72 results about "Discrete manufacturing" patented technology

The invention relates to the field of discrete manufacturing intelligence, in particular to an intelligent decision-making and business collaboration method based on an OAG ontology and an LLM large model, which comprises the following steps: collecting historical data and business documents, and performing directional fine tuning on a basic large language model to form the LLM large model; the method comprises the following steps: analyzing discrete manufacturing scene original data and business documents, and constructing an OAG ontology library; receiving field data in real time through the LLM large model, updating the OAG ontology library, and performing feedback optimization on the LLM large model to form bidirectional feedback; integrating real-time data and historical data, inputting the data into an LLM large model, converting the data into business knowledge through layering of an OAG ontology library, and generating a main and standby decision scheme; and establishing an agent federated center, issuing a decision scheme, collecting execution data and feeding back an LLM large model, and realizing cross-scene collaboration and full-process data closed loop. According to the invention, through a technical path of a whole-process data closed loop, a core pain point that an existing LLM does not understand a business is effectively solved, and whole-link value conversion of discrete manufacturing data is realized.

The invention relates to a manufacturing system risk control knowledge matching method based on semantic embedding and clustering analysis, and the method comprises the following steps: collecting and preprocessing risk control text data: collecting unstructured text data of a manufacturing system history record, and obtaining preprocessed risk control text data, constructing a professional corpus for a discrete manufacturing scene; text semantic embedding generation; semantic clustering modeling: performing unsupervised clustering modeling on all semantic vectors, mining semantic association and potential structures between texts, obtaining semantic representations of risk control knowledge through a clustering algorithm, and assisting in generating clustering tags; and a risk knowledge matching mechanism.

The invention is suitable for the technical field of intelligent manufacturing scheduling, and provides a discrete manufacturing workshop multi-stage dynamic scheduling method based on transportation state feedback, which comprises the following steps: determining a multi-stage structure of tasks in a workshop, and establishing a corresponding task flow chart and a resource dependence model; a scheduling modeling basis with resource constraint and physical connection is formed; based on the current state of the system, key state variables are extracted, and a scheduling action space is defined; a transportation index is introduced into the reinforcement learning structure, and a composite reward function is constructed; reinforcement learning is adopted for training, and scheduling strategy parameters are iteratively optimized based on interaction between the intelligent agent and the environment. According to the method, rapid restoration and resource continuity maintenance of a scheduling strategy in a dynamic disturbance environment are realized, the real-time response capability and scheduling adaptability of a scheduling system under complex resource constraints are remarkably improved, and the method is suitable for manufacturing scenes facing high-frequency task change and transportation bottleneck problems.

The invention designs a workshop dynamic scheduling method based on an improved genetic algorithm and multi-objective optimization. According to the method, in order to solve the problem that a scheduling scheme fails after dynamic events such as equipment failure, order insertion or material delay occur in a discrete manufacturing workshop, a greedy strategy is adopted for pre-scheduling, and rapid rescheduling is performed based on an improved genetic algorithm after the dynamic events occur. The algorithm improves search efficiency and scheduling stability through multi-population parallel evolution, differential evolution self-adaptive parameter adjustment and an elitist retention mechanism. And taking minimization of the maximum completion time, the total delay time and the equipment change frequency as multiple targets, and obtaining a comprehensive optimal solution through a weighted summation method. According to the method, the scheduling response speed and stability of the workshop in a dynamic environment can be remarkably improved, and the workshop production efficiency and the equipment utilization rate are improved.

The invention relates to a discrete manufacturing process evaluation method based on deep learning. Comprising an equipment performance data acquisition module for collecting key performance index data of various processing equipment; the equipment performance evaluation module adopts a weighted index combination and hierarchical evaluation mechanism, introduces a real-time state sensing mechanism, and evaluates the equipment performance in a multi-dimensional and multi-level manner; the process intelligent analysis module fuses historical process data and equipment performance data, constructs deep learning model input through feature extraction, and realizes identification of key difficulties of the processing process and prediction of the process success rate; and the process feasibility evaluation module combines the trained deep learning model with the equipment performance evaluation result to form the process feasibility automatic evaluation method for specific equipment and workpieces. According to the method, intelligent decision-making of equipment type selection and a process path in a manufacturing process can be realized, an enterprise is helped to realize optimization of production efficiency and product quality, and the production efficiency and the product quality of a manufacturing system are comprehensively improved.

The invention provides a discrete manufacturing production line process decision and optimization method and system based on digital twinning, and relates to the field of production intellectualization and digitalization, and the method mainly comprises the steps: constructing a high-fidelity digital twinning model corresponding to a physical production line through the combination of mechanism modeling and data-driven modeling; establishing a virtual-real consistency evaluation index system, and realizing deviation identification and dynamic consistency maintenance of the digital twinborn model; production disturbance, equipment state change and process deviation information are sensed in real time; a part-process-equipment-quality multi-dimensional correlation model is constructed; an improved stochastic gradient descent algorithm is introduced to carry out dynamic updating and convergence control on intelligent agent strategy network parameters, and a knowledge base self-evolution updating mechanism is constructed; according to the method, dynamic updating and consistency maintaining of the twinborn model are achieved, the dynamic response capability of the digital twinborn model under the dynamic operation condition is remarkably improved, and the systematicness and reusability level of process knowledge are effectively improved.

The invention provides a discrete manufacturing intelligent scheduling method based on a graph theory. The discrete manufacturing intelligent scheduling method comprises the steps of 1, constructing a heterogeneous graph model of scheduling elements; step 2, static scheduling optimization based on a critical path method; step 3, resource allocation optimization based on a multi-resource bipartite graph matching method; and 4, dynamic response and rescheduling are carried out. Based on a graph theory method, a complex'process-resource-constraint 'relationship is converted into a visual heterogeneous graph model, and a systematic scheduling solution based on the graph theory is constructed, so that the scheduling solution is suitable for a discrete manufacturing scene with multi-process, multi-equipment, multi-constraint and dynamic disturbance characteristics; the method is used for realizing static planning of production plan scheduling, resource optimization distribution and dynamic adjustment full-process optimization.

The invention discloses a discrete manufacturing multi-view BOM dynamic derivation method and system based on a tree-shaped network structure, and belongs to the technical field of intelligent manufacturing and product data management. The method comprises the following steps: constructing a tree body as a single product data source, and storing a hierarchical relationship, attribute definition and engineering semantics of a product in a tree structure; based on the tree body, dynamically matching derivation rules through a mesh mapping relation library; executing rule matching, semantic verification, structure reconstruction and dictionary verification by utilizing a dynamic view derivation engine to generate a target BOM view; semantic constraint and verification are carried out on the derivation process through a BOM data dictionary module; and realizing access control and audit based on role, scene and data sensitivity through the security and authority control module. According to the method, a new generation of xBOM dynamic derivation architecture which takes a tree form as an ontology, takes a net form as mapping, is configurable in rule and is strong in data specification is constructed; the fundamental problem of discrete manufacturing multi-view BOM management is solved, and engineering rationality and service flexibility are both achieved.

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 a discrete industrial agent-based production management method and system, and relates to the field of production management, and the method comprises the steps: collecting a production prediction sample data set and a quality inspection decision sample data set, carrying out the data weight division of the two data sets, and obtaining two sample weight sets; obtaining a production prediction and quality inspection decision path array, a first prediction accuracy rate set and a decision accuracy rate set after integrated training; combining the two path arrays to obtain a discrete industrial agent array, carrying out joint optimization training, and testing to obtain a second prediction and decision accuracy set; obtaining current production basic data, inputting the current production basic data into the agent array, outputting a predicted production yield and a decision quality inspection parameter, performing compensation according to an error between the second prediction and decision accuracy set and the first prediction and decision accuracy set, and obtaining a predicted production yield and decision quality inspection parameter interval for production management. The technical problem that data interaction and business collaboration of a plurality of complex and independent scenes in the discrete manufacturing industry are difficult to realize in production management is solved.

The invention discloses a cloud manufacturing optimization system for a linear cutting workshop. The cloud manufacturing optimization system comprises a user layer, a service center layer, a scheduling entity layer and a user feedback layer, the user layer is provided with a cloud terminal and is used for exchanging data with the service center layer; the business center layer comprises a model center, a data center, a search center, a computing center and a planning center; the scheduling entity layer comprises an enterprise level, a workshop level and a production line level, three levels of automatic scheduling systems exchange data with the service center layer respectively, and distributed automatic scheduling systems are arranged in the three levels of automatic scheduling systems respectively; the user feedback layer feeds back the work summarization condition to the user layer; according to the method, a cloud system of a whole wire cutting product is integrally redesigned by utilizing a new computing mode, a business mode and an application mode of cloud computing according to a design concept of'cloud centralized management of discrete manufacturing resources and on-demand service of overall planning of cloud resources.

The invention discloses an integrated intelligent monitoring system and a hierarchical isolation method and equipment based on a security domain, and relates to the technical field of industrial automation system integration, the method comprises a centralized data bus, a modular function service and a service interface, the centralized data bus is used for receiving original real-time data acquired from bottom equipment; the modularized function service is used for designing each function service of the discrete manufacturing workshop into a mutually independent and pluggable module form, and each function service subscribes to required data from the centralized data bus; and the service interface is used for publishing the generated new service data back to the centralized data bus after the function service consumes the subscription data, so that other function services can subscribe and consume. According to the invention, the problems of architecture chaos, data islands and security risks in multi-subsystem integration can be effectively solved.

A discrete manufacturing pipeline quality prediction method based on mechanism and data joint driving belongs to the field of intelligent manufacturing and industrial artificial intelligence, and the method comprises the following steps: first, data acquisition and preprocessing under the industrial scene of the discrete manufacturing pipeline; second, state space deduction and single variable nonlinear analysis of the sparse time sequence characteristics of the discrete manufacturing pipeline; third, mechanism joint training and real-time closed-loop intervention of the discrete manufacturing pipeline quality prediction model. The present application significantly improves the physical consistency of the discrete manufacturing pipeline quality prediction, realizes continuous inference of the hidden state under irregular sampling gaps, and enhances the safety decision-making ability and engineering application value of the automatic control of the discrete manufacturing pipeline.

The invention relates to the technical field of manufacturing data processing, and discloses a production business data processing system for a discrete manufacturing workshop, which comprises an asynchronous data acquisition module for acquiring an asynchronous event pulse sequence of a discrete execution node; the mapping transformation module establishes an addressing mapping relation between an event source and a memory logic bit plane and generates an original differential bitmap; the bitmap filtering module is used for hedging paired logic flipping signals by utilizing an exclusive-OR logic self-reversal principle and filtering jitter interference at the edges of the signals so as to generate a net value difference bit plane; the state synchronization processing module performs in-situ upset updating on the state global bitmap through address offset addressing according to the bit identifier, and through a bitmap differential synchronization mechanism, asynchronous state updating without transaction lock constraint is achieved, calculation blocking generated by high-frequency pulse impact is relieved, and the real-time performance and stability of system data throughput are enhanced.

The invention relates to the technical field of discrete manufacturing workshops, in particular to a discrete manufacturing unmanned intelligent workshop AGV number optimization method and a related device. The method comprises the following steps: constructing a mathematical combination optimization model for the discrete manufacturing unmanned intelligent workshop according to a preset constraint condition and a preset objective function; the process information of the discrete manufacturing unmanned intelligent workshop is determined, the process information comprises a plurality of processes, and each process corresponds to single demand information; and solving the demand information corresponding to each process based on the mathematical combination optimization model to obtain the number of AGVs required in each process. According to the invention, the number of AGVs in the discrete manufacturing unmanned intelligent workshop can be optimized, and the operation cost is reduced.

The application relates to the technical field of intelligent manufacturing teaching and training equipment, and discloses a lean production training platform based on production big data analysis, which comprises an entity production line module, a multi-source heterogeneous data acquisition system, an edge computing and control hub and a lean production training terminal. The entity production line module is used for simulating a production operation process in a real discrete manufacturing environment. The multi-source heterogeneous data acquisition system is distributedly arranged on the entity production line module and is used for collecting multi-dimensional data in a production process in real time. Through cooperation of an industrial sensor array, a machine vision unit, an RFID (Radio Frequency Identification) unit and a data aggregation terminal, multi-dimensional data such as equipment states, process parameters and material tracking are collected in real time, millisecond-level perception of whole-factor data such as people, machines, materials, methods and environments in the production process is realized, and the problems of single data dimension and rough perception granularity of a traditional training platform are fundamentally solved, so that a rich and accurate data basis is provided for lean analysis.

The invention relates to the technical field of intelligent manufacturing, in particular to a discrete manufacturing oriented production unit total factor digital twinning system, which comprises a digital twinning system and is used for realizing virtual mapping of discrete manufacturing production units, and the digital twinning system comprises a three-dimensional scene construction unit, a virtual environment corresponding to a physical production unit in a 1: 1 manner is created, and the virtual environment is used for realizing virtual mapping of the discrete manufacturing production units; model vertex capturing and aligning functions are realized; the equipment model library unit comprises digital models of an industrial robot, a numerical control machine tool, conveying equipment and a clamp, and physical attribute parameters are configured; the multi-protocol communication interface unit integrates Modbus TCP, S7 and OPC UA protocols and is connected with the PLC and a robot control system; through high-precision three-dimensional modeling, multi-protocol real-time communication, containerization motion control and virtual-real synchronization technologies, digital mapping and collaborative optimization of production units are realized, the production line planning efficiency and the operation reliability are remarkably improved, the debugging cost is reduced, and full-life-cycle management is supported.

This invention discloses a flexible scheduling method, system, and application for UPMS (Upright Manufacturing System) workshops based on an improved differential evolution algorithm. The method includes: establishing an independent parallel machine scheduling model with sequentially dependent mold-changing time as the objective of minimizing the maximum completion time; using real-valued vector encoding and decoding it into a scheduling scheme through the maximum position value rule; performing mutation and crossover operations of the differential evolution algorithm on the generated test vectors, with optimizations including load balancing optimization based on destruction and reconstruction and mold-changing optimization based on sequential smoothing; finally, updating the population and outputting the optimal scheduling scheme. This invention significantly reduces sequentially dependent mold-changing time, achieves dual optimization of load balancing and mold-changing cost, and improves solution accuracy and efficiency, making it particularly suitable for discrete manufacturing workshops such as textile printing and dyeing, and injection molding.

The invention relates to a discrete manufacturing workshop production simulation modular modeling method, which comprises the following steps of: establishing a modular model of production elements, and setting in functional attributes and a control method; establishing a modular model of a product, and setting in technological process attributes; based on the workshop layout, taking out a corresponding production element model from the module library, and constructing a production simulation model; on the basis of the technological process, a proposed discrete manufacturing workshop production and logistics simulation control method is adopted, corresponding production and logistics resources are called, production and circulation of products among different working procedures are achieved, and workshop full-process and full-factor production simulation is achieved; production simulation information is counted, production performance is analyzed, man-machine interaction adjustment and multi-factor experiments are carried out, and the production process and production resource configuration are optimized. The problems that current production simulation modeling is large in difficulty and long in period, and accuracy is difficult to guarantee are solved, discrete manufacturing workshop production and logistics simulation model modularization rapid construction is achieved, modeling efficiency is improved, and workshop production performance is optimized.

The invention discloses a workshop material distribution optimization method and system based on deep reinforcement learning, and relates to the technical field of industrial automation and intelligent manufacturing, and the method comprises the following steps: firstly, carrying out the unified modeling of two types of logistics activities of material distribution and material rack recovery in a workshop, key elements such as AGVs, stations, buffer areas and the like are abstracted into a system state, and an optimization function with the purpose of maximizing the productivity in unit time is constructed; secondly, a high-fidelity digital twinning environment is built according to actual workshop data, and simulation accuracy is ensured; a Markov decision process model is established in the environment, a graph attention network is utilized to encode the workshop state, and finally a near-end strategy optimization algorithm is adopted to train. According to the method, the adaptive capacity of the AGV scheduling system to the dynamic uncertainty of the workshop is effectively improved, the material distribution delay rate is remarkably reduced, the unit time yield is improved, and an efficient and reliable solution is provided for logistics optimization of the discrete manufacturing workshop.

The present application relates to the field of discrete manufacturing intelligence, and more particularly to a discrete manufacturing island production intelligent collaboration method and system based on an OAG ontology, comprising the following steps: real-time acquisition of cross-scene heterogeneous original data of island production full-scene, establishment of a mapping relationship, generation of a standardized mapping data set; based on the standardized mapping data set, combined with the core composition of the OAG vertical ontology, full-quantity data semantic conversion is completed, and an OAG ontology semantic information library is generated; combined with real-time production requirements, global situation analysis is completed, the OAG vertical ontology is used as a unified rule driver, a four-layer multi-agent system is started to complete collaborative verification and flexible collaborative decision-making, and standardized collaborative execution instructions are generated; the collaborative execution instructions are issued to the corresponding execution unit to complete full-link autonomous execution, and feedback data of the whole process is collected synchronously to complete closed-loop optimization. Through full-process autonomous collaboration and continuous iteration, the present application improves production efficiency and collaborative stability, and reduces scheduling cost and collaborative conflicts.

The application discloses a kind of robust scheduling optimization methods of discrete manufacturing workshop based on deep reinforcement learning, comprising: taking historical processing data, using neural network to fit the function of process processing duration about equipment, operator and production initial time;Establish the processing environment model of factory workshop, and the processing environment model includes the number of available equipment, on-duty operator and the inventory number of intermediate product;Obtain the product quantity that needs to be processed today;According to the processing environment model and the product quantity that needs to be processed today, construct the robust scheduling problem of discrete manufacturing workshop, and the objective function of the robust scheduling problem of discrete manufacturing workshop is to minimize the maximum completion time and minimize the difference between completion time and delivery time;Solve the robust scheduling problem of discrete manufacturing workshop.The application can obtain more actual, more robust production scheme.

The invention relates to the technical field of discrete manufacturing industry production process control, and discloses an automatic collaborative regulation system for a discrete manufacturing industry production process, which comprises a node control unit distributed at a process node and a collaborative control resolving unit, the cooperative control calculation unit converts the node operation rhythm into a time axis distribution parameter in a sequential logic correlation model, calculates a target adjustment vector based on the parameter, and superposes a compensation pulse at the output leading edge of the target adjustment vector by using a feed-forward shaping mechanism, so that an adjustment signal breaks through a physical execution dead zone of a bottom-layer driving execution unit, and the control of the bottom-layer driving execution unit is realized. According to the method, through signal shaping and smoothing of the dynamic impedance difference between the ideal control law and the mechanical actuator, the non-lagging response to extremely fine disturbance is realized, the non-linear amplification of errors in a rigid time sequence frame is blocked, and the stable convergence of a control loop is ensured.

The application relates to a kind of operation and maintenance fault monitoring system modeling method based on knowledge graph and OPC UA protocol. First, the knowledge and previous processing rules in the operation and maintenance field are summarized, the previous operation and maintenance fault event knowledge of workshop is extracted, the fault data in production is sorted out, the corresponding knowledge base is established, and the concept model of workshop operation and maintenance fault entity and relationship is constructed. Then, the operation and maintenance fault model data source is acquired, the data layer is built, and then the ontology, entity, relationship and attribute are perfected from bottom to top, and the knowledge graph is constructed. Further, based on the OPC UA protocol, the information model is abstracted into the software system, and the operation and maintenance fault information model is instantiated. The whole step is simple and clear, can be applied to various discrete manufacturing production systems, solves the fault handling problem in operation and maintenance scheduling, can express the whole content of operation and maintenance fault monitoring at the same time, and does not affect the subsequent production, and has great application value in the operation and maintenance field.

The invention discloses a hand key point identification method, system and device for a packaging worker in a discrete manufacturing workshop, and a storage medium. The method comprises the following steps: acquiring a hand image and preprocessing the hand image; detecting the preprocessed hand image based on the optimized YOLOv5s model so as to determine hand bounding box image information; carrying out adaptive affine transformation adjustment on the hand bounding box image information; inputting the hand bounding box image subjected to adaptive affine transformation into an HRNet model for key point estimation to obtain an image containing a plurality of hand key points; performing post-optimization processing on the image information containing the plurality of hand key points by adopting a post-processing optimization module to obtain an optimized hand key point image, and performing anomaly detection on the optimized hand key point image by adopting a preset packaging process to obtain an anomaly detection result; the method can better adapt to a complex and changeable dynamic environment, and has the advantages of high precision, low delay, good robustness and the like.

This invention provides a method for synchronizing digital twin data with real-world data in discrete manufacturing, belonging to the field of intelligent manufacturing and industrial information technology. It involves unified preprocessing of multi-source heterogeneous sensor data and using discrete manufacturing equipment data as a time reference to dynamically align the twin model data in time. Based on this, an adaptive correction mechanism based on historical synchronization errors is established to compensate for sensor drift, noise interference, and model prediction errors in real time. Simultaneously, through high- and low-frequency data rate matching and interpolation compensation, high-frequency acquired data and low-frequency simulation models can operate collaboratively on a unified time scale. Through these technical means, the digital twin model can continuously and stably reflect the real operating status of discrete manufacturing equipment, providing a reliable data foundation for production monitoring, status analysis, and intelligent decision-making.

The application provides a material distribution path design method, comprising the following steps: step 1: counting the transportation demands that have not been loaded, arranging the transportation demands that have not been loaded according to the time sequence, and selecting the site corresponding to the transportation demand with the earliest time arrangement as the departure site; step 2: for a specific departure site, according to all the transportation demands of the site, sorting the transportation demands that have not been loaded of the site according to the priority, and traversing a plurality of car departure schemes; the priority of the transportation demands is in the order from high to low as follows: the overnight starting time point demand, the overnight other time point demand, the current day starting time point demand and the current day other time point demand. The application solves the problems of multiple material distribution demands and multiple sites according to the research and batch mixed line research characteristics of the discrete manufacturing workshop, improves the material distribution efficiency in the discrete manufacturing workshop, and reduces the material distribution cost.

The application provides a discrete manufacturing capacity prediction method, medium and system based on AI multi-agent cooperation, belongs to the technical field of AI multi-agent cooperative manufacturing, and abstracts equipment, materials and human resources into agents by constructing a distributed multi-agent cooperation architecture and configuring a time synchronization mechanism, establishes an agent state perception layer, uses artificial intelligence technology to monitor the states of various resources in real time, constructs an agent cooperation decision network based on a graph neural network, uses game theory and a consistency algorithm to realize stable convergence, establishes a historical data preprocessing module, extracts key production features through data cleaning and feature engineering, selects a processing strategy according to a data missing rate, establishes a capacity prediction result output and feedback optimization mechanism, and adjusts model parameters adaptively according to a prediction error, so that the technical problem that the isolated and scattered state information of heterogeneous resources in a discrete manufacturing system leads to low capacity prediction precision and the incapability of real-time dynamic adjustment is solved.

The application discloses a kind of process parameters and production plan linkage's intelligent production scheduling optimization tool and method, belong to intelligent manufacturing and production scheduling optimization technical field.The tool includes data acquisition and processing module, process parameter optimization model module, production plan scheduling module, linkage optimization engine, cleaning time prediction module, dynamic rescheduling module, parameter-plan mapping knowledge base and visual interactive interface.The application will process parameter as the decision variable of production scheduling optimization, constructs joint decision space, and the processing sequence of product and each product process parameter are optimized by multi-objective evolutionary algorithm.Coincidentally, the application has dynamic rescheduling capability based on real-time data, can respond to equipment failure, urgent single insertion etc.Perturbation event.The application can be widely applied to automobile painting, furniture spraying, engineering machinery painting etc.Discrete manufacturing field, significantly improve production efficiency, reduce energy consumption, optimize product quality.

This invention belongs to the field of resource scheduling technology in discrete manufacturing, and discloses an intelligent management system and method for discrete manufacturing workshops. The system includes a scheduling module, an operation and maintenance module, a detection module, and an optimization module. The scheduling module collects workshop production data in real time and generates an optimal production scheduling plan through a scheduling algorithm. The operation and maintenance module dynamically updates the scheduling plan based on equipment operating status and sends it to the detection module. The detection module monitors product performance in real time, adaptively adjusts process parameters based on the updated scheduling plan, and uploads the updated parameters to the optimization module. The optimization module optimizes equipment operating parameters based on the process parameters, generates optimization instructions, and sends them to the discrete manufacturing workshop for execution. This system enables fully autonomous intelligent production control in discrete manufacturing workshops, significantly improves production energy consumption management, and makes production control more efficient, energy-saving, and intelligent.