6 results about "Machine selection" patented technology

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 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 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 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.