385 results about "Manufacturing sector" 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.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention discloses a reinforcement learning-based dynamic production scheduling optimization method and an ERP (Enterprise Resource Planning) integrated system, relates to the technical field of intelligent manufacturing and information technologies, and is used for solving the problem that the rapid development of the modern manufacturing industry puts forward higher requirements on production scheduling optimization. The flexibility and the adaptability are insufficient in the face of a real-time changing production environment; the intelligent level of the production process is improved through the multi-dimensional data processing capacity, the multi-dimensional quantization coding technology is adopted, dimension raising or dimension reduction processing is carried out based on the income-cost ratio of cross-domain technical parameters, and it is ensured that a calculation task is transferred to edge equipment and the PLC-level soft real-time requirement is met through edge calculation and lightweight framework deployment. On-time process completion and delay penalty are integrated, a global optimization item is combined, a meta-learning algorithm is used for dynamically adjusting the weight, and balance of delay, energy consumption and a safety target is ensured.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

The invention relates to the technical field of intelligent manufacturing, in particular to a flexible intelligent manufacturing system for multi-variety small-batch production, which comprises a flexibility enhancement module, a cost efficiency improvement module, an information interaction enhancement module, a technology depth fusion module and a decision ability optimization module, rapid switching and optimization of the production process can be achieved, so that production efficiency and product quality are improved, technological parameters are intelligently adjusted according to real-time production requirements, production requirements of diversified products are met, production preparation time is effectively shortened, the production cycle is shortened, meanwhile, by means of the Internet of Things technology, the system can monitor the state of production equipment in real time, and production efficiency is improved. In addition, through data analysis and a prediction model, the system can optimize inventory management, reduce the production cost, significantly improve the production flexibility and efficiency in a multi-variety small-batch production environment, and bring significant technical effects and economic benefits to the manufacturing industry.

Data from manufacturing is highly uncontextualized and siloed, requiring expert knowledge of context and substantial data pre-processing to support meaningful queries and visualizations. To address this problem, data for a number of sources in a manufacturing context can be retrieved and converted into an intermediate representation in a natural language or near-natural language form, which can in turn be ingested by a generative AI engine, along with suitable prompts by the user to summarize, analyze, and make recommendations based on the data.

The invention discloses an intelligent production line fault prediction and adaptive adjustment system and method based on artificial intelligence. In the modern manufacturing industry, a traditional production line maintenance mode is difficult to find potential faults in advance, so that shutdown loss is huge, and the problems of insufficient data utilization, poor prediction and adjustment effects and the like exist in the prior art. The system comprises a data acquisition module, a data processing module, a fault prediction module and a self-adaptive adjustment module. The data acquisition module acquires multi-modal data through various sensors and carries out edge calculation preprocessing on the multi-modal data. The data processing module cleans data by using deep learning, extracts features, and stores the features by using a block chain technology; the fault prediction module fuses multiple technologies to improve the prediction precision; and the adaptive adjustment module generates and optimizes an adjustment strategy by using reinforcement learning. The method comprises the steps of data acquisition, processing, fault prediction and adaptive adjustment. According to the method, accurate fault prediction and efficient adaptive adjustment are realized, the intelligent level of a production line is improved, the fault rate and the production cost are reduced, the enterprise competitiveness is enhanced, and the method has a wide application prospect in the field of intelligent manufacturing.

The invention discloses a manufacturing industry ERP dynamic resource optimization configuration system based on reinforcement learning, and relates to the technical field of manufacturing industry ERP resource configuration, and the system comprises an ERP state data perception module which is in butt joint with a manufacturing industry ERP system, collects multi-source heterogeneous data in real time, fuses the multi-source heterogeneous data in real time by using a Kalman filtering technology, detects an abnormal value, and sends the abnormal value to the ERP state data perception module; an improved Robust Scaling method is adopted for standardization processing, and initial data of a unified structure are output and transmitted to the feature extraction and integration processing representation module; multi-source heterogeneous data are collected in real time through the ERP state data sensing module, fusion is carried out through the Kalman filtering technology, dynamic changes in the production environment can be captured in time, strategies can be continuously adjusted according to the real-time dynamic change data through the reinforcement learning method, and the real-time dynamic change data can be obtained. And outputting an optimal resource allocation action adapting to a dynamic environment, so as to greatly improve the adaptability of the system to a complex production environment.

The invention is suitable for the technical field of production quality detection, and provides an intelligent detection method and system based on an aluminum alloy production line, and the method comprises the steps: firstly responding to a gradienter starting instruction, obtaining real-shot image information through an industrial camera, and then carrying out the image detection processing of the real-shot image information according to an image detection algorithm, according to the method, laser datum line information and workpiece edge contour line information are determined, then multiple pieces of actual measurement distance information are effectively generated according to the laser datum line information and the workpiece edge contour line information, and finally detection result information is accurately generated according to the multiple pieces of actual measurement distance information. According to the invention, the flatness of multiple positions of the aluminum alloy workpiece can be rapidly and accurately detected, the detection efficiency is remarkably improved, and the production requirements of the modern aluminum processing and manufacturing industry for efficient and high-precision detection are met.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

The invention discloses a milling chatter diagnosis method based on multi-step differential entropy, and belongs to the technical field of mechanical system fault diagnos.The method comprises the operation steps that the type and the installation position of a sensor are selected, and chatter diagnosis parameters are determined; acquiring synchronous vibration data X; the difference step number mu is set to be equal to 1, the embedded dimension is m, the time delay is tau, and the following calculation is repeated until the difference step number mu is larger than the maximum difference step number mu T; reconstructing a phase space P [mu] of the synchronous vibration data X; processing the phase space P [mu] according to a difference rule to obtain a phase space P '[mu]; calculating the cosine similarity between each two adjacent rows of data in the phase space P 'mu; the interval [-1, 1] is divided into epsilon intervals at equal intervals, probability distribution of the obtained cosine similarity in each interval is counted, and mu step difference entropy is calculated; making the difference step number mu = mu + 1 and the embedded dimension m = m0 + (mu-1), and returning to continue to calculate the mu step difference entropy; determining a flutter type according to a flutter diagnosis criterion; the method can effectively overcome the inherent defects of traditional entropy, remarkably improve the accuracy, timeliness and reliability of flutter diagnosis, provide solid technical support for flutter research and recognition in the machining process, practically improve the machining efficiency, reduce the influence of flutter on the machining quality and reduce the production cost. Besides, the method is highly in line with the trend of intelligent development of the manufacturing industry, has wide application prospects, and is expected to promote technical innovation and progress of the machining industry.

The invention relates to the technical field of automobile accessories, and discloses a manufacturing lean production management system based on transfer card work reporting, and the system comprises a data collection module which is used for collecting production data in real time through scanning a code transfer card; the production plan and circulation card configuration module is used for making a production plan and generating a circulation card matched with the production plan; the production process tracking module is used for dynamically monitoring the production state of the transfer card and the use condition of equipment; and the quality inspection and batch tracing module is used for binding the quality inspection data with the circulation card to realize quality problem tracing. By scanning a code transfer card, collecting production data in real time, and updating the state and task progress of each station in the production process to the system in real time, a manager can master the current production situation at any time, find potential problems in time and quickly take countermeasures, the response speed of sudden problems and the production efficiency are effectively improved, and the production efficiency is improved. And the production lean degree is improved.

The invention relates to the technical field of information retrieval, in particular to a part three-dimensional model retrieval method and system based on feature extraction. The part three-dimensional model retrieval method comprises the following steps that a manufacturing industry part design drawing is obtained; geometric structure feature extraction and material feature extraction are carried out according to a manufacturing industry part design drawing, and geometric structure data and material data are obtained; constructing a part three-dimensional model based on the geometric structure data and the material data; performing process processing feature extraction according to a manufacturing industry part design drawing to obtain part process processing data; and performing process treatment, including surface treatment, heat treatment and welding treatment, on the part three-dimensional model according to the part process treatment data to obtain surface treatment data, heat treatment data and welding treatment data. Based on the information retrieval technology, the part three-dimensional model retrieval precision and the assembly reliability are improved, and the part design optimization rate and the production quality control efficiency are remarkably improved.

The invention relates to the technical field of industrial master machine multi-axis coordination, and particularly discloses an industrial master machine multi-axis coordination method, which comprises the following steps of: selecting thermomagnetic sheets with thermomagnetic coupling characteristics, respectively embedding the thermomagnetic sheets into key parts of corresponding motion axes of a multi-axis machine tool, and simultaneously installing a high-precision magnetic field sensor in a main shaft of the multi-axis machine tool, the receiving module is used for receiving a magnetic field vector of each motion axis in each monitoring time point; collecting the temperature of each motion shaft in each monitoring time point, and calculating the thermal deformation amount of each motion shaft corresponding to the next time point; therefore, each motion shaft is dynamically judged and analyzed, and finally, the position compensation of each motion shaft corresponding to the next time point is realized, so that the machining precision and efficiency of the multi-shaft machine tool are improved, and the reliability, maintainability and intelligent level of the system are enhanced. The application of the technology has important practical significance and development potential in the modern manufacturing industry, and is a necessary means for realizing efficient, precise and intelligent manufacturing.

The invention relates to the technical field of information, in particular to a manufacturing industry-oriented multi-modal data deep fusion and cognitive intelligent system, which comprises a data acquisition module for acquiring a first type of physical field signals by deploying a distributed sensor network, and deploying second type of physical field signal acquisition equipment in the same area; the signal preprocessing and modal activation module is used for preprocessing the first type of physical signals, generating an activation instruction after judging that abnormity exists, and triggering second type of physical field signal acquisition equipment to enter an acquisition mode by the instruction; the data fusion module is used for carrying out space-time calibration on signals of the first type of physical field signal acquisition equipment and the second type of physical field signal acquisition equipment and constructing a complete evidence chain; and the anomaly cognition module is used for calculating the spatial distribution overlap ratio of the temperature anomaly region and the acoustic emission sound source positioning region, and judging structural damage according to a preset coefficient in combination with surface deformation characteristics. The first type and the second type of sensors are synchronously deployed, the problem of single information blind areas is solved, and the comprehensiveness of abnormal features is ensured.

The invention relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

The invention discloses a manufacturing industry supply chain processing method and system, and relates to the technical field of data processing, and the system comprises a capacity demand prediction end, a supply chain risk prediction end and an alternative strategy coping end. The capacity demand prediction end is used for reasonably formulating a supply chain execution strategy according to the supply demand of the prediction result, and balancing the capacity utilization of a manufacturing industry supply chain in real time by adopting a game theory; the supply chain risk prediction end is used for acquiring the delivery punctuality rate of suppliers in the manufacturing industry in real time and judging and predicting whether the supply of the suppliers is abnormal or not in real time; and the alternative strategy coping end is used for tracking the supply chain strategy deviation in real time and performing alternative strategy adjustment in real time according to the deviation. According to the manufacturing industry supply chain processing method and system, the situation of unbalanced capacity utilization is avoided, the safety and reliability of supplier supply during supply chain processing are ensured, and the alternative scheme of the supply chain is adjusted in time.

The invention discloses a verification method and device for a vehicle bill of materials, a medium and a product, and relates to the field of informatization of the manufacturing industry, the method comprises the steps that a to-be-verified bill of materials is acquired, and the bill of materials comprises all material numbers of materials needed by one vehicle type and the hierarchical relation between all the material numbers; generating a tree structure of the bill of materials based on each material number and the hierarchical relationship; based on the tree structure, generating a standard format file for each material number, the standard format file being used for describing basic information of the material number in the tree structure; sending the standard format file to a pre-trained random forest model, so that the random forest model verifies the bill of materials, and outputting a verification result; and when the verification result is that the error exists, determining that the error exists in the bill of materials or the rule in the random forest model is missing. According to the invention, the accuracy and efficiency of vehicle bill-of-material verification are improved.

The invention discloses a manufacturing industry intelligent decision-making-oriented fine-tuning large language model integration system, and the system achieves the capture of different feature subspace decision-making information through a plurality of parallel manufacturing industry fine-tuning large language models, and generates a plurality of preliminary decision-making schemes. Then modeling is directly carried out on the probability of the optimal candidate preliminary decision scheme to obtain a candidate sequence, and the candidate sequence is realized under the guidance of multiple metric heads; the top-ranked candidate schemes are then fused using the best fine tuning LLM to reduce deviation information in the final decision scheme. In conclusion, the fine-tuning large language model integration system for intelligent decision making in the manufacturing process aims at improving the context analysis capability of a manufacturing decision making system on complex association of the manufacturing system, and the probability of decision making deviation is reduced by integrating the advantages of all aspects through the sorting strategy and the fusion strategy.

The invention discloses a Beidou-based manufacturing industry contract logistics data fusion and scheduling method and system, and belongs to the field of data processing methods specially suitable for management. In the method, a basic geography and road network data module obtains basic geography data; the Beidou dynamic monitoring data module collects transport vehicle data; the cargo and contract data module stores cargo and contract data; the logistics node data module obtains logistics node data; the data fusion processing module performs spatial matching on the basic geographic data, the transport vehicle data, the cargo and contract data and the logistics node data to obtain a manufacturing industry logistics data association table; the data fusion processing module performs data fusion based on the manufacturing industry logistics data association table to obtain a unified data set; and the intelligent scheduling module generates an optimal scheduling control instruction based on the unified data set and a preset constraint condition, and sends the optimal scheduling control instruction to the corresponding vehicle-mounted terminal. The method and the device are used for improving the accuracy of a scheduling scheme in a complex manufacturing industry scene.

The tempering furnace comprises a base, a furnace body is arranged on the upper end face of the base, temperature equalizing mechanisms are arranged in the furnace body and on the base and used for keeping temperature balance of all areas of the furnace body, and a supporting frame is transversely arranged in the furnace body in a penetrating mode; sliding positioning assemblies used for positioning the racks are symmetrically arranged in the supporting frame. The device is simple in structure and reasonable in design, dynamic support is provided for the lower end face of the corresponding rack through continuous symmetrical sliding of the supporting rollers in the heat treatment process, the conditions of uneven tempering hardness, local stress concentration and the like of the rack due to the single clamping or supporting position are avoided, the overall performance of the rack is improved, and the service life of the rack is prolonged. And the actual use requirements in the field of the current high-end equipment manufacturing industry are better met, meanwhile, the temperature difference is eliminated, the performance balance of the rack in each area in the furnace body after tempering is guaranteed, and the tempering quality of the rack is further improved.

The invention is suitable for the technical field of computers, and provides an intelligent agent-based product production method, computer equipment and a computer program product.The intelligent agent-based product production method comprises the steps of collecting working state data of production equipment on a specified production line in real time based on a production task; respectively sending the working state data to each intelligent agent associated with the specified production line, wherein the intelligent agents represent specific equipment or processes on the specified production line; based on an operation result of a specified operation executed by each intelligent agent based on the working state data, detecting an output product of each production process to obtain a detection result; and if the detection result represents that the output product of each production process is a qualified product, determining the output product of the last production process as a target product required by the to-be-executed order. The product production efficiency of the manufacturing industry can be improved.

The invention relates to the field of manufacturing industry, in particular to a pomegranate peeling production line-oriented multi-process collaborative scheduling optimization method, which comprises the following steps of: acquiring and processing a first business data stream from an internet of things interface, and inputting historical sensor data into a business prediction model to generate a second business data stream; generating a third service data stream by using the data of the multi-mode sensor array; constructing a multi-target business cost function through a business decision optimization engine; starting an iterative optimization business simulation process to obtain an optimized scheduling scheme; and generating a corresponding digital production work order according to the optimized scheduling scheme. According to the method, equipment state, equipment health degree cost and quality indexes are analyzed through fusion of three business data streams and a multi-objective cost function construction method, comprehensive commercial value evaluation is generated, iterative optimization simulation and closed-loop optimization are started when conditions are met, scheduling optimization of timeliness and value fluctuation in perishable material scheduling is achieved, and the scheduling efficiency of perishable materials is improved. And the commercial value creation and self-adaptive scheduling capability of production is improved.

The invention discloses an automatic welding method, device and equipment, a storage medium and a product, and relates to the technical field of welding automation. According to the method, the three-dimensional point cloud data and the two-dimensional image data of the welding area are collected, the three-dimensional point cloud data and the two-dimensional image data are fused, and the fused image is obtained. The rough welding area is determined through semantic segmentation based on the two-dimensional information contained in the fused image, and the target welding seam is determined in the rough welding area based on the depth information contained in the fused image, so that the precision and robustness of welding seam positioning are remarkably improved by utilizing the characteristics of the fused image; the system can stably operate in a complex factory environment with uneven illumination, strong reflection or partial shielding; and the dependence of the algorithm on environmental conditions is reduced, so that the algorithm is more suitable for the application scene of an actual manufacturing industry factory. And on the basis of more accurate positioning, the welding gun is controlled to weld the target welding seam, and the accuracy of automatic welding can be remarkably improved.

The invention provides an enterprise production index monitoring method based on electric power data, and relates to the technical field of enterprise production index monitoring. The method comprises the following steps: acquiring real-time power data, water consumption, heat / gas energy consumption data and equipment sensor data of an enterprise, including vibration, temperature and pressure sensor data; calculating a production trend index and a product energy efficiency index by using the dynamic weight, and generating a comprehensive energy efficiency index CEI in combination with the equipment health coefficient; and when the CEI is lower than a threshold value, fault diagnosis is carried out based on the Bayesian network and early warning is triggered. And meanwhile, the electric power data is stored through a block chain technology, and data credibility verification and exception recheck are realized in combination with financial data cross validation. The system realizes multi-dimensional production state monitoring, has the advantages of dynamic adaptation to production fluctuation, intelligent fault diagnosis, data credibility guarantee and the like, can effectively improve enterprise production management efficiency, reduces energy consumption and fault loss, and is suitable for digital transformation requirements of high-energy-consumption industries such as chemical industry and manufacturing industry.

The invention relates to a dynamic intelligent stamping production line real-time monitoring strategy based on digital twinning, and adopts the technical scheme that a sensing layer acquires production total factor data related to a stamping production line in real time through multi-sensor fusion and an RFID (Radio Frequency Identification) technology, performs primary processing by utilizing edge calculation, and transmits the data to a real-time reaction layer; the real-time reaction layer quickly processes and analyzes multi-source data through edge calculation and a real-time database, detects faults in real time by using a multi-modal fusion technology, triggers an alarm mechanism, and feeds back to the interaction layer; and the interaction layer integrates intelligent production scheduling and NLP voice interaction through data interaction processing, error verification and multi-protocol adaptation to realize virtual factory visualization and equipment health real-time monitoring. Multi-modal data fusion is realized, an adaptive incremental learning and early warning mechanism is realized, an information island is broken, and millisecond-level response is realized. The strategy can improve the production efficiency, reduce the operation and maintenance cost, prolong the service life of equipment, and provide an efficient solution for intelligent transformation of the manufacturing industry.

The invention provides a process digital collaborative management method and system based on a data unified exchange platform, and belongs to the technical field of industrial software and intelligent manufacturing, and the method comprises the steps: constructing the data unified exchange platform, so as to achieve the data exchange among a process management system, a PLM system and a production management system; in the process management system, based on the EBOM obtained from the PLM system, constructing and managing a multi-state process BOM including a basic PBOM, a trial-production PBOM and a production PBOM; and a collaboration mechanism is constructed through a data unified exchange platform, so that business collaboration based on the multi-state process BOM is realized. According to the method, the problems of data consistency and flow collaboration which troubles the manufacturing industry for a long time are solved, and key technical support is provided for enterprises to realize digital transformation and intelligent upgrading through efficiency improvement, quality guarantee, cost control and knowledge precipitation.

The invention relates to the technical field of key distribution, in particular to a multi-cloud architecture-oriented dynamic key fragmentation storage management method and system, which comprises the following steps of: dividing a manufacturing process of the manufacturing industry into a plurality of time sequence stages corresponding to different data access authority levels and security requirements, and constructing corresponding quantum state evolution operators; driving the key state to evolve among different time sequence stages through a quantum state evolution operator according to the progress of the manufacturing process, and adjusting the access authority of the key; quantum entanglement connection is established between supply chain nodes, the trust degree between the nodes is measured and quantized through the association of the quantum entanglement state, and the distribution weight of the key fragments is adjusted based on the change of the trust degree; collecting constraint parameters of production takt, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; and when it is detected that the constraint condition changes, recalculating the key fragment distribution scheme based on the quantum state constraint condition and the credibility, and synchronously distributing the key fragment distribution scheme to each supply chain node.