Electronic price tag position identification method and system

Through multimodal sensors and deep learning algorithms, the problem of electronic price tag position recognition and price synchronization update in complex retail environments is solved, efficient and accurate identification and update are achieved, and the automation and adaptability of the system is improved.

CN119850930AActive Publication Date: 2025-04-18HANGZHOU ZKONG NETWORKS CO LTD
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Patent Information

Application Number
CN202510316449.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-18
Publication Date
2025-04-18
Estimated Expiration
2045-03-18

AI Technical Summary

Technical Problem

The existing electronic price tag identification methods are difficult to achieve efficient and accurate position identification and real-time price synchronization updates in complex and dynamic retail environments, especially under conditions such as lighting changes, product occlusion and different viewing angles.

Method used

Through multimodal sensor data (such as RGB images, depth sensors and infrared sensors) and deep learning algorithms (such as deep convolutional neural networks), real-time image data is obtained from retail environments, denoising, enhancing and standardizing processing, image features are extracted, real position coordinates of electronic price tags are calculated, and price information is synchronously updated.

Benefits of technology

In a complex retail environment, accurate location identification of electronic price tags and real-time synchronous update of price information are realized, which improves the automation level of the system and environmental adaptability, and avoids the identification failure and performance degradation caused by environmental changes in traditional methods.

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Abstract

The invention relates to the field of electronic price tag management, in particular to an electronic price tag position identification method and system. The method comprises the following steps: acquiring real-time image data from a retail environment, and enhancing the image data through a multi-modal sensor to obtain a standardized image sequence; carrying out denoising, enhancement and quality optimization on the image sequence, extracting image features through a deep convolutional neural network, and generating an electronic price tag position candidate region; the real position coordinates of the electronic price tag are calculated in combination with the image space information, and the commodity price is synchronously updated; the updated electronic price tag position and price information are sent to a cloud computing platform for storage, analysis and management; based on real-time feedback and historical data, an image processing and position identification strategy is optimized through reinforcement learning, and adaptive adjustment parameters are generated. According to the invention, the position of the electronic price tag can be efficiently and accurately identified in a complex retail environment, real-time price synchronous updating is realized, and automation and accuracy are high.
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