Communication processing system and method for enhancing computing power of low-orbit communication satellite based on constellation cooperative computing

By employing a constellation collaborative computing approach and utilizing a distributed computing power scheduling and optimization model within the low-Earth orbit (LEO) satellite constellation, the problems of high latency and high energy consumption when processing large-scale data by LEO satellites are solved, achieving efficient and secure data processing.

CN121664889APending Publication Date: 2026-03-13YINHE HANGTIAN (XIAN) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511940164.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-22
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

When processing large-scale data, low-Earth orbit satellites are constrained by energy, thermal management, and computing power, resulting in high data processing latency, high energy consumption, and the risk of data leakage. Existing systems mostly use transparent forwarding methods, which cannot effectively utilize the computing power within the constellation.

Method used

By employing a constellation-based collaborative computing approach, including modules for data acquisition, task generation, decomposition, distributed computing power scheduling, task allocation and optimization, execution, result fusion, and encryption, distributed task processing across satellites is achieved, and a multi-objective optimization model is constructed to minimize latency and energy consumption.

Benefits of technology

Significantly reduces task processing latency, decreases ground backhaul requirements, improves processing efficiency, ensures data security, saves energy, and enhances constellation task processing capabilities.

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Abstract

The invention discloses a task scheduling and energy consumption optimization system and method based on distributed satellite calculation, and the method comprises the following steps: obtaining the floating point calculation amount and data size of each subtask, obtaining the calculation power and link rate of each satellite, and calculating the estimated execution time of the subtasks on the satellites according to an execution time formula; further obtaining the calculation power, the communication power and the corresponding duration of each satellite, and estimating the total energy consumption through an energy consumption formula; and optimizing a task allocation scheme based on the energy consumption evaluation, thereby reducing the overall energy consumption of the satellite cluster.
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