12833 results about "Manufacturing line" patented technology

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The invention provides an intelligent factory data processing method and system based on the industrial Internet, and the method comprises the steps: firstly obtaining a real-time industrial data set collected by a multi-mode sensor in a target production region, covering various data, such as equipment vibration signals, carrying out the time sequence synchronization processing of the real-time industrial data set, and generating a synchronization data block; the method comprises the following steps of: extracting dynamic state characteristics of a production line from the data, calling a pre-trained anomaly detection model to carry out multi-dimensional anomaly detection, outputting an anomaly detection result, matching a preset expert knowledge base according to the anomaly detection result, and generating a dynamic control instruction set containing equipment adjustment parameters and the like; finally, production parameter configuration is adjusted based on the dynamic control instruction set and fed back to the industrial control terminal in real time, optimization of current production resource configuration is achieved, and the production efficiency and the resource utilization rate of an intelligent factory are effectively improved.

The invention relates to the technical field of industrial data processing, in particular to a suspension spring multi-station production line collaborative scheduling system based on digital twinning, which comprises a station control programming module, a multi-station autonomous collaborative module, a quality pre-control module, a manufacturing process tracing module and a multi-machine execution safety module, according to the system, through real-time data acquisition and monitoring of an integrated digital twin platform, self-adaptive generation of control programs of all stations and programmed issuing of production instructions are carried out; when the production disturbance is sensed, dynamic optimization of a task sequence and real-time self-adaptive adjustment of processing parameters are executed; feedforward control and active compensation are realized in combination with a quality prediction model; constructing a digital thread to support tracing and analysis of the whole manufacturing process; and dynamic program verification of security interlocking and resource conflicts is cooperatively executed on multiple machines. Intelligentized, high-efficiency and high-quality integrated optimization control over the multi-station production process of the suspension spring is achieved.

The invention provides an intelligent factory fault diagnosis method and system based on an AI prediction model, and the method comprises the steps: obtaining an equipment monitoring data flow of a target production line of an intelligent factory, carrying out the diagnosis feature construction processing of the equipment monitoring data flow, generating a state evolution feature and a component correlation feature, and carrying out the fault diagnosis of the target production line of the intelligent factory; and inputting the state evolution characteristics and the component association characteristics into a pre-trained fault prediction model for fault prediction, and generating diagnosis result data containing fault risk levels. And the potential fault type and the propagation characteristic information are identified according to the diagnosis result data, and finally the maintenance guidance data containing the fault positioning identifier are generated based on the potential fault type and the propagation characteristic information and are transmitted to the factory operation and maintenance system to trigger the fault intervention operation, so that the accuracy and the maintenance efficiency of intelligent factory fault diagnosis are effectively improved.

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention provides a new energy automobile control circuit board production process optimization control method and system, and relates to the technical field of production control, and the method comprises the steps: collecting production process parameters in real time, inputting the parameters into a process digital twin model, and obtaining process deviation data; and inputting the deviation data into a process parameter optimization model based on a deep reinforcement learning algorithm to generate an adjustment instruction, performing real-time parameter adjustment on the production line, and feeding back to a digital twinborn model to form closed-loop control. According to the invention, accurate monitoring and intelligent optimization of the production process can be realized, the production quality and the production efficiency of the circuit board are improved, and the rate of defective products is reduced.

The invention relates to the technical field of industrial production lines, and discloses an industrial production line multi-device dynamic collaborative scheduling method based on reinforcement learning, comprising the following steps: S1, modeling a three-dimensional state space; s2, hierarchical reinforcement learning architecture; and S3, edge-cloud cooperative execution. According to the industrial production line multi-device dynamic collaborative scheduling method and system based on reinforcement learning, device states, task constraints and resource occupation are integrated into a structured matrix through three-dimensional state space modeling, and a global decision-making layer captures production time sequence dependence by using a bidirectional long-short-term memory network; modeling equipment space association and process constraints through a graph attention network, and generating a global strategy including task allocation, capacity adjustment and resource pre-allocation; and after the edge layer detects the dynamic event, the cloud platform generates a candidate scheme through Monte Carlo tree search, and realizes dynamic event response and multi-target collaborative optimization by combining multiple targets such as global value network evaluation task completion time and equipment load balancing.

The invention discloses an artificial intelligence-based industrial production line dynamic scheduling method and system, and relates to the technical field of intelligent manufacturing and industrial intelligent scheduling, and the method comprises the steps: collecting real-time state data, carrying out the preprocessing, taking the maximization of productivity, the shortest delivery time and the lowest energy consumption as optimization objectives, constructing a multi-objective reinforcement learning model, and carrying out the optimization of the multi-objective reinforcement learning model; scheduling priority data is generated, and operation distribution of each process node is adjusted in combination with a current equipment load threshold value and production bottleneck node information; when an abnormal condition is detected, triggering a rescheduling mechanism according to scheduling priority data, and updating an operation sequence and a resource allocation result; and synchronously feeding back the updated job allocation result and execution effect to the multi-target reinforcement learning model, and carrying out iterative optimization on the multi-target reinforcement learning model through a priority experience playback mechanism to realize continuous optimization of a scheduling strategy. According to the method, through a mode of combining multi-target reinforcement learning and dynamic scheduling, the learning efficiency and the optimization effect are improved.

The invention relates to the technical field of program control systems, in particular to an intelligent daily chemical production line online control system. The system comprises an equipment digital twin modeling module, a process parameter optimization module, a modular production unit adjustment module, an automatic scheduling and Internet of Things transmission module, an equipment monitoring and data analysis module and a fault prediction and remote operation and maintenance module. Acquiring equipment real-time operation state data of the intelligent daily chemical production line, and performing digital twinborn modeling based on the equipment real-time operation state data to obtain an equipment digital twinborn model; and performing simulation optimization calculation on each process parameter based on the digital twin model of the equipment to obtain stable-state process parameter set data of the equipment. Through technological parameter optimization, modular production unit adjustment, equipment monitoring and fault prediction, efficient dynamic optimization of the production process is realized, the flexibility, stability and fault prevention capability of the production line are improved, the production cost is reduced, and the production efficiency is ensured.

The invention discloses an intelligent PE pipe manufacturing whole process digital management system and method. The system comprises a data acquisition module, a digital twinning module, an analysis optimization module, a detection module and an integration module. The data acquisition module forms a first signal; the digital twinning module receives the first signal and constructs a digital twinning model, material characteristic parameters and environmental data are fused to simulate a pipe crystallization process, and a second signal is formed; the analysis optimization module receives simulation data of the prime number second signal and forms a third signal; the detection module receives a defect image and wall thickness data of the detection instrument, forms a fourth signal and transmits the fourth signal to the digital twinning module to update the digital twinning model; and the integration module integrates the optimization instruction of the third signal and the quality constraint of the fourth signal, generates an equipment control instruction and issues the equipment control instruction to a production line. The intelligent PE pipe manufacturing whole process digital management system can solve the problems of data islands, quality fluctuation, energy consumption waste and quality tracing difficulty in PE pipe manufacturing.

The invention relates to the technical field of production line defect detection, in particular to an intelligent packaging production line defect detection method and system based on an image recognition model. The method comprises the following steps: carrying out packaging container surface defect analysis on an empty packaging container to generate a container inherent defect area; capturing a disturbance response time sequence image sequence based on the inherent defect area of the container after the liquid product packaging operation of the empty packaging container is completed; constructing a motion image recognition model, and performing motion area recognition on the disturbance response time sequence image sequence to obtain a time sequence motion area segmentation map; detecting internal and external impurity defects of the package according to the time sequence motion area segmentation map to obtain internal defect list data of the product; and when the product internal defect list data is non-empty, executing corresponding defective product removal control. High-precision intelligent identification of internal and external impurity defects of the liquid packaging product is realized through the image identification model, and the quality control level of a production line is remarkably improved.

The invention provides a stamping production line self-organizing production system and method based on a twin intelligent agent, and the twin intelligent agent comprises a data collection layer which is used for collecting the operation state data and production environment parameters of physical equipment; the virtual-real mapping layer is used for constructing a digital twin model of physical equipment and realizing real-time state synchronization and bidirectional control instruction transmission of a physical space and a virtual space; the optimization decision-making layer is used for predicting the performance degradation trend of the equipment based on a deep reinforcement learning algorithm and generating a game parameter adjustment strategy and a preventive maintenance scheme; and the collaborative arbitration layer generates a compromise optimization scheme based on Pareto frontier analysis when the multi-agent strategy conflicts, and the multi-dimensional targets of the production efficiency, the equipment life and the energy consumption are balanced. According to the invention, autonomous task allocation, real-time state monitoring and global resource balance of the stamping production line are realized.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention relates to the technical field of industrial automation and intelligent manufacturing, in particular to a rubber tube production line monitoring method and system.The method comprises the steps that multi-mode production data of a production line is collected and preprocessed, a virtual production line model is generated through a dynamic twin model and compared with actual data, and virtual-real difference data is generated; performing anomaly detection on the virtual-real difference data based on a classification algorithm and a rule matching structure, and optimizing parameters of an anomaly detection model; generating abnormal trend prediction data by using the optimized model, and proposing a system correction suggestion according to the prediction data; and dynamically adjusting the operation parameters of the equipment through a closed-loop feedback mechanism. According to the invention, through innovative dynamic twin modeling, deep learning optimization and closed-loop control technologies, real-time monitoring and intelligent adjustment of the production line are realized, and the production efficiency and the product quality are effectively improved.

The invention discloses an intelligent monitoring management system and method for a wire harness production line, and relates to the technical field of wire harness manufacturing, and the method comprises the steps: extracting data of crimping force and mold temperature from historical wire harness production data, coding the data into a real number coding gene chain, generating an initial gene pool in combination with material attributes, and constructing a digital twin model; the method comprises the steps of simulating stress field distribution and a heat conduction effect in a wire harness crimping process through finite element simulation, obtaining a twinning predicted value, pre-verifying process parameters in an initial gene bank based on a digital twinning model, screening process parameter combinations with deformation errors smaller than an error threshold value, and issuing the process parameter combinations to a crimping machine through a production line control center. According to the method, data of real-time wire harness deformation quantity is obtained, the data of the real-time wire harness deformation quantity is compared with a twinborn predicted value frame by frame, a deformation error rate is generated, and an abnormal traceability report is generated based on the deformation error rate, pre-verification is performed by constructing an initial gene pool and combining a digital twinborn model, so that optimization of process parameters is more accurate and effective.

The present invention relates to an intelligent material distribution and material taking device and method for solid fermentation of powdery and granular fertilizers, comprising at least one production line, the production line comprising: a longitudinal conveyor belt parallel to the length direction of the fermentation tank; a transverse conveyor belt arranged above the fermentation tank along the width direction of the fermentation tank; a discharge vehicle, including a longitudinal discharge vehicle and a transverse discharge vehicle; a material distribution conveyor belt for receiving the fermentation raw materials scattered by the longitudinal discharge vehicle; a longitudinal receiving hopper; a mobile spiral material taking machine, which is longitudinally slidably arranged in the fermentation tank to collect the intermediate products in the fermentation tank; a controller, which controls the intelligent material distribution and material taking device for solid fermentation of powdery and granular fertilizers to switch between the two working states of distribution and material taking, and controls the transverse conveyor belt and the longitudinal discharge vehicle to keep synchronous longitudinal movement. The controller is used to switch the working state of the material distribution and material taking device, so as to realize intelligent and automatic material distribution and material taking, so as to meet the needs of solid fermentation of powdery and granular fertilizers.

The invention discloses a columnar transparent part defect detection method, and relates to the technical field of optical detection and machine vision, and the detection method comprises a detection preparation stage, a detection pose configuration stage, a structured light stripe excitation stage, a machine vision detection stage, a model optimization evaluation stage, and a detection post-processing stage. According to the defect detection method for the columnar transparent part, the comprehensive performance of defect detection of the columnar transparent part is remarkably improved through the synergistic effect of coaxial pose optimization, composite structure light source design and improved YOLOv8 deep learning model; various common surface and subsurface defects in production are covered; the real-time detection requirement of an industrial production line is met through relative movement of the part and the light source and high-speed image acquisition and processing; the system has strong anti-interference capability, and can adapt to detection scenes of different materials, sizes and environmental conditions.

The embodiment of the invention provides a filling production line control method and system based on a virtual reality technology. According to the embodiment of the invention, the method comprises the steps: synchronously collecting a real-time dynamic parameter set of a fluid medium, constructing a dynamic coupling factor, generating a dynamic response coefficient matrix through a fluid coupling identification model, generating a nonlinear control parameter combination according to the dynamic response coefficient matrix, and inputting the nonlinear control parameter combination into a motion trail planner. Generating an optimized motion track instruction, driving an execution mechanism to execute filling operation according to the optimized motion track instruction through a servo controller, monitoring the deviation degree in real time, reconstructing the fluid coupling identification model when the deviation degree exceeds a preset threshold value, and updating a dynamic coupling factor; the nonlinear control parameter combination is corrected through the updated dynamic coupling factor, a closed-loop interlocking control mechanism is generated, and the technical scheme provided by the embodiment of the invention not only improves the intelligent level of a filling production line, but also improves the precision and stability in the filling process.

The invention belongs to the technical field of glue filling control, and particularly relates to a glue filling amount control method and system. According to the method, the glue filling deviation can be controlled within an extremely small range through multi-time iteration control of a stage theoretical value and real-time feedback, the filling consistency and the bonding strength are remarkably improved, initial compensation is conducted in combination with various working condition parameters such as temperature, pressure and materials, the pressure and the diffusion dynamic state are monitored in real time, and the working efficiency is improved. The system can automatically adapt to environmental fluctuation and glue performance change, accurately control the filling amount, respond to overflow and viscosity abnormity in time, reduce glue waste, avoid product reworking caused by excess or insufficient glue, combine closed-loop feedback with maintenance strategies, automatically identify and process abnormal states, improve the automation degree and operation stability of a production line, and improve the production efficiency. Manual intervention is reduced, rapid configuration and expansion can be carried out according to different workpiece shapes, materials and production working conditions, and the device is suitable for various glue filling scenes.

The invention relates to the technical field of clothing manufacturing, in particular to an AI scheduling optimization method for clothing flexible production and an ERP integrated system. Comprising the following steps: multi-source heterogeneous data acquisition and fusion: acquiring real-time equipment state data of a clothing production line through Internet of Things equipment, wherein the real-time equipment state data comprises a sewing machine rotating speed, a cutting bed load rate and a quality inspection station abnormal signal; obtaining an order delivery period, a material inventory and a BOM process path through an ERP system; the equipment data and the order data are stored in a distributed database after being aligned through timestamps; dynamic priority modeling: constructing a dynamic priority calculation model based on the order emergency degree, the production line bottleneck process load threshold and the material neat rate, and generating an order-insertable process-level priority queue; according to the scheme, the limitation of a traditional method can be broken through, and the modern production requirements of flexibility and high aging are met.

The invention discloses a terminal product surface defect intelligent judgment quality control system and method, and relates to the technical field of industrial automatic detection and intelligent quality control, and the system comprises the following steps: a data acquisition module generates an original detection signal containing environmental interference compensation; the dynamic detection module receives an original detection signal, performs illumination invariance processing through a self-optimization feature extraction network, and outputs a defect feature vector with confidence rating; the quality association module receives the defect feature vector and constructs a three-dimensional association map with real-time equipment state data, and generates a tracing analysis signal containing root cause probability distribution; and the feedback control module analyzes the key process parameter offset in the tracing analysis signal, generates an equipment adjusting instruction and feeds back the equipment adjusting instruction to the production line. The terminal product surface defect intelligent judgment quality control system and method can solve the problems of insufficient surface defect detection precision, difficulty in quality tracing and lack of process closed-loop control in industrial production.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The invention provides a food information tracing method and system based on Internet data, and relates to the technical field of industrial Internet, and the method comprises the following steps: generating an initial batch identifier of a to-be-produced product according to a product production plan, obtaining real-time external data of an associated region, and storing the real-time external data in a database; when it is judged that a batch adjustment event occurs in the production process of the to-be-produced product, based on real-time state data and real-time external data of a production line, dynamic batch decision is carried out to generate at least two sub-batch identifiers, and a tree-shaped association relationship is established between the sub-batch identifiers and the initial batch identifier; and receiving sub-batch detailed data of each sub-batch identifier, uploading the sub-batch detailed data to a block chain network, storing the batch topological relation in a block chain main chain, storing the sub-batch detailed data in a corresponding sub-chain node, responding to a tracing request of a user terminal, and according to the tree-shaped association relation, tracing the sub-batch data of each sub-batch identifier. And retrieving the full life cycle data of the corresponding target batch in the block chain network. The method overcomes the problem of tracing information splitting caused by batch splitting.

The invention provides a production line process scheduling optimization method and system based on an agent cluster. The method comprises the steps that the agent cluster comprising a plurality of distributed decision agent nodes is configured according to a production line process scheduling instruction; based on the production line architecture feature graph, a communication protocol between agent nodes is generated through node association relationship analysis, and a cooperation rule set is constructed in combination with a process priority rule and a resource constraint condition; the intelligent agent cluster is instructed to perform scheduling according to a communication protocol and a cooperation rule set, process tasks are dynamically allocated and adjusted through real-time state interaction among nodes, scheduling behavior logs are obtained, a reinforcement learning training sample set is constructed in combination with expected labels, decision parameters of intelligent agent nodes are optimized by using a multi-intelligent-agent reinforcement learning algorithm, and a reinforcement learning training result is obtained. According to the method, the dynamic response capability and the cooperation efficiency of automatic production line scheduling can be improved, and the method is suitable for efficient process scheduling in a complex production environment.

The invention discloses an intelligent fault diagnosis method and system for a cattle bone powder production line, and relates to the related technical field of production fault diagnosis, and the method comprises the steps: collecting equipment operation data and technological parameters of the cattle bone powder production line in real time through a multi-mode sensor, and obtaining a real-time monitoring data set; transmitting the real-time monitoring data set to an edge computing node for preprocessing; inputting the standard fusion data into the cloud digital twin, and dynamically simulating the operation state of the production line to obtain virtual-real mapped production line health state simulation data; analyzing through a hierarchical diagnosis engine to obtain a diagnosis result; and triggering a dynamic response according to a diagnosis result, and performing closed-loop intelligent operation and maintenance of the cattle bone powder production line. The technical problems that in the prior art, fault diagnosis is difficult to conduct on the cattle bone powder production line accurately in real time, the fault diagnosis result lags behind, and the production efficiency and the product quality are affected are solved, and the technical effect of improving the operation stability, the product quality and the production efficiency of the cattle bone powder production line is achieved.

The invention relates to a liquid silica gel injection molding process optimization method and system in the field of intelligent manufacturing, and the method comprises the steps: obtaining mold structure data adaptive to a shrinkage rate, combining geometric characteristics of a complex structure product, adopting a topological optimization algorithm to redesign a parting surface and a demolding path, and determining a lossless demolding mold parting scheme; whether the product damage risk is lower than a preset threshold value or not is judged by simulating stress distribution of the demolding path, and if the product damage risk is lower than the threshold value, final mold manufacturing parameters are generated, and an optimized production process configuration file is obtained; extracting production rhythm data from the optimized production process configuration file, dynamically adjusting the operation cycle of the injection molding machine in combination with the demolding efficiency monitored in real time, and determining equipment parameter configuration for efficient mass production; and generating a control instruction sequence according to the equipment parameter configuration, and transmitting the control instruction sequence to the injection molding equipment and the mold heating system through the industrial Internet of Things to obtain operation state data of the automatic production line.

The invention relates to the technical field of aircraft manufacturing, and discloses an aircraft part production quality optimization method based on data analysis. The method comprises the following steps: collecting multi-process real-time processing parameters and quality inspection data of a production line, and generating a dynamic quality weight matrix according to a process parameter coupling degree; extracting process path difference characteristics of qualified products and unqualified products in historical batches, and encoding the process path difference characteristics as a quality evolution chain matched with adjacent process parameter mutation relevance; optimizing process parameters at a quality evaluation node, driving a parameter combination to iterate to minimize quality fluctuation, generating an adjustment amount, updating an evolution chain constraint coefficient, and synchronously constructing a stability evaluation function of a correlation weight matrix and a defect propagation path; triggering process compensation according to an adjustment amount gradient, and verifying the cohesion through a tolerance rule by taking a reference parameter matched with the target evolution chain as compensation data; and generating a feedback matrix by using the quality fluctuation index and the compensation result, and correcting the mapping relation between the weight matrix and the evolution chain.

The invention relates to the technical field of supply chain management, in particular to a part supply chain full-process digital collaborative management system and method, and the method comprises the steps: evaluating supplier data through constructing a dynamic scoring model, and obtaining a supplier score; when the supplier score is lower than a predetermined threshold value, generating a multi-source purchase plan, and dynamically adjusting purchase distribution according to the supplier score; constructing a warehouse-in and warehouse-out part intelligent verification model to analyze warehouse-in and warehouse-out parts, generating an acceptance report and block chain record association, and triggering an alarm and freezing an order when a difference is found; a supply chain risk topological graph is constructed through the supplier fulfillment rate, the logistics data and the market demand fluctuation, and high-risk nodes are identified and early warned in real time; initiating emergency purchase to suppliers with high supplier scores based on high-risk nodes, and optimizing inventory scheduling based on geographic proximity; the inventory level is optimized in real time based on the production line state and the in-transit logistics data, and the safe inventory threshold is dynamically adjusted to cope with supply chain interruption.

The invention relates to the technical field of data control, in particular to a digital visual control method and system for a grease production line. The method comprises the following steps: acquiring full-process physical data; performing noise filtering on the whole-process physical data to obtain whole-process noise filtering data; abnormal data detection is carried out on the whole-process noise filtering data, and abnormal data are removed to obtain a whole-process data set of the grease production line; therefore, by constructing a feature driving mechanism of physical-data fusion of the grease production line, the problems of low data utilization rate, insufficient prediction precision and control optimization lag in a traditional grease production line are solved, and the operation efficiency and decision intelligent level of the grease production line under complex working conditions are improved.