Information processing apparatus and information processing method

Abstract

To propose illumination control information.SOLUTION: An information processing apparatus according to an embodiment includes an acquisition unit, a learning unit, and a proposal unit. The acquisition unit acquires existing building information regarding an illumination space of an existing building. The learning unit learns, on the basis of the existing building information acquired by the acquisition unit, a relation among a three-dimensional model obtained by reproducing the illumination space of the existing building, human flow information in the illumination space of the existing building, and illumination control information of each lighting fixture arranged in the illumination space of the existing building. The proposal unit proposes, on the basis of a learning result obtained by the learning unit, illumination control information of each lighting fixture to be arranged in an illumination space of a new building according to human flow information in the illumination space of the new building estimated from a new three-dimensional model obtained by reproducing the illumination space of the new building.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] Conventionally, there are lighting control devices that automatically control lighting fixtures. For example, Patent Document 1 discloses a technology that automatically adjusts the parameters of each lighting fixture so that the illuminance is set to a preset value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-95294 Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional techniques aim to optimize lighting control in existing buildings, and have not considered proposing lighting control for new buildings that are about to begin operation.

[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 propose lighting control for a new building. [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 existing building information related to the lighting space of an existing building. The learning unit learns the relationship between a 3D model that recreates the lighting space of the existing building, people flow information in the lighting space of the existing building, and lighting control information for each lighting fixture arranged in the lighting space of the existing building, based on the existing building information acquired by the acquisition unit. The proposing unit proposes lighting control information for each lighting fixture to be arranged in the lighting space of the new building, based on the people flow information in the lighting space of the new building estimated from the new 3D model that recreates the lighting space of the new building, based on the learning results by the learning unit. [Effects of the Invention]

[0007] According to the present invention, it is possible to propose lighting control for new buildings. [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 the illumination control information storage unit according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of information stored in a people flow information storage unit according to the embodiment. [Figure 6] FIG. 6 is a flowchart illustrating an example of the learning process according to the embodiment. [Figure 7] FIG. 7 is a flowchart illustrating an example of a 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 existing building information related to the lighting space of an existing building, a three-dimensional model that recreates the lighting space of the existing building based on the existing building information acquired by the acquisition unit 351, a learning unit 352 that learns the relationship between people flow information in the lighting space of the existing building and lighting control information for each lighting fixture arranged in the lighting space of the existing building, and a proposal unit 354 that proposes lighting control information for each lighting fixture to be arranged in the lighting space of the new building based on the people flow information in the lighting space of the new building estimated from the new three-dimensional model that recreates the lighting space of the new building based on the learning results by the learning unit 352.

[0010] Furthermore, the learning unit 352 described below learns the relationship for each use of the lighting space of the existing building, and the proposing unit 354 proposes lighting control information according to the use of the lighting space of the new building.

[0011] In addition, the three-dimensional model described below includes data that reproduces the fixtures placed in the lighting space of an existing building, and the proposal unit 354 proposes lighting control information based on the fixtures placed in the new three-dimensional model.

[0012] In addition, the lighting control information described below includes information regarding the type of each lighting fixture placed in the lighting space of the existing building, and the proposal unit 354 proposes lighting control information according to the type of each lighting fixture placed in the new three-dimensional model.

[0013] Furthermore, the learning unit 352 described below sets lighting spaces in existing buildings in which the overall power consumption of lighting fixtures has decreased as learning targets for the relationships.

[0014] Furthermore, the information processing method described below is an information processing method executed by a computer, and includes an acquisition step of acquiring existing building information regarding the lighting space of an existing building; a learning step of learning the relationship between a three-dimensional model that reproduces the lighting space of the existing building based on the existing building information acquired by the acquisition step, people flow information in the lighting space of the existing building, and lighting control information for each lighting fixture arranged in the lighting space of the existing building; and a proposal step of proposing lighting control information for each lighting fixture to be arranged in the lighting space of the new building based on the people flow information in the lighting space of the new building estimated from a new three-dimensional model that reproduces the lighting space of the new building based on the learning results of the learning step.

[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 lighting control information storage unit 342, a people flow information storage unit 343, and a learning result storage unit 344.

[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 lighting control information storage unit 342 will be described. The lighting control information storage unit 342 acquires lighting control information. The lighting control information is information relating to lighting control for each room in an existing building.

[0036] Here, the information stored in the illumination control information storage unit 342 will be described with reference to Fig. 4. Fig. 4 is a diagram illustrating an example of information stored in the illumination control information storage unit 342 according to the embodiment.

[0037] 4, the lighting control information storage unit 342 stores information items such as "Building ID," "Room ID," and "Lighting control information" in association with one another. The "Building ID" item stores an identifier for identifying the corresponding building.

[0038] The "Room ID" field stores an identifier for identifying each room in the corresponding building. The "Lighting Control Information" field stores information about the lighting control information for the room identified by the corresponding room ID. More specifically, the "Lighting Control Information" field stores an identifier for identifying each lighting fixture and information about the lighting fixture's on / off history, etc.

[0039] Returning to the explanation of FIG. 2, the people flow information storage unit 343 will be described. The people flow information storage unit 343 stores people flow information. The people flow information is information regarding the flow of people in each room. Here, an example of information stored in the people flow information storage unit 343 will be described with reference to FIG. 5. FIG. 5 is a diagram showing an example of information stored in the people flow information storage unit 343 according to the embodiment.

[0040] 5, the people flow information storage unit 343 stores information items 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.

[0041] 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.

[0042] Returning to the explanation of Figure 2, the learning result storage unit 344 will be described. The learning result storage unit 344 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 control information of the lighting fixtures placed in each room.

[0043] For example, the learning result is a model (e.g., AI; Artificial Intelligence) that has been trained to output lighting control information for lighting fixtures that is appropriate for the BIM data when BIM data is input. Also, the model may be a model that has been trained to output lighting control information for each lighting fixture that is appropriate for the BIM data, based on the BIM data (3D data) of each room, the layout of the lighting fixtures in each room, and information on the flow of people in each room.

[0044] 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).

[0045] 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.

[0046] The acquisition unit 351 also acquires people flow information and lighting control information related to 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 footage of each lighting space, or people flow information obtained by analyzing the camera footage. That is, the people flow information may be generated on the management device 100 side or on the information processing device 1 side. The lighting control information includes information about the history of turning on, turning off, and dimming each lighting fixture, information about the lighting pattern of each lighting fixture, etc. The information about the lighting pattern is information about the combination of lighting fixtures that are turned on or off in the existing building.

[0047] Based on the existing building information acquired by the acquisition unit 351, the learning unit 352 learns the relationship between a three-dimensional model (BIM data) that reproduces the lighting space of the existing building, the people flow information in the lighting space of the existing building, and the lighting control information for each lighting fixture placed in the lighting space of the existing building.

[0048] Specifically, the learning unit 352 first learns the relationship between BIM data and people flow information based on each BIM data and people flow information. The learning unit 352 generates integrated BIM data for each room that integrates the rooms, fixtures, and BIM data of the rooms, and generates learning data by superimposing actual people flow information on the integrated BIM data for each room. The integrated BIM data includes data on the area of ​​each room that will be the lighting space, ceiling height, room shape, column position, window position, interior material, lighting fixture placement, lighting fixture type, lighting fixture dimensions, lighting fixture attributes (luminous flux, power consumption, etc.), fixture placement, fixture type, etc.

[0049] The learning unit 352 then generates a people flow prediction model that has learned the relationship between the integrated BIM data and people flow. The people flow prediction model is a model that predicts people flow in a room reproduced in the BIM data when the integrated BIM data is input.

[0050] In addition, the learning unit 352 uses the integrated BIM data, people flow information, and lighting control information of each lighting fixture as learning data to generate a three-dimensional model (BIM data) that reproduces the lighting space of the existing building, and a lighting control prediction model that learns the relationship between the people flow information in the lighting space of the existing building and the lighting control information of each lighting fixture placed in the lighting space of the existing building.

[0051] The lighting control prediction model is a model trained to output lighting control information predicted for each lighting fixture when integrated BIM data and people flow information are input. Note that the learning unit 352 may generate a lighting control prediction model trained to output lighting control information predicted for each lighting fixture in response to input of integrated BIM data. For example, in this case, the lighting control prediction model may be a model that internally predicts people flow and predicts lighting control information based on the results.

[0052] Furthermore, the learning unit 352 may perform learning from the perspective of reducing the power consumption of lighting fixtures. For example, in such a case, the learning unit 352 narrows down the lighting control information to be used as learning data based on the transition of power consumption due to lighting fixtures being turned on in each room. Note that the information regarding power consumption is assumed to be obtained from the management device 100.

[0053] For example, the learning unit 352 uses data for learning from rooms whose power consumption reduction rate exceeds a threshold, based on the transition of power consumption in each room. In other words, in such cases, it is highly likely that the lighting control information in the room used as learning data has been optimized with the aim of reducing power consumption in actual operation. Therefore, by narrowing down the learning data in this way, it becomes possible to propose a lighting design that takes power consumption into consideration. Note that the power consumption here may be the total lighting time of each lighting fixture per cycle.

[0054] Furthermore, the learning unit 352 may perform learning for each use of each lighting space. That is, the learning unit 352 can learn lighting control information suitable for the use. Additionally, 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.

[0055] 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 three-dimensional model that is a three-dimensional model that reproduces the lighting space of the new building that is the target of the lighting design. For example, the new three-dimensional model is BIM data for each room, lighting, and fixtures. The new three-dimensional model also includes information about the purpose of each room.

[0056] The proposing unit 354 proposes lighting control information for each lighting fixture to be arranged in the lighting space of the new building based on people flow information in the lighting space of the new building estimated from a new three-dimensional model that reproduces the lighting space of the new building, based on the learning results by the learning unit 352. Specifically, the proposing unit 354 inputs the new three-dimensional model into the people flow prediction model learned by the learning unit 352. As a result, the people flow prediction model generates people flow prediction information for the new three-dimensional model.

[0057] Next, the proposing unit 354 inputs the new 3D model and people flow prediction information into the lighting control prediction model to obtain lighting control information. At this time, the proposing unit 354 may generate people flow prediction information and lighting control information using a people flow prediction model trained for each purpose, including the purpose of the room reproduced in the new 3D model, or a lighting control prediction model trained for each purpose.

[0058] Furthermore, the proposing unit 354 may generate lighting control information using a lighting control prediction model trained based on fixtures and interior decoration. In such a case, the lighting control prediction model is trained using the fixture arrangement, type, use, and interior decoration as learning parameters as learning data, and the proposing unit 354 inputs information about the fixture arrangement, type, use, and interior decoration into the lighting control prediction model to generate lighting control information.

[0059] The proposal unit 354 generates proposal materials using the people flow prediction information and lighting control information generated by each model (AI). The proposal materials are integrated BIM data that integrates the BIM data for each room, lighting, and fixture, and three-dimensional data that superimposes on the integrated BIM data the lighting environment estimated from the prediction results of the lighting control information and people flow data that represents the results of people flow predictions in three dimensions. 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.

[0060] 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.

[0061] Furthermore, in addition to the proposal materials, proposing unit 354 generates lighting control information in a format that can actually execute the lighting control information, for example, in a new building, and provides the generated information to client terminal 200. As a result, for example, management device 100 installed in a new building controls each lighting fixture based on the lighting control information proposed by proposing unit 354.

[0062] More specifically, for example, if the actual pedestrian flow pattern occurring in a new building corresponds to a pedestrian flow pattern included in the lighting control information, the management device 100 will control each lighting fixture using the lighting control information corresponding to the pedestrian flow pattern.

[0063] This will enable new buildings to operate lighting fixtures in a way that is optimized for pedestrian flow from the start.

[0064] Next, a processing procedure executed by the information processing device 1 according to the embodiment will be described with reference to Fig. 6 and Fig. 7. Fig. 6 is a flowchart showing an example of a learning process according to the embodiment. Fig. 7 is a flowchart showing an example of a proposal process according to the embodiment.

[0065] 6, in the learning process, the information processing device 1 first accumulates BIM data of the existing building acquired from the management device 100 of the existing building (see FIG. 1) (step S101). Next, the information processing device 1 accumulates lighting control information of the existing building also acquired from the management device 100 (step S102).

[0066] Next, the information processing device 1 accumulates the people flow information of the existing building similarly acquired from the management device 100 (step S103). Next, the information processing device 1 learns the relationship between the three-dimensional shape of each room in the existing building, the people flow, and the arrangement of lighting control information (step S104), saves the learning result (step S105), and ends the processing.

[0067] Next, the procedure of the proposal process will be described with reference to Fig. 7. In the proposal process, first, the information processing device 1 acquires BIM data of a new building from the client terminal 200 (step 111).

[0068] Next, the information processing device 1 estimates (predicts) the flow of people for each room based on the learning results of the learning process (step S112). Next, the information processing device 1 generates lighting control information in accordance with the estimated flow of people (step S113).

[0069] 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.

[0070] As described above, the information processing device 1 according to the embodiment includes an acquisition unit 351, a learning unit 352, and a proposal unit 354. The acquisition unit 351 acquires existing building information related to the lighting space of an existing building. The learning unit 352 learns the relationship between a 3D model that recreates the lighting space of the existing building, people flow information in the lighting space of the existing building, and lighting control information for each lighting fixture arranged in the lighting space of the existing building, based on the existing building information acquired by the acquisition unit 351. The proposal unit 354 proposes lighting control information for each lighting fixture to be arranged in the lighting space of the new building, based on the people flow information in the lighting space of the new building estimated from the new 3D model that recreates the lighting space of the new building, based on the learning result by the learning unit 352.

[0071] 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]

[0072] 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 Lighting control information storage unit 343 People flow information storage section 344 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 existing building information related to lighting spaces of existing buildings; a learning unit that learns the relationship between a three-dimensional model that reproduces the lighting space of the existing building, people flow information in the lighting space of the existing building, and lighting control information for each lighting fixture arranged in the lighting space of the existing building, based on the existing building information acquired by the acquisition unit; a proposal unit that proposes the lighting control information for each lighting device to be arranged in the lighting space of the new building based on the people flow information in the lighting space of the new building estimated from a new three-dimensional model that reproduces the lighting space of the new building, based on the learning result by the learning unit; An information processing device comprising:

2. The learning unit learning the relationship for each use of the lighting space of the existing building; The proposal unit propose the lighting control information according to the use of the lighting space of the new building; The information processing device according to claim 1 .

3. The three-dimensional model is The data includes data reproducing fixtures arranged in the lighting space of the existing building, The proposal unit proposing the lighting control information based on the fixtures arranged in the new three-dimensional model; The information processing device according to claim 1 .

4. The lighting control information information regarding the type of each of the lighting fixtures arranged in the lighting space of the existing building; The proposal unit and proposing the lighting control information according to the type of each of the lighting devices arranged in the new three-dimensional model. The information processing device according to claim 1 .

5. The learning unit The lighting space of the existing building in which the power consumption of all lighting fixtures has decreased is set as a learning target of the relationship. The information processing device according to claim 1 .

6. 1. A computer-implemented information processing method, comprising: an acquisition step of acquiring existing building information relating to lighting spaces in the existing building; a learning step of learning the relationship between a three-dimensional model that reproduces the lighting space of the existing building, people flow information in the lighting space of the existing building, and lighting control information for each lighting fixture arranged in the lighting space of the existing building, based on the existing building information acquired in the acquisition step; a proposing step of proposing the lighting control information for each lighting fixture to be arranged in the lighting space of the new building based on the people flow information in the lighting space of the new building estimated from a new three-dimensional model that reproduces the lighting space of the new building, based on the learning result of the learning step; Including, information processing methods.

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