WO2022205153A1

WO2022205153A1 - Information processing method and apparatus, and computing device and storage medium

Info

Publication number: WO2022205153A1
Application number: PCT/CN2021/084629
Authority: WO
Inventors: 王琪
Original assignee: 西门子股份公司; 西门子（中国）有限公司
Priority date: 2021-03-31
Filing date: 2021-03-31
Publication date: 2022-10-06

Abstract

The present disclosure relates to an information processing method and apparatus, and a computing device and a storage medium. The information processing method comprises: acquiring a source data model from an OPC-UA system; defining a template of a target data model of a target system, wherein the template at least defines the format of the target data model; and compiling the source data model and the template to generate the target data model.

Description

Information processing method, apparatus, computing device, and storage medium

technical field

The present disclosure generally relates to the technical field of the Internet of Things, and more particularly, to an information processing method, an apparatus, a computing device, and a storage medium.

Background technique

OPC-UA (Object Linking and Embedding (OLE) for Process Control, Unified Architecture) communication protocol is widely used in industry, including communication between field devices, and communication between field devices and management systems. For industrial IoT (Internet of Things) systems, OPC-UA plays an important role as a unified standard for providing data to IoT systems from different data sources.

However, in the process of integrating a system using the OPC-UA protocol with an IoT system, there are some problems:

1. Different IoT systems have different data models and applications, and there is no standard way to integrate OPC-UA systems with IoT systems.

2. Manual configuration of the data model, data points, and mapping between the OPC-UA system and the IoT system is required, which is error-prone and time-consuming when there are a large number of data points.

3. Configurations are usually static and not flexible for different types of applications.

In the prior art, there is a mapping from OPC-UA to Resource Description Framework (RDF), but its purpose is not to integrate OPC-UA with IoT systems.

There are also languages that support mapping from RDF to JSON, but do not support integration with IoT systems.

SUMMARY OF THE INVENTION

The following presents a brief summary of the present invention in order to provide a basic understanding of certain aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to identify key or essential parts of the invention nor to limit the scope of the invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.

In view of this, the present invention proposes a method that can map the data model of the OPC-UA system to the data model of other IoT systems, so as to realize the integration between the OPC-UA system and the IoT system simply and flexibly.

According to one aspect of the present disclosure, an information processing method is provided, comprising: acquiring a source data model from an OPC-UA system;

A template that defines a target data model of the target system, the template at least defines the format of the target data model; and the source data model and the template are compiled to generate a target data model.

In this way, the data model of the OPC-UA system can be easily mapped to the data model of other IoT systems.

Optionally, in an example of the above aspect, acquiring the source data model from the OPC-UA system further includes: using a semantic query language to define a query statement, and querying the OPC-UA system for the data required by the target system as the required data. Describe the source data model.

In this way, semantic-based queries to the OPC-UA system can be supported, so that it can adapt to the flexible needs of different applications.

Optionally, in an example of the above aspect, the target data model includes at least one of the following:

Cloud platform model, OPC-UA model, RDF data model, XML data model, JSON data model.

In this way, the integration of OPC-UA systems with many different IoT systems can be achieved.

Optionally, in an example of the above aspect, the template is a dynamic template implemented using logic statements.

In this way, the template can be flexibly applied to different types of IoT systems.

According to another aspect of the present disclosure, there is provided an information processing apparatus, comprising: a source data model acquisition unit configured to acquire a source data model from an OPC-UA system; a template definition unit configured to define a target data model of a target system a template, the template defines at least the format of the target data model; and a target data model generating unit configured to compile the source data model and the template to generate a target data model.

Optionally, in an example of the above aspect, the source data model obtaining unit is further configured to: define a query statement by using a semantic query language, and query the data required by the target system from the OPC-UA system as the required data. Describe the source data model.

According to another aspect of the present disclosure, there is provided a computing device comprising: at least one processor; and a memory coupled to the at least one processor, the memory for storing instructions, when the instructions are executed by the at least one processor When executed by the processor, the processor is caused to execute the method as described above.

According to another aspect of the present disclosure, there is provided a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the method as described above.

According to another aspect of the present disclosure, there is provided a computer program comprising computer-executable instructions which, when executed, cause at least one processor to perform the method as described above.

According to another aspect of the present disclosure, there is provided a computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions that, when executed, cause at least one A processor executes the method as described above.

According to the information processing method and device of the present invention, a unified method for conveniently and flexibly integrating the OPC-UA system with different IOT systems is provided; the semantic-based query of the OPC-UA system is supported, so as to adapt to the flexibility of different application programs Requirements; the method according to the present disclosure does not require excessive manual effort.

Description of drawings

The above and other objects, features and advantages of the present invention will be more easily understood with reference to the following description of the embodiments of the present invention in conjunction with the accompanying drawings. The components in the drawings are merely intended to illustrate the principles of the invention. In the drawings, the same or similar technical features or components will be denoted by the same or similar reference numerals. In the attached picture:

1 is a flowchart of an exemplary process of an information processing method 100 according to an embodiment of the present invention;

2 is a block diagram of an exemplary configuration of an information processing apparatus 200 according to an embodiment of the present invention;

3 shows a block diagram of a computing device 300 implementing information processing in accordance with an embodiment of the present disclosure.

Among them, the reference numerals are as follows:

100: Information processing method S102, S104, S106: steps

200: Information processing device 202: Source data model acquisition unit

204: Template definition unit 206: Target data model generation unit

300: Computing Equipment 302: Processor

304: memory

Detailed ways

The subject matter described herein will now be discussed with reference to example implementations. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and not to limit the scope of protection, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as desired. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may also be combined in other examples.

As used herein, the term "including" and variations thereof represent open-ended terms meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment." The term "another embodiment" means "at least one other embodiment." The terms "first", "second", etc. may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. The definition of a term is consistent throughout the specification unless the context clearly dictates otherwise.

In order to solve the above problems, the present invention proposes a method that can map the data model of the OPC-UA system to the data model of other IoT systems, so as to realize the integration between the OPC-UA system and the IoT system simply and flexibly.

The information processing method and apparatus according to the embodiments of the present disclosure will be described below with reference to the accompanying drawings.

FIG. 1 is a flowchart of an exemplary process of an information processing method 100 according to an embodiment of the present invention.

First, in step S102, the source data model is acquired from the OPC-UA system.

The source data model can be obtained, for example, from an OPC-UA server or a NodeSet file. The source data model is the data model of the target data model to be mapped to the target system to realize the integration of the OPC-UA system and the IoT system.

Below is a concrete example of the OPC-UA model.

Next, in step S104, a template of the target data model of the target system is defined.

In this disclosure, the target systems are collectively referred to as IoT systems. Specifically, the target data models of IoT systems may include, for example, cloud platform models, OPC-UA models in other systems (for example, through PubSub (subscription publishing)), and RDF data models. , XML data model or JSON data model, etc.

In order to realize the integration of the OPC-UA system and the target system, the source data model should be mapped to the target data model, so in step S104, templates should be defined according to different types of the target data model.

The template of the target data model is used to define the format of the target data model, and it can also define the interface transmission data format of the application program interface.

Those skilled in the art can refer to the existing application framework language to define corresponding templates for different types of target systems, which will not be described in detail here.

A specific example of a template is shown below.

The template shows the format of a certain cloud platform model, and the various variables are defined in the template.

Preferably, a dynamic language can be used to define the template, such as iterative, conditional and other logic statements, so that a dynamic template can be defined, so that the template can be flexibly applied to different types of systems (eg, JSON, XML, RDF, etc.). It can be seen that in the specific example of the above template, the "For" statement is used to implement the dynamic template.

In step S106, the source data model and the template are compiled to generate a target data model.

By compiling the source data model obtained in step S102 and the template of the target data model defined in step S104, the target data model in the target system can be generated. In this way, the OPC-UA source data model can be easily mapped to the target data model, which realizes the integration of the OPC-UA system and the IoT system.

In an example, step S102 further includes defining a query statement using a semantic query language (eg SPARQL), and querying the OPC-UA system to obtain the data required by the target system.

For example, according to the needs of the application program in the target system, the type of the variable to be obtained can be limited by a query statement, and the variable of the required type can be selected from the OPC-UA system to map to the target data model. In this way, the flexible needs of different applications can be supported.

For example, the following is a specific example of a query statement, through which a variable of variable type "ns=2; S=Device" can be selected from the OPC-UA server to map to the IoT model.

SELECT? node WHERE{

? node ua:HasTypeDefinition'ns=2;i=1002'.

}

A specific example of an IoT data model generated by using the above-mentioned IoT model template and the acquired source data model is shown below.

According to the method of the present disclosure, a unified method of integrating an OPC-UA system with different IOT systems is provided.

According to the method of the present disclosure, semantic-based query to the OPC-UA system is supported, so that the flexible requirements of different applications can be adapted.

According to the method of the present disclosure, excessive manual effort is not required.

FIG. 2 is a block diagram of an exemplary configuration of an information processing apparatus 200 according to an embodiment of the present invention.

As shown in FIG. 2 , the information processing apparatus 200 includes: a source data model acquisition unit 202 , a template definition unit 204 , and a target data model generation unit 206 .

The source data model acquisition unit 202 is configured to acquire the source data model from the OPC-UA system.

The template definition unit 204 is configured to define a template of the target data model of the target system, the template defining at least the format of the target data model.

The target data model generating unit 206 is configured to compile the source data model and the template to generate a target data model.

Preferably, the source data model obtaining unit 202 is further configured to: define a query statement using a semantic query language, and query the data required by the target system from the OPC-UA system as the source data model.

Wherein, the target data model includes at least one of the following: cloud platform model, OPC-UA model, RDF data model, XML data model, JSON data model.

Wherein, the template is a dynamic template implemented by using logic statements.

It should be noted that the structures of the information processing apparatus 200 and its constituent units shown in FIG. 2 are merely exemplary, and those skilled in the art can modify the structural block diagram shown in FIG. 2 as required.

Details of operations and functions of various parts of the information processing apparatus 200 may be, for example, the same or similar to relevant parts of the embodiment of the information processing method 100 of the present disclosure described with reference to FIG. 1 , and will not be described in detail here.

As above, with reference to FIGS. 1 to 2 , the information processing method and apparatus according to the embodiments of the present disclosure are described. Each unit of the information processing apparatus described above may be implemented by hardware, or may be implemented by software or a combination of hardware and software.

FIG. 3 shows a block diagram of a computing device 300 for implementing information processing in accordance with an embodiment of the present disclosure. According to one embodiment, computing device 300 may include at least one processor 302 that executes at least one computer-readable instruction (ie, the above-described in software form) stored or encoded in a computer-readable storage medium (ie, memory 304 ). implemented elements).

It should be understood that the computer-executable instructions stored in memory 304, when executed, cause at least one processor 302 to perform various operations and functions described above in connection with FIGS. 1-2 in various embodiments of the present disclosure.

According to one embodiment, a non-transitory machine-readable medium is provided. The non-transitory machine-readable medium may have machine-executable instructions (ie, the above-described elements implemented in software) that, when executed by a machine, cause the machine to perform various embodiments of the present disclosure above in conjunction with FIGS. 1-2 Various operations and functions are described.

According to one embodiment, there is provided a computer program comprising computer-executable instructions that, when executed, cause at least one processor to perform each of the various embodiments of the present disclosure described above in connection with FIGS. 1-2 operations and functions.

According to one embodiment, there is provided a computer program product comprising computer-executable instructions that, when executed, cause at least one processor to perform the various embodiments of the present disclosure described above in connection with FIGS. 1-2 Various operations and functions.

It should be understood that, the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the description of other embodiments. the difference. For example, for the above-mentioned embodiments about the apparatus, the embodiment about the computing device, and the embodiment about the machine-readable storage medium, since they are basically similar to the method embodiments, the descriptions are relatively simple, and the relevant details refer to the method implementation Part of the example can be explained.

Specific embodiments of the present specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Not all steps and units in the above processes and system structure diagrams are necessary, and some steps or units may be omitted according to actual needs. The device structure described in the above embodiments may be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities respectively, or may be implemented by multiple physical entities. Some components in separate devices are implemented together.

The detailed description set forth above in connection with the accompanying drawings describes exemplary embodiments and does not represent all embodiments that may be implemented or fall within the scope of the claims. The term "exemplary" as used throughout this specification means "serving as an example, instance, or illustration" and does not mean "preferred" or "advantage" over other embodiments. The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described embodiments.

The above description of the present disclosure is provided to enable any person of ordinary skill in the art to make or use the present disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of this disclosure . Thus, the present disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the present invention. within the scope of protection.

Claims

Information processing methods, including:

Obtain the source data model from the OPC-UA system;

a template defining a target data model of a target system, the template defining at least the format of the target data model; and

The source data model and the template are compiled to generate a target data model.
The method of claim 1, wherein obtaining the source data model from the OPC-UA system further comprises:

A query statement is defined using a semantic query language, and the data required by the target system is queried from the OPC-UA system as the source data model.
The method of claim 1 or 2, wherein the target data model includes at least one of the following:

Cloud platform model, OPC-UA model, RDF data model, XML data model, JSON data model.
The method of claim 1 or 2, wherein the template is a dynamic template implemented using logic statements.
An information processing device (200), comprising:

a source data model obtaining unit (202), configured to obtain a source data model from the OPC-UA system;

a template definition unit (204) configured to define a template of a target data model of a target system, the template defining at least a format of the target data model; and

A target data model generating unit (206) is configured to compile the source data model and the template to generate a target data model.
The apparatus of claim 5, wherein the source data model obtaining unit (202) is further configured to:

A query statement is defined using a semantic query language, and the data required by the target system is queried from the OPC-UA system as the source data model.
The apparatus of claim 5 or 6, wherein the target data model includes at least one of the following:

Cloud platform model, OPC-UA model, RDF data model, XML data model, JSON data model.
The apparatus of claim 5 or 6, wherein the template is a dynamic template implemented using logic statements.
A computing device (300) comprising:

at least one processor (302); and

a memory (304) coupled to the at least one processor (302) for storing instructions that, when executed by the at least one processor (302), cause the processor (302) ) to perform the method as claimed in any one of claims 1 to 4.
A non-transitory machine-readable storage medium storing executable instructions which, when executed, cause the machine to perform the method of any one of claims 1 to 4.
A computer program comprising computer-executable instructions which, when executed, cause at least one processor to perform the method of any one of claims 1 to 4.
A computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the execution according to claims 1 to 4 any of the methods described in .