AI Sensor Defense System for Water Quality Data Diagnosis
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Solution Overview
Problem
Inaccurate sensor data leads to misjudgment in AI prediction and decision-making systems, making it difficult to determine whether system or sensor issues are causing problems, particularly in IoT and AI industries, where sensor issues like signal offset, gradient descent, disconnection, delay, interference, and poor quality warnings are common.
Innovation Solution
An AI automatic sensor system that includes a data correction module for excluding bias data and a categorization module using a processor to determine sensor problems through paired, drifting, missing, zero, update timing abnormality, and unchanged data detection, distinguishing between sensor and system issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is used directly in AI prediction and decision-making systems, then the system operates continuously, but inaccurate sensor data leads to misjudgment and reduced reliability
Solution Approach 1:
The system performs preliminary verification of sensor data before it is used in AI prediction and decision-making. The verification module checks data quality indicators (such as signal strength, data format, and合理性) in advance, and only verified data is transmitted to the AI system. This prevents inaccurate data from causing misjudgment while maintaining system reliability.
Solution Approach 2:
A data verification module is introduced as an intermediary between the sensor and the AI system. This module acts as a mediator that filters and validates sensor data, ensuring that only high-quality data reaches the AI prediction and decision-making systems. The intermediary layer protects the AI system from poor quality data without requiring changes to the core AI architecture.
2Measurement precision
If multiple verification methods are implemented to ensure data quality, then measurement precision improves, but the detection and measurement process becomes more complex
Solution Approach 1:
The data verification process is segmented into multiple independent modules, each responsible for checking specific data quality indicators. For example, one module checks signal strength, another checks data format, and another checks data合理性. This segmentation makes the verification process more manageable and easier to implement while maintaining high measurement precision through comprehensive checking.
Solution Approach 2:
The system verifies data quality by checking multiple parameters simultaneously (signal strength, data format, temporal consistency, etc.). By changing from a single-parameter verification approach to multi-parameter verification, the system achieves higher measurement precision while the modular implementation keeps the process complexity manageable.
3Productivity
If sensor problems are not accurately classified, then the system operates without interruption, but the effectiveness of IoT and AI industry applications is reduced
Solution Approach 1:
The system implements a feedback mechanism where sensor data quality is continuously monitored and evaluated. When quality issues are detected, the system provides feedback about the specific problem type (e.g., signal offset, gradient descent, disconnection, delay, interference). This feedback loop enables accurate classification of sensor problems while maintaining system productivity by identifying and addressing issues systematically.
Solution Approach 2:
The system replaces manual sensor problem diagnosis with an automated AI-based classification system. The AI model analyzes sensor data patterns and automatically identifies the type of sensor problem, replacing what would otherwise require manual inspection and analysis. This substitution preserves diagnostic information while maintaining high productivity through automated processing.
Data Source
AI summary
The present invention provides the novel AI automatic sensor full defense system and method for determining the types of sensor problems in water quality data, which can be applied in the monitoring of the processing system.