摘要
环境噪声监测数据的质量直接影响着环境管理与政策决策的科学性。然而,现有监测体系在完整性、准确性、可追溯性及异常值识别方面存在着一定的局限性,难以满足精细化管理的要求。为此,应构建全流程质量控制体系,优化数据采集、传输、处理及存储环节,并引入智能技术以提升异常值识别和数据溯源能力。本文还提出了适应性优化模型,以实现质量控制体系的动态调整,提升智能化管理水平。同时,应建立多层级协同管理机制,加强数据共享与反馈,提高质量控制效能,并基于多维度评估体系,量化优化效果,确保体系能够长期稳定地运行。该研究成果为环境噪声监测的质量管理提供了系统化方案,提升了监测数据的科学价值及应用的可行性。
The quality of environmental noise monitoring data directly affects the scientificity of environmental management and policy decision-making.However,the existing monitoring system has limitations in completeness,accuracy,traceability,and outlier identification,making it difficult to meet the requirements of refined management.To this end,a full process quality control system should be established,optimizing data collection,transmission,processing,and storage processes,and introducing intelligent technology to enhance the ability to identify outliers and trace data.This article also proposes an adaptive optimization model to achieve dynamic adjustment of the quality control system and enhance the level of intelligent management.At the same time,establish a multilevel collaborative management mechanism,strengthen data sharing and feedback,improve quality control efficiency,and quantify optimization effects based on a multidimensional evaluation system to ensure long-term stable operation of the system.The research results provided systematic solution for the quality management of environmental noise monitored,enhanced the scientific value and application feasibility of monitoring data.
作者
刘中军
Liu Zhongjun(Zhenlai County Ecological Environment Monitoring Station,Baicheng 137300,China)
出处
《皮革制作与环保科技》
2025年第18期71-73,共3页
Leather Manufacture and Environmental Technology
关键词
环境噪声监测
数据质量控制
智能化优化
异常值识别
协同管理
environmental noise monitoring
data quality control
intelligent optimization
anomaly detection
collaborative management
