FEMS Network Repeater Optimization via Shaded Area Metrics
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Solution Overview
Problem
Constructing a factory energy management system (FEMS) network with minimized wireless communication shaded areas is challenging due to diverse channel environments in factories, making manual repeater configuration difficult and costly, and leading to either increased costs or degraded communication quality.
Innovation Solution
A method and apparatus that optimize repeater distribution in FEMS networks by using conditional probability metrics to identify and address communication shaded areas and remove unnecessary repeaters, ensuring error-free signal transmission and efficient communication path identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repeaters are manually configured to minimize wireless communication shaded areas, then communication quality is improved, but configuration cost and complexity increase
Solution Approach 1:
The system automatically configures repeaters by having communication nodes self-report their transmission metrics and shaded area conditions. The server collects this data and autonomously determines optimal repeater placements without manual intervention, enabling the system to serve itself in terms of network optimization.
Solution Approach 2:
The system implements a feedback loop where communication nodes continuously report transmission metrics (first metric) and shaded area information (second metric) to the server. The server processes this feedback and dynamically adjusts repeater configurations accordingly, creating a closed-loop control system for network optimization.
2Reliability
If more repeaters are deployed to cover shaded areas, then communication coverage is improved, but deployment cost increases
Solution Approach 1:
The server performs preliminary analysis of transmission metrics and shaded area predictions before deploying repeaters. By pre-calculating optimal placements based on predicted shaded areas and transmission quality, the system avoids unnecessary repeater deployments and ensures each repeater is placed only where truly needed.
Solution Approach 2:
The system dynamically adjusts repeater deployment decisions based on changing parameters such as transmission metrics, shaded area predictions, and network conditions. Rather than static deployment, the system modifies its deployment strategy in response to parameter changes, deploying repeaters only when and where parameters indicate a genuine need.
3Quantity of substance
If repeaters are reduced to lower deployment cost, then configuration cost is reduced, but communication quality in shaded areas degrades
Solution Approach 1:
The system replaces physical trial-and-error repeater placement with an information-based optimization system. Instead of mechanically deploying repeaters and testing coverage, the system uses transmitted metrics and predicted shaded area calculations to intellectually determine optimal placements, substituting physical experimentation with computational optimization.
4Ease of manufacture
If manual repeater configuration is performed, then initial setup is possible, but adaptability to changing factory environments is reduced
Solution Approach 1:
The system transitions from static manual configuration to dynamic automatic reconfiguration. Repeaters are not fixed in predetermined positions but are dynamically placed and repositioned based on real-time transmission metrics and changing environmental conditions, allowing the network to adapt flexibly to factory reconfigurations and environmental changes.
Data Source
AI summary
A method of constructing a factory energy management system (FEMS) network and an apparatus for performing the same are provided. The method includes obtaining a first metric and a second metric based on configuration information of the FEMS network and optimizing distribution of repeaters included in the network based on the first metric and the second metric.