Implicit reservoir operation rules map known reservoir conditions to discharge flow or water level decisions to guide reservoir operation. Existing data-driven operation rule extraction models are influenced by both features and hyperparameters, and lack subjective-objective coordination, hindering the formulation of reasonable water energy utilization strategies. To improve the comprehensive benefits of cascade reservoirs, this study proposes a novel multi-objective cascade reservoir operation rule derivation framework. First, a multi-objective optimal operation model for cascade reservoirs (MOOMCR) considering power generation, environment, and navigation is constructed and solved using a Speed-constrained Multi-objective Particle Swarm Optimization algorithm (SMPSO). The relationships among the requirements were explained using a four-dimensional Pareto front visualization analysis. Then, an Integrated Decision-making Method based on Multilevel Subspace Domination (IDMSD) is proposed to circumvent the subjective defects of single decision-making models and reliably select the optimal scheme with the most significant comprehensive benefits from all typical years. Finally, a feature-hyperparameter combination optimization technique based on the differential evolutionary algorithm (FHCC-DE/best/1) is proposed, which exhibits its strength in the identification of key influencing factors on the optimal discharge flow from the IDMSD. The method was embedded in a deep learning model to construct a Combinatorial Evolution Network (CEN) to extract the implicit reservoir operation rules from the optimal IDMSD schemes for the MOOMCR. The results show that the RMSE of the CEN simulating the optimal discharge flow is improved by 5.98%, 28.03%, and 27.89% for the XLD reservoir and by 11.01%, 1.66%, and 60.98% for the XJB reservoir, respectively, over the best results of all benchmark models. Meanwhile, the operation simulation results confirmed that the CEN outperforms conventional reservoir scheduling, with advanced multi-objective comprehensive effects. This suggests that multimethod integrated decision-making and deep learning evolution optimization can help improve the accuracy, generalization, and reliability of implicit reservoir operation rules.

隐式水库运行规则将已知水库条件映射到排放流量或水位决策以指导水库运行。现有数据驱动的运行规则提取模型受特征和超参数的双重影响，缺乏主客观协调性，阻碍了合理的水能利用策略的制定。为提高梯级水库综合效益，本研究提出了一种新颖的多目标梯级水库运行规律推导框架。首先，使用速度约束多目标粒子群优化算法 (SMPSO) 构建并求解考虑发电、环境和导航的梯级水库 (MOOMCR) 多目标优化运行模型。使用四维帕累托前沿可视化分析解释了需求之间的关系。然后，提出了一种基于多级子空间支配（IDMSD）的综合决策方法，以规避单一决策模型的主观缺陷，可靠地从所有典型年份中选择综合效益最显着的最优方案。最后，提出了一种基于差分进化算法（FHCC-德/最好/1) 被提出，这在识别 IDMSD 最佳排放流量的关键影响因素方面具有优势。该方法嵌入到深度学习模型中以构建组合进化网络 (CEN)，从 MOOMCR 的最优 IDMSD 方案中提取隐式水库运行规则。结果表明，CEN 模拟最优流量的 RMSE 分别比 XLD 水库提高了 5.98%、28.03% 和 27.89%，XJB 水库分别提高了 11.01%、1.66% 和 60.98%。所有基准模型的最佳结果。同时，运行模拟结果证实，CEN优于常规水库调度，具有先进的多目标综合效果。