WO2022245091A1 - Procédé et produit programme d'ordinateur pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction - Google Patents

Procédé et produit programme d'ordinateur pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction Download PDF

Info

Publication number
WO2022245091A1
WO2022245091A1 PCT/KR2022/007013 KR2022007013W WO2022245091A1 WO 2022245091 A1 WO2022245091 A1 WO 2022245091A1 KR 2022007013 W KR2022007013 W KR 2022007013W WO 2022245091 A1 WO2022245091 A1 WO 2022245091A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
work
risk
type
data
Prior art date
Application number
PCT/KR2022/007013
Other languages
English (en)
Korean (ko)
Inventor
윤재민
오형안
강신평
Original Assignee
주식회사 플럭시티
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020210064397A external-priority patent/KR102321897B1/ko
Priority claimed from KR1020220059729A external-priority patent/KR20230160106A/ko
Application filed by 주식회사 플럭시티 filed Critical 주식회사 플럭시티
Publication of WO2022245091A1 publication Critical patent/WO2022245091A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • the present disclosure provides a method and a computer program product for representing a degree of risk in a 3D modeling space of a construction site.
  • the person in charge inspects the site and judges the risk level of the construction site based on professional experience, so the risk level is subjectively judged and the risk level applied with the construction progress at the construction site cannot be calculated. have.
  • An object of the present invention is to provide a method and a computer program product for representing a degree of risk in a 3D modeling space of a construction site.
  • the technical problem to be achieved by the present embodiment is not limited to the technical problems described above, and other technical problems can be inferred from the following embodiments.
  • a first aspect of the present disclosure is a method for representing a risk level in a 3D modeling space of a construction site, at information on a work type, information on a work location, or at the time of work. predicting a type of disaster by inputting first data including information about a disaster to a first network; calculating a comprehensive risk index by inputting the first data and the disaster type into a second network; and visually displaying the comprehensive risk index in the 3D modeling space.
  • a color based on the comprehensive risk index may be displayed on an object corresponding to the first data in the 3D modeling space.
  • the 3D modeling space includes at least one spatial grid
  • the step of visually displaying the comprehensive risk index includes the comprehensive risk index and the first data in an object corresponding to the first data in a first spatial grid.
  • a color based on comprehensive risk indices of at least one object in the second spatial grid positioned below the first spatial grid may be displayed.
  • the information on the type of work includes information on the type of work performed by the person performing the task and information on the type of work performed by the person performing the task
  • the information on the work location includes the information on the type of work performed by the person performing the task
  • Information on the number of floors to be performed, and the information on the timing of the work may include information on the date, day, or time at which the work is performed.
  • the first network corresponds to a classification learner that receives the first data as an input and predicts any one disaster type among a plurality of preset disaster types
  • the second network receives the first data and the disaster It may correspond to a deep neural network that receives a shape as an input and calculates the comprehensive risk index.
  • a second aspect of the present disclosure is an apparatus for indicating a degree of risk in a 3D modeling space of a construction site, comprising: a memory for storing at least one program; and at least one processor representing a degree of risk by executing the at least one program, wherein the at least one processor includes first data including information about a work type, information about a work location, or information about a work time point. to the first network to predict the disaster type, input the first data and the disaster type to the second network to calculate the comprehensive risk index, and visually display the comprehensive risk index in the 3D modeling space.
  • a third aspect of the present disclosure may provide a computer-readable recording medium recording a program for executing the method according to the first aspect on a computer.
  • FIG. 1 is a diagram illustrating operations performed in a risk display device in a 3D modeling space according to an embodiment.
  • FIG. 2 is a diagram schematically illustrating a method of predicting a disaster type from first data using a first network according to an embodiment.
  • FIG. 3 is a diagram exemplarily illustrating learning data used to learn a first network according to an embodiment.
  • FIG. 4 shows a simplified representation of training data used to train a first network according to an embodiment.
  • FIG. 5 is a diagram illustrating a disaster type according to an embodiment by way of example.
  • FIG. 6 is a diagram schematically illustrating a method of calculating a comprehensive risk index using a second network according to an embodiment.
  • FIG. 7 is a diagram exemplarily illustrating a second network according to an exemplary embodiment.
  • FIG. 8 is a diagram illustrating preset risk indices according to an exemplary embodiment.
  • FIG. 9 is a diagram showing learning data used to learn a second network according to an exemplary embodiment.
  • FIG. 10 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a 3D modeling space according to an embodiment.
  • FIG. 11 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a 3D modeling space according to an embodiment.
  • FIG. 12 is a flowchart of a method of indicating a degree of risk in a 3D modeling space according to an embodiment.
  • FIG. 13 is a block diagram of a risk display device in a 3D modeling space according to an embodiment.
  • a method of expressing the degree of risk in a 3D modeling space of a construction site is provided.
  • the method of the present invention inputs first data including information on the type of work, information on the location of work, or information on the timing of work to the first network to predict the type of disaster, and to predict the type of disaster using the first data and the type of disaster.
  • first data including information on the type of work, information on the location of work, or information on the timing of work to the first network to predict the type of disaster, and to predict the type of disaster using the first data and the type of disaster.
  • 2 Input into the network to calculate the comprehensive risk index, and the comprehensive risk index can be visually displayed in the 3D modeling space.
  • Some embodiments of the present disclosure may be represented as functional block structures and various processing steps. Some or all of these functional blocks may be implemented as a varying number of hardware and/or software components that perform specific functions.
  • functional blocks of the present disclosure may be implemented by one or more microprocessors or circuit configurations for a predetermined function.
  • the functional blocks of this disclosure may be implemented in various programming or scripting languages.
  • Functional blocks may be implemented as an algorithm running on one or more processors.
  • the present disclosure may employ prior art for electronic environment setting, signal processing, and/or data processing. Terms such as “mechanism”, “element”, “means” and “component” may be used broadly and are not limited to mechanical and physical components.
  • connecting lines or connecting members between components shown in the drawings are only examples of functional connections and/or physical or circuit connections. In an actual device, connections between components may be represented by various functional connections, physical connections, or circuit connections that can be replaced or added.
  • FIG. 1 is a diagram illustrating operations performed in a risk display device in a 3D modeling space according to an embodiment.
  • the apparatus 100 for displaying risk in a 3D modeling space may receive first data and calculate a total risk point indicating a risk at a specific work position in a 3D modeling space.
  • the comprehensive risk index calculated in the 3D modeling space within the construction site can be visually displayed.
  • the 3D modeling space is a modeling of a construction site in a 3D space based on a digital twin.
  • a digital twin is a software representation of a physical asset, system or process.
  • the 3D modeling space may include object information such as structures, materials, facilities, and construction machines in a construction site and information on types of work being performed at the construction site. For example, object information may be generated through a lightening operation of object polygons in a construction site.
  • the risk level display device 100 of the 3D modeling space matches the coordinates of the real world with the coordinates of the 3D space, and reflects construction site data in the virtual 3D space to create a 3D modeling space.
  • the risk level display device 100 in the 3D modeling space may accurately reflect a specific actual location of a construction site in a virtual 3D space by applying a 3D grid precision address.
  • the risk level display device 100 in the 3D modeling space may display a comprehensive risk index representing the risk level of a specific type of work performed at a specific work location in the 3D modeling space in the 3D modeling space.
  • the risk level display device 100 of the 3D modeling space of FIG. 1 corresponds to a device that displays the risk level of a specific type of work performed at a specific work position in the 3D modeling space based on an artificial neural network.
  • An artificial neural network refers to a model in general that has problem-solving ability by changing synaptic coupling strength through learning of artificial neurons that form a network through synaptic coupling.
  • the risk display device 100 in the 3D modeling space may be implemented in various types of devices such as a personal computer (PC), a server device, a mobile device, and an embedded device, and as a specific example, 3D modeling using an artificial neural network. It may correspond to smartphones, tablet devices, AR (Augmented Reality) devices, IoT (Internet of Things) devices, autonomous vehicles, robotics, etc. that indicate the risk level of a specific type of work performed at a specific work location in a space, but is not limited thereto. don't
  • the risk display device 100 of the 3D modeling space may correspond to a dedicated hardware accelerator (HW accelerator) mounted on the above device.
  • the apparatus 300 for searching for a section in a video may be a hardware accelerator such as a neural processing unit (NPU), a tensor processing unit (TPU), or a neural engine, which are dedicated modules for driving an artificial neural network, but is not limited thereto.
  • NPU neural processing unit
  • TPU tensor processing unit
  • a neural engine which are dedicated modules for driving an artificial neural network, but is not limited thereto.
  • the apparatus 100 for displaying risk in a 3D modeling space may receive first data as an input.
  • the first data may include information on the type of work to be performed at a construction site, information on a work location, or information on a work time, but is not limited thereto.
  • the first data may be received from an external device through a communication unit included in the device 100 for displaying risk in a 3D modeling space.
  • the first data may be input from the user through the user interface of the risk display device 100 in the 3D modeling space.
  • the risk display device 100 in the 3D modeling space may output a comprehensive risk index corresponding to the first data based on the first data received as an input.
  • the risk display device 100 of the 3D modeling space may output a comprehensive risk index of a specific work type to be performed at a specific work location at a specific work time.
  • the risk display device 100 in the 3D modeling space is a comprehensive risk index (temporal characteristic) based on the first data. ), comprehensive risk index according to environmental characteristics (environmental characteristic) ), comprehensive risk index according to work-type characteristics ( ), comprehensive risk index according to work location (site) characteristics ( ) and comprehensive risk index according to accident-type characteristics ( ), at least one comprehensive risk index may be output.
  • the risk display device 100 in the 3D modeling space may determine a final comprehensive risk index by integrating the five comprehensive risk indexes.
  • the risk display device 100 of the 3D modeling space is a comprehensive risk index according to temporal characteristics based on the first data ( ) can be output.
  • the risk display device 100 in the 3D modeling space is based on at least one element of “month”, “week-day” and “time”, and the comprehensive risk index according to temporal characteristics. ( ) can be calculated.
  • the comprehensive risk index according to temporal characteristics ( ) can be calculated according to Equation 1.
  • Equation 1 is the coefficient for the "monthly” risk score, is the coefficient for the "day of the week” risk score, Represents the coefficient for the "time” risk score, is the "Monthly” risk score, is the “day of the week” risk score, represents the “time” risk score.
  • the risk display device 100 of the 3D modeling space is a comprehensive risk index according to environmental characteristics based on the first data ( ) can be output.
  • the risk display device 100 in the 3D modeling space is based on at least one element of "temperature”, “wind speed” and “rainfall”, and the comprehensive risk index according to environmental characteristics. ( ) can be calculated.
  • the comprehensive risk index according to environmental characteristics ( ) can be calculated according to Equation 2.
  • Equation 2 is the coefficient for the "temperature” risk score, is the coefficient for the "wind speed” risk score, Represents the coefficient for the "Rainfall” risk score, a "temperature” risk score; is the “wind speed” risk score, denotes the “Rain condition” risk score.
  • the risk level display device 100 of the 3D modeling space is a comprehensive risk index according to the work type characteristics based on the first data ( ) can be output.
  • the risk level display device 100 of the 3D modeling space is based on at least one element of "work type", “work process” and “temporary work”, based on the work type characteristics.
  • Comprehensive risk index ( ) can be calculated.
  • the comprehensive risk index according to work type characteristics ( ) can be calculated according to Equation 3.
  • Equation 3 is the coefficient for the "species” risk score, is the coefficient for the "task phase” risk score, Represents the coefficient for the "hypothesis work” risk score, is the "species” risk score; is the "task phase” risk score, represents the "hypothesis work” risk score.
  • the risk display device 100 of the 3D modeling space is a comprehensive risk index according to the work position characteristics based on the first data ( ) can be output.
  • the risk level display device 100 of the 3D modeling space is based on at least one element of "height (floor)" and “opening/end”, and comprehensively according to work position characteristics risk index ( ) can be calculated.
  • the composite risk index according to the characteristics of the work location ( ) can be calculated according to Equation 4.
  • Equation 4 is the coefficient for the "height (floor)” risk score, Represents the coefficient for the "opening/end” risk score, is the “height (floor)” risk score, represents the “opening/end” risk score.
  • the risk display device 100 of the 3D modeling space is a comprehensive risk index (according to the accident type characteristics based on the first data) ) can be output.
  • the risk level display device 100 in the 3D modeling space is "Fall Off”, “Fall Down”, “Hit”, “Struck By”, “Collapse””,”Jamming”,”Mutilation/Cut/Puncture”,”ElectricShock”,”Explosion/Blast”,"Fire”,”FallBeneath/Overturn”,”Contact on Abnormal Temperature”, “Imbalance/Immorate Motion”, “Exposure to Chemical Materials” , “Occupational Diseases” and “Others (ETC)", based on at least one element, the comprehensive risk index according to Chemical Materials” , “Occupational Diseases” and “Others (ETC)", based on at least one element, the comprehensive risk index according to Chemical Materials” , “Occupational Diseases” and “Others (ETC)", based on at least one element, the comprehensive risk index according to the
  • the comprehensive risk index according to the accident type characteristics ( ) can be calculated according to Equation 5.
  • Equation 5 is the coefficient for the "falling" risk score, is the coefficient for the "fall” risk score, is the coefficient for the "hit” risk score, is the coefficient for the "hit by object” risk score, is the coefficient for the "collapse” risk score, is the coefficient for the "entrapment” risk score, is the coefficient for the "cut/cut/puncture” risk score, is the coefficient for the "electric shock” risk score, is the coefficient for the "explosion/rupture” risk score, is the coefficient for the "fire” risk score, is the coefficient for the "run/over” risk score, is the coefficient for the "abnormal temperature contact” risk score, is the coefficient for the "imbalance and overexertion” risk score, is the coefficient for the "chemical leak/contact” risk score, is the coefficient for the "occupational disease” risk score, represents the coefficient for the "Other” risk score.
  • the "falling” risk score is the "fall” risk score, a "hit” risk score; is the “hit by object” risk score, is the “collapse” risk score, is the “entrapment” risk score; a “cut/cut/puncture” risk score; a “electric shock” risk score; is the “explosion/rupture” risk score, a "fire” risk score; is a “knock/overturn” risk score; a “overtemperature contact” risk score; a “imbalance and overexertion” risk score, is the “chemical leak/contact” risk score; is the “occupational disease” risk score, represents an “other” risk score.
  • the apparatus 100 for displaying risk in a 3D modeling space may predict a disaster type from first data using a first network 110 .
  • a method of predicting a disaster type from the first data using the first network 110 will be described later with reference to FIGS. 2 to 5 .
  • the apparatus 100 for displaying risk in a 3D modeling space calculates a comprehensive risk index from the first data and the type of disaster predicted from the first network 110 using the second network 120.
  • a method of calculating the comprehensive risk index from the first data and the disaster type using the second network 120 will be described later with reference to FIGS. 6 to 9 .
  • the risk display device 100 of the 3D modeling space may visually display the calculated comprehensive risk index in the 3D modeling space.
  • FIG. 2 is a diagram schematically illustrating a method of predicting a disaster type from first data using a first network according to an embodiment.
  • the risk level display device 100 in the 3D modeling space includes information about the type of work to be performed at the construction site using the first network, information about the work location, or information about the work time.
  • 1 Disaster type can be output from data.
  • the information on the type of work may include information on the type of work performed by the person performing the task and information on the type of work performed by the person performing the task.
  • the information about the work location may include information about the number of floors on which the work is performed or whether the work is performed outside or inside the building.
  • the information on the timing of the task may include information about the month, day, or time in which the task is performed.
  • the first network may correspond to a classification learner, and the first network may correspond to a neural network in which supervised learning is performed to predict a categorical variable. That is, the first network may be configured to predict one of several predefined class labels. Several predefined class labels may correspond to disaster types.
  • Supervised learning is a method of learning using learning data whose characteristics have already been determined.
  • the risk level display device 100 in the 3D modeling space analyzes disaster accidents obtained from disaster casebooks based on a certain classification system to configure learning data. can do.
  • the learning data may include data including information about work type, information about a work location where an accident occurred, or information about a work time point where an accident occurred, and a disaster type (corresponding to a class label) corresponding to the data.
  • FIG. 3 is a diagram exemplarily illustrating learning data used to learn a first network according to an embodiment.
  • the learning data 300 used to train the first network may include information 310 on the type of work, and the information 310 on the type of work is the injured person. It may include information 311 on the type of work in charge and information 312 on the type of work that caused the disaster.
  • the training data used to train the first network may include information 320 on a work location where a disaster has occurred, and the information 320 on a work location where a disaster has occurred is information 321 about the number of floors performed or information 322 about whether the work was performed inside or outside the building.
  • learning data used to train the first network may include information 330 on a work time point in which a disaster occurs, and information 330 on a work time point in which a disaster occurs It may include information 331 about the month performed, information 332 about the day of the week, or information 333 about the time.
  • learning data used to train the first network may include information 340 on the type of disaster that has occurred.
  • Information on the type of accident 340 may correspond to information corresponding to data including information on the type of work 310, information on the location where the accident occurred 320, or information about the time when the accident occurred. have.
  • the information 340 on the type of disaster may correspond to information about the location of the disaster or the cause of death or injury.
  • the risk display device 100 in the 3D modeling space can simply express and code the learning data 300 used to learn the first network, and use the simplified learning data to perform learning of the first network. can be done
  • FIG. 4 shows a simplified representation of training data used to train a first network according to an embodiment.
  • the information about the time of operation when the disaster occurred may be simplified to show only information about when the disaster occurred.
  • the information on the type of work may be simply expressed by coding information on the type of work (injured person's type of work) and information on the type of work that caused the accident (type of work for the victim). Codes may be preset for each of the various types of work types assigned by the victim and the various types of work types that caused the accident.
  • the information on the disaster type may use an abbreviation that can briefly express each of the various disaster types, as shown in FIG. 5 to be described later.
  • FIG. 5 is a diagram illustrating a disaster type according to an embodiment by way of example.
  • FIG. 5 illustrates various examples of types of disasters predicted in response to the first data when first data including information on work type, work location, or work time is input to the first network. . That is, FIG. 4 shows a plurality of predefined class labels.
  • disaster types include fall (FOF), fall (FDN), bump (HIT), object hit (STR), collapse (CLS), jamming (JAM), cut/cut/puncture (MCP), etc. may apply.
  • the first network may be configured to output a predicted disaster type corresponding to the input first data.
  • the risk level display device 100 in the 3D modeling space uses the first network to provide information that temporary construction will be performed outside a building on the 10th floor above the ground at 5:00 pm on May 12, 2021 (Wednesday).
  • a fall FOG
  • the first network may be configured to output a predicted disaster type corresponding to the input first data.
  • FIG. 6 is a diagram schematically illustrating a method of calculating a comprehensive risk index using a second network according to an embodiment.
  • the risk display device 100 in the 3D modeling space may output a total risk point from the first data and the disaster type predicted from the first data using the second network.
  • the first data may include information about work types, information about work locations, or information about work hours.
  • the second network may correspond to a deep neural network (DNN) or an n-layers neural network, but is not limited thereto.
  • the second network may correspond to Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), Deep Belief Networks, Restricted Boltzman Machines, and the like. have.
  • RNNs Recurrent Neural Networks
  • LSTMs Long Short-Term Memory Networks
  • BBDNNs Bidirectional Recurrent Deep Neural Networks
  • Deep Belief Networks Deep Belief Networks
  • Restricted Boltzman Machines Restricted Boltzman Machines, and the like.
  • FIG. 7 is a diagram exemplarily illustrating a second network according to an exemplary embodiment.
  • the second network may correspond to a deep neural network including an input layer, at least one hidden layer, and an output layer. Although only one hidden layer is shown in FIG. 7, this is merely an example and the second network may include a plurality of hidden layers.
  • first data including information on work type, work location, or work time
  • a disaster type output from the first network based on the first data may be input.
  • An operation may be performed in the hidden layer based on the input first data and the type of disaster, and finally, a comprehensive risk index may be calculated in the output layer.
  • the comprehensive risk index may be calculated in the form of a score or may be classified into a specific risk level according to a score value. Referring to FIG. 7 , it is shown that the risk level can be classified into 5 levels according to the range of score values of the comprehensive risk index, but the method of representing the comprehensive risk index is not limited thereto.
  • Each of the layers included in the second network is a plurality of artificial nodes, known as “neurons”, “processing elements (PEs)", “units” or similar terms.
  • the input layer may include p nodes and the hidden layer may include m nodes.
  • Nodes included in each of the layers included in the second network may be connected to each other to exchange data.
  • one node may receive data from other nodes, perform calculations, and output calculation results to other nodes.
  • the second network When the second network is implemented as a deep neural network architecture, it may include many layers capable of processing valid information. Meanwhile, the second network may include layers having various structures different from those shown in FIG. 7 .
  • the risk level display device 100 of the 3D modeling space includes first data including information that temporary construction will be performed outside a building on the 10th floor above the ground at 5:00 pm on May 12, 2021 (Wednesday), and When information that a fall (FOF) is predicted as a type of disaster is input corresponding to the first data, a comprehensive risk index corresponding thereto may be output using the second network.
  • first data including information that temporary construction will be performed outside a building on the 10th floor above the ground at 5:00 pm on May 12, 2021 (Wednesday)
  • a comprehensive risk index corresponding thereto may be output using the second network.
  • the risk display device 100 of the 3D modeling space includes a preset risk index for each work process, a risk index for each floor, a risk index for each month, a risk index for each day of the week, or a risk index for each hour.
  • the information can be used to train the second network to output a comprehensive risk index.
  • FIG. 8 is a diagram illustrating preset risk indices according to an exemplary embodiment.
  • each risk index is not limited to the one shown in FIG. 8 .
  • various types of risk index required to calculate the comprehensive risk index may be further stored.
  • a risk index for each type of work may be calculated based on statistical data.
  • calculation may be performed by applying a risk index for each work type included in the first data to the information on the work type included in the first data, and the information on the work location included in the first data may include a preset risk level for each floor number
  • the calculation may be performed by applying an index, and the calculation may be performed by applying a preset monthly risk index, day-of-week risk index, or hourly risk index to the information about the operation timing included in the first data.
  • calculation may be performed by applying a risk index for each disaster type to the disaster type predicted from the first network.
  • FIG. 9 is a diagram showing learning data used to learn a second network according to an exemplary embodiment.
  • the device 100 for displaying the degree of risk in a 3D modeling space may configure learning data by analyzing disaster accidents obtained from a casebook of disasters based on a certain classification system.
  • the learning data used to train the second network is information on the type of work (type of work for the injured, type of work for the injured), information on the work location where the accident occurred (number of floors, inside/outside), and information on the type of work where the disaster occurred, as in FIG. 4 described above. It can include information about the time of operation (month, day, time) or information about the type of disaster that occurred.
  • the risk display device 100 in the 3D modeling space calculates the work type risk index by applying the risk index for each work type described above in FIG.
  • the floor number risk index may be calculated by applying the risk index for each floor number described above in FIG.
  • a time risk index can be calculated.
  • a disaster type risk index may be calculated by applying the risk index for each disaster type described above with reference to FIG. 8 to information on the occurred disaster type.
  • the learning data used to learn the 2 networks may further include information about each risk index described above.
  • the risk display device 100 in the 3D modeling space can learn the second network using the above-described learning data so that the second network can output a comprehensive risk index in response to the input first data and disaster type. have.
  • FIG. 10 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a 3D modeling space according to an embodiment.
  • the risk display device 100 in the 3D modeling space may display a color based on the overall risk index on an object corresponding to the first data in the 3D modeling space.
  • the comprehensive risk index may be calculated in the form of a score or may be classified into a specific risk level according to a score value.
  • a specific risk level classified according to a score value may correspond to a specific color.
  • the risk level may be classified into 5 levels according to the range of score values of the comprehensive risk index, and there may be 5 colors corresponding to each risk level, but this is only an example and the score of the comprehensive risk index
  • the number of risk levels according to values and colors corresponding to the risk levels may also vary.
  • the 3D modeling space may include at least one spatial lattice, and the spatial lattice may correspond to a specific actual location of a construction site.
  • the risk level display device 100 in the 3D modeling space is a comprehensive risk level output to an object in any one spatial grid corresponding to first data including information about work types, information about work locations, or information about work hours. Index can be displayed visually.
  • the risk display device 100 in the 3D modeling space may display a color based on a comprehensive risk index output from the second network on an object within any one space grid corresponding to the first data.
  • the risk display device 100 in the 3D modeling space may display a color corresponding to a risk level according to a score value of a comprehensive risk index.
  • each hatch may indicate a color of a different risk level.
  • FIG. 11 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a 3D modeling space according to an embodiment.
  • FIG. 11 shows an example in which the risk display device 100 in the 3D modeling space displays a color based on a comprehensive risk index on an object corresponding to first data in the 3D modeling space, similar to FIG. 10 .
  • the spatial lattice corresponding to the first data including information about the type of work, information about the work location, or information about the work time may further include at least one spatial lattice positioned below it. .
  • the risk display device 100 of the 3D modeling space provides a comprehensive risk index corresponding to the first data to an object in the spatial grid corresponding to the first data and a first layer located at a lower layer of the spatial grid corresponding to the first data. All comprehensive risk indices corresponding to at least one object in the two-space grid may be reflected and displayed visually.
  • the risk display device 100 in the 3D modeling space can visually display the overlapping overall risk index as it goes up from the spatial grid located on the lower layer to the spatial grid located on the upper layer.
  • FIG. 12 is a flowchart of a method of indicating a degree of risk in a 3D modeling space according to an embodiment.
  • the device for displaying the risk level of the 3D modeling space inputs first data including information about the type of work, information about the work location, or information about the time of work to the first network to determine the type of disaster. can predict
  • the information on the type of work may include information about the type of work assigned by the person performing the task and information about the type of work performed by the person performing the task.
  • the information about the work location may include information about the number of floors on which the work is performed or information about whether the work is performed outside or inside the building.
  • the information on the timing of the task may include information about the month, day, or time in which the task is performed.
  • the first network may correspond to a classification learner that receives the first data as an input and predicts any one disaster type among a plurality of preset disaster types.
  • the first network may correspond to a neural network in which supervised learning has been performed to predict a categorical variable.
  • the first network includes data including information on work type, information on a work location where an accident occurred, or information on a time point at which a disaster occurred, and learning data including a disaster type (corresponding to a class label) corresponding to the data. It can be supervised learning using .
  • the risk display device of the 3D modeling space may calculate a comprehensive risk index by inputting the first data and the disaster type to the second network.
  • the second network may correspond to a deep neural network that calculates a comprehensive risk index by receiving the first data and the type of disaster predicted from the first network as inputs.
  • first data including information on work type, work location, or work time
  • a disaster type output from the first network based on the first data are input.
  • An operation may be performed in the hidden layer based on the input first data and the type of disaster, and finally, a comprehensive risk index may be calculated in the output layer.
  • the risk display device of the 3D modeling space uses information including a preset risk index for each work process, a risk index for each floor, a risk index for each month, a risk index for each day of the week, or a risk index for each hour. 2
  • the network can be trained to output a comprehensive risk index.
  • the risk display device in the 3D modeling space may visually display the comprehensive risk index in the 3D modeling space.
  • the comprehensive risk index may be calculated in the form of a score or may be classified into a specific risk level according to a score value. Depending on the score value, a specific risk level may correspond to a specific color.
  • the risk display device in the 3D modeling space may display a color based on the comprehensive risk index on an object corresponding to the first data in the 3D modeling space.
  • the 3D modeling space includes at least one spatial grid, and a comprehensive risk index for an object corresponding to the first data in a first spatial grid and a space located below the first spatial grid A color based on a comprehensive risk index of at least one object in the grid may be displayed.
  • FIG. 13 is a block diagram of a risk display device in a 3D modeling space according to an embodiment.
  • the apparatus 1300 for displaying the degree of risk in a 3D modeling space may include a communication unit 1310, a processor 1320, and a DB 1330.
  • the risk level display device 1300 of the 3D modeling space of FIG. 13 only components related to the embodiment are shown. Accordingly, those skilled in the art can understand that other general-purpose components may be further included in addition to the components shown in FIG. 13 .
  • the communication unit 1310 may include one or more components that allow wired/wireless communication with the risk level display device 1300 in the 3D modeling space.
  • the communication unit 1310 may include at least one of a short-range communication unit (not shown), a mobile communication unit (not shown), and a broadcast reception unit (not shown).
  • the DB 1330 is hardware for storing various data processed in the risk level display device 1300 in the 3D modeling space, and may store programs for processing and control of the processor 1320.
  • the DB 1330 includes random access memory (RAM) such as dynamic random access memory (DRAM) and static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and CD-ROM. ROM, Blu-ray or other optical disk storage, hard disk drive (HDD), solid state drive (SSD), or flash memory.
  • the processor 1320 controls the overall operation of the risk display device 1300 in the 3D modeling space.
  • the processor 1320 may generally control an input unit (not shown), a display (not shown), a communication unit 1310, and the DB 1330 by executing programs stored in the DB 1330.
  • the processor 1320 may control the operation of the risk display device 1300 in the 3D modeling space by executing programs stored in the DB 1330 .
  • the processor 1320 may include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. It may be implemented using at least one of micro-controllers, microprocessors, and electrical units for performing other functions.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • controllers and microcontrollers. It may be implemented using at least one of micro-controllers, microprocessors, and electrical units for performing other functions.
  • Various embodiments of the present disclosure may be implemented as software (eg, a program) including one or more instructions stored in a storage medium readable by a machine.
  • the processor of the device may call at least one command among one or more commands stored from a storage medium and execute it. This enables the device to be operated to perform at least one function according to the at least one command invoked.
  • the one or more instructions may include code generated by a compiler or code executable by an interpreter.
  • the device-readable storage medium may be provided in the form of a non-transitory storage medium.
  • 'non-temporary' only means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and this term refers to the case where data is stored semi-permanently in the storage medium. It does not discriminate when it is temporarily stored.
  • signals e.g., electromagnetic waves
  • the method according to various embodiments of the present disclosure may be included and provided in a computer program product.
  • Computer program products may be traded between sellers and buyers as commodities.
  • a computer program product is distributed in the form of a device-readable storage medium (eg compact disc read only memory (CD-ROM)), or through an application store (eg Play StoreTM) or between two user devices. It can be distributed (e.g., downloaded or uploaded) directly or online. In the case of online distribution, at least part of the computer program product may be temporarily stored or temporarily created in a device-readable storage medium such as a manufacturer's server, an application store server, or a relay server's memory.
  • a device-readable storage medium such as a manufacturer's server, an application store server, or a relay server's memory.
  • the method according to various embodiments of the present disclosure may be provided in the form of a web service through the Internet.
  • users may access a cloud server and execute software stored in the cloud server, and the software may include one or more instructions capable of implementing a method according to various embodiments of the present disclosure.
  • unit may be a hardware component such as a processor or a circuit, and/or a software component executed by the hardware component such as a processor.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Biomedical Technology (AREA)
  • Geometry (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Computer Graphics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Economics (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction. Le procédé de la présente invention peut consister à : saisir, dans un premier réseau, des premières données comprenant des informations relatives à un type de travail, des informations relatives à un lieu de travail ou des informations relatives à un moment de travail de façon à prédire un type de catastrophe ; saisir les premières données et le type de catastrophe dans un second réseau de façon à calculer un indice de degré de risque global ; et visuellement afficher l'indice de degré de risque global dans un espace de modélisation tridimensionnel.
PCT/KR2022/007013 2021-05-18 2022-05-17 Procédé et produit programme d'ordinateur pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction WO2022245091A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR1020210064397A KR102321897B1 (ko) 2021-05-18 2021-05-18 건설 현장의 3차원 모델링 공간에서 위험도를 나타내는 방법 및 컴퓨터 프로그램 제품
KR10-2021-0064397 2021-05-18
KR1020220059729A KR20230160106A (ko) 2022-05-16 2022-05-16 건설 현장의 3차원 모델링 공간에서 위험도를 나타내는 방법 및 컴퓨터 프로그램 제품
KR10-2022-0059729 2022-05-16

Publications (1)

Publication Number Publication Date
WO2022245091A1 true WO2022245091A1 (fr) 2022-11-24

Family

ID=84141479

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2022/007013 WO2022245091A1 (fr) 2021-05-18 2022-05-17 Procédé et produit programme d'ordinateur pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction

Country Status (1)

Country Link
WO (1) WO2022245091A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015114706A (ja) * 2013-12-09 2015-06-22 三菱重工業株式会社 危険度算出システムおよび危険度算出方法
KR101805713B1 (ko) * 2017-07-31 2017-12-06 한국가스안전공사 위험지도를 이용한 위험도 분석 시스템
KR20190063000A (ko) * 2017-11-29 2019-06-07 주식회사 오경컴텍 에이알기반 재해자 위치표시시스템 및 방법
KR20200001938A (ko) * 2018-06-28 2020-01-07 중앙대학교 산학협력단 건설 현장의 안전 관리 방법 및 이를 수행하는 서버
KR102244978B1 (ko) * 2020-12-23 2021-04-28 주식회사 케이씨씨건설 작업 현장의 위험성을 판단하는 인공지능 모델의 학습 방법, 장치 및 컴퓨터프로그램
KR102321897B1 (ko) * 2021-05-18 2021-11-08 주식회사 플럭시티 건설 현장의 3차원 모델링 공간에서 위험도를 나타내는 방법 및 컴퓨터 프로그램 제품

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015114706A (ja) * 2013-12-09 2015-06-22 三菱重工業株式会社 危険度算出システムおよび危険度算出方法
KR101805713B1 (ko) * 2017-07-31 2017-12-06 한국가스안전공사 위험지도를 이용한 위험도 분석 시스템
KR20190063000A (ko) * 2017-11-29 2019-06-07 주식회사 오경컴텍 에이알기반 재해자 위치표시시스템 및 방법
KR20200001938A (ko) * 2018-06-28 2020-01-07 중앙대학교 산학협력단 건설 현장의 안전 관리 방법 및 이를 수행하는 서버
KR102244978B1 (ko) * 2020-12-23 2021-04-28 주식회사 케이씨씨건설 작업 현장의 위험성을 판단하는 인공지능 모델의 학습 방법, 장치 및 컴퓨터프로그램
KR102321897B1 (ko) * 2021-05-18 2021-11-08 주식회사 플럭시티 건설 현장의 3차원 모델링 공간에서 위험도를 나타내는 방법 및 컴퓨터 프로그램 제품

Similar Documents

Publication Publication Date Title
CN108628286B (zh) 机台维修系统与方法
WO2018030772A1 (fr) Procédé de commande de signal de trafic de réponse et appareil associé
EP3144901B1 (fr) Système de gestion de conception immersive
CN109840672B (zh) 作业者管理装置
KR101810937B1 (ko) 가상 재난 시나리오 대응 훈련 시스템 및 방법
WO2020022639A1 (fr) Procédé et appareil d'évaluation à base d'apprentissage profond
KR102655865B1 (ko) 건설 현장의 3차원 모델링 공간에서 위험도를 나타내는 방법 및 컴퓨터 프로그램 제품
WO2019168336A1 (fr) Appareil de conduite autonome et procédé associé
EP3637390A1 (fr) Système de présentation de contenu
KR101646720B1 (ko) 원자력 발전소 운전에 영향을 미치는 인적요소에 대한 분석 및 평가 시스템
WO2022245091A1 (fr) Procédé et produit programme d'ordinateur pour indiquer un degré de risque dans un espace de modélisation tridimensionnel d'un site de construction
CN115079710A (zh) 多无人机电力巡检智能调度方法和系统
CN112669345A (zh) 一种面向云端部署的多目标轨迹跟踪方法及系统
KR20170110278A (ko) 재난 대응을 위한 기능성 게임 시스템
WO2019190171A1 (fr) Dispositif électronique et procédé de commande associé
Shah et al. Artificial intelligence in advancing occupational health and safety: an encapsulation of developments
CN103162984B (zh) 基于熵理论的在役桥梁安全预警方法
CN111739376A (zh) 一种机场管制员模拟训练系统
Matos et al. Implementation of advanced technologies into Aeronautic integrated maintenance concept-Use of virtual reality in ground-floor training maintenance execution
CN111583567A (zh) 一种森林防火预警方法及装置
Woolley et al. A naval damage incident recoverability toolset: Assessing naval platform recoverability after a fire event
WO2023027283A1 (fr) Système de simulation pour développement de commande de robot en nuage, et procédé associé
US11403845B2 (en) Dynamic detection of building structure
JP2008180784A (ja) シミュレーション装置
WO2024111809A1 (fr) Procédé et dispositif de commande d'exécution de tâche d'inférence par inférence partagée de réseau neuronal artificiel

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22804945

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22804945

Country of ref document: EP

Kind code of ref document: A1