208 results about "Production control system" patented technology

The invention discloses a flexible automatic production management and control system for a new energy high-power high-frequency transformer, and relates to the field of production management and control, the flexible automatic production management and control system comprises a production demand receiving module, a production resource management module, a production planning module, a production execution module and a quality control module, an order is received through an API or EDI, and a work order is generated; collecting equipment and material information in real time, and constructing a production resource association network; carrying out production scheduling and process matching by adopting an improved deep learning model; scheduling materials, procedures and equipment based on a multi-level analysis control model; the quality is evaluated by using a two-dimensional cloud model, and data recording is performed through a distributed database, so that the traceability of the quality problem is ensured. According to the system, the production efficiency and the quality management level are effectively improved, the problem that existing rigid automatic equipment lacks flexibility is solved, and efficient and accurate production of the new energy high-power high-frequency transformer is ensured through cooperative work of a plurality of modules so as to meet different production requirements.

The invention relates to the technical field of control systems, and discloses a thick broad-bean sauce production control system and method, which are used for solving the problems of quality fluctuation and pollution risk caused by insufficient precision control in the existing thick broad-bean sauce fermentation process. The method comprises the following steps: acquiring temperature, humidity, pH value and microbial activity indexes in a fermentation process in real time to form a dynamic parameter data set; analyzing the deviation degree of the data set, calculating the matching degree with a standard fermentation curve, and identifying potential unstable factors; generating a preliminary adjustment instruction based on the deviation result, and performing automatic fine adjustment on the temperature and humidity to stabilize the environment; monitoring the activity change of the adjusted microorganisms, feeding back and verifying a control effect through a sensor, and calculating an activity recovery rate; optimizing fermentation model parameters, and iteratively adjusting a control algorithm according to a recovery rate to realize self-adaptive precision improvement; and outputting a final control report, and feeding back optimized parameters to the system to realize closed-loop precision control.

The invention provides a puree production line microbial pollution risk real-time monitoring and early warning method, which comprises the following steps: deploying multiple types of sensors, an industrial camera and an operation log interface, collecting environmental temperature and humidity, pH value, dissolved oxygen, image and operation behavior multi-modal data in a fermentation tank in real time, and carrying out edge calculation and standardized preprocessing to obtain the microbial pollution risk of a puree production line. Data missing, abnormity and time sequence difference are eliminated; multi-modal features and a dynamic knowledge graph constructed based on a production process and a pollution event are fused, and a deep learning model and a graph neural network are utilized to realize microbial pollution risk probability intelligent prediction; risk grading and a dynamic weighting algorithm are introduced, grading early warning signals and disposal suggestions are automatically generated, and a production control system is linked to execute response measures; according to the subsequent production state, continuous feedback is carried out, the model and the early warning threshold are adjusted in a self-adaptive mode, and the accuracy, response efficiency and production safety of pollution early warning are remarkably improved.

The invention relates to an abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention relates to the technical field of equipment control, in particular to a feeding and discharging machining production control system and method based on a PLC, and the system comprises a multi-mode data sensing module which collects a three-dimensional point cloud and image sequence of a working scene through a laser radar and a visual sensor, and combines a vibration signal and a current signal of a machining main shaft; and after feature extraction, outputting a comprehensive state vector including the workpiece pose and the equipment health state. And the digital twinborn collaborative planning module receives the comprehensive state vector, drives a digital twinborn model to be synchronized with a physical environment in a virtual space, performs Monte Carlo tree search by applying a reinforcement learning algorithm, and generates a flexible control strategy with a time sequence mark. And the digital twinborn collaborative planning module adjusts a task allocation strategy according to the early warning signal, and corrects digital twinborn model parameters based on the execution log. According to the operation process corresponding to the modules, collaborative optimization of the feeding and discharging system and the machining rhythm is achieved, and the equipment utilization rate and the production efficiency are improved.

The application discloses a rapid batch online detection method and system for micro bearings, and relates to the technical field of bearings.The method comprises the following steps: cooperatively collecting micro bearings to generate a multidimensional detection data set; performing dynamic space registration on key detection areas to locate the key detection areas of the micro bearings; performing defect feature extraction on the key detection areas to generate a defect probability distribution graph, performing dynamic segmentation on the defect probability distribution graph, and generating a defect type label group; performing online sorting on the micro bearings based on the defect type label group to obtain a sorting instruction set, generating a detection quality report, and feeding back the detection quality report to a production control system.The application solves the technical problems of low detection efficiency, insufficient sampling accuracy, and difficulty in real-time online detection and quality control of the micro bearings in the prior art, realizes rapid batch online detection of the micro bearings, and improves the detection efficiency and accuracy of the micro bearings.

The invention belongs to the field of automation control, particularly relates to an ampoule bottle production control system based on a PLC, and aims to solve the problems of lack of a feed-forward adjusting mechanism and control lagging during ampoule bottle production control in the prior art. The system comprises a sensing unit which is configured to obtain raw material characteristic parameters used for representing raw material component fluctuation and process state parameters used for representing product quality in the ampoule bottle production process; the processing unit is in communication connection with the sensing unit so as to receive the raw material characteristic parameters and the process state parameters; the control execution unit is in communication connection with the processing unit, and the control execution unit is configured to receive a global optimization instruction; and analyzing the global optimization instruction into a control instruction for at least one station execution mechanism, and driving the at least one station execution mechanism to execute corresponding operation based on the control instruction. According to the invention, the process parameters of the downstream station can be adjusted in advance through a feed-forward adjustment mechanism.

The invention relates to the technical field of control systems, in particular to a plate production control system with a heating function, which comprises a cross-domain data fusion module for acquiring deformation data, energy transfer data, electrical signal data, acoustic emission signals and stress distribution images through a distributed sensor network; generating a characteristic matrix containing a mapping relation of thermal resistance distribution, stress gradient and current density based on a physical field coupling model; the multi-parameter coupling control module synchronously outputs a temperature gradient adjustment signal, a pressure distribution control signal and a current density modulation signal according to the characteristic matrix; the heat flow coordinated regulation and control module combines conductive layer power data and temperature field characteristics; the digital twinning optimization module constructs a thermodynamic electric coupling virtual model, optimizes a weight coefficient through a genetic algorithm and returns the weight coefficient to the control module. Through cross-domain data association and multi-target cooperative control, heat conduction path stability and interface stress dynamic digestion are realized.

The invention provides an anomaly analysis method and system applied to a diborane production control system, and the method comprises the steps: firstly obtaining a multi-source production data set which is generated in the operation of the diborane production control system and comprises data with timestamp marks, such as equipment states, raw material conveying and reaction parameters, and then constructing a production association knowledge graph; the method comprises the following steps: carrying out physical connection between production equipment or parameters corresponding to production nodes and between equipment or logic association between parameters corresponding to production edges, then carrying out graph feature extraction on a production association knowledge graph, calling a pre-training graph neural network model to carry out graph structure analysis, and generating an anomaly detection result containing different anomaly type detection confidence degrees; and determining an abnormal event type and distribution characteristic information in the knowledge graph according to an anomaly detection result, and finally generating a production early warning instruction containing an event positioning identifier and sending the production early warning instruction to the production control terminal to trigger an anomaly response operation, thereby comprehensively and accurately analyzing the anomaly in diborane production, and improving the production safety and stability.

The invention provides a coal dressing decision-making method and system based on offline and online data fusion, and relates to the technical field of coal washing and dressing processing. According to the method, firstly, a dynamic mapping relation between an offline basic data set and an online real-time data set in the coal dressing process is established; then, a collaborative constraint condition across data types is generated based on the relationship. And then a decision influence evaluation model of the coal dressing process parameters is constructed through the conditions. And generating a coal preparation process adjustment scheme according to a model output result. And finally, the scheme is sent to a coal dressing production control system to be executed, the dynamic mapping relation and the collaborative constraint conditions are optimized according to execution result data, and coal dressing decision optimization is achieved.

The invention provides a heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity drainage, and belongs to the field of oil-gas field development. The experimental device comprises a three-dimensional oil reservoir simulation system, a microwave-assisted heating system, a horizontal well injection-production regulation and control system and a multi-field monitoring system; the microwave auxiliary heating system comprises a microwave generation regulation and control system, a microwave transmission system and a shaft integrated microwave radiator, the microwave generation regulation and control system comprises an industrial grade microwave generator, a power regulator and a PLC, and the microwave transmission system comprises a microwave coaxial cable and a microwave waveguide tube bundle; and the microwave generator is continuously connected with the microwave waveguide tube bundle and the shaft integrated microwave radiator. According to the heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage, the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce a'thermal-flow-force 'multi-field coupling environment, the microwave heating adaptability is poor, and the experimental scale and the field deviation is large are solved. The heavy oil thermal-flow coupling development experimental device and method based on microwave-assisted gravity oil drainage solve the technical problems that an existing deep heavy oil thermal recovery experimental device cannot reproduce the'thermal-flow-force' multi-field coupling environment.

The invention discloses a welding wire production control system and method based on visual feedback, and belongs to the technical field of precision machining. The system comprises a multi-mode intelligent sensing subsystem, a self-adaptive model prediction controller and a safety and exception handling subsystem, and a two-stage control architecture of a quality closed loop and an execution closed loop is constructed. According to the method, the microcosmic roughness, macroscopic geometry, defects and other multi-dimensional quality characteristics of the surface of the welding wire are obtained in real time through multi-modal sensing and fused into a comprehensive quality index; carrying out rolling optimization by adopting a model prediction control algorithm for updating parameters on line, and generating a control instruction; meanwhile, trend early warning based on statistical process control and sudden fault diagnosis based on model residual analysis are operated in parallel, and active safety protection is achieved. According to the method, the problems of unstable quality, control lag and passive safety caused by supplied material fluctuation and tool time variation in the welding wire flaking machining process are solved, and self-adaptive accurate control and intelligent safety protection in the machining process are achieved.

The invention relates to the technical field of industrial internet, in particular to a control system and method for stirrer production. Comprising an edge data preprocessing unit, a flexible production instruction distribution unit and a working condition diagnosis and protection control unit. By constructing an integrated control parameter mapping model, an industrial operation signal is converted into a collaborative sensing digital primitive; the production task is compiled into an intelligent production execution process cluster based on a resource scheduling algorithm, and equipment is driven to execute differentiated production; and performing situation assessment on the digital primitives by using the anomaly detection model, and generating a regulation and control instruction to execute process logic recombination and safety protection. According to the invention, the problems of low production information integration level and tight data in each link of a production control system are solved, and flexible scheduling and active security defense in the production process are realized.

The application provides a rotating electrode atomization powder production control system and a control method, a multivariate machine learning prediction model is established between an electrode rotating speed, a plasma arc power, a protective gas flow and powder particle size and sphericity, according to target powder particle size and sphericity, the multivariate machine learning prediction model is used to calculate optimal electrode rotating speed, plasma arc power and protective gas flow, the optimal process parameters are applied to a powder production process, and a powder production system is operated, the electrode rotating speed, the arc power and the gas pressure are monitored in real time in the atomization process, and the process parameters are dynamically adjusted to compensate for fluctuations, an image acquisition unit is used to obtain images of powder particles in flight, AI vision model is used to analyze particle size distribution and sphericity in real time, and the electrode rotating speed, the plasma arc power and the protective gas flow are adjusted according to the analysis results.

The invention discloses an aluminum wiring terminal, a wiring terminal unit and an assembling method, and the method comprises the steps: carrying out the laser etching of a joint surface of an aluminum conductive sheet, forming a pretreated conductive sheet with a micro-nano structure, depositing a protective coating on the joint surface, and carrying out the curing, and obtaining a conductive sheet with a protective coating. And then crimping and assembling with an insulating shell assembly to form a crimping assembly, and fixing bolts are arranged in the insulating shell assembly. Applying resonance fastening of characteristic frequency to a fixing bolt in the crimping assembly, injecting a lubricating medium in the fastening process, performing synchronous detection and verification of multi-physical field parameters on the fastening assembly, and feeding back detection data to a production control system to optimize assembly process parameters; the micro-nano structure is constructed on the joint surface of the conducting strip and the protective coating is deposited, so that the interface conductivity and oxidation resistance are remarkably improved, online detection and dynamic feedback optimization are integrated, and the consistency and reliability of the product are improved.

The invention relates to the technical field of assembly management, and provides an engine production control system and method, and the system comprises an MES system which is configured to generate a production task according to the equipment capability and an operation plan, and transmits the production task to an LES logistics system; the LES logistics system is configured to generate a material demand order according to the production task issued by the MES system and monitor the completion progress of the order; the WCS system is configured to generate a production plan based on the material demand order and the production task so as to drive the assembly production line to execute part assembly work; the assembly production line comprises a vertical warehouse system, an assembly execution unit and an AGV central control system. According to the method, the part machining precision is improved, and the problems that planning and execution are disjointed, logistics response is lagged, and the equipment cooperation efficiency is low in traditional manufacturing are solved.

The invention discloses a pet food intelligent production management and control system and a traceability code spraying device thereof, and relates to the technical field of pet food production, and the pet food intelligent production management and control system comprises a production planning unit, a raw material management unit, a production control unit, an intelligent mistake proofing unit and a quality traceability unit. The production planning unit optimizes a production plan and accurately allocates resources, the raw material management unit performs supplier qualification evaluation, raw material multi-level quality inspection and intelligent inventory regulation and control, quality is controlled from the source, the supply chain risk is reduced, the production control unit depends on the equipment real-time monitoring and fault early warning technology, the stability of production parameters is guaranteed, and the production efficiency is improved. The intelligent error-proofing unit integrates OCR verification, threshold detection and prediction model construction, rejects error detection data, prevents data missing, realizes real-time early warning and closed-loop processing of quality risks, and eliminates quality problems in the bud stage, and the quality tracing unit performs real-time early warning and closed-loop processing on the quality risks through full-link data acquisition and a unique tracing code. And accurate positioning and rapid recall of problem products are supported.

The invention provides a soft extraction paper product production control system. The hardware platform comprises a central control all-in-one machine, an intelligent gateway, a folding machine, a semi-finished product paper storage frame, a cutting machine, a single packaging machine, a finished product paper storage frame, a medium packaging machine and a box filling machine. The software platform comprises a monitoring system running on the central control all-in-one machine and is provided with an adaptive feedback learning control method based on PID; according to the intelligent monitoring system, in the normal production process of a papermaking production line, firstly, an SCADA system is responsible for carrying out data acquisition on all PLCs, execution mechanisms and sensors on the production line, a WINCC control platform is responsible for data monitoring and processing, and the adaptive feedback learning control method based on PID is implemented. The control system is convenient to set, is simple and easy to use, saves manpower, improves the productivity of a papermaking production line and the market competitiveness of products, is low in comprehensive cost, and is very suitable for monitoring and control of online papermaking production line equipment.

The invention discloses an enterprise intelligent comprehensive carbon emission reduction method and system, and the method comprises the steps: collecting the carbon source emission, energy consumption and technological parameters of each production link of an enterprise, and transmitting the parameters to a carbon intelligence brain decision platform; inputting an intelligent carbon flow topology analysis model to construct a carbon flow topology network, and analyzing a carbon flow input and output relationship and intensity distribution; key influence factors are extracted, and multiple groups of emission reduction schemes are generated by combining a carbon emission reduction target input adaptive carbon trajectory optimization algorithm; cost, efficiency and potential parameters are introduced into the carbon intelligence brain decision-making platform to evaluate and screen an optimal scheme, and the optimal scheme is converted into a control instruction; and issuing to a production control system to adjust equipment and process parameters so as to realize closed-loop control. The system comprises a carbon data acquisition unit, an intelligent carbon flow analysis unit, an adaptive optimization unit, a scheme evaluation unit, a scheme screening unit and an instruction execution unit. The carbon emission reduction precision and the carbon emission reduction landing performance are improved, and the production efficiency and the cost are both considered.

The invention discloses a wide vermicelli production control system with a safety monitoring function, and relates to the technical field of automatic control, and the wide vermicelli production control system comprises an online state sensing unit which is used for collecting diffuse reflection spectrum vectors and core temperature in real time; the state estimation and prediction unit is used for receiving the diffuse reflection spectrum vector, calculating a predicted glass transition temperature, calculating a safety margin and generating a dynamic temperature set point and a core safety constraint; the dynamic optimization control unit is used for receiving the core temperature, the dynamic temperature set point and the core security constraint, and performing rolling optimization calculation according to a multi-objective optimization function to generate an optimal control instruction; and the process parameter execution unit is used for receiving the optimal control instruction and adjusting the microwave power and the vacuum degree. Through real-time product state sensing, online prediction and scientific safety boundary construction, complete self-adaptive control of the production process is realized, the drying efficiency and the product quality are cooperatively improved, and the production stability and the yield are greatly enhanced.

The invention relates to the technical field of on-line control, in particular to a magnesium oxide production regulation and control system and method based on big data analysis, and the method specifically comprises the steps: obtaining three-dimensional shape data and element composition distribution data of a microcell on the surface of a substrate; based on the curvature continuity index, separating out a high-activity grain boundary region and generating a first active grain boundary distribution diagram; obtaining an optimized second active grain boundary distribution diagram according to the silicon activity influence factors; and calculating the reaction adaptation degree of the magnesium oxide coating on the silicon steel substrate, and determining a hydration degree control target and a particle size distribution control target of magnesium oxide raw material production. According to the method, the problem that in the prior art, the insulating property of the produced magnesium oxide coating is not stable due to the difference of surface characteristics, such as grain boundary activity, element distribution and three-dimensional morphology, of different production batches of silicon steel substrates is solved.

The invention relates to the technical field of chemical production automatic control, in particular to a hexachlorodisilane automatic production control system and method. Data are corrected through the temperature compensation model, side reaction caused by inaccurate temperature is avoided, the temperature and pressure of the reaction kettle are adjusted through negative feedback of the self-adaptive temperature and pressure control model, a gain function is dynamically adjusted according to the reaction time, and the control precision is improved; on the basis of online component analysis, a mathematical regression analysis model is established, coefficients are determined through machine learning, and a production parameter optimization module is used for calculating the opening degree of a return valve and optimizing parameters of a rectifying tower so as to reduce by-products and guarantee the quality of hexachlorodisilane; according to a data analysis result, automatic safety inspection is automatically carried out, the explosion risk is quantitatively evaluated, leakage is detected and speculated through a leakage positioning algorithm, graded protection signals are matched, safety measures are executed, and production safety is guaranteed.

The invention relates to the technical field of production control, in particular to a bearing production control system with data analysis, which comprises the following modules: a data acquisition module which is responsible for collecting operation parameters and running condition data of bearing production equipment in real time; the data processing module is used for analyzing the collected data and comprises an intelligent learning unit, the intelligent learning unit integrates a deep learning technology and a reinforcement learning algorithm into the unit, learning from the data is achieved, and a production strategy is dynamically adjusted; the reinforcement learning algorithm continuously optimizes production parameter setting through real-time interactive learning with a production system so as to improve efficiency and quality; and the production adjustment module is used for automatically adjusting production parameters and equipment settings according to the analysis result provided by the data processing module so as to improve the production efficiency and the product quality. According to the invention, the production control system can flexibly respond to changes in the production process.

The invention relates to the technical field of rock wool board production, in particular to a hot-melt slag regenerated rock wool board production control system based on an intelligent sensor. The data acquisition module comprises an image sensor and a thickness sensor; the production control module comprises an areal density determination unit used for determining whether areal density distribution of the cotton blank is uniform or not according to the areal density distribution characterization value and determining whether the areal density of the cotton blank is qualified or not according to the areal density distribution characterization value, and a correction unit used for determining whether to correct the areal density of the abnormal position or not based on the compensation area; the pendulum bob control unit is used for adjusting the lap joint overlapping degree of the cotton blanks based on the characterization difference value and adjusting the linear speed of the pendulum bob device based on a preset area, and is used for determining whether the thickness stability of the rock wool board is qualified or not based on the thickness fluctuation characterization value; and the curing oven control unit is used for adjusting the chain plate distance of the curing oven or optimizing the preset distribution characterization value. The thickness stability of the rock wool board is improved.

The invention provides a discrete workshop self-adaptive production control system based on a control theory. The problem that an existing production control system is insufficient in adaptability in a dynamic and changeable workshop environment is solved. A traditional modeling method cannot effectively analyze dynamic behaviors of a system and is difficult to deal with transient changes caused by disturbance such as emergency order insertion and equipment faults. By introducing an event-driven mechanism and a multi-target coordination control strategy, dynamic optimization and self-adaptive control of a workshop production system are realized. According to the system, work-in-process and overstock tasks are used as control targets, and dynamic adjustment is achieved by controlling the order input rate and the actual productivity. And the control strategy planning layer and the workshop operation decision-making layer are linked by adopting a centralized event-driven mechanism, and meanwhile, a distributed event-driven mechanism is used in the workshop operation decision-making layer to construct an intelligent unit autonomous decision-making mechanism. The method is suitable for dynamic production control of discrete manufacturing workshops, overcomes the defects in the prior art, and meets the requirements of complex, dynamic and changeable workshop operation environments.

The invention relates to the technical field of digital production control, in particular to a glass fiber cloth multi-process collaborative digital production control system, which is characterized in that a real-time tension sequence data stream is established through a data acquisition module, and a modal entropy extraction module is used for analyzing and generating a signal modal entropy sequence; mapping the sequence amplitude distribution interval into a virtual damping compensation coefficient by using an amplitude deviation mapping model, calculating an adjustment gradient in combination with a first-order differential characteristic, and generating a collaborative sampling control instruction subjected to smooth weighting through an adaptive correction model; the collaborative sampling control instruction drives the cyclic feedback execution module to dynamically adjust or lock the current sampling frequency, and the mechanism effectively gives consideration to the acute capture of transient tension abnormities and the efficient suppression of steady-state data redundancy during operation, and achieves the dynamic balance of the feature extraction precision and the calculation resource utilization rate.

The present application relates to the technical field of production process control, in particular to a prebaked anode grinding production control system based on feedback control, the system comprises: a deviation calculation module acquires acoustic vibration, flow and particle size and calculates filling state, output and particle size deviation, a state modeling module constructs an observation vector based on the deviation and performs Kalman filtering, a weighted comprehensive grinding state index is generated, a regulation and decision module performs PID operation on the index to generate static blade and turbine adjustment instructions, and a feedback module generates pulses and voltage to be sent to an actuator. In the present application, an observation vector is constructed and a state estimation value is output by filtering and removing noise, the deviation is weighted and fused to generate a comprehensive index reflecting the load of the mill, accurate driving instructions are obtained according to the index, dynamic closed-loop control is realized to compensate for the real-time fluctuation of the raw material characteristics, and the estimated results are used to cooperatively adjust the classification parameters to ensure that the finished product particle size meets the standards and improve the overall energy efficiency of the grinding operation.

The invention provides a steel rail curvature detection method and system based on machine vision, and the method comprises the steps: synchronously collecting three-dimensional point cloud data and a two-dimensional image of a steel rail; voxel filtering and statistical filtering are carried out on the three-dimensional point cloud data to remove noise, and distortion correction and gray level equalization processing are carried out on a two-dimensional image; processing the two-dimensional image by using a deep learning semantic segmentation algorithm, extracting the edge contour of the steel rail, and generating a three-dimensional contour model of the steel rail in combination with the three-dimensional point cloud data; fitting the three-dimensional contour point cloud of the steel rail by adopting an RANSAC algorithm to obtain a central axis of the steel rail; bending angles of the steel rail in the vertical direction and the tangential direction are calculated, included angles of normal vectors of adjacent sections are calculated, and the torsion degree is determined; bending and torsion angle threshold values are set, when a detection value exceeds the threshold values, sound-light alarm is triggered, real-time communication with a production line PLC system is carried out through an industrial Ethernet, a bending degree value and an abnormal state signal are transmitted to a production control system, and automatic rolling mill parameter adjustment or shutdown maintenance triggering is supported.

The application relates to the technical field of carbon fiber die forming of automobile hubs, in particular to a production control system and method for carbon fiber die forming of automobile hubs, which comprises a pressure coordination control module, a temperature field dynamic adjustment module, a defect prediction and compensation module and a multi-source data tracing module. Through segmented pressure curve and temperature-pressure coupling control, combined with multi-source data collected by a distributed sensor array, accurate regulation and control and abnormal tracing in the forming process are realized. The deformation compensation and exhaust control submodule improves process stability, and space-time data modeling locates the root cause of the abnormality, thereby ensuring product quality. Through the cooperative design of hardware and algorithms, the application solves the problems of poor material characteristic adaptability, unstable deformation control and low process quality tracing efficiency, and improves the precision and reliability of carbon fiber hub production.

The present invention discloses a lysozyme fermentation temperature control method based on LSTM-PID. First, a temperature mathematical model of a lysozyme fermenter is established through a mechanism modeling method. Then, a random walk strategy and a Levy flight strategy are used to improve the optimization ability of a whale optimization algorithm. Then, the improved whale optimization algorithm is used to determine the initial stage parameters for the LSTM. Then, the output of the LSTM neural network is set as three K values ​​of a PID controller and the most suitable K value is trained and substituted into the PID controller to obtain control parameters. The control parameters are passed to the fermenter production control system for temperature control. The whale algorithm is optimized using Levy flight and random walk strategies to solve the problem that the whale optimization algorithm is prone to falling into local extreme values, i.e., premature convergence, when facing complex models such as neural network LSTM.