Digital marketing big data processing method and system

By processing marketing data using encrypted communication index tables, the isolated forest algorithm, and the BitFit algorithm, the problems of distribution imbalance and anomaly detection in multi-source data processing are solved, achieving efficient data mapping and intelligent scheduling, and improving the accuracy and response speed of marketing strategies.

CN121997190AInactive Publication Date: 2026-05-08GUANGZHOU BIG EVENT NETWORK TECH CO LTD
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Patent Information

Application Number
CN202511979117.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-05-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In existing technologies, multi-source marketing data processing suffers from problems such as unbalanced distribution, low positioning efficiency, inaccurate bid value filtering, insufficient anomaly detection, lack of scheduling flexibility, and low level of intelligence, resulting in insufficient data reliability and processing efficiency.

Method used

The algorithm employs an encrypted communication index table, an isolated forest algorithm, and a BitFit algorithm to process advertising bid values, generating a secure aggregated gradient set. It then generates a locally adapted weight matrix through inner product calculation and matrix cutting. Combined with index rearrangement and scheduling task allocation, it achieves targeted data scheduling and anomaly detection.

Benefits of technology

It enables efficient mapping and allocation of multi-source heterogeneous data, improves data reliability and processing efficiency, enhances the intelligence and data-driven capabilities of marketing strategies, and shortens response time.

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Abstract

The invention relates to the technical field of data processing, in particular to a digital marketing big data processing method and system.The method comprises the steps that channel distribution and click fields are extracted from marketing participant identifiers and training round numbers, and segmentation left shift XOR and remainder calculation is executed; an advertisement bid value is converted into an integer through decimal truncation and a multiple product, an abnormal score is calculated based on isolated forest multi-tree splitting depth after an out-of-interval value is removed, an abnormal item is removed, bit addition and carry are executed through a peak value, a valley value and a modulus, and an accumulated encrypted text sequence is generated; obtaining a safe aggregation gradient set through comparative statistics of bit-by-bit and abstract bit sequences, executing index item-by-item product and row-level accumulation on the aggregation gradient set corresponding to row vectors to obtain an update segment, then adding the update segment with corresponding column vectors item by item, and multiplying the update segment by a set scaling factor to execute local update; a BitFit mode is adopted to realize weight adjustment only aiming at the key parameters of the sub-matrix, so that global synchronization is avoided, and the calculation load is reduced.
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