Research on cooperative technologies in watershed monitoring systems based on end-edge-control-cloud architecture

ABSTRACT

A layered diagram illustrating the EECC (End-Edge-Control-Cloud) architecture in a watershed monitoring system. The architecture is divided into four main tiers. At the bottom, the "End layer" depicts a river basin with sensors and monitoring devices. Above this is the "Edge layer," consisting of local processing nodes represented by two square devices receiving data from the end layer. The next tier is the "Control layer," shown as a platform facilitating centralized coordination and featuring a prominent "AI" cloud icon; text on the right highlights a "Core Innovation: Architecture-Driven Genetic Algorithm (EECC-GA)" and the ability to "Maintain robustness under noisy WAN conditions with jitter and packet loss." At the top is the "Cloud layer," represented by a large cloud containing a globe icon, indicating central cloud computing. Blue arrows indicate the flow of data and communication between the different layers.

Sudden watershed incidents under extreme climate conditions often trigger high-concurrency, spatiotemporally coupled alerts, posing a fundamental challenge to emergency response systems: how to reconcile the agility of distributed sensing with the optimality of centralized decision-making within stringent minute-level deadlines. This tension manifests as a Decision Granularity Gap – a mismatch between the decision-making scale of conventional architectures and the intrinsic coupling scale of hydrological processes. To bridge this gap, we propose the end-edge-control-cloud (EECC) collaborative architecture, whose core innovation is the introduction of a control layer as an intelligent coordination hub aligned with watershed segments. This layer enables regional event graph construction for cascading risk inference and dynamic priority re-ranking, thereby achieving an organic integration of localized rapid response and regional collaborative optimization. Critically, our approach embodies a ‘co-design paradigm’ where the architecture drives algorithm design and algorithms, in turn, empower architectural efficacy. Evaluated on real flood-season data from a major Chinese watershed in 2022, EECC not only reduces average response time by 50% but validates the necessity of a regional coordination layer in large-scale hydro-cyber-physical systems. This work provides both a deployable solution for smart water conservancy and a generalizable methodological framework for hierarchical intelligent decision-making in other spatiotemporal-coupled CPS domains.