41 results about "Distributed manufacturing" patented technology

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

The invention relates to the technical field of industrial internet, and discloses a digital production process management and optimization platform based on the industrial internet, and the platform comprises a dynamic data calculation module, a constraint model construction module, a production task distribution module, a task monitoring feedback module and a deviation iteration correction module. Compared with a production management method which mainly depends on a static task allocation rule and a single optimization algorithm in the prior art, the production management method has the advantages that in a distributed manufacturing scene, under the conditions of complex priorities of long-production business orders, dynamic logistics network changes and heterogeneous production capacity of sub-factories, the production management efficiency is greatly improved; according to the technical scheme of the invention, the real-time optimization capability of the production task in the distributed manufacturing scene is remarkably improved, and the problems of resource waste and delivery delay caused by static planning are avoided. And the dynamic adaptability of task allocation, the utilization rate of production resources and the overall production efficiency are improved.

The invention relates to the technical field of manufacturing data acquisition, and discloses a distributed manufacturing data acquisition system based on NB-IoT. The system comprises a heterogeneous node sensing layer, a narrowband signaling scheduling layer, an edge fusion processing layer, a cloud collaborative decision-making layer and a closed-loop optimization feedback layer. The heterogeneous node sensing layer obtains multi-source data through a sensor array, generates an equipment operation state thermodynamic diagram and divides region priorities; the narrowband signaling scheduling layer deploys NB-IoT nodes, transmits time division multiplexing signaling and generates a node load distribution diagram; the edge fusion processing layer synchronizes the clock, fuses the multi-source data and the load distribution map, and generates fusion confidence; the cloud collaborative decision-making layer converts the fusion confidence into an executable instruction and issues the executable instruction; and the closed-loop optimization feedback layer monitors the load change of the system, generates an optimization index and dynamically optimizes an acquisition strategy. The system can penetrate through network congestion, improves the real-time performance and accuracy of data acquisition, dynamically adapts to the change of a manufacturing environment, and is suitable for a distributed manufacturing scene.

The invention relates to the technical field of intelligent optimization of discrete combination problems, in particular to a distributed heterogeneous machining workshop order allocation method and system based on simulation, workshops are accurately described by constructing simulation models of the machining workshops, and a hybrid genetic algorithm is combined with simulation to find order allocation schemes meeting requirements, so that the optimization of the machining workshops is realized. According to the method, a factory delivery date relaxation calculation mode is improved, maximization and minimum factory delivery date relaxation are taken as a target, a high-quality order allocation scheme is efficiently generated, whether each order can be completed in time or not is verified through production simulation of each machining workshop, an order transfer operation is designed to adjust overdue order allocation, and the order allocation efficiency is improved. Compared with a traditional method, the efficiency of processing heterogeneous workshops is higher, order allocation of simulation verification is more real and reliable, the resource utilization rate and the on-time completion rate can be improved in a complex distributed manufacturing scene, the order delay risk is reduced, and the method is suitable for the field of multi-factory collaborative production scheduling.

The invention relates to a distributed manufacturing multi-production task-oriented resource configuration method, a service platform and a medium. The method comprises the following steps: determining the task completion degree of a manufacturing task associated with each manufacturing unit when an interrupt instruction is received; according to the task completion degree, current manufacturing tasks are determined from all the manufacturing tasks corresponding to the manufacturing configuration information, an alternative manufacturing unit associated with each current manufacturing task is determined, and the alternative manufacturing units are associated with at least one manufacturing device; performing hybrid coding based on the current manufacturing task, the task completion degree and the manufacturing equipment to generate a plurality of hybrid coding bodies; after the fitness of the hybrid coding bodies is determined according to a preset fitness function, performing corresponding genetic evolution operation iteration and simulated annealing search iteration by utilizing a genetic simulated annealing algorithm based on the plurality of hybrid coding bodies and the corresponding fitness until the iteration is performed for a preset number of times; and selecting a target hybrid coding body from the plurality of generated candidate hybrid coding bodies to obtain a resource configuration result.

A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

The present invention provides a method and device for allocating manufacturing tasks for multimodal transport under multi-factor constraints, which solves the problem that it is difficult to consider the optimization of resource allocation from a comprehensive perspective, so there are problems such as low efficiency and unreasonable service resource allocation in task allocation. The method for allocating manufacturing tasks for multimodal transport under multi-factor constraints includes: obtaining order requirements, decomposing the order requirements into multiple task requirements; obtaining the time cost and economic cost of completing each task based on the task requirements; obtaining the total time cost and total economic cost of completing the order based on the time cost and the economic cost of all tasks; optimizing the time cost and the economic cost of each task and the total time cost and the total economic cost of the order to perform resource allocation.

The invention discloses an outsourcing production and logistics combined scheduling method for an overseas manufacturing base. The method comprises the following steps: S1) acquiring data of a customer order, an outsourcing factory, an overseas manufacturing base workshop and a delivery ship; s2) establishing constraint conditions according to the factory, workshop and marine transportation data, wherein the constraint conditions comprise a processing workshop constraint, a parallel machine constraint and a task order time window constraint; s3) an outsourcing production and logistics combined scheduling model of the overseas manufacturing base is established according to the constraint conditions, and the target of the combined scheduling model is to minimize the maximum processing completion time and the total cost; and S4) designing a two-stage joint optimization strategy to combine with an improved decomposition multi-objective optimization algorithm of reinforcement learning to solve the production and logistics joint scheduling model in the distributed manufacturing environment, and obtaining an optimal scheme of production distribution scheduling. According to the constraint condition of the actual production workshop, the optimal joint scheduling scheme is solved, and the overall cost of the supply chain can be reduced.

The invention relates to the technical field of distributed manufacturing resource intelligent scheduling, in particular to a distributed manufacturing resource intelligent scheduling method and system, and the method comprises the following steps: obtaining equipment operation state data and workpiece quality data; based on the equipment operation state data and the workpiece quality data, evaluating the equipment performance degradation degree; correcting equipment capability parameters in the scheduling system according to the equipment performance degradation degree; before task allocation, task risk assessment information is generated based on the equipment performance degradation degree and task requirements; and according to the task risk assessment information, assisting a dispatcher to make a task allocation decision. The intelligent level of the scheduling system and the accuracy of decision making can be improved.

A system and method for making made-to-order architectural millwork of custom dimensions; including the design of wood joints and a system for deploying them within the overall structural design of individual architectural millwork units, as well as methods for online design and ordering, automated writing of machine code, robotic part preparation, and simplified on-site installation. With the automation and generation of digital code for manufacturing custom architectural components, this method makes the formation of distributed manufacturing centers possible. Through this method and with minimum re-tooling and / or training, these centers are able to produce custom millwork more efficiently and effectively than traditional custom millwork woodshops.

The invention discloses a production and logistics integrated decision-making method for improving distributed manufacturing efficiency. The method comprises the following steps: S1, obtaining process information of a workpiece to be processed, processing workshops specified by the process, processing machines of the process, corresponding processing time, the number of logistics vehicles, the maximum cargo capacity of each logistics vehicle and logistics time between the workshops; s2, the production and logistics integrated decision model of the distributed manufacturing system is utilized, decision variable values of the production and logistics process are obtained through an improved teaching optimization algorithm, and the decision variable values comprise the machining sequence of all procedures on a machine and the goods taking and delivering sequence of all workpieces. According to the method, production and logistics completion time is taken as an optimization target, a multi-load logistics vehicle is taken as a logistics tool, a processing sequence and a logistics task execution sequence are optimized in a collaborative manner, and an improved teaching optimization algorithm is designed, so that the solvability of the model is ensured, and the distributed manufacturing efficiency is effectively improved.

The present subject matter relates to a method comprising: receiving, at a messaging middleware (325), a data access request in a first format from a first application (321) of a public network (237B); processing the data access request by the messaging middleware (325), the processing including converting a first format of the data access request to a second format of a second application (306) of a particular enterprise network (237A); and sending the data access request in the second format to the second application (306); processing, by the second application (306), the data access request, thereby generating data; the generated data is sent by the second application (306) to the first application (321) through the messaging middleware (325).

The present invention provides a production order scheduling method, system, storage medium and electronic device under a distributed manufacturing mode, and relates to the field of production order scheduling. In the present invention, firstly, an order set and a factory set are obtained; secondly, triangular fuzzy numbers are used to represent the fuzzy transportation time of the order from the factory to the customer and the fuzzy expiration date of the order, respectively, to construct customer satisfaction; again, based on the order set and the factory set, a multi-objective optimization scheduling model is constructed with the goal of minimizing cost and maximizing customer satisfaction; finally, the GA-VNS algorithm is used to solve the optimization scheduling model, and the Pareto front surface is obtained to obtain the optimal production order scheduling solution. By incorporating factory qualification restrictions, fuzzy transportation time and customer satisfaction into the model framework, the problem is closer to reality. At the same time, an optimization algorithm combining genetic algorithm and variable neighborhood search algorithm is designed, which can make reasonable scheduling decisions for new energy vehicle companies within a limited time.

The present invention discloses a task set allocation method for collaborative optimization of production and transportation based on cloud manufacturing, which relates to the field of cloud manufacturing production technology. The method includes encoding the allocation of manufacturing service providers of all task sets using a one-dimensional positive integer vector method and randomly generating an initial solution. The present invention realizes efficient modeling of the allocation relationship of task set groups on distributed manufacturing service providers by adopting a one-dimensional positive integer vector encoding method. The method combines the optimal sorting method for minimizing the completion time of a single MSP to simultaneously determine the optimal processing sequence of the workpiece group and the processing sequence of the workpieces within the group. The closed-form expression of the completion time of each machine is obtained based on mathematical derivation, thereby effectively evaluating the performance of the overall scheduling scheme. The method forms a multi-level and multi-angle search collaborative mechanism by introducing a contrast perturbation mechanism, a multi-neighborhood search structure and an iterative greedy strategy, which significantly enhances the diversity retention and deep search capabilities of the algorithm in the solution space.

The present invention discloses a monitoring system for heterogeneous equipment in a distributed manufacturing system, comprising: a distributed manufacturing workshop and a cloud service unit, wherein one cloud service unit corresponds to a plurality of distributed manufacturing workshops; the distributed manufacturing workshop comprises: a heterogeneous data acquisition module and a heterogeneous data processing module; the cloud service unit comprises: a central database, a data monitoring and analysis module, and an interactive module; the present invention realizes effective access to the bottom layer heterogeneous equipment of the workshop, performs corresponding adaptation according to the type of equipment and the type of communication protocol, has a fast configuration speed, can adopt a one-to-one or one-to-many form, and flexibly realizes access to heterogeneous equipment.

A method for reducing carbon dioxide emissions using trusted on-demand distributed manufacturing includes, through operation of a processor in a system, automatically calculating carbon dioxide reduction (carbon offset) of a manufactured product, storing the calculated carbon dioxide reduction, automatically transmitting the calculated carbon dioxide reduction to a certification body, and receiving carbon offset credit issued by the certification body.

The present invention discloses a system and method for making custom-sized building wood products produced to order, including: design of wood joints, system for deploying said wood joints in the overall structural design of a single building wood product unit, and methods for online design and ordering, automatic writing of machine codes, automated parts preparation, and simplified field installation. With the automation of manufacturing custom building components and the generation of digital codes, this method enables the formation of distributed manufacturing centers. Compared with traditional custom woodworking shops, through the method, the center can produce custom wood products more efficiently and more effectively with minimal reassembly and / or training.

The invention relates to the technical field of production scheduling, in particular to an optimal scheduling method for distributed production and manufacturing of notebook parts. The method comprises the following steps: obtaining notebook product structure data and production equipment data; setting electronic component surface mounting parameters according to the notebook product structure data; performing surface mounting simulation according to the production equipment data and the surface mounting parameters of the electronic component to obtain surface mounting data; component missing solder detection is carried out based on the surface mounting data, and component missing solder data are obtained; determining the soldering amount of soldering paste according to the missing soldering data of the component; evaluating the wear degree of the scraper based on the welding amount of the soldering paste; and surface mounting precision parameter adjustment is performed on the surface mounting data according to the abrasion degree of the scraper, and surface mounting adjustment parameters are obtained. According to the invention, real-time monitoring and optimization adjustment in the notebook computer production process are realized based on the production scheduling technology, so that the production efficiency and the qualified rate of the product quality are improved.

Disclosed herein is a method for redundant transmission in a distributed manufacturing automation system. The method includes: sending by an automation apparatus of the system a data item to a receiver through a first cellular network using a first sub-band of an industry frequency band, the first cellular network including a first base station and a first core network device; and sending by the automation apparatus the data item to the receiver through a second cellular network using a second sub-band of the industry frequency band, the second cellular network including a second base station and a second core network device.

The application discloses a kind of manufacturing collaborative systems based on cloud edge architecture, it is related to the field of intelligent manufacturing.It includes cloud layer for global planning and analysis, production edge layer for real-time scheduling and execution, and basic equipment layer.Edge layer is deployed with multiple edge execution MES and internet-of-things interface gateway, rule engine and event listening mechanism are built-in edge execution MES, support event triggering, condition judgment and timing scheduling, and can run autonomously when disconnected, guarantee production continuity;Internet-of-things interface gateway is used to uniformly access devices of multiple industrial communication protocols, through protocol adaptation and device abstraction, realize standardized access to heterogeneous devices, reduce system integration complexity;Multiple edge execution MES realizes event-driven collaborative work through standardized interface and asynchronous message mechanism. The present application can realize the efficient cooperation of cloud centralized decision and edge autonomous execution, and further improve the real-time performance, reliability and scalability of distributed manufacturing system.

According to the cross-layer closed-loop optimization method for the strip steel hot continuous rolling process, cross-layer closed-loop multi-objective optimization is carried out on the strip steel hot continuous rolling process by using a nested model predictive control (MPC) frame, and then the requirements for high quality, low energy consumption and other production objectives in the hot continuous rolling process are met; and meanwhile, the optimization efficiency in the strip steel hot continuous rolling process is improved, and the optimization quality is improved. For a distributed manufacturing process, a cross-layer closed-loop optimization architecture based on cloud side end cooperation is designed by using a cloud side end cooperation mechanism. By adopting the method disclosed by the invention, the calculation efficiency can be improved, the global and local optimization targets of the strip steel outlet thickness and the system energy consumption in the finish rolling area in the strip steel hot continuous rolling process can be better considered, and a finer and more dynamic industrial process control strategy is realized.

The present application relates to the technical field of distributed manufacturing resource intelligent scheduling, and particularly relates to a distributed manufacturing resource intelligent scheduling method and system, the method comprising the following steps: obtaining equipment operation state data and workpiece quality data; based on the equipment operation state data and the workpiece quality data, evaluating the equipment performance degradation degree; according to the equipment performance degradation degree, correcting the equipment capability parameters in the scheduling system; before task allocation, based on the equipment performance degradation degree and the task requirements, generating task risk assessment information; according to the task risk assessment information, assisting the dispatcher to make task allocation decisions. The above can improve the intelligent level of the scheduling system and the accuracy of the decisions.

The present subject matter relates to a method comprising creating, in a distributed manufacturing automation system (100, 200), overlay networks (300, 400, 500, 600) having nodes, where each of the overlay networks (300, 400, 500, 600) spans one or more levels (101, 113, 115, 117) of an automation pyramid, where the nodes represent system resources of a network (130, 133, 135, 137) of the system (100, 200). An application may be created for performing a manufacturing task. Applications may be partitioned into application components according to predefined functionality and information streams, where the application components belong to one or more levels (101, 113, 115, 117) of the automation pyramid. One or more overlay networks (300, 400, 500, 600) that enable execution of the application components may be selected. A connection to the selected overlay network (300, 400, 500, 600) may be established. The application components may be deployed in the selected overlay networks (300, 400, 500, 600) using the established connections. The application may be used to plan and / or control manufacturing in the distributed manufacturing automation system (100, 200).

The present subject matter relates to a method comprising creating in a distributed manufacturing automation system (100, 200) overlay networks (300, 400, 500, 600) of nodes, wherein each overlay network of the overlay networks (300, 400, 500, 600) spans one or more levels (101, 113, 115, 117) of an automation pyramid, wherein the nodes represent system resources of networks (130, 133, 135, 137) of the system (100, 200). An application for performing a manufacturing task may be created. The application may be divided into application components according to the predefined functions and information flow, wherein the application components belong to one or more levels (101, 113, 115, 117) of the automation pyramid. One or more overlay networks (300, 400, 500, 600) that enable execution of the application components may be selected. Connections to the selected overlay networks (300, 400, 500, 600) may be established. The application components may be deployed in the selected overlay networks (300, 400, 500, 600) using the established connections. The application may be used for planning and / or controlling the manufacturing in the distributed manufacturing automation system (100, 200).

A system (100) for enabling trusted on-demand distributed manufacturing includes a first interface (120) configured to receive product data from a product data source (110-A, 110-B, 110-N), a second interface (130) configured to exchange manufacturing data from a manufacturing data source (140-A, 140-B, 140-N), a trust anchor module (170) configured via computer executable instructions to add a physically unclonable function (PUF) to a product, generate a non-fungible token (NFT) to represent the product on an NFT platform (172), a matching module (174) configured via computer executable instructions to match the product characteristics with requirements of a buyer and manufacturing capabilities of a producer, and a traceability module (176) to verify success of a manufacturing process.

The invention relates to the technical field of computers, and discloses a socialized productivity scheduling method and system based on real-time inventory matching priority, and the system comprises a user terminal which is used for receiving an order request and displaying a scheduling result; the intelligent scheduling server is in communication connection with the user terminal and is used for processing the order request and executing a scheduling algorithm; the database is connected with the intelligent scheduling server and is used for storing producer information, equipment states, inventory data and historical order data; wherein the intelligent scheduling server comprises a real-time state acquisition module which is used for acquiring the equipment state of the distributed production node in real time through an API (Application Program Interface) or active reporting of a producer. According to the invention, by integrating social idle distributed manufacturing resources, a multi-level collaborative production network covering local street graphic and text stores to source factories is constructed, and accurate and efficient matching of production orders and optimal producers is realized by using real-time state monitoring and a multi-dimensional weight scoring algorithm.

The embodiment of the invention discloses a multi-task optimization method for multi-user target requirements of a distributed manufacturing system, and relates to the technical field of intelligent manufacturing. According to the scheme, the method comprises the steps that a distributed manufacturing platform receives manufacturing demand information uploaded by a client terminal and establishes a manufacturing task set; performing matching processing on the manufacturing task set and the manufacturing resource set to obtain a candidate service set corresponding to the manufacturing task; and determining service information matched with the sub-tasks of the manufacturing tasks in the manufacturing task set from the candidate service set, obtaining a feasible service combination corresponding to the manufacturing tasks, and then sending order information to a corresponding enterprise terminal according to the obtained feasible service combination. The method can accurately and effectively reflect the effect that the scheduling scheme optimizes different user targets, alleviates the calculation pressure when a scheduling control system of the distributed manufacturing system deals with task scheduling caused by too many tasks and targets, and promotes the improvement of the operation efficiency of the distributed manufacturing system.

A system (100) for reducing carbon dioxide emissions using trusted on-demand distributed manufacturing, with a processor (102) and a memory (104), includes a first interface (120) configured to receive product data from a product data source (110-A, 110-B, 110-N), a second interface (130) configured to exchange manufacturing data from a manufacturing data source (140-A, 140-B, 140-N), a matching module (174) configured via computer executable instructions to match the product data with the manufacturing data based on manufacturing characteristics for producing a product (154) as described in the product data, a carbon footprint module (180) configured via computer executable instructions to determine a traditional carbon footprint and an actual carbon footprint of the product (154), and determine a carbon dioxide reduction based on the traditional and the actual carbon footprint of the product (154), and wherein the system (100) is configured to automatically transmit the carbon dioxide reduction to a certification body (240).