12 results about "Manufacturing services" patented technology

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 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 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 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.

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.