Abstract: Taking the intelligent management and control platform for coal mine excavation equipment as the research object, this study adopts a combination of qualitative and quantitative analysis methods. Based on technical means such as literature research, field investigation, and data analysis, combined with the results of expert interviews and questionnaires, it conducts an in-depth analysis of the technical foundation, core value, and implementation path for platform construction. The research shows that through the in-depth integration of multiple technologies including digital twin, 5G communication, and artificial intelligence, a closed-loop management and control system covering the entire life cycle of equipment (from selection, procurement, operation, maintenance to scrapping) is established, realizing the comprehensive integration and intelligent analysis of equipment operation data. At the strategic value level, the platform significantly reduces operating costs through equipment collaborative optimization and intelligent energy consumption control; effectively improves operational safety by building a closed-loop safety management and control system of “monitoring-early warning-disposal”; and maximizes the investment benefit of equipment through full-cycle dynamic management. In terms of technical architecture, the platform operates collaboratively relying on a 5-layer structure (interaction layer, presentation layer, service layer, data layer, and infrastructure layer). It achieves effective integration with production management and control platforms, enterprise asset management (EAM), enterprise resource planning (ERP) and other systems through standardized data interfaces, ensuring data sharing and functional collaboration. The study demonstrates the necessity of equipment upgrading from dimensions such as the difference between economic life and physical life, and safety redundancy evaluation, and scientifically formulates maintenance and renewal strategies based on the performance of equipment in different service stages. Aiming at key problems such as extensive equipment investment decisions, high failure rates, excessively high maintenance cost ratios, and fragmented data across multiple systems, an investment decision model based on equipment economic analysis and a predictive maintenance strategy are proposed. The platform adopts a “pilot-iteration” implementation model. On the basis of the achievements in the intelligent upgrading of shearers, it provides data foundation and technical support for the subsequent collaborative optimization of the “three machines” (shearer, scraper conveyor, and hydraulic support) and the intelligent operation and maintenance of hydraulic supports, forming an overall solution for the full-life cycle management of equipment.