KR102321897B1 - Method and computer program product for representing the degree of risk in a 3d modeling space of a construction site - Google Patents

Abstract

A method for representing risk in a three-dimensional modeling space of a construction site is provided. The method of the present invention comprises the following steps of: predicting a disaster type by inputting first data including information about a work type, information about a work location, or information about a work time into a first network; inputting the first data and a disaster type into a second network to calculate a comprehensive risk index; and visually displaying the comprehensive risk index in the three-dimensional modeling space.

Description

METHOD AND COMPUTER PROGRAM PRODUCT FOR REPRESENTING THE DEGREE OF RISK IN A 3D MODELING SPACE OF A CONSTRUCTION SITE

The present disclosure provides a method and a computer program product for representing a degree of risk in a three-dimensional modeling space of a construction site.

Conventionally, for safety management of the construction site, the person in charge inspects the site and judges the level of risk of the construction site based on professional experience. have.

Alternatively, in the prior art, for safety education at the construction site, education was conducted to familiarize the risk level based on the two-dimensional site information such as the verbal explanation of the site supervisor and CAD drawings before performing the work, but daily workers or foreigners without professional knowledge There is a problem in that the effectiveness of education for workers decreases.

Accordingly, there is a need for a method that can calculate an objective level of risk that reflects the progress of construction on site and intuitively convey the calculated level of risk to workers.

An object of the present invention is to provide a method and a computer program product for representing risk in a three-dimensional modeling space of a construction site. The technical problem to be achieved by the present embodiment is not limited to the technical problems as described above, and other technical problems may be inferred from the following embodiments.

As a technical means for achieving the above-described technical problem, the first aspect of the present disclosure is a method for representing a degree of risk in a three-dimensional modeling space of a construction site, information about the type of work, information about the work position, or the time of work predicting a disaster type by inputting first data including information on the first network into 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 overall risk index in the three-dimensional modeling space.

In addition, the step of visually displaying the overall risk index may include displaying a color based on the overall risk index on the object corresponding to the first data in the three-dimensional modeling space.

In addition, the three-dimensional modeling space includes at least one spatial grid, and the step of visually displaying the composite risk index includes: the composite risk index and the second object corresponding to the first data in a first spatial grid. A color based on the overall risk indices of at least one object in the second spatial grid positioned below the first spatial grid may be displayed.

In addition, the information on the type of work includes information about the type of work performed by the person performing the work and information about the type of work performed by the person performing the work, and the information about the work location includes the work It includes information on the number of floors to be performed, and the information on the time of the operation may include information on the date, day or time at which the operation is performed.

In addition, the first network corresponds to a classification learner that receives the first data as an input and predicts a disaster type of any one of a plurality of preset disaster types, and the second network includes the first data and the disaster It may correspond to a deep neural network that receives a form as an input and calculates the overall risk index.

A second aspect of the present disclosure provides an apparatus for indicating a degree of risk in a three-dimensional modeling space of a construction site, comprising: a memory for storing at least one program; and at least one processor indicating a degree of risk by executing the at least one program, wherein the at least one processor is first data including information about a work type, information about a work location, or information about a work time input into the first network to predict the disaster type, input the first data and the disaster type into the second network to calculate a comprehensive risk index, and visually display the comprehensive risk index in the three-dimensional modeling space. can

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.

According to the problem solving means of the present disclosure described above, it is possible to calculate an objective degree of risk reflecting the construction progress status of the site.

In addition, according to the problem solving means of the present disclosure, as the calculated risk level is visually expressed in the three-dimensional modeling space of the construction site, information on the risk level can be intuitively transmitted to workers at the construction site.

1 is a diagram illustrating an operation performed by an apparatus for displaying a risk level in a three-dimensional modeling space according to an exemplary embodiment.
2 is a diagram schematically illustrating a method of predicting a disaster type from first data using a first network according to an embodiment.
3 is a diagram exemplarily illustrating learning data used to train a first network according to an embodiment.
4 shows a simplified representation of learning data used to train a first network according to an embodiment.
5 is a diagram exemplarily illustrating a disaster type according to an embodiment.
6 is a diagram schematically illustrating a method of calculating a comprehensive risk index using a second network according to an embodiment.
7 is a diagram exemplarily illustrating a second network according to an embodiment.
8 is a diagram exemplarily showing preset risk indices according to an embodiment.
9 is a diagram exemplarily illustrating learning data used to train a second network according to an embodiment.
10 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a three-dimensional modeling space according to an embodiment.
11 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a three-dimensional modeling space according to an embodiment.
12 is a flowchart of a method for indicating a degree of risk in a 3D modeling space according to an exemplary embodiment.
13 is a block diagram of an apparatus for displaying a risk level in a 3D modeling space according to an exemplary embodiment.

Advantages and features of the present invention, and methods for achieving them will become apparent with reference to the detailed description in conjunction with the accompanying drawings. However, it should be understood that the present invention is not limited to the embodiments presented below, but may be implemented in a variety of different forms, and includes all transformations, equivalents, and substitutes included in the spirit and scope of the present invention. . The embodiments presented below are provided to complete the disclosure of the present invention, and to fully inform those of ordinary skill in the art to the scope of the present invention. In describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Some embodiments of the present disclosure may be represented by functional block configurations and various processing steps. Some or all of these functional blocks may be implemented in various numbers of hardware and/or software configurations that perform specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors, or by circuit configurations for a given function. Also, for example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented as an algorithm running on one or more processors. Also, the present disclosure may employ prior art techniques for electronic configuration, signal processing, and/or data processing, etc. Terms such as "mechanism", "element", "means" and "configuration" will be used broadly. and are not limited to mechanical and physical configurations.

In addition, the connecting lines or connecting members between the components shown in the drawings only exemplify functional connections and/or physical or circuit connections. In an actual device, a connection between components may be represented by various functional connections, physical connections, or circuit connections that are replaceable or added.

Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating an operation performed by an apparatus for displaying a risk level in a three-dimensional modeling space according to an exemplary embodiment.

The apparatus 100 for displaying the risk of the 3D modeling space according to an embodiment may receive the first data and calculate a total risk point indicating the degree of risk at a specific working position in the 3D modeling space. In addition, it is possible to visually display the overall risk index calculated in the three-dimensional modeling space within the construction site.

The three-dimensional modeling space is a model of a construction site in a three-dimensional 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 the construction site and information on the type of work being performed at the construction site. For example, the object information may be generated by reducing the weight of object polygons in a construction site.

According to an embodiment, the apparatus 100 for displaying the risk of the 3D modeling space matches the coordinates of the real world with the coordinates in the 3D space, thereby reflecting the data of the construction site in the virtual 3D space to create the 3D modeling space. can create The risk level display apparatus 100 of the three-dimensional modeling space may accurately reflect the specific actual location of the construction site in the virtual three-dimensional space by applying the three-dimensional grid precision address.

The apparatus 100 for displaying the risk of the three-dimensional modeling space may display a comprehensive risk index indicating the degree of risk of a specific type of work performed at a specific working position in the three-dimensional modeling space in the three-dimensional modeling space.

For example, the apparatus 100 for displaying the risk of the 3D modeling space of FIG. 1 corresponds to a device for displaying the level of risk of a specific type of work performed at a specific working position in the 3D modeling space based on an artificial neural network. can do. An artificial neural network refers to an overall model that has problem-solving ability by changing the bonding strength of synapses through learning in which artificial neurons that form a network by combining synapses.

The apparatus 100 for displaying the risk of a three-dimensional 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, three-dimensional modeling using an artificial neural network It may correspond to a smartphone, tablet device, AR (Augmented Reality) device, Internet of Things (IoT) device, autonomous vehicle, robotics, etc. that display the degree of risk of a specific type of work performed at a specific work location in space, but is not limited thereto does not

Furthermore, the risk level display apparatus 100 of the 3D modeling space may correspond to a dedicated hardware accelerator (HW accelerator) mounted on the device as described above. Alternatively, the apparatus 300 for searching a section in a video may be a hardware accelerator such as a neural processing unit (NPU), a tensor processing unit (TPU), a neural engine, etc., which are dedicated modules for driving an artificial neural network, but is not limited thereto. does not

Referring to FIG. 1 , the risk level display device 100 in the 3D modeling space may receive first data as an input. The first data may include, but is not limited to, information about a type of work to be performed at a construction site, information about a work location, or information about a work time.

For example, the first data may be received from an external device through a communication unit included in the risk level display device 100 of the 3D modeling space. Alternatively, the first data may be input from the user through the user interface of the risk level display apparatus 100 in the 3D modeling space.

The risk level display device 100 of the 3D modeling space may output a comprehensive risk index corresponding to the first data based on the first data received as an input. For example, the risk display device 100 of the three-dimensional modeling space may output a comprehensive risk index of a specific work to be performed at a specific working time at a specific working location.

Referring to FIG. 1 , the apparatus 100 for displaying a risk level of a 3D modeling space according to an embodiment may predict a disaster type from first data using the 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 .

In addition, the apparatus 100 for displaying the risk of the three-dimensional modeling space according to an embodiment calculates a comprehensive risk index from the disaster type predicted from the first data and the first network 110 using the second network 120 . can A method of calculating a comprehensive risk index from the first data and disaster type using the second network 120 will be described later with reference to FIGS. 6 to 9 .

Finally, the apparatus 100 for displaying the risk in the three-dimensional modeling space according to an embodiment may visually display the calculated overall risk index in the three-dimensional modeling space.

2 is a diagram schematically illustrating a method of predicting a disaster type from first data using a first network according to an embodiment.

According to an embodiment, the apparatus 100 for displaying the risk of the three-dimensional 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 The disaster type can be output from the data.

The information on the type of work may include information about the type of work entrusted to the person performing the work and information about the type of work performed by the person performing the work. 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. In addition, the information on the time of the operation may include information on the month, day or time in which the operation is performed.

For example, 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, and the device 100 for displaying the risk level of a three-dimensional modeling space analyzes disaster accidents obtained from disaster case books, etc. based on a schedule classification system to construct learning data. can do. The learning data may include data including information on the type of work, information on a work location where a disaster occurred, or information on a work time when a disaster occurs, and a disaster type (corresponding to a class label) corresponding to the data.

3 is a diagram exemplarily illustrating learning data used to train a first network according to an embodiment.

Referring to FIG. 3 , the learning data 300 used to train the first network according to an embodiment may include information 310 on the type of work, and the information 310 on the type of work is the victim. It may include information 311 about the type of work that the person undertakes and information 312 about the type of work that caused the disaster.

In addition, the learning data used to train the first network according to an embodiment may include information 320 about the work location where the disaster occurred, and the information 320 about the work location where the disaster occurred is the It may include information 321 about the number of floors performed or information 322 about whether the work was performed inside or outside the building.

In addition, the learning data used to train the first network according to an embodiment may include information 330 about the work time point when the disaster occurred, and the information 330 about the work time point when the disaster occurred It may include information 331 about the month performed, information 332 about the day of the week, or information 333 about time.

In addition, the learning data used to train the first network according to an embodiment may include information 340 about the type of disaster that has occurred. Information about the type of disaster 340 may correspond to information corresponding to data including information about the type of work 310, information 320 about the work location where the disaster occurred, or information about the work time when the disaster occurred. have. The information 340 on the type of disaster may correspond to information on the location of the disaster or the cause of death or injury.

On the other hand, the risk display device 100 of the three-dimensional modeling space can be expressed simply by coding the learning data 300 used to learn the first network, and the learning of the first network using the simplified learning data. can be done

4 shows a simplified representation of learning data used to train a first network according to an embodiment.

Referring to FIG. 4 , information on a work time point when a disaster occurs may be simplified to indicate only information about a time when a disaster occurs. In addition, the information on the type of work can be expressed briefly by coding the information about the type of work that the injured person is in charge of (the type of work for the injured person) and the information about the type of work that caused the disaster (the type of work that is caused by the accident). Codes may be preset for each of the various types of work performed by the injured person and the various types of work that caused the disaster. In addition, 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.

5 is a diagram exemplarily illustrating a disaster type according to an embodiment.

5 shows various examples of disaster types predicted in response to the first data when first data including information about work types, information about work locations, or information about work hours is input to the first network; . That is, FIG. 4 shows a plurality of predefined class labels.

Referring to FIG. 5 , the disaster types include falling (FOF), falling (FDN), hitting (HIT), hitting an object (STR), collapsing (CLS), caught (JAM), cut/cut/pierced (MCP), etc. may be applicable.

The first network may be configured to output a predicted disaster type corresponding to the input first data. For example, the risk display device 100 of the three-dimensional modeling space receives information that the temporary construction will be performed outside the building on the 10th floor at 5 pm on Wednesday, May 12, 2021 using the first network. When the included first data is input, a result that a fall (FOF) is predicted as a disaster type may be output.

6 is a diagram schematically illustrating a method of calculating a comprehensive risk index using a second network according to an embodiment.

According to an embodiment, the apparatus 100 for displaying the risk of 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. . As described above, the first data may include information about the type of work, information about the work location, or information about the work time.

For example, the second network may correspond to a deep neural network (DNN) or an n-layer neural network, but is not limited thereto. For example, the second network may correspond to Recurrent Neural Networks (RNN), Long Short-Term Memory Network (LSTM), Bidirectional Recurrent Deep Neural Network (BRDNN), Deep Belief Networks, Restricted Boltzman Machines, etc. have.

7 is a diagram exemplarily illustrating a second network according to an embodiment.

Referring to FIG. 7 , 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 only an example and the second network may include a plurality of hidden layers.

In the input layer, the first data including information about the type of work, information about the work location, or information about the work time, and the disaster type output from the first network based on the first data may be input. Calculation 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.

For example, the overall risk index may be calculated in the form of a score or may be classified into a specific risk level according to the score value. Referring to FIG. 7 , it is shown that the risk level can be classified into 5 levels according to the range of the score value of the overall risk index, but the method of representing the overall risk index is not limited thereto.

Each of the layers included in the second network is a plurality of artificial nodes, known as “neuron”, “processing element (PE)”, “unit” or similar terms. may include For example, as shown in FIG. 7 , the input layer may include p nodes, and the hidden layer may include m nodes. However, this is only an example, and each of the layers included in the second network may include a variable number of nodes.

Nodes included in each of the layers included in the second network may be connected to each other to exchange data. For example, one node may receive data from other nodes to perform an operation, and may output an operation result to other nodes.

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 .

For example, the risk display device 100 of the three-dimensional modeling space includes first data including information that temporary construction will be performed outside the building on the 10th floor at 5 pm on Wednesday, May 12, 2021 and When information that a fall (FOF) is predicted as a form of disaster is received in response to the first data, a comprehensive risk index corresponding thereto may be output using the second network.

On the other hand, the risk display device 100 of the three-dimensional modeling space according to an embodiment includes a preset risk index for each work process, a risk index for each floor, a monthly risk index, a risk index for each day or a risk index for each hour, etc. Using the information, the second network can be trained to output a comprehensive risk index.

8 is a diagram exemplarily showing preset risk indices according to an embodiment.

8 is a risk index for each preset work type stored in the risk display device 100 of the three-dimensional modeling space, the risk index for each floor, the monthly risk index, the risk index for each day of the week, the risk index for each hour, and the risk for each type of disaster. An example of the index is shown, and each risk index is not limited to the bar illustrated in FIG. 8 . In addition, in addition to the risk index shown in FIG. 8 , various types of risk indices required to calculate the overall risk index may be further stored.

For example, the risk index for each type of work, the risk index for each floor, the monthly risk index, the risk index for each day of the week, the hourly risk index, and the risk index for each type of disaster can be calculated based on statistical data.

In the second network, the information on the type of work included in the first data may be calculated by applying a preset risk index for each type of work, and the information about the work location included in the first data includes the risk by the preset number of floors. A calculation may be performed by applying an index, and a preset monthly risk index, a daily risk index, or an hourly risk index may be applied to the information on the work time included in the first data to perform the calculation. In addition, with respect to the disaster type predicted from the first network, a risk index for each type of disaster may be applied and calculation may be performed.

9 is a diagram exemplarily illustrating learning data used to train a second network according to an embodiment.

Referring to FIG. 9 , the apparatus 100 for displaying a risk level of a three-dimensional modeling space may configure learning data by analyzing a disaster accident obtained from a disaster case book based on a schedule classification system. The learning data used to train the second network is information on the type of work (injured type of work, the type of work done by the victim), information about the work location where the disaster occurred (number of floors, inside/outside), where the disaster occurred, as in FIG. 4 described above. It may include information about the time of operation (month, day, time) or information about the type of disaster that occurred.

According to an embodiment, the apparatus 100 for displaying the risk of the three-dimensional modeling space calculates the risk index for the type of work by applying the risk index for each type of work described above in FIG. can In addition, the number of floors risk index can be calculated by applying the risk index for each floor number described above in FIG. In addition, by applying each of the above-described monthly risk index, day-of-week risk index, and hour-by-hour risk index in FIG. A time risk index can be calculated. In addition, the disaster type risk index may be calculated by applying the risk index for each type of disaster described above with reference to FIG. 8 to information on the type of disaster that has occurred. Accordingly, the training data used to train the two networks may further include information about each risk index described above.

The risk display device 100 of the three-dimensional modeling space can learn the second network using the above-described learning data so that the second network can output the comprehensive risk index in response to the input first data and the type of disaster. have.

10 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a three-dimensional modeling space according to an embodiment.

According to an embodiment, the apparatus 100 for displaying the risk in the 3D modeling space may display a color based on the overall risk index on the object corresponding to the first data in the 3D modeling space.

For example, as described above in FIG. 7 , the overall risk index may be calculated in the form of a score or may be classified into a specific risk level according to the score value. A specific risk level classified according to a score value may correspond to a specific color. For example, the risk level may be classified into 5 levels according to the range of the score value of the composite risk index, and 5 colors corresponding to each risk level may exist, but this is only an example and the score of the composite risk index The number of risk levels according to values and colors corresponding to the risk levels may also vary.

Referring to FIG. 10 , the 3D modeling space may include at least one spatial grid, and the spatial grid may correspond to a specific actual location of a construction site. The risk level display device 100 of the three-dimensional modeling space is a comprehensive risk level output to an object in any one spatial grid corresponding to the first data including information about the type of work, information about the work location, or information about the work time Indices can be displayed visually. For example, the apparatus 100 for displaying the risk of the three-dimensional modeling space may display a color based on the comprehensive risk index output from the second network on an object in any one spatial grid corresponding to the first data. For example, the risk display device 100 in the three-dimensional modeling space may display a color corresponding to the risk level according to the score value of the comprehensive risk index. For example, in FIG. 10 , each hatch may represent a color of a different risk level.

11 is a diagram exemplarily illustrating a method of visually displaying a comprehensive risk index in a three-dimensional modeling space according to an embodiment.

11 shows an example in which the risk display apparatus 100 of the 3D modeling space displays a color based on the overall risk index on the object corresponding to the first data in the 3D modeling space, similar to FIG. 10 .

Referring to FIG. 11 , the spatial grid corresponding to the first data including information about the type of work, information about the work location, or information about the working time may further include at least one spatial grid positioned below. .

In this case, the risk display device 100 of the three-dimensional modeling space is located in the lower layer of the spatial grid corresponding to the first data and the comprehensive risk index corresponding to the first data in the object in the spatial grid corresponding to the first data. 2 The total risk index corresponding to at least one object in the spatial grid may be reflected and displayed visually.

That is, the risk display device 100 of the 3D modeling space may visually display the overlapping comprehensive risk index as it goes up from the spatial grid located on the lower layer to the spatial grid located on the upper layer.

12 is a flowchart of a method for indicating a degree of risk in a 3D modeling space according to an exemplary embodiment.

Referring to FIG. 12 , in step 1210, the device for displaying the risk of the three-dimensional modeling space inputs first data including information on the type of work, information on the work location, or information on the time of the work to the first network to form a disaster can be predicted

According to an embodiment, the information on the type of work may include information about the type of work performed by the person performing the work and information about the type of work that the person performing the work has. 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. In addition, the information on the time of the operation may include information on the month, day or time in which the operation is performed.

According to an embodiment, 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 is performed to predict a categorical variable. The first network includes data including information on the type of work, information on the work location where the disaster occurred, or information on the timing of the work at which the disaster occurred, and learning data including the disaster type (corresponding to the class label) corresponding to the data can be supervised using

In step 1220, the device for displaying the risk of the three-dimensional modeling space may calculate the overall risk index by inputting the first data and the disaster type into the second network.

According to an embodiment, the second network may correspond to a deep neural network that receives the first data and a disaster type predicted from the first network as inputs and calculates a comprehensive risk index. In the input layer constituting the second network, the disaster type output from the first network based on the first data and the first data including information about the type of work, information about the work location, or information about the work time is input. can Calculation 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 device for displaying the risk of the three-dimensional modeling space according to an embodiment uses information including a preset risk index for each work process, a risk index for each floor, a monthly risk index, 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 composite risk index.

In operation 1230, the risk display device in the 3D modeling space may visually display the overall risk index in the 3D modeling space.

For example, the overall risk index may be calculated in the form of a score or may be classified into a specific risk level according to the score value. Depending on the score value, a specific risk level may correspond to a specific color.

According to an embodiment, the device for displaying the risk of the 3D modeling space may display a color based on the overall risk index on the object corresponding to the first data in the 3D modeling space.

In addition, according to an embodiment, the three-dimensional modeling space includes at least one spatial grid, and a space located below the overall risk index and the first spatial grid in the object corresponding to the first data in the first spatial grid. A color based on the overall risk index of at least one object in the grid may be displayed.

13 is a block diagram of an apparatus for displaying a risk level in a 3D modeling space according to an exemplary embodiment.

Referring to FIG. 13 , the risk level display device 1300 of the 3D modeling space may include a communication unit 1310 , a processor 1320 , and a DB 1330 . Only the components related to the embodiment are shown in the risk level display device 1300 of the 3D modeling space of FIG. 13 . Accordingly, it can be understood by those skilled in the art 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 for performing wired/wireless communication with the risk level display device 1300 of the 3D modeling space. For example, 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 receiving unit (not shown).

The DB 1330 is hardware for storing various data processed in the risk level display device 1300 of the 3D modeling space, and may store a program for processing and controlling the processor 1320 .

DB 1330 is a random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), CD- It may include a ROM, Blu-ray or other optical disk storage, a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. Controls the overall operation. For example, the processor 1320 may generally control the input unit (not shown), the display (not shown), the communication unit 1310 , the DB 1330 , and the like by executing programs stored in the DB 1330 . The processor 1320 may control the operation of the risk level display device 1300 in the 3D modeling space by executing programs stored in the DB 1330 .

The processor 1320 is ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers (controllers), microcontroller It may be implemented using at least one of (micro-controllers), microprocessors, and other electrical units for performing 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. For example, the processor of the device may call at least one of the one or more instructions stored from the storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the at least one command called. 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. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain a signal (eg, electromagnetic wave), and this term is used in cases where data is semi-permanently stored in the storage medium and It does not distinguish between temporary storage cases.

According to an embodiment, the method according to various embodiments of the present disclosure may be provided by being included in a computer program product. Computer　program　products can be traded between sellers and buyers as commodities. Computer　programs　products are 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 Store™) or between two user devices. It can be distributed directly or online (eg, downloaded or uploaded). In the case of online distribution, at least a part of a computer, a program, and a product may be temporarily stored or temporarily created in a machine-readable storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.

Also, according to an embodiment, the method according to various embodiments of the present disclosure may be provided in the form of a web service through the Internet. For example, users may access a cloud server to execute software stored in the cloud server, and the software may include one or more instructions that may implement a method according to various embodiments of the present disclosure.

Also, in this specification, “unit” may be a hardware component such as a processor or circuit, and/or a software component executed by a hardware component such as a processor.

The scope of the present embodiment is indicated by the following claims rather than the detailed description, and it should be construed as including all changes or modifications derived from the meaning and scope of the claims and their equivalents.

Claims (5)

In the device representing the degree of risk in the three-dimensional modeling space of the construction site,
a memory in which at least one program is stored; and
A processor for performing an operation by executing the at least one program,
The processor is
Predict the type of disaster by inputting first data in text form including information on the type of work, information on the location of work, or information on the time of work into the first network,
Input the first data and the disaster type into a second network to calculate a comprehensive risk index,
Visually display the overall risk index in the three-dimensional modeling space,
The three-dimensional modeling space corresponds to the reflection of the data of the construction site, including object information in the construction site and information on the type of work being carried out at the construction site, in a virtual three-dimensional space, the apparatus. The method of claim 1,
The processor is configured to display a color based on the overall risk index on an object in a first spatial grid corresponding to the first data in the three-dimensional modeling space. The method of claim 1,
The three-dimensional modeling space includes at least one spatial grid,
The processor is
Displaying a color based on the composite risk index and the composite risk index of at least one object in the second spatial grid located below the first spatial grid on the object in the first spatial grid corresponding to the first data, Device. The method of claim 1,
The information on the type of work includes information about the type of work that the person performing the work is in charge of and information about the type of work actually performed by the person performing the work,
The information about the work location includes 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 time of the operation, the device including information about the date, day or time when the operation is performed. The method of claim 1,
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 corresponds to a deep neural network that receives the first data and the disaster type as inputs and calculates the overall risk index.

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