Abstract: Various embodiments include a data acquisition method including: generating a list of data points to be acquired from a field device; and performing the following separately for each data point. Parsing an address, and determining values of a region type, a DB offset, a byte offset and a bit offset in the address; using a first rule to calculate a sorting indicator of the data point according to the values determined; using a second predetermined rule to calculate a grouping indicator of the data point according to the determined values; sorting all of the data points according to the sorting indicators; dividing all of the sorted data points into multiple groups on the basis of the grouping indicator and a byte length of each data point and the length of a protocol data unit of the field device; and sending a data request for each group to the field device.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: In the field of industrial automation, a method and device are for determining a data reading period for determining a data reading period of data in an industrial control system, and are capable of automatically configuring the data reading period to obtain a better configuration result. In embodiments of the present invention, an industrial control system in different states is simulated by using simulation software to obtain a simulation model and simulation data. Data features of the industrial control system in different states that is simulated can be extracted respectively, and a data reading period is determined according to the extracted data features. Automatic configuring of a data reading period is implemented.

Abstract: A system, a cloud platform, a device and a method are for configuring edge devices. The system includes one or more edge devices generating corresponding one or more field data; a controller connected to one or more edge devices to acquire one or more field data; and a cloud platform connected to the controller to configure one or more edge devices. The cloud platform includes a cloud platform receiving module receiving one or more field data from the controller; an asset database including one or more templates corresponding to the configuration data of one or more edge devices; and a configuration module configuring one or more edge devices according to one or more field data and the corresponding templates. A specific protocol is used, in at least one embodiment, to packetize the field data generated by edge devices and utilizes the corresponding templates and field data to configure edge devices.

Abstract: In the field of industrial automation, a method and device are for determining a data reading period for determining a data reading period of data in an industrial control system, and are capable of automatically configuring the data reading period to obtain a better configuration result. In embodiments of the present invention, an industrial control system in different states is simulated by using simulation software to obtain a simulation model and simulation data. Data features of the industrial control system in different states that is simulated can be extracted respectively, and a data reading period is determined according to the extracted data features. Automatic configuring of a data reading period is implemented.

Abstract: A system, a cloud platform, a device and a method are for configuring edge devices. The system includes one or more edge devices generating corresponding one or more field data; a controller connected to one or more edge devices to acquire one or more field data; and a cloud platform connected to the controller to configure one or more edge devices. The cloud platform includes a cloud platform receiving module receiving one or more field data from the controller; an asset database including one or more templates corresponding to the configuration data of one or more edge devices; and a configuration module configuring one or more edge devices according to one or more field data and the corresponding templates. A specific protocol is used, in at least one embodiment, to packetize the field data generated by edge devices and utilizes the corresponding templates and field data to configure edge devices.

Abstract: A titanium composite material includes a titanium matrix material and a powder reinforced composite material with a volume ratio of 10%-70%. The titanium matrix material is disposed at an ? phase, a ? phase, an ?+? phase, an omega phase, or an intermetallic ?-1, ?-2, ?-3 phase. The powder reinforced composite material is selected from a ceramic powder material, a powder material with electric features or a magnetic powder material. Thus, the powder reinforced composite material is added into and combined with the titanium matrix material to form the titanium composite material by casting, agglomerating or pressing, so that the titanium matrix material contains the physical, chemical or electric features of the titanium matrix material and the powder reinforced composite material.