Patent Publication Number: US-2022237678-A1

Title: Method and device for establishing information model and non-volatile computer readable recording medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of Taiwan application serial no. 110102671, filed on Jan. 25, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND 
     Technical Field 
     The disclosure relates to a technique for converting a description data of a product into an information model which conforms to an international standard, and particularly relates to a method and a device for establishing an information model, and a non-volatile computer-readable recording medium. 
     Description of Related Art 
     In the architecture of Industry 4.0, it is expected that a highly interoperable information model for information exchange of descriptions (e.g., product specification descriptions) of products (e.g., electronic circuits or equipment) can be achieved among multiple manufacturers. However, current digital information models are mainly established manually and take much time to operate. In addition, currently, the establishment of information models does not effectively use the information model specifications of international standards, so that information generally cannot be directly communicated among the information models established by different manufacturers. 
     SUMMARY 
     The disclosure provides a method and a device for establishing an information model, and a non-volatile computer-readable recording medium, which can efficiently and automatically establish an information model of a product and improve the convenience of the information model in information exchange. 
     An embodiment of the disclosure provides a method for establishing an information model, including steps below. A description data of a product is obtained. A resource description framework (RDF) graph is generated according to the description data. The RDF graph is compared with at least one first international standard model. An information model corresponding to the product is established according to a comparison result. The information model is configured to provide information related to the product. 
     An embodiment of the disclosure further provides a device for establishing an information model, including a storage circuit and a processor. The storage circuit is configured to store a description data of a product. The processor is coupled to the storage circuit and is configured to generate an RDF graph according to the description data, compare the RDF graph with at least one first international standard model, and establish an information model corresponding to the product according to a comparison result. The information model is configured to provide information related to the product. 
     An embodiment of the disclosure further provides a non-volatile computer-readable recording medium. The non-volatile computer-readable recording medium stores a program code, and the program code is executed by a processor to obtain a description data of a product, generate an RDF graph according to the description data, compare the RDF graph with at least one first international standard model, and establish an information model corresponding to the product according to a comparison result. The information model is configured to provide information related to the product. 
     Based on the above, after a description data of a product is obtained, an RDF graph may be generated according to the description data. The RDF graph is compared with at least one first international standard model. An information model corresponding to the product is established according to the comparison result. The information model is configured to provide information related to the product. Accordingly, the information model of the product can be established efficiently and automatically, and the convenience of the information model in information exchange can be improved. 
     To make the aforementioned more comprehensible, several embodiments accompanied with drawings are described in detail as follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view of a management system of an information model according to an embodiment of the disclosure. 
         FIG. 2  is a functional block diagram of a device for establishing an information model according to an embodiment of the disclosure. 
         FIG. 3  is a schematic view of an operation of automatically establishing an information model according to an embodiment of the disclosure. 
         FIG. 4  is a schematic view of an RDF according to an embodiment of the disclosure. 
         FIG. 5  is a schematic view of an RDF graph according to an embodiment of the disclosure. 
         FIG. 6  is a schematic view showing training and using a classifier according to an embodiment of the disclosure. 
         FIG. 7  is a flowchart of a method for establishing an information model according to an embodiment of the disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
       FIG. 1  is a schematic view of a management system of an information model according to an embodiment of the disclosure. Referring to  FIG. 1 , a system (also referred to as a management system of an information model)  10  includes a device (also referred to as a device for establishing an information model)  11 . The device  11  may include a desktop computer, a notebook computer, a tablet computer, an industrial computer, a server, or other computer devices having data processing functions. The device  11  may obtain a description data of one or more products. For example, the product may include various electronic components, electronic circuits, electronic equipment, industrial equipment, commercial equipment, household appliances, etc. The description data of the product may include at least one of a product catalog data, a circuit diagram, a mechanical structure diagram, an international product classification number, a webpage link, and a communication interface specification data related to the product. The device  11  may automatically generate and establish an information model corresponding to the product according to the description data of the product, so as to provide information related to the product through the information model. It is noted that the information model may conform to a specific international standard (e.g., RAMI, SSN, AML, SCORVoc, etc.). Accordingly, different manufacturers (i.e., client-end devices) may access the information model according to the internationally recognized standard to obtain information related to the product. 
       FIG. 2  is a functional block diagram of a device for establishing an information model according to an embodiment of the disclosure. Referring to  FIG. 2 , the device  11  includes a processor  21 , a storage circuit  22 , and an input/output (I/O) interface  23 . The processor  21  is configured to control the overall or part of the operation of the device  11 . For example, the processor  21  may include a central processing unit (CPU), other programmable general-purpose or specific-purpose microprocessor, digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), programmable logic device (PLD), or other similar devices, or a combination of the above devices. 
     The storage circuit  22  is coupled to the processor  21  and is configured to store data. For example, the storage circuit  22  may include a volatile storage circuit and a non-volatile storage circuit. The volatile storage circuit is configured to volatilely store data. For example, the volatile storage circuit may include a random access memory (RAM) or similar volatile storage media. The non-volatile storage circuit is configured to non-volatilely store data. For example, the non-volatile storage circuit may include a read only memory (ROM), a solid state disk (SSD), and/or a conventional hard disk drive (HDD), or similar non-volatile storage media. 
     The I/O interface  23  is coupled to the processor  21  and is configured to transmit signals. For example, the processor  21  may receive an input signal or transmit an output signal via the I/O interface  23 . For example, the I/O interface  23  may include various I/O devices such as a network connection interface, a mouse, a keyboard, a screen, a touch panel, and/or a speaker. 
     In an embodiment, the storage circuit  22  stores a description data  201 . The description data  201  records information related to a specific product. In an example where the specific product is a sensor, the description data  201  may include a product catalog data, a circuit diagram, a mechanical structure diagram, an international product classification number, a webpage link, and/or a communication interface specification data related to the sensor. In addition, different types of products may correspond to different types of description data  201 . 
     In an embodiment, the storage circuit  22  further stores a classifier  202 . The classifier  202  may perform image recognition on a specific image and may classify this image. For example, in the process of generating an information model corresponding to the specific product, a resource description framework (RDF) graph may be temporarily used. The classifier  202  may perform image recognition on this RDF graph and determine whether the RDF graph conforms to a specific international standard model. In addition, the classifier  202  may be trained to improve the accuracy of its image recognition and/or classification. 
     It is noted that, in the embodiment of  FIG. 2 , the classifier  202  is implemented in the form of software and stored in the storage circuit  22 . However, in another embodiment, the classifier  202  may also be implemented in the form of a hardware circuit, for example, as one or more image processing chips. The classifier  202  implemented in the form of a hardware circuit may be installed inside the processor  21  or independent of the processor  21 . 
       FIG. 3  is a schematic view of an operation of automatically establishing an information model according to an embodiment of the disclosure. Referring to  FIG. 2  and  FIG. 3 , assuming that the description data  201  includes a description data  31 , the processor  21  may obtain the description data  31  of a product  301  from the storage circuit  22 . For example, the description data  31  may include a product catalog data, a circuit diagram, a mechanical structure diagram, an international product classification number, a webpage link, and/or a communication interface specification data related to the product  301 . The processor  21  may analyze the description data  31  and generate an RDF graph  32  according to the analysis result of the description data  31 . 
     Then, the processor  21  may compare the RDF graph  32  with international standard models (also referred to as first international standard models)  33 ( 1 ) to  33 ( n ). “n” may be any positive integer. According to the comparison result, the processor  21  may automatically establish an information model  34  corresponding to the product  301 . The information model  34  may be configured to provide information related to the product  301 . For example, the information related to the product  301  may include information recorded in the product catalog of the product  301 , information recorded in the circuit diagram of the product  301 , information recorded in the mechanical structure diagram of the product  301 , information of the international product classification number of the product  301 , information of the webpage link of the product  301 , the communication interface specification data of the product  301 , and/or any other information related to the product  301 . 
     In an embodiment, the processor  21  may communicate with a user via a dialogue service interface. Then, the processor  21  may obtain at least part of the data in the description data  31  according to the communication result. For example, the dialogue service interface may be presented on a screen of the device  11  and/or output voice messages via a speaker of the device  11  to communicate (or interact) with the user. For example, the user may input a data required by the processor  21  via this dialogue service interface, such as a type of the product  301 , a name of the product  301 , an identification (e.g., an ID, a webpage link, or a QR code) of the product  301 , a function of the product  301 , an attribute of the product  301 , an applicable communication interface specification of the product  301 , and/or other useful information. The processor  21  may add the data received from the user via the dialogue service interface into the description data  31  as at least part of the data in the description data  31 . 
     In an embodiment, the original description data  31  is uploaded by the user (e.g., uploading to the storage circuit  22 ). In an embodiment, the processor  21  may determine whether the content of the description data  31  uploaded by the user is sufficient and/or determine the lacking content, so as to determine whether to activate the dialogue service interface and/or determine the type of data to request the user via the dialogue service interface. 
     In an embodiment, the processor  21  may analyze a text data corresponding to the description data  31  by natural language processing (NLP). For example, the processor  21  may convert the description data  31  into a text data. This text data may record at least part of the information mentioned in the description data  31  in a computer-readable text format. The processor  21  may generate the RDF graph  32  according to the analysis result of the text data. 
     In an embodiment, in the process of generating the RDF graph  32 , the processor  21  may analyze the text data corresponding to the description data  31  by natural language processing to generate an RDF. For example, the processor  21  may tag parts of speech for at least part of the data content of the text data corresponding to the description data  31  according to a predetermined professional vocabulary. Then, the processor  21  may perform a series of natural language processing on the tagging result to generate an RDF. Afterwards, the processor  21  may generate the RDF graph  32  according to the RDF. 
     In an embodiment, the text data corresponding to the description data  31  may include information conforming to a production system communication data exchange format (.aml). This information may be applied to a production system to describe information such as a topological structure data, a mechanical data, a system data associated with electricity, gas, oil, etc., a product function description data, and a control process data, and the information belongs to an open international standard. The processor  21  may analyze the information conforming to the production system communication data exchange format (.aml) to generate the RDF graph  32 . 
     In an embodiment, the processor  21  may annotate the text data corresponding to the description data  31  in relation to at least one industrial semantic vocabulary. Then, the processor  21  may map the annotated text data to a web ontology language (OWL) format. Afterwards, the processor  21  may generate the RDF graph  32  according to the mapping result. For example, the industrial semantic vocabulary may include aml, rami, sto, etc. 
       FIG. 4  is a schematic view of an RDF according to an embodiment of the disclosure.  FIG. 5  is a schematic view of an RDF graph according to an embodiment of the disclosure. Referring to  FIG. 4  and  FIG. 5 , by natural language processing, the processor  21  may analyze the text data corresponding to the description data  31  and/or analyze the information in the text data which conforms to the production system communication data exchange format (.aml) to generate an RDF  41 . Then, the processor  21  may query at least one industrial semantic vocabulary and map the RDF  41  to the web ontology language format according to the query result to generate an RDF graph  51 . It is noted that the RDF graph  51  may serve as an example of the RDF graph  32 . However, the contents shown in the RDF  41  in  FIG. 4  and the RDF graph  51  in  FIG. 5  are only examples and may be adjusted according to the practical requirements. 
     Referring back to  FIG. 3 , in an embodiment, after the RDF graph  32  is initially obtained, the processor  21  may determine whether a defect is present in the RDF graph  32 . For example, the defect may include information which cannot be automatically annotated in the RDF graph  32 . If a defect is present in the RDF graph  32 , the processor  21  may receive a user operation and add a supplementary data into the RDF graph  32  according to the user operation. Taking  FIG. 5  as an example, assuming that, in the RDF graph  51 , it is not clear to which vocabulary the word “With” belongs, so a corresponding annotation cannot be generated (i.e., a defect is present). In an embodiment, the processor  21  may allow the user to annotate in the RDF graph  51  the vocabulary to which the word “With” belongs, e.g., belonging to “sto” or other vocabularies, by this user operation. In addition, if there is no defect in the RDF graph  32 , the processor  21  may skip this operation. 
     In an embodiment, the processor  21  may compare, by the classifier  202 , the RDF graph  32  with the international standard models  33 ( 1 ) to  33 ( n ). For example, the processor  21  may input the RDF graph  32  to the classifier  202 . Then, the processor  21  may establish the information model  34  corresponding to the product  301  according to the output of the classifier  202 . 
       FIG. 6  is a schematic view showing training and using a classifier according to an embodiment of the disclosure. Referring to  FIG. 6 , in an embodiment, the processor  21  may train the classifier  202  by using training datasets  61 ( 1 ) to  61 ( n ) corresponding to the international standard models  33 ( 1 ) to  33 ( n ). For example, a training dataset  61 ( i ) among the training datasets  61 ( 1 ) to  61 ( n ) may include one or more folded graphs of the model topology corresponding to an international standard model  33 ( i ). The trained classifier  202  may be used to identify a graph class which conforms to at least one of the international standard models  33 ( 1 ) to  33 ( n ). In an embodiment, as the types of the training datasets  61 ( 1 ) to  61 ( n ) used to train the classifier  202  increases and/or the number of the folded graphs in the topology model increases, the identification capability of the trained classifier  202  with respect to the graph class also improves. 
     In an embodiment, the classifier  202  may generate output values V( 1 ) to V(n) according to the inputted RDF graph  32 . An output value V(i) may reflect a probability value by which the classifier  202  believes that the currently inputted RDF graph  32  conforms to the international standard model  33 ( i ). “i” may be any positive integer between 1 and n. A larger output value V(i) means a higher probability by which the classifier  202  believes that the RDF graph  32  conforms to the international standard model  33 ( i ). Conversely, a smaller output value V(i) means a lower probability by which the classifier  202  believes that the RDF graph  32  conforms to the international standard model  33 ( i ). In an embodiment, the sum of V( 1 ) to V(n) may be “1”. 
     In an embodiment, the processor  21  may determine whether the output value V(i) of the classifier  202  is greater than a predetermined value. For example, this predetermined value may be “0.75”. If the output value V(i) is greater than the predetermined value, the processor  21  may determine that the RDF graph  32  conforms to the international standard model  33 ( i ). However, in an embodiment, if none of the output values V( 1 ) to V(n) of the classifier  202  is greater than the predetermined value, the processor  21  may determine that the current classifier  202  fails to identify the graph class of the RDF graph  32 . 
     In an embodiment, if graph class identification fails (i.e., none of the output values V( 1 ) to V(n) is greater than the predetermined value), the processor  21  may receive a user operation and determine an international standard model (also referred to as a second international standard model) to which the RDF graph  32  conforms according to the user operation. It is possible that this second international standard model is not included in the international standard models  33 ( 1 ) to  33 ( n ). In other words, once the classifier  202  cannot correctly identify the international standard model to which the RDF graph  32  conforms, the processor  21  may immediately allow the user to intervene to manually determine the international standard model to which the RDF graph  32  truly conforms. 
     In an embodiment, after determining the international standard model (i.e., the second international standard model) to which the RDF graph  32  conforms according to the user operation, the processor  21  may update the international standard models  33 ( 1 ) to  33 ( n ) according to the second international standard model. For example, the processor  21  may add the second international standard model into the international standard models  33 ( 1 ) to  33 ( n ). In addition, the processor  21  may add the training dataset corresponding to the second international standard model into the training datasets  61 ( 1 ) to  61 ( n ) to expand the types of the training datasets  61 ( 1 ) to  61 ( n ). Accordingly, the trained classifier  202  may later be used to identify an RDF graph conforming to the second international standard model. 
     It is noted that, in the above embodiment of  FIG. 6 , the total number of the output values V( 1 ) to V(n) is equal to the total number of the international standard models  33 ( 1 ) to  33 ( n ) (or the training datasets  61 ( 1 ) to  61  ( n )). However, in another embodiment of  FIG. 6 , it is possible that the total number of the output values V( 1 ) to V(n) is not equal to the total number of the international standard models  33 ( 1 ) to  33 ( n ) (or the training datasets  61 ( 1 ) to  61 ( n )), and the disclosure is not limited thereto. 
     In an embodiment, the processor  21  may further extract at least part of the information from the RDF graph  32  according to the international standard model to which the RDF graph  32  conforms. Then, the processor  21  may store the extracted information (e.g., storing in the storage circuit  22 ) based on an OPC Unified Architecture (OPC UA). In other words, the processor  21  may further map (or convert) the RDF graph  32  from the original specific international standard model to a model architecture conforming to the OPC UA for storage. Accordingly, other devices (e.g., remote devices) may connect to the device  11  and access the above information related to the product  301  based on the more universal OPC UA model architecture. Compared with the case where different client-end devices may access the information model by different international standard models, storing the information model based on the more universal OPC UA model architecture can further improve the convenience of accessing the information model among the client-end devices. 
     Referring back to  FIG. 1 , in an embodiment, the system  10  further includes a server  12 . The device  11  may be connected to the server  12  via a network (e.g., the Internet). In an embodiment, the processor  21  may store the established information model  34  corresponding to the product  301  to the server  12  via the I/O interface  23 , so as to allow other client-end devices (or remote devices) to connect to the server  12  to access the information model  34 . Specifically, the information model  34  transmitted to the server  12  may be stored based on a specific international standard (e.g., RAMI, SSN, AML, SCORVoc, etc.) or a more universal OPC UA model architecture, and the disclosure is not limited thereto. 
     An embodiment of the disclosure provides a non-volatile computer-readable recording medium. The non-volatile computer readable recording medium stores a program code. A processor (e.g., the processor  21  in  FIG. 2 ) in a computer may execute (or run) this program code to execute the above functions and operations. 
       FIG. 7  is a flowchart of a method for establishing an information model according to an embodiment of the disclosure. Referring to  FIG. 7 , in step S 701 , a description data of a product is obtained. In step S 702 , an RDF graph is generated according to the description data. In step S 703 , the RDF graph is compared with at least one first international standard model. In step S 704 , an information model corresponding to the product is established according to a comparison result, where the information model is configured to provide information related to the product. 
     Details of each step in  FIG. 7  have been described as above and will not be repeatedly described herein. It is noted that each step in  FIG. 7  may be implemented as multiple program codes (e.g., software modules) or circuits (e.g., circuit modules), and the disclosure is not limited thereto. In addition, the method in  FIG. 7  may be applied in conjunction with the above exemplary embodiments or may be applied alone, and the disclosure is not limited thereto. 
     In summary of the above, in the embodiments of the disclosure, an information model corresponding to the product is automatically generated and established according to the description data of the product, so as to provide information related to the product through the information model. Accordingly, different manufacturers (e.g., client-end devices) may access the information model according to the internationally recognized standard to obtain information related to the product. 
     Compared to the conventional approach which requires to manually refer to specification descriptions of the product to establish the required information model, in the embodiments of the disclosure, the information model of the product is automatically established according to the description data of the product, which can effectively improve the efficiency of establishing the information model and the accuracy of the data. In addition, compared with the conventional approach in which the information models established among different client-end devices may not be compatible with each other, in the embodiments of the disclosure, the established information model conforms to the common international standard, so multiple client-end devices may easily access the information model established by each other to obtain the required product information, which thereby improves the convenience of the information model in information exchange. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed embodiments without departing from the scope or spirit of the disclosure. In view of the foregoing, it is intended that the disclosure covers modifications and variations provided that they fall within the scope of the following claims and their equivalents.