Proactive Harmful Algal Bloom (HAB) Monitoring & Early Warning

Harmful Algal Blooms (HABs) pose a global challenge to drinking water safety, ecosystem health, and water-dependent economies. Traditional monitoring methods, reliant on manual patrols and fixed stations, suffer from delayed response, limited coverage, and high costs, failing to meet the goal of proactive management through “early detection, early warning, and early action.”

Building upon a mature low-altitude remote sensing framework for aquatic ecology, we introduce the Proactive Harmful Algal Bloom (HAB) Intelligent Monitoring and Early Warning Solution. This solution deeply integrates the Y-1 unmanned autonomous drone-in-a-box network, high-resolution multispectral remote sensing, and SwiftAI water quality inversion models, all orchestrated through the Swift4D integrated platform. It establishes a persistent, air-ground integrated monitoring capability, driving the transformation of water body management from passive response to a data-driven, intelligently-alert, and precise governance model.

Our Differentiated Solution Framework

Our framework is a complete technological closed loop integrating automated sensing, intelligent analytics, and decision management.

1. Fully Autonomous Wide-Area Low-Altitude Sensing Network
The Y-1 Low-Altitude Unmanned System forms the scalable, automated backbone of our monitoring network. A single station, with a 50 km operational radius and single-mission coverage exceeding 7,000 km², enables high-frequency, all-weather data acquisition through 5-minute rapid automatic battery swapping and 7x24 unmanned operation. This overcomes the bottlenecks of traditional satellite remote sensing (long revisit cycles, cloud obstruction) and inefficient manual patrols.

2. SwiftAI-Powered Precision Identification & Inversion Engine
At the core of the solution lies the SwiftAI Intelligence Center. It first employs computer vision to automatically and accurately extract water boundaries from imagery. Subsequently, utilizing validated multispectral remote sensing inversion algorithms, it performs quantitative analysis on the extracted water bodies, accurately calculating key water quality parameters such as Chlorophyll-a (Chl-a), Total Suspended Solids (TSS), and the Trophic Level Index (TLI). Finally, through trained deep learning models, it intelligently identifies the spatial distribution, density, and dynamic trends of algal blooms, achieving the leap from data to actionable insight.

3. Swift4D Integrated Intelligent Management & Decision Platform
The Swift4D Platform serves as the unified "command center," integrating full-chain functionalities including mission planning, data management, AI analytics, and visualization. The platform not only presents a "Unified Water Monitoring Dashboard" for an intuitive overview but also features an embedded intelligent early-warning engine. This engine automatically triggers multi-level alerts based on SwiftAI's inversion results and identification conclusions. It further links with the task management system to achieve a closed-loop digital workflow from anomaly detection and work order dispatch to field verification and feedback, significantly enhancing operational and management efficiency.

Application Scenarios & Client Value