KR102638529B1 - Ontology data management system and method for interfacing with power system applications - Google Patents

Abstract

An ontology data management system and method for interfacing with a power system application according to an embodiment provides a system including an ontology storage for efficient interfacing with an application that generates power information. Through an embodiment, an ontology database can be built from the perspective of data exchange with an application, and only the information needed to run the application can be converted to CIM-XML based on profile information for the application, and the converted data can be exchanged. In addition, by building an entire ontology database through the example, adding information needed for the application, and extracting only the data needed for the application, it is differentiated from the conventional technology that stores ontology data by profile.

Description

Ontology data management system and method for interfacing with power system applications {ONTOLOGY DATA MANAGEMENT SYSTEM AND METHOD FOR INTERFACING WITH POWER SYSTEM APPLICATIONS}

This disclosure relates to a power system ontology data management system for executing power system applications and an interface with applications. Specifically, a system that provides efficient management of CIM-based power system data specialized for application execution and a data exchange interface with applications. and methods.

Unless otherwise indicated herein, the material described in this section is not prior art to the claims of this application, and is not admitted to be prior art by inclusion in this section.

Ontology is a tool for structuring and expressing knowledge in artificial intelligence and knowledge engineering, and is used to indicate the meaning of knowledge and the connection between meanings. Ontologies allow computer systems to process and reason about complex knowledge. To exchange information between systems based on ontology, several communication models and protocols are used.

IEC61970 is a data exchange information model standard for power information between power EMS developed in IEC TC57 WG13. The three standards IEC61970 (transmission and substation power model), IEC61968 (distribution and metering), and IEC62325 (electricity trading) are defined as CIM (Common Information Model). The data exchange information model standard models (profiles) all logical, physical, and behavioral information of the power system based on UML for CIM-based power information data exchange, and the XML format (CIM-XML) of the data and information for exchanging the data. Define the expression method. Additionally, the data exchange information model standard excludes lower layer protocols or communication methods for data exchange.

Figure 1 is a diagram showing the system configuration for ontology database construction and CIM-XML data exchange. Referring to Figure 1, the system for constructing an ontology database and exchanging CIM-XML data includes a CIM object management unit 10, an ontology database 20 storing static information, and an ontology database 30 storing real-time information. ), a relational database 40, an ontology database, and a database 50 that stores relational data mapping information. EMS (Energy Management System) and power applications generate power information according to the CIM format and exchange data with each other through CIM-XML.

Figure 2 is a diagram showing a process for how to use the CIM standard information model according to the prior art.

Referring to Figure 2, a profile such as RDFS can be created using the schema created after extracting only the necessary parts from the UML model. And EMS can exchange data with external systems using XML documents based on RDF (Resource Description Framework) that protects the schema.

Profiles defined in the CIM standard include Equipment Profile based on IEC 61970-452, IEC 61970-456 Analog Measurements Profile, and Discrete Measurements Profile. The prior art has the form of individually constructing various profile-specific repositories based on these profiles and storing measurement ontology information, equipment ontology information, and connection structure ontology information, respectively.

Figure 3 is a diagram showing an example of performing data exchange by integrating information in the profile extractor and each ontology repository when exchanging data with an application in a power system operating system or EMS based on the prior art.

Referring to Figure 3, in order to configure information about the ontology repository in a form that is easy to delete and add, profile information for data exchange for each application applied to EMS must be defined, and data information is modified for each application.

The process of modifying data for each application requires processing a large amount of data, which may result in data inconsistencies or contradictions. In addition, with conventional technology, it is difficult to efficiently store and search large amounts of ontology data, and the process of integrating ontology data from various sources is difficult.

1. US Patent Registration No. 11714792 (2023.08.01) 2. US Patent Registration No. 11714956 (2023.08.01) 3. U.S. Patent Publication No. 20230206661 (2023.06.29) 4. US Patent Registration No. 11663411 (2023.05.30) 5. Japanese Patent Publication No. 34551253 (2022.12.08) 6. Korean Patent Registration No. 10-2141784 (2020.07.31) 7. Korean Patent Registration No. 10-2113050 (2020.05.14)

An ontology data management system and method for interfacing with a power system application according to an embodiment provides a system including an ontology storage for efficient interfacing with an application that generates power information.

Through an embodiment, an ontology database can be built from the perspective of data exchange with an application, and only the information needed to run the application can be converted to CIM-XML based on profile information for the application, and the converted data can be exchanged.

In addition, by building an entire ontology database through the example, adding information needed for the application, and extracting only the data needed for the application, it is differentiated from the conventional technology that stores ontology data by profile.

In addition, through the example, redundant data loading elements are eliminated by generating CIM-XML containing only the information necessary for the application.

Additionally, in the embodiment, even when the profile changes due to adding or modifying an application, only specific data related to the change can be changed.

Additionally, the embodiment provides an ontology storage structure to streamline the data search process to generate CIM-XML for data exchange with applications.

In the embodiment, the role of the profile extraction unit, which extracts and merges information from the storage for each profile, is reduced. For example, the profile extractor only performs the role of loading application interface data defined in the profile into the ontology repository.

Additionally, in the embodiment, a profile, which is an interface necessary for running an application that generates power information, is created. Since each application requires equipment information, equipment status, measurement information, etc., in the embodiment, the required profile information is built in the ontology repository. Through this, when querying data to create CIM-XML necessary for data exchange, profile information can be used to query.

An ontology data management system for interfacing with a power system application according to an embodiment includes an ontology database for interfacing with an application that generates power information; A data management server that converts only the information necessary for application operation into CIM-XML based on profile information, which is input and output data required for application operation, and exchanges the converted CIM-XML with the application; Includes.

The ontology data management system and method for interfacing with power system applications as described above improves data inquiry efficiency by providing an ontology storage.

Through an embodiment, data processing efficiency can be improved by reducing the role of the profile extractor to loading the application interface defined in the profile into the ontology storage with the corresponding information.

In addition, through the embodiment, the efficiency, reliability, and safety of the power system can be improved by efficiently managing and processing data related to the power system.

The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

Figure 1 is a diagram showing the system configuration for ontology database construction and CIM-XML data exchange.
Figure 2 is a diagram showing a process for how to use the CIM standard information model according to the prior art.
Figure 3 is a diagram showing an example of performing data exchange by integrating information in the profile extractor and each ontology repository when exchanging data with an application in a power system operating system or EMS based on the prior art.
Figure 4 is a diagram showing an ontology data management system for interfacing with a power system application according to an embodiment.
Figure 5 is a diagram showing the configuration of a data management server according to an embodiment
6 is a diagram for explaining the concept of profile information according to an embodiment
Figure 7 is a diagram showing a data source of an ontology repository according to an embodiment
Figure 8 is a block diagram of a data management server according to an embodiment
Figure 9 is a diagram showing an example of a model or module stored in a data management server according to an embodiment
Figure 10 is a diagram showing the data processing flow of the ontology data management server according to an embodiment

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of the present invention are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

In this specification, 'part' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more pieces of hardware, and two or more units may be realized using one piece of hardware.

In this specification, some of the operations or functions described as being performed by a terminal, apparatus, or device may instead be performed on a server connected to the terminal, apparatus, or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

Hereinafter, the present invention will be described in detail with reference to the attached drawings.

Figure 4 is a diagram showing an ontology data management system for interfacing with a power system application according to an embodiment.

Referring to FIG. 4, an ontology data management system for interfacing with a power system application according to an embodiment may be configured to include an ontology database 200 and a data management server 100.

In embodiments, power system applications are various types of software applications used to monitor, control, and analyze power systems. These applications enable power system operators, engineers and managers to maintain and improve the stability, efficiency and reliability of power networks.

The ontology database 200 stores a series of data for interfacing with an application that generates power information. In an embodiment, the ontology database 200 may include a profile repository 210, a relational data repository 220, an ontology repository 230, and a mapping information repository 240.

In an embodiment, the profile storage 210 stores profile information, which is a file defining input and output data required for application operation in an EMS (Energy Management System).

In the embodiment, profile information is a file that defines input and output data required for application operation, as shown in FIG. 6. In the embodiment, the profile storage 210 refers to a storage for defining profiles. In the embodiment, the profile storage 210 is a database designed for the purpose of extracting information required for each application from the ontology storage when data is requested from the upper EMS. The profile storage 210 stores data defining input profiles and output profiles for each application. In the embodiment, when data is transmitted from the data management server 100 to the EMS, only necessary information can be extracted and transmitted through the profile storage 210 of the ontology database 200.

The relational data store 220 stores time series information including real time information. Time Series Information is information that represents patterns, trends, and changes in recorded data over time. Time series information includes data points that occur in a series of time sequences. In the embodiment, time series information required to run the application is accumulated in a relational database.

The ontology repository 230 is a system used to structure and express knowledge, and can store information, concepts, relationships, etc. related to the power system. In an embodiment, the ontology repository 230 stores static information. Static system information refers to information that has a fixed value regardless of the passage of time and does not change. For example, an object's unique identification number, manufacturer information, model name, size, color, etc. may be static information. Static information describes the characteristics of an entity and does not change over time. Specifically, in an embodiment, static information may include power facility information and attribute information. In an embodiment, the ontology storage 230 may store static system information required to run an application in a graph form.

The mapping information storage 240 stores a series of data mapping information necessary for system operation. Mapping information may include data defining the mapping relationship, ontology measurement value key, relational database measurement value key, measurement-related mRID, and facility type information. In the embodiment, the main mapping relationships are ontology facility keys and relational database measurement value keys, and in the embodiment, the data management server 100 defines several mapping relationships necessary for system operation.

In an embodiment, the data management server 100 converts only the information required to run the application into CIM-XML based on profile information for the application and exchanges the converted CIM-XML with the power system application to extract data to be transmitted to the EMS or application. do.

For this purpose, the data management server 100 extracts profile information about the application. Profile information may include data structures, fields, properties, connections, etc. required by the application. Thereafter, the data management server 100 selects or filters only the information directly necessary for running the application from the profile information. During the filtering process, information that is not necessary for the application is excluded. Afterwards, the data management server 100 converts the filtered information into CIM-XML, a standard format of CIM. CIM is an international standard for power and energy system information modeling and uses the XML format to represent data. Afterwards, the data management server 100 transmits or stores the converted CIM-XML data to the application. In the embodiment, CIM-XML data is appropriately utilized according to the requirements of the application.

In an embodiment, the data management server 100 analyzes all profile information to determine what data is present, the format of each data field, and what data can affect application operation in order to filter information required to run the application. Find out, etc. Afterwards, the data management server 100 analyzes the requirements of the application. In embodiments, data management server 100 may analyze application requirements to determine what information is associated with the core functionality of the application, what information is essential, and what information is optional. Thereafter, the data management server 100 identifies and extracts information directly necessary for running the application, and excludes or masks unnecessary or sensitive information.

In the embodiment, information directly required to run the application includes user authentication and authorization information, core settings and environment variable information, business data, real-time data and notifications, logs and audit information, cache and performance optimization data, and survey and analysis data. can do. In an embodiment, user authentication and authorization information is information needed to identify and authenticate a user of an application. Specifically, it includes the user's ID, password, token, etc., and includes user and administrator authority and role information for managing access control and security of the application.

Core configuration and environment variable information includes the core configuration information and environment variables of the application. Specifically, it may include database connection information, API keys of external services, language settings, etc. In an embodiment, business data includes data related to the main functions of the application. Real-time data and notification data are data related to the real-time functions of the application. Log and audit information is necessary for operating and monitoring the application, and is information needed to track the operation of the application and solve problems. Cache and performance optimization data is cache data or optimization-related information to improve application performance. Survey and analysis data may include survey or analysis data to improve the user experience of the application.

The type of information required may vary depending on the purpose and function of each application, and various types of information in addition to the types mentioned above may be required to run the application.

Afterwards, the data management server 100 refines the selected information to maintain data consistency and quality. In embodiments, tasks such as removing data duplication or correcting inaccurate data may be performed. Afterwards, the data management server 100 converts or formats the selected information into a format that can be easily used in the application. The data management server 100 can adjust the format of data or, if necessary, combine data to create a new form. Afterwards, the data management server 100 sets up a data access mechanism to provide filtered information to the application. For example, interaction between the application and the data management server is implemented through database queries, API calls, etc.

Additionally, in the embodiment, the data management server 100 deletes unnecessary data that is not required to run the application during the filtering process. For example, the data management server 100 may delete sensitive personal information, security-related information, unnecessary duplicate data, expired data, data for testing and development, unnecessary log data, incorrect or old information, and unused data. Security-related information deleted in the embodiment may include information that may affect the security of the application, for example, a password hash value or an encryption key. Unnecessary duplicate data is duplicate data that can degrade application performance or damage data consistency. Embodiments may reduce database size and optimize data manipulation operations by removing redundant information. Expired data is data that is no longer valid or used in the application. For example, it may include expired session information or user profiles that have not been used for a long time. Unnecessary log data is data that is not necessary for operation or problem solving among log data.

CIM-XML (Common Information Model-Extensible Markup Language) is one of the standard data formats for exchanging and sharing information in power systems. CIM (Common Information Model) is a model that provides a standardized way to model and express power system and energy-related information. CIM is a standard developed by the International Electrotechnical Commission (IEC) and the American Electrotechnical Commission (IEEE), and is used for power system operation, monitoring, planning, and interconnection. In an embodiment, CIM-XML is a data format based on XML (Extensible Markup Language) for exchanging and sharing CIM data. XML is a format that expresses data in a hierarchical structure and gives semantic meaning using tags. It facilitates data exchange between computers by clearly expressing the structure and meaning of data.

In an embodiment, CIM-XML is used to exchange information between other systems by expressing power system-related data in XML format according to the CIM model. This enables standardized data exchange between various components or systems of the power system and supports information sharing and efficient work in various fields such as operation, monitoring, and planning of the power system.

Additionally, when the profile changes due to adding or modifying an application, the data management server 100 changes only specific data related to the change. To this end, the data management server 100 first determines whether there are any changes, including any additions or modifications, to the application. Changes may include adding new fields or changing the format of existing fields. Afterwards, the data management server 100 analyzes how the changed content is related to the profile data. For example, the data management server 100 checks which fields have been added and which field values have been changed. Afterwards, the data management server 100 extracts only data related to the changed part. For example, only data corresponding to added or modified fields is selected and extracted. In an embodiment, the data management server 100 updates profile data using the extracted data. For example, added fields are updated with new values, and modified fields are updated with changed values.

Figure 5 is a diagram showing the configuration of a data management server according to an embodiment.

Referring to FIG. 5, the data management server 100 according to the embodiment may be configured to include a CIM object management unit 110 and a profile extraction unit 120. The term 'part' used in this specification should be interpreted to include software, hardware, or a combination thereof, depending on the context in which the term is used. For example, software may be machine language, firmware, embedded code, and application software. As another example, hardware may be a circuit, processor, computer, integrated circuit, integrated circuit core, sensor, Micro-Electro-Mechanical System (MEMS), passive device, or a combination thereof.

The CIM object management unit 110 determines the mRID status of the facility in the CIM-XML data. For example, the CIM object management unit 110 determines whether an mRID (master resource identifier) is assigned to the facility in the CIM-XML data and whether the mRID is applied according to the rules. At this time, when the CIM object management unit 110 determines that there is a facility for which an mRID has not been assigned or that the mRID assignment method is not applied according to preset rules, the CIM object management unit 110 redefines the mRID for the facility and stores it in the mapping information storage (240). ) to save it.

A master resource identifier (mRID) is an identifier used to uniquely identify resources in the energy and power systems field. This identifier is defined according to CIM and is used for unique identification of various elements or resources of the power system. CIM is a model that provides a standardized way to model and express information in power systems, and mRID is used to uniquely identify each element within this model. This manages connectivity between various components or resources of the power system and facilitates data exchange and integration. Additionally, mRID is a type of unique address or name that is used to accurately identify and share power system information between various systems or applications. This helps maintain data consistency and efficiency.

In the embodiment, the CIM object management unit 110 first collects CIM-XML data to determine the mRID status of the equipment in the CIM-XML data. CIM-XML data contains information related to the power system and includes information representing the attributes and relationships for each facility. Afterwards, the CIM object management unit 110 checks whether an mRID exists for each facility. mRID is an identifier that uniquely identifies each facility and must not be duplicated. Therefore, the CIM object management department checks whether there is an mRID for all equipment.

Additionally, the CIM object management unit 110 performs mRID rule checking. In embodiments, mRID must follow certain rules. These rules may vary depending on the CIM standard and the policies of the system in question. The CIM object management unit checks whether each mRID is created according to the rules. Specifically, it checks whether the mRID format, length, character limit, etc. follow the rules. Afterwards, the CIM object management unit 110 checks for duplicate mRID. Since the mRID for all equipment must be unique, the CIM object management unit 110 checks whether there is a duplicate mRID to prevent duplication. Additionally, in the embodiment, the CIM object management unit 110 records logs and audit information about the mRID assignment and rule inspection process.

In the embodiment, the CIM object management unit 110 determines the mapping relationship between various data defined through the mapping information storage 240 and processes the data.

The profile extraction unit 120 checks information on the profile in which the interface required for the application is defined and provides data about the corresponding facility to the ontology storage 230. The form of data provided in the embodiment is not standardized.

To this end, when an application requests a profile that defines necessary functions or data interfaces, the profile extraction unit 120 extracts information necessary for the application and checks the contents of the profile. In an embodiment, the content of the profile includes what type of data is needed and in what format it should be expressed.

In an embodiment, the profile extractor 120 extracts and verifies information required for an application and then interacts with the ontology repository 230 to create or provide data for the corresponding facility. For example, the profile extractor 120 checks whether data corresponding to the ontology repository exists based on the extracted profile information. If the profile does not match the ontology model or contains inaccurate information, the profile extractor 120 may detect an error. Thereafter, the profile extraction unit 120 creates data about the corresponding facility or provides already existing data to the ontology repository 230 based on the confirmed profile information. In this process, data is created according to the ontology model, and the data accurately reflects the characteristics and properties of the facility. In an embodiment, the profile extraction unit 120 may tag or label data regarding information necessary for the operation of an application for the corresponding equipment and store it in the ontology storage 230. For example, the profile extractor 120 extracts a profile defining information needed for an application. In an embodiment, the profile describes what functionality or data interfaces the facility requires. Depending on the extracted profile information, data about the facility is identified as a tag or label. This tag or label serves to indicate the characteristics, purpose, and connectivity of the equipment, and explicitly expresses the information needed for the application. Thereafter, the profile extraction unit 120 stores or connects the tagged or labeled data to the ontology storage 230. At this time, the profile extraction unit 120 structures and stores information related to the facility according to the data model of the ontology repository. In an embodiment, data stored in the ontology storage 230 is managed by the profile extraction unit 120 along with a tag or label indicating information for which application the corresponding facility requires information. This allows you to track the information needed for each application and quickly access the information when necessary. Additionally, in the embodiment, because the data stored in the ontology storage 230 indicates which application the information is needed for through tags or labeling, it is possible to search for and utilize the information needed in other applications or systems.

Figure 7 is a diagram showing a data source of an ontology repository according to an embodiment.

Referring to FIG. 10, the ontology storage 230 according to the embodiment collects and stores system equipment information and equipment status information among equipment information in CIM-XML format from the CIM object management unit 110. Additionally, the ontology storage 230 may collect and store application information to be run among the application profile information from the profile extractor 120.

Figure 8 is a block diagram of a data management server according to an embodiment.

Referring to FIG. 8, the data management server 100 according to an embodiment may include a communication unit 150, a memory 170, and a processor 190.

Here, the configuration of the data management server 100 shown in FIG. 8 is only a simplified example. In one embodiment, the data management server 100 may include different configurations for performing the computing environment of the data management server 100, and only some of the disclosed configurations may configure the data management server 100.

In an embodiment, the server 100 is a computing system that provides services to other computers or devices in a computer network or stores and manages data. In an embodiment, the data management server 100 accepts requests from other computers or devices called clients, including EMS and applications, and provides responses or data to the requests.

The communication unit 150 can be configured regardless of the communication mode, such as wired or wireless, and can be configured with various communication networks such as a personal area network (PAN) and a wide area network (WAN). In addition, the communication unit 150 can operate based on the known World Wide Web (WWW) and uses wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. You can also use it. For example, the communication unit 150 may be responsible for transmitting and receiving data necessary to perform a technique according to an embodiment of the present disclosure.

Memory 170 may refer to any type of storage medium. For example, memory 170 may be a flash memory type, hard disk type, or multimedia card type. micro type), card type memory (e.g. SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory) ), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. This memory 170 may form the database shown in FIG. 1.

Memory 170 may store at least one instruction that can be executed by processor 190. Additionally, the memory 170 may store any type of information generated or determined by the processor 190 and any type of information received by the server 200. For example, the memory 170 stores RM data and RM protocols according to users, as will be described later. Additionally, the memory 170 stores various types of modules, instruction sets, or models.

The processor 190 may perform technical features according to embodiments of the present disclosure, which will be described later, by executing at least one instruction stored in the memory 170. In one embodiment, the processor 190 may be comprised of at least one core, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and tensor processing of a computer device. It may include a processor for data analysis and/or processing, such as a tensor processing unit (TPU).

In an embodiment, the memory 170 may store a set of instructions for performing ontology data management for interfacing with a power system application. The corresponding command set may include the CIM object management unit 110 and profile extraction unit 120 shown in FIG. 5.

Additionally, various types of modules or models may be stored in the memory 170. This is shown in Figure 8.

Figure 9 is a diagram showing an example of a model or module stored in a data management server according to an embodiment. Referring to FIG. 9, a database construction model (1), a data generation model (2), and a data extraction model (3) may be stored in the memory 120. Each module or model may be in the form of an application executable by the processor 190.

The database construction model (1) is a model for building an ontology database, which includes the profile storage (210), relational data storage (220), ontology storage (230), and mapping information required for ontology data management for interfacing with power system applications. This is a model for building a storage 240. In the embodiment, the database construction model (1) is a relational database model, an object-oriented database model, a document database model, and a key-value database model. Database Model) and graph database model, etc., but are not limited thereto.

The data generation model (2) is a model that generates profile information required for ontology data management. In the embodiment, the data generation model 2 is a model that generates profile information that describes and expresses the characteristics, behavior, and attributes of an entity. In the embodiment, the data generation model 2 includes, but is not limited to, a probabilistic data generation model, a statistical data generation model, a simulation model, a machine learning model, a time series data generation model, etc.

The data extraction model 3 is a model that extracts only data needed for power system applications, and may include a model that extracts necessary information from various data sources and a filtering model. The data extraction model (3) performs the process of identifying, extracting, and processing or analyzing desired data from a data source. In the embodiment, the data extraction model 3 is mainly used in various data collection tasks such as data integration, data mining, and web scraping. In the embodiment, the data extraction model 3 includes functions such as web scraping, API call and data extraction, database query, file system navigation and data extraction, image and video data extraction, data conversion and cleansing, and log data extraction. can be performed.

In the embodiment, when data is transmitted from the data management server 100 to the EMS, only necessary information can be extracted and transmitted through the profile storage 210 of the ontology database 200 through the extraction model 3.

A model in this specification may refer to any type of computer program that operates based on a network function, artificial neural network, and/or neural network. Throughout this specification, model, neural network, network function, and neural network may be used interchangeably. In a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer.

A deep neural network (DNN) may refer to a neural network that includes a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), transformer, etc. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

A neural network can be learned in at least one of supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. You can. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of supervised learning, data in which the correct answer is labeled (labeled data) can be used in each learning data, and in the case of unsupervised learning, data in which the correct answer is not labeled in each learning data (unlabeled data) can be used. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. Additionally, to prevent overfitting, methods such as increasing learning data, regularization, dropout to disable some nodes, and batch normalization layer can be applied.

In one embodiment, the model may borrow at least a portion of a transformer. The transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. A transformer may have a structure that receives a series of data, goes through encoding and decoding steps, and outputs a series of data of different types. In one embodiment, a series of data can be processed into a form that can be operated by a transformer. The process of processing a series of data into a form that can be operated by a transformer may include an embedding process. Expressions such as data token, embedding vector, embedding token, etc. may refer to data embedded in a form that a transformer can process.

In order for the transformer to encode and decode a series of data, the encoders and decoders within the transformer can be processed using an attention algorithm. The attention algorithm calculates the similarity of one or more keys for a given query, reflects the given similarity to the value corresponding to each key, and returns the reflected similarity value ( It may refer to an algorithm that calculates the attention value by weighting the values.

Depending on how queries, keys, and values are set, various types of attention algorithms can be classified. For example, if attention is obtained by setting the query, key, and value all the same, this may mean a self-attention algorithm. In order to process a series of input data in parallel, when the dimension of the embedding vector is reduced and attention is obtained by obtaining individual attention heads for each divided embedding vector, this refers to a multi-head attention algorithm. can do.

In one embodiment, the transformer may be composed of modules that perform a plurality of multi-head self-attention algorithms or a multi-head encoder-decoder algorithm. In one embodiment, the transformer may also include additional components other than the attention algorithm, such as embedding, normalization, and softmax. A method of configuring a transformer using an attention algorithm may include the method disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

Transformers can be applied to various data domains such as embedded natural language, segmented image data, and audio waveforms to convert a series of input data into a series of output data. In order to convert data with various data domains into a series of data that can be input to the transformer, the transformer can embed the data. Transformers can process additional data expressing relative positional or phase relationships between sets of input data. Alternatively, a series of input data may be embedded by additionally reflecting vectors expressing relative positional relationships or phase relationships between the input data. In one example, the relative positional relationship between a series of input data may include, but is not limited to, word order within a natural language sentence, relative positional relationship of each segmented image, temporal order of segmented audio waveforms, etc. . The process of adding information expressing relative positional or phase relationships between a series of input data may be referred to as positional encoding.

In one embodiment, the model includes at least one of a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM) network, a Deep Neural Network (DNN), a Convolutional Neural Network (CNN), and a Bidirectional Recurrent Deep Neural Network (BRDNN). It can be done and is not limited to this.

In one embodiment, the model may be a model learned using transfer learning. Here, transfer learning obtains a pre-trained model with the first task by pre-training a large amount of unlabeled learning data using a semi-supervised learning or self-learning method, and obtains a pre-trained model with a first task. It represents a learning method that implements a target model by fine-tuning the model to suit the second task and learning the labeled learning data using a supervised learning method.

Below, we will sequentially explain the ontology data management method for interfacing with power system applications. Since the operation (function) of the ontology data management method for interfacing with the power system application according to the embodiment is essentially the same as the function of the system, descriptions overlapping with FIGS. 1 to 8 will be omitted.

Figure 10 is a diagram showing the data processing flow of the ontology data management server according to an embodiment.

Referring to FIG. 10, in step S100, the data management server constructs ontology data for an interface with an application that generates power information in a database. In the S100 stage, an ontology database is built and the information needed for the application is added to the built database. In the embodiment, when the profile changes due to adding or modifying an application, only specific data related to the change is changed.

In step S200, the data management server converts only the information required to run the application into CIM-XML based on profile information, which is input and output data required for application operation, and exchanges the converted CIM-XML with the application. Step S200 includes the process of loading application interface data defined in the profile into the ontology repository. At this time, the profile in which the interface required for the application is defined is checked, and equipment data corresponding to the profile is provided to the ontology database. In addition, step S200 is a step of determining whether to grant an mRID to the facility in the CIM-XML data and whether the mRID is applied according to the rules; Includes. In the embodiment, if there is a facility that has not been assigned an mRID or the mRID assignment method is not applied according to the rules, the mRID for the facility is redefined, the redefined mRID is stored in the mapping information repository, and the mapping stored in the mapping information repository is performed. Determine the mapping relationship between data based on the relationship definition data.

The ontology data management system and method for interfacing with power system applications as described above improves data inquiry efficiency by providing an ontology storage.

Through an embodiment, data processing efficiency can be improved by reducing the role of the profile extractor to loading the application interface defined in the profile into the ontology storage with the corresponding information.

In addition, through the embodiment, the efficiency, reliability, and safety of the power system can be improved by efficiently managing and processing data related to the power system.

The disclosed content is merely an example, and various modifications and implementations may be made by those skilled in the art without departing from the gist of the claims, so the scope of protection of the disclosed content is limited to the above-mentioned specific scope. It is not limited to the examples.

Claims (20)

In the ontology data management system for interfacing with power system applications,
Ontology database for interfacing with applications that generate power information;
A data management server that converts only the information necessary for running the application into CIM-XML based on profile information, which is input and output data required for application operation, and exchanges the converted CIM-XML with the application; includes
the data management server; Is
In order to filter the information required to run the application, all profile information is analyzed to identify the type of data present in the profile information, the format of the data field, and data that affects the application run, and the requirements of the application are analyzed and analyzed. According to the results, the information directly needed to run the application is extracted, unnecessary information is excluded, sensitive information is masked,
the data management server; Is
Remove data duplication from the information needed to run the application, correct inaccurate data, format it into a format usable by the application, and set up a data access mechanism to provide the application with the information needed to run the filtered application.
the data management server; Is
During the filtering process, delete sensitive personal information, security-related information, unnecessary duplicate data, expired data, data for testing and development, unnecessary log data, incorrect or old information, and unused data that are not required to run the application. The security-related information to be deleted is information that may affect the security of the application and includes a password hash value and encryption key,
the data management server; Is
When the profile changes due to adding or modifying an application, identify changes including additions and modifications to the application in order to change only specific data related to the change. Analyze the relationship between changes and profile data, select and extract only the data related to changes, including added or modified fields, and use the extracted data to update profile data;
the ontology database; Is
Profile storage that stores profile information required for application operation in an EMS (Energy Management System);
Ontology repository that stores static information;
Relational data store that stores time series information; and
A mapping information repository that defines mapping relationships between data; includes
the data management server; Is
a profile extractor that loads application interface data defined in the profile into an ontology repository, checks a profile in which an interface required for the application is defined, and provides equipment data corresponding to the profile to the ontology database;
A CIM object management unit that determines whether mRID is assigned to equipment in CIM-XML data and whether mRID is applied according to the rules; includes
The CIM object management unit; Is
If there is a facility that has not been assigned an mRID, or the mRID assignment method is not applied according to the rules, redefine the mRID for the facility, store the redefined mRID in the mapping information repository, and store the mapping relationship stored in the mapping information repository. Determine mapping relationships between data based on definition data
the data management server; Is
An ontology data management system that builds an ontology database, adds information needed for the application to the constructed database, and generates CIM-XML in which the information needed for the application is stored.
delete delete delete delete delete delete delete In the ontology data management method for interfacing with power system applications,
(A) The data management server constructs ontology data for interfacing with an application that generates power information in a database; and
(B) the data management server converting only the information necessary for running the application into CIM-XML based on profile information, which is input and output data required for application operation, and exchanging the converted CIM-XML with the application; includes
Step (B) above; Is
In order to filter the information required to run the application, all profile information is analyzed to identify the type of data present in the profile information, the format of the data field, and data that affects the application run, and the requirements of the application are analyzed and analyzed. According to the results, the information directly necessary to run the application is extracted, unnecessary information is excluded, sensitive information is masked,
Remove data duplication from the information needed to run the application, correct inaccurate data, format it into a format usable by the application, and set up a data access mechanism to provide the application with the information necessary to run the filtered application.
During the filtering process, delete sensitive personal information, security-related information, unnecessary duplicate data, expired data, data for testing and development purposes, unnecessary log data, incorrect or old information, and unused data that are not required to run the application. The security-related information to be deleted is information that may affect the security of the application and includes a password hash value and encryption key,
When the profile changes due to application addition or modification, changes including additions and modifications to the application are identified in order to change only specific data related to the change. Analyze the relationship between changes and profile data, select and extract only the data related to changes, including added or modified fields, and use the extracted data to update profile data;
Step (B) above; Is
(B-1) Loading application interface data defined in the profile into the ontology repository; and
(B-2) determining whether mRID is assigned to the facility in CIM-XML data and whether mRID is applied according to the rules; contains and includes
Step (B-1) above; Is
Check the profile in which the interface required for the application is defined, and provide equipment data corresponding to the profile to the ontology database.
Step (B-2) above; Is
If there is a facility that has not been assigned an mRID, or the mRID assignment method is not applied according to the rules, redefine the mRID for the facility, store the redefined mRID in the mapping information repository, and store the mapping relationship stored in the mapping information repository. An ontology data management method that determines mapping relationships between data based on definition data.


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