In order to identify inventory changes of different goods, smart shelves need to rely on the coordination of a variety of advanced technologies. In terms of sensor technology, each shelf of the smart shelf is equipped with a high-precision weight sensor, which can keenly sense the weight changes when goods are placed or taken away. With the weight data of different commodities entered in advance, the system can judge the increase or decrease of specific commodity inventory according to the weight difference. For goods with relatively stable weight, such as bottled drinks, the weight sensor can quickly and accurately feed back inventory changes, and even if only one bottle is taken, it can be caught in time. At the same time, the intelligent shelf is also equipped with a high-definition image sensor to take real-time pictures and analyze the goods on the shelf with the help of computer vision technology. Through the image recognition algorithm, the system can distinguish the types, packaging forms and other characteristics of different commodities. When the number of commodities decreases, the commodity arrangement screen captured by the image sensor changes, and the change of the commodity inventory can be determined through algorithm analysis. For commodities with obvious appearance differences, this recognition method has a significant effect.
At the algorithm optimization level, the deep learning algorithm performs deep analysis and processing on the data collected by the sensor. Through the training of a large number of sample data of commodity inventory changes, the algorithm continues to optimize the identification model to improve the accuracy and stability of the identification of different commodity inventory changes. As time goes by, it can identify even in the face of complex situations such as irregular commodity placement and partial commodity occlusion. In addition, in view of the fact that environmental factors such as temperature and humidity may interfere with the accuracy of sensor data, the intelligent shelf adopts a dynamic calibration algorithm to monitor environmental parameters in real time, and dynamically calibrate the sensor data accordingly, so as to ensure that under different environmental conditions, the identification of commodity inventory changes is always.
In terms of system collaboration, the intelligent shelf and the enterprise's inventory management system achieve seamless docking. Once the changes in commodity inventory are identified, the data will be transmitted to the management system in real time. The management system will update the inventory information based on these data in a timely manner, providing accurate basis for the enterprise's purchase and replenishment decisions, and realizing inventory management. In large retail locations, multiple intelligent shelves form a network. Through data association analysis, the system can comprehensively judge the flow of goods, accurately identify the transfer of goods between different shelves, and avoid confusion of inventory data due to changes in the location of goods.
