Water Treatment Module Control for Usage-Based Filter Replacement
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Solution Overview
Problem
Small-scale water treatment equipment, such as household water purifiers, face challenges in maintaining water treatment functionality due to varying water usage patterns and demands, leading to inefficient filter replacement and maintenance.
Innovation Solution
A water treatment system that includes a network of water treatment apparatuses and a server that acquires data on water usage patterns, generates a trained model to predict demand, and adjusts the combination of water treatment modules based on actual usage and environmental factors, allowing for dynamic module selection and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filter replacement is based on fixed water treatment volume, then water treatment function can be maintained, but frequent replacement occurs for high usage scenarios reducing efficiency
Solution Approach 1:
The system dynamically adjusts filter replacement timing based on actual water usage patterns detected by sensors. Instead of fixed intervals, the replacement schedule adapts to real-time consumption data, allowing extended intervals for low-usage households while ensuring timely replacement for high-usage scenarios, thus optimizing both reliability and productivity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring water usage through sensors and adjusting filter replacement decisions based on this feedback. The server receives usage data from multiple sources, analyzes it, and provides optimized replacement recommendations that reflect actual system conditions rather than predetermined schedules
2Productivity
If water treatment equipment is customized for each household usage pattern, then treatment efficiency improves, but system complexity increases
Solution Approach 1:
The system employs a universal server platform that handles multiple functions: data collection from various sensors, usage pattern analysis, filter replacement optimization, and equipment control. This centralized multi-functional approach allows customization for each household without requiring separate complex systems, achieving high treatment efficiency while managing complexity through consolidation
Solution Approach 2:
The server acts as an intermediary between the water treatment equipment and the user environment. It collects data from sensors, processes usage patterns, and translates this information into optimized control decisions for the treatment equipment, simplifying the overall system architecture while enabling personalized treatment for each household
3Measurement precision
If multiple sensors are deployed to track usage patterns, then resource allocation accuracy improves, but device complexity and cost increase
Solution Approach 1:
The system merges data from multiple sensor sources (water flow sensors, usage pattern detectors, environmental sensors) into a unified analysis framework on the server. By combining these measurement inputs and processing them collectively, the system achieves high measurement precision for resource allocation while avoiding the complexity of separate processing systems for each sensor type
Data Source
AI summary
A water treatment apparatus comprising a control unit is provided. The control unit functions as: a module for receiving at least one of a data set relating to an environment in which a water treatment apparatus operates or a data set relating to water treatment of the water treatment apparatus in the environment; and a module for generating a trained model based on the acquired data set.