OPC UA Server Data Crawling and Search System
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Solution Overview
Problem
Current industrial automation systems face inefficiencies in searching for information stored in OPC UA servers, requiring manual browsing and lacking robustness in dynamically changing environments, which is time-consuming and often incomplete due to the need for expert knowledge and lack of standardized information models.
Innovation Solution
An industrial information identification and retrieval system comprising a crawler, search engine, result processor, and web UI that identifies OPC UA servers, extracts and stores data items, searches, and ranks results using domain knowledge and similarity metrics, enabling efficient and accessible information retrieval within industrial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual browsing of OPC UA servers is used to search for information, then information can be retrieved, but the process is time-consuming and requires expert knowledge
Solution Approach 1:
The system performs preliminary actions by automatically crawling and indexing data from OPC UA servers before search requests are made. The crawler component proactively discovers devices, extracts data items, and stores them in a searchable database structure in advance, eliminating the need for manual browsing at search time and significantly reducing search time while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary search system that mediates between users and OPC UA servers. Instead of directly browsing servers, users interact with the search interface which queries the pre-indexed database, and results are returned through the interface. This intermediary layer abstracts the complexity of OPC UA navigation and provides user-friendly search capabilities.
2Loss of information
If manual browsing of plant catalogues is used to find information, then information can be located, but the process is incomplete and requires expert knowledge
Solution Approach 1:
The system implements self-service by automatically discovering devices and extracting data items from OPC UA servers without human intervention. The crawler autonomously navigates the OPC UA address space, identifies data items, and populates the database, ensuring comprehensive information capture without requiring expert knowledge of OPC UA structures.
Solution Approach 2:
The search interface acts as an intermediary that simplifies information access. It provides a user-friendly interface where users can search using simple terms rather than navigating complex OPC UA hierarchies, making the system accessible to non-experts while maintaining complete information retrieval through the underlying automated crawling mechanism.
3Productivity
If standardized information models are implemented for OPC UA servers, then information retrieval becomes more systematic, but implementation complexity increases
Solution Approach 1:
The system applies parameter changes by transforming raw OPC UA data items into a standardized search-ready format during the crawling process. Data items are extracted with their properties (name, description, data type, etc.) and stored in a normalized database structure that enables efficient querying. This standardization occurs automatically during data ingestion rather than requiring complex model implementation at the server level.
Solution Approach 2:
The crawl controller serves as an intermediary that handles the complexity of OPC UA information models. It manages the crawling process, interprets OPC UA address space structures, and transforms complex server data into simple searchable records. This mediator layer isolates the complexity from both the users and the OPC UA servers, allowing standardized processing without requiring complex model implementation anywhere in the system.
4Loss of time
If automated crawling is implemented to discover OPC UA servers, then information retrieval becomes faster, but system complexity increases
Solution Approach 1:
The system segments the information retrieval functionality into distinct modular components: a crawler for automatic discovery, a crawl controller for managing the crawling process, a search engine for querying, and a user interface for interaction. Each component has a specific responsibility and can be developed, maintained, and scaled independently. This segmentation manages complexity by distributing functionality across specialized modules rather than requiring a monolithic complex system.
Solution Approach 2:
The crawl controller acts as an intermediary that manages the complexity of automated crawling. It coordinates server discovery, controls the crawling process, handles authentication and connection management, and processes extracted data. By centralizing these complex operations in a dedicated controller component, the system achieves automated fast discovery without requiring the entire system to be complex, as other components can remain simple and focused on their specific tasks.
Data Source
AI summary
An industrial information identification and retrieval system includes: a crawler; a search engine; a result processor; and a web user interface “web UL” The crawler identifies devices and their associated Open Platform Communication Unified Architecture “OPC UA” servers within a network as identified OPC UA servers. The crawler browses the identified OPC UA servers and extracts and stores data items in a database as extracted data items. The search engine searches the extracted data items and provides search results to the result processor. The result processor determines a score for each search result. The web UI presents the search results according to the scores.