107 results about "Global temperature" patented technology

The invention relates to the field of electromagnetic induction heating, and discloses a global temperature control method for an electromagnetic induction heating system, which comprises the following steps of: acquiring a two-dimensional temperature distribution diagram of a heating area at a plurality of key moments through a multi-point infrared temperature acquisition and induction heating coil synchronous excitation mechanism; performing multi-dimensional region segmentation on the global temperature scene data set, and identifying a temperature abnormal region and a thermal diffusion path feature based on a boundary thermal gradient change rate and an energy transfer path analysis algorithm; a distributed power regulation and control method and a historical thermal field evolution rule are introduced based on the regional thermal response feature map; thermal field feedback information is collected in real time in the temperature control process, and the current global thermal field state is judged in combination with a thermal distribution stability judgment criterion; and based on the updated control parameter configuration table, in combination with the thermal performance parameters of the material and the geometric information of the workpiece, generating an operation instruction set of the heating process, and executing intelligent regulation and control. The induction heating device has the advantage of improving the induction heating quality.

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

The invention relates to the field of environment simulation control, in particular to an environment simulation method and system for a high and low temperature test box. The method comprises the following steps: acquiring initial state monitoring parameters of a high and low temperature test box, performing regional environment parameter division and time point gradient change fitting, and constructing a plurality of regional temperature and humidity gradient change curves; identifying a test object in the high and low temperature test box; performing transient thermal change limit evolution excavation on the test object according to the plurality of regional temperature and humidity gradient change curves to generate a transient thermal change limit curve of the current test object; acquiring a preset test box environment simulation log, performing global temperature change cross period optimization according to the transient thermal change limit curve of the current test object, and constructing a global temperature change optimization simulation period; and executing environment simulation control operation according to the global temperature change optimization simulation period, and collecting high and low temperature simulation environment monitoring data. According to the invention, quick-response, accurate and stable environment simulation control is realized.

The invention relates to a turbine blade global temperature field reconstruction method based on a multi-modal physical constraint network, and belongs to the technical field of aero-engine thermal management. Aiming at the problems of insufficient global heat conduction path modeling, lack of physical rules and blank region differentiation perception, carrying out normalization and position coding on sparse temperature measurement points to form a Transform encoder input sequence; extracting features through an encoder and compressing the features into global feature vectors through self-attention pooling; decoding the image into a temperature field image by adopting a U-Net generator fused with jump connection; a multi-scale discriminator is constructed, a semantic weighting mechanism is introduced, and differentiated weights are distributed to cooling holes, tenons and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on a heat conduction equation, and a weighted training model is combined with confrontation loss and temperature error loss. The method is used for providing high-precision temperature distribution data support for turbine blade structure optimization design, cooling system efficiency evaluation and thermal fatigue prevention.

The invention relates to the field of digital twinborn technology and industrial equipment intelligent operation and maintenance, and discloses an electric power screen cabinet heat dissipation efficiency improvement and thermal management comprehensive analysis system, which comprises an offline modeling module for constructing a thermal order reduction model with efficient calculation through a model order reduction method based on high-fidelity CFD simulation data; the real-time data acquisition module is used for acquiring real-time power consumption, sparse sensor temperature and an expected load curve; the online state estimation module is used for fusing physical prediction of the thermal reduced-order model with real-time sparse measurement data and reconstructing a global three-dimensional temperature field of the power screen cabinet in real time; and the prospective scheduling analysis module is used for taking the current global temperature field as an initial condition, driving the thermal reduced-order model to perform prospective deduction by utilizing an expected load curve, and calculating and outputting a future thermal margin to early warn a thermal risk. According to the invention, comprehensive real-time sensing, prospective risk prediction and self-adaptive correction of the thermal state of the electric screen cabinet are realized, and thermal management is converted from passive response to active prevention.

The invention provides a multi-sensor fusion welding temperature monitoring method and a related device, and the method comprises the steps: dividing a welding region into a plurality of sub-regions according to the spatial distribution of the welding region, and obtaining the initial temperature data corresponding to each sub-region according to various temperature sensors; performing time alignment on the initial temperature data through difference resampling of different sampling points to generate a time sequence temperature matrix; performing real-time quality detection on the time sequence temperature matrix to generate a real-time quality score, and performing weighted combination on the real-time quality score and a preset confidence coefficient parameter table to obtain a dynamic confidence coefficient result set; fusing the effective temperature data exceeding a preset confidence threshold in the dynamic confidence result set according to a fusion rule corresponding to each sub-region to obtain a fused temperature result; and the fusion temperature results of the sub-regions are subjected to time sequence splicing to obtain a global temperature curve of the welding process, so that the accuracy of temperature detection in the welding process can be effectively improved.

The invention relates to the field of thermal fault early warning of machine room inspection robots, in particular to a multi-modal sensing thermal fault early warning method of a machine room inspection robot, which comprises the following steps: acquiring infrared thermal image, acoustics, vibration and gas concentration data through a multi-modal sensor array, and generating voxelization temperature distribution; according to the method, temperature features are extracted through wavelet transform and a fractional Brownian motion algorithm and are fused into an equipment global temperature feature set; constructing noise, vibration and gas concentration tower extraction auxiliary features, and combining non-extensive entropy and a graph attention model to be fused into a high-dimensional fault feature pool; building a fiber bundle model by taking the machine room topology as a base space, and locating a thermal fault after optimizing parameters by a simulated annealing algorithm; and finally, a dynamic thermodynamic diagram and the like are generated through digital twinning, so that accurate early warning is realized.

The invention discloses a temperature real-time monitoring method and system based on mass concrete continuous pouring, and the method comprises the steps: collecting data in a mass concrete continuous pouring process in real time through the arrangement of sensors, forming a global temperature set of temperature values at each collection moment, obtaining the temperature value of each layer at each collection moment according to pouring layering, and carrying out the real-time monitoring of the temperature of each layer; forming a layered temperature set of the temperature values of each layer; determining a concrete pouring abnormal index; determining a dynamic early warning threshold value in the concrete continuous pouring process, determining a multi-stage early warning and response mechanism according to comparison between the concrete abnormal index and the threshold value, and generating an early warning control signal; and based on the early warning control signal, controlling the water cooling equipment and carrying out real-time monitoring feedback. According to the method, the specific temperature conditions of each layer at different acquisition moments can be precisely known, the positions of the layers with abnormal temperature can be more precisely positioned, structural cracks caused by temperature incoordination between the layers are avoided, and real-time monitoring of the temperature of mass concrete continuous pouring is achieved.

The invention relates to the technical field of chip processing, and discloses an intelligent temperature control heating platform based on a SiC substrate and a chip processing method. The method comprises the following steps: modeling SiC substrate temperature distribution characteristics to obtain a three-dimensional temperature field distribution model; performing temperature parameter analysis to obtain a SiC substrate surface temperature distribution parameter set; performing hot area analysis to obtain a heating unit contribution degree coefficient matrix and a target hot area distribution map; calculating a power distribution value to obtain a heating power setting matrix; performing four-stage hierarchical control on the heating power setting matrix to obtain a stable transition control curve; according to the method, stable transition from the room temperature to the process temperature is achieved, extra preheating equipment is not needed, a stable processing temperature environment is provided for the SiC substrate, energy consumption is reduced, and efficient utilization of energy in the processing process of the SiC substrate chip is achieved.

The invention provides a temperature distribution inversion method based on a multi-vertex grid and a high-robustness error functional, and the method comprises the steps: 1, obtaining a temperature gradient change rule of a measured structure under a typical thermal load condition, and determining a three-dimensional multi-vertex grid division scheme and a temperature sensor layout form of the measured structure; step 2, establishing an m vertex grid basis function model, and constructing a conversion matrix between a regular grid and an irregular grid; 3, establishing a high-robustness error functional between the theoretical temperature of the centroid position of each surface of the m-vertex grid and the actually measured temperature of the sensor; 4, a temperature matrix equation containing a temperature coefficient matrix and a temperature amplitude matrix is constructed, temperature vectors of all grid vertexes are solved, the m-vertex grid basis function model is combined, three-dimensional temperature distribution in the structure space is inverted, and according to the method, only few sensors need to be adopted, the global temperature response of the structure can be obtained, and the inversion precision is high; the method is simple, convenient and high in real-time performance.

The invention discloses a data center global temperature control optimization method based on online-offline hybrid reinforcement learning, and the method comprises the steps: constructing an interpretable global state space fusing physical information, and guaranteeing that a state variable has a clear physical meaning; a combined action space is defined, and cooperative control of the cold source side and the tail end side is achieved; designing a multi-objective reward function based on physical model driving, and comprehensively considering energy consumption, temperature stability, carbon emission and physical optimization indexes; obtaining a basic security policy through offline pre-training, and extracting reliable behaviors from historical data by using a conservative Q learning algorithm; strategy optimization is achieved through online safety fine adjustment, and gradual adjustment is conducted under multiple constraints to adapt to real-time changes; and finally, deploying an optimization strategy to realize closed-loop control of the system, and establishing a continuous learning mechanism to cope with long-term drift. According to the method, a complete offline-online mixed learning system is established, and the energy efficiency of the data center temperature control system is improved.

The invention relates to the technical field of spraying control, and discloses a spraying process self-adaptive adjustment method and system based on temperature measurement, and the method comprises the steps: presetting multi-sensor parameters, calculating a dynamic weight, calibrating sensors, and solving the problems of real-time interference and cross-process sensitivity difference; acquiring environmental data of a target spraying process, generating a global temperature field thermodynamic diagram, associating the film thickness with an environmental interference label, and providing a three-dimensional reference; coating workpiece heat flux and empty workpiece heat flux are collected, net heat flux and real-time reaction enthalpy change are calculated, a three-dimensional curing degree model is constructed, and the real-time curing degree is calculated to reflect the coating film curing progress; based on real-time curing degree fusion temperature spatial-temporal characteristics and phase change characteristics, core characteristics are extracted, a digital twin model is established to preview a process adjustment effect, an optimal control vector is output by constructing a spray gun three-dimensional optimization function and an improved quantum genetic algorithm, a targeted actuator is triggered for regulation and control, and a coating thermodynamic parameter library is constructed. And updating model parameters to optimize the overall situation.

The invention discloses an industrial mainboard computer heat dissipation optimization method based on AI regulation and control, and particularly relates to the technical field of industrial mainboard heat dissipation regulation and control. A multi-source sensor is used for synchronously collecting temperature, performance load, cooling feedback and environment data, a dynamic heat map is generated based on mainboard physical layout, a hot spot area is identified and predicted, and the heat dissipation optimization of an industrial mainboard computer is realized. Global temperature monitoring is realized, and local overheating is effectively prevented; the load and temperature of a future time window are predicted through a hybrid prediction model, the fan rotating speed is adjusted in advance in combination with feedforward control, and delayed response is avoided; a multi-objective optimization model is constructed, an optimal fan rotating speed sequence is solved under constraint conditions of temperature upper limit and power consumption limit, feedback compensation and feedforward control are combined, cooling requirements are accurately distributed, excessive cooling is effectively avoided, energy consumption is reduced, and meanwhile the heat dissipation effect is guaranteed. Reactive type control based on the threshold value is converted into predictive type control based on the AI, and the response speed and accuracy are remarkably improved.

The invention provides an injection mold temperature field regulation and control method and device based on multi-sensor fusion, and the method comprises the steps: recognizing a non-uniform region based on a surface radiation temperature distribution image, discrete point temperature data, an instantaneous heat flow direction and heat flow intensity information; based on each non-uniformity area and the heating and cooling unit adjacent to the non-uniformity area, constructing a space mapping relation, and based on the space mapping relation, the instantaneous heat flow direction and the heat flow intensity information, generating a heat flow path guide instruction; triggering a corresponding heating and cooling unit to execute alternate heating and cooling operation according to a preset phase difference based on the heat flow path guide instruction, so that the heat flow directionally migrates in the non-uniform region along a specified path; and monitoring that the temperature gradient change trend of each non-uniform area tends to be uniform based on a dynamic heat balance state formed by the heat flow after directional migration in the mold, and entering a steady-state maintenance mode. According to the invention, high-precision sensing and dynamic cooperative regulation and control of the global temperature field of the injection mold are realized.

The invention relates to the technical field of power supply management, in particular to a low-temperature emergency start power supply control system, which comprises a temperature acquisition unit used for acquiring dynamic temperature field data of a vehicle power supply operation environment in real time through a multi-source temperature sensing network to obtain environment temperature field distribution data; the trend prediction unit is used for performing temperature field evolution trend prediction based on the environment temperature field distribution data to obtain temperature field evolution trend data; and the risk assessment unit is used for carrying out risk coupling assessment based on the temperature field evolution trend data and obtaining comprehensive risk grade data based on the power supply health state data and the vehicle operation historical data. According to the method, global temperature information is obtained by constructing the multi-source temperature sensing network and the dynamic temperature field, the evolution trend of the temperature field is predicted by using algorithms such as wavelet transform and a long-short-term memory network, the problem of response lag of a traditional mode is avoided, and thermal management can be timely and accurately carried out on the battery.

The invention relates to the technical field of distribution network operation and maintenance, in particular to a distribution network operation and maintenance system based on digital twinning, and the system executes the following steps: constructing a distribution network digital twinning body of a physical distribution network, driving the distribution network digital twinning body to update the electrical state of the distribution network digital twinning body and equipment microenvironment parameters, outputting a global electrical stress distribution diagram and a global microenvironment distribution diagram at the current moment; a virtual temperature sensing grid is generated, thermal dynamic simulation is carried out, and a global temperature field distribution cloud picture is output; and analyzing the global temperature field distribution cloud picture, identifying and tracking a dynamic overheating risk trajectory, predicting an evolution path of the overheating risk germination point in a subsequent prediction environment and a planned load change trend, generating a dynamic migration trajectory of the overheating risk germination point, and outputting targeted early warning and routing inspection positioning information. According to the method, a traditional hysteresis operation and maintenance mode taking'temperature overrun 'as a judgment basis is broken, and qualitative changes from static monitoring to dynamic prediction and from passive warning to active pre-control are truly realized.

The invention discloses a data center machine room temperature field equalization method based on a multi-scale model learning strategy. Fusing the equipment tolerance, the refrigeration system performance and the environmental thermodynamic boundary to construct a dynamic safe operation boundary; a machine room-cabinet-equipment multi-scale reverse model is constructed based on a physical guidance neural operator, and backstepping from observation of a temperature field to an optimal control strategy is realized; the method comprises the following steps: dynamically dividing a machine room into a plurality of logic control areas, deploying a reinforcement learning agent for each area, deciding and adjusting the air supply speed under the constraint of a safety boundary, and meanwhile, learning the minimum feasible air speed on line to optimize energy conservation; and when temperature abnormity which cannot be processed by local control is detected, generating and executing a global coordinated regulation and control instruction according to the global temperature field data and an analysis result of the multi-scale model. According to the method, on the premise of guaranteeing the safety of equipment, the temperature field of the data center is subjected to refined and self-adaptive balanced regulation and control, and the energy efficiency and the operation reliability of a refrigeration system are remarkably improved.

The invention discloses a rotor bearing cooperative temperature control method, which comprises the following steps of: acquiring rotor bearing end face hot spot region information, millimeter-level spatial resolution local temperature field distribution information and vibration spectrum and local temperature information, and fusing to generate rotor bearing global temperature field model information; according to the rotor bearing global temperature field model information, a double closed-loop control system is operated or a phase change microcapsule coolant is triggered; when the double-closed-loop control system is operated, the viscosity and the flow of lubricating oil are adjusted, and / or the flow of cooling water is dynamically distributed through a variable nozzle array, and operation data of the double-closed-loop control system are output; when the phase change microcapsule coolant is triggered, the rotor bearing is subjected to point-shaped cooling; and running data of the double-closed-loop control system is subjected to deep reinforcement learning dynamic optimization and fault mode knowledge graph pre-judgment and then is transmitted back to the double-closed-loop control system. The operation reliability and the service life of the rotor bearing can be remarkably improved.

The invention provides a ship refrigerated container cluster redundancy control method and system, and the method comprises the steps: deploying a plurality of temperature sensors in each refrigerated container, and obtaining high-reliability environment temperature information through employing a multi-source data fusion technology; monitoring the running state of the local main controller of the refrigerated container in real time, judging whether the local main controller fails or not, and sending out an alarm signal; the redundant system monitors and stores all key temperature data of the refrigerated container in real time, and performs simple preprocessing to form a historical temperature database; comparing the control efficiency of the local main / backup controller and the control efficiency of the redundant system, and deciding an output signal weight; an accumulated historical database is utilized to dynamically adjust and optimize an adaptive fuzzy PID control rule in the redundant system; and collecting and counting models of local main / standby controllers and comparison and selection results of controller states and efficiency, and uploading the comparison and selection results to the cloud to construct an equipment efficiency knowledge base, so that faults can be accurately identified in a short time, the global temperature field characterization precision is improved, and unified regulation and control of containers of multiple brands are realized.

The invention discloses an anomaly detection method, electronic equipment, a storage medium and a program product, and relates to the technical field of computers, and the method comprises the steps: determining a first weight of an influence degree of each temperature detection device on a plurality of preset position temperatures on a first board card based on first temperatures and first positions detected by a plurality of temperature detection devices; and according to the plurality of first temperatures, the first weight and the second position of each device in the board card, generating a first image of a target point with abnormal temperature and a position relationship between the target point and each device, so that the problem of a detection blind area caused by the fact that the whole area of the board card cannot be covered due to limited deployment position of a temperature sensor in the prior art can be effectively avoided; the technical problem of low efficiency due to the fact that the heating condition of the position without the sensor needs to be checked manually is solved, and the technical effects of automatically reconstructing the global temperature distribution of the board card, accurately positioning the incidence relation between the abnormal heating point and the device and improving the anomaly detection efficiency and accuracy are achieved.

The invention discloses a data center machine room energy-saving intelligent regulation and control system and method based on data driving. The system is provided with a data acquisition module, an edge intelligent processing node, a relation matrix construction module, a global optimization control module and a strategy execution and visualization module. Multi-point temperature data in a cold channel and a hot channel are collected in real time, preprocessing is carried out through edge calculation, and the mapping relation between temperature distribution and control behaviors is established through a relation matrix. In actual operation, the system continuously accumulates the environment state, the air conditioner set value, the air supply speed and the corresponding temperature response data, continuous modeling and control strategy optimization are conducted through a self-learning algorithm, and dynamic adjustment of the precise air conditioner air supply temperature, the air return temperature and the floor air supply outlet air speed is achieved. Compared with the prior art, machine room energy-saving management of global temperature control can be realized, the total energy consumption of the machine room is greatly reduced, and the coordination control requirement between the temperature stability of the cold and hot channels and the energy efficiency is met.

The application provides a three-dimensional stacked chip, a temperature control method and an electronic device. The chip comprises a logic die and a storage die stacked on the logic die; a first temperature sensor and a hierarchical temperature controller disposed in the logic die, the temperature controller being configured to control the first temperature sensor to collect first temperature data of the logic die; a second temperature sensor disposed in each layer of the storage die, and a temperature output pin shared by the storage dies, the temperature output pin being configured to output second temperature data of the storage die, the second temperature data being determined based on temperature collected by the second temperature sensor; and a temperature fusion controller connected with the temperature controller and the temperature output pin, and configured to perform scene classification based on the first temperature data and the second temperature data, and perform global temperature collaborative control or local area temperature control based on a scene classification result. Through the application, temperature collaborative control of the three-dimensional stacked chip can be realized.

The invention belongs to the technical field of temperature control, and particularly relates to a machine room temperature control method and system based on distributed optical fiber temperature sensing, and the method comprises the steps: collecting a first temperature set and a second temperature set through two or one distributed optical fiber sensing network which is disposed in a machine room or a machine room cooling unit and is used for the refrigerant outlets of an energy-using cabinet and a refrigerating cabinet; within the set unit time, the sum of the temperature rise of all the energy consumption cabinets and the sum of the temperature change of all the refrigerant outlets are calculated; and then, inputting the operation parameters into a control model constructed based on a heat energy balance principle, calculating to obtain the operation control parameters of the refrigeration equipment, and accurately adjusting the output according to the operation control parameters. High-precision and large-range direct temperature sensing is achieved through the DTS technology, and dynamic precise matching and energy saving of heat production and refrigeration of the machine room are achieved through the global temperature difference and the control strategy combining calculation and a physical model.

The invention relates to the technical field of water-salt distribution, and discloses a simulation system and method for water-salt evolution of an island aquifer under the influence of underground structures, and the method comprises the steps: prefabricating standard dyeing seawater by adopting NaCl and carmine; diluting standard dyeing seawater in proportion to prepare saturated sand column standard samples with different salinity gradients, and collecting profile images of the saturated sand column standard samples; extracting the average gray value of the section image of each saturated sand column standard sample and the corresponding global temperature and salinity; fitting the average gray value, the global temperature and the salinity corresponding to the saturated sand column standard sample, and establishing a global temperature coupled salinity inversion model; acquiring an experiment profile image and an experiment global temperature of the sea island aquifer water-salt evolution experiment under the influence of the underground structure; and generating a two-dimensional salinity distribution data field according to the salinity inversion model, the experimental profile image and the experimental global temperature. The full-profile image of the aquifer to be measured is collected in real time, the relation between the light intensity and the salinity is established, interpolation processing is not needed, and the simulation result is more accurate.

Disclosed in the present invention are a monitoring system and method for the global temperature and humidity of a main cable of a suspension bridge. The monitoring system for the global temperature and humidity of a main cable of a suspension bridge comprises a demodulation module, a data processing module and a plurality of measurement cable strands. It is only necessary to provide a small number of measurement cable strands in an original main cable to implement global state monitoring of the main cable. Mass temperature and humidity data acquired by means of the monitoring system can provide real data support for state assessment of the main cable. Temperature grating array sensing optical fibers and humidity grating array sensing optical fibers, which are arranged in the measurement cable strands, are small in terms of size and have high measurement precision, are resistant to electromagnetic interference, do not require a power supply, can be directly installed in place as the cable strands are being pulled, and have a good economical performance.

The present invention relates to a method for reconstructing the global temperature field of a turbine blade based on a multimodal physical constraint network, and belongs to the field of aero-engine thermal management technology. In order to solve the problems of insufficient global heat conduction path modeling, missing physical rules, and regional differentiated perception gaps, the sparse temperature measurement points are normalized and position-encoded to form a Transformer encoder input sequence; features are extracted by the encoder and compressed into a global feature vector through self-attention pooling; a U-Net generator with fused jump connections is used to decode it into a temperature field image; a multi-scale discriminator is constructed and a semantic weighting mechanism is introduced to assign differentiated weights to cooling holes, tenons, and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on the heat conduction equation, and a weighted training model is formed by combining adversarial loss and temperature error loss. It is used to provide high-precision temperature distribution data support for turbine blade structural optimization design, cooling system performance evaluation, and thermal fatigue prevention.

The invention discloses a multi-source temperature early warning method and system for a lithium ion battery. The method comprises the steps that three paths of independent temperature data are collected; processing the acquired three paths of temperature data to obtain a real-time temperature value, and judging the working state of the temperature sensor based on a preset threshold value and a data rationality rule; performing cross validation and data fusion on the three paths of temperature values in combination with a working state, and executing a composite judgment strategy algorithm to obtain a reliable temperature value; and outputting a reliable temperature value, and updating a global temperature sensor state flag for fire alarm logic decision. By fusing the data of the three heterogeneous temperature sensors and combining state judgment and composite judgment strategies, the system can still output credible temperature value judgment when any one or two sensors fail, are interfered or fail, the robustness and safety of the lithium ion battery thermal management system are remarkably enhanced, and the reliability of the lithium ion battery thermal management system is improved. And the temperature monitoring reliability and the fault-tolerant capability are improved.

The invention discloses a method for repairing abnormal curvature of curved glass, which comprises the following steps of: positioning a main repairing area, and identifying a compensation area of a new defect caused by heat conduction by adopting a prediction model. In the repairing process, through the synergistic effect of the main heat source and the auxiliary heat source, parameters of the main heat source and the auxiliary heat source are adjusted according to a global temperature field monitored in real time, management of the thermodynamic state in the repairing process is achieved, and the problem that the temperature field is uneven due to local heating is solved. The technical problem that new profile tolerance is out-of-tolerance or optical distortion is induced while original curvature abnormity is repaired is solved, and it is ensured that the shape precision and the optical performance of the repaired curved glass are synchronously improved.

This invention provides a computational method, system, and computer equipment for fluid-structure interaction heat transfer. Addressing the problem of extremely slow convergence in the later stages of long-tail iterations in existing weakly coupled algorithms, this invention proposes a two-stage decoupling acceleration strategy. The first stage employs a fluid-solid weakly coupled algorithm with an early truncation mechanism: when the global temperature rise has not yet converged, but the convective heat transfer coefficient at the interface reaches a stable convergence state first, the bidirectional fluid-solid iteration is stopped, and the current convective heat transfer coefficient is extracted. The second stage applies this convective heat transfer coefficient as a separate thermal boundary condition to the solid computational domain, stopping the flow field equation calculation and rapidly solving the solid's heat conduction equation in a unidirectional decoupled state until a complete steady state is reached. This invention cleverly utilizes the spatiotemporal differences in the convergence speed of characteristic physical quantities, eliminating redundant iterations without sacrificing engineering accuracy and significantly improving the solution efficiency of heat transfer simulation.

The application relates to the technical field of battery thermal management, and particularly discloses a lithium battery module charging and discharging process temperature control system and method, which comprises a thermal field dynamic perception module, a heat flow path prediction module, an adaptive regulation and control decision module and an executor array coordination module. By constructing a global temperature field digital twin model, predicting future heat flow paths and hot spots, and driving the executor array to perform spatial directional coordinated regulation and control, closed-loop, dynamic and accurate control of the internal temperature of the lithium battery module is realized, and the thermal safety and temperature uniformity are improved.