一种基于AI的海洋生态调查记录方法及系统
By using an AI-based marine ecological survey and recording method, multi-source data is received and processed in real time. Combined with an ecological disturbance rule base, dynamic adjustments are made to generate structured records. This solves the problems of insufficient efficiency and data reliability in existing marine ecological surveys, and realizes efficient and sustainable intelligent surveys.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- URBAN PLANNING & DESIGN INST OF SHENZHEN UPDIS
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-17
AI Technical Summary
Existing marine ecological survey technologies struggle to achieve efficient, accurate, and sustainable intelligent surveys. In particular, in complex and ever-changing marine environments, seamless collaboration and real-time optimization of the entire process of data collection, intelligent identification, and structured recording are difficult to achieve. Furthermore, the potential impact of observational behavior on data authenticity is not incorporated into system regulation, resulting in limited improvements in survey efficiency, data consistency, and the credibility of results.
An AI-based marine ecological survey and recording method is adopted. By receiving multi-source data in real time, combining it with an ecological disturbance rule base for disturbance assessment, dynamically adjusting the data collection operation parameters, and using an AI model cluster for real-time processing, structured survey records are generated and synchronized to a cloud platform for management and iterative optimization.
It achieves integrated and synchronous acquisition of multi-source data, actively minimizes interference with the observation target, ensures the availability and accuracy of data, generates a complete and traceable data evidence chain, and has self-evolution capabilities through iterative optimization of the cloud platform, thus solving the problem of insufficient ecological adaptation capabilities in traditional technologies.
Smart Images
Figure CN122087371B_ABST