134 results about "Discrete manufacturing" patented technology

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

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 belongs to the technical field of equipment fault detection, and discloses a Petri network-based discrete manufacturing system toughness enhancement method and system. According to the method, redundancy design is carried out by determining a key area and reasonably configuring standby equipment and redundant personnel; petri net modeling is carried out, the intelligent agent carries out cyclic updating of a petri net model by using a deep reinforcement learning algorithm according to reward feedback, and an optimal scheduling strategy in different system states is learned; and optimizing the Petri network model according to the optimal scheduling strategy and the real-time state information obtained by learning to complete stable operation of the intelligent agent in a complex environment. Accurate execution of the instruction is ensured by comparing actual data through simulation. The intelligent agent gradually converges to an optimal strategy through an exploration rate attenuation mechanism to adapt to a complex production environment. The process waiting time is recorded through the timestamp, so that the overstock of products in process is reduced, and the process connection efficiency is improved.

The present invention discloses a method for constructing an intelligent computation engine of an artificial intelligence cross-platform model based on knowledge self-evolution. The method comprises: determining source and target moments; dividing a discrete manufacturing system data set; initializing a dynamic discrete manufacturing system model; preprocessing data and constructing a task pool; constructing a meta learning framework; migrating a trained neural network to new tasks; iterating until convergence and storing model parameters; and testing in a new environment. This invention shortens the convergence time of model parameters, significantly benefiting the training of dynamic discrete manufacturing models subject to temporal disturbances in actual production.

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 the technical field of manufacturing and order splitting, in particular to an order splitting method and system based on discrete manufacturing and a storage medium. The method comprises the following steps: obtaining task flow data of a manufacturing execution unit, collecting information of a machining part of the manufacturing unit, and executing order splitting processing of the machining part to generate order splitting data; evaluating the matching deviation condition based on the split data, detecting the dynamic fatigue accumulation condition of the cutter, and predicting the progressive imbalance trend of the function of the cutter; further measuring the cutter path deviation degree and the spindle vibration increasing condition, and calculating the part machining precision reduction degree; and finally, comprehensively analyzing the abnormal condition of the order splitting suitability, realizing the optimization management of the order splitting data, and outputting the optimized processing part order splitting data. According to the invention, the split order is optimized, so that the distribution of the manufacturing split order is more accurate.

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 discloses a discrete manufacturing equipment efficiency maximization strategy determination system and method, effectively achieving the purpose of improving the equipment utilization rate, and the system comprises a multi-source data collection unit which comprises a multi-source data collection module and an edge calculation module; the intelligent decision-making unit comprises a prediction module, a multi-objective optimization module and a weight fusion module; the execution control unit comprises a strategy issuing module, a self-adaptive adjustment module and a dynamic scheduling module; the feedback optimization unit comprises a digital twin simulation module and an incremental learning module; the device is novel in structure, ingenious in conception and easy and convenient to operate, the joint manufacturing efficiency of manufacturing equipment is effectively improved, the energy consumption cost is reduced, the delivery punctuality rate is increased, and carbon emission is reduced.

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 discrete manufacturing enterprise product cost accounting method, and belongs to the technical field of commercial data processing, and the method comprises the steps: dividing the production process of each part in a product into a plurality of production processes; according to the inflow cost and the material picking cost of one production process, the in-process cost, the waste product cost and the completion cost of the production process are obtained through the processing motivation in the production process, and the inflow cost of the next production process is determined according to the completion cost; through circulation of the completion cost between continuous production processes, the completion cost of each part is obtained, so that the product cost is obtained. According to the method, the part production procedures are divided, cost circulation is carried out, refined cost control is achieved, cost accounting is independently carried out in each procedure, and data are accurate. By reasonably sharing the manufacturing cost and the waste cost, the cost transfer of cross-workshop circulation is simplified, and the cost accounting accuracy is 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 a discrete manufacturing production line equipment automatic configuration method based on a knowledge graph, and the method comprises the following steps: 1, collection and fusion of production line data: dividing the type of a discrete manufacturing production line, determining key parameters of equipment, refining the hierarchical structure of the production line, and constructing an equipment database; 2, defining production line resource requirements: extracting product requirement keywords, analyzing a mapping relation between equipment parameters and product requirements, and creating a requirement conversion system; step 3, construction and storage of a knowledge graph: constructing a production line equipment configuration knowledge graph containing a hierarchical structure and an association relationship; 4, defining a production line equipment selection evaluation standard: dividing parameter grades and assigning scores according to equipment key parameters and cost, and calculating and generating a comprehensive score of an equipment configuration scheme; 5, production line equipment configuration based on the knowledge graph: outputting equipment nodes matched with the knowledge graph, and selecting an optimal scheme in combination with an evaluation standard; and 6, outputting and displaying a configuration result.

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 multi-variety variable-batch manufacturing process quality characteristic evaluation method and system. The method comprises the steps that the total number of processes, the total number of products and the total number of poor processes in product historical production task process quality data are clustered according to product similarity and then combined; the total process number N, the total number N (X) of all the processes, the work report amount Nb of the type b products, the X process defect number P (X) and the total process defect number P are calculated; dimension factors of quality evaluation are calculated, the dimension factors comprise the procedure support degree SX, the procedure confidence coefficient CX, the procedure bad support degree SPX and the procedure reject ratio PRX, the quality characteristic PQX of each procedure is calculated accordingly, and the procedure with the large relative value is recognized as the current quality bottleneck procedure. According to the method, standardized, self-adaptive and convenient quality characteristic comprehensive evaluation can be carried out on discrete manufacturing type product procedures under the conditions of mixed lines of different product types, batch production, different procedure complexity and long technological process by integrating procedure historical production quality abnormity records.

The invention relates to the technical field of aircraft pipeline assembly, and discloses an off-line visual coordinated assembly method and system.The off-line visual coordinated assembly method comprises the steps that firstly, aircraft pipelines are classified and defined, and a pipeline system serves as a basic assembly unit; then, according to the standard tightening torque of the piping system, the tightening turns of coordinated assembly of the piping system and tightening torque control values of the guide pipe in the different tightening turns are determined; the method comprises the following steps: acquiring and storing tightening torque and rotation angle data in a coordinated assembly process of a piping system, and splicing the acquired tightening torque and rotation angle data according to a tightening sequence and tightening turns to generate a corner torque curve of different tightening point positions in the tightening process; and finally evaluating the coordinated assembly quality of the piping system according to the continuity of the tightening curve at the splicing position. The problem that a traditional tool cannot collect assembly data and perform visual analysis is solved, the tool adapts to a narrow space and a discrete manufacturing environment, and the assembly efficiency and quality are 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 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 relates to an automatic guided vehicle scheduling control optimization method and device, equipment and a medium, and the method comprises the steps: randomly generating a plurality of different task execution scenes through employing a preset second scheduling control algorithm, determining the vehicle position distance between the automatic guided vehicles based on the task execution speed corresponding to each automatic guided vehicle, and triggering a plurality of path conflict solving strategies corresponding to the task execution scene when detecting that the vehicle position distance is smaller than a preset distance threshold value at a certain moment, the transport path track of each automatic guided vehicle under the plurality of path conflict solving strategies is determined; and calculating and determining the path conflict probability between the transportation path tracks of each automatic guided vehicle under the corresponding path conflict solving strategy, and taking the conflict solving strategy with the minimum path conflict probability as the optimal path conflict solving strategy under the task execution scene. The transportation task scheduling efficiency and flexibility of the discrete manufacturing workshop can be remarkably 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 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 workshop scheduling experience learning method based on a heterogeneous graph neural network, which aims at a flexible job workshop scheduling problem and constructs an experience library based on historical data through a dependency and compatibility relationship between a heterogeneous graph modeling process and a machine. The method comprises the following steps: 1) constructing a heterogeneous graph containing 8-dimensional process features, 3-dimensional machine features and 1-dimensional process-machine pair features; 2) designing a heterogeneous graph neural network, and fusing features through sub-graph decomposition, graph convolution and multi-head attention; 3) dynamic mask constraint decision space is used to optimize the strategy by weighting cross entropy, the method does not need reinforcement learning trial and error, training time is reduced by 67%-97% compared with traditional DRL, performance is better than that of a heuristic algorithm, a general framework of the method can be expanded to the fields of discrete manufacturing, logistics, cloud computing and the like, and the method is suitable for multi-scene intelligent scheduling and has low training cost and high generalization ability.

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.