Intelligent processing method and device for big data traffic of mobile terminal

CN120804627APending Publication Date: 2025-10-17BEIJING NANSHAN TONGXING TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510925647.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-06
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Existing technologies are unable to effectively manage and process large-scale data in mobile terminals, resulting in transmission redundancy and excessive storage pressure, affecting the real-time performance of key tasks. In addition, existing optimization models are difficult to balance accuracy, efficiency and adaptability, and there is a problem of ambiguous judgment.

Method used

By performing multi-dimensional feature extraction on the data entries received by the mobile terminal, constructing a feature matrix, and using the context prediction model to generate an importance vector, a sparse optimization model is constructed to solve locally, generate a scheduling weight vector, generate a scheduling strategy and perform data processing operations to form an optimization closed loop.

Benefits of technology

It achieves efficient management of large-scale data under limited computing resources and energy constraints, improves processing efficiency, generates reasonable scheduling results, adapts to changes in user behavior, and enhances the model's adaptability and the rationality of decision execution.

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Abstract

The invention relates to the technical field of wireless communication, and discloses a big data traffic intelligent processing method and device for a mobile terminal, and the method comprises the steps: S1, extracting data entry features, and constructing a feature matrix; s2, in combination with historical context information, calculating a predicted importance value of the data entry through a context prediction model to form an importance vector; s3, constructing a sparse optimization model based on the feature matrix and the importance vector, and obtaining a scheduling weight vector; s4, generating a scheduling strategy and a scheduling instruction, and executing uploading, caching or discarding operation; s5, executing corresponding processing operation on the data items; and S6, dynamically updating model parameters based on feedback, and constructing an optimized closed loop. According to the method, the feature matrix is constructed, so that the mobile terminal can perform unified structured processing on heterogeneous data, input is provided for a prediction and scheduling model, the processing flow is simplified, the efficiency is improved, and the problem of fuzzy judgment caused by inconsistent data structures is avoided.
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