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KR-102963163-B1 - DIGITAL TWIN-BASED INDUSTRIAL SAFETY MANAGEMENT SYSTEM

KR102963163B1KR 102963163 B1KR102963163 B1KR 102963163B1KR-102963163-B1

Abstract

The embodiments disclosed in this disclosure aim to provide an integrated safety management system that fuses real-time data from industrial sites with past accident history to quantitatively predict risk levels by process, section, and time period, simulates potential risks in a digital twin-based virtual space, and automatically recommends and deploys safety equipment based on analysis results. Furthermore, the embodiments disclosed in this disclosure aim to provide an intelligent safety management system capable of proactively ensuring safety at industrial sites based on digital twin technology. Additionally, the invention enables the quantification and prediction of potential risks in real time by fusing and analyzing past accident history data with real-time sensor data.

Inventors

  • 한성욱

Assignees

  • 주식회사 아이티유

Dates

Publication Date
20260512
Application Date
20250912

Claims (7)

  1. Memory for storing at least one instruction for digital twin-based industrial safety management; and It includes a processor that performs an operation according to the above instruction, The above processor is, A digital twin generation module that models actual industrial sites into a 3D virtual space; A data collection module that reflects real-time data collected from IoT sensors and location tracking devices at an industrial site into the digital twin; An artificial intelligence analysis module that predicts risk levels by process, section, and period by learning from historical accident data; Equipment recommendation module that automatically derives necessary personal protective equipment (PPE) based on predicted risk and accident types; and An integrated monitoring module that visualizes the above risk level and equipment recommendation results and provides them to manager and worker terminals; including The above equipment recommendation module is It includes a rule-based mapping table between risk types and response equipment, automatically recommends the optimal Personal Protective Equipment (PPE) based on the analyzed accident type, and Integrated with ERP or inventory management systems, it checks real-time equipment inventory information, suggests alternative equipment or sends notifications to managers in case of stock shortages, tracks workers' locations via RTLS (Real-Time Location System), and guides them to receive recommended PPE at the nearest distribution center or smart kiosk. The above equipment recommendation module is Propose PPE by considering at least one of the individual worker's past wearing history, accident experience, and physical characteristics, and The above artificial intelligence analysis module is When risk scores are calculated for each process, section, and period, the simulation engine detects sections where the risk score exceeds a certain threshold, virtually executes an accident reconstruction scenario based on similar past cases within the digital twin space, and In sections where the risk of pinching exceeds a certain level, it generates scenarios where worker movement paths and equipment movement paths collide, predicts the collision time, location, and scale of damage, and in sections where the probability of fire exceeds a certain level, it simulates flame spread paths based on temperature rise and gas leakage conditions, Digital twin-based industrial safety management device.
  2. In claim 1, the artificial intelligence analysis module is Time-series labeling of past disaster data to train LSTM and Transformer-based time-series classification models, and calculating risk scores by process, section, and period, Digital twin-based industrial safety management device.
  3. In claim 1, the digital twin generation module is Generating a 3D point cloud model based on photogrammetry data using drones, cameras, and mobile devices, and aligning it with BIM/CAD data to virtually recreate actual industrial sites, Digital twin-based industrial safety management device.
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  7. In paragraph 1, the integrated monitoring module is Generates predicted risk as risk maps at the process and space levels, and visualizes them on a digital twin-based 3D interface using colors, icons, and animation effects to provide to managers and operators, Digital twin-based industrial safety management device.

Description

Digital Twin-Based Industrial Safety Management System The present disclosure relates to the field of industrial safety management, and more specifically, to an intelligent industrial safety management system capable of constructing a virtual model of an industrial site using digital twin technology, predicting risks by process, section, and time period by combining real-time data collected from IoT sensors and location tracking devices with past accident history, and automatically recommending and deploying optimal personal protective equipment (PPE) based on the analyzed results. Unless otherwise indicated in this specification, the contents described in this section are not prior art for the claims of this application, and are not to be recognized as prior art simply because they are included in this section. Recently, as advanced automation facilities, large-scale equipment, and high-risk processes are operated in combination at industrial sites, the likelihood of safety accidents is steadily increasing. In particular, in industrial sectors such as construction, manufacturing, and energy plants, factors such as confined spaces, work at heights, the operation of heavy equipment, and the high density of workers are intertwined, which can lead to large-scale casualties and property damage if an accident occurs. Existing industrial safety management systems have primarily operated based on passive analysis derived from past accident reports, risk prediction relying on worker visual inspections and managerial experience, and reactive response following accidents. This approach has limitations, including low predictive accuracy, difficulties in real-time response, and an inability to reflect detailed risk characteristics that vary by process, section, and time period. In addition, while the collection of field data is actively taking place due to the recent proliferation of IoT sensors, location tracking systems, and smart wearables, the technology for comprehensively analyzing collected data and reflecting it in a 3D virtual space to establish preemptive safety measures remains in its early stages. In particular, there is a lack of systems that can comprehensively perform time-series risk pattern analysis based on process progress, visualize spatial risk, and automatically recommend and manage the distribution of optimal Personal Protective Equipment (PPE) for each situation. Digital Twin technology is attracting attention as a key technology to overcome these limitations. Digital Twins can precisely reproduce actual industrial sites in a virtual space and dynamically analyze process conditions and risk factors on a virtual model by reflecting real-time data collected from sensors and equipment. However, existing applications of Digital Twins are mainly concentrated in the fields of productivity optimization, equipment maintenance, and quality control, while research and system development specialized exclusively for safety management are relatively lacking. Figure 1 is a diagram showing the configuration of a digital twin-based industrial safety management system according to an embodiment. FIG. 2 is a block diagram of a digital twin-based industrial safety management device according to an embodiment. FIG. 3 is a diagram showing the configuration of an instruction set stored in memory according to an embodiment. Figure 4 is a diagram illustrating a digital twin-based industrial safety management process according to an embodiment. Hereinafter, various embodiments of the present disclosure are described in conjunction with the accompanying drawings. As various embodiments of the present disclosure may be subject to various modifications and may have various forms, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the various embodiments of the present disclosure to specific forms, and it should be understood that they include all modifications and/or equivalents and substitutions that fall within the spirit and scope of the various embodiments of the present disclosure. In relation to the description of the drawings, similar reference numerals have been used for similar components. In various embodiments of the present disclosure, terms such as “comprising” or “having” are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. In various embodiments of the present disclosure, expressions such as “or” include any and all combinations of the words listed together. For example, “A or B” may include A, may include B, or may include both A and B. Expressions such as "first," "second," "first," or "second" used in various embodiments of the present disclosure may modify various componen