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KR-102959503-B1 - METHOD AND SYSTEM FOR CREATING AND MANAGING DIGITAL TWIN CONTENT BASED ON PHYSICAL ARTIFICIAL INTELLIGENCE

KR102959503B1KR 102959503 B1KR102959503 B1KR 102959503B1KR-102959503-B1

Abstract

A method and system for generating and managing digital twin content based on physical AI are disclosed. A system according to one embodiment of the present disclosure comprises a plurality of types of sensors; a plurality of edge nodes; and a cloud server, wherein a first edge node among the plurality of edge nodes acquires first sensing data from at least one first sensor among the plurality of types of sensors; and based on the first sensing data acquires i) first data related to a first 3D (dimension) model and ii) first environment data within a first area based on the current location of the at least one first sensor; and transmits the first data and the first environment data to the cloud server, and the cloud server can generate virtual reality (VR) content based on a database related to a 3D model and an environment database received from each of the plurality of edge nodes.

Inventors

  • 이미애
  • 이호근
  • 함동빈

Assignees

  • 주식회사 시즈

Dates

Publication Date
20260507
Application Date
20250623

Claims (5)

  1. Multiple types of sensors; Multiple edge nodes; and Including cloud servers, The first edge node among the plurality of edge nodes above is: Acquiring first sensing data from at least one first sensor among the plurality of types of sensors; Based on the first sensing data above, i) first data related to a first 3D (dimension) model and ii) first environmental data within a first area based on the current position of at least one first sensor; and The above first data and the above first environment data are transmitted to the cloud server, and The above cloud server is: Virtual reality (VR) content is generated based on a database and an environment database related to 3D models received from each of the plurality of edge nodes, and The above at least one first sensor includes a sensor located within a second area based on the current location of the first edge node among the plurality of types of sensors, and The above at least one first sensor includes a temperature sensor, an illuminance sensor, a humidity sensor, a barometric pressure sensor, a LiDAR (Light Detection and Ranging) sensor, and an image sensor. The above first edge node is: Acquiring a first point cloud for a first plurality of objects within the first area through the above LiDAR sensor; The above first point cloud is converted into a mesh form to obtain first approximate data; i) based on the first approximation data and ii) first feature data related to image data for the first plurality of objects acquired through the image sensor, data related to the first 3D model corresponding to the plurality of objects is acquired; and First environmental data within the first area is obtained through the temperature sensor, the humidity sensor, the illuminance sensor, and the atmospheric pressure sensor, and The first environmental data above includes temperature, humidity, illuminance, and atmospheric pressure within the first area, and The above cloud server is: Based on the above first environment data, determine the environment settings related to the first area within the VR content; Data related to the first 3D model is placed within the first area; and A first avatar based on information about the first user is placed within the VR content, and The above cloud server is: Calculate an exercise recommendation index associated with the first region by applying a plurality of weights to a normal function associated with each of the temperature, humidity, illuminance, and atmospheric pressure within the first region; and Based on the fact that the above exercise recommendation index is below a first threshold, a message recommending that the first user experience the VR content using the first avatar on the terminal device used by the first user is transmitted, and The magnitude of each of the plurality of weights applied to the normal function associated with each of the temperature, humidity, illuminance, and atmospheric pressure within the first region is determined based on the sensitivity of each of the first user's temperature, humidity, illuminance, and atmospheric pressure, and The terminal device of the first user above is: Based on the selection of a specific 3D model within the VR content, a first user interface (UI) screen containing image data corresponding to the specific 3D model is displayed on the VR content; Information about a specific object is obtained by inputting image data corresponding to the above specific 3D model into a search application; Adding information for purchasing the above-mentioned specific object to the above-mentioned first UI screen; and Based on the fact that the above specific object is purchased by the first user, purchase information regarding the first user's specific object is transmitted to the cloud server, and The above cloud server is: A first hash value is obtained by applying a predefined hash function to the feature data of each of the avatar image of the first user and the image of the specific object. issuing a non-fungible token (NFT) based on the first hash value above; and A system for uploading the above-mentioned issued NFT to the first user's electronic wallet.
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  5. In paragraph 1, The information regarding the first user includes image data of the first user, body information of the first user, and voice data of the first user. The above cloud server is: A system for obtaining information about the first avatar by inputting information about the first user into a first artificial intelligence (AI) model.

Description

Method and System for Creating and Managing Digital Twin Content Based on Physical Artificial Intelligence The present disclosure relates to technology related to digital twin content, and more specifically, to a method and system for generating and managing digital twin content based on physical artificial intelligence (AI). Digital twin can be a general term for a technology that accurately digitally replicates physical objects of the real world (e.g., machines, factories, cities, buildings, etc.) within a virtual environment to simulate and analyze them by reflecting real-time data. Digital twins go beyond simply virtually replicating physical objects; they include technology that reflects a state nearly identical to reality in real time, based on various information collected through different types of sensors and IoT devices. With the recent advancement of artificial intelligence, machine learning, and simulation technologies, the utilization of digital technology is increasing significantly. For example, AI and machine learning can be used to analyze collected data to create predictive models, detect anomalies early, or suggest optimal operational methods. FIG. 1 is a drawing for explaining a system for generating and managing digital twin content based on physical AI according to one embodiment of the present disclosure. FIG. 2 is a diagram illustrating the configuration of a first device for generating and managing digital twin content based on physical AI, according to one embodiment of the present disclosure. FIG. 3 is a diagram illustrating a method for generating and managing digital twin content based on physical AI according to one embodiment of the present disclosure. The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the present disclosure, and the present disclosure is defined only by the scope of the claims. The terms used herein are for describing the embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, "comprises" and/or "comprising" do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and "and/or" includes each of the mentioned components and all combinations of one or more thereof. Although terms such as "first," "second," etc., are used to describe various components, they are not limited by these terms. These terms are used merely to distinguish one component from another. Accordingly, the first component mentioned below may be the second component within the technical scope of this disclosure. Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise. Spatially relative terms such as "below," "beneath," "lower," "above," and "upper" may be used to easily describe the relationship between one component and another, as illustrated in the drawings. Spatially relative terms should be understood as encompassing the different directions of the components during use or operation, in addition to the directions depicted in the drawings. For example, if a component depicted in a drawing is inverted, a component described as being "below" or "beneath" another component may be placed "above" the other component. Therefore, the exemplary term "below" may encompass both the downward and upward directions. Components may also be oriented in other directions, and accordingly, spatially relative terms may be interpreted according to the orientation. The following is a diagram illustrating a method and system for generating and managing digital twin content based on artificial intelligence algorithms. FIG. 1 is a drawing for illustrating a system for generating and managing digital twin content based on physical AI (or physical informed AI) according to one embodiment of the present disclosure. As illustrated in FIG. 1, a system (1000) for generating and managing digital twin content based on physical AI may include a cloud server (100), a plurality of edge nodes (200-1, 200-2, …, 200-N) (N is a natural number greater than or equal to 1) and a plurality of types of sensors (300-1, 300-2, …, 300-M) (M is a