partition temperature setting model, dividing a solid conveying section, a melting transition section and a melt homogenization section and setting a reference temperature interval; deploying a distributed temperature sensing array to collect temperatures at key positions in real time; establishing a melt thermal state prediction model based on a non-Newtonian fluid heat conduction equation; dynamically regulating and controlling heating and cooling through a PID compound algorithm to realize closed-loop control of axial gradient and radial uniformity; and combining a process database to perform adaptive parameter correction. The application can significantly improve temperature control precision and melt homogeneity, enhance adaptability to raw material changes and high-speed production, effectively reduce bottle blank internal stress, wall thickness deviation and acetaldehyde content, and guarantee product quality and food safety.

This invention discloses a dynamic control method and system for data center cooling systems based on a large model. The method involves preprocessing the collected data to obtain preprocessed data; using a graph convolutional network (GCN) to model the thermal environment of the data center to obtain spatial features; and using a gated recurrent unit (GRU) to receive the data and spatial features, capturing the dynamic dependencies in time to obtain temporal features. A spatiotemporal attention mechanism is introduced to evaluate the weights of different sensor nodes in predicting future thermal states at different time points. Based on these weights, the spatial and temporal features are adjusted to output predicted temperature values. A deep reinforcement learning agent (DRL) is constructed, and based on the predicted temperature values, a cooperative control strategy is obtained through proximal policy optimization (PPO) to reduce the total energy consumption of the cooling system and is then executed. This achieves a dynamic balance between data center energy consumption and heat dissipation.

This invention discloses a liquid-cooled collaborative intelligent temperature control method and system based on layered PCM, relating to the field of liquid-cooled temperature control technology. The method includes: S1, real-time acquisition of liquid-cooled temperature control data and preprocessing of the data; S2, calculation of the sensible and latent heat absorption of the outer and inner PCM layers, assessment of the risk of thermal imbalance on the conductor surface, determination of the current thermal state, and output of a thermal risk warning signal; S3, assessment of the degree to which cable bending weakens thermal coupling performance, triggering a layered PCM collaborative temperature control strategy, and execution of graded control commands for the power pump, circulation pump, and three-way valve; S4, assessment of the deviation between the current heat exchange capacity and the heat load demand, correction of the cooling response coefficient, and optimization of the PCM collaborative temperature control strategy. This solves the problems of unstable thermal coupling between the coolant pipe and the heating conductor in existing liquid-cooled cables, and the tendency for pipe deformation and contact thermal resistance and localized hot spots to occur with frequent bending.

The present application relates to the technical field of power equipment, and more particularly to an intelligent thermal management system for a cleaning robot, comprising a state sensing unit, a risk assessment unit, a thermal state calculation unit, a data decision unit, an execution unit and a feedback correction unit, the state sensing unit collects internal temperature, humidity and key component temperature; the risk assessment unit calculates dew condensation trend and overheating trend representation values accordingly; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision unit determines the actual adjustment amount of heating and cooling power based on the above parameters; the execution unit includes heating, cooling and load management modules, and adjusts the output power accordingly; the feedback correction unit corrects the thermal state according to the operation deviation. Through multi-parameter fusion decision and dynamic power adjustment, the present application realizes the collaborative management of heat dissipation and dew condensation prevention, and improves the working reliability of the cleaning robot in harsh environments.

This invention relates to the field of medical ablation technology, and discloses a method and system for controlling the temperature of an ablation electrode. The method includes: during the ablation process, acquiring in real time the output power of the ablation host and the biological tissue impedance between the ablation electrode and the lesion tissue monitored by the ablation host; calculating the real-time heat generation power of the ablation electrode acting on the lesion tissue; acquiring the inlet temperature, outlet temperature, and instantaneous flow rate of the cooling medium flowing through the internal cooling channel of the ablation electrode, and quantifying the real-time heat dissipation power of the cooling medium according to the heat transfer relationship of the medium; determining thermal state parameters based on the real-time difference between the real-time heat generation power and the real-time heat dissipation power, the thermal state parameters reflecting the heat accumulation trend; dynamically adjusting the flow rate of the cooling medium using a feedforward method; and in the feedforward dynamic adjustment, collecting and associating successful ablation parameter combinations of biological tissue to construct a self-learning parameter library for the ablation electrode. This invention can improve the accuracy of ablation electrode temperature control.

This invention provides a method and system for temperature control in cold chain logistics transportation of agricultural products, belonging to the field of cold chain temperature control technology. The method includes: acquiring operating condition data characterizing the thermal state and airflow state during cold chain transportation; processing the operating condition data to characterize dynamic temperature features, obtaining feature parameters reflecting these features, and training a temperature rebound prediction model based on these feature parameters; generating a temperature rebound prediction curve based on the temperature rebound prediction model, and applying a preset correction rule to the basic target temperature curve corresponding to the transportation task based on the temperature rebound prediction curve to generate a corrected target temperature curve; generating target control parameters for driving the refrigeration unit and circulating fan based on the corrected target temperature curve and the operating condition data, and performing temperature regulation based on the target control parameters. This invention enables cold chain transportation temperature regulation to better match real-time heat load changes and maintain more stable temperature control performance.

This application provides a battery thermal management method and device based on a dynamic aerogel thermal insulation pad. The battery thermal management method can monitor the thermal state information inside the battery and the temperature-sensitive aerogel substrate in real time through an embedded sensing network, and input the monitored thermal state information into a physical information neural network model deployed in the intelligent control unit. The physical information neural network model predicts the temperature field and heat flow field inside the battery and outputs coordinated control commands for the temperature-sensitive aerogel substrate and the external thermal management system. In this way, by dynamically coordinating the temperature-sensitive aerogel substrate and the external thermal management system, the dynamic aerogel thermal insulation pad is transformed from a passive thermal insulation material into a "temperature-adaptive thermal management intelligent medium" for the battery, fundamentally solving the technical contradiction between the static material characteristics of existing aerogel thermal insulation pads and the dynamic thermal management requirements of batteries.

A method (100) for determining a ground state (1000) of a quantum system is disclosed, comprising the following steps: 6) determining (102) the thermal state (1020) of the quantum system at a temperature value (1001', 1001) by optimizing the free energy (3020) of the thermal state (1020) at the temperature value (1001', 1001) by iteratively adjusting gate parameters (1022', 1022) by means of a variational quantum thermalizer (1010) until a first termination criterion is met; 7) checking (1025) whether the previously found thermal state meets a second termination criterion; 8) if the second termination criterion is not met (10250): decreasing (1026) the temperature value (1001', 1001); initializing (301) the quantum circuit (1011) of the variational quantum thermalizer using the gate parameters (1022', 1022) from the last iteration of step 2); repeating steps 2) and 3); 9) if the second termination criterion is met (10251): providing (103) an energy spectrum (1030) of the quantum system using the gate parameters (1022) of the last iteration of step 2) before the second termination criterion (10251) is met; determining (104) the ground state (1000) of the quantum system by selecting the state of the energy spectrum that is assigned the lowest energy.

This application provides a method and system for monitoring the condition of equipment in a production workshop based on the Internet of Things (IoT), relating to the field of equipment condition monitoring technology. The method includes: firstly, collecting multi-angle infrared data and control system switching data from the equipment via the IoT; then, processing the infrared data using a region growing algorithm to obtain three-dimensional temperature data, and calculating temperature gradient and divergence data accordingly; next, labeling the above data with the switching data to form thermal state data; then, constructing a structural relationship diagram of the equipment, and using a graph convolutional neural network in conjunction with the thermal state data to generate a feature vector reflecting the overall thermal state of the equipment; subsequently, inputting this feature vector and the switching data into a long short-term memory neural network to predict future temperature changes; finally, generating equipment health status monitoring results by comparing the prediction results with dynamic thresholds. This application achieves accurate predictive maintenance for early abnormal heating and potential failures of key equipment in a production workshop.

This invention discloses an adaptive load-based method and system for temperature regulation of a hydro-generator, relating to the field of hydropower control technology. It involves real-time acquisition of multi-source operating data, time alignment, and feature fusion. The fusion result is input into a physically constrained thermal state prediction model to obtain the generator's predicted thermal state and instantaneous heat generation information, generating an advanced cooling regulation amount based on the heat generation mechanism. The system employs a control architecture that parallels baseline safety versions and candidate strategy versions. Candidate strategies are updated online in the background, and control commands to be verified are output. Before issuance, all control commands undergo an online safety verification mechanism to assess command consistency and condensation risk. If the verification fails, the system automatically blocks and triggers strategy rollback or switches to a degraded control mode. This invention overcomes the temperature regulation lag problem caused by thermal inertia, balancing regulation advancement, model physical consistency, and operational safety under complex operating conditions, and is suitable for the safe operation of large hydro-generator units.

The application discloses a conductive light-shielding FPC special adhesive curing process control method and system, relates to the circuit board manufacturing technical field, and the method comprises the following steps: obtaining the target rheological property information and target reactivity information of the glue in the glue dispensing equipment; obtaining the target substrate thermal state information and the environmental parameter information at the entrance of the curing oven; obtaining the curing progress information and temperature distribution information inside the glue layer; generating a target parameter adjustment instruction, which comprises a glue dispensing parameter adjustment instruction and a curing parameter adjustment instruction; adjusting the glue dispensing equipment parameters according to the glue dispensing parameter adjustment instruction to control the coating state of the glue, and adjusting the curing oven parameters according to the curing parameter adjustment instruction to control the curing progress of the glue layer. The application can generate different parameter adjustment instructions to adjust the glue dispensing equipment parameters and the curing oven parameters, so as to realize the curing process control, improve the accuracy and product quality.

The present application relates to a kind of asphalt pavement paving temperature self-adaptive control method, specifically relates to the technical field of asphalt pavement construction equipment, by real-time identification heat diffusivity, convective heat transfer coefficient and the equivalent temperature of lower base, so that system matrix, control matrix and disturbance matrix are dynamically updated with material thermal state, and then the time constant of exponential decay expected temperature trajectory is adaptively adjusted, to avoid heating mismatch caused by material batch fluctuation or environmental change;Combined with model predictive control rolling optimization, under the upper and lower limit of heating power density, the increment change rate and the upper limit protection constraint of surface layer temperature, actively look ahead to adjust heating power density, effectively suppress the temperature difference mutation between new and old asphalt layer, improve interlayer bonding strength and compaction uniformity, while reducing the asphalt aging and energy waste caused by over-heating, realize the collaborative optimization of paving quality and energy efficiency.

The application relates to the technical field of fuel cell control, and discloses a fuel cell adaptive optimization calculation method and system based on deep learning, which comprises the following steps: acquiring environmental parameters and operating state parameters of a fuel cell system; identifying the thermal state characteristics of a stack in the fuel cell system based on the environmental parameters and the operating state parameters; constructing a dynamic target temperature optimization model according to the thermal state characteristics and the environmental parameters, and outputting a dynamic target temperature; constructing a collaborative control model comprising a main control intelligent agent and a thermal management intelligent agent for a fan of an air-cooled stack in the fuel cell system, so as to generate a fan collaborative control instruction; predicting and compensating the fan collaborative control instruction according to the thermal state characteristics and the dynamic target temperature, so as to overcome the response lag caused by thermal inertia; and realizing adaptive optimization control of the fuel cell system based on the fan collaborative control instruction after the prediction and compensation, so that the control target and strategy can be dynamically adjusted according to the real-time changes of the environment and working conditions.

The application discloses a deep-groove wrapped lithium battery liquid cooling heat dissipation system based on fuzzy PID control and relates to the technical field of battery thermal management. The system comprises a battery module unit, a liquid cooling circulation loop, a data acquisition unit and a main controller. The battery module unit is integrated with square lithium batteries and deep-groove wrapped heat exchangers. The liquid cooling circulation loop is used for realizing external energy transfer. The data acquisition unit is used for collecting temperature signals reflecting the thermal state of the square lithium batteries in real time. The main controller performs logical reasoning on the temperature signals through a fuzzy PID control program, generates a pulse width modulation (PWM) signal, and adjusts the cooling liquid flow rate of a variable frequency water pump and the heat exchange intensity of a radiator assembly. Through the deep cooperation of the deep-groove wrapped heat exchange structure and the fuzzy PID control program, the heat exchange efficiency, temperature uniformity and temperature control precision of the lithium battery module are greatly improved, the optimal balance of heat dissipation energy efficiency is realized, and the operation reliability, safety performance and cycle life of the battery system are enhanced.

The application provides a low-temperature double-pulse test platform and method, and relates to the technical field of power device testing.The platform comprises a refrigeration compressor, a cooling platform, an enclosure, an air extraction unit, a temperature acquisition unit, a parameter acquisition unit and a control unit.The control unit is used for acquiring surface temperature information of the power device to be tested, platform temperature information of the cooling platform and thermistor parameters under the action of a calibration pulse during the cooling process, determining the corrected equivalent junction temperature parameters of the power device to be tested, and controlling the double-pulse test loop to perform double-pulse testing when the corrected equivalent junction temperature parameters enter a target test temperature window.The method introduces a calibration pulse and thermistor parameters on the basis of a low-temperature environment, corrects and characterizes the actual thermal state of the power device to be tested, and controls the double-pulse testing time in combination with the target test temperature window, so as to improve the accuracy, consistency and repeatability of low-temperature double-pulse testing.

This application provides a control method and system for a trackless rubber-tired vehicle in underground mines based on equipment thermal state perception, belonging to the field of trackless rubber-tired vehicle control technology. The method includes: planning a travel path based on the target task type, a digital map of the roadway in the target travel area, and positioning information; controlling the trackless rubber-tired vehicle to travel along the travel path and collecting environmental image data, temperature data, and real-time operating condition data of the roadway ahead; obtaining the environmental thermal risk level based on the environmental image data and the expected travel trajectory corresponding to the travel path; inputting the temperature data, real-time operating condition data, and environmental thermal risk level into a thermal state prediction model to obtain a predicted temperature change curve; determining the thermal state assessment level based on the current temperature value of the target component and the predicted temperature change curve; and inputting the environmental thermal risk level, thermal state assessment level, target task type, target travel area, and real-time operating condition data into a collaborative decision engine to generate a collaborative control instruction set.

This invention relates to the technical field of cold box control methods, and particularly to a method and apparatus for rapid and stable control of a cold box. The proposed technical solution includes: a cold box historical state feature library, a dynamic feedforward compensation calculation module, and a collaborative control output module; specifically, it includes the following steps: S1: Constructing the cold box historical state feature library; S2: Historical state modeling and dynamic feedforward compensation control strategy. By utilizing the historical optimal operating trajectory for feedforward-led control, valve adjustment can be performed more actively and in closer accordance with the actual thermal state of the cold box, significantly shortening the time required for the cold box to reach the target stable operating condition. This significantly improves temperature control stability and safety, greatly reduces the risk of equipment cracking, leakage, or even damage, and extends the service life of the cold box; it also improves the comprehensive utilization efficiency of applications such as liquid-air energy storage; and by reducing the risk of thermal stress damage, it reduces equipment maintenance costs and unexpected downtime losses.

The application provides a bulldozer brake fault early warning and protection control method and system, and belongs to the technical field of hydrostatic bulldozers, and comprises the following steps: collecting operation data of a hydrostatic bulldozer; based on the operation data, a thermodynamic model is used to estimate a brake thermal state in real time, so that a core estimated temperature and a thermal load rate of the brake are obtained; based on the core estimated temperature and the thermal load rate, a multi-feature fusion diagnosis is used to obtain a risk comprehensive level of the brake, and a position of the brake with the highest risk is located; according to the risk comprehensive level and the position of the brake with the highest risk, a pump-motor collaborative adaptive fault-tolerant control algorithm is used to control a hydrostatic transmission system of the bulldozer to realize brake protection. The application solves the friction plate sintering fault of the hydrostatic bulldozer brake, formulates a brake solenoid valve fault protection control strategy, and improves the stability of the whole vehicle.

This invention relates to the field of industrial control technology, and more specifically, to a method for precise pressure adjustment in the injection molding holding process based on iterative learning control. The method includes: acquiring a reference holding pressure curve, an actual feedback pressure sequence, and injection molding holding control commands for the current holding stage; calculating thermal hysteresis characteristics characterizing changes in physical state; calculating a thermal drift evolution index characterizing the thermal drift rate; generating a nonlinear adaptive gain adjustment factor constrained within a safe range; and calculating and updating the injection molding holding control commands for the next holding stage. This invention more accurately reflects the comprehensive impact of changes in physical state, scientifically assesses the thermal drift rate and the difference between current and historical data, dynamically adapts to thermal drift to ensure control stability under different thermal conditions, effectively solves the problems of holding pressure overshoot, oscillation, and slow response, improves the dimensional consistency of injection molded products and the control accuracy of the holding process, and ensures production quality and efficiency.

The application discloses a construction method of a newly-built intelligent arch dam, and the method comprises the following steps: in the arch dam construction process, a moderate perception module is established to collect monitoring data based on a multi-source monitoring device; a real analysis module is established to construct an analysis model based on the monitoring data, so as to analyze the thermal state, mechanical response state and structural stability of the arch dam; an intelligent decision module is established to determine an analysis model and a decision model matched with a scheduling scenario through a large language model and a knowledge graph, to optimize and solve decision variables under the analysis support of the analysis model by the decision model, and to obtain scheduling decision information; and an automatic control module is established to execute a control operation on the arch dam related physical device through a pre-encapsulated control module, so as to realize automatic execution of the decision instruction. The application realizes integrated construction of the moderate perception, real analysis, intelligent decision and automatic control modules of the intelligent arch dam in the arch dam construction process, so that the arch dam is built into an intelligent arch dam.

The application provides a kind of traction motor bearing internal temperature inversion system and method based on external temperature measuring point, it is related to equipment state monitoring technical field.System includes temperature acquisition module, operating condition parameter acquisition module, temperature inversion model module and result output module;Temperature acquisition module acquires the temperature of multiple external temperature measuring points on the outer surface of motor, operating condition parameter acquisition module acquires motor operating condition parameters, temperature inversion model module fuses two types of data to calculate bearing internal target position inversion temperature, and result output module completes result output.The application establishes the mapping relationship between external measurable information and internal temperature by multi-source data fusion, combined with circumferential multi-measuring point layout, consistency check weighted fusion and prediction deviation adaptive correction, improves the accuracy, reliability and anti-interference ability of inversion temperature, realizes non-invasive temperature inversion, and provides easy engineering implementation technical support for traction motor bearing thermal state evaluation, fault early warning and life prediction.

The application discloses a goaf intelligent fire prevention control method based on residual coal dynamic evaluation and relates to the technical field of mine fire prevention safety, and comprises the following steps: in the process of goaf intelligent fire prevention control, in view of the situation that the inert gas is prone to backflow due to temperature difference in a hot environment, a continuous temperature and pressure monitoring link is established, temperature, pressure and flow direction information of the inert gas in different channels are acquired in real time in a time sequence mode, and a continuous monitoring data set is formed. Through continuous monitoring and dynamic oriented control, stable forward flow of the inert gas in the goaf is realized, and backflow and cooling blind area are eliminated; through introduction of a periodic flow fluctuation and a dynamic compensation loop, a self-balancing state of the airflow and the temperature is maintained, a continuous cooling zone is formed, and therefore the real-time response capability and overall stability of the fire prevention process are improved.

The application discloses an artificial intelligence control energy storage liquid cooling system method, and relates to the technical field of energy storage system cooling; comprising: step 1: improving the liquid cooling energy storage system, step 2: collecting the thermal energy data of the liquid cooling energy storage system through the sensor, step 3: analyzing and processing the thermal energy data to obtain the analysis result, obtaining the operation state, heat distribution and heat dissipation effect data of the liquid cooling energy storage system according to the analysis result, step 4: based on the operation state, heat distribution and heat dissipation effect data of the liquid cooling energy storage system, constructing and training an artificial intelligence prediction model, using the artificial intelligence prediction model to predict the future thermal state development trend by learning the historical data and real-time data, and making corresponding decisions according to the current thermal state, step 5: according to the thermal state development trend predicted by the artificial intelligence prediction model and the provided decisions, formulating and executing corresponding thermal energy control strategies: according to the predicted thermal state development trend, adjusting the operation parameters of the liquid cooling energy storage system, controlling each component of the liquid cooling energy storage system to real-time regulate and control the liquid cooling energy storage system.