Network-Based Backup Battery Control Using Traffic Profiles
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Solution Overview
Problem
Conventional cable modems require external battery backup units with communication links, which are inefficient and lack power management strategies to optimize battery usage.
Innovation Solution
A network-based battery backup system that uses machine learning to generate traffic profiles for devices, enabling them to enter low power modes during low usage times and normal power modes during high usage times, thereby optimizing battery usage and reducing the size of backup batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional external battery backup units are used with communication links, then battery backup functionality is provided, but device complexity and power inefficiency increase
Solution Approach 1:
The patent extracts the intelligence and control functions from the battery backup unit and relocates them to the network infrastructure (CMTS and monitoring server). The battery backup unit itself is simplified to only perform power switching and status reporting, eliminating the need for complex communication links while maintaining reliable backup functionality.
Solution Approach 2:
The patent introduces a monitoring server as an intermediary between the battery backup units and the network. This server consolidates the intelligence for managing multiple battery units, allowing simple battery units without communication links to be coordinated centrally, thus resolving the contradiction between reliability and device complexity.
2Reliability
If battery backup mode is used during power failures, then continuous operation is maintained, but battery thrashing and energy waste occur
Solution Approach 1:
The patent implements dynamic power management by continuously monitoring traffic patterns and device activity. The system adjusts the operational state of battery backup units in real-time, switching between low-power and full-power modes based on actual needs, thereby preventing battery thrashing while ensuring continuous operation when necessary.
Solution Approach 2:
The monitoring server receives feedback from battery backup units regarding their operational status and power conditions. Based on this feedback and analyzed traffic profiles, the server sends control signals to adjust battery unit operations, creating a closed-loop system that prevents energy waste from unnecessary battery engagement while maintaining reliability.
3Productivity
If full power mode is maintained continuously, then device performance is optimized, but energy consumption increases
Solution Approach 1:
The patent implements periodic monitoring of traffic patterns and device activity through the monitoring server. Based on these periodic assessments and created traffic profiles, the system periodically adjusts power modes, maintaining full power during high-activity periods and low-power modes during low-activity periods, thus optimizing the balance between productivity and energy consumption.
Solution Approach 2:
The system changes operational parameters (power mode) of battery backup units based on traffic profile analysis. The monitoring server adjusts voltage, current, or operational state parameters dynamically according to actual traffic demands, ensuring optimal device performance during high usage while reducing energy consumption during low usage periods.
4Ease of manufacture
If battery size is reduced to lower cost, then system cost decreases, but backup duration and reliability are compromised
Solution Approach 1:
The patent enables battery backup units to operate autonomously in low-power modes during low-traffic periods, effectively 'servicing themselves' by reducing their power consumption without external intervention. This self-service capability allows smaller batteries to provide adequate backup duration by intelligently managing their own energy consumption, thus reducing system cost while maintaining reliability.
Solution Approach 2:
The monitoring server performs preliminary analysis of traffic patterns and creates predictive profiles that anticipate future power needs. Based on these preliminary assessments, the system proactively adjusts battery operational states before power failures occur, ensuring that smaller batteries are optimally charged and positioned to provide necessary backup duration when actually needed, thereby reducing cost without compromising reliability.
Data Source
AI summary
Obtain, at a monitoring server of a network, usage data from a plurality of traffic sensors associated with a plurality of devices of interest of a plurality of network users. Based on the obtained usage data, create a plurality of traffic profiles for the plurality of devices of interest of the plurality of network users. From time to time, cause first signals to be sent, based on the plurality of traffic profiles, to the plurality of devices of interest. The first signals cause the plurality of devices of interest to enter a low power mode during first times. From time to time, cause second signals to be sent, based on the plurality of traffic profiles, to the plurality of devices of interest. The second signals cause the plurality of devices of interest to leave the low power mode for a normal power mode during second times.