KR-20260067853-A - Real-Time Energy Intensity Monitoring and Anomaly Cause Diagnosis System

KR20260067853AKR 20260067853 AKR20260067853 AKR 20260067853AKR-20260067853-A

Abstract

The present invention relates to a real-time energy unit monitoring and anomaly cause diagnosis system. It is a system that monitors the energy unit of equipment and processes in real time through a web-based logic tree, automatically analyzes the cause through root cause analysis when an anomaly occurs, and provides the results to the user in the form of a visual dashboard. This system consists of a data collection unit, a logic tree configuration unit, a cause diagnosis unit, and a visualization unit. The data collection unit communicates with various data sources, such as OPC and DataPARC, to store real-time data in a central database. The logic tree configuration unit allows users to set up logic trees using a drag-and-drop method. The cause diagnosis unit monitors unit energy consumption in real time and automatically analyzes the cause of anomalies when consumption deviates from standards. Finally, the visualization unit provides analysis results via a dashboard to intuitively convey the location and cause of anomalies to the user. Through the present invention, field workers can detect abnormalities in energy unit consumption in real time and respond quickly, thereby increasing the efficiency of energy unit consumption management.

Inventors

김원호
이건의

Assignees

주식회사 인포트롤테크놀러지

Dates

Publication Date: 20260513
Application Date: 20241106

Claims (2)

In a real-time energy unit monitoring and anomaly cause diagnosis system, a data collection unit that communicates in real-time with various data sources (OPC, DataPARC, InfluxDB, etc.) to collect equipment and process data, stores it in a central database to maintain a real-time data flow, and continuously provides data necessary for unit diagnosis; a logic tree configuration unit that provides a user interface allowing the user to configure and save an energy unit anomaly diagnosis logic tree by selecting a shape of a desired level from a shape list using a drag-and-drop method, thereby supporting the application of consistent diagnosis rules when an anomaly occurs; a cause diagnosis unit that monitors units in real-time, automatically analyzes the cause of the unit anomaly according to the configured logic tree if the unit deviates from a standard, and stores the results in a time-series database to enable future analysis and review; and a visualization unit that provides diagnosis results and unit data in a dashboard format, enabling the user to intuitively understand the unit status and the cause of the anomaly. A real-time energy unit monitoring and anomaly cause diagnosis system characterized by including
In claim 1, the visualization unit generates an energy intensity histogram and a major factor comparison chart based on the inquiry period and facilities selected by the user on a dashboard, based on data collected by the data collection unit; provides the cause of the energy intensity anomaly diagnosed by the cause diagnosis unit in the form of a table or chart; and provides the analysis results and the cause of the anomaly in the form of a dashboard linked with visualization tools such as Tableau and Microsoft Power BI, thereby enabling the user to intuitively understand the analysis results and the cause of the anomaly.

Description

Real-Time Energy Intensity Monitoring and Anomaly Cause Diagnosis System The present invention aims to provide a real-time energy intensity monitoring and anomaly cause diagnosis system capable of monitoring the energy intensity of equipment or processes and diagnosing the causes of abnormalities by constructing a web-based logic tree to interface with field data sources to monitor energy intensity in real time, and diagnosing the cause through root cause analysis when an energy intensity anomaly occurs, and delivering the diagnosis results to the user. Energy intensity is a critical management indicator in manufacturing and energy-intensive industries, widely utilized to measure and improve the energy efficiency of facilities and processes. Analyzing energy intensity requires processes such as data collection, preprocessing, filtering under identical operating conditions, calculating the intensity, and identifying the cause if the intensity increases. Conventional manual diagnosis of the causes of energy consumption anomalies is complex and time-consuming, and tends to rely on the operator's experience and subjective judgment. This not only increases the complexity and cost of maintenance but also presents limitations, such as the difficulty of real-time response due to a lack of consistency in diagnosis results and restrictions on diagnosis speed. To maximize the effectiveness of the real-time energy intensity management system, energy intensity analysis and countermeasures must be carried out by immediately identifying the cause and taking action upon the occurrence of abnormal operation. Therefore, there is a need for a real-time energy anomaly diagnosis and analysis system capable of enhancing diagnostic accuracy and enabling immediate response. FIG. 1 is a schematic diagram showing a real-time energy unit monitoring and abnormal cause diagnosis system according to one embodiment of the present invention. FIG. 2 is a flowchart showing the configuration of a real-time energy unit monitoring and abnormal cause diagnosis system server according to one embodiment of the present invention. FIG. 3 is a drawing showing a logic tree editing screen according to an embodiment of the present invention. FIG. 4 is a diagram showing a list of shapes in which a logic tree configuration shape according to one embodiment of the present invention is shown. FIG. 5 is a drawing showing a logic tree viewer screen according to an embodiment of the present invention. FIG. 6 is a flowchart illustrating a method for analyzing the cause of an abnormal occurrence of a unit according to an embodiment of the present invention. FIG. 7 is a diagram showing an energy unit abnormality diagnosis and analysis dashboard according to one embodiment of the present invention. FIG. 8 is a diagram showing an example of a logic tree configuration according to one embodiment of the present invention. The terms used in this invention have been selected to be as widely used as possible; however, in specific cases, terms arbitrarily selected by the applicant may be included. In such cases, the meaning of the term should be clearly interpreted in light of the detailed description of the invention or the context in which it is used, rather than merely as a name. The technical configuration of the present invention will be described in detail below with reference to the attached drawings. However, the present invention is not limited to the embodiments described and may be implemented in various forms, and throughout the specification, the same reference numerals indicate the same components. FIG. 1 is a schematic diagram of a real-time energy unit monitoring and abnormal cause diagnosis system according to one embodiment of the present invention, and FIG. 2 is a flowchart showing the system server configuration of the present invention. Referring to FIGS. 1 and 2, the real-time energy unit monitoring and abnormal cause diagnosis system (200) of the present invention monitors the energy unit using data collected by the data collection unit (201) from the field data source (100) through a logic tree (see FIG. 7) generated by the logic tree configuration unit (202). If the energy unit deviates from the standard, the cause diagnosis unit (203) analyzes the cause of the abnormality and stores the diagnosis result in the central database (205), and the visualization unit (204) visualizes the result in the form of a dashboard as shown in FIG. 6. Here, the above field data source (100) refers to a data source collected from field equipment or process instruments such as OPC, dataPARC, and influxDB. In addition, the cause of the energy unit abnormality analyzed by the cause diagnosis unit (203) is stored in a time-series database such as InfluxDB and visualized in the form of a table and a pie chart in the visualization unit (204). The central database (205) stores time-series data of the energy unit, key factors, and identical operating condition factors, a