An inventory processing method and apparatus and a device are provided. The method includes: A first device receives a first command, where the first command is used for inventory and obtaining code information of a target device; and the first device determines, according to the first command, whether to feed back code information of the first device.

Provided is information processing device, which includes: an information acquisition unit that acquires acquisition information as network information related to a prediction target container based on web access information included in an information storage carrier attached to a container to be filled with a content and thus constituting a product; and a generation processing unit that generates demand-supply information corresponding to the network information of the prediction target container when the network information acquired by the information acquisition unit related to the prediction target container is input to a learning model trained by machine learning with a correlation between the network information of a training target container and demand-supply information including, for each of the types of the contents, information related to demand or supply of the product in which the different container having the specification identical to the specification of the container has been filled.

A computer-implemented method and apparatus, and a computer program product. The computer-implemented method comprises: by means of a first input interface, receiving a material query instruction inputted by a user; in response to the material query instruction, acquiring, from a data source, data of a plurality of materials for manufacturing one or more product components, wherein the data of the plurality of materials comprises the available inventory of the plurality of materials; calculating the maximum consumption of the plurality of materials with respect to at least one of the one or more product components; and according to the maximum consumption of the corresponding materials of the plurality of materials and the available inventory of the corresponding materials of the plurality of materials, determining whether the amount of one or more materials for manufacturing the one or more product components is insufficient.

A data prediction subsystem stores hourly event potential data indicating an expected amount of removal events as a function of time of day. Based at least in part on event data, it is determined that a first item associated with a first location has an empty status at a time after a start of a day. For the day, an anticipated event value is determined for the first item at the first location. Using the anticipated event value and the hourly event potential data, a time-adjusted event value is determined. Based at least in part on the time-adjusted event value, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

One or more first images depicting removal of a first inventory item of a plurality of inventory items of a particular item type from an inventory storage area are obtained. The one or more first images are processed with one or more machine-learned computer vision models to generate one or more model outputs. The one or more model outputs identify an item type for the inventory item. The one or more model outputs comprise values extracted from a label of the first inventory item. The first inventory item is identified from the plurality of inventory items of the particular item type based on the values extracted from the label of the first inventory item. Responsive to identifying the first inventory item, a status is assigned to the first inventory item indicating that the first inventory item has been removed from the inventory storage area.

Systems and method of tracking cargo. The tracking system includes a wireless system with tags configured to be connected to the cargo and to emit identification data, and locators configured to be connected to the vehicle and receive the identification data emitted from the tags. A vision system includes cameras positioned in the vehicle and configured to capture images of the cargo. A control unit includes processing circuitry configured to identify the cargo and track a position of the cargo based on signals transmitted from the tags and received by the locators and track the position of the cargo based on the images captured by the vision system.

A computerized communication device running an item tracking application comprising a CPU executing computer software instruction stored in an associated memory to create an item record to track an item. A computer server linked wirelessly with the computerized communication device wherein the computer server host a database comprising a plurality of item records. Each item record comprises a tag name for a tracking tag, an owner name, and owner contact information, and each item record further comprises a tag record encoded on the tracking tag.

A system for automated photo and/or video capture of pallets in a supply chain environment includes a photo/video capture tunnel with a frame structure. The system may include cameras mounted on the frame structure. At least one mobile device may be configured to capture metadata associated with pallets positioned within the photo/video capture tunnel. The mobile device may also transmit a signal to initiate photo and/or video capture. A cloud-based storage system may be configured to receive and store photos and/or videos and associated metadata from the cameras. Each of the cameras may be configured to receive the signal to initiate photo and/or video capture. The cameras may capture at least one photo and/or video of the pallets positioned within the photo/video capture tunnel. The cameras may transmit the at least one photo and/or video and the associated metadata to the cloud-based storage system.

A goods logistics system is provided. The system can include: a master computer, and at least one input unit. The input units can generate a trigger signal and transmit the trigger signal to the master computer. The master computer can generate, change, or delete a transport task to be carried out in a transport region by driverless industrial trucks operating based on the trigger signal with the application of a stored transport task definition and to transmit a transport task signal linked to this transport task to the industrial truck. The transport task signal prompts the industrial truck to perform the transport task. The master computer can change a status loading aid stations assigned to the input units based on the trigger signal with the application of a stored status change definition. The transport task definition and the status change definition are changeable.

Systems and methods for tracking assets are disclosed. Each asset is associated with a zone of a site and a stock keeping unit (SKU). Assets have a tag affixed thereto. The tag includes a beacon broadcasting a signal. Gateways in the site receive the signals and determine a signal characteristic for each, extract data encoded in the signals, and transmit the data, the signal characteristic and a time associated with each radio signal to a processing unit. The processing receives the data, the signal characteristics and the times, computes asset locations based on the signal characteristics, process some asset locations and the data associated with a given asset to generate event data, store the event data and the asset locations, and aggregates a subset of the event data related to a subset of the assets associated with a given SKU and to provide business intelligence related to the given SKU.