Advertisement putting method and system based on charging pile

By constructing a third-order behavior tensor and information entropy value to filter advertisements, the problems of single user portrait and slow strategy update are solved, high-dimensional semantic modeling of user behavior and dynamic advertising delivery are achieved, and the accuracy of advertising recommendations and system adaptability are improved.

CN120807054AActive Publication Date: 2025-10-17BEIJING REAL ESTATE INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511001629.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-21
Publication Date
2025-10-17
Estimated Expiration
2045-07-21

AI Technical Summary

Technical Problem

Existing advertising delivery technologies have a single user portrait dimension, delayed behavioral response, and slow strategy updates, making it difficult to integrate multi-dimensional data for dynamic adjustments.

Method used

By collecting the interaction data between users and charging piles, a third-order behavior tensor with user identification, time segment and geographic location as dimensions is constructed. Tensor decomposition is used to generate user behavior embedding vectors, which are then used to filter advertisements based on information entropy values. The model is then updated based on user response behavior to dynamically adjust the advertising delivery strategy.

Benefits of technology

It achieves high-dimensional semantic modeling of user behavior, accurately depicts the evolution path of user interests, dynamically identifies information to focus on advertising content, quickly adapts to changes in user behavior, and improves the accuracy of advertising recommendations and system adaptability.

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Abstract

The invention relates to the technical field of new energy charging and advertisement putting, and discloses an advertisement putting method and system based on a charging pile, and the method comprises the steps: collecting user interaction, behavior, position, time and environment data, and constructing a behavior feature data set; constructing a third-order behavior tensor based on the behavior characteristics, and decomposing to generate a user behavior embedding vector; calculating an information entropy value of advertisement selection; screening the target advertisement with the maximum entropy reduction amplitude for putting; displaying the target advertisement, collecting user response and updating behavior characteristics; retraining the behavior tensor model to adjust the advertisement strategy; the system comprises a data acquisition module, a tensor modeling module, a strategy optimization module, a strategy screening module, an edge putting module and a strategy updating module. According to the method, the behavior tensor taking the user identifier, the time slice and the geographic position as the three-dimensional index is constructed, and the behavior embedding vector is extracted by introducing the tensor decomposition method, so that the high-dimensional semantic modeling of the user behavior is realized.
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