IoT Cross-Charging with Priority Management Rules
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Solution Overview
Problem
Existing wireless cross-charging systems for IoT devices lack personalized charging management rules that automatically prioritize charging based on user needs, leading to inefficient battery balancing and potential usage interruptions.
Innovation Solution
A method and apparatus for cross-charging IoT devices that involve receiving device battery status data, generating usage patterns based on user profiles, assigning priority management rules, and transmitting charging instructions to optimize battery balancing and prioritize charging among devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wireless cross-charging is implemented without personalized management rules, then device charging flexibility is improved, but battery balancing efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts charging priorities and power distribution based on real-time device usage patterns, battery status, and user-defined rules. The charging management is not static but adapts continuously to changing conditions, optimizing battery balancing efficiency while maintaining charging flexibility through automated rule-based control.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring device battery status, usage patterns, and charging progress. This feedback loop enables the system to automatically adjust charging strategies, prioritize devices based on predicted usage, and optimize power distribution among multiple devices, thereby improving battery balancing efficiency without compromising user control.
2Productivity
If automated charging management rules are implemented, then charging efficiency is improved, but system complexity increases
Solution Approach 1:
The system enables devices to automatically manage their own charging processes based on pre-defined user rules and real-time conditions. Each device can independently determine its charging priority and power requirements, reducing the need for complex centralized control while maintaining high charging efficiency through autonomous decision-making at the device level.
Solution Approach 2:
The charging management system is segmented into modular components: user rule definition, usage pattern analysis, real-time monitoring, and automated control. This segmentation allows the system to handle complexity in manageable modules, making the overall system more manageable while achieving high charging efficiency through coordinated operation of these independent but interconnected components.
3Reliability
If battery status monitoring is continuously performed, then device usage interruption is reduced, but energy consumption increases
Solution Approach 1:
The system performs battery status monitoring at optimized intervals rather than continuously, reducing energy consumption while maintaining reliable device usage. The monitoring frequency is adjusted based on device activity states, charging phases, and predicted usage patterns, ensuring that battery status is updated sufficiently to prevent usage interruptions without wasteful continuous monitoring.
Solution Approach 2:
The system uses machine learning to predict future battery requirements based on historical usage patterns and scheduled events. By anticipating when battery levels will become critical, the system can proactively initiate charging actions before usage interruptions occur, reducing the need for frequent real-time monitoring and thereby lowering energy consumption while maintaining device availability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively reduces usage interruptions by achieving battery balancing and prioritization, ensuring that IoT devices are charged efficiently based on predicted usage patterns and user needs, thereby maintaining device functionality and extending battery life.
Implementation Method 1
Wireless charging, also known as inductive charging or cordless charging, allows a device to charge its battery wirelessly from a power source
Data Source
AI summary
Cross-charging among a set of wireless charging IoT devices using prioritizing management rules is disclosed. Device battery status data is received from each wireless charging device in the set of wireless charging devices. A device usage pattern is generated based on user profile data for each wireless charging device in the set of wireless charging devices. At least one priority management rule (PMR) is assigned to at least one wireless charging device in the set of wireless charging devices based on the device battery status data and user profile data from each wireless charging device in the set of wireless charging devices. At least one cross-charging instruction is transmitted to at least one wireless charging device in the set of wireless charging devices based on the at least one PMR.