71 results about "Machine selection" patented technology

The invention discloses a workshop scheduling method considering AGV transportation time based on a near-end strategy optimization algorithm, and the method comprises the steps: obtaining an action space, a process local state, a machine local state and an AGV local state according to a current workshop state, carrying out the coding in sequence, and obtaining a process state feature, a machine state feature and an AGV state feature; inputting the state characteristics into a trained strategy network for decoding to obtain action values of the process action, the machine action and the AGV action; the method comprises the following steps: performing normalization processing on an action value, selecting and executing a process selection action, a machine selection action and an AGV selection action with the highest probability in an action space, updating a current workshop environment based on the executed actions, and repeating the steps until all scheduling processes are completed, so that the expansibility of an algorithm and the overall operation efficiency are improved.

The invention relates to the technical field of powder concentrator control, in particular to an unpowered powder concentrator air volume control method and system, which reduce the time and frequency of shutdown adjustment, improve the continuity and stability of a production line and finally improve the overall production efficiency through real-time and dynamic control. The method comprises the following steps: collecting a plurality of material grading samples obtained by grading and powder selecting of an unpowered powder selecting machine; based on preset material grading target features, target deviation feature extraction is carried out on each material grading sample, and a material grading deviation vector corresponding to the collection time is obtained; obtaining material grading deviation vectors at a plurality of collection times, and arranging the material grading deviation vectors according to a time sequence to obtain a material grading deviation matrix; and inputting the material grading deviation matrix into a pre-constructed powder concentrator grading precision evaluation model to obtain a material grading precision index corresponding to the unpowered powder concentrator.

The invention discloses a flexible workshop production resource scheduling method based on multi-strategy deep reinforcement learning, and relates to the field of intelligent manufacturing, the method constructs three strategy networks and three value networks of work, machines and AGVs, and at each scheduling moment, the work selection strategy network selects a scheduling rule according to a work state and maps the scheduling rule to a specific operation; the AGV selection strategy network selects a scheduling rule according to the AGV state and maps the scheduling rule to a # imgabs0 # table AGV # imgabs1 # of transportation operation; a machine selection strategy network is mapped to a # imgabs2 machine # imgabs3 # of processing operation according to a machine state, it is determined that the operation is transported from an AGV # imgabs4 # to a machine # imgabs5 # for processing through multi-strategy cooperation, the starting time and the ending time of the operation are calculated, states, actions and rewards are stored in corresponding experience pools, and a value network samples data from the experience pools, calculates losses and updates network parameters. And after the experience pool is emptied, entering a next round of iteration. The method can dynamically respond to production demand changes, and optimize resource allocation and scheduling decisions in real time.

The invention provides a processing and assembling combined production workshop scheduling optimization method considering dynamic disturbance, and the method comprises the steps: building a combined scheduling model: dividing a product production process into a part processing process, an assembling process and an assembling subsequent process, defining the correlation constraint of processing and assembling, and carrying out the processing and assembling operation; comprising part machining sequence consistency, part machining completion requirements before assembly, buffer area capacity limitation and a dynamic disturbance event response mechanism; establishing a multi-objective optimization function: taking minimization of maximum completion time, equipment idle time, processing energy consumption cost and tool switching times as optimization objectives, and generating a scheduling model in combination with process time dependence, machine selection uniqueness and tool switching time constraint; dynamic disturbance processing: when an equipment fault occurs or a new order is inserted, updating an available machine state or combining tasks to be processed, and triggering rescheduling to generate a new scheme; an improved MOEA / D algorithm is used for solving; and outputting and applying a scheduling scheme: selecting a final scheme for production guidance according to the non-dominated solution set.

The invention belongs to the related technical field of workshop scheduling, and discloses a flexible workshop production scheduling method and system with a double-track type guide vehicle, and the method comprises the steps: building a flexible workshop production scheduling model with the double-track type guide vehicle in consideration of the machine constraint of a flexible workshop machining process and the carrying constraint of the track type guide vehicle; performing iterative solution on the flexible workshop production scheduling model through an optimization algorithm to obtain a flexible workshop production scheduling method; during iterative solution, the procedure processing sequence, the procedure processing machine selection and the procedure guide vehicle selection are coded respectively to form chromosomes, and the three chromosomes correspond to one solution; and a decoding operator based on motion conflict repair is executed on a solution generated by each iteration. According to the method, processing machine constraints and track guided vehicle constraints of processes are considered, a three-layer coding structure is provided, and a decoding mode based on motion conflict repair is designed, so that collaborative optimization scheduling of a flexible manufacturing system with double RGVs can be realized.

The invention provides a collaborative data migration scheduling method based on topology awareness and deep reinforcement learning, and the method comprises the steps: initializing system configuration, constructing a weighted graph comprising a source server, a working machine, a target storage and network equipment, carrying out the embedded processing of a graph structure through a graph neural network, and generating a node and a path representation vector; in combination with source data, a working machine and a target storage state, constructing a system state vector, inputting the system state vector into a deep reinforcement learning agent for decision making, and outputting a composite action including data block selection, working machine selection, path indexing and migration rate suggestion; the network control module converts path information into flow rules, the flow rules are issued and executed through the software defined network controller, the task management module continuously monitors the migration state and processes failed data blocks, and the integrity and non-repeatability of migration tasks are ensured. According to the method, the defects of an existing data migration system in the aspects of task allocation, path selection, resource control and dynamic adaptability can be overcome.

Systems and methods for managing task assignments (214) for a plurality of work machines (107) at a site (100) are disclosed. A distribution engine (220) may: receive first status data (206) for a work machine including historical data, operating conditions, and location data, and second status data (206) for a venue (100) including feature data for materials and a plurality of available tasks; predicting performance data and energy consumption data of the working machine for the task; selecting a task for the work machine by inputting the first state data (206) and the second state data (206) into a trained reinforcement learning model (228), where: the model (228) has been trained to learn an allocation policy (230) that optimizes the reward function (232) such that the learned policy selects a task for at least one work machine from a plurality of available tasks of the venue (100); and causing the at least one work machine to operate according to the at least one task assignment (214).

The invention discloses a reconfigurable flexible job shop scheduling optimization method with secondary clamping constraint, and relates to the technical field of intelligent manufacturing and production optimization. The method comprises the following steps of: 1) establishing a mixed integer linear programming model considering a reconfigurable flexible job shop scheduling problem of secondary clamping by taking minimization of maximum completion time and minimum number of chemical workers as targets; 2) designing a three-segment coding mode and a decoding mode corresponding to the mixed integer linear programming model based on process sorting, machine selection and worker selection; and 3) based on the three-segment coding mode and the decoding mode, adopting an improved multi-target genetic algorithm to solve an optimal scheduling scheme of the mixed integer linear programming model. According to the method, processing machine selection, auxiliary module selection, processing sequence sorting and secondary clamping worker selection of a manufacturing workshop can be considered at the same time, the workshop production efficiency is improved, and the method has the advantages of being good in model performance, small in result fluctuation and high in stability.

The invention belongs to the field of workshop scheduling, and particularly discloses a hybrid flow workshop dynamic scheduling method and system considering machine predictive maintenance, and the method comprises the steps: taking the minimization of total completion time, maintenance cost and processing cost as the target, building a hybrid flow workshop scheduling problem as a multi-target joint optimization model, and carrying out the optimization of the multi-target joint optimization model; setting intelligent agents with the same number as the processing stages, and constructing a Markov decision process; each agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on a Markov decision process, training an intelligent agent, maintaining a workshop at an operation and maintenance point, and respectively calling a workpiece scheduling network and a machine scheduling network at a scheduling point to select workpieces and machines; and after training is completed, workshop dynamic scheduling is realized based on the trained intelligent agent. According to the method, the problem of hybrid flow shop dynamic scheduling considering machine predictive maintenance is effectively solved through workshop scheduling integrating machine operation and maintenance, and the method has good dynamics and adaptivity.

The invention provides a dynamic flexible workshop multi-target online rescheduling method based on hierarchical MADDQN driving, aiming at the problem of dynamic multi-target rescheduling in actual production. According to the method, two performance indexes, namely the minimum total tardiness and the maximum machine utilization rate, are optimized. The MADDQN comprises two DDQN agents, and the DDQN of the higher level is a controller for determining the temporary optimization target of the DDQN of the lower level. At each rescheduling point, the high-level DDQN considers a target which needs to emphasize optimization at present, the low-level DDQN considers process machine selection under the current optimization target, and the two intelligent agents cooperate with each other to carry out iterative training. Compared with other rules-based algorithms, the multi-target dynamic scheduling method has certain advantages in each performance index when processing a multi-target dynamic scheduling problem, and still has good performance when solving a large-scale problem.

The invention provides a distributed flexible workshop scheduling method and system based on a multi-target grey wolf algorithm, and belongs to the technical field of workshop scheduling. The method comprises the following steps: initializing algorithm parameters, and defining a coding and decoding mechanism by adopting three-layer coding including a process sequence, machine selection and factory selection; initializing the initial wolf pack by adopting a hybrid strategy based on three-layer coding; dividing non-dominated levels of the initial wolf pack through non-dominated sorting and sorting population crowding degree; determining the position of a wolf pack leader based on the number of non-dominated hierarchies and crowding degree sorting; performing crossover variation and wolf pack combination on the individual positions of the wolf pack, and updating the combined wolf pack to obtain a non-dominated elite solution set; setting a destruction operator and a repair operator, dynamically adjusting the selection probability of the destruction operator and the repair operator, introducing a simulated annealing algorithm to expand a search solution space, and optimizing an elite solution set to obtain a final solution. The scheduling efficiency of the distributed flexible job shop is improved, local optimum is avoided, and the production benefit and quality are guaranteed.

The invention provides a whole-process production scheduling method considering processing and assembling, which comprises the following steps: constructing a two-stage flexible flow shop scheduling model, scheduling processing tasks of all parts of a plurality of products on a parallel machine in the first stage, and scheduling assembling of the product parts and processing tasks of semi-finished products in the second stage; minimizing the maximum completion time and the total energy consumption of the machine is taken as a double-optimization target; a multi-target swarm intelligence algorithm is adopted to solve the model, population individuals represent a scheduling scheme through two-segment coding, the first segment of coding defines a process execution sequence, and the second segment of coding defines a machine selection result; in an iteration process, alternately executing a Thompson sampling strategy and a dedirectional sampling and generating strategy according to a preset probability, adaptively selecting a bottom layer optimization operator to realize global search, and constructing a guide solution set to realize local mining; and outputting a non-dominated solution set after iteration is ended, and obtaining a corresponding whole-process scheduling scheme after decoding.

The invention provides a remanufacturing workshop scheduling method based on reinforcement learning and evolutionary algorithm fusion, relates to the technical field of production scheduling optimization, and solves the problem of remanufacturing system scheduling in three stages of disassembly, reprocessing and assembly. A four-dimensional decision model covering process sorting, factory distribution, machine selection and speed gear scheduling is constructed, and an initial population with diversity and high-quality characteristics is generated by adopting a multi-strategy hybrid initialization method. A reinforcement learning decision module based on Q-learning is introduced, an optimal combination is dynamically selected from various crossover operators, mutation operators and neighborhood search strategies, the crossover probability and the mutation probability are dynamically corrected according to the distribution characteristics of the Pareto leading edge, and a dual escape mechanism is integrated to enhance the capability of the algorithm to jump out of local optimum. According to the scheme, the group search advantage of the evolutionary algorithm is exerted, the dynamic optimization of the search process is realized, and an efficient and accurate scheduling solution is provided for a complex remanufacturing system.

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

A method for assisting the piloting of an aircraft comprising a head-up display and an automatic flight control system that comprises a human-machine selection interface for choosing an operating mode from among several predetermined operating modes comprising a disengaged mode and an engaged mode, the automatic flight control system being configured, in the engaged mode, to control at least one actuator in order to bring a current value of a controlled parameter towards a setpoint value. The method comprises: i) detecting, with a control system, an applied current mode from among the several predetermined operating modes; and ii) controlling and displaying, on the head-up display, a symbol carrying the current value according to a graphic charter specific to the current mode, a first graphic charter applied during the disengaged mode being different from a second graphic charter applied during the engaged mode.

The invention discloses a flexible job shop integrated scheduling optimization method, device and equipment considering AGV electric quantity constraint, through the algorithm, process processing sorting and machine selection of a population individual solution are completed through two-segment coding, in order to improve the quality of an initial population solution, a population is initialized in a manner of combining heuristic generation and random generation, and the quality of the initial population solution is improved. During decoding, scheduling of the AGV is completed by using a heuristic rule, the problems of power consumption and charging of the AGV are considered, generation of an unreasonable solution caused by pre-designation of the AGV in a coding link is effectively avoided, and the problem that a genetic algorithm is prone to falling into local optimum is solved by designing multiple neighborhood structures for local search.

A flexible job shop scheduling method based on improved HHO of co-evolution, steps include S1 data collection; S2 abstract FJSP as a mathematical model; S3 divide FJSP into machine selection sub-problem and process processing sequence sub-problem; S4 machine selection sub-problem processing: adopt DI coding method coding, map discrete machine selection sequence to continuous solution space of HHO algorithm; Each process selects the decision of the process in the machine set as a variable; After iteration solution by HHO algorithm, DI coding is mapped back to discrete machine selection sequence by decoding; S5 process processing sequence sub-problem processing: adopt OS coding method coding, and adopt genetic operator to co-evolution; S6 select one individual from DI population and OS population respectively, combine into DIOS code; Calculate the maximum completion time of DIOS code; S7 DI population and OS population co-evolution; Decode the optimal solution and output.

The present invention provides a method for solving a flexible workshop scheduling problem based on an improved golden jackal optimization algorithm, including: generating an initialization population based on a combination of GLS initialization and RI initialization, and calculating the fitness values ​​of individuals in the initialization population; comparing the fitness values ​​of the individuals, and determining male golden jackals and female golden jackals according to the comparison results; setting the current maximum number of iterations to 0, respectively updating the positions of a machine selection part and a process sorting part, and iterating according to the position update results, and updating the current optimal solution based on the iterative processing results; judging whether the current iteration meets the iteration termination condition, if the iteration termination condition is met, outputting the optimal solution, otherwise executing step 3 for the next iteration. While improving the local search capability of the golden jackal optimization algorithm, the global search capability is improved to ensure that the best solution that meets the global deployment of the workshop is provided for the flexible workshop scheduling problem.

The invention discloses an archive information data management system and method based on big data, and relates to the technical field of archive information management.The archive information data management method comprises the steps that a detection node is preset based on an archive storage library port, and sending of an inquiry event to a management port is triggered according to a preset judgment condition; determining an archive storage mode according to the inquiry event feedback information; the archive warehousing mode comprises a manual warehousing mode and a machine selection intelligent warehousing mode; when the archive warehousing mode is machine selection intelligent warehousing, starting a machine selection intelligent analysis model to analyze and store the warehousing archive information; the machine selection intelligent analysis model carries out information node analysis processing on the warehousing archives; constructing a three-party association library based on the analysis and processing results of the warehousing archive information nodes, and carrying out classified storage and associated archive information calling storage on warehousing archives; according to the invention, an efficient archive data storage management mode is realized, so that flexible storage and associated calling of the archive data are realized.

The invention discloses a kind of original flavor green bean and a production method thereof. The production method comprises the following steps: machine selection-soaking-cleaning-two-stage frying-oiling-deoiling-mixing-sorting-cooling-packaging; in the soaking step, the green bean is soaked in water containing sodium bicarbonate with a mass concentration of 0.1% to 1%; in the two-stage frying step, the green bean is first fried in palm oil at 135 to 150 DEG C for 3 to 4 minutes; and then fried in palm oil at 165 to 175 DEG C for 4 to 5 minutes; the production method can ensure that the green bean product has a green appearance, reduce the shell breakage rate, and highlight the bean fragrance of the green bean itself.

The present invention discloses a flexible job shop batch scheduling method considering carbon emissions, and solves the flexible job shop batch scheduling problem with completion time and carbon emissions as targets by improving the NSGA-Ⅱ algorithm. First, a mathematical model for flexible job shop batch scheduling is established; the relevant parameters of the improved NSGA-Ⅱ algorithm are set, and the individuals of the population are encoded using a four-segment encoding method, thereby initializing the population. Then, crossover and mutation operators are designed to perform crossover and mutation operations on the four genes of batch division, sub-batch workpiece quantity division, process sorting, and machine selection, respectively. The concept of a dominant and recessive gene is proposed, and a method for distinguishing dominant and recessive genes is designed, by which individuals can be effectively decoded. Then, excellent individuals are selected and retained through non-dominated sorting and crowding. Finally, the Pareto optimal solution set is output when the number of iterations is reached.

A construction management system includes an output unit that causes a display device to display a remotely operable work machine, a selection data acquisition unit that acquires machine selection data indicating specification of the work machine, and a remote operation permission unit that permits start of a remote operation of the work machine based on the machine selection data.

The invention provides a flexible production workshop batch flow scheduling method, electronic equipment and a storage medium, and the method comprises the following steps: constructing a process sequence code, a machine selection code and a worker distribution code based on the production batch of each order product and the process route of each workpiece, and carrying out the calibration through a pointer calibration method, constructing a sub-batch strategy code; establishing a scheduling model taking the maximum completion time and the total production energy consumption as targets; optimizing a hunting exploration stage and an escape development stage of the secretary bird algorithm through discretization operation, and dynamically adjusting a neighborhood search strategy by utilizing reinforcement learning to obtain an improved discrete secretary bird algorithm; and based on a four-layer coding strategy of sub-batch strategy coding, process sequence coding, machine selection coding and worker allocation coding, solving the scheduling model through an improved discrete secretary bird algorithm to generate a scheduling scheme. And the overall advantages of synergistic interaction are formed in the aspects of shortening the production cycle, reducing the energy consumption, improving the scheduling flexibility and the like.

A drilling machine for selecting pin holes in strength training equipment is characterized by two transverse guide rails and a slide plate at the front of the worktable, with a lead screw and lead screw motor underneath the slide plate. Above the slide plate are drilling square columns and center hole square columns. Behind the drilling square columns are two vertical drilling guide rails, with a drilling head on each guide rail. In front of the drilling head are a vertical drilling lead screw and a drilling feed motor, and above the drilling head are a drilling gearbox and a drilling spindle motor. Behind the center hole square columns are two vertical center hole guide rails, with a center hole drilling head on each guide rail. In front of the center hole drilling head are a vertical center hole lead screw and a center hole feed motor, and above the center hole drilling head are a gearbox and a spindle motor. At the rear of the worktable, multiple rows of transverse positioning blocks are arranged longitudinally, with a clamping cylinder in the gap between each row of positioning blocks. This design solves the problems of low efficiency and poor precision in machining selection pin holes using ordinary drilling machines, making it suitable for use by fitness equipment manufacturers.

The invention provides an automatic guided vehicle scheduling method and device, computer equipment and a storage medium, and belongs to the technical field of production scheduling, and the method comprises the steps: obtaining the number of workpieces transported by an automatic guided vehicle, the number of procedures and the number of parallel machines for machining the workpieces; according to the number of the workpieces, the number of the working procedures and the number of the parallel machines, the priority of workpiece transportation and the parallel machine selection of each workpiece in each working procedure are coded, and an initial scheduling scheme is obtained; for any workpiece, determining a transportation plan of the workpiece in the current working procedure, corresponding to the state of the parallel machine and the position of the automatic guided vehicle in the idle state, and determining a transportation plan of the workpiece in the next working procedure; constructing a parallel multi-population hybrid genetic algorithm, and carrying out iterative optimization on the initial scheduling scheme according to a transportation plan of any workpiece transportation in the next process to obtain an optimal scheduling scheme; and scheduling the automated guided vehicle according to the optimal scheduling scheme. Therefore, the utilization efficiency of AGV scheduling can be effectively improved.

The invention discloses an intelligent task scheduling method and system based on labels and cue words. The method comprises the following steps of obtaining total resources and residual resources of each machine currently used for executing tasks; adding a plurality of initial labels and cue words for each task, and assigning a weight for each initial label and cue word; converting the cue word into an additional label by using a natural language analysis method; and according to the initial label, the additional label, the weight, and the total resource and the residual resource of each machine, utilizing a preset machine selection formula and a task execution sequence formula to calculate and obtain a machine where each task is executed and a task execution sequence when the same machine executes. The resource utilization rate is improved, the task waiting time is shortened, and the user experience is improved.

This invention provides a scheduling method for additive manufacturing workshops based on safety reinforcement learning, relating to the field of deep learning technology. The method includes: determining a workshop state vector S based on workshop simulation environment parameters; where T represents the current time, L represents the number of tasks being processed in the current batch, Tard represents the total delay time of all tasks, C represents the total completion time of completed tasks, and Index represents the task currently being scheduled; using the DQN algorithm, combined with the workshop state vector S and a reward function, determining the priority of tasks to be scheduled, and determining the batch of tasks to be scheduled based on the priority; using an improved A3C algorithm for machine selection, allocating the tasks to be scheduled in different batches to target machines; the improved A3C algorithm is an A3C algorithm incorporating a safety shield mechanism. By comprehensively considering task urgency, processing time, and machine load, the method dynamically optimizes production scheduling, improving scheduling efficiency and resource utilization, and achieving multi-objective balance.

The invention discloses a flexible job shop scheduling method based on a Harris eagle optimization algorithm, and the method comprises the steps: collecting basic data of a flexible job shop, and constructing a processing time length matrix of the flexible job shop and a flexible job shop scheduling mathematical model; performing standardization processing on the processing duration matrix, determining the probability that the machine selects to process the process in combination with the machinable mask of the process-machine, and generating an initial solution of a flexible job shop scheduling scheme; the generated initial solution serves as an eagle individual, a flexible job shop scheduling mathematical model serves as a fitness function, and the maximum number of iterations is set; calculating the fitness value of each eagle individual by using a fitness function, retaining the eagle individual with the minimum fitness value, calculating a nonlinear energy factor according to the current iteration number, and adjusting the optimization strategy of the eagle individuals according to the nonlinear energy factor until the maximum iteration number is reached, and taking the eagle individual with the minimum fitness function value as the optimal flexible job shop scheduling scheme.

To provide a technique for efficiently selecting Ising machines.SOLUTION: An Ising machine selection device includes: a feature quantity vector calculation unit configured to calculate a feature quantity vector of a graph corresponding to an Ising Hamiltonian to be selected; a first selection unit configured to select an Ising Hamiltonian Hm_0 corresponding to a feature quantity vector with the minimum distance to the feature quantity vector using a relationship R2 between an Ising Hamiltonian Hm and a feature quantity vector Xm of the graph corresponding to the Ising Hamiltonian Hm; and a second selection unit configured to select an allowable calculation time Tk_0 that is closest to an allowable time for calculating the Ising Hamiltonian to be selected and select a predetermined number of Ising machines corresponding to a value Vm_0,n,k_0 in order from the smallest one using a relationship among the Ising Hamiltonian Hm, an Ising machine In, an allowable calculation time Tk, and a value Vm,n,k of the Ising Hamiltonian Hm at the time when the allowable calculation time Tk has elapsed since the Ising machine In started calculation.SELECTED DRAWING: Figure 4