The invention relates to a method for the distributed calculation of calculation tasks by means of field devices of an industrial plant, wherein a plurality of field devices are coupled to a task distribution unit by means of a data link, the field devices effect a control of the industrial plant in an operating state in each case, at least one of the field devices receives a calculation task from the task distribution unit in an idle state and changes to a calculation state in which the calculation task is processed.

The present disclosure provides an Industrial Internet of Things for implementing a production task plan, including at least one user platform, a service platform, a management platform, a sensor network platform, and one or more object platforms that are interacted sequentially from top to bottom; the service platform is arranged in an independent layout, the management platform is arranged in a front sub platform layout, and the sensor network platform is arranged in a rear sub platform layout; the one or more object platforms are configured as one or more intelligent manufacturing devices or one or more manufacturing management devices.

The present disclosure provides an industrial IoT for correction and regulation of a defective product, a control method, and a storage medium. The industrial IoT includes a user platform, a service platform, a management platform, and a sensing network platform that interact in sequence. The service platform, the management platform, and the sensing network platform are all arranged in a front-sub-platform layout. The control method is applied in the industrial IoT. The industrial IoT may calculate whether a defective product is correctable according to a correction cost of the defective product, and may reduce the correction cost and a time consumption of a corresponding correction device, thereby reducing correction cost of the defective product and a loss in a manufacturing process, further ensuring a normal product manufacturing efficiency of the correction device, and thus reducing a correction loss of a waste product and the defective product.

A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.

A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.

Various systems and methods for implementing a software defined industrial system are described herein. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. In response to a node failing, a module may be redeployed to a replacement node. In an example, self-descriptive control applications and software modules are provided in the context of orchestratable distributed systems. The self-descriptive control applications may be executed by an orchestrator or like control device and use a module manifest to generate a control system application. For example, an edge control node of the industrial system may include a system on a chip including a microcontroller (MCU) to convert IO data. The system on a chip includes a central processing unit (CPU) in an initial inactive state, which may be changed to an activated state in response an activation signal.

In example embodiments, a multi-stage PFS diagram generation technique is used to iteratively define the layout of a PFS diagram from a subset of engineering data in a 3D model of an industrial process. The multi-stage PFS diagram generation technique may repeatedly call an automatic layout generator, which each time solves for one unknown quality of the PFS diagram (e.g., relative positions of components in the PFS diagram, positions on components in the PFS diagram, sizes of the components in the PFS diagram). The PFS diagram may be adapted based on user preferences, for example to define the subset of engineering data, or to constrain aspects of its layout. Updated PFS diagrams may be generated by selecting different user preferences.

Various systems and methods may be used to implement a software defined industrial system. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. The orchestrated system may include an orchestration server, a first node executing a first module, and a second node executing a second module. In response to the second node failing, the second module may be redeployed to a replacement node (e.g., the first node or a different node). The replacement mode may be determined by the first node or another node, for example based on connections to or from the second node.

A method for testing an equipment automation program may be implemented using a hardware device and may include the following steps: receiving user input through a user interface of the device; automatically identifying a test scenario based on the user input; automatically and sequentially fetching a plurality of messages according to the test scenario; and automatically and sequentially sending the messages to the equipment automation program.

The present disclosure discloses a method and system for assisting a robot to navigate in a warehouse setting. In some embodiments, robot is configured to receive a reserved route and move according to the reserved route as it navigates inside a warehouse. A reserved route includes one or more waypoints. Each waypoint represents a location and a size, the size being the space needed for accommodating a robot at the location. The one or more waypoints are listed in a sequence for a robot to follow sequentially. In some embodiments, each waypoint may further include a timestamp representing the time that the robot given the reserved route is supposed to arrive at the location.