IOT News

In water pollution control efforts, monitoring scenarios such as drainage outlets and river inlets is the first step. Establishing an efficient and reliable online monitoring system for sewage outlet water quality enables the rapid identification of abnormal fault information, facilitating quick location, diagnosis, and implementation of measures to ensure timely control of water pollution and prevent the escalation of pollution incidents.

By connecting water quality monitors at various factory sewage outlets through a hydraulic and hydrological gateway RTU, key water quality indicators such as permanganate, chemical oxygen demand (COD), ammonia nitrogen content, phosphorus content, and nitrogen content are collected in real time.

The collected data is then transmitted to a remote monitoring and management platform via 4G networks using protocols such as HJ212, MQTT, and Modbus, enabling real-time monitoring, early warning, and efficient management to provide scientific decision-making support for ecological and environmental protection efforts.

Implemented Functions

1. The hydraulic and hydrological gateway RTU accurately collects key water quality indicators and transmits the data to the remote platform via 5G/4G networks in accordance with SL651, Modbus, and other protocols, ensuring real-time and accurate upload of data even from remote sewage outlets.

2. The remote platform analyzes and processes the data in real time, displaying it in charts and curves, and annotates the locations and water quality conditions of monitoring points on maps using GIS technology, allowing management personnel to grasp the regional water quality distribution at any time.

3. An integrated water quality standard threshold library enables real-time comparison and analysis of data. When thresholds are exceeded, early warnings are triggered and relevant personnel are notified through interface prompts, SMS, email, WeChat, etc., to prevent water quality deterioration.

4. The platform stores water quality data over the long term to form a historical database. Management personnel can flexibly set conditions for querying and analyzing long-term trends in water quality, providing data support for governance plans.

5. Algorithmic models are employed to deeply mine and analyze data, identify the distribution and migration patterns of pollution sources, predict water quality changes, generate reports and recommendations, and assist in environmental protection decision-making and enterprise production optimization.