基于大数据与AI技术的西南河流梯级开发水生境变异识别与高维度、高动态生态调度研究——以乌江梯级为例

Application of big data modeling and AI technology to construct river cascade development holographic big data and hydrological-hydrodynamic-water environment-water ecological multidimensional information mining to study river hydrology, hydrodynamics, water environment and water ecological factors before and after river cascade development Evolution. Identify the most active and most distinctive habitat factors in the river habitat system before and after the cascade development. To explore the space-time evolution mechanism and basic characteristics of the process of ecological and environmental impacts on the development of the southwest river cascade, identify its driving factors and intrinsic law of evolution, and reveal the sensitivity and vulnerability based on the dual constraints of cascade development and water ecological environmental health requirements. The dynamic game relationship between multi-dimensional water demand, and the use of artificial intelligence technology for deep learning, establishes the self-adaptive adjustment of hydrological-hydrodynamic-water environment-water ecological mutual feedback of river systems and the high-dimensional, high-dynamic water games. Coordinated regulation, adaptive control to achieve the fine configuration of the water system. Taking the Wujiang Cascade Development as an example, this paper proposes the distribution and scheduling modes of the water system elements of the southwest river cascades under the water ecological background of the Yangtze River Economic Belt. Taking the southwest river cascade development as an example, the Yangtze River Economic Belt is proposed to “grasp the protection and not engage in "Development" measures for water ecology, water environment, and water safety.

应用大数据及AI技术构建河流梯级开发全息大数据库并进行水文-水动力-水环境-水生态多维信息挖掘，识别梯级开发前后河流生境系统中最活跃、变异特征最明显的生境因子，研究河流水文、水动力、水环境与水生态要素在河流梯级开发前后的变异情况，探究西南河流梯级开发对生态和环境影响过程的时空演化机理和基本特征，及其演化的驱动因素和内在变异规律，揭示在梯级开发和水生态环境健康要求双重约束下，河流发电、供水、灌溉、航运、生态等高维度用水需求之间的动态博弈关系，并使用人工智能技术深度学习，建立河流水系统水文-水动力-水环境-水生态各环节互馈作用的自适应调节以及高维度、高动态水博弈的协同调控模型，以自适应调控实现河流梯级开发水系统高动态化生态调度。最终以乌江梯级开发为例，提出在长江经济带生态保护背景下的西南河流梯级开发水系统各要素分配及调度模式和长江经济带“共抓大保护、不搞大开发”的水生态、水环境措施建议。

大数据与AI智能算法在水文水生态环境领域，尤其是河流梯级开发与生态调度方面研究较少。本项目基于大数据模型架构，建立了涵盖生态要素、工程要素、水文气象要素、水环境要素、经济社会等主要要素的河流全息大数据库，全面识别并分析了河流梯级开发前后的变异情况。识别乌江上游平寨水库建库前后河流生境系统中最活跃、变异特征最明显的水环境因子。通过乌江流域水电开发对评价区陆生植被影响后评价研究、贵州黔中地区土壤和植物要素与喀斯特生态系统退化的关系研究、乌江流域水电开发对国家重点保护植物及古树影响的后评价研究，乌江流域水电开发对水生系统相应关系研究。建立了水资源、水环境、水生态三元动态博弈模型，分析水资源采取/不采取节水措施，水环境采取/不采取治污措施，水生态保障/不保障生态流量下放的动态均衡演化过程。基于机器学习的人工智能算法，建立乌江梯级的来水与水动力-水环境-水生态不同纬度的互馈机制，进行河流梯级开发高维度、高动态、高智能水系统模拟，建立了机器学习的人工智能自适应协同调控模型系统。