Abstract: In the broader context of the digital and intelligent transformation of ecological and environmental monitoring,intelligent laboratories are rapidly transitioning from localized pilot projects to large-scale applications. Their core operational characteristics including full-process automation, algorithm-driven decision-making, and full-chain data traceability are posing fundamental challenges to the traditional “human-controlled” quality management model. Meanwhile,current laboratory quality management systems struggle to effectively address the novel risk points derived from intelligence,such as algorithm validation,data security,and system synergy. Based on a systematic review of relevant domestic and international standards and practical cases,this paper identifies the systemic risks in the promotion and application of intelligent laboratories. Consequently,it proposes a full-life-cycle quality management system and an intelligent control framework covering seven key elements: personnel, machine, material,method,environment, measurement, and data. This study aims to provide a theoretical basis and practical reference for the standardized construction, quality control mechanism design, and data security assurance of intelligent laboratories, thereby facilitating the paradigm shift from “human-controlled ” to “intelligence-controlled ” in the ecological and environmental monitoring quality management.