船载海洋调查装备数据质控系统构建与实践应用OA

Observation data acquired by shipborne marine survey equipment serve as a critical foundation for marine resource exploration,ecological environment monitoring,and marine scientific research.The quality of these data directly determines the reliability and application value of survey outcomes.However,the traditional data quality control(QC)model has long been plagued by prominent issues,including fragmented standards,heavy reliance on manual operations,and poor timeliness,which renders it incapable of meeting the core requirements of modern marine surveys for high precision,high efficiency,and high reliability of data.To address these issues and achieve full-process anomaly identification and closed-loop quality management and control of raw observation data from shipborne survey equipment,this study establishes a three-dimensional QC technical framework based on"element-standard-process".It breaks through the key technologies of full-chain visual quality control and non-intervention automation,and develops an intelligent QC system featuring"real-time+scheduled"dual modes.This achieves a fundamental transformation of the QC model from post-hoc error correction to real-time intelligent management and control.The system has been successfully demonstrated and applied in nearly 20 scientific research cruises aboard the Research Vessel Kexue.Field tests show that the error detection rate for on-site equipment reaches over 89％,the time required for manual QC is reduced by more than 90％,and the QC timeliness is shortened from several days post-voyage to real-time or hourly scheduled intervals.The research findings provide technical support for the high-quality acquisition and reliable application of data from shipborne marine survey equipment,and lay a solid foundation for the intelligent upgrading of such equipment.