Event Detection Using Life Cycle Models and Nutrition Functions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional event detection methods directly detect original values and often ignore potential conditions, leading to inaccurate event tracking and a high likelihood of false alarms, as they do not effectively account for event strength progression and evolution.
Innovation Solution
An event detection method and system that utilizes historical event data, a nutrition growing function library, and firing point rules to calculate strength value variations, determining if these correspond to historical event variations, and using a life cycle model to accurately identify event firing points and deliver relevant notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional event detection methods directly detect original values and use rule-based determination, then the detection process is simple and intuitive, but potential conditions for generated events are ignored leading to false alarms
Solution Approach 1:
The patent segments the event detection process into multiple stages: initial event generation using rule-based methods, event strength calculation using nutrition growing functions, and final event determination using life cycle models. This segmentation allows the system to maintain simplicity in event generation while adding sophisticated analysis stages to improve accuracy and reduce false alarms.
2Ease of manufacture
If conventional methods use rule-based event determination, then the method is easy to implement, but the ability to track event progression and evolution is limited
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing nutrition growing functions and life cycle models based on historical event data before actual event detection. This allows the system to have sophisticated event tracking and evolution capabilities ready in advance, while maintaining easy implementation through pre-prepared computational frameworks.
3Ease of operation
If conventional methods detect events based on original values only, then the detection is straightforward, but false alarms increase due to ignoring event strength progression
Solution Approach 1:
The patent introduces nutrition growing functions and life cycle models as intermediary elements between raw event data and final event determination. These intermediaries process and evaluate event strength progression, filtering out false alarms while maintaining straightforward detection through the structured intermediate evaluation layers.
Data Source
AI summary
An event detection method is disclosed. At least one most adaptable life cycle model is generated according to at least one historical event data, at least one nutrition growing function, and at least one firing point rule. Event data is received and a strength value thereof is calculated according to a life cycle model corresponding to the event data. It is determined whether an event firing point is achieved according to the strength value variation. If the event firing point is achieved, an event corresponding to the event data is sent. The event detection method enhances the ability of event tracking and development so event firing is more accurate to fit real event occurring situations, realize event evolution, and filter false alarms.