Data Processing Device for Automated IoT Monitoring
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Solution Overview
Problem
Current data monitoring and analysis systems require manual user intervention to select and order data items, making it difficult to automate the process, especially with the increasing complexity of IoT data monitoring.
Innovation Solution
A method and device for processing time-series data that identifies data item types, processes the data using specific calculations, adds new data items, and calculates scores to automate the selection and ordering of data items based on information display on a screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and ordering of data items is performed, then data display accuracy is improved, but automation level deteriorates
Solution Approach 1:
The system automatically identifies data item types, selects relevant data items, and determines display order without requiring manual user intervention. The data processing device performs self-service by autonomously analyzing data characteristics and making selection decisions based on predefined criteria and algorithms.
Solution Approach 2:
The system changes parameters such as data item type identification, selection criteria, and display order based on data characteristics. By dynamically adjusting these parameters according to the data being processed, the system achieves both automation and accurate data display.
2Extent of automation
If automated data selection is implemented, then automation level is improved, but data display accuracy deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the data processing device continuously evaluates the effectiveness of automated selections and adjusts its algorithms accordingly. By analyzing user interactions and data patterns, the system refines its selection criteria to maintain high display accuracy while preserving automation.
Solution Approach 2:
The system performs preliminary actions by pre-defining data item types, selection criteria, and processing rules before actual data processing occurs. This preparation enables the automated system to make accurate selections without real-time manual intervention, maintaining both automation level and display accuracy.
3Loss of information
If multiple data items are monitored, then information completeness is improved, but device complexity deteriorates
Solution Approach 1:
The system segments data items into distinct types with specific characteristics and processing rules. By categorizing data into manageable segments, the system can monitor multiple data items efficiently without overwhelming complexity, as each segment can be processed according to its specific type.
Solution Approach 2:
The data processing device is designed with universal capabilities to handle multiple data item types through a unified framework. By implementing multi-functional processing logic that can adapt to different data types, the system monitors comprehensive information while avoiding the complexity of separate processing systems for each data type.
4Loss of information
If data processing calculations are added, then information quality is improved, but processing time deteriorates
Solution Approach 1:
The system applies partial processing by performing only the necessary calculations required for each data item type rather than exhaustive processing of all possible operations. By selectively applying processing operations based on data priorities and requirements, the system maintains information quality while reducing overall processing time.
Data Source
AI summary
The method includes: identifying a type of a data item in which the data is stored, using an overlap pattern indicating the type of the data item and a method for identifying the type; processing the data stored in the data item, using calculation designated for each type of the data item, and adding at least one or more new data items to the type of the data item storing the processed data; and calculating scores obtained by quantifying an amount of information displayed on a display screen for the data items including the added data items and arranging the data items on the basis of the scores.