Dynamic Parameter Collection Tuning for Incident Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information technology infrastructure management systems lack efficient methods for predicting and preventing incidents such as security breaches and hardware failures, often resulting in unexpected downtime and increased maintenance costs.
Innovation Solution
An adaptive and dynamic monitoring platform that uses distributed sensors to collect and analyze data parameters, applying machine learning models to detect incident patterns and dynamically adjust data collection to focus on essential parameters, enabling proactive preventative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection is performed to detect all potential incidents, then detection accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The monitoring system is segmented into multiple independent modules: data collection module, machine learning model module, and incident detection module. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive monitoring capabilities through distributed sensor nodes.
Solution Approach 2:
Machine learning models are trained in advance on historical data to learn incident patterns before actual monitoring begins. This preliminary training enables the system to detect incidents efficiently during operation without requiring complex real-time analysis of all collected data, thus reducing operational complexity.
2Reliability
If continuous monitoring of all parameters is performed, then reliability is improved, but energy consumption increases
Solution Approach 1:
The system performs periodic data collection at scheduled intervals rather than continuous monitoring. Sensors collect data at defined frequencies, and the machine learning model processes data periodically, reducing energy consumption while maintaining sufficient monitoring coverage for reliable incident detection.
Solution Approach 2:
The machine learning model automatically determines which parameters require attention based on learned patterns, enabling the system to self-adjust monitoring intensity. When incidents are detected, the model automatically triggers focused data collection on relevant parameters, reducing overall energy consumption while maintaining high reliability for critical events.
3Measurement precision
If detailed data collection is performed to verify incident patterns, then detection accuracy is improved, but response time increases
Solution Approach 1:
The machine learning model performs preliminary analysis of incoming data streams to identify potential incident patterns before full verification is required. This preliminary detection allows the system to prepare verification procedures in advance, reducing the time needed for detailed data collection and analysis when incidents are suspected.
Solution Approach 2:
The system dynamically adjusts data collection intensity based on incident likelihood. When the machine learning model detects patterns suggestive of incidents, the system automatically increases data collection frequency and detail for verification. During normal operation, data collection operates at lower intensity, reducing overall response time while maintaining verification accuracy when needed.
Data Source
AI summary
Collected data of a first set of parameters is received via a network from one or more devices. Using machine learning, at least a portion of the collected data of the first set of parameters is analyzed to automatically identify one or more additional data parameters to be obtained to verify a detection of an incident pattern. The one or more additional data parameters are indicated to be obtained to at least a portion of the one or more devices. Collected data responsive to the indicated one or more additional data parameters is received. Based at least in part on the responsive collected data, the detection of the incident pattern is verified and a responsive action is performed.