AI Network Connection Management via Relay Devices
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Solution Overview
Problem
Existing network connection management systems fail to maintain stability in devices due to environmental factors, leading to loss of network connectivity and disruption in information provision and control.
Innovation Solution
An electronic apparatus and control method that utilize artificial intelligence models to predict device disconnections and maintain network connections through relay devices, ensuring seamless data transmission and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network connection management is performed without prediction capability, then the system structure remains simple, but network connection stability deteriorates due to inability to prevent disconnections
Solution Approach 1:
The patent applies preliminary action by using AI models to predict potential network disconnections before they occur. The system analyzes environmental factors and device states in advance to identify devices at risk of disconnection, allowing the management server to take preventive measures such as switching to alternative communication paths or notifying users beforehand, thereby improving connection stability without requiring complex real-time intervention systems
Solution Approach 2:
The patent introduces an intermediary approach by incorporating AI prediction models as a mediator between the network management system and the devices. These models process environmental information and device states to predict disconnection risks, enabling the management server to make informed decisions about connection maintenance without directly controlling each device, thus balancing reliability improvement with system complexity management
2Reliability
If automatic connection switching is implemented, then network connection stability improves, but operation complexity increases due to automated decision-making requirements
Solution Approach 1:
The patent applies self-service by enabling the network management system to automatically monitor connection statuses, predict potential disconnections, and execute switching decisions without requiring manual user intervention. The system autonomously processes environmental information, evaluates connection risks, and maintains optimal connections based on pre-configured policies, thereby improving reliability while keeping the user interface simple and operationally straightforward
3Ease of operation
If prediction information is provided to users, then user convenience improves by maintaining information availability during disconnections, but information processing complexity increases
Solution Approach 1:
The patent applies preliminary action by providing users with prediction information about upcoming connection issues before they actually occur. The system analyzes device states and environmental factors to forecast potential disconnections and notifies users in advance, allowing them to prepare for or mitigate the impact. This approach improves user convenience by maintaining information availability while the automated prediction system handles the complex processing in the background
Data Source
Figure 1~2A
Figure 2B~3
Figure 4A
AI summary
An electronic apparatus and a control method are provided. The electronic apparatus includes a transceiver, a memory configured to store an artificial intelligence (AI) model, and a processor configured to control the transceiver to receive environment information from at least one of a plurality of devices that are connected to the electronic apparatus, determine that a predicted device of the plurality of devices will lose a network connection based on the first AI model and the environment information, and in response to determining the predicted device will lose the network connection, maintain the network connection of the predicted device through another device of the plurality of devices. The electronic apparatus may use a rule-based model or an AI model trained by using at least one of a machine learning algorithm, a neural network algorithm, or a deep-learning algorithm.