Provided are a work content analysis apparatus, method, and program for analyzing a state of congestion or proximity of persons such as workers in an analysis target region such as a factory, for each area in the analysis target region. According to an embodiment a work content analysis apparatus includes a first database, an accumulation unit, and a classification unit. The database stores position information indicative of a position of a person in the analysis target region together with time information, in association with identification information of the person. The accumulation unit acquires a cumulative value in a predetermined item based on the position information and the time information stored in the first database in association with the identification information of the person. The classification unit classifies the each area based on the cumulative value acquired by the accumulation unit.

Methods, industrial Internet of Things systems, and storage mediums for controlling production line detection data are provided. The industrial Internet of Things system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform and the management platform use a centralized arrangement. The management platform sends a data request and receives production line detection data. The sensor network platform uses an arrangement of a front sub-platform, the sensor network platform includes a total platform and a plurality of sub-platforms, the sub-platforms are child node gateway devices in different communication networks, and the total platform is a total node gateway device aggregated by all the communication networks. The sensor network platform retrieves the production line detection data from the total node gateway device or the child node gateway devices based on a data request sent by the management platform.

Methods, industrial Internet of Things systems, and storage mediums for controlling production line detection data are provided. The industrial Internet of Things system includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform and the management platform use a centralized arrangement. The management platform sends a data request and receives production line detection data. The sensor network platform uses an arrangement of a front sub-platform, the sensor network platform includes a total platform and a plurality of sub-platforms, the sub-platforms are child node gateway devices in different communication networks, and the total platform is a total node gateway device aggregated by all the communication networks. The sensor network platform retrieves the production line detection data from the total node gateway device or the child node gateway devices based on a data request sent by the management platform.

Provided is a softsensor analysis and measurement system to provide the output by new progress variables based on collected data by a sort of sensors. The system comprises: an online sensor, an inline sensor, or an offline sensor; a machine learning/deep learning module; and a soft sensor server for collecting and storing online sensor data collected by the on-line sensor through a sensor network and a gateway, Ethernet, or WLAN (Wi-Fi), collecting and storing inline sensor data measured by an IoT device equipped with the inline sensor or offline sensor data measured by the offline sensor through an analysis device (dedicated analyzer), and outputting a result by applying new process variables (input) of a manufacturing process by using the machine learning/deep learning module, and feeding back and applying the new process variables of the manufacturing process to a control computer of the manufacturing process.

A processor may analyze a physical environment. One or more portions of the physical environment may have an edge computing resource requirement. The processor may determine, based on the analyzing, one or more additional edge computing resources to be placed in a surrounding area associated with the one or more portions of the physical environment. The processor may situate, automatically, the one or more additional edge computing resources on a material handling device in the surrounding area and that is directed toward the one or more portions of the physical environment.

A processor may analyze a physical environment. One or more portions of the physical environment may have an edge computing resource requirement. The processor may determine, based on the analyzing, one or more additional edge computing resources to be placed in a surrounding area associated with the one or more portions of the physical environment. The processor may situate, automatically, the one or more additional edge computing resources on a material handling device in the surrounding area and that is directed toward the one or more portions of the physical environment.

A ruggedized edge computing assembly is provided, which includes an edge computing device having a processor configured to control a controlled device. The ruggedized edge computing assembly includes a field connector configured to connect to the edge computing device via a plurality of pins and to the controlled device via a coupling. The ruggedized edge computing assembly further includes a housing overmolded around each of the field connector and the edge computing device. The housing includes two portions which are a field connector portion configured to accommodate the field connector and an edge computing device portion configured to accommodate the edge computing device. The two portions are configured to interlockingly engage together at an interface.

A ruggedized edge computing assembly is provided, which includes an edge computing device having a processor configured to control a controlled device. The ruggedized edge computing assembly includes a field connector configured to connect to the edge computing device via a plurality of pins and to the controlled device via a coupling. The ruggedized edge computing assembly further includes a housing overmolded around each of the field connector and the edge computing device. The housing includes two portions which are a field connector portion configured to accommodate the field connector and an edge computing device portion configured to accommodate the edge computing device. The two portions are configured to interlockingly engage together at an interface.

Disclosed is a blockchain-enabled edge computing method for production scheduling. The method includes modeling a smart contract between a device and a manufacturing unit, and using the smart contract to perform production scheduling on the device inside the manufacturing unit; one of the manufacturing units includes multiple devices; mounting each device on the blockchain operating node, the MES issues production instructions to the nodes of each manufacturing unit, at the same time, the nodes acquire production data of the device through multiple data sources of the device, the operating state data and process parameter data of each device are acquired in real time, and the data is directly chained from the device level; according to the production instructions and device parameters obtained by the manufacturing unit, using edge computing to dynamically adjust the device load, efficiency, and utilization.

Disclosed is a blockchain-enabled edge computing method for production scheduling. The method includes modeling a smart contract between a device and a manufacturing unit, and using the smart contract to perform production scheduling on the device inside the manufacturing unit; one of the manufacturing units includes multiple devices; mounting each device on the blockchain operating node, the MES issues production instructions to the nodes of each manufacturing unit, at the same time, the nodes acquire production data of the device through multiple data sources of the device, the operating state data and process parameter data of each device are acquired in real time, and the data is directly chained from the device level; according to the production instructions and device parameters obtained by the manufacturing unit, using edge computing to dynamically adjust the device load, efficiency, and utilization.