河湖透彻感知的理论与技术体系框架OA

Addressing the governance demands for river and lake restoration,ecological health,and supervision,tra-ditional monitoring mainly focuses on displaying phenomenal indicators,struggling to reveal underlying evolutionary mechanisms.This study introduces the concept of"Genuine River-Lake"as the direct object of thorough perception,emphasizing the integrated representation of the physical space,river-lake material flow processes,and life charac-teristics of rivers and lakes,as well as the continuous depiction of their historical evolution,current state,and future scenarios.By overcoming the spatiotemporally limitations of traditional monitoring methods,this approach enables a deep understanding and systematic reconstruction of the entire process and all elements of the healthy life of rivers and lakes.Unlike existing studies that focus on state visualization and process reproduction,this paper concentrates on building the capability to connect phenomenon observation,key parameter acquisition and scenario projection,establishing a theoretical and technical framework for river-lake thorough perception oriented towards mechanism analysis and scenario projection assessment.The framework covers four cardinal features of"Thorough Perception":"Throughness"emphasizes penetrative sensing to acquire hidden state variables and key model parameters;"Com-pleteness"emphasizes comprehensiveness and holographic representation to form a spatiotemporally continuous data foundation across the entire domain;"Sensing"emphasizes extracting scene-specific features and driving informa-tion;and"Cognition"emphasizes interpretation and scenario projection under the constraints of mechanism consis-tency.Furthermore,a technical pathway driven by scenarios,empowered by AI,and enabled by model projection is proposed:perception elements,indicator systems,and monitoring networks are customized around key river and lake governance scenarios;key parameters are identified and aggregated through data fusion and joint inversion-assimilation;mechanism models and physics-informed large models collaboratively enhance projection robustness under unprecedented and unexpected conditions;and interactive scenario presentation is achieved through Q&A-style analysis and immersive reality technologies.The proposed framework offers a theoretical and technical founda-tion for decision support in river and lake governance.