基于船厂分段时空数据的分段状态识别及转运监测

Ship Block State Identification and Transfer Monitoring Based on Time-Site Data of Blocks

船舶分段转运保障了分段在各个工艺之间的有序流动,但消耗了大量成本.船厂管理者需要监测实际转运过程,特别是监测因堆场内分段互相阻挡和返工产生的两类非生产性转运.S船厂由于场地紧张必须一场多用,现有监测技术难以从分段时空数据中获取分段状态,进而难以实现两类非生产性转运的监测.针对这个问题,研究了转运过程中分段状态的时序转化规律和耗时特征,建立了4个隐马尔可夫模型并使用有监督的方法学习其参数,通过维特比算法实现了分段状态识别,其在测试集上的准确率最高达到了93.5%.将其中1个隐马尔可夫模型应用于船厂分段时空数据,实现了船厂的两类非生产性转运的监测,根据监测结果为优化船厂分段转运过程提出初步建议.

Ship block transfer is important to the orderly flow of blocks between crafts, which is costly. Shipyard managers have to monitor the actual transfers, especially the unproductive transfers that occur when blocks are obstructed or reworked. A high-load shipyard, called S, often uses one site for multiple purposes, and the difficulties in obtaining the state of ship blocks through the time-site data of blocks provided by the existing monitoring technology make it difficult to monitor two types of unproductive transfers. To address this problem, four hidden Markov models whose parameters are calculated by a supervised approach are proposed, and a Viterbi algorithm based method is proposed to identify the state of blocks, achieving an accuracy of up to 93.5% on the test dataset. One of the hidden Markov models is applied to the time-site data of blocks to monitor two types of unproductive transfers in shipyards. Preliminary suggestions for improving the blocks transfer process based on monitoring results are proposed.