Multilingual cross-cultural marketing content adaptive generation and evaluation system

By decoupling heterogeneous data streams with high-dimensional sparse features in a big data processing system, and using the covariance matrix to monitor coupling strength and generate parameterized feature maps, the computational complexity problem in multilingual and cross-cultural marketing content generation is solved, achieving efficient data processing and accurate content generation.

CN122175614APending Publication Date: 2026-06-09ZHEJIANG NORMAL UNIV
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Patent Information

Application Number
CN202610358921.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-23
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing big data processing systems cannot effectively decouple heterogeneous data streams with high-dimensional sparse features when processing multilingual and cross-cultural marketing content, resulting in a geometric increase in computational complexity and a tendency for imbalances in computing power allocation and pipeline blockages.

Method used

By performing orthogonal decoupling in the system topology, monitoring the coupling strength using the maximum eigenvalue of the covariance matrix, skipping the iteration stage using a bypass control signal, generating a parameterized feature map, and adjusting the feature weights using a dynamic time-series decay operator, linearization of data processing is achieved.

Benefits of technology

It effectively reduces the computational complexity of generating multilingual and cross-cultural marketing content, prevents system computational deadlock, and improves the structural fidelity and adaptation accuracy of data processing.

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Abstract

This invention relates to the field of big data processing technology and discloses a multilingual cross-cultural marketing content adaptive generation and evaluation system, comprising: a feature acquisition module for acquiring business fact features; a benchmark feature library module for storing benchmark feature vectors; a correlation analysis module for determining the mapping path signal based on the maximum eigenvalue of the covariance matrix between features; a topology mapping module for responding to the signal and iteratively adjusting the feature anchor coordinates to minimize the mapping topology loss value and generate a parameterized feature map; and a dimensionality compression module for adjusting the feature addressing weights using a dynamic temporal decay operator. This invention avoids coupling interference caused by the dimensionality collapse of high-dimensional sparse features by establishing a parameterized map mapping mechanism based on constraint propagation, ensuring the integrity of the data structure in cross-domain conversion, and maintaining the data processing complexity at the linear order.
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Citation Information

Patent Citations

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