1、 System architecture and module optimization
Modular design enhancement
Adopting a highly modular architecture, the sensor group, data acquisition unit, control module, communication module, etc. are designed as independent functional units, supporting quick replacement and upgrade. For example, the sensor group integrates conventional parameter sensors such as pH, dissolved oxygen, turbidity, conductivity, etc., while reserving interfaces to expand specialized parameter sensors for heavy metals (such as lead and cadmium), organic matter (such as COD, TOC), etc., to meet the needs of different monitoring scenarios.
Core hardware selection upgrade
Sensor technology: Select high-precision and long-term stable sensors, such as dissolved oxygen sensors based on fluorescence quenching principle (error ≤± 0.1mg/L) and graphite electrode conductivity sensors (stability ± 0.5%), to ensure data accuracy.
Data acquisition unit: using industrial grade microprocessors or embedded systems, supporting multi-channel synchronous acquisition, sampling intervals can be set (1-60 minutes), and data efficiency exceeds 99%.
Communication module: Integrated with wireless communication methods such as 4G/5G, LoRa, NB IoT, etc., suitable for remote areas or complex environments, while supporting wired transmission such as Ethernet and fiber optic to ensure real-time data transmission (transmission delay ≤ 5 seconds).
2、 Improvement of data processing and analysis capabilities
Edge computing and cloud collaboration
Local data processing: deploy edge computing units at monitoring stations to realize preliminary filtering, smoothing and outlier marking of data and reduce data transmission. For example, by using built-in algorithms to automatically identify data that exceeds the range or undergoes sudden changes, and triggering a retest mechanism after labeling.
Cloud based big data analysis: Upload data to cloud platforms and utilize cloud computing resources for in-depth analysis, including trend prediction, pattern recognition, and pollution tracing. By using machine learning algorithms such as LSTM neural networks, a water quality change model is constructed to predict the water quality trend for the next 72 hours with an accuracy rate of over 90%.
Intelligent warning and decision support
Multi level warning mechanism: setting threshold warning (triggering first level warning when indicators exceed the standard, SMS+platform notification), trend warning (predicting the risk of exceeding the limit based on data from the past 7 days, triggering second level warning), and regional linkage warning (initiating collaborative warning when multiple monitoring points in the same basin are abnormal).
Automatic report generation: Generate monitoring reports on a daily/weekly/monthly basis, including indicator trend charts, compliance rate statistics, and pollution event analysis. Support exporting in PDF/Excel format to assist decision-making.
3、 Energy management and operation optimization
Low power design and energy self-sufficiency
By using low-power sensors and control units, combined with a solar power supply system (such as 11W solar panels+60Ah lithium batteries) and backup batteries, the system ensures continuous operation for at least 30 days in an unmanned environment.
Optimize the device sleep mode, automatically enter a low-power state during non collection periods, and reduce energy consumption.
Remote operation and fault self diagnosis
Remote control function: supports remote calibration of sensors, starting of samplers, and adjustment of monitoring frequency through mobile apps or PC terminals, with a response time of ≤ 30 seconds.
Intelligent fault diagnosis: The system has a built-in self-test program that regularly checks the status of sensors, communication links, and power modules. When a fault occurs, it automatically locates the problem point (such as sensor blockage or communication interruption) and pushes repair suggestions through the platform.
4、 Deployment form and scenario adaptation
Diversified deployment plan
Immersive sensor deployment: suitable for small scenarios such as wetlands and park water bodies. The sensor is directly deployed into the water body and connected to the host through cables. The host uses 4G network to transmit data. Regular cleaning of probe sediment is required to ensure monitoring accuracy.
Buoy type monitoring station: It uses a buoy body as a carrier, integrates sensors, solar power supply system, and RTU, and is suitable for open water areas such as rivers and lakes. The buoy body is designed with a semi elliptical sphere made of PC material, and the bottom is an aluminum alloy semi cone. It is equipped with a mechanical rotating self-cleaning device to reduce the impact of pollution.
Shore based monitoring station: The box is constructed with color steel or stainless steel material, integrating water collection, distribution, detection and control units, suitable for long-term monitoring scenarios such as water source areas and national and provincial control sections. The water collection unit adopts a float or riverbed buried pipe design to ensure the representativeness of the water sample.
Scenario based parameter configuration
Surface water monitoring: Key monitoring indicators such as pH, dissolved oxygen, turbidity, ammonia nitrogen, total phosphorus, etc., combined with GIS geographic information technology to achieve watershed water quality ranking and pollution traceability.
Industrial wastewater monitoring: Increase monitoring of characteristic pollutants such as heavy metals (such as lead and mercury) and organic compounds (such as volatile phenols and VOCs) to meet environmental discharge standards.
Monitoring of drinking water sources: Integrated biological toxicity or Escherichia coli analyzers to ensure the safety of drinking water.
5、 System Security and Data Management
Data security protection
Adopting encrypted transmission protocols (such as HTTPS, MQTTover TLS) to ensure data transmission security and prevent data leakage or tampering.
Local storage adopts 16MB built-in storage and 256GB TF card expansion, supporting power-off resume function to ensure data integrity.
Data sharing and collaboration
Build an open data interface that supports integration with systems from environmental protection, water conservancy, emergency response, and other departments to achieve real-time data sharing (latency ≤ 5 minutes).
Provide API interfaces to allow third-party platforms to call monitoring data and promote cross departmental collaborative governance.