基于关键区域的井下人员轨迹挖掘方法
Underground Personnel Trajectory Mining Method Based on Key Areas
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摘要: 提出了基于关键区域的井下人员轨迹挖掘框架，该框架由关键位置发现算法和移动对象轨迹挖掘算法组成。首先利用关键位置发现算法将矿井下的定位数据转化成有特定语义的关键位置序列；然后利用移动对象轨迹挖掘算法将关键位置序列聚类关键区域，从而发现井下移动对象的日常轨迹，之后利用轨迹结构相似度筛选出异常轨迹。利用矿工定位数据集进行试验表明：基于关键区域的井下人员轨迹挖掘框架解决了多密度区域的识别问题，能够准确识别出矿工日常轨迹和异常轨迹。Abstract: A trajectory mining framework for underground personnel based on key areas is proposed. The framework consists of key location discovery algorithm and moving object trajectory mining algorithm. Firstly, the key location discovery algorithm is used to transform the positioning data under the mine into a key position sequence with specific semantics. Then the moving object trajectory mining algorithm is used to cluster the key position sequences into key regions, so as to find the daily trajectory of the moving objects in the underground, and then use the trajectory. Structural similarity screens out anomalous trajectories. Experiments using miners’ positioning datasets show that the trajectory mining framework based on key areas solves the problem of multi-density area identification, which can accurately identify the daily trajectory and abnormal trajectory of miners.
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Keywords:
- personnel positioning system /
- key region /
- trajectory mining /
- multi-density /
- clustering algorithm
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