Information processing device, information processing method, and information processing program

Abstract

To reduce the computational resources required when calculating dimming parameters according to the purpose. [Solution] The information processing device according to the embodiment comprises a generation unit, a reception unit, and an estimation unit. The generation unit generates illuminance distribution information that shows the illuminance distribution when each lighting fixture installed in a predetermined space is turned on. The reception unit receives lighting mode conditions, which are conditions relating to the desired lighting mode in the predetermined space. The estimation unit uses the illuminance distribution information of each lighting fixture to estimate a lighting mode that satisfies the conditions indicated by the lighting mode conditions.

Description

Technical Field

[0001] Embodiments of the present invention relate to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Conventionally, when calculating dimming parameters related to dimming of a lighting fixture in order to bring indicators of the lighting fixture such as illuminance and uniformity at the user's hand closer to target values, it is necessary to execute an illuminance simulator that simulates illuminance distribution under various conditions.

[0003] For example, there is known a technique for calculating an output illuminance vector that minimizes an evaluation function composed of the distance between a detected illuminance vector and a target illuminance vector based on the contribution degree from the illuminance detected by each illuminance detector (Patent Document 1 below). Also, there is known a technique for controlling the dimming level of each of m lighting fixtures that perform lighting in n regions based on n illuminance information, one or more human information, and n target illuminance information (Patent Document 2 below).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in Patent Documents 1 and 2 above, when calculating dimming parameters according to the purpose using an illuminance simulator, a large number of simulations are required, so the calculation resources tend to increase.

[0006] The problem that this invention aims to solve is to provide an information processing device, an information processing method, and an information processing program that can reduce the computational resources required when calculating dimming parameters according to the purpose. [Means for solving the problem]

[0007] The information processing device according to the embodiment comprises a generation unit, a reception unit, and an estimation unit. The generation unit generates illuminance distribution information that shows the illuminance distribution when each lighting fixture installed in a predetermined space is turned on. The reception unit receives lighting mode conditions, which are conditions relating to the desired lighting mode in the predetermined space. The estimation unit uses the illuminance distribution information of each lighting fixture to estimate a lighting mode that satisfies the conditions indicated by the lighting mode conditions. [Effects of the Invention]

[0008] According to the present invention, the computational resources required to calculate dimming parameters according to the purpose can be reduced. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a conceptual diagram of the generation of illuminance distribution information. [Figure 2] Figure 2 is a conceptual diagram relating to the generation of illuminance distribution information according to this embodiment. [Figure 3] Figure 3 shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] Figure 4 shows an example of a condition information storage unit according to the embodiment. [Figure 5] Figure 5 shows an example of an illuminance distribution information storage unit according to the embodiment. [Figure 6] Figure 6 shows an example of an office space according to the embodiment. [Figure 7] Figure 7 shows an example of illuminance distribution information according to the present invention. [Figure 8] Figure 8 is a flowchart showing an example of information processing performed by the information processing device according to the embodiment. [Figure 9]Figure 9 shows the illuminance distribution information for each lighting fixture in application example (1) according to the embodiment. [Figure 10] Figure 10 shows the illuminance distribution information in application example (1) according to the embodiment. [Figure 11] Figure 11 is a diagram showing the variation in evaluation values ​​in application example (1) according to the embodiment. [Figure 12] Figure 12 shows the illuminance distribution information for each lighting fixture in application example (2) according to the embodiment. [Figure 13] Figure 13 is a diagram showing the illuminance distribution information in application example (2) according to the embodiment. [Figure 14] Figure 14 is a hardware configuration diagram showing an example of a computer that implements the functions of an information processing device. [Modes for carrying out the invention]

[0010] The information processing device according to the embodiment described below comprises a generation unit, a reception unit, and an estimation unit. The generation unit generates illuminance distribution information that shows the illuminance distribution when each lighting fixture installed in a predetermined space is turned on. The reception unit receives lighting mode conditions, which are conditions relating to the desired lighting mode in the predetermined space. The estimation unit uses the illuminance distribution information of each lighting fixture to estimate a lighting mode that satisfies the conditions indicated by the lighting mode conditions.

[0011] Furthermore, in the information processing device according to the embodiment described below, the estimation unit estimates a lighting mode that satisfies the conditions indicated by the lighting mode conditions based on the weighted sum of the illuminance distribution information of each lighting fixture.

[0012] Furthermore, in the information processing device according to the embodiment described below, the generation unit generates illuminance distribution information when each lighting fixture is lit based on predetermined lighting conditions.

[0013] Furthermore, in the information processing device according to the embodiment described below, the generation unit generates illuminance distribution information when a lighting fixture is lit based on predetermined lighting conditions.

[0014] Furthermore, in the information processing apparatus according to the embodiment described below, the receiving unit further receives spatial information relating to a predetermined space, including the layout of the predetermined space, and the generating unit generates illuminance distribution information based on the spatial information.

[0015] Furthermore, in the information processing apparatus according to the embodiment described below, the receiving unit further receives lighting information relating to each lighting fixture, including the illuminance conditions of each lighting fixture, and the generating unit generates illuminance distribution information based on the lighting information.

[0016] Furthermore, in the information processing apparatus according to the embodiment described below, the receiving unit further receives simulation conditions including the ambient light conditions of a predetermined space, and the generating unit generates illuminance distribution information based on the simulation conditions.

[0017] Furthermore, in the information processing apparatus according to the embodiment described below, the reception unit receives, as lighting mode conditions, the illuminance conditions of each lighting fixture that irradiates light onto a person located in a predetermined space or an area included in the predetermined space.

[0018] Furthermore, in the information processing apparatus according to the embodiment described below, the reception unit further receives information regarding the person's location and orientation.

[0019] Furthermore, in the information processing apparatus according to the embodiment described below, the reception unit further receives conditions related to uniformity as lighting mode conditions.

[0020] Furthermore, in the information processing device according to the embodiment described below, the reception unit further receives the power consumption of each lighting fixture as a lighting mode condition.

[0021] Furthermore, the information processing device according to the embodiment described below further comprises an output unit that outputs dimming parameters for each lighting fixture calculated based on the lighting pattern estimated by the estimation unit.

[0022] Furthermore, in the information processing device according to the embodiment described below, the output unit outputs one of the following as dimming parameters: the dimming rate, uniformity, power consumption, and the difference between the illuminance of each lighting fixture and a predetermined illuminance.

[0023] The following describes in detail, with reference to the drawings, the embodiments for implementing the information processing apparatus, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing apparatus, information processing method, and information processing program according to the present application. Furthermore, each embodiment can be appropriately combined as long as the processing content is not inconsistent. Also, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0024] [Embodiment] [1. Overview of the Embodiment] Traditionally, when calculating dimming parameters for lighting fixtures in order to bring indicators such as illuminance and uniformity at the user's location closer to target values, it is necessary to run an illuminance simulator under various conditions to simulate the illuminance distribution. Alternatively, dimming parameters may be calculated by trying various dimming parameters in the actual environment where the illuminance distribution is to be obtained and repeatedly measuring the illuminance. In the following, the simulation results of the illuminance distribution may be referred to as illuminance distribution information.

[0025] However, with conventional technology, calculating dimming parameters according to the purpose using an illuminance simulator requires numerous simulations, which tends to increase computational resources. Furthermore, when measuring illuminance with various dimming parameters, for example, a large number of illuminance sensors are required, and comprehensive measurements are necessary, which tends to increase the time required to calculate the appropriate dimming parameters.

[0026] As an example, Figure 1 will be used to explain a conventional method for generating illuminance distribution information. Figure 1 is a conceptual diagram of the generation of illuminance distribution information. For example, let's assume that lighting fixtures I1 to I4 are installed in the office space O1 shown in Figure 1. In this case, the dimming parameter for lighting fixtures I1 to I4 is set to a dimming rate for each lighting fixture I1 to I4. In the example in Figure 1, the dimming rate of lighting fixture I1 is 100%. The dimming rate of lighting fixture I2 is 12.5%. The dimming rate of lighting fixture I3 is 50%. The dimming rate of lighting fixture I4 is 25%.

[0027] When a simulation is performed using an illuminance simulator based on the dimming rates of the above-mentioned lighting fixtures I1 to I4, illuminance distribution information SI1 is obtained. In this way, by setting dimming parameters such as the dimming rates of lighting fixtures I1 to I4 under various conditions, it is possible to generate an illuminance distribution for a desired lighting pattern. For example, if many simulations are required, the computational resources tend to increase. Also, depending on the computing environment, even a single simulation using an illuminance simulator may take a considerable amount of time.

[0028] Therefore, in order to address the above problem, this invention proposes an information processing device that estimates a lighting mode that satisfies the conditions indicated by lighting mode conditions, which are conditions relating to a desired lighting mode in a given space, by using illuminance distribution information obtained when each lighting fixture installed in a given space is turned on.

[0029] The method for generating illuminance distribution information according to the embodiment will be explained using Figure 2. Figure 2 is a conceptual diagram of the generation of illuminance distribution information according to the embodiment. Figure 2 explains the case of generating illuminance distribution information in the same office space O1 as shown in Figure 1. In Figure 2, it is assumed that lighting fixtures I1 to I4 are installed in the office space O1.

[0030] First, the information processing device according to the embodiment generates illuminance distribution information for each lighting fixture I1 to I4. For example, the information processing device generates illuminance distribution information SR1 when lighting fixture I1 is lit at 100% dimming and lighting fixtures I2 to I4 are off. The information processing device also generates illuminance distribution information SR2 when lighting fixture I2 is lit at 100% dimming and lighting fixtures I1, I3 to I4 are off.

[0031] Furthermore, the information processing device generates illuminance distribution information SR3 when lighting fixture I3 is lit at 100% dimming and lighting fixtures I1-I2 and I4 are off. The information processing device also generates illuminance distribution information SR4 when lighting fixture I4 is lit at 100% dimming and lighting fixtures I1-I3 are off.

[0032] For example, let's assume that the dimming rates of each lighting fixture I1 to I4 shown in Figure 2 are the same as the dimming rates of each lighting fixture I1 to I4 shown in Figure 1. That is, the dimming rate of lighting fixture I1 is 100%. The dimming rate of lighting fixture I2 is 12.5%. The dimming rate of lighting fixture I3 is 50%. The dimming rate of lighting fixture I4 is 25%.

[0033] Next, the information processing device generates illuminance distribution information TSR1 for lighting modes that satisfy the dimming rate conditions for each of the lighting fixtures I1 to I4, based on the weighted sum of the illuminance distribution information SR1 to SR4 of each lighting fixture I1 to I4 that have been generated in advance.

[0034] For example, the information processing device multiplies the illuminance distribution information SR1 by the dimming rate of lighting fixture I1, which is 100%. It then multiplies the illuminance distribution information SR2 by the dimming rate of lighting fixture I2, which is 12.5%. Furthermore, it multiplies the illuminance distribution information SR3 by the dimming rate of lighting fixture I3, which is 50%. Finally, it multiplies the illuminance distribution information SR4 by the dimming rate of lighting fixture I4, which is 25%. The information processing device then calculates the sum of the multiplied illuminance distribution information SR1 to SR4 as the illuminance distribution information TSR1. In this way, the information processing device estimates the illuminance distribution information TSR1 based on the weighted sum of the illuminance distribution information SR1 to SR4.

[0035] Thus, in this invention, since the illuminance distribution information TSR1 is estimated based on the weighted sum of the illuminance distribution information SR1 to SR4 of each pre-generated lighting fixture I1 to I4, computational resources can be reduced. Therefore, this invention can reduce the computational resources required when calculating dimming parameters according to the purpose. As a result, this invention can shorten the computation time required to calculate dimming parameters according to the purpose.

[0036] The issues listed above are merely examples, and the problems that this application seeks to solve are not necessarily limited to those listed above; other problems may also be addressed.

[0037] [2. Configuration of the Information Processing Device] Next, an example of the functional configuration of the information processing device 100 according to the embodiment will be described using Figure 3. Figure 3 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Figure 3, the information processing device 100 has a communication unit 110, a storage unit 120, a control unit 130, an input unit 140, and a display unit 150.

[0038] (Regarding Communications Unit 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to a network such as the Internet by wired or wireless connection and transmits and receives information with various other devices.

[0039] (Regarding memory unit 120) The memory unit 120 stores programs and other data necessary for control by the control unit 130. The memory unit 120 can be implemented using, for example, semiconductor memory elements such as RAM (Random Access Memory) or flash memory, or storage devices such as hard disks or optical discs.

[0040] For example, the memory unit 120 includes an illuminance simulator 121, a condition information storage unit 122, and an illuminance distribution information storage unit 123. For example, the illuminance simulator 121 is a simulator that simulates the illuminance distribution.

[0041] (Regarding the condition information storage unit 122) The condition information storage unit 122 stores various information used to generate illuminance distribution information. Here, Figure 4 shows an example of the condition information storage unit 122 according to the embodiment. Figure 4 is a diagram showing an example of the condition information storage unit 122 according to the embodiment.

[0042] In the example shown in Figure 4, the condition information storage unit 122 has items such as "condition ID (Identifier)", "spatial information", "lighting information", "simulation conditions", and "lighting mode conditions".

[0043] The "Condition ID" is an identifier that identifies the conditions under which illuminance distribution information is generated. The "Spatial Information" is spatial information set by the conditions associated with the "Condition ID," and is spatial information relating to a given space. For example, the spatial information includes the layout of the given space.

[0044] "Lighting information" refers to lighting information set according to the conditions associated with the "condition ID," and is lighting information for each lighting fixture. For example, the lighting information includes the illuminance conditions for each lighting fixture.

[0045] "Simulation conditions" are the conditions related to the simulation that are associated with the "condition ID". For example, simulation conditions include the ambient light conditions of a given space and mesh information related to the mesh, which is the unit used to calculate illuminance.

[0046] "Lighting mode conditions" are lighting mode conditions in a predetermined space, set by conditions associated with other conditions. For example, lighting mode conditions include illuminance conditions for each lighting fixture that illuminates a person located in the predetermined space or an area included in the predetermined space, information regarding the position and orientation of a person, conditions regarding uniformity, and the power consumption of each lighting fixture.

[0047] For example, in Figure 4, "C1," identified by the condition ID, has spatial information of "SP1," lighting information of "II1," simulation conditions of "SC1," and lighting mode conditions of "IM1."

[0048] In the example shown in Figure 4, spatial information is represented by abstract codes such as "SP1," but spatial information may also be represented by numerical values, strings, or file formats containing various types of information indicating spatial information.

[0049] (Regarding the illuminance distribution information storage unit 123) The illuminance distribution information storage unit 123 stores the generated illuminance distribution information. Here, Figure 5 shows an example of the illuminance distribution information storage unit 123 according to the embodiment. Figure 5 is a diagram showing an example of the illuminance distribution information storage unit 123 according to the embodiment.

[0050] In the example shown in Figure 5, the illuminance distribution information storage unit 123 has items such as "illuminance distribution information ID", "lighting fixture ID", "condition ID", and "illuminance distribution information".

[0051] The "Illuminance Distribution Information ID" is an identifier that identifies the illuminance distribution information. The "Lighting Fixture ID" is an identifier that identifies the lighting fixture that generated the illuminance distribution information associated with the "Illuminance Distribution Information ID".

[0052] The "Condition ID" is an identifier that identifies the conditions under which the illuminance distribution information associated with the "Illuminance Distribution Information ID" was generated. The "Illuminance Distribution Information" is the illuminance distribution information associated with the "Illuminance Distribution Information ID".

[0053] For example, in Figure 5, "S1," identified by the illuminance distribution information ID, has a lighting fixture ID of "I1," a condition ID of "C1," and illuminance distribution information of "IDI1."

[0054] In the example shown in Figure 5, the illuminance distribution information was represented by an abstract code such as "IDI1," but the illuminance distribution information may also be represented by numerical values, strings of characters, or a file format containing various information indicating the illuminance distribution.

[0055] (Regarding the control unit 130) The control unit 130 executes various processes of the information processing device 100. The control unit 130 can be realized by a control circuit equipped with a processor and memory. Each functional part of the control unit 130 is realized, for example, by executing instructions written in a program (e.g., an information processing program) read from internal memory by the processor, using the internal memory as a working area. The program read from internal memory by the processor includes the OS (Operating System) and application programs.

[0056] Furthermore, each functional unit of the control unit 130 may be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0057] As shown in Figure 3, the control unit 130 includes a receiving unit 131, a generation unit 132, an estimation unit 133, and an output unit 134, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 3, and other configurations are also possible as long as they perform the information processing described later. Furthermore, the connection relationships of the various processing units in the control unit 130 are not limited to the connection relationships shown in Figure 3, and other connection relationships are also possible.

[0058] (Regarding reception desk 131) The reception unit 131 receives various types of information. Specifically, the reception unit 131 receives spatial information relating to a predetermined space, including the layout of that space.

[0059] Here, we will explain spatial information using Figure 6. Figure 6 is a diagram showing an example of an office space according to the embodiment. The office space O1 shown in Figure 6 is a space that is subject to lighting, such as a living room. In the example of Figure 6, the office space O1 includes lighting fixtures I1 to I4, a window W1, and a partition P1.

[0060] For example, spatial information includes the layout of windows, partitions, etc. More specifically, spatial information includes the size of the office space O1, the reflectivity of each surface, the location, shape, size, or height of windows, partitions, etc. In addition to windows and partitions, spatial information may also include furniture such as chairs and desks, and objects that block lighting.

[0061] In the example shown in Figure 6, the reception area 131 receives information that the dimensions of the office space O1 are a width F1 of 6.0m, a depth D1 of 8.0m, and a height H1 of 4.0m. Although the office space O1 shown in Figure 6 is a rectangular prism, it is not limited to this.

[0062] Furthermore, the reception unit 131 receives information regarding the reflectivity of each surface, such as the reflectivity of the ceiling being 50%, the reflectivity of the walls being 30%, and the reflectivity of the floor being 10%. Note that the reflectivity may be set for each surface. Also, different reflectivity values ​​may be set even within the same surface.

[0063] The reception unit 131 also receives information about the window. For example, information about the window includes the window's location, size, transmittance, or reflectance. In the example in Figure 6, the window is located on the south wall. The window is 100 cm high and 650 cm wide. The window's transmittance is 60%. The window's reflectance is 40%. Although Figure 6 shows an example with one window, it is not limited to this, and the number of windows can be any number.

[0064] The reception unit 131 also receives information about the partition. For example, information about the partition includes the position coordinates within the space in which the partition is installed, the shape of the partition, the height of the partition, and its transmittance or reflectance.

[0065] In the example in Figure 6, the position coordinates within the office space O1 where the partition is installed are x=3.0, y=4.0. The partition is L-shaped, and its height is 120 cm. The partition's transmittance is 0%, and its reflectance is 30%. Although Figure 6 shows an example with one partition, the example is not limited to this, and the number of partitions can be any number.

[0066] Furthermore, the reception unit 131 receives lighting information relating to each lighting fixture, including the illuminance conditions for each lighting fixture. For example, the lighting information includes the name of the lighting fixture, lighting performance information, the position coordinates of the lighting fixture, the height of the lighting fixture, or the maintenance factor. For example, the lighting performance information includes the light distribution of the light emitted by the lighting fixture and the actual luminous flux value.

[0067] Furthermore, the reception unit 131 accepts simulation conditions. For example, simulation conditions include ambient light conditions for a given space and mesh information. For example, ambient light conditions include the date and time, and the latitude and longitude indicating the location of office space O1. To give a more specific example, ambient light conditions include conditions such as the window facing south, the date being 2024 / 10 / 23, the time being 11:00, the latitude being 35 degrees 30 minutes 12 seconds north, the longitude being 139 degrees 42 minutes 20 seconds east, the elevation being 3.5m, and the horizontal illuminance being 3200 lx. Note that the illuminance obtained may differ depending on the date, time, weather, etc. Therefore, the reception unit 131 may accept at least two or more ambient light conditions.

[0068] Furthermore, the mesh information includes information indicating the fineness of the mesh, information about the calculation surface, and the height of the calculation surface. The calculation surface referred to here is surface CS1 shown in Figure 6. To give a more specific example, the mesh information includes information such as the mesh width being 100 cm, the mesh depth being 80 cm, the calculation surface width being 6.0 m, the depth being 8.0 m, and the calculation surface height being 0.8 m.

[0069] Note that the shape of the computational surface is not limited to a rectangle. Similarly, the shape of the mesh is not limited to a rectangle. Furthermore, while the above example assumes a two-dimensional computational surface as the simulation target, it is not limited to this; for example, a three-dimensional computational space could be used as the simulation target. For instance, the computational space could be a rectangular prism.

[0070] Furthermore, the reception unit 131 receives lighting conditions for a predetermined space. For example, the reception unit 131 receives illuminance conditions for each lighting fixture that illuminates a person located within the predetermined space, or an area included in the predetermined space, as lighting conditions. Illuminance conditions here refer to conditions for bringing the illuminance of a specific location closer to a predetermined illuminance value. Hereafter, these illuminance conditions may be referred to as target illuminance conditions. The reception unit 131 also receives further information regarding the position and orientation of people.

[0071] To give a more specific example, let's assume that in the illumination mode conditions, the positions of people PE1 and PE2 are set as the target positions. In this case, the reception unit 131 receives information that the position coordinates of person PE1 are x=2.5m, y=3.6m, and that person PE1 is facing west. The reception unit 131 also receives information that the illumination at the location of person PE1 is 150lx.

[0072] The reception unit 131 also receives information that the position coordinates of person PE2 are x=3.5m, y=2.0m, and that person PE2 is facing east. The reception unit 131 also receives information that the illuminance where person PE2 is located is 140lx.

[0073] Furthermore, the reception unit 131 accepts conditions related to uniformity (hereinafter sometimes referred to as uniformity conditions) as lighting type conditions. For example, the uniformity condition indicates the uniformity of illumination by the lighting fixture. Uniformity here refers to the uniformity of the illuminance distribution within a specific range. For example, uniformity is expressed as the ratio of the highest illuminance to the lowest illuminance. To give a more specific example, uniformity takes values ​​from 0 to 1. The closer the uniformity is to 1, the higher the uniformity. For example, lighting with high uniformity is lighting with little unevenness.

[0074] For example, the reception unit 131 receives instructions to calculate based on the illuminance values ​​of a 3x2 mesh in front of people PE1 and PE2, given their positions. For example, since person PE1 is facing west, the average value of the illuminance of a 3x2 mesh in front of person PE1 facing west is calculated. Then, the uniformity is calculated from the ratio of the maximum and minimum values ​​of the calculated illuminance.

[0075] Furthermore, the reception unit 131 accepts energy-saving conditions to reduce the power consumption of lighting fixtures as lighting mode conditions. For example, the reception unit 131 accepts the power consumption of each lighting fixture as a lighting mode condition. To give a more specific example, the reception unit 131 accepts information such as the power consumption of 12W when the dimming rate of lighting fixture I1 is 100%. This power consumption is linked to the model number of the lighting fixture, etc.

[0076] Furthermore, this power consumption may be measured data representing the power consumption when the lighting fixture is actually turned on. Also, if the relationship between the dimming rate and power consumption is not proportional, the relationship between the dimming rate and power consumption may be set using a predetermined mathematical formula or an approximate formula calculated using piecewise linear functions, spline functions, etc.

[0077] (Regarding the generation unit 132) The generation unit 132 generates illuminance distribution information for when each lighting fixture installed in a predetermined space is turned on. For example, the generation unit 132 generates illuminance distribution information for each lighting fixture based on the spatial information stored in the condition information storage unit 122, the lighting information, and the simulation conditions. The generation unit 132 then stores the generated illuminance distribution information in the illuminance distribution information storage unit 123, associating it with each lighting fixture.

[0078] Here, we will describe an example of generating illuminance distribution information when each luminaire I1 to I4 is lit based on predetermined lighting conditions. For example, the generation unit 132 generates illuminance distribution information SR1 when luminaire I1 is lit at 100% dimming and luminaires I2 to I4 are off (corresponding to predetermined lighting conditions). The generation unit 132 also generates illuminance distribution information SR2 when luminaire I2 is lit at 100% dimming and luminaires I1, I3 to I4 are off.

[0079] Furthermore, the generation unit 132 generates illuminance distribution information SR3 for the case where lighting fixture I3 is lit at 100% dimming and lighting fixtures I1-I2 and I4 are off. The generation unit 132 also generates illuminance distribution information SR4 for the case where lighting fixture I4 is lit at 100% dimming and lighting fixtures I1-I3 are off. The generation unit 132 then stores the generated illuminance distribution information SR1-SR4 in the illuminance distribution information storage unit 123, associating them with each of the lighting fixtures I1-I4.

[0080] Here, we will explain the illuminance distribution information using Figure 7. Figure 7 is a diagram showing an example of illuminance distribution information according to the embodiment. In the example in Figure 7, the office space O1 is assumed to include partition P1.

[0081] As described above, the illuminance distribution information SR1 shown in Figure 7 represents the illuminance distribution when lighting fixture I1 is lit at 100% dimming and lighting fixtures I2 to I4 are turned off. Here, the illuminance distribution information SR1 is composed of grid-like regions. These regions correspond to meshes. The values ​​displayed in these regions are illuminance in lx units. Note that this illuminance is the average illuminance of the mesh.

[0082] (Regarding Estimation Section 133) The estimation unit 133 uses the illuminance distribution information of each lighting fixture to estimate lighting modes that satisfy the conditions indicated by the lighting mode conditions. For example, the estimation unit 133 estimates lighting modes that satisfy the conditions indicated by the lighting mode conditions stored in the condition information storage unit 122 based on the weighted sum of the illuminance distribution information SR1 to SR4 stored in the illuminance distribution information storage unit 123.

[0083] For example, the estimation unit 133 estimates lighting modes that satisfy the conditions indicated by the lighting mode conditions using the following formulas (1) to (4).

[0084]

number

number

number

number

[0085] Here, F target F energy F kinseido This shows each item of the evaluation function F. target , W energy , W kinseido This indicates the weighting constants that determine the proportion of each item in the evaluation function F.

[0086] K represents a set of lighting fixtures. H represents a set of meshes for which target illuminance levels are set. R(x) represents a set of meshes for which maximum and minimum values ​​are calculated when calculating uniformity for mesh x.

[0087] b(k) represents the dimming rate of luminaire k. L(x) represents the illuminance of mesh x. T(x) represents the target illuminance of mesh x. E(k) represents the power consumption when luminaire k is lit at maximum luminous intensity.

[0088] The right-hand side of equation (1) represents the sum of the violations of the target illumination condition, energy saving condition, and uniformity condition. For example, by minimizing the evaluation function F in equation (1), a solution that satisfies the target illumination condition, energy saving condition, and uniformity condition can be obtained.

[0089] The right-hand side of equation (2) shows the square error of the illuminance L(x) to the target illuminance T(x) for each mesh x∈H. For example, F in equation (2) target Minimizing this corresponds to obtaining a dimming ratio that satisfies the target illumination conditions as much as possible.

[0090] The right-hand side of equation (3) represents the power consumption for each lighting fixture at a dimming factor b(k). For example, F in equation (3) energy Minimizing this corresponds to obtaining a dimming rate that satisfies energy-saving conditions as much as possible.

[0091] The right side of the formula (4) indicates the difference between the maximum value and the minimum value of the illuminance for the calculation range R(x′) for each mesh x′ ∈ H. For example, F in the formula (4) kinseido minimizing it corresponds to obtaining a dimming rate that satisfies the uniformity condition as much as possible.

[0092] Note that it is not necessary to be limited to the above example, and a formula that minimizes max x∈R(x′) L(x) / min x∈R(x′) L(x) may be used. Also, for example, a formula that maximizes (e.g., approaches 1) min x L(x) / ave x∈R(x′) L(x) with ave x∈R(x′) as the average value of x may be used. Also, a modified formula using the four-point method that classifies the lattice points constituting the region R(x) into corner points, edge points, and interior points may be used.

[0093] (Regarding the output unit 134) The output unit 134 outputs the dimming parameters of each lighting fixture calculated based on the lighting mode estimated by the estimation unit 133. For example, the output unit 134 outputs any one of the dimming rate, uniformity, power consumption of each lighting fixture, and the difference between the illuminance of each lighting fixture and a predetermined illuminance as the dimming parameter.

[0094] Also, the output unit 134 may output the illuminance distribution information SR1 to SR4 stored in the illuminance distribution information storage unit 123.

[0095] Note that when outputting the dimming rate, if the relationship between the dimming rate and the light quantity is not a proportional relationship, the relational expression between the dimming rate and the light quantity may be set by an approximate expression calculated using a predetermined formula, a piecewise linear function, a spline function, or the like.

[0096] (Regarding the input unit 140) The input unit 140 receives various information inputs from the user. For example, the input unit 140 receives various information inputs via a keyboard, a mouse, etc. connected to the information processing apparatus 100.

[0097] (Regarding the display unit 150) The display unit 150 displays various types of information. For example, the display unit 150 is a display screen realized by a liquid crystal display or an organic EL (Electro-Luminescence) display connected to the information processing device 100, and is a display device for displaying various types of information.

[0098] The information processing device 100 may also have an input unit 140 such as a touch panel for receiving various types of information from users, and a display unit 150 such as a liquid crystal display for displaying various types of information.

[0099] [3. Regarding the processing procedure] Next, using Figure 8, the procedure for information processing performed by the information processing device 100 according to the embodiment will be described. Figure 8 is a flowchart showing an example of information processing performed by the information processing device according to the embodiment.

[0100] [3-1. Processing Procedure] As shown in Figure 8, the reception unit 131 receives spatial information, lighting information, simulation conditions, and lighting mode conditions (step S101). Specifically, if the reception unit 131 has not received spatial information, lighting information, simulation conditions, and lighting mode conditions (step S101; No), it waits until it receives spatial information, lighting information, simulation conditions, and lighting mode conditions.

[0101] On the other hand, when the receiving unit 131 receives spatial information, lighting information, simulation conditions, and lighting mode conditions (step S101; Yes), the generation unit 132 generates illuminance distribution information for each lighting fixture k when each lighting fixture k is lit individually at a dimming rate of 100%. The generation unit 132 then stores the illuminance of each mesh x in a(k, x) (step S102).

[0102] Here, if the computational domain is two-dimensional (for example, a computational surface), the mesh x will consist of two numerical values, (x, y), consisting of the x and y coordinates. If the computational domain is three-dimensional (for example, a computational space), the mesh x will consist of three numerical values, (x, y, z), consisting of the x, y, and z coordinates.

[0103] Next, the estimation unit 133 determines F * ←+∞ (step S103). For example, the estimation unit 133 repeats steps S104 to S106 shown below N times based on the following formulas (5) to (10) (n=1, 2, ..., N). Here, N is any value.

[0104]

number

number

number

number

number

number

[0105] Here, F target F energy F kinseido This shows each item of the evaluation function F. target , W energy , W kinseido This indicates the weighting constants that determine the proportion of each item in the evaluation function F.

[0106] X represents the set of meshes for the calculation surface. K represents the set of luminaires. H represents the set of meshes for which target illuminances have been set. R(x) represents the set of meshes for which the maximum and minimum values ​​are calculated when calculating uniformity for mesh x.

[0107] T(x) represents the target illuminance of mesh x. E(k) represents the power consumption when luminaire k is lit at maximum luminous intensity. b(k) represents the dimming rate of luminaire k. L(x) represents the illuminance of mesh x. A(k, x) represents the illuminance of mesh x when luminaire k is lit alone with a dimming rate of 100%.

[0108] Equation (5) is the objective function. Equations (6), (7), and (8) are formulas for calculating the terms that make up the objective function. Equation (9) is a formula for calculating the illuminance of each mesh from the dimming rate of each lighting fixture. Equation (10) is a formula that restricts the dimming rate to be between 0% and 100%.

[0109] Then, the estimation unit 133 generates the next candidate b(k) for the dimming rate parameter of each lighting fixture k∈K (step S104). Subsequently, the estimation unit 133 calculates L(x)=Σ k∈K The illuminance L(x) of each mesh x on the calculation surface is calculated using A(k, x) × b(k) (step S105).

[0110] Then, the estimation unit 133 calculates the evaluation function F based on L(x), and F is the evaluation value F * If it becomes smaller than F * ←F, b * ←Update to b (step S106). Here, b represents the set of all b(k) adopted in step S104. Next, the estimation unit 133 performs F * , b * The value is estimated (step S107). Then, the output unit 134 is F * , b * The output is generated (step S108). Subsequently, the output unit 134 terminates the information processing.

[0111] [3-2. Variations of Processing Procedures] In step S102 of the flowchart above, if daylight is used, all dimming rates may be set to 0, and the illuminance of each mesh x when the simulation is performed using only daylight may be stored in a(0, x).

[0112] Furthermore, the generation unit 132 may correct the generated illuminance distribution information using detection information detected by sensors such as cameras and illuminance sensors. For example, the generation unit 132 may correct the information by replacing only a specific area with the detection information. For example, the generation unit 132 may denote the replaced value as a(k, x).

[0113] Furthermore, the generation unit 132 may use a(k, x) as the weighted sum of the illuminance distribution information and the detection information. Also, if the user inputs simulation reliability data for each mesh, the generation unit 132 may adjust the weights for each mesh based on this reliability. And the generation unit 132 may use a(k, x) as the weighted sum of the illuminance distribution information and the detection information.

[0114] Furthermore, in step S104, when dealing with daylight, the generation unit 132 has L(x) = Σ k∈K The illuminance L(x) can also be calculated using A(k, x) × b(k) + A(0, x).

[0115] Furthermore, the method for generating the next candidate b(k) for the dimming rate parameter in step S104 when repeated N times is not limited to the example in the flowchart above. For example, the generation unit 132 may generate b(k) using a gradient method, GA (Genetic Algorithm), SA (Simulated Annealing), etc.

[0116] Furthermore, the generation unit 132 solves the optimization problem by mathematically modeling the above formulas, etc., without repeating steps S104 to S106 N times, F * ←F, b * ←You may also obtain b.

[0117] Furthermore, if the definitions of each evaluation item in formula (5) can be described using linear or quadratic equations for the variables, the optimal solution may be obtained using a general-purpose solver such as Gurobi optimizer, CPLEX, or SCIP. Alternatively, the best solution obtained within the time limit may be used as the optimal solution.

[0118] Furthermore, in formula (6), F target =Σ x∈H (L(x)-T(x)) 2 F target =|Σ x∈H Alternatively, the optimal solution can be obtained by using an optimization problem in which the expression is replaced with (L(x)-T(x)|. This substitution eliminates the quadratic expression from the mathematical model, leaving only linear expressions, making the problem easier to solve.

[0119] Furthermore, part of the objective function may be replaced with constraint equations. For example, F target =Σx∈H(L(x)-T(x)) 2 This can also be replaced with the inequality L(x)≧C, in which case C is a predetermined constant.

[0120] F energy =Σ k∈K E(k)·b(k) is Σ k∈K It can also be replaced with the inequality E(k)·b(k)≦C. kinseido =Σ x′∈H max x∈R(x′) L(x)-min x∈R(x′) L(x) is max x∈R(x′) L(x) / min x∈R(x′) L(x) ≤ C, or min x∈R(x′) L(x) / ave x∈R(x′) You can also substitute this with an inequality such as L(x)≧C.

[0121] Furthermore, equation (10) may be modified to allow dimming rates greater than 100%, and to impose a penalty if the rate is greater than 100%. In this case, the optimal solution for dimming rates greater than 100% can be obtained. This allows us to propose that better evaluation values ​​can be achieved by replacing the lighting fixtures.

[0122] [4. Examples of application] Below, we will explain two application examples with different office space layouts using Figures 9-13.

[0123] [4-1. Application Example (1)] First, we will explain an example of applying the estimation process for estimating lighting patterns using Figures 9 to 11. First, we will explain the preconditions for the estimation process using Figure 9. Figure 9 is a diagram showing the illuminance distribution information for each lighting fixture in application example (1) according to the embodiment. In Figure 9, we will explain the case where illuminance distribution information is generated in the same office space as the office space O1 shown in Figure 1. In Figure 9, we will also assume that lighting fixtures I21 to I24 are installed in the office space O1.

[0124] Furthermore, the power consumption of each lighting fixture I21 to I24 at its maximum illuminance is assumed to be 12W. The calculation area is assumed to have a width of 6m and a depth of 10m. The target point (hereinafter sometimes referred to as the target point) is assumed to be one location. The target illuminance is assumed to be 150lx. The evaluation function F is assumed to be the target illuminance plus energy saving, with each weighted 50%. That is, F = 0.5 × F target +0.5×F energy That is the case.

[0125] In such cases, the estimation unit 133 generates the next candidate b(k) for the dimming rate parameter of each lighting fixture k∈K. At this time, it is assumed that illuminance distribution information for each lighting fixture I21 to I24 is generated, as shown in Figure 9. In the example in Figure 9, the generation unit 132 generates illuminance distribution information SR21 when lighting fixture I21 is lit at 100% dimming rate and lighting fixtures I22 to I24 are off. The generation unit 132 also generates illuminance distribution information SR22 when lighting fixture I22 is lit at 100% dimming rate and lighting fixtures I21, I23 to I24 are off.

[0126] Furthermore, the generation unit 132 generates illuminance distribution information SR23 when lighting fixture I23 is lit at 100% dimming rate and lighting fixtures I21-I22 and I24 are off. Also, the generation unit 132 generates illuminance distribution information SR24 when lighting fixture I24 is lit at 100% dimming rate and lighting fixtures I21-I23 are off.

[0127] Next, an example of estimating illuminance distribution information TSR22 based on illuminance distribution information SR21 to SR24 will be explained using Figure 10. Figure 10 is a diagram showing illuminance distribution information in application example (1) according to the embodiment. In the example in Figure 10, the dimming rate of each lighting fixture I21 to I24 is assumed to be 25%.

[0128] In the example shown in Figure 10, the estimation unit 133 estimates the illuminance distribution information TSR22 for a lighting configuration in which the illuminance and power consumption at the target location TA21 satisfy predetermined conditions, based on the weighted sum of the illuminance distribution information SR21 to SR24.

[0129] To give a more specific example, if the dimming rate of each lighting fixture I21 to I24 is 25%, the illuminance at the target point TA21 is calculated as 0.25 × (229 + 135 + 104 + 116) = 146 lx. Also, the power consumption is calculated as 0.25 × 12 + 0.25 × 12 + 0.25 × 12 + 0.25 × 12 = 12 W. In this case, F target =(150-146)×(150-146)=16.0, F energy = 12.0, therefore F = 0.5 × F target +0.5×F energy = 0.5 × 16.0 + 0.5 × 12.0 = 14.0 is calculated. Therefore, the evaluation value F is * This becomes 14.0. Also, b * is, b * ={25%, 25%, 25%, 25%}. By repeating this calculation, the estimation unit 133 can estimate the dimming rate according to the purpose.

[0130] Next, we will explain the variation in evaluation values ​​due to iterative calculations using Figure 11. Figure 11 is a diagram showing the variation in evaluation values ​​in application example (1) according to the embodiment. In the example in Figure 11, we will explain an example in which the iterative calculation was attempted 10 times. Below, Table 1 shows the number of trials and the evaluation value F * This is a table summarizing the information.

[0131] [Table 1]

[0132] As shown in Table 1, when the number of trials is "1", the evaluation value F * The result is "14.0". If the number of trials is "2", the evaluation value F * The result is "16.1". If the number of trials is "3", the evaluation value is F * The result is "12.1". If the number of trials is "4", the evaluation value is F. * The result is "12.5". If the number of trials is "5", the evaluation value F * The result is "18.3". If the number of trials is "6", the evaluation value F * The value is "13.0".

[0133] If the number of trials is "7", the evaluation value is F. * The result is "8.0". If the number of trials is "8", the evaluation value F * The result is "10.2". If the number of trials is "9", the evaluation value is F. * The result is "5.3". If the number of trials is "10", the evaluation value F * That is "9.4".

[0134] In the example in Figure 11, the evaluation value F is given when the number of trials is "3". * The evaluation value F when the score is "12.1" and the number of trials is "7". * The evaluation value F when the score is "8.0" and the number of trials is "9". * The score "5.3" is updated. And the final rating is F. * This is calculated to be 5.3.

[0135] Below is the evaluation value F * Let's take the case where is 5.3 as an example. For example, let's assume that the dimming rates of each lighting fixture I21 to I24 are 45%, 35%, 0%, and 0%, respectively. In this case, the illuminance at the target point is calculated to be 0.45 × 229 + 0.35 × 135 = 151 lx. Also, the power consumption is calculated to be 0.45 × 12 + 0.35 × 12 + 0 × 12 + 0 × 12 = 9.6 W. In this case, F target =(151-150)×(151-150)=1.0, F energySince this becomes 9.6, F = 0.5 × F target +0.5×F energy = 0.5 × 1.0 + 0.5 × 9.6 = 5.3 is calculated. Therefore, the evaluation value F * It will be updated to 5.3. Also, b * is, b * ={45%, 35%, 0%, 0%}. Thus, the calculated evaluation value F * This represents the optimal solution when the dimming rate of each light is adjusted in 5% increments. In this case, a 20% energy saving is achieved compared to when all dimming rates are set to 25%.

[0136] [4-2. Application Example (2)] Next, we will explain an example of applying the estimation process for estimating lighting patterns using Figures 12-13. First, we will explain the preconditions for the estimation process using Figure 12. Figure 12 is a diagram showing the illuminance distribution information for each lighting fixture in application example (2) according to the embodiment. Figure 12 explains the case in which illuminance distribution information is generated in an office space where partition P31 is installed in the office space O1 shown in Figure 1. Note that lighting fixtures I31-I34 are installed in the office space in Figure 12.

[0137] Furthermore, the power consumption of each lighting fixture I31 to I34 at its maximum illuminance is assumed to be 12W. The calculation area is assumed to have a width of 6m and a depth of 10m. There are two target locations. The first target illuminance at the first target location is assumed to be 150lx. The second target illuminance at the second target location is assumed to be 100lx. The evaluation function F is assumed to be the ratio of target illuminance to energy saving, with each weighted 50%. That is, F = 0.5 × F target +0.5×F energy That is the case.

[0138] In such cases, the estimation unit 133 generates the next candidate b(k) for the dimming rate parameter of each lighting fixture k∈K. At this time, it is assumed that illuminance distribution information for each lighting fixture I31 to I34 is generated, as shown in Figure 12. In the example in Figure 12, the generation unit 132 generates illuminance distribution information SR31 when lighting fixture I31 is lit at 100% dimming rate and lighting fixtures I32 to I34 are off. The generation unit 132 also generates illuminance distribution information SR32 when lighting fixture I32 is lit at 100% dimming rate and lighting fixtures I31, I33 to I34 are off.

[0139] Furthermore, the generation unit 132 generates illuminance distribution information SR33 when lighting fixture I33 is lit at 100% dimming rate and lighting fixtures I31-I32 and I34 are off. Also, the generation unit 132 generates illuminance distribution information SR34 when lighting fixture I34 is lit at 100% dimming rate and lighting fixtures I31-I33 are off.

[0140] Next, an example of estimating illuminance distribution information TSR32 based on illuminance distribution information SR31 to SR34 will be described using Figure 13. Figure 13 is a diagram showing illuminance distribution information in application example (2) according to the embodiment. In the example in Figure 13, the dimming rate of each lighting fixture I31 to I34 is assumed to be 25%.

[0141] In the example shown in Figure 13, the estimation unit 133 estimates illuminance distribution information TSR32 for a lighting configuration in which the illuminance at the first target location TA31, the illuminance at the second target location TA32, and the power consumption satisfy predetermined conditions, based on the weighted sum of illuminance distribution information SR31 to SR34.

[0142] To give a more specific example, if the dimming rate of each lighting fixture I31 to I34 is 25%, the illuminance at the first target point TA31 is calculated to be 88.1 lx. The illuminance at the second target point TA32 is calculated to be 100 lx.

[0143] Furthermore, the power consumption is calculated as 0.25 × 12 + 0.25 × 12 + 0.25 × 12 + 0.25 × 12 = 12W. In this case, F target=(150 - 90)×(150 - 90)=3600.0, F energy =12.0, so F = 0.5×F target +0.5×F energy =0.5×3600.0 + 0.5×12.0 = 1806.0 is calculated. Therefore, the evaluation value F * becomes 1806.0.

[0144] If this calculation is repeated, assume that the dimming rates of each lighting fixture I31 - I34 are 0%, 0%, 50%, and 80% respectively. In this case, the illuminance at the first target point is calculated as 0.50×154 + 0.80×30 = 101 lx. Also, the illuminance at the second target point is calculated as 0.50×108 + 0.80×120 = 150 lx.

[0145] Also, the power consumption is calculated as 0×12 + 0×12 + 0.5×12 + 0.8×12 = 15.6 W. In this case, F target =(101 - 100)×(101 - 100)+(150 - 150)×(150 - 150)=1.0, F energy =15.6, so F = 0.5×F target +0.5×F energy =0.5×1.0 + 0.5×15.6 = 8.3 is calculated. Therefore, the evaluation value F * is updated to 8.3. Thus, even when there are obstacles such as partitions, it was confirmed that the target illuminances are achieved at the two target points respectively.

[0146] [5. Use Case] Next, examples will be given and explained for several use cases. For example, the user who performs the above estimation process is a building manager or a planner who designs the layout of a room when newly constructing, moving, or changing the floor plan, etc.

[0147] For example, a user could repeatedly run a simulation, changing the ambient light conditions (season, time of day, weather, etc.) among the simulation conditions. As a result, by repeatedly running the simulation while appropriately changing the parameters of the ambient light conditions, the user can obtain the optimal dimming rate. For example, a user can obtain the optimal dimming rate that takes into account differences in illuminance due to season, differences in illuminance due to time of day on the same day, differences in illuminance due to weather, etc. It is also possible to automatically calculate dimming rates such as a rate that ensures the illuminance at the target location is 150 lux or more throughout the year, a rate that ensures the illuminance at the target location is 150 lux or more throughout the day, and a rate that ensures the illuminance is 150 lux or more even in the darkest conditions considering the weather forecast for the day.

[0148] Another example is that the user can repeatedly run the simulation by changing the spatial information parameters among the simulation conditions. As a result, by repeatedly running simulations for room layouts, the user can obtain the optimal room layout, the optimal configuration of windows and blinds, and so on.

[0149] Another example is a user repeatedly performing a process to determine the optimal dimming ratio by changing one or more of the following lighting conditions: the illuminance conditions of each lighting fixture that illuminates people located in a predetermined space or areas included in a predetermined space; information on the position and orientation of people; information on the number of people; the power consumption of each lighting fixture; and the maximum illuminance of each lighting fixture. As a result, the user can obtain the optimal dimming ratio for the expected number of people and their positions when building a new house or moving. Furthermore, the user can also automatically calculate dimming ratios and other parameters that result in favorable evaluation values ​​for any of several expected patterns of the number of people.

[0150] Alternatively, one could input an upper limit for illuminance exceeding the rated illuminance, and output results indicating that a good evaluation value is achieved at an illuminance value greater than 100%.

[0151] Another example is when a user performs multi-objective optimization in a simulation to satisfy two or more conditions from among target illumination conditions, energy saving conditions, and uniformity conditions. Multi-objective optimization, in this context, refers to an optimization method that attempts to optimize multiple objectives simultaneously. For example, by performing multi-objective optimization, a user can find not just one solution, but many suitable solutions that take into account trade-offs between different objectives. Furthermore, the user can obtain information by plotting the results of multiple simulations on a two-dimensional graph, for example, with uniformity on the horizontal axis and energy saving conditions on the vertical axis.

[0152] [6. Variant Example] The information processing device 100 described above may be implemented in various other forms besides the embodiment described above. Therefore, other embodiments will be described below.

[0153] [6-1. Lighting Information] The lighting information is not limited to the above embodiment and may further include information regarding how the lighting fixture is installed. For example, the lighting fixture may emit spot light depending on how it is installed. Alternatively, the lighting fixture may emit light to illuminate a wide area depending on how it is installed. In this case, the receiving unit 131 receives information regarding how the lighting fixture is installed as lighting information.

[0154] Furthermore, the lighting information may also include information about the color of light. For example, the color of light may be daylight, cool white, or incandescent. In this case, the reception unit 131 receives information about the color of light as lighting information.

[0155] Furthermore, the lighting information may also include conditions such as the use of a common switch in the lighting layout, where the dimming rate is the same for multiple lights. In this case, the reception unit 131 accepts such conditions as lighting information.

[0156] [6-2. Lighting Conditions] Furthermore, the lighting mode conditions are not limited to the above embodiment and may also include wear and tear conditions. For example, the wear and tear conditions may include the load factor of each lighting fixture and the maximum load factor. For example, if a target value for the load factor is set in the wear and tear conditions, the estimation unit 133 estimates a lighting mode that does not exceed that target value. This allows the estimation unit 133 to reduce damage to the lighting fixtures.

[0157] Furthermore, the target illuminance condition included in the lighting mode condition may also include the ratio of indirect light. The ratio of indirect light referred to here is, for example, a ratio calculated based on the proportion of indirect and direct light included in the target illuminance.

[0158] For example, the target illumination conditions include the ratio of indirect light for each target person. Generally, indirect light is preferable to direct light at night because it can strain the eyes. Also, indirect light may be preferable depending on the use of the living room. Therefore, by including the ratio of indirect light in the target illumination conditions, the estimation unit 133 can estimate the lighting pattern that is most suitable for the user.

[0159] Furthermore, the target illumination conditions may be replaced with conditions related to the user's work content. For example, they may be set at 300 lux or more for general office work and 150 lux or more for incidental office work.

[0160] Furthermore, the target illumination conditions may also include information regarding the user's lighting preferences. For example, information regarding lighting preferences may include a desire to avoid lighting fixtures only directly overhead, a preference for reflected light, a preference for lighting from the front, or a preference for lighting from behind.

[0161] Furthermore, the constraints of the evaluation function F may be modified based on the above lighting conditions. For example, if a target value PV is set as the upper limit of the dimming rate for each lighting fixture based on the wear and tear condition, an objective function may be added that imposes a penalty when L(x) ≥ PV.

[0162] Furthermore, if target values ​​for direct or indirect light are set, target illuminance conditions may be set for direct or indirect light. Also, if individual lighting preferences are set, these preferences may be modeled in advance. In this case, the modeling is done in such a way that a penalty is imposed when the preferences are not met. For example, if front lighting is preferred, an objective function may be added that imposes a penalty when only the rear lighting is on.

[0163] [6-3. Storage section] Furthermore, the condition information storage unit 122 may store information regarding the installation method of the lighting fixture. For example, the condition information storage unit 122 may store various types of lighting information for each installation method of the lighting fixture.

[0164] Furthermore, the condition information storage unit 122 may store information related to light color. For example, the condition information storage unit 122 may store various types of lighting information for each light color. In addition, the condition information storage unit 122 may further store information related to color temperature.

[0165] Furthermore, the condition information storage unit 122 may store conditions for setting the dimming rate to the same value for multiple lights. Also, the condition information storage unit 122 may store various types of lighting information separately for indirect light and direct light.

[0166] Furthermore, the illuminance distribution information storage unit 123 may store the generated illuminance distribution information for each time period based on ambient light conditions.

[0167] Furthermore, the memory unit 120 may store a history of illuminance distribution information generated in the past. In this case, for example, the estimation unit 133 may estimate a lighting mode that satisfies the conditions indicated by the lighting mode conditions based on the weighted sum of the illuminance distribution information generated in the past.

[0168] [6-4. Output Processing] Furthermore, the output unit 134 may output information regarding the ratio of indirect light. In addition, the output unit 134 may output recommended work content and work area for each mesh based on the illumination conditions corresponding to the user's work.

[0169] [6-5. Other generation processes] Furthermore, the lighting fixtures from which illuminance distribution information is generated may be grouped into any number of units. For example, two lighting fixtures may be grouped together. In this case, the generation unit 132 may generate illuminance distribution information for each such group. This allows the generation unit 132 to reduce the number of calculations required.

[0170] [7. Hardware Configuration] Furthermore, the information processing device 100 according to the above-described embodiment is implemented by a computer 1000 having a configuration such as that shown in Figure 14. Figure 14 is a hardware configuration diagram showing an example of a computer 1000 that implements the functions of the information processing device 100.

[0171] Computer 1000 comprises a CPU (Central Processing Unit 1) 1001, an input device 1002, a display device 1003, a communication device 1004, and a storage device 1005, and each device is interconnected by a bus 1006.

[0172] The CPU 1001 is the control unit and arithmetic unit of the computer 1000. The CPU 1001 performs arithmetic processing based on data and programs input from each device connected via the bus 1006 (for example, the input device 1002, the communication device 1004, and the storage device 1005), and outputs the calculation results and control signals to each device connected via the bus 1006 (for example, the display device 1003, the communication device 1004, and the storage device 1005).

[0173] Specifically, the CPU 1001 executes the operating system and information processing programs of the computer 1000, and controls each of the devices that make up the computer 1000. An information processing program is a program that causes the computer 1000 to implement the aforementioned functional configurations of the information processing device 100. By executing the information processing program, the computer 1000 functions as the information processing device 100.

[0174] The input device 1002 is a device for inputting information into the computer 1000. The input device 1002 is, for example, a keyboard, a mouse, and a touch panel, but is not limited to these. Users can input information by using the input device 1002.

[0175] The display device 1003 is a device for displaying images or videos. The display device 1003 is, for example, an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube), or a PDP (Plasma Display Panel), but is not limited to these. These can be used to display the optimization results on the display device 1003.

[0176] The communication device 1004 is a device that allows the computer 1000 to communicate with an external device wirelessly or via a wired connection. The communication device 1004 is, for example, a modem, a hub, and a router, but is not limited thereto. Input information may also be received from the external device via the communication device 1004.

[0177] The storage device 1005 is a storage medium that stores the operating system of the computer 1000, information processing programs, data necessary for the execution of information processing programs, and data generated by the execution of information processing programs. The storage device 1005 includes main memory and external storage devices. The main memory is, for example, RAM, DRAM (Dynamic RAM), or SRAM (Static RAM), but is not limited to these. The external storage devices are, for example, hard disks, optical disks, flash memory, and magnetic tapes, but are not limited to these.

[0178] The computer 1000 may be equipped with one or more CPUs 1001, input devices 1002, display devices 1003, communication devices 1004, and storage devices 1005. Peripheral devices such as printers and scanners may also be connected to the computer 1000.

[0179] Furthermore, the information processing device 100 may consist of a single computer 1000, or it may be configured as a system consisting of multiple interconnected computers 1000.

[0180] Furthermore, the information processing program may be pre-stored in the storage device 1005 of the computer 1000, stored on a storage medium such as a CD-ROM, or uploaded to the internet. In any case, the information processing device 100 can be configured by installing and executing the information processing program on the computer 1000.

[0181] The specific forms of distribution or integration of each device described in the above embodiments and modifications are not limited to those shown in the figures, and all or part of them may be configured by functionally or physically distributing or integrating them in any unit according to various loads, usage conditions, etc. Furthermore, the contents of the information processing described in the above embodiments and modifications can be combined as appropriate within a non-contradictory range.

[0182] While embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out 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 variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0183] 100 Information Processing Devices 110 Communications Department 120 Storage section 121 Illuminance Simulator 122 Condition information storage unit 123 Illuminance distribution information storage unit 130 Control Unit 131 Reception Department 132 Generation part 133 Estimation Department 134 Output section 140 Input section 150 Display section

Claims

1. A generation unit that generates illuminance distribution information showing the illuminance distribution when each lighting fixture installed in a predetermined space is turned on; A reception unit that receives lighting conditions, which are conditions relating to the desired lighting configuration in the aforementioned predetermined space; An estimation unit that estimates a lighting mode that satisfies the conditions indicated by the lighting mode conditions using the illuminance distribution information of each of the aforementioned lighting fixtures; An information processing device equipped with the following.

2. The estimation unit, Based on the weighted sum of the illuminance distribution information of each of the aforementioned lighting fixtures, a lighting mode that satisfies the conditions indicated by the lighting mode conditions is estimated. The information processing apparatus according to claim 1.

3. The generating unit is The system generates the illuminance distribution information when each of the lighting fixtures is turned on based on predetermined lighting conditions. The information processing apparatus according to claim 2.

4. The generating unit is This generates illuminance distribution information when a lighting fixture is turned on based on the predetermined lighting conditions. The information processing apparatus according to claim 3.

5. The aforementioned reception unit is The spatial information relating to the predetermined space, further including spatial information including the layout of the predetermined space, The generating unit is Based on the spatial information, the illuminance distribution information is generated. The information processing apparatus according to claim 1.

6. The aforementioned reception unit is Lighting information relating to each of the aforementioned lighting fixtures, further including lighting information including the illuminance conditions of each of the aforementioned lighting fixtures, The generating unit is Based on the aforementioned lighting information, the illuminance distribution information is generated. The information processing apparatus according to claim 1.

7. The aforementioned reception unit is The simulation conditions, including the ambient light conditions of the aforementioned predetermined space, are further accepted. The generating unit is Based on the simulation conditions, the illuminance distribution information is generated. The information processing apparatus according to claim 1.

8. The aforementioned reception unit is The lighting configuration conditions include the illuminance conditions of each lighting fixture that irradiates light onto a person located within the predetermined space, or an area included within the predetermined space. The information processing apparatus according to claim 1.

9. The aforementioned reception unit is Further information regarding the position and orientation of the said person is received. The information processing apparatus according to claim 8.

10. The aforementioned reception unit is The aforementioned lighting mode conditions may further accept conditions related to uniformity. The information processing apparatus according to claim 8.

11. The aforementioned reception unit is The lighting mode conditions further accept the power consumption of each of the lighting fixtures. The information processing apparatus according to claim 8.

12. The system further comprises an output unit that outputs dimming parameters for each lighting fixture calculated based on the lighting patterns estimated by the estimation unit. The information processing apparatus according to claim 1.

13. The output unit is, The dimming parameters to be output include one of the following: the dimming rate, uniformity, power consumption, and the difference between the illuminance of each lighting fixture and a predetermined illuminance. The information processing apparatus according to claim 12.

14. A method of information processing performed by a computer, A generation process for generating illuminance distribution information that shows the illuminance distribution when each lighting fixture installed in a predetermined space is turned on; A receiving process for receiving lighting conditions, which are conditions relating to the desired lighting configuration in the aforementioned predetermined space; An estimation step of estimating a lighting mode that satisfies the conditions indicated by the lighting mode conditions using the illuminance distribution information of each of the aforementioned lighting fixtures; Information processing methods including

15. A generation procedure for generating illuminance distribution information that shows the illuminance distribution when each lighting fixture installed in a given space is turned on; A reception procedure for receiving lighting conditions, which are conditions relating to the desired lighting configuration in the aforementioned predetermined space; An estimation procedure for estimating a lighting mode that satisfies the conditions indicated by the lighting mode conditions, using the illuminance distribution information of each of the aforementioned lighting fixtures; An information processing program that causes a computer to execute something.

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