16 results about "Machine selection" patented technology

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 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 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.

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 belongs to the related technical field of workshop scheduling, and discloses a two-stage batch flow flexible job workshop dynamic scheduling method and equipment, and the method comprises the steps: (1) extracting product processing technology features to construct a multi-dimensional batch feature set, carrying out the automatic selection and combination of batch features in the multi-dimensional batch feature set through employing a genetic programming algorithm, and carrying out the automatic selection and combination of the batch features in the multi-dimensional batch feature set; generating a plurality of batch scheduling rules through evolution operation, wherein the plurality of batch scheduling rules form an initial batch scheduling rule set; and (2) carrying out weight dynamic optimization on the initial batch scheduling rule set by adopting a deep reinforcement learning algorithm, and then determining a current optimal workpiece sorting and machine selection strategy so as to obtain a scheduling scheme. Through cooperative work of the genetic programming algorithm and the deep reinforcement learning algorithm, real-time response and adaptive scheduling of dynamic disturbance such as new workpiece arrival and machine faults in the production process are realized, the optimization efficiency is effectively improved, and the workshop production efficiency and the resource utilization rate are improved.

The application discloses a three-stage hybrid algorithm for solving a flexible job shop scheduling method, and solves a flexible job shop scheduling problem with the minimum maximum completion time as the target through a three-stage hybrid algorithm. In the search process, three stages are divided, and a population is divided into common individuals, elite individuals and alert individuals. In the first stage, a variable neighborhood breadth search algorithm is proposed to extensively search a solution space of process selection coding and update machine selection coding through a simplified Nopt1 neighborhood. When facing a small-scale scheduling problem, fast convergence to a global optimal effect can be realized. In the second stage, an adaptive elite individual number updating formula is proposed, and a crossover mutation operation is used to help the algorithm better exploit the elite individuals obtained in the previous stage. In the last stage, the alert individuals are updated to increase the ability of individuals in the population to escape from a local optimum. The method has the advantages of not being easily trapped in a local solution and high solution precision.

The invention relates to an aerospace component incidence relation mining method. The method comprises the following steps: preprocessing aerospace component selection list data; carrying out space navigation component selection list similarity analysis: constructing a selection list disorder tree and an electronic single machine selection component adjacent matrix, calculating selection list structure similarity and selection component specification similarity, and screening similar component lists; and for the screened similar component list, iteratively retrieving all component association selection frequent item sets which have the support degree greater than the minimum support degree threshold and possibly have association relationships, constructing a rule meeting the minimum confidence coefficient from the frequent item sets, and setting the support degree and the confidence coefficient value to obtain the component association selection frequent item sets. And adopting an Apriori algorithm to obtain a high-frequency association rule in the aerospace component selection process. According to the method, pre-analysis is performed from the two aspects of the component analysis range and the list similarity in a targeted manner, the problem of low mining efficiency is solved, manual subjective judgment is not needed, and the data mining accuracy is improved.

The application provides a wafer box carrying method, an electronic device and a readable storage medium, the method comprising: obtaining current state information of each wafer box; determining a first target wafer box to be fetched according to the current state information of each wafer box and a first preset wafer box fetching rule; obtaining current state information of each machine in the area where the first target wafer box to be fetched is located; determining a target carrying machine according to the current state information of each machine and a preset machine selection rule; and controlling an automatic carrying system to carry the first target wafer box to be fetched to the target carrying machine according to current position information of the first target wafer box to be fetched and position information of the target carrying machine, and controlling the target carrying machine not to implement a locking action on the first target wafer box to be fetched. The application can realize automatic carrying of wafer boxes, effectively save manpower, improve carrying efficiency and reduce the possibility of human errors.

The application discloses a flexible job shop AGV and machine combined scheduling method, and relates to the technical field of industrial automation. The method comprises the following steps: acquiring workshop scheduling basic data, and constructing combined scheduling input data; adopting a genetic algorithm to jointly optimize process sequencing, machine selection and AGV distribution, and utilizing a static path search algorithm to quickly estimate transportation time, and generating structured interface data; generating discrete reference path data satisfying space-time mutual exclusion constraints based on space-time discrete search, and generating continuous trajectory data satisfying AGV nonholonomic kinematic constraints through rolling horizon optimization; when continuous trajectory optimization fails to be solved, a soft kinematic constraint iterative relaxation strategy is executed; when multiple vehicle stagnation or interlocking is detected, a multi-scenario deadlock resolution mechanism is triggered. The application realizes unified coordination from discrete scheduling decision to continuous physical execution, and improves the robustness and engineering feasibility of flexible job shop multi-AGV collaborative scheduling.

The application discloses a kind of scheduling method of distributed manufacturing based on empire competition algorithm, comprising the following steps: establishing the objective function including minimizing maximum completion time, minimizing maximum machine load and minimizing total energy consumption, and setting the constraint condition of objective function according to the assembly process of product;Set coding and decoding rules, encode process sequencing and machine selection to obtain process sequencing vector OSV and machine selection vector MAV;Set the hybrid initialization rule of OSV and MAV, generate initial solution set;Calculate the objective function value of all initial solutions in initial solution set, select the better solution as the colony from it, the remaining initial solution is as the colony, adopt OX cross to realize the assimilation between colony, adopt POX cross to realize the assimilation between colony and colony, generate new solution set;Design the neighborhood of OSV and MAV, carry out variable neighborhood descent search to colony, obtain the optimal production scheduling scheme.

The application belongs to the field of workshop scheduling, and particularly discloses a mixed flow shop dynamic scheduling method and system considering machine predictive maintenance, which comprises the following steps: taking minimizing total completion time, maintenance cost and processing cost as the target, establishing a mixed flow shop scheduling problem as a multi-objective joint optimization model, setting the same number of intelligent agents as the number of processing stages, and constructing a Markov decision process; each intelligent agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on the Markov decision process, the intelligent agents are trained, the workshop is maintained at the operation and maintenance point, and the workpiece and machine selection are respectively performed by calling the workpiece scheduling network and the machine scheduling network at the scheduling point; after the training is completed, the trained intelligent agents are used to realize the dynamic scheduling of the workshop. The application effectively overcomes the mixed flow shop dynamic scheduling problem considering machine predictive maintenance by integrating the workshop scheduling of machine operation and maintenance, and has good dynamic and adaptability.

The application discloses a kind of multi-agent multi-objective optimization workshop scheduling method based on man-machine cooperation.First, three objective functions including process sequencing, machine selection, speed selection and worker selection are constructed to optimize maximum completion time, total energy consumption and machine load balancing.Second, a distributed constraint processing mechanism based on local resource agent (LRA) is designed, and each LRA autonomously decides speed and worker allocation based on local resource state to ensure the feasibility of the scheduling scheme.Finally, a hierarchical multi-agent evolutionary algorithm is used to evolve collaboratively between global planning layer and field execution layer, and efficiently search for the Pareto optimal solution set through distributed evaluation, environment selection and adaptive policy adjustment.The application overcomes the problems of single resource constraint, rigid processing model and limited optimization objectives in traditional scheduling, achieving multi-objective collaborative optimization under double resource and multi-speed constraints, and significantly improving the greenness, balance and efficiency of production scheduling.

The present application relates to the technical field of new energy system operation control, and discloses a new energy station group maximum available power calculation method, device and medium, the present application constructs the space-time characteristics of fusing time sequence law and the correlation between units, and carries out dynamic partition and template machine selection based on the space-time characteristics, and then calculates the maximum available power of the whole field, which can reflect the influence of environmental condition change on the output of the station group in real time, avoid the defects that fixed template machine or fixed power curve cannot adapt to environmental fluctuations and other actual working conditions, significantly improve the estimation accuracy of the maximum available power of large-scale new energy station group, provide more accurate available power boundary for station group dispatching control, and help to improve the power generation efficiency and operation economy of new energy station group.

This application relates to a cleaning machine driving method, system, smart terminal, and storage medium, belonging to the field of cleaning machine technology. It includes acquiring preset cleaning machine start-up trigger information; acquiring cleaning machine selection gear information based on the start-up trigger information; determining cleaning gear information of the cleaning machine according to the selected gear information and preset gear-head relationship information; acquiring cleaning distance information between the cleaning machine and a preset area to be cleaned; determining whether the cleaning distance information meets the requirements of the cleaning gear information; if not, analyzing the cleaning distance information and gear-head relationship information to determine gear change information, and controlling the cleaning machine to change gears according to the gear change information before cleaning; if it meets the requirements, controlling the cleaning machine to clean according to the selected gear information. This application improves the ease of use of cleaning machines.

This invention discloses a distributed heterogeneous flexible assembly workshop scheduling method with transfer functionality. It includes the following steps: setting assumptions and basic parameters; proposing corresponding constraints based on the problem under study; constructing a bi-objective optimization model with the objectives of minimizing maximum completion time and minimizing total processing energy consumption. This bi-objective optimization model is used to optimize the flexible assembly workshop scheduling considering factory processing capacity, transportation stages, and workpiece transfer. Based on the process sequence, factory selection, and machine selection, a three-segment encoding and decoding mode corresponding to the bi-objective optimization model is designed. Based on the three-segment encoding and decoding modes, an improved multi-objective genetic algorithm based on Q-Learning is used to solve for the optimal scheduling scheme. This invention considers factors such as workshop processing capacity, collaborative processing, and transportation stage time, arranging the process sequence, factory, machine, and assembly workshop selection from a global optimization perspective.