Intelligent recommendation system for rectification of construction safety hazards based on deep learning

Zhen LIU; Song ZHAO; Tao YANG; Taiwei CAI

doi:10.11959/j.issn.2096-0271.2023076

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Intelligent recommendation system for rectification of construction safety hazards based on deep learning

STUDY | 更新时间：2024-06-03

- Intelligent recommendation system for rectification of construction safety hazards based on deep learning
- Big data research Vol. 9, Issue 6, Pages: 124-136(2023)
- 作者机构：
  
  1. 广东粤海珠三角供水有限公司，广东广州 511455
  2. 云南大学信息学院，云南昆明 650504
  3. 深圳市科荣软件股份有限公司，广东深圳 518063
  4. 华南师范大学华南先进光电子研究院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2023076
  CLC： TP183
- Online First：2023-11，
  
  Published：15 November 2023
- 稿件说明：
移动端阅览
Zhen LIU, Song ZHAO, Tao YANG, et al. Intelligent recommendation system for rectification of construction safety hazards based on deep learning[J]. Big data research, 2023, 9(6): 124-136.
DOI：

Zhen LIU, Song ZHAO, Tao YANG, et al. Intelligent recommendation system for rectification of construction safety hazards based on deep learning[J]. Big data research, 2023, 9(6): 124-136. DOI： 10.11959/j.issn.2096-0271.2023076.

摘要

水利工程施工安全隐患治理正向信息化与智能化转型，为了高效地从大量非结构化的施工安全隐患数据中挖掘出有价值的潜在信息，提出了基于深度学习的施工安全隐患整改智能推荐系统。该算法基于词频逆向文档频率算法，提取施工安全隐患的特征词，构建安全隐患关联桑基图，展示施工标段、隐患特征、隐患类型之间的信息流动特征；基于FP-Growth算法挖掘历史数据中的关联规则；结合序列相似度匹配（sequence similarity matching，SSM）算法和Doc2Vec模型，优化案例检索推荐的过程。该算法利用珠江三角洲水资源配置工程2019—2023年记录的80 953条施工安全隐患信息作为数据源。实例验证表明，该算法能够为当前的施工安全隐患匹配出较为准确的整改措施，可有效辅助施工安全管理者排查治理隐患问题。

Abstract

The management of safety hazards in water conservancy engineering construction is transitioning towards informatization and intelligence.In order to efficiently mine valuable potential information from a large amount of unstructured construction safety hazard data

an intelligent recommendation system for construction safety hazard rectification based on deep learning is proposed.This paper is based on the TF-IDF algorithm to extract feature words of hidden danger

construct a safety hazard association Sankey diagram and display the information flow characteristics among construction sections

hazard features and hazard types.Then

this paper mines association rules in historical data based on the FP-Growth algorithm.In addition

the process of case retrieval recommendation is optimized by combining the sequence similarity matching algorithm and the Doc2Vec model.This paper uses 80 953 construction safety hazard information as the data source

which is recorded in the water resources allocation project of Pearl River Delta from 2019 to 2023.Example verification shows that the proposed method can match accurate rectification measures for current construction safety hazards

effectively assisting construction safety managers to identify and address hidden danger.

关键词

Keywords

references

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