Adaptive Fan Speed Control for Network Device Cooling
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Solution Overview
Problem
Existing methods fail to effectively control fan rotation speed in network devices to balance noise reduction and power consumption while preventing excessive temperature, leading to energy waste and potential device failure.
Innovation Solution
A method and apparatus that adjust fan duty cycles using algorithms like PID, Greedy, and DDPG to determine optimal rotation speeds based on reward parameters, ensuring reduced noise and power consumption while maintaining device temperature within safe limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the fans rotate at a higher speed, then the network device can be cooled down more effectively, but the fans may produce larger noise and consume more power
Solution Approach 1:
The patent applies dynamic control by adjusting fan rotation speed based on real-time temperature monitoring and algorithmic optimization (PID, Greedy, DDPG). The system transitions from static high-speed operation to dynamic adaptive speed control, optimizing the balance between cooling effectiveness and energy consumption according to actual thermal conditions.
Solution Approach 2:
The system changes the operational parameters of the fan by adjusting rotation speed to multiple discrete levels based on temperature thresholds and algorithmic decisions. This parameter adjustment allows the system to achieve effective cooling while minimizing energy consumption by operating at the lowest necessary speed.
2Temperature
If the fans rotate at a higher speed, then the network device can be cooled down more effectively, but the fans may produce larger noise
Solution Approach 1:
The system dynamically adjusts fan speed based on actual cooling requirements rather than operating continuously at high speed. This dynamic control reduces noise generation by maintaining fans at lower speeds during periods when maximum cooling is not required, while still responding promptly to temperature increases.
Solution Approach 2:
The system uses predictive algorithms (PID, Greedy, DDPG) to anticipate cooling requirements and adjust fan speed proactively. This preliminary action allows the system to maintain lower fan speeds during stable thermal conditions, reducing noise before temperature problems arise.
3Loss of energy
If the fans rotate at a lower speed, then the fans may produce smaller noise and consume less power, but the network device will be cooled down less effectively
Solution Approach 1:
The system implements continuous feedback monitoring of temperature, power consumption, and noise levels. The algorithms (PID, Greedy, DDPG) use this feedback to optimize fan speed, ensuring the lowest possible speed that still maintains effective cooling. This feedback loop prevents under-cooling while minimizing energy consumption.
Solution Approach 2:
The system applies partial action by operating fans at reduced speeds during periods when full cooling capacity is not needed, while maintaining the ability to increase speed rapidly when required. This partial operation achieves energy savings without compromising cooling effectiveness.
4Object-generated harmful factors
If the fans rotate at a lower speed, then the fans may produce smaller noise, but the network device will be cooled down less effectively
Solution Approach 1:
The system continuously monitors both noise levels and temperature, using feedback to determine the optimal fan speed that achieves effective cooling at the lowest possible noise level. The algorithms adjust speed in real-time to maintain the quietest operation that still provides adequate cooling.
Solution Approach 2:
The system changes fan operational parameters by implementing multiple speed levels and transitioning between them based on thermal conditions. This parameter optimization allows the system to operate at lower speeds (reducing noise) while maintaining cooling effectiveness through strategic speed adjustments.
Data Source
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AI summary
The present disclosure provides a method and apparatus for controlling a rotation speed of a fan and a device. The method includes: determining an initial fan duty cycle currently used by each fan; based on a target sequence of the plurality of fans, sequentially traversing out each fan as a candidate fan; performing multiple adjustments on the initial fan duty cycle corresponding to the candidate fan to obtain multiple different candidate fan duty cycles; for each candidate fan duty cycle, obtaining reward data of the network device under the candidate fan duty cycle, and based on the reward data, determining a reward parameter value corresponding to the candidate fan duty cycle; selecting an optimal reward parameter value from the reward parameter values corresponding to the multiple candidate fan duty cycles, and determining the candidate fan duty cycle corresponding to the optimal reward parameter value as a target fan duty cycle corresponding to the candidate fan; after the target fan duty cycle corresponding to each fan is obtained, based on the rotation speed corresponding to the target fan duty cycle corresponding to each fan, controlling the fan. By the technical solution of the present disclosure, the rotation speed of each fan can be effectively controlled and the power consumption and noise of the fan can be reduced.