433 results about "Thermal state" patented technology

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention relates to the technical field of cold box control methods, in particular to a rapid and stable control method and device for a cold box. According to the technical scheme, the system comprises a cold box historical state feature library, a dynamic feed-forward compensation calculation module and a cooperative control output module; the method specifically comprises the following steps: S1, constructing a cold box historical state feature library; and S2, historical state modeling and dynamic feed-forward compensation control strategy. The feed-forward dominant control is performed by using the historical optimal moving trajectory, so that the valve can be adjusted more actively and more closely to the actual thermal state of the cold box, and the time required for the cold box to reach the target stable working condition is remarkably shortened. The temperature control stability and safety are remarkably improved, the risks of cracks, leakage and even damage of equipment are greatly reduced, and the service life of the cold box is prolonged; the comprehensive utilization efficiency of applications such as liquid-air energy storage is improved; and by reducing the risk of thermal stress damage, the equipment maintenance cost and the accidental shutdown loss are reduced.

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 relates to the technical field of intelligent detection, and discloses a high-temperature alloy casting size on-line detection system based on machine vision, and the system comprises an imaging fusion module which collects image data of a high-temperature alloy casting, carries out the pixel-level registration of infrared image data and visible light image data, and generates a multispectral fusion image. The feature extraction module performs temperature distribution analysis and brightness feature extraction based on the multispectral fusion image, generates an edge confidence map and extracts a feature point set. And the calculation module performs weighted fitting on the feature points according to the feature point set to obtain thermal state size parameters. And the thermal compensation module performs thermal compensation and geometric correction on the thermal-state size parameters to obtain cold-state size parameters. And the judgment module carries out comparison to judge whether the casting is a qualified casting. According to the invention, stable imaging and accurate registration in high-temperature radiation and strong reflection environments are realized, and the detection efficiency and the size control level of the high-temperature alloy casting are improved.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention relates to the technical field of motors, and discloses a high-temperature-resistant direct-current coreless gear motor and a test system, and the system comprises a temperature monitoring module, a current regulation and control module, a heat dissipation optimization module, a load response module, a cloud analysis module and a virtual verification module. By arranging the temperature monitoring module, multi-dimensional temperature data during operation of the motor can be acquired in real time, and a temperature distribution diagram can be generated, so that the system can comprehensively sense the internal thermal state of the motor and accurately identify local overheating risk points, thereby improving the operation reliability and safety of the motor under a high-temperature working condition; through cooperative operation and closed-loop control of the current regulation and control module, the heat dissipation optimization module and the load response module, according to real-time temperature and load changes, driving current parameters and heat dissipation resource distribution can be dynamically adjusted, and the heat dissipation efficiency is improved. And the output performance and the torque stability of the motor under high-temperature and high-load conditions are ensured.

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 a surge margin prediction method and device of a gas compressor, equipment and a storage medium. The method comprises the steps that key parameter point sets obtained through load step iteration of the gas compressor in all the operation stages are obtained, all the key parameter point sets are mapped to performance diagrams of temperature identification to obtain corresponding mapping performance diagrams, discrete points in the same temperature layer in all the mapping performance diagrams are fitted, and the corresponding mapping performance diagrams are obtained; and the thermal state performance curves of the gas compressor in all the operation stages are determined, the surge critical points of all the thermal state performance curves are obtained, the predicted surge margin of the gas compressor in all the operation stages is determined according to the surge critical points of all the thermal state performance curves and all the key parameter point sets, and load step iteration is achieved based on multi-physics field coupling of the gas compressor. By adopting the method, the surge margin of the gas compressor in different operation stages can be predicted based on the key parameter point set obtained by load step iteration realized by considering multi-physics field coupling in each operation stage of the gas compressor, so that the accuracy of a prediction result is improved.

The invention discloses a virtual sensing method for thermal characteristics of an internal combustion engine. The method comprises the following steps: S1, acquiring temperature distribution data; s2, time sequence temperature data mapping; s3, acquiring fusion temperature data; s4, obtaining thermal characteristics; and S5, abnormal judgment of a combustion chamber, an exhaust pipeline and a cooling system. According to the method, the comprehensiveness and the accuracy of thermal state analysis are remarkably improved by constructing a global sensing system of a three-dimensional space temperature field of the internal combustion engine and combining a multi-source sensor collaborative acquisition and deep learning feature fusion mechanism. Through closed-loop coupling of time-space sequence temperature data and a thermodynamic mechanism model, quantitative correlation between thermal characteristics and combustion efficiency, heat energy loss and cooling efficiency core performance parameters is established. According to the LSTM-CNN-MLP collaborative framework, the spatio-temporal evolution law of a temperature field is extracted in a self-adaptive mode, and the robustness of thermodynamic state perception is remarkably enhanced.

The invention belongs to the technical field of new energy automobile charging equipment, and relates to a charging pile power adaptive adjustment system and method based on temperature feedback, and the method comprises the steps: carrying out the data fusion of a plurality of collected temperature values through employing a Kalman filtering algorithm, and obtaining a target temperature fusion value representing the overall thermal state of a charging pile; the target temperature fusion value is compared with a preset temperature safety threshold value, and the output power of the charging pile is dynamically adjusted according to a comparison result; the future temperature trend is predicted through a charging pile internal temperature prediction model and an air cooling / liquid cooling cooperative temperature adjustment mechanism, and a system can take actions in advance before the temperature does not reach a dangerous threshold but is determined to exceed the standard, so that the passive situation of traditional thermal management is thoroughly changed, real preventive control is realized, and the safety of the system is improved. The output power can be actively reduced when it is detected that the temperature in the pile rises, and overheating damage is prevented; and when the temperature returns to normal, the original power is automatically recovered, so that the reliability and the service life of the charging pile are improved.

The invention discloses a building energy consumption control method. The method comprises the following steps: acquiring thermal state data of equipment; based on the thermal state data, a quantitative thermal urgency index fusing static temperature difference, dynamic change and instant demand dimension is generated; and performing cooperative scheduling on the communication resources allocated to the equipment according to the quantitative thermal urgency index. According to scheduling, candidate communication links are scored through a multi-dimensional evaluation system at least comprising a thermal cooperation dimension, a target link is determined through marginal utility evaluation under dual budget constraints of communication energy consumption and equipment power, and a communication resource scheduling scheme comprising time slot allocation and power authorization is generated and executed. According to the method, the physical state requirement of the equipment is deeply coupled with communication resource scheduling, collaborative optimization of cross-domain resources is realized, and the response speed and the overall energy efficiency of building energy consumption control are improved.

The invention relates to the technical field of power electronic conversion, and discloses a high-efficiency rectification conversion system and a control method thereof, and the system comprises the following modules: a detection module, an input compatible module, a hybrid power device module, a control module, a thermal management module and a frequency modulation module. The control method comprises the following steps: acquiring input power grid voltage, output current and load state parameters; calculating an input voltage phase difference; dynamically selecting the type and channel configuration of the silicon carbide or gallium nitride power device according to the load rate; outputting a device configuration instruction and a switching frequency control instruction; adjusting the cooling system through the evaluated thermal state of the conversion system; and the control module receives the control instruction output by the control module and is used for implementing a corresponding pulse width modulation strategy. According to the method, a multi-system power grid identification and self-adaptive topology switching strategy is adopted, the technical effect of automatically configuring the optimal rectification architecture under different input types is achieved, and the problems of response delay and poor compatibility of the method in the environment of variable power grid types are solved.

The invention discloses a hybrid power vehicle collaborative thermal management method and system based on energy flow intelligent perception. The method comprises the following steps: sensing and predicting multi-dimensional energy flow characteristics of an engine in real time through a pre-trained time sequence neural network model; the predicted value is used as a feedforward signal, real-time temperature feedback of the power battery is combined, and a thermal management cooperative control instruction is generated through feedforward-feedback cooperative decision logic; and finally, a heat exchange path between the engine cooling system and the battery heat management system and respective execution mechanisms are dynamically adjusted, so that prospective active cooperative temperature control is realized. The problems of passive response, control delay and low energy efficiency of an existing thermal management system are solved, and accurate and efficient two-way optimization management of the thermal states of the engine and the battery is achieved.

The invention discloses a mass concrete construction temperature control anti-cracking integrated system and method, and the system comprises a construction module which is used for constructing a cooling water pipe fine model based on concrete thermodynamic parameters and construction conditions, solving a heat flow coupling control equation through a heat flow coupling algorithm, and determining a concrete internal temperature field and stress field distribution cloud chart; the monitoring module is used for collecting attribute data of concrete; the comparison module is used for comparing the attribute data with the concrete internal temperature field and stress field distribution cloud atlas, correcting model parameters of the heat flow coupling model according to a comparison result, generating a regulation and control instruction and sending the regulation and control instruction to the execution module; the execution module is used for receiving and executing the regulation and control instruction; the regulation and control instruction comprises at least one of temperature regulation and pouring time sequence optimization. A temperature field and a stress field are accurately predicted, complex construction working conditions are matched, mechanical and thermal state changes of the whole life cycle of concrete are captured in real time, temperature control adjustment is effectively conducted, and the construction quality is improved.

The invention belongs to the technical field of heat supply systems, and discloses a regional heat supply system optimization control method for multi-heat-source networking operation. The method comprises the steps that S1, physical entity data, hydraulic working condition data and temperature data in a regional heat supply pipe network are collected in real time, an adjacent matrix is constructed according to the actual connection relation of the physical entity data, and a digital network architecture is formed; s2, dividing the digital network architecture into a plurality of sub-architectures based on an improved GN algorithm, and establishing a state space model for each sub-architecture; and S3, constructing a rapid simulation model based on the state space model, and continuously fusing each physical entity data and a preset deviation range through a physical information neural network. According to the method, the thermal state change caused by hydraulic adjustment and the reaction of the thermal change on the hydraulic resistance characteristic can be captured in real time, meanwhile, all physical entity data and hydraulic working conditions are processed in a unified mode, and deviation caused by decoupling simplification in a traditional method is reduced.

The invention relates to the technical field of refrigeration energy efficiency monitoring of refrigeration houses, in particular to a dynamic monitoring system and method for refrigeration energy efficiency of a refrigeration house. A multi-objective optimization scheme analysis module in the system is used for analyzing the refrigeration energy efficiency of the refrigeration house based on the running state of each temperature zone of the current refrigeration house, the heat state of stored goods and an external electricity price signal; and by taking the minimization of the total operation energy consumption of the refrigeration house and the minimization of the temperature fluctuation of each temperature zone as optimization objectives, constructing a multi-objective optimization function, and generating at least one candidate energy-saving operation scheme in a future time window of a preset duration. According to the method, through multi-dimensional data perception, a digital twinborn model for specific characteristics of goods and refrigeration house scenes is established; a multi-objective optimization function with the lowest total energy consumption, the lowest temperature difference fluctuation of the temperature zone and the highest energy efficiency ratio of the system as the core is constructed; and a plurality of candidate energy-saving schemes are dynamically generated by using the function, and an optimal scheme is screened out through a normalized comprehensive evaluation system, so that clear energy efficiency data and optimization suggestions are provided for managers, and invalid energy consumption is avoided.

The invention discloses a heat dissipation control method and device of a liquid cooling charging pile and a medium, is applied to a liquid cooling control panel, and relates to the technical field of electric vehicle charging, and the method comprises the steps: collecting the original temperature data of a plurality of key nodes in a charging pile liquid cooling system in real time; the key node at least comprises a charging gun head and a cooling liquid inlet / outlet; comparing the original temperature data with a preset multi-stage temperature threshold value, and determining a current thermal state of the charging pile liquid cooling system according to a comparison result; according to the current thermal state, control parameters of a heat dissipation actuator in the charging pile liquid cooling system are generated through a pre-constructed temperature control engine; and generating a heat dissipation driving signal based on the control parameter, and sending the heat dissipation driving signal to a heat dissipation actuator so as to adjust the operation state of the heat dissipation actuator, so that the temperature of the charging pile liquid cooling system is maintained within the target temperature range. And according to different thermal state intelligent switching control strategies, it is ensured that the liquid cooling system can quickly respond and maintain the optimal thermal balance under any working condition.

The invention relates to the technical field of power equipment, in particular to an intelligent thermal management system for a cleaning robot, which comprises 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, and is characterized in that the state sensing unit is used for acquiring internal temperature and humidity and key component temperature; the risk assessment unit calculates the characterization values of the dew formation trend and the overheating trend; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision-making unit determines the actual adjustment amount of the heating and cooling power according to the parameters; the execution unit comprises a heating module, a cooling module and a load management module and correspondingly adjusts output power. And the feedback correction unit corrects the thermal state according to the operation deviation. According to the invention, through multi-parameter fusion decision and dynamic power adjustment, collaborative management of heat dissipation and condensation prevention is realized, and the working reliability of the cleaning robot in a severe environment is improved.

The invention discloses a single-phase immersion battery intelligent thermal management control method and device for an energy storage power station, and relates to the technical field of battery thermal control. The intelligent thermal management control method and device for the single-phase immersed battery for the energy storage power station comprises the steps that S1, battery state data and external environment data in the operation process of the energy storage power station are collected and preprocessed, and a standardized thermal state data set is constructed; s2, performing working condition evaluation on the current working condition state by combining the battery heat flow density, and dynamically adjusting the flow direction of the cooling liquid; s3, the current heat exchange load and the phase state safety boundary are comprehensively analyzed, and the valve opening degree is driven to change; and S4, carrying out stability evaluation on the local thermal stability, and adjusting a power supply loop safety response strategy in real time. The problems that a traditional heat management system lacks the bidirectional regulation and control capacity for coping with wide-temperature-range extreme climate and cannot give consideration to the collaborative requirements of high-temperature heat dissipation and low-temperature heat supply, and consequently system structure redundancy, energy consumption increase and operation and maintenance complexity are caused are solved.

The invention relates to the technical field of electrochemical energy storage safety, particularly discloses a multistage thermal runaway protection and AI early warning method for an energy storage system, and aims to solve the problems that traditional early warning depends on macroscopic signal lag, single-mode misinformation is high, and a protection strategy is rigid. According to the method, thermal state data are collected through a multi-modal monitoring system, core features are extracted after the data are preprocessed, global feature vectors are generated through weighted fusion, a CNN-LSTM hybrid model is input to predict normal, slight thermal runaway risks, moderate thermal runaway risks, severe thermal runaway risks and the risk level of thermal runaway, and the risk level of thermal runaway is calculated. Dynamically triggering grading protection based on the risk grade; early early warning and accurate gradient intervention of thermal runaway are achieved, and the active safety protection capacity of the energy storage system is remarkably improved.

The invention relates to the technical field of battery thermal safety management, and discloses an energy storage battery thermal runaway intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining an energy storage system cooling layout parameter and a sensor data flow, generating a data missing mode recognition result containing a missing time period, a missing position and a missing type; probability distribution of temperature and flow physical quantity of the missing sensor is inferred through a message passing mechanism, and a numerical value distribution inference result of the missing sensor is output; posterior state distribution is calculated through Bayesian reasoning, and a virtual-real mixed thermal state estimation result is generated; and analyzing a thermal state estimation result based on a confidence coefficient weighted multi-objective optimization algorithm, constructing a multi-objective function including weighted risk minimization and cooling energy consumption cost minimization, and outputting a cooling control instruction. The technical problem that a traditional digital twinning system is highly dependent on data integrity is solved.

The invention belongs to the technical field of photoelectric detection, and discloses a refrigeration type handheld observation device and a dynamic temperature control method thereof. An infrared thermal imager is installed at the cold end of a semiconductor chilling plate and is connected with a heat dissipation structure through a composite phase change structure and a heat pipe array; acquiring environment parameters and equipment thermal state parameters by using an environment and equipment parameter acquisition unit; and the dynamic temperature control unit is used for matching the refrigeration power in real time according to the environment parameters and the equipment thermal state parameters, and the working current of the semiconductor refrigeration sheet is adjusted according to the target refrigeration power obtained through matching. The cooling efficiency of the refrigeration type handheld observation device is actively adjusted, the temperature control precision is improved, and heat management of the refrigeration type handheld observation device is optimized.

The invention relates to the technical field of liquid cooling electric control temperature adjustment, and discloses an air temperature adjustment system for a liquid cooling electric control room based on multi-mode switching. The system comprises a multi-source thermal state monitoring unit, a dynamic thermal coupling evaluation unit, a cooling requirement grading unit and a multi-mode decision-making unit. The multi-source thermal state monitoring unit collects the temperature of a liquid cooling loop of a frequency converter and a fracturing rectifier, the environment temperature of an electric control room and equipment operation power data in real time. The dynamic thermal coupling evaluation unit constructs a nonlinear heat conduction model based on the collected data, and evaluates the dynamic thermal coupling effect between the two devices; the cooling demand grading unit calculates the cooling demand grades of the two devices in combination with the thermal coupling evaluation result and a preset thermal load threshold value; and the multi-mode decision-making unit generates a combined forced cooling, combined natural cooling or mixed cooling mode decision through a ternary state judgment mechanism. The system can flexibly switch cooling modes, is adaptive to different thermal load states of equipment, and gives consideration to both the cooling effect and the energy efficiency.

The invention discloses a regulation and control method and device for a vehicle cooling system, and the method comprises the steps: collecting a temperature signal and a flow speed signal of cooling liquid in real time, obtaining an engine rotating speed signal and a load signal, carrying out the fusion processing, and generating a real-time working condition parameter of the system. And on the basis of the real-time working condition parameters of the system, a pre-calibrated degassing flow control mapping table is inquired, target degassing flow is obtained through calculation, and an adjustment value of the opening degree of the degassing channel is determined accordingly. Generating a control instruction to drive an actuator to adjust the opening degree of the degassing channel according to the adjustment value of the opening degree of the degassing channel in combination with an environment temperature signal; and meanwhile, an adjusted cooling liquid temperature feedback signal is collected, and the actual heat state parameter of the system is calculated based on the difference value between the cooling liquid temperature feedback signal and the target temperature. Therefore, the system has the advantages of dynamically responding to the working condition change of the engine, optimizing the flow distribution of the cooling liquid and maintaining the pressure balance of the system, and can effectively improve the heat management efficiency and the system reliability.

The invention discloses a multi-scale bidirectional coupling sweating cooling simulation method which comprises the following steps: firstly, on a mesoscale, constructing a mesoscopic flow model, simulating flow of a coolant, and obtaining parameters such as porosity, permeability and inertia coefficient of a porous medium; introducing the obtained parameters into a porous medium panel area of a thrust chamber geometric model constructed under a macroscale to realize cross-scale parameter coupling; the flow and heat transfer process in the thrust chamber geometric model after cross-scale parameter coupling is simulated, and the speed, pressure, temperature and local heat flux density distribution of a combustion chamber and a porous medium panel area under the macroscale are obtained; and taking the local heat flow density as a new wall surface boundary condition, capturing the flow and heat transfer characteristics of a coolant in pores under a thermal state working condition based on the mesoscopic flow model, and further evaluating the overall cooling performance and the thermal protection effect of the porous medium panel. Through coupling calculation of the mesoscopic pores and the macroscopic combustion chamber, the accuracy and reliability of prediction of the cooling performance of the porous medium are improved.

The invention relates to the technical field of industrial control, in particular to an error compensation method for numerical control machining of an automobile stamping die, which comprises the following steps of: acquiring temperature of a plurality of thermo-sensitive points of a numerical control machine tool and thermal displacement data of a tool nose; the effective thermal driving potential is calculated by considering the time lag effect of the temperature level and the historical temperature change; calculating a thermal phase direction gradient in combination with the temperature difference value and the nonlinear gain; combining the effective thermal driving potential and the thermal phase direction gradient into a feature vector to train a support vector regression model; and inputting the feature vector in real time to obtain a compensation value, and superposing the compensation value to the coordinate instruction by dynamic origin offset. The method can accurately reflect the thermal inertia, capture the dynamic evolution of the thermal state, comprehensively represent the thermal state to improve the adaptability of the model, and correct the thermal error in real time, thereby improving the machining precision and surface quality of the automobile stamping die.

The invention provides a transformer heavy overload evaluation analysis method and device, and the method comprises the steps: building a thermal circuit model of load-induced heat point temperature rise of a transformer according to the heating power, equivalent heat capacity and equivalent heat resistance of an internal winding of the transformer, and load-induced heat top oil temperature rise of the transformer; converting a heat flow source in the heat circuit model into a hot pressure source based on the Thevenin theorem, constructing an equivalent heat circuit model of the heat circuit model, and calculating load heating hot spot temperature rise of the transformer under different load rates according to the equivalent heat circuit model; according to the historical environment heating temperatures of the transformer under different load rates, obtaining predicted environment heating temperatures of the transformer under different load rates; and obtaining winding hot-spot temperatures of the transformer under different load rates according to the load heating hot-spot temperature rise and the predicted environment heating temperature of the transformer under the same load rate. According to the invention, the thermal state sensing precision of the transformer under the dynamic load is improved.

The invention discloses a heat management and control parameter dynamic optimization method and system for a liquid-cooled lithium iron phosphate battery, and relates to the field of battery heat management and control parameter dynamic optimization, and the method comprises the steps: building a carrier heat state offline training database based on a carrier state, a heat state, a cooling system and environment parameters, and carrying out the data cleaning and alignment; based on a LightGBM model, constructing a general thermal state prediction basic model of the carrier; verifying whether the thermal state of the predicted carrier is reliable or not by combining an actual monitoring result; dynamically optimizing the carrier general thermal state prediction basic model based on an online gradient descent algorithm; dynamically optimizing the flow velocity of the cooling liquid based on a heat balance equation; a sensor set is arranged and used for collecting current state parameters and thermal state actual data in real time. The method has the advantages that data driving and a physical model are fused, the heat management and control parameters are dynamically optimized through online learning, and the heat management precision, adaptability and safety of the liquid-cooled lithium iron phosphate battery are remarkably improved.

The invention provides an underwater robot intelligent thermal management energy-saving method, which comprises the following steps: acquiring temperature data and an operation load peak value of a processor in real time through a heat-sensitive sensor arranged in an underwater robot cluster, generating a thermal state vector group, and storing the thermal state vector group in a distributed data node to obtain an initial thermal state record set; analyzing task queue congestion conditions of other underwater robots based on the task capability limited list through a task transfer mechanism under the support of a cluster communication bandwidth, redistributing affected tasks, generating a task takeover distribution table, and determining a target takeover underwater robot combination; and according to the task takeover distribution table, the navigation trajectory of the target takeover robot is recalculated in combination with the water flow resistance change of the underwater environment and the terrain shielding coefficient, path overlapping conflicts are avoided, and an adjusted navigation trajectory group is obtained.

The invention relates to the technical field of numerical control machine tools, and discloses a thermal error dynamic modeling and compensation method for a numerical control machine tool. The method comprises the following steps: continuously collecting heat state original data through a temperature sensing device arranged at a key part of a numerical control machine tool; real-time preprocessing is carried out on the thermal state original data to eliminate noise and abnormal values, and a standardized thermal data sequence is generated; key thermal characteristic parameters are extracted from the standardized thermal data sequence, and the key thermal characteristic parameters are input into a self-adaptive thermal error prediction module to output a current thermal error prediction value; comparing the current thermal error predicted value with a preset thermal error threshold value to generate a thermal error state indication signal; starting a compensation strategy selection mechanism when the thermal error state indication signal indicates abnormity; the compensation strategy selection mechanism retrieves a compensation scheme library according to the thermal error state indication signal to determine an adaptive compensation instruction; and executing the adaptive compensation instruction to drive the numerical control machine tool to adjust processing parameters for thermal error correction.