Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data

Round 1

Reviewer 1 Report

Comments are attached

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Author Response

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Reviewer 2 Report

This manuscript presents a new model for intelligent management of mine safety risks, which has significant effects on scientific and in-depth mine safety risk analysis. The research content is a hot issue and the analysis of the result is rich with clear ideas. Overall, this manuscript is positive to be accepted after minor revision. However, this manuscript also has the following shortcomings and is needed to be further improved. The specific problems are as follows:

(1) Please recheck the format of figures 1 to 6 and revise them, which should be consistent with the expression of the full text.

(2) The construction of the system described in the article used some open-source software and tools. It is necessary to make appropriate declarations at relevant positions in the article.

(3) Please carefully check the citation method and format of the references to make them more in line with the template requirements.

(4) In general, the presented article leaves a positive impression and, after eliminating these comments and taking into account the recommendations made, it can be recommended for publication in the journal "Sustainability".

(1) There are still some grammar and expression problems in this manuscript. Please check and correct them carefully.

(2) Keywords need to be modified. Please use words not combinations of words or phrases.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

The article “Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data” presents the results of an intelligent model analysis that provides support for identifying and preventing mine safety risks, as well as formulating control and emergency response strategies.

In this article, a big data management and integration structure was built by sorting the data resources accumulated by the existing safety management system, collecting, standardizing, and normalizing heterogeneous mine safety information from multiple sources, and forming underground metal mine safety data assets.

The aim of this study was to develop a system using big data analysis technology to implement an intelligent mine safety risk analysis and early warning model that provides support for identifying and preventing safety risks, classifying hazard sources, formulating a control strategy and emergency response.

In the course of the study, a system for intelligent analysis of security risks and decision making was installed. According to the process of transferring data to information, models for maintenance, management for decision making, the overall architecture of security risk mining and decision making system is formulated, including the infrastructure layer, the data resource layer, the data management layer, the solution analysis layer, and the complex display layer.

The developed intelligent mining safety risk analysis and decision-making system interacts with other existing systems in the mine, including safety monitoring, business safety, human factor, which is undoubtedly a great advantage compared to other systems.

One of the strengths of this article is the detailed explanation of the technical aspects of the study technology. In the course of the study, a safety risk information analysis and decision-making system was built, which provides efficient and intelligent real-time application and analysis services for the safe production of underground metal mines, and the entire information management process of collecting, aggregating, processing, analyzing and visual display of mine safety risk information from multiple sources.

The relevance of the article is beyond doubt. Production safety is a very hot topic in mining, as well as one of the important components of smart mine construction.

Building an intelligent system for safety risk analysis and decision-making is an active exploration of new ideas and safety management systems, which makes the organization and management of the mine more complete, safety management more efficient, and the use of data more profound, forming a new model.

However, the study needs further research, as the current application of security monitoring data in the system is limited to the analysis of environmental monitoring data, and video surveillance data is not used, as reported by the authors.

Undoubtedly, the advantage is that the authors have ideas for future developments and research to improve this system.

The article is well written and well structured, with a clear introduction, methodology, results and discussion section. The authors effectively communicate the results of their research and their significance. The article is the result of the collaboration of several authors with different backgrounds, which enhances the overall quality and depth of the study.

The literature review effectively sets the stage for the authors' own research. The authors present the results of their experiments in a clear and understandable manner, with tables and figures that effectively illustrate the main findings.

The purpose and significance of the study are clearly articulated and the research method is appropriate as it is based on experimental measurements with real examples. This is an important aspect of the article, since the examples of experimental data given by the authors are very interesting. The experimental work is very well explained and contributes a lot to the understanding of the research.

The authors' study is well done and the results are clearly presented, making it a valuable contribution to the field. I would recommend this article to anyone who is interested study intelligent security risk analysis systems. This article is original work that may be published in Sustainability magazine.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx