340 results about "Production control system" patented technology

The present invention relates to a production and energy system based on a new energy-powered submerged arc furnace, and a related control method. The production and energy system comprises a power transmission and distribution system, a renewable energy power generation system, an energy storage system, and a submerged arc furnace-based production system. The power transmission and distribution system is respectively connected to the renewable energy power generation system, the energy storage system, and the submerged arc furnace-based production system. The production and energy system further comprises an energy control system. The energy control system comprises: an integrated regulation and control system used for performing integrated regulation and control on the production and energy system by means of the power transmission and distribution system; a renewable energy power generation control system used for controlling operation of the renewable energy power generation system; an energy storage control system used for controlling operation of the energy storage system; and a submerged arc furnace-based production control system used for controlling operation of the submerged arc furnace-based production system. Compared with the prior art, the present invention has the advantages of optimizing energy utilization, achieving environmental protection and low-carbon development, improving the stability and reliability of the energy system, etc.

The invention discloses an accurate fertilizer proportioning production control system based on data analysis, and relates to the technical field of intelligent agriculture, the system collects soil and crop related data in real time through a sensor group and monitoring equipment, and after preprocessing, a crop growth environment data set is formed; summarizing and calculating a soil microorganism activity index, a root respiration rate index and a photosynthesis efficiency index; the indexes and the soil environment data are combined to determine the specific feeding demand quantity of nitrogen, phosphorus and potassium, the specific feeding demand quantity of nitrogen, phosphorus and potassium is summarized, a comprehensive fertilizer proportion index is calculated, evaluation is performed according to a preset first fertilizer demand threshold value and a preset second fertilizer demand threshold value, and when an evaluation result triggers a proportion adjustment mechanism, the fertilizer is adjusted. And the fertilizer ratio regulation and control module dynamically adjusts the fertilizer ratio according to the actual crop and soil conditions, and optimizes the ratio effect through a feedback mechanism. According to the system, the nutrient supply precision of crops is improved, fertilizer waste can be effectively reduced, and sustainable agricultural development is promoted.

The invention discloses a hard alloy production control method and system based on digital twinning, and relates to the technical field of digital twinning. A hard alloy production control system based on digital twinning comprises a twinning processing model building module and a hard alloy production control module. According to the method, the digital twinborn model is constructed for the full-process alloy production equipment, the historical production data and the equipment loss data are introduced for calibration regulation and control, the virtual model can truly map the equipment operation state and the process behavior, and the model prediction precision and adaptability are remarkably improved; on the premise that the quality target and the cost constraint are met, the candidate process paths are generated in the multiple equipment clusters, and screening is performed in combination with the raw material feature prediction portrait and the historical database, so that the scientificity and operability of path planning are improved, and the blindness and resource waste of process path selection are effectively reduced.

The invention provides a coal bed gas productivity prediction method and system combined with artificial intelligence, and the method comprises the steps: firstly obtaining a multi-source coal bed gas productivity data set, which comprises geological structure data, reservoir physical property data and historical production dynamic data, of a target block, and then carrying out the time-space alignment processing of the multi-source coal bed gas productivity data set, the method comprises the following steps: obtaining a standardized coal bed gas productivity characteristic set, then performing characteristic extraction on the standardized coal bed gas productivity characteristic set to generate a prediction characteristic set containing geological attribute characteristic and dynamic attribute characteristic dual codes, and then performing iterative prediction on the prediction characteristic set based on a reinforcement learning strategy network. Finally, an optimization strategy set is generated according to the target coalbed methane productivity prediction result and error distribution of historical production dynamic data and fed back to a coalbed methane production control system to trigger parameter adjustment operation, and therefore the accuracy of coalbed methane productivity prediction can be improved, and optimization control over the production process is achieved.

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

The invention relates to the technical field of coal mine fully mechanized mining, in particular to a coal mine communication network system based on a time-sensitive network, and the system comprises a wired communication system, the backbone network of the wired communication system is composed of a plurality of TSN network switches which are in ring network topology connection, and the wired communication system is used for accessing coal mine equipment as a terminal to a TSN network; the wireless communication system is connected with the wired communication system, consists of wireless communication equipment compatible with a TSN protocol, and is used for accessing the coal mine equipment into a TSN network through wireless connection; a communication system based on a time sensitive network (TSN) is established, a real-time, determined and reliable communication network is provided for a coal mine production system, wired and wireless communication systems are adopted, the whole coal mine production control system is covered together, and the requirement of automatic control for data communication is met.

The invention relates to the technical field of program control, and discloses a high-activity selenium yeast intelligent production control system based on gradient feeding monitoring. The system comprises a data acquisition module, a control parameter acquisition module, a gradient calculation module, an intelligent control module and a material supplementing control module. The system collects process production data, extracts key control parameters and weights, calculates real-time gradient values of the parameters, forms six-dimensional gradient vectors, generates control gradient indexes in combination with the parameter weights and time sequence attenuation weights, and then forms an addition control decision. And according to the decision and the process state dynamic data, the material supplementing opportunity is identified, an active adding instruction is generated, programmed control and adjustment of the raw materials and the additives are achieved, and the control precision and the production automation level are improved.

The invention discloses a new material production whole process monitoring method, system and device and a medium. The method comprises the following steps: collecting monitoring data of a plurality of process links in a new material production process and preprocessing the monitoring data; performing correlation analysis on the monitoring data of different process links, identifying a coupling relationship between the previous and later process links, and establishing a correlation model between the process stages; fusing the monitoring data of the plurality of process links to generate a dynamic key monitoring index under the current production condition; performing anomaly detection on the real-time monitoring data based on the association model and the dynamic key monitoring index, and judging whether abnormal data deviating from an expected range exists in the production process or not; and pushing the detected abnormity or potential risk to an operator, and / or generating a parameter optimization and adjustment suggestion of the production process, and sending the parameter optimization and adjustment suggestion to a production control system and the operator. According to the monitoring method provided by the invention, the monitoring precision can be enhanced, and the fine management level of the production process is improved.

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 the technical field of hazardous chemical substance production, and provides a hazardous chemical substance safety production management and control system, in the aspect of data acquisition, all-around acquisition of equipment operation, environment and personnel operation data is realized, a high-precision sensor ensures data accuracy and far exceeds traditional finite dimension and precision acquisition, and during data analysis and processing, edge calculation is combined with deep learning, so that the safety production management and control of hazardous chemical substances is realized. A risk assessment model is dynamically adjusted, the limitation of a traditional fixed model is broken through, production change and risk early warning can be adapted in real time, risk levels are dynamically adjusted, multi-element early warning and visual display are achieved, compared with single early warning and simple display, risk perception is more timely and accurate, accurate reasons are found through a unique accident analysis model, and the risk assessment efficiency is improved. According to the method, the production management and operation improvement can be promoted, each module of the system can be optimized, a closed-loop lifting mechanism is formed, the safety production management and control level of the hazardous chemical substances is comprehensively improved from data acquisition, analysis, early warning and optimization, the safety risk is greatly reduced, and the production safety and stability are guaranteed.

The invention discloses an intelligent injection molding product quality prediction method, which comprises the steps of collecting product structure information, mold parameters and injection molding equipment operation parameters, and constructing a scientific mold testing process window based on simulation analysis; designing an orthogonal experiment scheme to generate a simulation data set, and forming a multi-source data training sample in combination with field test model data; training a quality prediction model by using a distributed gradient boosting tree model, inputting process parameters, and outputting quality indexes such as product gram weight, volume shrinkage and buckling deformation; the model interpretability is realized through a shop value analysis method, and the influence weight of the process parameters on the quality indexes is evaluated; the model is deployed in a production control system or a cloud platform, the product quality is predicted in real time, a dynamic feedback mechanism is established, and when the error between actual detection data and a predicted value exceeds a threshold value, model retraining and parameter correction are automatically triggered to form closed-loop control. The method effectively improves the accuracy, transparency and engineering adaptability of quality prediction of the injection molding product, and can be popularized.

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 discloses a chemical production control system, and relates to the technical field of chemical production automation, the system comprises the following components: a data acquisition module, a data processing module, a fault diagnosis module and an early warning module, the data acquisition module is used for acquiring various data in a chemical production process in real time; the data processing module is used for processing and analyzing the collected data; the fault diagnosis module is used for performing automatic fault diagnosis based on the processed data; according to the invention, through a plurality of fault diagnosis models in the fault diagnosis module, different types of faults can be accurately identified, and the system can rapidly position and give out early warning no matter equipment faults, process abnormalities or operation errors, and in addition, the fault diagnosis accuracy is improved. The early warning module can send early warning signals in multiple modes, it is ensured that an operator can rapidly receive information and take corresponding countermeasures, and the rapid response mechanism is beneficial for preventing fault expansion and further guaranteeing safe proceeding of chemical production.

The invention discloses a dark tea extract intelligent production control system, and relates to the technical field of plant extraction, the system comprises a data acquisition and monitoring module, an intelligent control module, an analysis optimization module and a visualization module; the data acquisition and monitoring module acquires environmental data and eurotium cristatum growth data in real time; the intelligent control module automatically adjusts the temperature, the humidity and the oxygen concentration in the fermentation process based on the environmental data and the eurotium cristatum growth data; the analysis and optimization module predicts and adjusts production conditions in real time by using a neural network and an optimization model; the visualization module provides real-time monitoring and remote operation functions through a graphical user interface, so that an operator can monitor the state of the system in real time. According to the method, the growth environment of eurotium cristatum can be accurately controlled, the fermentation and extraction processes are optimized, the quality consistency and production efficiency of dark green tea are improved, and the problems of quality fluctuation and inaccurate control in a traditional production method are solved.

The present application relates to the field of intelligent manufacturing technology, and discloses an intelligent ring network cabinet automatic production control system and production control method. The system includes: a data acquisition and preprocessing module, which is used to collect equipment status, task progress, and material supply data in the production process in real time, and preprocess the data; a multi-stage recursive optimization scheduling module, which is used to generate production scheduling decisions based on preprocessed data, and recursively optimize at each production stage to minimize the total production time and maximize production efficiency; the method includes: real-time collection of production equipment status, task progress, and material supply information, and preprocessing the collected data. The present invention adopts a real-time data acquisition and preprocessing technical solution based on Internet of Things devices, sensors, and production management software, achieving the technical effect of real-time and accurate collection of key data such as equipment status, task progress, and material supply in the production process.

The invention discloses a rapid batch online detection method and system for miniature bearings, and relates to the technical field of bearing relevance, and the method comprises the steps: carrying out the cooperative collection of miniature bearings, and generating a multi-dimensional detection data set; traversing the key detection area to carry out dynamic space registration, and positioning the key detection area of the miniature bearing; traversing the key detection area to carry out defect feature extraction, generating a defect probability distribution diagram, and carrying out dynamic segmentation on the defect probability distribution diagram to generate a defect type label group; and on-line sorting is conducted on the miniature bearings based on the defect type label group, a sorting instruction set is obtained, and a detection quality report is generated and fed back to a production control system. The technical problems that in the prior art, the miniature bearing detection efficiency is low, the sampling inspection precision is insufficient, and the quality of the bearing is difficult to detect and control online in real time are solved, rapid batch online detection of the miniature bearing is achieved, and the technical effect of improving the miniature bearing detection efficiency and precision is achieved.

A production control system for a hydrogen storage tank includes: an operating unit, a design terminal, and a processor. The processor may be communicatively connected to the operating unit and the design terminal, respectively. The design terminal may be configured to operate a finite element simulation command stored in a storage unit. The processor may be configured to obtain the target laying parameter from the design terminal, generate an operation command based on the target laying parameter, send the operation command to the winding mechanism; and determine whether to generate an optimization command. In response to a determination of generating the optimization command, the optimization command may be generating and sent to at least one of the operating unit, the monitoring unit, and the design terminal.

The invention provides a ZP8 final inspection mistake and neglected loading prevention detection method, device, equipment and system and a storage medium. The method comprises the following steps: acquiring a KNR number of a to-be-detected vehicle; determining the type and configuration of the to-be-detected vehicle according to the KNR number of the to-be-detected vehicle; determining a currently used trained detection model corresponding to the vehicle type and the configuration; acquiring real shot images of a plurality of parts of the to-be-detected vehicle; inputting the actually shot image into a currently used trained detection model to obtain a detected PR number of the part; according to the KNR number of the to-be-detected vehicle, vehicle configuration information of the to-be-detected vehicle is obtained from a production control system, and the vehicle configuration information comprises configuration part PR number information; consistency comparison is carried out on the detected PR number of the part and the PR number information of the configured part, and a detection result is generated; and generating alarm information under the condition that the detected PR number of the part is inconsistent with the PR number information of the configured part. According to the invention, the cost of automatic detection is reduced, the detection rate of wrong and neglected loading defects is improved, and the workload of inspectors is reduced.

The invention discloses a lithium hexafluorophosphate production control system and a method thereof, and relates to the technical field of lithium hexafluorophosphate production, and the method specifically comprises the following steps: obtaining crystallization dynamic information of each area in a reaction kettle in a lithium hexafluorophosphate crystallization process in real time, and analyzing after obtaining to obtain a crystallization dynamic result; respectively generating a crystal growth driving force coefficient and a particle uniform distribution index of each region; constructing a particle size distribution evaluation model for the generated crystal growth driving force coefficient and particle uniform distribution index of each region, generating a particle size distribution coefficient of each region, and performing comprehensive analysis after generation to generate an evaluation coefficient; and analyzing the generated evaluation coefficient, and evaluating the uniformity degree of particle size distribution in the reaction kettle in the lithium hexafluorophosphate crystallization process. The problem of uneven particle size distribution in the lithium hexafluorophosphate crystallization process is solved, and the effect of improving the particle size uniformity and the product quality stability is achieved.

The invention provides a cloud factory production control system for a carbon-based microbial fertilizer, and the system comprises the steps: obtaining associated data for producing the carbon-based microbial fertilizer, carrying out the feature extraction of the associated data, and constructing a time sequence association matrix; inputting the time sequence incidence matrix into a long short-term memory network model for dynamic proportion prediction to obtain an optimal raw material proportion scheme; performing real-time simulation verification on the optimal raw material proportioning scheme, and generating an equipment control instruction after the verification is passed; wherein the equipment control instruction comprises production process parameters; executing the equipment control instruction in a distributed manner based on edge computing nodes so as to produce the carbon-based microbial fertilizer; and performing causal analysis on the quality data and the production process parameters of the produced finished product, and identifying key influence factors to generate a quality optimization strategy. According to the method, the defects that the standard production requirement is difficult to meet and the root of the quality problem is difficult to quickly position at present are overcome.

The invention relates to the technical field of industrial Internet of Things intelligent production control, and discloses an industrial Internet of Things cloud edge collaboration-based equipment intelligent operation method and system, and the method comprises the steps: extracting production flow information and production demands in each product industrial production order, analyzing the elastic production flow related information of the product industrial production order, and carrying out the cloud edge collaboration of the product industrial production order; querying artificial production resources and industrial production auxiliary equipment resources, solving a product industrial production order allocation sub-strategy and an industrial production auxiliary equipment configuration sub-strategy, and issuing the sub-strategies to each production line production edge management terminal and each production line production edge execution terminal; and a production executor is driven to control the production processing equipment to execute the production operation of independent manual production or equipment-assisted production. According to the invention, seamless application and connection of the production auxiliary equipment under the industrial Internet of Things intelligent production control system are realized, and the overall energy efficiency of industrial production is improved while the distribution rationality and scientificity of the production processing orders and the production auxiliary equipment are improved.

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 provides an anomaly analysis method and system applied to a hydrogel production control system, and the method comprises the steps: obtaining an initialized basic anomaly diagnosis neural network and X prior anomaly diagnosis neural networks with different anomaly diagnosis capabilities, and carrying out the abnormal state chain diagnosis prediction of system operation data, and performing guided learning on the initialized basic network by using a first prior anomaly diagnosis neural network with the weakest capability in the X prior anomaly diagnosis neural networks to generate a basic network after the first round of guided learning. And then utilizing an ith prior anomaly diagnosis neural network with a stronger anomaly diagnosis capability to continue guided learning on the basic network after the (i-1) th round of guided learning (i is an incremental integer greater than 1 and not greater than X), and finally outputting the basic network after the Xth round of guided learning as a target anomaly diagnosis neural network. The method is used for carrying out abnormal state chain diagnosis prediction on input system operation data, and the accuracy and reliability of abnormity analysis of the hydrogel production control system can be improved.

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 discloses a cross-linking agent production control system and method based on the Internet of Things, and relates to the technical field of chemical production control, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module for obtaining spectral data in a reaction kettle and obtaining temperature data and pressure data of different positions on the wall of the reaction kettle; the data processing and control module is used for converting the spectral data into reaction data of a cross-linking agent intermediate product based on a meta-calibration model; on the basis of the temperature data and the pressure data of different positions, state data, including a local overheating area and a pressure abnormal area, of a cross-linking agent intermediate product are identified by adopting an image identification and mode analysis algorithm; and the decision execution module is used for establishing a dynamic model fusing the production characteristics of the cross-linking agent based on the reaction data and the state data, training an ML model by using historical data and real-time IoT data based on the dynamic model, and automatically generating and executing an optimal control strategy under security constraints.

The invention relates to a machine core calibration device for an adjustable rack of a tube push bench. The machine core calibration device comprises an annular rack, and a machine core supporting structure is arranged at one axial end of the annular rack; a calibration block is arranged at the other axial end of the annular rack in an extending manner; the center line of a roll hole pattern on an adjustable rack core loaded on the core supporting structure is coaxial with the center line of the calibration block; a plurality of detection rods are arranged on the annular rack, the detection rods are connected with a detection driving part, the detection driving part drives the detection rods to press against the roller in the machine core of the adjustable rack until the roller abuts against the calibration block, and a displacement sensor is arranged on the detection driving part; and the control part is used for receiving the displacement data output by the displacement sensor and transmitting the displacement data to a production control system. Roll gap measurement of the machine core of the pipe jacking adjustable rack is completed, manual detection is replaced, the working efficiency and detection precision of machine core assembly and detection can be improved, meanwhile, the intelligent operation level of a factory is achieved, and the device is compact in structural form, convenient to use, high in working efficiency and capable of meeting the working requirement between rollers.