23 results about "Manufacturing services" patented technology

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 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.

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 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 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 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 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 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 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.

The invention relates to a cloud manufacturing service matching optimization method based on a double-layer immune optimization algorithm, and belongs to the field of cloud manufacturing services. The method comprises the following steps: establishing historical evaluation result data, and storing the historical evaluation result data in a database; when a new service request is received, similar items meeting the requirements of the new service request are searched in the historical database to serve as candidate services, and evaluation indexes of the similar items are extracted; the evaluation indexes are corrected through a double-layer immune optimization algorithm, and the optimal service is determined; wherein the bottom layer adjusts a weight coefficient of each evaluation index through a global optimization immune algorithm, calculates an evaluation result corresponding to a candidate service under a new service request, and transmits the evaluation result to the top layer; and the top layer adopts fuzzy weighting comprehensive evaluation to calculate the comprehensive evaluation value of each candidate service, and provides the candidate service with the optimal comprehensive evaluation value as the optimal service to the new service request. The method can dynamically adjust the evaluation indexes, and is suitable for various actual industrial scenes with different scales and complexity degrees.

The invention discloses a manufacturing service combination optimization method based on an improved multi-objective particle swarm algorithm, and relates to the technical field of intelligent manufacturing, in particular to the manufacturing service combination optimization method based on the improved multi-objective particle swarm algorithm. The objective of the invention is to solve the problem of low reliability and accuracy of a solving result caused by the problems of premature convergence to a local non-dominated solution, falling into a local optimal solution and the like when a traditional multi-objective particle swarm optimization algorithm is used for solving a multi-objective optimization problem. The method comprises the following steps: step 1, establishing a manufacturing service combination optimization evaluation index system; 2, establishing a manufacturing service combination optimization objective function according to the manufacturing service combination optimization evaluation index system; establishing a multi-objective optimization problem mathematical model of the manufacturing service combination based on the objective function of the manufacturing service combination optimization; and step 3, solving the manufacturing service combination multi-objective optimization problem mathematical model by adopting an improved multi-objective particle swarm algorithm, and providing a multi-objective optimization solution for the manufacturing service combination optimization problem.

A machine tool equipment resource adaptive optimization method in an industrial interconnection environment based on an improved PPO-RND algorithm comprises the following steps: 1) modeling machine tool equipment resources, and establishing a production disturbance model; 2) modeling a machine tool equipment resource optimization process as a six-tuple MR-MDP; 3) designing a reward function of the machine tool equipment resource optimization process MR-MDP; 4) constructing an improved PPO-RND algorithm framework; 5) solving the machine tool equipment resource optimization process MR-MDP by using an improved PPO-RND algorithm framework to obtain each machine tool equipment resource combination and corresponding action; and after production disturbance occurs, re-optimization is carried out on each machine tool equipment resource combination and corresponding actions. The method has excellent robustness, and provides certain theoretical method support for stable and reliable operation of industrial interconnection manufacturing service.

The invention 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 initial values of two rows of elements; s3, preliminarily determining the number of two rows; s4, resistance coefficient item optimization estimation; s5, generating a final satellite two-line element according to a two-line element rule; the two-line element generated by the method can be accessed to an Internet collaborative manufacturing platform and an Internet intelligent manufacturing service platform to adapt to industrial data integration service and industrial Internet basic environment operation service requirements, so that the contradiction between autonomy and universality of the satellite two-line element is solved; the method provides technical support for high-quality industrial internet service in the field of satellite measurement, operation and control, and is suitable for the technical field of remote sensing satellite measurement, operation and control and industrial internet related scenes. The invention relates to the technical field of remote sensing satellite measurement, operation and control, and is applied to the scope of industrial internet technology system service and industrial big data resource service.

To provide a pricing system, pricing method, and program that can facilitate the introduction of EMS (Essential Manufacturing Services). [Solution] The system includes an EMS cost calculation means that calculates the cost of introducing an EMS to a facility including a storage battery and power generation equipment. The EMS outputs information on buying and selling electricity, and the EMS cost calculation means uses a portion of the profits obtained by introducing the EMS to the facility as the cost.

The embodiment of the application discloses an optimization method and device of manufacturing resources, a storage medium and an electronic device, and belongs to the computer field. Order demand information is decomposed into multiple manufacturing tasks, and a chromosome is constructed. The number of gene groups included in the chromosome is equal to the number of manufacturing tasks, each gene group in the chromosome contains a manufacturing task serial number, an atomic manufacturing task serial number and an atomic manufacturing service serial number, and a primary population of individuals containing the above chromosome structure is iteratively processed by using a genetic algorithm to obtain an optimal solution for processing multiple manufacturing tasks. In this way, when a manufacturing enterprise produces and sells in the market, the manufacturing resources are optimized in combination with supplier raw materials, production base capacity, warehouse storage and transportation tool constraints, so that the total cost and total time are minimized, and the market competitiveness of the manufacturing enterprise is improved.