巷道堆垛式立体车库车辆存取测控方法

发布时间：2018-04-22 18:52

本文选题：立体车库 + 并行优化控制　；参考：《江苏大学学报(自然科学版)》2017年02期

【摘要】：针对智能立体车库车辆存取时间长、系统运行故障率高等问题,研究了巷道堆垛式立体车库车辆传送策略及智能测控方法.基于进程互斥原理、采用基于邻域的有界深度优先搜索算法实现多存取进程并行优化控制,给出车库主体立式框架结构设计方案,研究多通道多层面条件下的车辆传送运送策略,运用混合可切换控制手段完成不同服务环境中高峰及其他时间段车库车辆的存取传送.采用工控计算机和单元车库控制器PLC作为网络节点,利用现场总线组建一主多从的二级监控车库控制管理网络,采用MCGS、VC++编制PLC组态控制软件及上位机管理软件.结果表明:多通道多层面巷道式车辆堆垛及其智能测控能够最大限度节约用地,仅双层设置就会使地面使用率提升约85%;且所提出测控方法能够解决车辆自动存取的优化控制问题,为进一步提高车库运行效率提供有效途径.
[Abstract]:In order to solve the problems of long storage time and high failure rate of the intelligent stereoscopic garage, this paper studies the transport strategy and intelligent measurement and control method of the roadway stacking stereoscopic garage vehicle. Based on the principle of mutual exclusion of processes, the parallel optimization control of multi-access processes is realized by using the border-based bounded depth first search algorithm, and the design scheme of the main body vertical frame structure of the garage is given. The vehicle transport strategy under multi-channel and multi-layer conditions is studied, and the hybrid switchable control method is used to complete the access and delivery of garage vehicles in different service environments and other periods. The industrial control computer and the unit garage controller PLC are used as the network nodes, and a secondary monitoring garage control and management network is constructed by using the field bus. The PLC configuration control software and the upper computer management software are programmed by using MCGSV VC. The results show that the multi-channel and multi-layer roadway stacking and its intelligent measurement and control can save land as much as possible. The two-layer setting alone will increase the ground utilization rate by about 85 points, and the proposed measurement and control method can solve the problem of optimal control of vehicle automatic access, and provide an effective way to further improve the efficiency of garage operation.
【作者单位】：哈尔滨理工大学测控技术与通信工程学院;哈尔滨理工大学测控技术与仪器黑龙江省高校重点实验室;
【基金】：国家自然科学基金资助项目(61401126)
【分类号】：U491.71

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