A method by a computing device obtains a digital image of a physical map, identifies features in the digital image, and obtains map augmentation information based on the identified features. The method then generates an augmented map based on the map augmentation information, and provides the augmented map for display. Related mobile devices and computer program products are disclosed.

A method by a computing device obtains a digital image of a physical map, identifies features in the digital image, and obtains map augmentation information based on the identified features. The method then generates an augmented map based on the map augmentation information, and provides the augmented map for display. Related mobile devices and computer program products are disclosed.

A method for analyzing a viewing direction of an electronic component includes inputting a package type and a file image of an electronic component, with the file image having at least one engineering drawing image, and the at least one engineering drawing image being a view of the electronic component in at least one viewing direction; querying and acquiring a viewing direction detection model meeting the package type from a database, with the database storing respective viewing direction detection models of different package types of electronic components; inputting the file image into the viewing direction detection model of the package type to identify the viewing direction of the at least one engineering drawing image; and outputting the viewing direction of the at least one engineering drawing image of the electronic component.

A method for analyzing a specification parameter of an electronic component includes inputting a package type and at least one engineering drawing image of an electronic component; acquiring a probability value that in each view of the different viewing directions each of the plurality of specification parameter of the electronic component is labeled; taking the view of each of the plurality of specification parameters in the view direction with a highest probability value as a recommended view; performing a box selection on the plurality of specification parameters for at least one engineering drawing image with the same viewing direction as that of the recommended view by an object detection model; and identifying box-selected specification parameters to acquire a size value of identified specification parameters from the at least one engineering drawing image, and converting the size value into a corresponding editable text for output.

An object of the invention is to efficiently create a 3D model of a plant with attributes from a 3D model of a plant with no attributes. In order to solve the above problems, in the invention, a connection information conversion part 5 converts a connection relationship of parts extracted from a 3D model with no attributes 2 into connection information of a system diagram, an extraction information comparing part 6 compares the connection information with the connection relationship extracted from an attribute system diagram to create an conversion correspondence DB 7, and a 3D model with attributes 9 is created based on the conversion correspondence DB from the 3D model with no attributes 2.

An information processing apparatus (10) is for supporting work by a user who uses drawings for a plant. The information processing apparatus (10) includes a controller (15). The controller (15) is configured to convert a drawing including elements configuring the plant into an abstract model represented by element information indicating the elements and connection information indicating a connection relationship between the elements. The controller (15) is configured to generate display information, when it is judged that a difference exists between one abstract model based on one drawing and another abstract model based on another drawing, for displaying the differing portion in a different form than another portion.

Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

A hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.

A hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.

A map file includes two-dimensional or three-dimensional geometric data items collectively representing layout of a building. The map file is parsed and the geometric data items are analyzed to identify building elements including rooms, floors, and objects of the building, and to identify containment relationships between the elements. A space model having a space graph is constructed. The space graph includes nodes that correspond to the respective building elements and links forming relationships between nodes that correspond to the identified containment relationships. Each node may include node metadata, rules or code that operate on the metadata, and a node type that corresponds to a type of physical space. Some nodes may include user representations or device representations that represent physical sensors associated therewith. The representations may receive data from the respectively represented sensors, and the sensor data becomes available via the space model.