标题： 一种仿生方法用于珊瑚礁启发的群体智能实现碳中和污水处理 显示英文标题

标题： A Biomimetic Way for Coral-Reef-Inspired Swarm Intelligence for Carbon-Neutral Wastewater Treatment

摘要： 随着废水处理费率的增加，实现能源中性净化具有挑战性。 我们引入了一种受珊瑚礁启发的群体交互网络，用于碳中和废水处理，结合形态发生抽象与多任务碳意识。 可扩展性源于线性标记复杂度，缓解了能耗问题。 与七个基线方法相比，我们的方法实现了96.7%的去除效率，0.31~kWh~m$^{-3}$的能耗，以及14.2~g~m$^{-3}$ CO$_2$排放。 方差分析表明在传感器漂移下具有鲁棒性。 现场场景——岛屿泻湖、酿酒厂峰值和沙漠温室——显示出最高可达22%的柴油节省潜力。 然而，数据科学人员配置仍然是一个障碍。 未来的工作将在项目范围内集成AutoML包装器，尽管治理限制带来了可解释性挑战，需要进一步的可视化分析。 显示英文摘要

摘要： With increasing wastewater rates, achieving energy-neutral purification is challenging. We introduce a coral-reef-inspired Swarm Interaction Network for carbon-neutral wastewater treatment, combining morphogenetic abstraction with multi-task carbon awareness. Scalability stems from linear token complexity, mitigating the energy-removal problem. Compared with seven baselines, our approach achieves 96.7\% removal efficiency, 0.31~kWh~m$^{-3}$ energy consumption, and 14.2~g~m$^{-3}$ CO$_2$ emissions. Variance analysis demonstrates robustness under sensor drift. Field scenarios--insular lagoons, brewery spikes, and desert greenhouses--show potential diesel savings of up to 22\%. However, data-science staffing remains an impediment. Future work will integrate AutoML wrappers within the project scope, although governance restrictions pose interpretability challenges that require further visual analytics.