Deepshift: AI-Powered Shift Management and Safety Analytics on an Immutable Blockchain

Abstract

Underground coal mining requires shift information that is timely, accurate, and verifiable. However, many sites still rely on fragmented paper workflows that are difficult to validate and slow to audit. This paper presents Deepshift, an Artificial Intelligence (AI)-assisted digital governance platform for regulated mine environments. The system combines role-based workflow control, structured shift documentation, automated cross-role validation, and tamper-evident integrity anchoring to improve safety communication and compliance confidence. Unlike standalone tools that focus only on digitization or analytics, Deepshift integrates operational capture (pre-shift reports, attendance, shift logs, incidents, and checklists), intelligence support (Natural Language Processing (NLP)-enabled AI-generated shift summaries), and trust infrastructure (Secure Hash Algorithm 256 (SHA-256) hashing with blockchain anchor metadata) in one lifecycle. The implementation uses Flutter and Firebase with modular service layers for shift processing, AI summarization, report generation, and integrity verification. Feature-wise validation indicates improved handover clarity, stronger supervisory continuity, and better audit traceability. The system also includes escalation pathways for disputes and safety incidents, supporting transparent resolution and regulator-facing review.

Introduction

Coal mine operations involve complex, safety-critical workflows with multiple roles, where traditional paper-based reporting leads to delays, inconsistencies, and weak auditability. These limitations create gaps between field observations and compliance visibility, making it difficult to ensure reliable decision-making and traceability.

To address this, the proposed system, Deepshift, introduces a digital, cloud-based governance platform that integrates role-based workflows, AI-assisted analysis, and tamper-evident record management. The goal is to transform fragmented paper records into a continuous, verifiable, and transparent operational lifecycle.

The problem lies in unstructured reporting, lack of real-time validation, weak record integrity, and poor escalation tracking. Existing solutions often focus on isolated aspects like monitoring or analytics, but fail to combine workflow control, intelligence, and audit reliability into a unified system.

Deepshift overcomes these challenges through a structured methodology that includes role-based authentication, pre-shift data capture, standardized logging, cross-role validation, AI-generated summaries, supervisory approval, and cryptographic record verification. This approach enhances safety, improves consistency, and ensures stronger compliance and audit readiness in coal mine operations.

Conclusion

This paper presents Deepshift as an AI-assisted and tamper-evident digital governance framework for underground mine shift management. The proposed system addresses known weaknesses of manual workflows by combining structured operational capture, cross-role validation, intelligent summarization, and cryptographic integrity support. Results from prototype implementation indicate meaningful gains in handover clarity, supervisory continuity, and audit confidence. The study suggests that future mine-safety software should be evaluated as integrated governance infrastructure rather than isolated modules. Future work will focus on long-duration field trials, quantitative before-and-after benchmarking, enhanced predictive safety analytics, and broader interoperability with institutional compliance ecosystems.

References

[1] Government of India, “The Mines Act,” 1952. [2] Directorate General of Mines Safety (DGMS), “The Coal Mines Regulations,” 2017. [3] Verma and Chaudhari, “Safety of Workers in Indian Mines: Study, Analysis, and Prediction,” 2017. [4] Tripathy and Ala, “Identification of Safety Hazards in Indian Underground Coal Mines,” 2018. [5] Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. [6] International Organization for Standardization (ISO), “ISO 45001:2018 Occupational Health and Safety Management Systems,” 2018.

Copyright

Copyright © 2026 Vedant Bagave, Pallavi Bajpai, Priyanka Bhopi, Vaishnavi More, Kirti Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.