28 results about "Cloud manufacturing" patented technology

The invention provides an energy-saving cloud manufacturing multi-target scheduling method and system for improving rate-driven heterogeneous aggregation, and the method comprises the steps: A, setting algorithm parameters, job attributes and machine constraints, generating a weight vector and a neighborhood, and randomly binding an initial aggregation method; b, generating an initial population, performing heuristic decoding, and initializing an ideal point and an external archive set; c, calculating a dynamic switching threshold value based on the current iteration progress; d, executing sequential crossover and swap mutation operators to generate offspring individuals, and performing heuristic decoding based on consistency increment evaluation; e, updating an ideal point and maintaining an external archive set; f, executing self-adaptive environment selection according to the dominating relation and the relative improvement rate, and updating a neighborhood solution and a bound aggregation method; and G, if the termination condition is not met, returning to the step D, otherwise, outputting a non-dominated scheduling scheme set. The method has the advantages that the convergence problem under the multi-target conflict is effectively solved through self-adaptive cooperation of heterogeneous strategies. According to the method, heuristic batch decoding and time sequence linkage are adopted, a heuristic decoding algorithm with cluster constraints is designed, through real-time calculation of idle increments and switching losses, deep fusion of cross-process and cross-region resources is achieved, the cooperation efficiency of the whole cloud manufacturing process is guaranteed, and the maximum completion time and the total manufacturing cost can be balanced on the premise that production constraints are guaranteed; and thus, a high-quality collaborative scheduling solution set is stably obtained.

The invention discloses a service combination method for driving benefit balance of both parties by cooperation capability in a cloud manufacturing environment, which comprises the following steps of: loading a manufacturing task to be processed at the current moment, decomposing the manufacturing task into a plurality of sub-tasks, acquiring a plurality of state characteristics of each sub-task at the current moment, and splicing the state characteristics into a state vector representing the current state of the sub-task; and inputting the state vector representing the current state of the subtask into a reinforcement learning model of the cloud manufacturing platform, selecting a real-time optimal service with the maximum probability distribution from the candidate service set according to a pre-training parameter of the reinforcement learning model, and sequentially selecting a corresponding real-time optimal service for each subtask according to the execution sequence number, the real-time optimal services of all the sub-tasks are combined in sequence until the optimal service combination chain of the manufacturing task is constructed, the cooperative feasibility and operation stability between the services are considered, the service combination chain with the higher adaptive capacity is generated, and the service ecological sustainability and the development capacity of a platform are remarkably improved.

The invention discloses a cloud manufacturing optimization system for a linear cutting workshop. The cloud manufacturing optimization system comprises a user layer, a service center layer, a scheduling entity layer and a user feedback layer, the user layer is provided with a cloud terminal and is used for exchanging data with the service center layer; the business center layer comprises a model center, a data center, a search center, a computing center and a planning center; the scheduling entity layer comprises an enterprise level, a workshop level and a production line level, three levels of automatic scheduling systems exchange data with the service center layer respectively, and distributed automatic scheduling systems are arranged in the three levels of automatic scheduling systems respectively; the user feedback layer feeds back the work summarization condition to the user layer; according to the method, a cloud system of a whole wire cutting product is integrally redesigned by utilizing a new computing mode, a business mode and an application mode of cloud computing according to a design concept of'cloud centralized management of discrete manufacturing resources and on-demand service of overall planning of cloud resources.

The invention belongs to the technical field of resource scheduling, and provides a cloud manufacturing machine tool service resource scheduling method and system oriented to customer income, and the method comprises the steps: distinguishing customer types based on customer personalized preference demands, carrying out the weighting of machine tool service key indexes according to the customer types, and constructing a cloud manufacturing customer task income function; solving the cloud manufacturing customer task revenue function by adopting an improved genetic algorithm to obtain an optimal machine tool service resource scheduling scheme; according to the improved genetic algorithm, an individual fitness function is reconstructed based on a non-cooperative game, a machine tool service resource scheduling scheme is optimized, and multi-task income Nash equilibrium is achieved. The technical problem that dynamic balance of client task income under the influence of multiple factors is usually difficult to consider by an existing scheduling method is solved.

The invention provides a wide-area cloud manufacturing industrial service system and method based on an Internet of Things large model. The system comprises a service cloud platform and a user platform, wherein the service cloud platform is configured to obtain a constraint index; determining first service data from historical service data corresponding to the user based on the constraint index; responding to the condition that the first service data meets user requirements, and displaying the first service data to the user through a user platform; and in response to the condition that the first service data does not meet the user demand, taking one or more candidate service data of which the matching degree meets a preset matching condition as one or more second service data, and presenting the one or more second service data to the user through the user platform. Through the system, real demands of a manufacturing enterprise can be obtained, and accurately matched service data is provided for the manufacturing enterprise, so that high-quality operation of wide-area cloud manufacturing industrial services is supported.

The application discloses a kind of considering preference cloud manufacturing system digital twin migration modeling method, based on the production relationship of cloud manufacturing system, the knowledge graph of cloud manufacturing system is constructed;The digital twin model set in the cloud model library of cloud manufacturing system is constructed;The digital twin model includes geometric model, behavior model, logic model and performance model;Based on the characteristics of cloud manufacturing digital twin model migration, the demand set of digital twin modeling of cloud manufacturing service demand side is constructed;According to the interaction and preference relationship between the modeling demand and the model to be selected, a recommendation algorithm is constructed to select the optimal model from the digital twin model library for migration.The migration modeling method considering preference provided by the application provides an efficient and accurate modeling method for the precise application of cloud manufacturing system digital twin, which has important value for the intelligent improvement of cloud manufacturing system.

The invention relates to the technical field of cloud manufacturing services, in particular to a common evolution analysis method for cloud manufacturing low-carbon cooperation and related products. According to the method, suppliers and demander groups are connected through a small world network, the suppliers select learning neighbors based on the companion effect, supplier group strategies are updated through a Fermi rule, and interaction between the suppliers is achieved. And the demanders update the demander group strategy through the Aspiration-drive rule, and update the willing level based on the conformity effect to realize interaction between the demanders. Therefore, in the double-layer network of the supplier group and the demander group, the common evolution process of low-carbon cooperation of the supplier and the demander group is analyzed, and strategy evolution rules of the supplier group and the demander group are designed based on the companion effect and the conformity effect. Reference is provided for the policy formulation of low-carbon cooperative excitation of the cloud platform to the supply and demand parties, and low-carbon transformation of enterprises is promoted.

The present application relates to the cloud manufacturing production regulation technical field, especially to a kind of cloud edge production scheduling regulation method based on GRN-RL under high-frequency disturbance environment, monitoring and regulation are carried out through edge, and the value optimization of edge model is carried out using cloud.The specific configuration optimization in production process and process monitoring are carried out by local production area edge task gene regulation network model by such processing.And when the preset update condition is triggered, the optimization of relevant parameters is carried out by cloud.Using the method, even in high-frequency disturbance environment, the optimization configuration of equipment resource can be realized in production execution process in time and effectively, and high-quality dynamic scheduling regulation service is provided for cloud manufacturing task in workshop site.And the effect of scheduling regulation is also improved, which is conducive to the landing application of cloud manufacturing mode in the vast number of discrete manufacturing enterprises workshop bottom.

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 relates to the technical field of cloud manufacturing services, in particular to a cloud manufacturing enterprise low-carbon cooperative evolution analysis method and related products. The evolutionary game process of suppliers, demanders and supply and demand parties is analyzed from the three aspects of transverse low-carbon cooperation between supplier clusters, transverse low-carbon cooperation between demander clusters and longitudinal low-carbon cooperation between suppliers and demanders; therefore, suppliers, demanders and supply and demand parties can be guided to make specific low-carbon cooperation decisions. And meanwhile, the cloud platform can be guided to select low-carbon cooperation incentive decisions matched with suppliers, demanders and supply and demand parties, so that the transformation of low-carbon cooperation of supply and demand enterprises is better promoted.

The application discloses a scheduling method and system for minimizing total service completion time in a cloud manufacturing environment, the method comprising: initializing pheromone; selecting a job set from the current batch job set as a to-be-added job set; according to the pheromone τ jbm and heuristic information, a selected-job-rule algorithm is used to jointly select a job from the to-be-added job set; based on the selected job and the current batch, a selected-batch-machine-rule algorithm is used to select an optimal batch processing from the available batches of all machines; a selected-next-batch strategy is used to select a candidate batch; the candidate batch is added to the current job set; the above steps are repeated until all jobs are completed production and delivery, and a scheduling scheme is obtained; an optimization algorithm is applied to optimize the scheduling scheme; and then, according to the optimized scheduling scheme, the pheromone is updated to minimize the total service completion time of all jobs. Through the cooperation of production and delivery, the application achieves efficient production of jobs and reduces the waste of idle resources.

This invention discloses a novel method for adaptive robust service composition and optimal selection in cloud manufacturing, belonging to the field of cloud manufacturing technology. The method includes the following steps: 1) establishing an Adaptive Robust Service Composition and Optimal Selection (ARSCOS) model; 2) solving the model in step 1) using the Enhanced Multi-Objective Artificial Hummingbird Algorithm (EMOAHA). The ARSCOS model of this invention enhances the anti-interference capability of CMS and reduces the negative impact of uncertainty on the task execution process.

The present application relates to cloud manufacturing service technical field, especially based on the cloud manufacturing service combination optimization method of improved multi-objective artificial swallow algorithm, through the establishment of a double target SCOS model, the service quality (QoS) and energy consumption are considered simultaneously, to realize the sustainable green development of cloud manufacturing and ensure the efficient solution of SCOS problem, through the design of an efficient improved multi-objective artificial swallow algorithm (IMOAHA) for solving SCOS problem.IMOAHA adopts the reverse learning strategy to strengthen the exploration of initial population, and redesigns a kind of improved field foraging strategy based on leader mechanism, the comprehensive performance of IMOAHA algorithm is significantly improved, and the solving efficiency and accuracy are higher than existing algorithm.

The invention discloses a cloud manufacturing multi-task scheduling method considering service occupation in an uncertain environment, which adopts a cloud model to describe uncertain information, converts a linguistic variable into a numerical variable to model and reflect the fuzziness and randomness of the information, thereby improving the matching degree of a user order and a service. A scheduling period is divided into a plurality of equilong time windows to effectively represent a service occupancy state, a cloud manufacturing multi-task scheduling model is established, and an improved hyper-heuristic algorithm based on deep reinforcement learning is used for solving. During solving, a matrix coding scheme is adopted to more comprehensively represent a scheduling solution space, and seven low-layer heuristic operators based on a hybrid strategy are fused to realize collaborative optimization of global and local search. A high-level strategy based on a near-end strategy optimization algorithm is integrated to adaptively select a low-level heuristic operator. The cloud manufacturing multi-task scheduling method has obvious effectiveness and superiority for cloud manufacturing multi-task scheduling.

The present application relates to the technical field of cloud manufacturing, and particularly relates to a cloud manufacturing quality control and defect prediction method and system based on deep learning. The method comprises the following steps: acquiring production site data in a manufacturing process through a data acquisition device and uploading the data to the cloud for next step analysis; extracting information features of the production site data and performing feature fusion to obtain an information data feature set; constructing a joint deep learning prediction model, training the joint deep learning prediction model using the information data feature set; and completing cloud manufacturing quality control and defect prediction through the trained joint deep learning prediction model. The present application extracts various features of production site data, trains a joint deep learning prediction model, and then completes cloud manufacturing quality control and defect prediction, thereby solving the problems of quality and defect prediction in the manufacturing process, effectively improving production efficiency, optimizing product quality, and promoting the intelligentization and automation upgrade of the manufacturing process.

The invention discloses a task time sequence tight coupling cloud manufacturing energy consumption and service quality collaborative scheduling optimization method, belongs to the field of cloud computing, intelligent manufacturing and resource scheduling optimization, and aims to solve the problems of preheating energy consumption waste, service occupation conflict, supply and demand benefit imbalance, insufficient optimization performance and the like in the prior art. Comprising the following steps: decomposing a customized task into independent sub-tasks and matching a candidate service set; constructing a bilevel planning model in which the preheating energy consumption of the upper layer focuses on the minimum supplier and the lower layer is based on the QoS weighting preference of the demand side; designing an energy perception scheduling generation scheme containing service occupation scheduling and connection degree scheduling; and solving an optimal Pareto solution through an adaptive NSGA-II algorithm integrated with MEOS, and outputting a service combination and a subtask execution plan. On the premise that QoS constraints are met, preheating energy consumption is averagely reduced by 2%-8%, service occupation conflicts are effectively avoided, benefits of a supply party and a demand party are balanced, the Pareto frontier is better, the method is suitable for cloud manufacturing scenes with preheating processes, and diversified requirements of personalized manufacturing tasks can be met.

The invention discloses a machine tool equipment cloud service dynamic optimization configuration method with an idle time window, and relates to the technical field of cloud manufacturing. According to the invention, by analyzing the focus of a machine tool equipment resource supplier in cloud service cooperation, a multi-target optimization configuration model giving consideration to service benefits and service risks under the constraint of a machine tool idle time window is constructed; according to the machine tool equipment cloud service dynamic optimization configuration method with the idle time window, the problem of comprehensive quantification and collaborative optimization of cloud manufacturing task benefits and risks in minute-level fragmentary time periods is solved, the matching process of cloud manufacturing service resources is divided into a combination stage and an optimization stage, and in the combination stage, the cloud manufacturing task benefits and risks are optimized in a collaborative mode. Generating all feasible candidate subtask combination schemes based on an idle time window of the machine tool; in the optimization stage, an improved GOOSE-ESC algorithm is used for solving the feasible schemes, and a global optimal task scheduling scheme with the maximum service benefit and the minimum service risk is obtained.

The application discloses a kind of resource conflict resolution methods based on improved genetic algorithm, it is characterized in that, including the following steps: step one. construct mathematical model based on time cost TT;Step two. establish mathematical model based on resource use efficiency BR;Step three. construct the objective evaluation function P of cloud manufacturing resource conflict resolution;Step four. according to the quantity and task situation elements of supply and demand parties built by the objective evaluation function P of cloud manufacturing resource conflict resolution, configure constraint condition;Step five. the objective function of cloud manufacturing resource conflict resolution is solved, and the optimization result of resource matching is obtained.The application compared with prior art: the effectiveness and practicality of cloud manufacturing resource allocation optimization are good, resource utilization is high, optimization process is fast, resource allocation is reasonable, the allocation efficiency of manufacturing resource is high, and regional allocation is more balanced.

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 provides a kind of cloud manufacturing environment under automobile parts die casting resource intelligent scheduling method, and the technical field is artificial intelligence and intelligent manufacturing.It is characterized by: the method combines the production characteristics of automobile parts die casting under cloud manufacturing environment, and carries out the task decomposition of automobile parts die casting order project and under cloud manufacturing environment, and classifies die casting resources;Establish a die casting resource scheduling model, determine the objective function and constraint condition of optimization scheduling;And using deep policy gradient (DDPG) algorithm to solve the die casting resource scheduling problem of automobile parts.The application is widely used in automobile parts die casting enterprises and vehicle manufacturing enterprises, can meet the real-time scheduling needs of automobile parts die casting resources, and can effectively solve the difficulty of automobile parts die casting resource scheduling problem under current cloud manufacturing environment.

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 cloud manufacturing scheduling method based on discrete event simulation and multi-agent reinforcement learning, and belongs to the technical field of intelligent manufacturing and cloud manufacturing service scheduling. According to the method, each manufacturing task is modeled as an independent agent, a partially observable Markov decision process model is constructed, information fusion among the agents is realized through a strategy network, and global value estimation is performed by adopting a value hybrid network meeting monotonicity constraint; in a discrete event simulation environment, an event-driven mechanism is used for triggering an agent collaborative decision, factory distribution actions are mapped into manufacturing or logistics events, and a priority experience playback mechanism is used for network training. According to the method, the sequence limitation of a traditional method of sequencing first and then selecting is broken through, synchronous joint optimization of service selection and task scheduling is achieved, the scheduling efficiency and the sample utilization rate are remarkably improved in a complex manufacturing scene, and the method is suitable for a multi-task and multi-factory distributed cloud manufacturing environment.

Provided is a wide-area cloud manufacturing industrial service system and method based on an Internet of Things large model. The system includes a service cloud platform and a user platform. The service cloud platform is configured to: acquire a constraint indicator; determine first service data from historical service data corresponding to a user based on the constraint indicator; in response to the first service data satisfying a user demand, display the first service data to the user; in response to the first service data not satisfying the user demand, determine a candidate service pool based on the constraint indicator and the user demand, and generate a matching degree between each candidate service data and the user demand; and take one or more candidate service data whose matching degree satisfies a preset matching condition as one or more second service data, and present the second service data to the user.

This application provides a collaborative method, apparatus, electronic device, and readable storage medium based on cloud manufacturing. The method includes: decomposing the development task of the product to be developed and determining a collaborative development decomposition process; assigning corresponding R&D personnel to each collaborative development node in the collaborative development decomposition process and granting them corresponding collaborative development permissions; for each R&D personnel, responding to their R&D operations, generating corresponding R&D results, and synchronizing these results with other R&D personnel in the collaborative development decomposition process, so that other personnel can conduct R&D based on the synchronized results to obtain unified model data of the product to be developed; the unified model data includes development data related to the development task of the product to be developed. This method aims to improve the efficiency of product development and manufacturing.

The application discloses an LED light splitting ribbon real-time defect detection compensation method and system based on edge calculation. The method comprises the following steps: obtaining an LED target image collected for pre-processing to obtain an image sequence; using a network model deployed on an edge calculation device to perform real-time inference on the image sequence to obtain an inference result, wherein the inference result comprises an appearance defect and a position offset; generating a rejection control instruction according to the appearance defect, and performing online rejection on the LED chip with defects before entering the ribbon process; generating a real-time motion control compensation instruction according to the position offset, and sending the real-time motion control compensation instruction to a pick-and-place execution mechanism of a ribbon machine to correct the placement position of the LED chip; and uploading the detection result, compensation data and device state information processed by the edge calculation device to a cloud manufacturing execution system asynchronously. The application improves the mounting precision and yield, realizes edge-cloud collaborative real-time defect detection, fuses visual and force sensing double compensation, and improves the detection precision.

The invention discloses a supply chain carbon footprint multi-source information collaborative management method and system based on a cloud manufacturing platform, and the method comprises the steps: defining a product system boundary, and determining all stages needing to be considered in a product life cycle; constructing a product supply chain network based on a product system boundary, and completing unified identity authentication on the cloud manufacturing platform; based on a data interface and a service management interface of the cloud manufacturing platform, a supplier enterprise is cooperated to call a product carbon footprint life cycle evaluation platform, carbon footprint evaluation work is carried out, and data interaction and storage are realized; based on a data interface and a service management interface of a cloud manufacturing platform, a supplier enterprise is cooperated to call a product carbon footprint life cycle evaluation platform, carbon footprint evaluation work is carried out, and data interaction and storage are realized. Through cooperative management of the cloud manufacturing platform and the supply chain, efficient collection, cooperative processing and optimization analysis of the carbon footprint data are realized, the transparency and efficiency of supply chain carbon footprint management are improved, and construction of a green supply chain is promoted.

The application provides a process flow simulation method and system for a cloud manufacturing mode, relates to the technical field of industrial virtual simulation, acquires process flow collection data and determines collection environment parameters, further performs multi-environment parameter evaluation to acquire multi-environment estimated collection data, acquires cooperation process collection data sets by using a cloud manufacturing platform, acquires simulation process product demand parameters and performs standard demand parameter deviation value calculation, sets cooperation process collection data set weight values, assembles local training data to perform federated learning, optimizes model parameters according to cooperation process collection data set weight values, and determines process flow simulation parameter information, so that the technical problem that the acquisition method for missing parts in the collection data has certain limitations in the prior art, the acquisition process is not rigorous enough, the finally determined data is not sufficient in actual fitting degree, and the accuracy of simulation is affected is solved, and the accurate simulation that is consistent with the process flow simulation demand is realized.

The invention provides an intelligent scheduling method for automobile part die-casting resources in a cloud manufacturing environment, and belongs to the technical field of artificial intelligence and intelligent manufacturing. The method is characterized by comprising the following steps of: decomposing automobile part die-casting order items and tasks and classifying die-casting resources in the cloud manufacturing environment by combining the die-casting production characteristics of the automobile parts in the cloud manufacturing environment; establishing a die-casting resource scheduling model, and determining an objective function and constraint conditions of optimal scheduling; and a depth strategy gradient (DDPG) algorithm is adopted to solve an automobile part die-casting resource scheduling problem. The method is widely applied to automobile part die-casting enterprises and finished automobile manufacturing enterprises, the real-time scheduling requirement of the automobile part die-casting resources can be met, and the difficulty of the automobile part die-casting resource scheduling problem in the current cloud manufacturing environment can be effectively solved.