Abstract: The disclosure provides a shopping shelf. The shopping shelf includes: a shelf support; and a rack detachably mounted to the shelf support; where the rack includes: a mounting bracket; a weight sensing device above the mounting bracket; a suspension bracket base above the weight sensing device; and a suspension bracket having one end detachably mounted to the suspension bracket base.

Abstract: A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.

Abstract: A goods information collection device includes: a box; a carrier plate disposed on an inner bottom surface of the box for placing goods and an image capturing device. The image capturing device is configured to collect at least one image of the goods. The goods information collection device further includes a storage for storing the image of the goods and a data processing device connected to the image capturing device and the storage.

Abstract: The disclosure provides a shopping shelf. The shopping shelf includes: a shelf support; and a rack detachably mounted to the shelf support; where the rack includes: a mounting bracket; a weight sensing device above the mounting bracket; a suspension bracket base above the weight sensing device; and a suspension bracket having one end detachably mounted to the suspension bracket base.

Abstract: A sensing system and a sensing method are provided based on weight monitoring. The sensing system includes: a goods database generator that generates a goods database; a weight collector that collects a weight sensing value of the rack in real time; a pick or place status judger that determines whether the weight sensing value of the rack changes or not, where if the weight sensing value of a rack becomes smaller, it determines that there are goods taken away from the rack; and if the weight sensing value of the rack becomes larger, it determines that there are articles placed on the rack; and a goods database updater that stores the weight sensing value in the goods database to update the weight sensing value of the rack in the goods database in real time.

Abstract: The disclosure provides a target positioning system and a target positioning method. The target positioning system includes: a 3D image acquisition device that acquires at least one frame of 3D image in real time, where the 3D image comprises all or part of images of at least one target object; and a target coordinate generator in a data processing equipment, that establishes a 3D coordinate system in a closed space, and acquires coordinates or a group of coordinates of the target object in the 3D coordinate system in real time according to the at least one frame of 3D image.

Abstract: A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.