针对堤防工程综合安全监测体系,基于物联网技术,建立堤防工程海量数据资源的采集、汇集、交换与共享云平台;同时针对堤防工程质量、险情演化、致溃机理,基于大数据和人工智能技术,构建堤防工程大数据开放平台和人工智能计算平台,提供海量、多源、异构数据的融合、共享,实现风险识别、评估、预警模型的构建、运算。针对堤防工程安全防护、加固中填筑石料粒径级配要求,利用堤防工程物联网监测平台进行数据采集汇集,在堤防工程人工智能计算平台构建砂石爆破开采级配预测模型,解决多层非线性问题,进行爆破开采优化设计和石料粒径级配控制。实例证明,基于人工智能的堤防工程大数据安全管理平台可将堤防填筑石料开采级配平均相对误差率控制在21%以内,满足开采级配设计要求,控制石料块度,从而保障了堤防工程质量,实现了堤防工程的安全管理。研究成果可为堤防工程填筑石料开采设计提供技术参考,保障工程质量。
Abstract
A big data safety management platform for dyke engineering is presented in this paper. The internet of things (IoT) technology is adopted to build a cloud platform for the monitoring, collection, exchange, and sharing of massive data in the monitoring system. Meanwhile, big data and artificial intelligence technologies are employed for the fusion and sharing of massive, multi-source and heterogeneous data to identify and evaluate risks and construct early-warning model. Through the data acquisition and collection by the IoT monitoring platform, the model predicting the gradation after sandstone blasting is built to design the optimum blasting scheme and control the particle gradation of sandstone material. Engineering practice has demonstrated that the average relative error rate of controlling the gradation is within 21%, which meets the requirement and guarantees the dyke construction quality.
关键词
堤防工程 /
安全管理平台 /
大数据 /
人工智能 /
深度学习 /
物联网 /
开采级配设计要求
Key words
dyke engineering /
safety management platform /
big data /
artificial intelligence /
deep learning /
IoT /
design requirements of gradation
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基金
国家重点研发计划项目(2017YFC1502604,2018YFC0407904)
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