41 results about "Manufacturing services" patented technology

The invention relates to the technical field of cloud manufacturing, in particular to a cloud manufacturing service combination and scheduling integrated optimization method. Comprising the steps that 1, a complex matching network of cloud manufacturing services and cloud manufacturing tasks is established, the cloud manufacturing services are divided into cloud manufacturing production services and cloud manufacturing logistics services, and the cloud manufacturing tasks also comprise cloud manufacturing production tasks and cloud manufacturing logistics tasks; 2, a cloud manufacturing service combination and scheduling integration optimization model is established, QoS of all cloud manufacturing services in the network is quantified through weighted summation of a service quality parameter and a service reliability parameter, the two parameters are dynamically described by a time domain, and an initial value is an evaluation value obtained in advance; and step 3, solving the integrated optimization model by adopting a multi-population co-evolution algorithm based on meta-knowledge adaptive migration to obtain a multi-objective optimization Pareto solution set, and decoding the multi-objective optimization Pareto solution set into an optimization scheduling scheme. And performing combined configuration and scheduling optimization on services in the cloud manufacturing platform.

The invention discloses an industry integrated digital intelligent management system based on AI and cloud computing. The system comprises a cloud platform layer, a data platform layer, an AI engine layer and an application layer. The cloud platform layer builds a basic support framework based on the cloud computing technology, provides elastic computing power, storage resources and distributed deployment capability, and supports access of multiple terminals (computers, mobile devices and the like). By constructing an integrated intelligent management platform based on AI and cloud computing, centralized integration and modeling analysis of multi-source heterogeneous data are achieved, a data island of a traditional system is broken, the data utilization efficiency and the business processing intelligent level are remarkably improved, the system supports cross-industry modular configuration and flexible expansion, and the system is suitable for popularization and application. The method has high concurrent processing capability, automatic decision-making capability and full-process closed-loop management capability, effectively reduces manual intervention cost, improves resource scheduling efficiency and operation response speed, and is suitable for digital transformation and intelligent upgrading in multiple fields of manufacturing, service, government affairs and the like.

A method for onboarding a client includes: sending a request to a global manufacturing service (GMS) module to register the client; receiving a GMS agent from the GMS module; executing the GMS agent on the client to collect a dataset; initiating displaying of the dataset to a user of the client, in which the user signs the dataset to generate a signed dataset; sending the signed dataset to the GMS module, in which, in response to the sending the signed dataset, a device initialization (DI) agent is received from the GMS module; executing the DI agent on the client to collect a second dataset; sending the second dataset to the GMS module, in which, in response to the sending the second dataset, a notification is received; retrieving an ownership voucher (OV); making a determination that the OV comprises the signed dataset; and completing the onboarding of the client.

The invention discloses a service combination method based on a colored Petri net and a deep reinforcement learning agent. The method comprises the following steps: S1, constructing a colored Petri net model for manufacturing a service combination system; s2, constructing a deep reinforcement learning environment based on the model, and defining a state space, an action space and a reward function; s3, interacting the intelligent agent with the environment, dynamically generating an action mask by using a Petri net model to constrain an action space, and training the intelligent agent to converge by using a near-end strategy optimization algorithm; and S4, taking the strategy obtained by training as an optimal service combination strategy, and triggering transition in the Petri network to generate a final manufacturing service combination scheme. According to the method, the formalized modeling capability of the colored Petri network and the self-adaptive decision-making capability of deep reinforcement learning are deeply coupled, so that the problem that in a dynamic complex industrial interconnection environment, the formalized modeling capability of the colored Petri network and the self-adaptive decision-making capability of the deep reinforcement learning are influenced is solved; the problems of low decision-making efficiency, poor adaptive ability and opaque optimization process caused by huge decision-making space and complex logic constraints in a manufacturing service combination method are solved.

The present application relates to a kind of benchmark test method and system of network platform manufacturing service cooperation optimization, comprising the following steps:1.resolve network platform manufacturing service cooperation optimization element relationship, construct network platform manufacturing service cooperation optimization complex network model;2.according to actual problem situation, configure network platform manufacturing service cooperation optimization problem model as needed;3.considering test problem demand, import or generate test data as needed;4.considering test algorithm situation, import or configure the algorithm of solving problem as needed;5.solve the problem multiple times, and record the test results of this problem.The present application can effectively simulate and simulate network platform manufacturing service cooperation optimization environment and process, and can be configured and tested according to the actual needs of user to problem, data and algorithm and compare, to solve the problem of lack of unified benchmark test system tool for previous different problems and algorithm.

The application provides a manufacturing cloud service recommendation generation method, comprising: obtaining manufacturing service descriptive information and label information of a cloud platform; using an LDA topic model to vectorize and model the manufacturing service descriptive information, obtaining p topic information of the manufacturing service, and obtaining p*q-dimensional service topic word vectors through Word2vec mapping expansion; inputting all the word vectors into CNN to learn the deep features of the manufacturing service; further calculating the manufacturing service similarity matrix through the deep feature similarity; using an improved K-Means clustering to cluster the manufacturing service label information to obtain the manufacturing service category, further obtaining a user category matrix model and a service category matrix model, and obtaining a coefficient to construct a user preference matrix; and combining the manufacturing service similarity matrix and the user preference matrix to obtain a manufacturing service recommendation list. The application solves the problems of incomplete manufacturing service information feature extraction and sparse user service matrix in the prior art.

The present invention discloses a modular design method for a manufacturing service system based on intelligent case-based reasoning, belonging to the technical field of multi-source heterogeneous service-oriented manufacturing service system design, including S1, feature extraction, constructing a target case based on the description of the user's requirements for the system design scheme, extracting the key features of the target case and analyzing the modular composition of the target case; S2, case retrieval, retrieving historical cases with high similarity to the target case in the case library; S3, case reuse, based on reinforcement learning, reorganizing the modules of the retrieved historical cases with high similarity to quickly generate a system design scheme with high fit with the target case; S4, feasibility evaluation. The present invention adopts the above-mentioned modular design method for a manufacturing service system based on intelligent case-based reasoning, and realizes the modular design of the manufacturing service system by integrating case-based reasoning and machine learning technologies, helping service-oriented manufacturing enterprises reduce the system development cycle and cost.

The invention discloses a stateless robot motion control method and system based on vision, and belongs to the field of robot intelligent control, and the method comprises the steps: collecting multi-view visual observation data through a top main camera and double-wide-angle wrist cameras installed above and below an end effector; inputting the visual observation data into a pre-trained strategy network, wherein the strategy network outputs a relative pose increment of an end effector; and the current pose of the end effector is updated according to the relative pose increment, and the robot is controlled to execute corresponding actions. The strategy network is trained based on a stateless imitation learning framework, only visual observation data and corresponding expert action labels are used in the training process, and state information is sensed without depending on a robot body; according to the method, pure-vision-driven end-to-end control is realized, and the method has strong space generalization ability, high data efficiency and excellent cross-platform adaptability, and is suitable for complex operation tasks such as industrial manufacturing and service robots.

This paper provides a manufacturing service recommendation method based on a knowledge graph and graph convolutional networks. This method fully exploits the information contained in manufacturing services and tasks within the manufacturing service-task information knowledge graph and implements feature representations for manufacturing services and tasks using a graph convolutional network. When aggregating manufacturing service and task neighbor information, neighbors are selected based on their degree centrality. Neighbor information is selectively aggregated based on the relationship scores and self-attention scores of manufacturing service neighbor nodes to reduce noise and generate manufacturing service representations. A self-attention mechanism is then used to aggregate task neighbor information and generate task representations. This effectively addresses the issue of recommendation limitations caused by insufficient modeling on the other end, improving the accuracy of recommendation results.

The invention discloses an electronic component production line full-process cooperative control method, and relates to the technical field of production line control, and the method comprises the steps: carrying out the data cleaning of collected data, and carrying out the formatting and standardization preprocessing of the data; integrating and fusing the data of each device; based on audience demands of the components, intelligent manufacturing service of a customized production line is provided; establishing an equipment fault prediction model, and predicting a future fault trend and a possible occurrence time point of the equipment; and establishing a component performance prediction model, predicting future performance and market competitiveness of the component, and optimizing the intelligent manufacturing service based on the market competitiveness. The intelligent manufacturing service of the customized production line is provided, the future performance and market competitiveness of the components are predicted, the intelligent manufacturing service is optimized, the production parameters of the components are controlled according to market feedback, and therefore it can be guaranteed that the produced components have good market benefits.

The invention discloses a data association method, device and equipment for a manufacturing process and a storage medium, and the method comprises the steps: carrying out the material tracking through a technology BOM and a material BOM, obtaining preparation type action related data, obtaining and associating technology parameters through a technology route, and obtaining execution type action related data; the method comprises the following steps: acquiring preparation-type action related data and execution-type action related data, fusing the preparation-type action related data and the execution-type action related data to obtain an equipment manufacturing service flow, then acquiring a process dimension data flow and a plan dimension data flow, fusing the process dimension data flow and the plan dimension data flow to obtain an application service flow, and finally fusing the equipment manufacturing service flow and the application service flow. And data association of production process data from planning to action business execution is realized. The integration and fusion of the manufacturing business flow and the planning business flow of the microwave component manufacturing process data are completed by combining the equipment manufacturing business flow and the application business flow, so that the real flow and the data flow of an automatic production line are associated through time and events.

The present invention provides a project management system based on an industrial cloud platform and an implementation method, comprising: a cloud industrial platform, users, and suppliers; wherein the cloud industrial platform comprises a database module for collecting user input data, an intelligent module for data processing, optimization, and generation of feasible solutions, and a decision support module for solution evaluation and comparison; the cloud platform collects user manufacturing service requests and supplier quotations, processes them through an intelligent decision-making algorithm, and finds the best manufacturing solution according to user needs; the intelligent decision-making algorithm generates solutions based on a nested algorithm, helping suppliers and users to minimize time and cost according to specific solution feature factors, and has good stability and system performance.

The utility model relates to the field of electronic manufacturing service, and discloses a substrate ejection jig which comprises a jacking plate, a transmission column is fixedly connected to the top of the jacking plate, a pressing handle is rotationally connected to the top of the transmission column, toggle clamp transverse pressing molds are rotationally connected to the two sides of the outer wall of the pressing handle, toggle clamp fixing plates are fixedly connected to one sides of the outer walls of the toggle clamp transverse pressing molds, and the toggle clamp fixing plates are fixedly connected to the toggle clamp fixing plates. The top of the toggle clamp fixing plate is fixedly connected with a lower bottom plate, the bottom of the lower bottom plate is fixedly connected with the interior of the upper bottom plate, the interior of the upper bottom plate is connected with a cutting jig belt product, and the interior of the upper bottom plate is provided with a guiding assembly used for providing guiding and positioning. According to the base plate sorting device, the effects that manual base plate sorting work can be replaced, and the labor intensity of operators is reduced are achieved, the problems that time and labor are consumed, labor intensity is extremely high, and then production efficiency is remarkably reduced due to the fact that cut base plates are taken out depending on manual work are solved, and the production efficiency is improved.

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 invention provides a manufacturing service recommendation method based on a knowledge graph and a graph convolutional network, which fully excavates information contained in manufacturing services and tasks in a manufacturing service-task information knowledge graph, and realizes feature representation of the manufacturing services and the tasks through the graph convolutional network. In the process of aggregating manufacturing service neighbor information and task neighbor information, neighbors are selected according to degree centrality of nodes, neighbor information is selectively aggregated according to relation scores and self-attention scores of manufacturing service neighbor nodes, noise is reduced, manufacturing service representation is generated, and task neighbor information is aggregated by using a self-attention mechanism. The problem that the recommendation result has limitation due to the fact that modeling representation at the other end is insufficient is effectively solved, and the accuracy of the recommendation result is improved.

The invention discloses a manufacturing service fusion supply chain field-oriented inductive relationship prediction method, and belongs to the field of knowledge maps, an APIRP model provided by the method can compensate for the influence of closed path loss on model prediction performance by constructing an anchoring path set of a given supply chain field knowledge map; defining a neighborhood relation structure of each relation by creating a relation weight graph of the knowledge graph, and aggregating and representing relation embedding by utilizing an attention mechanism of fusion relation weight grouping information; adopting a neural network model to perform structure vector representation on the anchoring path by utilizing relation embedding representation; performing semantic representation on the anchoring path fusing the entity relationship description information by utilizing a pre-training model; according to the method, training is carried out by taking scores of maximized path representation and semantic representation as targets, and a training mode of structure and semantic information is combined, so that the APIRP model provided by the invention can more accurately predict new entities and relationships during reasoning, and the inductive reasoning capability of the model is improved.

The present application aims to provide a satellite two-line element generation method based on telemetry data. The method comprises the following steps: S1, satellite telemetry data processing; S2, setting two-line element initial value; S3, preliminary determination of two-line element; S4, optimization estimation of drag coefficient term; S5, generating final satellite two-line element according to two-line element rule; the generated two-line element can be connected to an internet collaborative manufacturing platform and an internet intelligent manufacturing service platform, and is suitable for industrial data integration service and industrial internet basic environment operation service demand, which not only solves the contradiction between satellite two-line element autonomy and universality, but also provides technical support for high-quality industrial internet service in the field of satellite measurement and control, and is suitable for remote sensing satellite measurement and control technical field and industrial internet related scene. The present application relates to the field of remote sensing satellite measurement and control technology, and is also applied to the field of industrial internet technology system service and industrial big data resource service.

The invention discloses a multi-chain block chain performance optimization method based on a manufacturing service cooperation platform, and the method comprises the steps: carrying out the evaluation of the comprehensive capability of a platform participant based on the historical production data of the platform participant, and distributing a block chain organization and authority according to a score; performing grouping and preference analysis on the manufacturing services based on the historical service quality data of the manufacturing services; based on a generalized random Petri network, performing modeling analysis on each stage of service execution and log uplink recording; the model is simulated and evaluated based on discrete event simulation, and key performance parameters of the block chain are optimized through an improved optimization algorithm. According to the method, through automatic modeling, simulation, optimization and testing, the problems of correctly evaluating the capability of a participant and allocating organizations and permissions to the block chain in the aspect of block chain management are solved; the problem that the cost is too high due to the fact that the requirements of different manufacturing services for the block chain log performance are remarkably different, and the block chain performance becomes a bottleneck or far exceeds the requirements is solved.

The invention provides a wave energy electric field dynamic scheduling method based on a game, and the method comprises the steps: 1, distributing a batch of different types of tasks submitted by a service demander to a service provider in a pre-decision scheduling layer for a local fault problem of a wave energy power generation field; respectively putting the unallocated task set and the corresponding candidate service set into a virtual manufacturing static task pool and a virtual manufacturing service pool; and step 2, constructing a static scheduling game, searching Nash equilibrium of the static scheduling game by using a double ant colony optimization algorithm, and when a local fault occurs in the wave power plant, performing simulation and solving. When a local fault occurs in the wave power plant, the algorithm model provided by the invention can perform efficient simulation and solution.

The invention provides a wafer manufacturing network system. A production access network is provided through a core switch; the network optical line terminal equipment comprises at least one optical line terminal, isolates a production access network into a plurality of local area networks corresponding to different services and distributes bandwidths of the local area networks; the key service local area network has a preset guarantee bandwidth and is divided into a plurality of slice domains, and each slice domain corresponds to one type of manufacturing service; the optical splitter is connected with the network optical line terminal equipment and the optical network unit and transmits the service local area network to the optical network unit; the at least four optical network units are connected with terminal devices of different services through different physical isolation ports of the optical network units, and only semiconductor devices of the same kind of manufacturing services can communicate with the slice domain through the same physical isolation port. According to the invention, the local area networks are mutually independent, so that the bandwidth of the key service local area network is ensured when the abnormal flow occurs in the link, and the influence degree of safety accidents is reduced.

The present invention relates to the field of recovering metal values from solder dross. More particularly, the present invention relates to a method of recovering metal values from solder dross of electronic manufacturing services in an economical manner. The process provided by the present invention discloses selective recovery of metal values by selective precipitation. Further, the method of recovering metal values from solder dross is clean, green and economical.

The present invention discloses an optimization method for a furniture customization manufacturing service system based on multi-objective grey wolf optimization, including obtaining customized furniture orders; decomposing the furniture orders into parts of various specifications, and classifying the parts into standardized parts and non-standardized parts; imposing constraints on the makespan and the total cost; improving the search mechanism of the basic grey wolf optimization algorithm; and optimizing the makespan and the total cost based on the improved grey wolf optimization algorithm. By adopting the above method, the present invention uses the grey wolf optimization algorithm, with the minimization of the makespan and the total cost as the optimization objectives, realizes the adjustment of the production scheduling of the hybrid manufacturing workshop, improves the production efficiency and the flexibility of the manufacturing process, and meets the requirements of different customers.

The invention discloses a manufacturing service exception handling method, device and equipment for a numerical control machine tool, and a medium, relates to the technical field of manufacturing service scheduling, is applied to a capacity sharing platform, and comprises the steps: monitoring the exception state information of the manufacturing service of a target numerical control machine tool when the manufacturing service runs according to an initial capacity supply and demand scheme; in response to the capacity supply and demand scheme adjustment instruction, adjusting the initial capacity supply and demand scheme by using a preset supply and demand adjustment model based on the abnormal state information; the preset supply and demand adjustment model is a model constructed based on a principle of maximizing adjustment satisfaction and minimizing adjustment amplitude; the adjustment satisfaction comprises satisfaction values of the demand side and the supplier; and manufacturing service operation of the target numerical control machine tool is controlled according to the adjusted target capacity supply and demand scheme, so that manufacturing service abnormity processing is completed. Therefore, the influence of service abnormity on the demand side and the supply side can be fully considered, the response capability and robustness of the capacity sharing platform are improved, and the acceptance of a user to a scheme adjustment result is enhanced.

Provided is a system for providing an online fitting glasses manufacturing service using a 3D head shape, comprising: a user terminal that photographs a face and a head shape, selects a glasses frame, and uploads an optometry report; and a manufacturing service providing server including a modeling unit for generating a 3D head shape on the basis of the face and the head shape photographed by the user terminal, a selection unit for selecting a sample head shape most similar to the 3D head shape from among pre-manufactured sample head shapes, an output unit that compares the selected sample head shape with the 3D head shape and outputs parts having different sizes to a store terminal, and a fitting guide unit that outputs a pre-stored glasses frame fitting process to the store terminal if the size of the part of the sample head shape has been adjusted like the 3D head shape.

The invention provides a collinear workshop element dynamic reconstruction method based on space-time urgency degree deduction, and belongs to the technical field of intelligent manufacturing. According to the method, aiming at the problems of response lag and system oscillation caused by order fluctuation in a multi-variety variable-batch collinear production scene, a manufacturing service collaborative network model embedded with task batch and workload intensity is constructed. The method comprises the following steps: calculating local resource urgency of each community in real time, and calculating network coupling urgency based on betweenness centrality and an upstream neighbor state; in combination with the first-order momentum trend of the urgency and the historical fluctuation risk, constructing a risk-sensitive and trend-perceived space-time urgency deduction evaluation function; and finally, executing a constrained evolutionary game mechanism with a hysteresis characteristic, and driving resource dynamic reconstruction under the condition that an evolutionary threshold value and reconstruction cost constraints are met. According to the method, through a multi-dimensional urgency degree deduction and lag buffering mechanism, the problem of supply and demand mismatching is effectively solved, and self-adaptive adjustment and global optimization of workshop productivity are achieved.

The application discloses a kind of cloud manufacturing service combination optimization method, device, computing device and storage medium, cloud manufacturing service combination is subtask and manufacturing resource combination, method includes: to the multiple combinations of subtask and manufacturing resource one by one coding, obtain multiple chromosomes, construct population comprising multiple chromosomes;According to the processing information of subtask and manufacturing resource corresponding to chromosome, the fitness value of chromosome is calculated;Determine whether the fitness value of chromosome is greater than preset optimization threshold;If yes, according to the chromosome of the fitness value greater than preset optimization threshold, obtain corresponding cloud manufacturing service combination;If not, obtain the optimization algebra of chromosome, according to the comparison of optimization algebra and preset algebra threshold, based on first optimization algorithm or second optimization algorithm, cross processing or difference mutation processing is carried out to chromosome, continue to calculate the fitness value of processed chromosome and judge, until the fitness value is greater than preset optimization threshold.

The invention discloses a manufacturing service cooperation negotiation method under dynamic pressure, which comprises the following steps: constructing a three-level pressure evaluation model under dynamic pressure based on the number of negotiators, proposal situations, demand urgency and other characteristics in manufacturing service cooperation; constructing a multi-attribute incomplete information game model based on the attribute change characteristics of the demander and the manufacturing service provider and a three-level pressure evaluation model; based on a Markov decision process, a Bayesian Nash equilibrium algorithm for seeking expected utility maximization is designed. The method considers that the demander and the service provider share the same manufacturing service negotiation scene, studies the multi-round negotiation problem under the incomplete information dynamic pressure condition formed by the demander and the manufacturing service provider, and has the advantages of improving the task completion rate, shortening the negotiation period of the manufacturing service and improving the negotiation efficiency of the manufacturing service. And meanwhile, the satisfaction degree of a demander and a manufacturing service provider is improved.

The application discloses a layered blockchain-based upstream and downstream enterprise manufacturing service cooperation structure and method, which comprises the establishment of an upstream and downstream enterprise alliance, the application and joining of a manufacturing service cooperation platform, the publishing and undertaking of a cooperation task, and the delivery and certification of a product. The application can establish a trust relationship in the cooperation process of upstream and downstream enterprises and perform efficient and safe manufacturing service cooperation. The designed cooperation task undertaking algorithm can ensure that the undertaking of the cooperation task is not affected by the dark-box operation of privileged persons on the platform and does not damage the integrity, consistency and traceability of transaction-related data. The double-incentive mechanism can continuously encourage cooperators while reducing malicious behaviors of participants on the platform.

The invention provides a manufacturing task processing method, device and equipment, and belongs to the technical field of computer information processing. The method comprises the steps of obtaining a manufacturing service optimal scheme initial set of a manufacturing task and a preset low-level heuristic operator queue; determining a low-level heuristic operator performance ranking table and a first solution set according to the manufacturing service optimal scheme initial set and a preset low-level heuristic operator queue; determining a target low-level heuristic operator of the manufacturing service optimal scheme according to the first solution set and a low-level heuristic operator performance ranking table; determining a second solution set according to the first solution set and the target low-level heuristic operator; obtaining a fitness landform vector and a ranking weight; according to the fitness landform vector and the ranking weight, determining a target manufacturing service optimal scheme solution set; and according to the target manufacturing service optimal scheme solution set, determining a manufacturing task corresponding to the manufacturing service optimal scheme and scheduling the manufacturing task. The scheme has self-adaptive capability, and efficient and accurate selection of manufacturing services is realized.

The application relates to a distributed platform manufacturing service scheduling algorithm test method and system, and belongs to the technical field of industrial internet, and comprises the following steps: platform information sensing, which is used for describing platform dynamic characteristics, sensing manufacturing task attributes and real-time information of service nodes; platform state evaluation, which is used for evaluating the urgency of a manufacturing task and the load state and shortage degree of a service node; manufacturing service scheduling algorithm test, which is used for constructing constraint conditions and an objective function of platform manufacturing task allocation and manufacturing service scheduling problems, and further screening a suitable scheduling algorithm for test; algorithm evaluation, which is used for evaluating the running result of a manufacturing service scheduling algorithm and the performance of the algorithm itself; and algorithm recommendation, which is used for analyzing the current platform operation state and performing algorithm recommendation in combination with algorithm evaluation data. The application effectively improves the efficiency of platform manufacturing task allocation and service scheduling, and greatly reduces the occurrence of service queue congestion.