Reservoir

Scene Introduction

Ruhr IoT leverages its self-developed smart hardware, large language models, and AI intelligent agent technology to create a comprehensive IoT solution for reservoir safety. Integrating technologies such as visual deformation, GNSS, and rainfall sensing, it conducts all-weather, high-precision safety monitoring of critical reservoir facilities. The system integrates intelligent analysis, risk warning, dispatch assistance, and emergency command capabilities, empowering the intelligent and safe operation and maintenance management of reservoirs.

Demand Analysis

Weak Infrastructure

Outdated equipment result in blind spots in monitoring. The coverage of automated monitoring in small and medium-sized reservoirs is low, and the three-dimensional monitoring system is incomplete. Communication and power supply methods are limited, making them prone to disconnection and data loss in extreme environments

Insufficient Data Governance

Systems from multiple vendors cannot interoperate, creating data silos. Data is disorganized, outdated, and prone to errors and omissions. The lack of high-quality datasets hinders the practical application of AI models

Superficial Technology Application

Data only collected and displayed in a basic manner; the application of AI and digital twin technologies is insufficient. There is a lack of specialized water conservancy-specific models, no intelligent hazard identification and coordinated response capabilities, and reliance on manual inspections, making rapid early warning and response impossible

Program Highlights

Sensing: Integrated Monitoring Framework

Deploy structural safety, water and rainfall monitoring, engineering condition monitoring, and video surveillance equipment to create a comprehensive monitoring and perception system.
Model: Simulation and Deduction

By leveraging AI algorithms and multimodal large models, the deformation and crack trends of the dam body can be simulated and predicted to assist in management decisions.
Surveillance: Real-Time Monitoring System

Establish a multi-dimensional IoT monitoring network to automatically collect data and monitor the safety status of engineering operations in real time.
Analysis: Risk Assessment

Risk levels are classified based on the degree of structural damage, development trend, and potential harm, supporting multi-mode alarms and unified management of early warning information.
Enhancement: Closed-loop Management

Sensors are able to promptly detect abnormal situations and rely on AI intelligent agents to support various tasks such as reservoir operation and maintenance, safety management, scheduling decisions and emergency response.