108 results about "Discrete manufacturing" patented technology

Disclosed in the present invention are a multimodal industrial data fusion method and system for discrete manufacturing. The method comprises: collecting discrete manufacturing industrial multimodal data in real time, performing feature extraction to obtain feature data, and inputting the feature data into a differentiable computer network to obtain a data fusion result, wherein a differentiable computer is further used for accomplishing a prediction objective on the basis of the data fusion result, and the differentiable computer network comprises a data fusion module and a memory system, the data fusion module fusing the feature data in an agent, and the memory system being used for storing and reading / writing refused data in the agent; and inputting the data in the memory system into an evaluation system to calculate a reward value, guiding the agent in data fusion on the basis of the reward value, and updating the memory system. The present invention increases the capacity of a memory system without leading to an increase in training parameters, and fuses complex multimodal industrial data, thereby facilitating decision-making in the discrete manufacturing industry.

The invention relates to the technical field of product detection, in particular to a discrete manufacturing product defect AI visual detection method based on multi-modal fusion, which comprises the following steps: acquiring a visible light image and a thermal infrared image of a target product in time alignment, carrying out photometric distortion correction on the visible light image, carrying out radiation calibration correction on the thermal infrared image, and obtaining a target product defect AI visual detection result. Performing spatial registration on the visible light image after the luminosity distortion correction and the thermal infrared image after the radiation calibration correction to obtain a registered visible light image and a registered thermal infrared image; feature level fusion based on multi-level DWT and decision level fusion based on the DS evidence theory are carried out on the registered visible light image and the registered thermal infrared image, and a fused image is obtained; and inputting the fused image into a visual detection model running on an embedded AI computing platform, and obtaining a detection result of the target product output by the visual detection model. According to the method, the defect detection precision is improved, and a more reliable guarantee is provided for the discrete manufacturing industry.

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 provides a discrete manufacturing capacity prediction method based on AI multi-agent collaboration, a medium and a system, and belongs to the technical field of AI multi-agent collaboration manufacturing. Equipment material human resources are abstracted into agents by constructing a distributed multi-agent collaboration architecture, and a time synchronization mechanism is configured; an intelligent agent state sensing layer is established, various resource states are monitored in real time by applying an artificial intelligence technology, an intelligent agent collaborative decision network based on a graph neural network is constructed, and stable convergence is realized by adopting a game theory and a consistency algorithm; a historical data preprocessing module is established, key production features are extracted through data cleaning and feature engineering, a processing strategy is selected according to a data missing rate, a productivity prediction result output and feedback optimization mechanism is established, and model parameters are adaptively adjusted according to prediction errors; the technical problems of low productivity prediction precision and incapability of real-time dynamic adjustment caused by isolated and dispersed heterogeneous resource state information in a discrete manufacturing system are solved.

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 discloses a workshop layout optimization method for a discrete manufacturing system, and aims to construct a multi-row facility layout mathematical model considering a channel loading and unloading point mechanism aiming at the process storage behavior and cross-row logistics path problems in the manufacturing process. According to the method, on the basis of analysis of workshop parameters and logistics paths, reasonable assumed conditions are set, decision variables such as facility space positions and arrangement relations are defined, a constraint system meeting uniqueness, non-overlapping performance and boundary limitation of facilities is established, and material handling cost minimization is taken as a target. And a Manhattan distance and a Floyd algorithm are adopted to measure the same-row transportation distance and the inter-row transportation distance respectively. In order to solve the model, an improved genetic algorithm fusing bidding selection and a random immigrant mechanism is designed, and the algorithm convergence performance and the global search capability are improved. According to the method, the inter-bank logistics cost can be remarkably reduced, the compactness of the facility layout and the path continuity are optimized, and the method is suitable for manufacturing scenes with process storage characteristics such as welding, assembling and warehousing transfer.

The invention discloses a discrete manufacturing full-life-cycle intelligent computing system and method, the system comprises a data knowledge layer, a computing adaptation layer and a parallel service layer, a virtual matching mechanism is used between the data knowledge layer and the computing adaptation layer, that is, primary mapping is formed according to task demand description and manufacturing resource description in a twin world, an optimal algorithm is called for a specific task, the most reasonable resources are adapted, an optimal service strategy is obtained, and a mapping process from a service demand to the service strategy is realized; a virtual-real parallel computing mechanism is used between a computing adaptation layer and a parallel service layer, that is, a mutual linkage relation is established in a physical world according to a service execution strategy obtained in a twin world, so that the requirement of discrete manufacturing full-life-cycle intelligent service is met, and the mapping process from a service strategy to service execution is achieved. And through two times of mapping, autonomous computing adaptation of task-knowledge-algorithm-resource in the whole life cycle of discrete manufacturing is realized.

The invention discloses an equipment energy consumption simulation prediction method and system based on a discrete manufacturing system and a storage medium, and the method comprises the following steps: S1, collecting the average power data of all process equipment in the discrete manufacturing system under each state and during the conversion of different states, and constructing an equipment power database; s2, collecting process and logistics information of a workshop; s3, integrating the equipment power database into the simulation model, and calling corresponding power data in the equipment power database according to the process equipment operation state determined by simulation; s4, inputting production parameters, operating the simulation model, and calculating an energy consumption prediction result; according to the method, the state switching energy consumption can be quantified, and accurate calculation of multi-process equipment energy consumption under various production scenes is supported.

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 application relates to the technical field of product detection, in particular to a discrete manufacturing product defect AI visual detection method based on multi-modal fusion, which comprises the following steps: acquiring time-aligned visible light images and thermal infrared images of a target product; performing photometric distortion correction on the visible light images; performing radiation calibration correction on the thermal infrared images; performing space registration on the visible light images after photometric distortion correction and the thermal infrared images after radiation calibration correction to obtain registered visible light images and registered thermal infrared images; performing feature level fusion based on multi-level DWT and decision level fusion based on DS evidence theory on the registered visible light images and the registered thermal infrared images to obtain a fusion image; and inputting the fusion image into a visual detection model running on an embedded AI computing platform to obtain a detection result of the target product output by the visual detection model. The method improves the defect detection precision and provides more reliable guarantee for the discrete manufacturing industry.

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 discloses an AGV scheduling system and scheduling method, relates to the technical field of flexible workshop job scheduling, and solves the problems that local optimization damages global circulation efficiency, characterization defects and invalid actions frequently occur and robustness is poor in the prior art. The system comprises a manufacturing execution system, a data acquisition module, a decision-making system and an environment interaction module, the manufacturing execution system decomposes a production plan into discrete manufacturing tasks and stores the discrete manufacturing tasks in a dynamically updated task pool, a workshop environment generates state information in real time, the state information comprises environment dynamics and AGV instant observation data, and the AGV real-time observation data is transmitted to the manufacturing execution system. The decision-making system adopts a neural network model based on multi-agent reinforcement learning, multiple groups of candidate scheduling strategies are generated by processing real-time observation data, the environment interaction module is responsible for verifying feasibility verification and performance evaluation of actions, iterative updating of the system state is achieved by generating observation values, and the decision-making system is in real-time scheduling. The modular design ensures the real-time performance and adaptability of the scheduling decision.

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 present invention belongs to the technical field of equipment fault detection and discloses a method and system for enhancing the resilience of discrete manufacturing systems based on Petri nets. The method performs redundant design by determining key areas and rationally configuring spare equipment and redundant personnel; performs Petri net modeling, and the intelligent agent uses a deep reinforcement learning algorithm to cyclically update the Petri net model based on reward feedback to learn the optimal scheduling strategy under different system states; based on the learned optimal scheduling strategy and real-time status information, the Petri net model is optimized to achieve stable operation of the intelligent agent in a complex environment. The present invention ensures the precise execution of instructions by comparing actual data through simulation. The intelligent agent gradually converges to the optimal strategy through an exploration rate decay mechanism and adapts to complex production environments. The waiting time of the process is recorded by timestamp to reduce the backlog of work-in-progress and improve the efficiency of process connection.