A method and system for power consumption coordination control of an in-vehicle server

CN122387658APending Publication Date: 2026-07-14SHENZHEN LTIME IN VEHICLE ENTERTAINMENT SYST CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN LTIME IN VEHICLE ENTERTAINMENT SYST CO LTD
Filing Date
2026-04-15
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve a dynamic balance between energy consumption and performance of onboard servers in intelligent transportation and autonomous driving scenarios. Traditional response mechanisms cannot predict dynamically changing computing demands, leading to response delays and resource waste.

Method used

By employing a long short-term memory network model combined with a particle swarm optimization algorithm, the processor core frequency and voltage are adjusted in a coordinated manner by predicting future computing needs, constructing a frequency and voltage search range, gradually increasing the frequency to match the optimal working combination, and generating coordinated control instructions.

Benefits of technology

It achieves a balance between real-time performance and energy efficiency under dynamic load and road condition changes, improves the accuracy of resource pre-allocation and system stability, and solves the problems of response lag and resource waste in existing technologies.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the technical field of intelligent transportation, and discloses a power consumption cooperative control method and system of a vehicle-mounted server. The method comprises the following steps: acquiring a running state sequence, a real-time occupancy rate time sequence, a real-time working frequency, a real-time voltage level, a real-time road condition load, an actual demand value and a stable state sequence, and performing demand prediction to obtain a preliminary demand prediction value; performing particle swarm iteration optimization according to the preliminary demand prediction value to obtain a target frequency; performing step-by-step frequency increase according to the target frequency to obtain an energy consumption control parameter; performing sample amplification according to the energy consumption control parameter to obtain a comprehensive training data set; performing model training and optimization according to the comprehensive training data set to obtain an improved prediction model; inputting the running state sequence and the real-time occupancy rate time sequence into the improved prediction model to obtain an effectiveness confirmation result; and generating a cooperative control instruction according to the effectiveness confirmation result and executing the cooperative control instruction. The method can realize dynamic balance between energy consumption and performance.
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