Information processing apparatus and information processing method

Abstract

To provide appropriate support for lighting design.SOLUTION: An information processing apparatus according to an embodiment includes an acquisition unit, a learning unit, and a proposal unit. The acquisition unit acquires a three-dimensional model obtained by reproducing a lighting space of an existing building. The learning unit learns, on the basis of the three-dimensional model acquired by the acquisition unit, a relation between a three-dimensional shape of the lighting space of the existing building and lighting fixtures arranged in the lighting space of the existing building. The proposal unit proposes lighting fixtures to be arranged in a new three-dimensional model which is a three-dimensional shape obtained by reproducing a lighting space of a new building on the basis of a learning result by the learning unit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] There are currently technologies for supporting lighting design. For example, Patent Document 1 discloses a technology that calculates the type and quantity of lighting fixtures typically required for each room based on architectural drawings, and generates a lighting equipment drawing that is superimposed on the architectural drawings. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 08-87535 Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional techniques merely generate lighting equipment drawings in a simplified manner, and there is room for improvement in terms of providing appropriate support for lighting design.

[0005] The present invention has been made in view of the above, and has an object to provide an information processing device and an information processing method that can appropriately support lighting design. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, an information processing device according to the present invention includes an acquisition unit, a learning unit, and a proposing unit. The acquisition unit acquires a 3D model that recreates the lighting space of an existing building. The learning unit learns the relationship between the 3D shape of the lighting space of the existing building and the lighting fixtures arranged in the lighting space of the existing building based on the 3D model acquired by the acquisition unit. The proposing unit proposes lighting fixtures to be arranged in a new 3D model that recreates the lighting space of a new building based on the learning results by the learning unit. [Effects of the Invention]

[0007] According to the present invention, lighting design can be appropriately supported. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an overview of a control system according to an embodiment. [Figure 2] FIG. 2 is a block diagram of the information processing apparatus according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of information stored in a BIM data storage unit according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of information stored in a people flow information storage unit according to the embodiment. [Figure 5] FIG. 5 is a flowchart illustrating an example of the learning process according to the embodiment. [Figure 6] FIG. 6 is a flowchart illustrating an example of the proposal process according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The information processing device 1 according to the embodiment described below includes an acquisition unit 351 that acquires a three-dimensional model that reproduces the lighting space of an existing building, a learning unit 352 that learns the relationship between the three-dimensional shape of the lighting space of the existing building and the lighting fixtures arranged in the lighting space of the existing building based on the three-dimensional model acquired by the acquisition unit 351, and a proposal unit 354 that proposes lighting fixtures to be arranged in a new three-dimensional model, which is the three-dimensional model that reproduces the lighting space of a new building, based on the learning results of the learning unit 352.

[0010] In addition, the learning unit 352, which will be described below, learns the relationships between the uses of the lighting spaces of existing buildings, and the proposing unit 354 proposes lighting fixtures to be placed in the new 3D model according to the uses of the lighting spaces of the new building.

[0011] Furthermore, the acquisition unit 351, which will be described below, acquires people flow information regarding the flow of people in the lighting space of the existing building, the learning unit 352 learns the relationship between the three-dimensional shape of the lighting space of the existing building, the lighting fixtures placed in the lighting space of the existing building, and the people flow information of the lighting space of the existing building, and the proposal unit 354 proposes lighting fixtures to be placed in the new three-dimensional model based on the people flow prediction information predicted from the new three-dimensional model.

[0012] In addition, the three-dimensional model described below is data that reproduces the fixtures to be placed in the lighting space of an existing building, and the proposal unit 354 proposes lighting fixtures to be placed in the new three-dimensional model based on the fixtures to be placed in the new three-dimensional model.

[0013] The three-dimensional model described below is data that reproduces the interior of the lighting space, and the proposing unit 354 proposes lighting fixtures to be arranged for the new three-dimensional model based on the interior of the new three-dimensional model.

[0014] Furthermore, the information processing method described below is an information processing method executed by a computer, and includes an acquisition process of acquiring a three-dimensional model that reproduces the lighting space of an existing building, a learning process of learning, based on the three-dimensional model acquired by the acquisition process, the relationship between the three-dimensional shape of the lighting space of the existing building and the lighting fixtures arranged in the lighting space of the existing building, and a proposal process of proposing lighting fixtures to be arranged in a new three-dimensional model, which is the three-dimensional model that reproduces the lighting space of a new building, based on the learning results of the learning process.

[0015] (Embodiment) Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the following embodiments do not limit the technology disclosed by the present invention. Furthermore, the same components in each embodiment are designated by the same reference numerals, and redundant explanations will be omitted.

[0016] First, an overview of a control system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of a control system according to an embodiment. The control system S shown in Fig. 1 is a system that supports lighting design for a new building based on information about an existing building.

[0017] 1, the control system S includes an information processing device 1, a management device 100, and a client terminal 200. The information processing device 1, the management device 100, and the client terminal 200 are connected via a predetermined network N.

[0018] The information processing device 1 executes various processes for supporting lighting design. For example, the information processing device 1 acquires various information about an existing building from a management device 100 installed in the existing building, and analyzes this information to make various proposals about lighting design for a new building.

[0019] The management device 100 is a terminal device set in an existing building. In this disclosure, a case will be described in which the existing building is a facility such as a commercial facility or an office building. The management device 100 stores BIM (Building Information Modeling) data for each room in the existing building. The BIM data is a three-dimensional model that reproduces the three-dimensional shape of each room. In this disclosure, the BIM data includes information about lighting fixtures, furniture, and the like installed in each room.

[0020] Furthermore, the management device 100 acquires camera images of each room in the existing building and lighting control information of lighting fixtures installed in each room, and provides these to the information processing device 1. For example, cameras are installed on the ceiling of each room in the existing building, and the management device 100 acquires camera images from each camera installed in each room. Furthermore, the management device 100 is connected to, for example, a lighting control device (not shown) that controls the lighting in each room, and acquires lighting control information of lighting fixtures installed in each room from the lighting control device.

[0021] The client terminal 200 is a terminal device owned by a client who requests lighting design for a new building from the information processing device 1. The client is, for example, a design office or the like that has been requested to design a new building such as a commercial facility or an office building.

[0022] The client creates BIM data (three-dimensional model) of a new building and requests the information processing device 1 to design a lighting for the new building through the client terminal 200. As a result, the information processing device 1 proposes a lighting design for the requested new building to the client through the client terminal 200.

[0023] Next, an example of the configuration of the information processing device 1 will be described with reference to Fig. 2. Fig. 2 is a block diagram of the information processing device 1 according to the embodiment. As shown in Fig. 2, the information processing device 1 has a communication unit 31, a display unit 32, an operation unit 33, a storage unit 34, and a control unit 35.

[0024] The communication unit 31 is implemented, for example, by a predetermined communication circuit such as a NIC (Network Interface Card), and performs data communication with the management device 100, client terminal 200, etc. via a communication network such as Ethernet (registered trademark) or LAN.

[0025] The display unit 32 is implemented by, for example, a liquid crystal monitor, a touch panel, etc. The operation unit 33 is implemented by, for example, a mouse, a keyboard, etc., and receives various operations from the operator.

[0026] The storage unit 34 is implemented by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. In the example shown in Figure 2, the storage unit 34 has a BIM data storage unit 341, a people flow information storage unit 342, and a learning result storage unit 343.

[0027] The BIM data storage unit 341 stores BIM data. The BIM data is data for reproducing the shape of a building, etc., in software that reproduces an object in a three-dimensional shape.

[0028] Here, an example of information stored in the BIM data storage unit 341 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of information stored in the BIM data storage unit 341 according to the embodiment.

[0029] 3, the BIM data storage unit 341 stores information on items such as "Building ID," "Room ID," "Use," and "BIM" in association with each other. The "Building ID" item stores an identifier for identifying the corresponding building.

[0030] The "Room ID" field stores an identifier for identifying each room in the corresponding building. The "Use" field stores information about the use of the room identified by the corresponding room ID. If the building is an office building, possible uses include entrance floor, desk floor, conference area, reception area, break area, etc.

[0031] The "BIM" item stores BIM data related to a room identified by a corresponding room ID. As shown in Figure 3, the "BIM" item includes the items "Room," "Lighting," and "Fixtures."

[0032] The "Room" item stores BIM data for a room identified by the corresponding room ID. More specifically, the "Room" item stores 3D data that recreates the corresponding room. The 3D data is data that recreates, for example, the positions of walls, pillars, and windows, as well as the placement of ventilation vents, spring cladding, and other ceiling fixtures. The 3D data may also recreate the interior of the room (for example, wallpaper, floor materials, etc.). The "Room" item may also include information about the sunlight entering through the windows, such as the latitude, longitude, and direction of the room.

[0033] The "Lighting" item stores BIM data for lighting in a room identified by the corresponding room ID. The lighting BIM data includes information on the type, dimensions, and placement of lighting fixtures.

[0034] The "Fixtures" item stores BIM data for the fixtures in a room identified by the corresponding room IDN. The BIM data for fixtures is data on the type, dimensions, and placement of the fixtures. The BIM data for fixtures can also be linked to the purpose of each piece of fixture. For example, if the fixture is a desk, it can be used for desk work, meetings, etc.

[0035] Returning to the explanation of FIG. 2, the people flow information storage unit 342 will be described. The people flow information storage unit 342 stores people flow information. The people flow information is information relating to the flow of people in each room. Here, an example of information stored in the people flow information storage unit 342 will be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of information stored in the people flow information storage unit 342 according to the embodiment.

[0036] 4, the people flow information storage unit 342 stores information such as "building ID," "room ID," and "people flow information" in association with each other. The "building ID" item stores information related to an identifier for identifying the corresponding building.

[0037] The "Room ID" field stores an identifier for identifying the corresponding room. The "People Flow Information" field stores people flow information for a room identified by the corresponding room ID. The people flow information is information related to the history of people flow in the corresponding room. In the present disclosure, people flow information is generated by analyzing camera footage captured by a camera installed on the ceiling of the room. However, the analysis of camera footage is not limited to people flow information; for example, the behavior of people captured in camera footage may be identified and included in the people flow information.

[0038] Returning to the explanation of Figure 2, the learning result storage unit 343 will be described. The learning result storage unit 343 stores the learning results. The learning results are the results of learning the relationship between the BIM data (three-dimensional data) of each room in an existing building and the lighting fixtures placed in each room.

[0039] For example, the learning result is a lighting placement model (e.g., AI; Artificial Intelligence) that has been trained to place lighting fixtures appropriate for the BIM data when BIM data is input. The lighting placement model may also be a model that has been trained to place lighting fixtures appropriate for the BIM data based on the BIM data (3D data) of each room, the placement of lighting fixtures in each room, and information on the flow of people in each room.

[0040] Next, the control unit 35 will be described. The control unit 35 is a controller that controls the entire information processing device 1. For example, the control unit 35 can be implemented by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). Alternatively, the control unit 35 may be implemented by an integrated circuit such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0041] In the example of FIG. 2, the control unit 35 includes an acquisition unit 351, a learning unit 352, a reception unit 353, and a proposal unit 354. The acquisition unit 351 acquires various information related to existing buildings. For example, the acquisition unit 351 acquires BIM data (three-dimensional models) that reproduce the lighting space of the existing building from the management device 100 (see FIG. 1) of each existing building via the network N. At this time, the acquisition unit 351 may also acquire BIM data of lighting fixtures and BIM data of fixtures.

[0042] The acquisition unit 351 also acquires people flow information about the flow of people in each existing building from the management device 100 of each existing building at a predetermined cycle. The people flow information may be camera images of each lighting space, or may be people flow information obtained by analyzing the camera images. In other words, the people flow information may be generated on the management device 100 side or on the information processing device 1 side.

[0043] Based on the three-dimensional model acquired by the acquisition unit 351, the learning unit 352 learns the relationship between the three-dimensional shape of the lighting space of the existing building and the lighting fixtures arranged in the lighting space of the existing building.

[0044] More specifically, first, the learning unit 352 generates learning data from the BIM data of rooms, lighting, and fixtures. The learning data includes data on the area of ​​each room that will be the lighting space, ceiling height, room shape, pillar positions, window positions, interior materials, lighting fixture placement, lighting fixture types, lighting fixture dimensions, lighting fixture attributes (luminous flux, power consumption, etc.), fixture placement, fixture types, etc.

[0045] Then, the learning unit 352 uses the generated learning data to learn the relationship between the three-dimensional shape of the lighting space and the lighting fixtures arranged in the lighting space, and outputs the learning result as a lighting setting model (AI) that has learned these.

[0046] In this case, the learning unit 352 may perform learning for each purpose of each lighting space. By performing learning for each purpose in this way, it becomes possible to propose a lighting design suited to the purpose. In addition, the learning unit 352 may narrow down the learning data based on an instruction from the client terminal 200. In such a case, the client terminal 200 can specify an existing building or room to be used for learning, or specify the developer who constructed the existing building.

[0047] The learning unit 352 may also perform learning including actual people flow information in each lighting space. In this case, the learning unit 352 generates a lighting setting model that has learned the relationship between the BIM data of rooms and fixtures and the actual people flow information in each room.

[0048] The learning unit 352 may separately generate a people flow prediction model that is trained to output people flow prediction results from the BIM data of each room based on the BIM data of the rooms and fixtures and the actual people flow information of each room. For example, the people flow prediction model is a model that, when BIM data of a room or fixtures is input, outputs people flow prediction results in that room.

[0049] The reception unit 353 receives a request for lighting design for a new building from the client terminal 200 via the network N. For example, when a lighting design is requested, the reception unit 353 acquires from the client terminal 200 a new 3D model that is a 3D model that reproduces the lighting space of the new building that is the target of the lighting design. For example, the new 3D model is BIM data of rooms and fixtures before lighting BIM data is created. The new 3D model also includes information about the purpose of each room.

[0050] The proposing unit 354 proposes lighting fixtures to be placed in a new 3D model that is a 3D model that reproduces the lighting space of a new building, based on the learning results of the learning unit 352. Specifically, the proposing unit 354 inputs the new 3D model into the lighting setting model (AI) that has been learned by the learning unit 352. As a result, the lighting setting model (AI) creates lighting BIM data for the new 3D model.

[0051] In this case, the proposal unit 354 may generate lighting BIM data for the new 3D model based on the purpose of the room reproduced in the new 3D model, using a lighting setting model (AI) that has been trained for that purpose.

[0052] In addition, the proposing unit 354 may use the people flow prediction model generated by the learning unit 352 to predict people flow in a room reproduced in the new 3D model, and then use the information on the new 3D model and the people flow prediction to generate BIM data for lighting.

[0053] The proposal unit 354 generates proposal materials using lighting BIM data generated by the lighting setting model (AI). The proposal materials are three-dimensional data in which integrated BIM data that integrates the BIM data of rooms, lighting, and fixtures, and people flow data that represents the results of people flow predictions in three dimensions are superimposed on the integrated BIM data. In other words, the proposal materials are data that visualize the lighting environment and people's behavior in each room. The proposal materials may be images or moving images.

[0054] Then, the proposing unit 354 provides the generated proposal material to the requesting client terminal 200 via the network N. This allows the client to view the proposal material via the client terminal 200.

[0055] Next, a processing procedure executed by the information processing device 1 according to the embodiment will be described with reference to Fig. 5 and Fig. 6. Fig. 5 is a flowchart showing an example of a learning process according to the embodiment. Fig. 6 is a flowchart showing an example of a proposal process according to the embodiment.

[0056] 5, in the learning process, the information processing device 1 first accumulates BIM data of existing buildings acquired from the existing building management device 100 (see FIG. 1) (step S101). Next, the information processing device 1 accumulates people flow information of existing buildings also acquired from the management device 100 (step S102).

[0057] Next, the information processing device 1 learns the relationships between the three-dimensional shape of each room in the existing building, the flow of people, and the arrangement of lighting fixtures (step S103), stores the learning results (step S104), and ends the process.

[0058] Next, the procedure of the proposal process will be described with reference to Fig. 6. In the proposal process, first, the information processing device 1 acquires BIM data of a new building from the client terminal 200 (step 111).

[0059] Next, the information processing device 1 calculates the type and arrangement of lighting fixtures for each room based on the learning results of the learning process (step S112). Next, the information processing device 1 updates the BIM data of the new building based on the calculated type and arrangement of lighting fixtures (step S113).

[0060] Next, the information processing device 1 generates proposal materials including images of the lighting environment and people flow data in the new building (step S114), and ends the process.

[0061] As described above, the information processing device 1 according to the embodiment includes the acquisition unit 351, the learning unit 352, and the proposing unit 354. The acquisition unit 351 acquires a 3D model that recreates the lighting space of an existing building. The learning unit 352 learns the relationship between the 3D shape of the lighting space of the existing building and the lighting fixtures arranged in the lighting space of the existing building, based on the 3D model acquired by the acquisition unit 351. The proposing unit 354 proposes lighting fixtures to be arranged in a new 3D model, which is a 3D model that recreates the lighting space of a new building, based on the learning results of the learning unit 352.

[0062] Therefore, the information processing device 1 according to the embodiment can appropriately support lighting design.

[0063] Although an embodiment of the present invention has been described, this embodiment is presented as an example and is not intended to limit the scope of the invention. This embodiment can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as the inventions described in the claims and their equivalents. [Explanation of symbols]

[0064] 1. Information processing equipment 31 Communications Department 32 Display section 33 Operation section 34 Storage section 35 Control Unit 100 Management device 200 client terminals 341 BIM Data Storage Unit 342 People flow information storage section 343 Learning result memory unit 351 Acquisition Department 352 Learning Department 353 Reception Department 354 Proposal Department N Network S Control System

Claims

1. an acquisition unit that acquires a three-dimensional model that reproduces the lighting space of an existing building; a learning unit that learns a relationship between a three-dimensional shape of a lighting space of the existing building and lighting fixtures arranged in the lighting space of the existing building based on the three-dimensional model acquired by the acquisition unit; a suggestion unit that suggests lighting fixtures to be placed in a new three-dimensional model that is the three-dimensional model that reproduces the lighting space of a new building based on a learning result by the learning unit; An information processing device comprising:

2. The learning unit Learning the relationship according to the purpose of the lighting space of the existing building; The proposal unit Proposing lighting fixtures to be placed on the new three-dimensional model according to the intended use of the lighting space of the new building; The information processing device according to claim 1 .

3. The acquisition unit Acquire people flow information regarding people flow in the lighting space of the existing building; The learning unit learning a relationship between a three-dimensional shape of the lighting space of the existing building, lighting fixtures arranged in the lighting space of the existing building, and people flow information in the lighting space of the existing building; The proposal unit and proposing lighting fixtures to be arranged with respect to the new three-dimensional model based on people flow prediction information predicted from the new three-dimensional model. The information processing device according to claim 1 .

4. The three-dimensional model is Data reproducing fixtures to be placed in the lighting space of the existing building, The proposal unit Proposing lighting fixtures to be placed on the new three-dimensional model based on the fixtures to be placed on the new three-dimensional model; The information processing device according to claim 1 .

5. The three-dimensional model is Data reproducing the interior of the lighting space, The proposal unit Proposing lighting fixtures to be placed on the new three-dimensional model based on the interior design of the new three-dimensional model; The information processing device according to claim 1 .

6. 1. A computer-implemented information processing method, comprising: An acquisition step of acquiring a three-dimensional model that reproduces the lighting space of the existing building; a learning step of learning a relationship between a three-dimensional shape of the lighting space of the existing building and lighting fixtures arranged in the lighting space of the existing building based on the three-dimensional model acquired in the acquisition step; a proposing step of proposing lighting fixtures to be placed in the new three-dimensional model, which is the three-dimensional model that reproduces the lighting space of a new building, based on the learning result of the learning step; An information processing method, including:

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