472 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 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 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 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 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 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 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 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 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 relates to a solar term temperature-based power load prediction method, which comprises the following steps of: obtaining 24 solar term reference temperatures and long-term trends of solar term temperatures, and correcting the long-term trends of the solar term temperatures; generating a continuous basic seasonal temperature curve; generating a high-resolution final simulation temperature sequence; and inputting the high-resolution final simulation temperature sequence into a power load prediction model, and performing power load prediction. Through throttle window smoothing, Gaussian process regression kernel function physical constraint and physical amplitude limiting random noise design, high-frequency random noise and chaos disturbance in original observation temperature are effectively stripped, the sensitivity of a power load prediction model to local atypical weather fluctuation is remarkably reduced, overfitting is relieved fundamentally, and the power load prediction method has the advantages of being high in robustness and high in reliability. The generalization capability and the prediction stability of the power load prediction model are greatly improved; and main physical driving factors of temperature change are subjected to decoupling and explicit modeling, so that the interpretability of the model is enhanced.

The invention relates to the technical field of building construction, in particular to a digital twinning-based building foundation construction method, which comprises the following steps of: constructing a digital twinning body based on a BIM (Building Information Modeling) technology; during the foundation pit overwintering period, the stress and the soil body temperature of the supporting structure are collected at the preset detection frequency and synchronized to the digital twinborn body; the freeze-thaw damage characterization value of the supporting structure is calculated according to the hysteretic loop area surrounded by the stress-temperature curve within the preset temperature period, and the construction risk level of the deep foundation pit is determined according to the freeze-thaw damage characterization value; when the three types of construction risk levels are judged, the displacement of the supporting structure at the key depth is collected, a deformation recovery characterization value is obtained, an adjustment strategy when the standard is not reached is determined according to the deformation recovery characterization value, the adjustment strategy is fed back to a physical construction site to be executed, and the data and the disposal result are recorded in a case library; and optimizing the digital twinning model. The intelligent level of building foundation construction is improved.

The invention discloses a battery thermal management method and system and a computer readable storage medium, which are applied to the technical field of thermal management, and the method comprises the following steps: obtaining battery pack operation data, the battery pack operation data comprising battery cell temperature information and battery pack heat dissipation information; storing the operation data of the battery pack to form historical data; presetting a temperature prediction model based on a preset machine learning algorithm and historical data; generating a current temperature curve based on the battery pack operation data and the temperature prediction model; and if it is determined that the battery pack has a thermal runaway trend based on the current temperature curve, controlling the thermal management controller to perform temperature control. The temperature of the battery cell is monitored through the operation data of the battery cell of the battery pack, the temperature curve is generated according to the temperature prediction model, the thermal runaway trend is performed according to the temperature curve, the thermal management controller can be actively controlled to perform temperature control when the temperature of the battery is abnormal or before the thermal runaway trend occurs, and the thermal management accuracy of the vehicle is improved.

The invention provides a PI control method and device for laser soldering based on observation compensation, a controller and a program product, and the method comprises the steps: building a laser soldering thermodynamic model containing a thermal disturbance term based on a lumped parameter method, and constructing a state-space equation; designing a state observer based on the state-space equation, and estimating a state variable of the state-space equation and external disturbance acting on the state variable in real time; calculating a feedforward control quantity according to a target temperature curve and the thermodynamic model, calculating a compensation control quantity according to a disturbance value estimated by an observer, and calculating a PI control quantity according to the deviation between the output temperature and the target temperature to obtain a total control quantity; and in each sampling period, the total control quantity is dynamically adjusted according to the deviation between the current welding spot temperature and the target temperature, so that the temperature error tends to zero. According to the method, the disturbance observation technology and the feedforward-PI control are combined, and high-precision and high-robustness control over the welding temperature is achieved.

The invention relates to the technical field of power battery data analysis, and discloses a multi-source data fusion analysis method which comprises the following steps: S1, respectively extracting multi-dimensional health factors reflecting a battery degradation state from a voltage curve, a temperature curve and an incremental capacity curve of a battery; the health factors comprise a voltage sampling value and a voltage curve slope extracted from a voltage curve, a differential temperature characteristic parameter extracted from a temperature curve, and a peak value characteristic extracted from an incremental capacity curve; s2, respectively inputting the health factors into at least two different types of basic prediction models to obtain a preliminary health state estimation result of each basic prediction model; and S3, fusing the preliminary health state estimation results through an integrated learning model to obtain a final estimation result of the battery health state. According to the method, the limitation that a traditional method depends on a single data source is overcome, and the complex degradation state of the battery can be represented more comprehensively, more stably and more accurately.

The invention provides a stress evaluation method and system for a TSV packaging structure, and relates to the technical field of semiconductor packaging, and the method comprises the steps: constructing a global coarse mesh model, cutting to generate a local TSV sub-model, and generating a failure feature mapping table; based on the failure feature mapping table and a preset process temperature curve, coupling solution is carried out on the local TSV sub-model, and a multi-physical field data set is output; training a failure probability prediction model by combining the failure feature mapping table based on the multi-physics field data set, and generating a digital failure atlas template; performing dynamic risk assessment based on the digital failure atlas template, the real-time process parameters and the dynamically calculated electrothermal coupling parameters, and generating a failure risk report; analyzing the failure risk report, and generating a mode-based optimization guide; and performing spatial deviation comparison based on the verification test data executed by the mode-based optimization guide, and when the spatial deviation exceeds a preset threshold value, outputting a failure map version updating mark and triggering the failure feature mapping table to iteratively update.

The invention discloses a computer mainboard welding temperature correction control method based on machine learning, which relates to the technical field of intelligent control, and comprises the following steps: predicting a reference welding temperature curve by using a machine learning model based on historical operation data of reflow welding of a computer mainboard; dividing a thermal management area based on the thermal characteristics of the component; determining an optimal welding temperature curve; on the basis of the divided heat management areas, regionalization adjustment is conducted on the optimal welding temperature curve, and temperature area parameter fine adjustment strategies for the different heat management areas are generated; and based on the optimal welding temperature curve and the fine adjustment strategy, a final temperature control instruction set is synthesized, and the reflow soldering furnace is controlled for batch production. The method has the advantages that through fusion of machine learning and real-time data driving optimization, on the premise that the welding reliability is guaranteed, the production efficiency and energy consumption are intelligently balanced, and the adaptability and the intelligent level of a production line are remarkably improved.

The invention discloses a liquid-cooling heat dissipation system regulation and control method for a liquid-cooling super-intelligent fusion computer, and relates to the technical field of computer heat dissipation control, and the method comprises the steps: collecting and preprocessing state data of a liquid-cooling heat dissipation system and detailed information of a next calculation task, and generating a real-time task-state fusion data set; thermal fluid simulation calculation is conducted on the real-time task-state fusion data set, a future temperature curve is predicted, a simulation result data set is obtained, an aggressive energy efficiency strategy is started according to the simulation result data set, and an ideal rotating speed control instruction sequence is generated; and according to the preprocessed state data of the liquid cooling heat dissipation system, dynamically calculating an estimated dead zone time length, and sending an ideal rotating speed control instruction sequence ahead of the estimated dead zone time length to generate a time advanced control instruction. According to the method, the ideal rotating speed control instruction sequence is generated, so that prospective simulation and energy efficiency optimization decision making of the thermal load of the calculation task are realized, the energy consumption is reduced, and the heat dissipation adaptability is improved.

The invention relates to an automatic parameter optimization method for a high-density multi-layer PCB lamination process, and the method comprises the steps: obtaining the plate thickness, layer number structure and copper foil distribution data of a multi-layer printed circuit board through a sensor collection system, and generating a basic data set; constructing a multi-dimensional feature vector according to the basic data set, and generating a feature parameter matrix by adopting a normalization processing method; heat conduction coefficients and stress distribution data of the candidate pressing batch combinations are obtained through thermal characteristic simulation calculation, and the thermal response consistency of the plates in the groups is judged; according to the thermodynamic response consistency optimization grouping, a parameter optimization algorithm is adopted to calculate an optimal lamination process parameter combination, and a temperature curve, a pressure gradient and dwell time are generated; and verifying the optimized grouping scheme through a compatibility evaluation algorithm, and generating a final pressing grouping strategy and process parameter configuration.

The invention relates to the technical field of fermentation tank temperature control, and discloses a fermentation tank temperature control method and system, and the method comprises the steps: arranging a plurality of temperature sensors at equal intervals in each temperature control region; constructing a temperature curve in the vertical direction of the fermentation tank according to the regional average temperature value, and judging the overall temperature stability of the fermentation tank according to the temperature smoothness; if the judgment result of the overall temperature stability is stable, calculating the average temperature value of the fermentation tank according to the regional average temperature value, and adjusting the overall temperature according to the average temperature value of the fermentation tank and the fermentation standard temperature; and if the judgment result of the overall temperature stability is unstable, the temperature of each temperature control area is adjusted according to the average temperature value of the area. The invention provides reliable temperature guarantee for the fermentation process.

The invention relates to the technical field of vacuum brazing furnace temperature control, and discloses a vacuum brazing uniform heating and cooling system with multi-temperature-zone cooperative control. According to the system, a temperature deviation trend spectrum is generated by matching a multi-zone actual temperature curve with a workpiece target temperature curve, multi-temperature-zone energy dynamic rebalance is started accordingly, and a preliminary power scheduling atlas and collaborative cooling trigger nodes are generated. And then heat flow disturbance compensation amount caused by heat radiation of the hearth is calculated and integrated with the atlas and the nodes to form a unified final process control atlas, and the atlas comprises heating correction and cooling scheduling instructions at the same time. Predictive regulation and control of the furnace temperature change trend and active compensation of temperature interval thermal disturbance can be achieved, the heating process and the cooling process are highly coordinated, and the temperature uniformity and the process consistency of a large or complex workpiece in the brazing process are improved.

The invention discloses a brake drum heat treatment process method based on digital twin drive, which comprises the following steps: acquiring real-time process parameters including temperature, atmosphere and heating time from a heat treatment furnace group, acquiring and transmitting the real-time process parameters to a central data processing module through a sensor network to obtain a standardized parameter data set; according to the synchronous adjustment instruction, the temperature curve and atmosphere control parameters of each heat treatment furnace are updated in real time, cooperative adjustment is achieved through a communication protocol between devices, and a consistent process execution state is obtained; extracting sensor data from the consistent process execution state, and generating a data record binding a physical entity and a digital model in combination with a timestamp and a device identifier to obtain a traceable data set; and aiming at the traceable data set, encrypting and storing the mapping relation between the sensor data and the process result by adopting a block chain technology, judging whether the data is tampered or not, and obtaining a tamper-proof verification result.

The invention relates to a cooperative control method and system for lean-burn emission of a marine low-zero carbon fuel engine. The method comprises the steps that real-time in-cylinder pressure and temperature data are collected, and an in-cylinder pressure curve and an in-cylinder temperature curve are obtained through time sequence alignment, filtering denoising and interpolation fitting processing; extracting time sequence data based on the curve, calculating average effective pressure and peak temperature in a cylinder to determine a current load interval, and marking a risk tag sequence; calculating the generation rate of the nitrogen oxides and the particulate matters by matching with reaction condition parameters, and extracting reverse variation characteristics to obtain strong mutual exclusion correlation of the nitrogen oxides and the particulate matters; and based on the associated starting model, predicting and controlling the output oil injection quantity and the air inflow adjusting quantity, calculating the transient time sequence lag quantity through a dynamic lag model, and after superposition, calibrating through a combustion phase matching algorithm to generate an oil injection timing and air inlet valve opening control instruction after transient adjustment. The method is adaptive to the combustion characteristics of low-zero-carbon fuel, emission contradictions are accurately captured, transient emission peak values are avoided, and multi-pollutant collaborative control is achieved.