Monitoring Control System for Automated Phenomena Detection
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Solution Overview
Problem
Existing monitoring systems require manual intervention to detect phenomena and determine appropriate actions, leading to inefficiencies in processing and response times.
Innovation Solution
A monitoring control system that automatically detects phenomena from multiple data sources using predefined rules and outputs control information for notifications or predetermined actions, integrating data collection, phenomenon detection, and action determination in a sequential processing flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to detect phenomena and determine actions, then accuracy can be maintained through human judgment, but response time increases and processing efficiency decreases
Solution Approach 1:
The system pre-defines multiple phenomena types with their detection rules and multiple action types with determination rules before operation. When data is received, the system automatically matches it against these pre-defined patterns to detect phenomena and determine actions, eliminating the need for manual analysis while maintaining accuracy through comprehensive pre-configured rules
Solution Approach 2:
The patent replaces the mechanical manual intervention system with an automated information processing system. The phenomenon detecting section and determination section use algorithmic rule-based processing to substitute human judgment, achieving both speed (automated processing) and accuracy (pre-defined comprehensive rules)
2Productivity
If automated processing is implemented to reduce manual intervention, then response time improves, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a phenomenon detecting section that identifies phenomena using detection rules, and a determination section that selects actions using determination rules. This modular segmentation allows each section to handle specific tasks independently, simplifying the overall system architecture while maintaining automated processing efficiency
Solution Approach 2:
The patent introduces rule sets as intermediary elements between raw data and decision-making. The detection rules and determination rules act as intermediaries that translate complex data patterns into actionable insights, reducing the complexity of direct automated processing by providing structured intermediate representation
3Measurement precision
If comprehensive data from multiple sources is processed, then detection accuracy improves, but data processing complexity and time increase
Solution Approach 1:
The system pre-defines detection rules that specify what constitutes each phenomenon type across multiple data sources. These pre-configured rules establish clear criteria for interpreting data from various sources, enabling accurate detection without requiring complex real-time analysis of all possible data combinations
Solution Approach 2:
The determination rules serve as a universal mechanism that can evaluate multiple data sources and detected phenomena against a common set of action criteria. This universal rule-based approach allows the system to handle diverse data inputs through a unified processing framework, reducing overall system complexity
Data Source
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AI summary
[PROBLEM] To provide a monitoring control system for sequentially processing data collected from various data sources to find (detect) a possible phenomenon, producing an appropriate action (event) for the found change or phenomenon, and outputting control information for causing a system to perform a notification or predetermined control. [SOLVING MEANS] A monitoring control system according to an embodiment includes a phenomenon detecting section configured to detect a phenomenon leading to a particular result from contiguous data received from data sources serving as data transmission element based on detecting rule definition data previously specifying a change attribute of contiguous time-series data pieces for each phenomenon leading to a particular result, and a determination section configured to extract action information associated with the particular result detected by the phenomenon detecting section to produce an event for a predetermined system based on action definition data previously specifying action information for each particular result.