Automated Network Condition Identification via Location-Based Grouping
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Solution Overview
Problem
Monitoring and maintaining large data networks is complex and labor-intensive, often requiring manual dispatch of technicians to identify and correct issues, leading to delayed problem resolution and inadequate information about problem sources or extents.
Innovation Solution
An automated system that analyzes performance data from multiple network devices, identifies groups experiencing problems by location, and communicates this information for monitoring by service personnel, using a network polling server, subscriber database server, and analysis server to generate tables and perform algorithms that flag problem zones and update event databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service personnel are dispatched to each customer location to identify and correct plant-related network issues, then problems can be corrected, but response time is delayed and labor costs increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network devices and automatically identifying problems before customers report them. The automated system proactively detects plant-related issues, determines affected customer locations, and prepares service dispatch information in advance, eliminating the waiting period for customer complaints and technician availability.
Solution Approach 2:
The system enables self-service by automating the problem identification and analysis functions that previously required human technicians. The automated network monitoring system independently polls devices, analyzes performance data, identifies plant-related issues, and generates service information without human intervention, freeing technicians from routine diagnostic tasks.
2Reliability
If service personnel are dispatched to each customer location, then plant-related problems can be identified and corrected, but information about the source and extent of problems is inadequate
Solution Approach 1:
The system implements feedback by continuously polling network devices for performance data and using this information to automatically identify plant-related problems. The automated analysis of performance metrics provides detailed feedback about problem sources, affected devices, and extent of issues, enabling informed service dispatch decisions without relying on incomplete customer complaints.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between network devices and service personnel. This intermediary automatically polls devices, analyzes performance data, identifies plant-related issues, and synthesizes comprehensive problem information including source location and extent of affected customers, providing technicians with actionable intelligence before they arrive on site.
3Reliability
If manual monitoring and dispatch of service personnel is used, then network problems can be addressed, but the process becomes increasingly complex and labor-intensive as network size grows
Solution Approach 1:
The system enables self-service by automating the monitoring, analysis, and dispatch functions that previously required human operators. The automated system independently polls network devices, analyzes performance data, identifies plant-related problems, determines affected customer groups, and generates service dispatch information without human intervention, significantly reducing operational complexity as network size increases.
Solution Approach 2:
The system achieves universality by creating a multi-functional automated platform that performs polling, performance analysis, problem identification, geographic mapping, and service dispatch coordination through a single integrated system. This universal system can monitor and manage plant-related issues across the entire network infrastructure regardless of size, eliminating the need for separate manual processes for each network segment.
Data Source
AI summary
Performance data relating to each of multiple network devices distributed in a geographic region is analyzed. That data can include values for various parameters measured automatically by routine polling of subscriber devices and/or network elements serving those subscriber devices. Measured parameter values can then be stored in a database and made available, together with information about subscriber device locations, to one or more analysis servers that analyze different portions of the network. As part of that analysis, groups of devices experiencing performance problems are identified based on device location. Information about those groups is then communicated and can be made available for, e.g., monitoring by service personnel.