674 results about "Temperature curve" patented technology

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention relates to the technical field of intelligent control, and discloses a self-adaptive temperature curve control system for high-temperature alloy vacuum casting, which comprises a sensing module, a control module and a control module, and is characterized in that the sensing module acquires multi-point temperature information and preprocesses the information to obtain temperature distribution in a furnace; the acquisition module obtains vacuum environment parameters. The simulation module establishes a three-dimensional dynamic coupling model based on heat conduction, radiation, convection and phase change heat effect, and obtains a real-time temperature evolution prediction result. The generation module obtains a target temperature curve of the corresponding stage. The prediction control module collects a real-time temperature evolution prediction result and a target temperature curve, deviation calculation and trend analysis are carried out, and a heating power and cooling flow control instruction is generated. And collecting product feedback errors and carrying out self-learning correction on prediction model parameters to obtain an optimized control output result. The control and optimization of the high-temperature alloy vacuum casting temperature field are realized, and the structure uniformity of castings and the quality stability of finished products are improved.

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

The invention discloses a nuclear fusion superconducting coil heat treatment temperature control system and method. The temperature control system comprises a fuel gas heating control subsystem, an electric heating control subsystem, a wind speed control subsystem, a temperature acquisition subsystem and a temperature analysis feedback subsystem. The temperature control method comprises the following steps: performing an electric heating zoning temperature control process in a heat treatment temperature rise stage; and a gas heating and electric heating zoning cooperative temperature control process in the heat treatment heat preservation stage is carried out. According to the method, the power load peak value is remarkably reduced, and meanwhile, the method is suitable for the heat treatment scene of the nuclear fusion superconducting coil by dynamically distributing the power proportion of gas and electric heating; the actual temperature of each temperature control subarea of the heating furnace body and the superconducting coil is detected in real time and compared with the preset target temperature value of the corresponding process temperature curve in real time, the top layer is combined with stirring fan rotating speed dynamic adjustment and gas-electric power complementation, a closed-loop feedback chain from a local part to an overall part is formed, and the response speed and stability are effectively improved.

The invention discloses an artificial intelligence-based refining process efficiency optimization and real-time temperature control method and system, and the method achieves the efficient optimization and precise temperature control of a refining process through the innovation of four steps: 1, breaking through the limitation of traditional single-point monitoring, and deploying a multi-modal sensing array and a feed port near infrared spectrum sensor according to three-dimensional coverage; process-raw material two-dimensional data are collected in real time and associated, and the problem of data islands is solved; 2, innovatively constructing a'process-raw material 'two-dimensional feature vector, predicting a finished product rate by adopting an XGBoost model, and judging a dynamic power adjustment demand in combination with a threshold value to replace a traditional experience decision; thirdly, on the basis of the value output by the DQN model, an optimal dynamic temperature curve is calculated in a staged mode, and then the problem of fixed curve stiffness is solved through a generated precise power instruction; and fourthly, a three-level aging standard reaching mechanism is created for the first time, and an effect closed loop is formed corresponding to a differentiated scheme of model strengthening, local compensation and cross-furnace repairing.

The invention discloses an optimal control method for a straw pyrolysis carbonization process. The method comprises: performing multipoint temperature real-time acquisition and data processing on a carbonization zone of a carbonization furnace, obtaining temperature information and calculating a deviation from a preset ideal temperature curve (S01); dynamically adjusting an operating parameter of the combustion zone using a control algorithm based on the temperature deviation (S02); performing on-line monitoring on the flue gas components (S03); and regulating and controlling the return air flow and the secondary air supply amount of the combustion area in a linkage manner according to the flue gas monitoring result so as to optimize combustion and energy recovery (S04). Through accurate and intelligent process control, efficient, environment-friendly and high-valued utilization of the straw carbonization process is realized.

The invention relates to the technical field of state monitoring, in particular to an electric drive system overheating protection monitoring method and system based on big data, and the method comprises the following steps: obtaining a power rising section and extracting a temperature curve, judging whether the temperature rise lags behind or not, marking response delay, recognizing thermal fluctuation lag through an expansion window, and extracting peak valley to judge trend deviation. And the positioning node tracks the heat path, identifies the interrupt structure and generates an early warning identifier. In the electric drive system operation process, the coupling relation between the power change trend and the temperature rise time sequence is combined, accurate recognition of temperature rise response delay is achieved, the position distribution of a heat path interruption structure is defined through continuous tracing of the heat node temperature rise sequence relation, and linkage performance between heat interruption structures is combined, so that the reliability of the electric drive system is improved, and the reliability of the electric drive system is improved. Concurrent characteristics of overheating abnormity are identified, the response capability and early warning accuracy of an electric drive system in the early stage of overheating risks are improved, and rapid positioning of potential thermal fault risks and intelligent division of risk levels are achieved.

The invention discloses an intelligent assembly method of a precise connector, which comprises the following steps of: adding phase change particles with different melting points into tin-silver-copper-based solder according to the difference of functional areas of welding spots in the connector, generating an area formula index, and obtaining composite solder with partition identification; according to the area formula index, the composite welding flux is deposited to preset welding spot positions according to the functional areas, and particle distribution information is recorded; performing reflow soldering on different functional areas by adopting corresponding temperature curves according to the melting point parameters of the phase change particles, and recording the temperature history of each area; applying pulse thermal excitation to the packaged connector, and collecting a surface temperature change curve of each welding spot to obtain thermal response data; and performing correlation comparison on the thermal response data and process historical data, identifying the welding spot defect type according to curve deviation characteristics, and generating process adjustment parameters. According to the scheme, after the connector is completely packaged, rapid nondestructive detection of internal tiny welding spots can be realized, hidden defects can be effectively identified, and intelligent optimization and adjustment of process parameters can be realized.

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 technical field of concrete construction, and particularly discloses a concrete pavement construction quality monitoring system based on Internet of Things, which comprises an environmental data acquisition end, a stress sensor, a temperature sensor and an analysis module, the environmental data acquisition end is used for acquiring environmental parameters of a construction area; the plurality of groups of stress sensors and the plurality of groups of temperature sensors are arranged in concrete and are used for monitoring stress change data and temperature change data in the concrete at different position points in real time; the analysis module is used for obtaining a predicted stress curve and a predicted temperature curve according to the environmental parameters, and judging the construction quality of the concrete pavement according to a comparison result of stress change data of different position points and the predicted stress curve and a comparison result of temperature change data of different position points and the predicted temperature curve; and the quality of the solidified soil is judged more accurately through the consistency of the data change trend and the consistency of the data change rate.

The invention relates to the technical field of injection molding quality monitoring, in particular to a real-time monitoring method and system for injection molding production quality. The method comprises the following steps: acquiring a standard pressure curve and a standard temperature curve of each production stage in each injection molding production cycle; calculating the local intensity of each sampling moment; obtaining an abnormal fluctuation coefficient of each sampling moment, and constructing a multi-dimensional coupling dynamic weight of each sampling moment; multiplying the abnormal fluctuation coefficient by the multi-dimensional coupling dynamic weight to obtain a weighted dynamic fluctuation curve; integrating the weighted dynamic fluctuation curve to obtain a form deviation index; and performing anomaly detection on the current production cycle based on the form deviation index. And the monitoring precision of injection molding production is improved.

The invention relates to the technical field of welding, in particular to a hot riveting welding head synchronous temperature control method and system based on the temperature change rate, and the method comprises the steps: executing full-power heating pulse triggering operation on a welding head, calculating the pulse duration according to a start timestamp and an end timestamp, calculating the temperature rise, and eliminating peak noise through a digital filtering algorithm; environment temperature and relative humidity are collected in real time, and equivalent heat capacity is calculated; the initial temperature difference between any welding heads is calculated, and a dynamic target temperature curve is constructed; and calculating an actual temperature change rate, synchronously extracting a target change rate of the dynamic target curve at a corresponding moment, calculating a change rate deviation between the two change rates, judging whether intervention adjustment is needed or not, calculating an output signal by using an incremental PID algorithm if intervention adjustment is needed, and adjusting the heating power according to the output signal. According to the scheme, the stability of the hot riveting welding quality is effectively improved by accurately quantifying the thermal characteristics of the heterogeneous welding head, dynamically planning a personalized temperature rising track and performing high-frequency closed-loop regulation and control.

The invention discloses an aircraft shell surface quality detection method. According to the method, a passenger cabin pressure difference curve and a shell outer surface temperature curve are calculated to obtain a pressure difference change node and a temperature difference change node respectively, and then partitioning is carried out to obtain phase thermoelastic coupling partitions; when a temperature difference change node reaches a phase change threshold value, performing beta-phase to alpha-phase transformation on the polyvinylidene fluoride microcapsule premixed in the finish paint, and forming a polarized memory cavity at a defect cementing position; when a voltage difference change node reaches a peak value, triggering a polarized memory hole to release charges, exciting a high-order thin film elastic wave, and performing integer ratio phase locking on the high-order thin film elastic wave and a natural vibration wave of a machine body to obtain a defect harmonic signal; carrying out acoustic-mechanical coupling calculation according to the amplitude of the defect harmonic signal to obtain a virtual work increment, and recording the virtual work increment to a damage matrix; and performing multi-leg accumulation calculation on the virtual work increment, and outputting a defect early warning signal when an accumulation value exceeds a material fatigue critical value. According to the invention, the hidden fatigue defect of the surface of the aircraft shell under the dynamic load can be detected.

The invention relates to the technical field of metal material heat treatment, in particular to a gradient temperature control method for a stainless steel cold rolling annealing process. Comprising the steps that an annealing furnace is divided into independent temperature control sections in the strip steel conveying direction, each temperature section is provided with an electric heating wire set, a forced convection cooling air duct and a high-precision infrared temperature measurement array, temperature data are collected in real time, and a target gradient temperature curve is generated; constructing a phase change power-temperature correlation model, and outputting a theoretical gradient temperature curve; a three-stage MPC is adopted to cooperatively control and output a control signal; and the heating power and the cooling strength are adjusted in real time according to the control signal, if the temperature fluctuation exceeds the limit, the power is compensated through a PID algorithm, the strip steel speed is adjusted, and closed-loop feedback is formed. Through multi-section independent temperature control, thickness-temperature dynamic modeling and a three-level cooperative control framework, accurate tracking of a gradient temperature curve is achieved, and the quality and efficiency of the annealing process are improved.

The invention relates to the technical field of electrical performance testing, in particular to a method and system for measuring loss of an inductance winding, and the method comprises the following steps: applying a direct current to a measured inductance winding, synchronously monitoring the temperature, and multiplying a pre-measured direct current resistance value by a direct current value to obtain a direct current loss power value. The direct current is applied to the inductance winding, the temperature is monitored synchronously, the direct-current loss power is obtained by combining the direct-current resistance value and the current value, the temperature curve in the initial stage is extracted, the temperature rise rate is calculated, and therefore the winding heat capacity parameter is obtained, loss measurement can be closely associated with thermal response, and the measurement accuracy is improved. Introduction of a heat capacity factor is added in loss calculation, dynamic characteristics of a winding material on a thermodynamic level can be reflected, instantaneous current with load step change is applied after an inductor is cooled, an instantaneous loss power curve is generated by collecting a voltage sequence and a current sequence at the same time, and loss calculation is not limited to average value or steady state measurement any more.

The invention provides an infrared pulse thermal radiation grinding burn nondestructive testing method and system, and belongs to the technical field of nondestructive testing. Comprising the following steps: inputting an excitation signal to an induction coil, and inducing a pulsed eddy current in a tested part for heating; a thermal infrared imager and a depth camera are used for obtaining a thermal response temperature matrix of a measured area of the part and a three-dimensional point cloud of the measured area; then, constructing a thermal response model of the part; extracting a time sequence temperature curve of the heating stage and the natural cooling stage of the detected area, and establishing a grinding burn discrimination function to judge whether a grinding burn area exists or not; and the thermal infrared imager and the depth camera are calibrated, the grinding burn area under the coordinate system of the thermal infrared imager is projected into the three-dimensional point cloud of the depth camera, the position of the grinding burn area under the world coordinate system is obtained, and detection and positioning of the grinding burn area are achieved.

The invention relates to the technical field of carbon fiber mold optimization, and discloses a carbon fiber box mold optimization method and system based on multiple targets. A multi-target definition library of the system stores a design constraint set composed of a geometric accuracy threshold value, a material strength threshold value and thermal deformation tolerance; the real-time sensing network collects data of layering tension distribution, temperature gradient and resin flow front edge; the topological dependency analyzer constructs a dependency relationship graph of the design parameters and the real-time sensing data; the material characteristic library stores carbon fiber layering parameters and resin curing kinetic parameters; the optimization calculation scheduler dynamically allocates multi-target optimization calculation task nodes; an iterative optimization engine generates a layering path sequence and curing temperature curve combination scheme; and the result verifier compares the combination scheme with the design constraint, and outputs a scheme deviation index. According to the system, the design standardization, the production monitoring comprehensiveness and the optimization verification accuracy of the carbon fiber box mold can be improved, and the product quality and the production stability are guaranteed.

The invention relates to the field of metal powder processing, in particular to a sintering process intelligent control method and system based on a powder metallurgy metal material, and the method comprises the steps: obtaining a temperature curve of a powder metallurgy sintering process, determining the offset of each curve through a cross-correlation function, and carrying out the alignment; the method comprises the following steps: determining a segmentation length according to segmentation adaptability of a local area, analyzing an abnormal reference degree based on correlation between a mahalanobis distance of each segment and sintering quality, obtaining an overall abnormal degree of each curve through normalization processing and weighted calculation, and judging whether an abnormal temperature curve exists according to the overall abnormal degree and a preset abnormal degree threshold value. And if the abnormal degree exceeds the preset abnormal degree threshold value, early warning that the sintering process is abnormal, and prompting adjustment control. According to the invention, local abnormity is accurately detected, temperature control is optimized, the production cost is reduced, and the product quality and the production efficiency are improved.

The invention discloses a Foster network model-based cable transient temperature prediction method and a related device. The method comprises the steps of obtaining target cable basic data, actual operation power data, actual environment temperature data and heat dissipation boundary conditions of a target cable; constructing a cable simulation model based on the basic data of the target cable; controlling the cable simulation model to perform transient simulation according to the actual operation power data, the actual environment temperature data and the heat dissipation boundary conditions to obtain a target simulation temperature curve; a target Foster network model is constructed; performing transient temperature prediction through the target Foster network model to obtain a target prediction temperature curve; target model precision is determined according to the target simulation temperature curve and the target prediction temperature curve; and when the target model precision is greater than the preset precision, determining the transient temperature of the target cable according to the target predicted temperature curve. By adopting the embodiment of the invention, the prediction error of the transient temperature is reduced.

The invention relates to a large-scale high-temperature alloy blank casting method capable of controlling the casting size, and belongs to the technical field of high-temperature alloy casting. Measuring a temperature curve of the sand mold after the high-temperature alloy is poured; a process model is created, simulation software is used for carrying out stress deformation simulation, a part model of the casting is created, the simulation result of the part model and the simulation result of the process model are compared, the reduced scale, correction and the machining amount are adjusted in real time, and the process model is obtained; manufacturing a process pattern according to the process model, and producing a sand mold by utilizing the process pattern; creating a sand core model, inputting sand mold temperature curve parameters into simulation software, carrying out sand core deformation stress simulation, measuring sand core deformation stress, and manufacturing a core box according to the sand core model; a core bar is manufactured according to the design, the manufactured core bar is placed in a core box, and a sand core is manufactured; demolding the sand core; combining the sand core with the sand mold to obtain a casting cavity; and molten steel is poured into a casting cavity, and a casting blank is formed after cooling and solidification.

The invention discloses an air conditioner air outlet control method and device, an air conditioner and a storage medium, and relates to the technical field of air conditioner control. Determining a target temperature curve according to the set data and the environment temperature data; determining a target air outlet temperature and a target air speed based on the target temperature curve; and controlling a compressor and a fan according to the target air outlet temperature and the target air speed. The target temperature curve is determined according to the set data and the environment temperature data, and the target air outlet temperature and the target air speed are determined through the target temperature curve, so that the compressor and the fan are controlled at the same time, the air outlet temperature and the indoor temperature are controlled at the same time, and the comfort and the energy-saving rate of an existing variable frequency air conditioner are further improved.

The invention discloses a method for inhibiting temperature fluctuation of an oriented silicon steel BAF furnace, which comprises the following steps: adding 0.005-0.01 wt% of Nb, and forming a nanoscale Nb (C, N) precipitate; a three-dimensional heat conduction model is established in a mode of linkage of infrared thermal imaging and electromagnetic induction heating in combination with a finite element method, and real-time monitoring and accurate control of a temperature field in the furnace are achieved. On the basis of a fuzzy PID hybrid control strategy, the optimal heating power and scanning path are predicted in combination with a convolutional neural network, and closed-loop control of local temperature compensation is achieved; a random forest model with the temperature gradient delta T, the Als content and the Nb precipitated phase density as input parameters is constructed, technological parameters are iteratively optimized in combination with a genetic algorithm, and dynamic self-adaptive adjustment of an annealing temperature curve is achieved. According to the method, uniform growth of Gaussian grains in the secondary recrystallization process is achieved, and the effects of reducing annealing temperature fluctuation, improving secondary recrystallization uniformity and optimizing grain orientation distribution are achieved.

The invention provides an antibacterial ceramic glaze sintering process optimization and intelligent temperature control method and system. According to the method, the molecular vibration characteristics of the antibacterial agent at different temperatures are dynamically obtained, and a thermal decomposition dynamic fingerprint database is constructed according to the attenuation law of the molecular vibration characteristics; meanwhile, a three-dimensional temperature field of the glaze layer is reconstructed based on a coupling relation between sound wave propagation parameters and glaze thermal expansion. And carrying out transfer learning on the fingerprint database and historical process data, predicting a glaze thermal stability attenuation parameter, and calculating a temperature gradient and molecular chain fracture compensation coefficient. And fusing the temperature field and the attenuation parameter, and dynamically regulating and controlling the sintering temperature curve and preserving heat by combining the compensation coefficient. The adjusted parameters generate closed-loop feedback by verifying the matching degree of the glaze microstructure and the antibacterial indexes, the compensation coefficient is corrected, and it is ensured that the antibacterial performance of the glaze is stabilized within the process allowance. The temperature control precision and the antibacterial performance stability of antibacterial ceramic glaze sintering are improved.

The invention discloses a temperature control system of an electric heating rice steaming cabinet, which not only can acquire and analyze key characteristic data in a heating process in real time, but also can dynamically adjust PID parameters according to the data by introducing a parameter self-adaption and staged self-adaption control mechanism. The flexible and accurate regulation and control of the heating process of the rice steaming cabinet under different working conditions are realized. Specifically, the system carries out deep analysis on data acquired in an initial heating stage by utilizing time sequence feature coding and a machine learning model, so that an optimal target temperature curve and a PID parameter set corresponding to each stage are generated, and the most suitable control strategy is automatically switched according to the current stage. By means of the mode, the response capacity of the electric heating rice steaming cabinet to environment disturbance and load sudden change can be effectively improved, and intelligent and self-adaptive temperature management under wider working conditions is achieved while the rice quality and energy consumption optimization are guaranteed.

The invention discloses a high-frequency induction intelligent control system and a thermal fatigue test method, and belongs to the technical field of thermal fatigue tests.The method specifically comprises the steps that a to-be-tested sample piece is installed, heating parameters are set, heating is started, heating is stopped after the to-be-tested sample piece is heated to a preset temperature, and the to-be-tested sample piece is cooled until the temperature drops to a preset lower limit; in the heating and cooling process, the surface and core temperature of a to-be-detected sample piece is collected in real time, the heating-cooling step is repeatedly executed according to the preset number of cycles or to a preset damage threshold value, the temperature curve, the power curve and the cycle count of each cycle are generated, the recorded temperature curve, the power curve and the cycle count are analyzed, and the temperature of the to-be-detected sample piece is obtained. Outputting the thermal fatigue life value of the to-be-tested sample piece; the temperature is intelligently controlled through high-frequency induction, the uniformity of the temperature is ensured, the thermal fatigue life is accurately predicted through multi-dimensional life evaluation and layered modeling, and the speed, precision and reliability of thermal fatigue testing are remarkably improved.

The invention relates to the field of thermal management of electronic equipment, and discloses a heat dissipation control method and system for a set top box and a storage medium, and the method comprises the steps: firstly obtaining a hardware layout corresponding to the set top box, positioning an adaptive heat dissipation node, and collecting initial data; according to the operation temperature data flow, generating a temperature curve, extracting an overheating risk point, and calculating a heat dissipation urgency value to generate an adjusting instruction; analyzing an instruction execution effect, and calculating a heat dissipation risk value by means of an effect deviation vector; and finally, determining an adjustment mode in combination with an adaptive mechanism, and extracting core data to generate a heat dissipation control report. The heat dissipation stability of the set top box is improved.

The invention discloses a geothermal favorable area prediction method and device based on numerical simulation, equipment and a medium. The method comprises the following steps: determining a substrate earth heat flow in a research area; establishing a two-dimensional geologic model, and counting the thermal conductivity value of each lithology of each stratum above the bedrock in the research area; performing thermal conductivity assignment on the two-dimensional geologic model, performing thermal conduction numerical simulation, and storing a temperature curve with a fixed depth; according to the temperature curve of the fixed depth, drawing a ground temperature contour plane graph of the fixed depth in the research area; analyzing geothermal conditions in the research area according to the basic geological data and the geochemical data; and determining the correlation between the ground temperature isoline plane graph and the geothermal conditions, and predicting the geothermal favorable area of the research area. According to the method for predicting the geothermal resource favorable area through numerical simulation, the temperature information in the research area can be accurately obtained, the precision is high, the efficiency is good, the method is more economical and practical, and convenience is provided for detection of the ground surface geothermal abnormal area.

The invention provides a registration type battery thermophysical parameter measurement method. A battery to be measured and a standard component group with known thermophysical properties are placed in the same air environment for synchronous heating and temperature measurement. The simulation curve and the actually measured temperature curve of the standard component are overlapped through the registration technology, the convective heat transfer coefficient of the environment is accurately inverted, and the function relation of the convective heat transfer coefficient is established. The function relation is applied to the battery to be tested, heat lost by convective heat transfer is compensated, and the specific heat capacity and the heat conductivity are synchronously obtained through one experiment process. According to the invention, the dependence of a traditional measurement method on a vacuum insulation environment is broken through, the problems of high measurement cost, low efficiency and poor data collaboration in the prior art are solved, and high-precision integrated measurement in a common environment is realized.

The invention relates to the technical field of vulcanized rubber processing, and discloses an intelligent vulcanized rubber processing system and method. According to the method, a physical property parameter set of a rubber raw material and real-time working condition data of vulcanization equipment are obtained, the physical property parameter set comprises the rubber content, the filler distribution density and the heat conduction coefficient, and the real-time working condition data comprises a vulcanization temperature curve, a pressure gradient sequence and a mold state identifier; multi-modal feature fusion processing is carried out on the physical property parameter set and the real-time working condition data, a feature fusion atlas containing thermodynamic response features and mechanical stress distribution is generated, and the atlas is associated with a phase change critical point and a material deformation track in the vulcanization process; and finally, a process parameter optimization model is called to perform dynamic scheduling analysis on the feature fusion atlas, and a vulcanization process control instruction set containing a time sequence operation sequence based on the temperature compensation parameters and the pressure adjustment step length is generated.

The invention discloses a transcritical working medium thermophysical property intelligent prediction method suitable for physical property mutation characteristics, and the method comprises the steps: obtaining the physical property-temperature data of a target working medium under a specified working pressure through a standard physical property database, and employing a piecewise linear regression change point detection algorithm based on residual sum of squares and dynamic monitoring; recognition of inflection points of a working medium physical property-temperature curve and self-adaptive division of temperature intervals are achieved, polynomial order optimization is carried out on each segmented interval based on a Bayesian information criterion to balance fitting precision and complexity, continuity constraints at segmented connection points are applied, and a globally continuous segmented polynomial function is constructed. According to the method, the full-process automatic transcritical working medium physical property-temperature function modeling of data acquisition-inflection point identification-order optimization-function generation can be realized, the subjectivity and limitation of artificial experience segmentation are effectively overcome, and the over-fitting risk is avoided while the model precision is ensured; and reliable support is provided for engineering simulation and thermodynamic system design under the transcritical working condition.