Prediction system, prediction method, and program

Abstract

To provide a prediction system capable of easily predicting the brightness of a space when one or more objects are placed therein while reducing labor for inputting information.SOLUTION: A prediction system 100 includes an acquisition unit 11, a determination unit 12, and a prediction unit 13. The acquisition unit 11 acquires characteristics information that includes information regarding the reflection of light from one or more objects that may be placed in a space, but does not include information regarding spatial coordinates of one or more objects. The determination unit 12 determines reflection information regarding light reflection in the space when one or more objects are placed based on the characteristics information. The prediction unit 13 predicts the brightness of the space when one or more objects are placed therein based on the reflection information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a prediction system, a prediction method, and a program. [Background technology]

[0002] Patent Document 1 discloses a lighting design device. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-9475 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention provides a prediction system, a prediction method, and a program that can easily predict the brightness of a space when one or more objects are placed, while reducing the effort required to input information. [Means for solving the problem]

[0005] A prediction system according to one aspect of the present invention includes an acquisition unit, a determination unit, and a prediction unit. The acquisition unit acquires characteristic information that includes information about light reflection of one or more objects that can be placed in a space, but does not include information about the coordinates of the one or more objects in the space. The determination unit determines reflection information regarding light reflection in the space when the one or more objects are placed, based on the characteristic information. The prediction unit predicts the brightness of the space when the one or more objects are placed, based on the reflection information.

[0006] A prediction method according to one aspect of the present invention acquires characteristic information that includes information about the light reflection of one or more objects that can be placed in a space, but does not include information about the coordinates of the one or more objects in the space. The prediction method determines reflectance information about the light reflection in the space when the one or more objects are placed based on the characteristic information. The prediction method predicts the brightness of the space when the one or more objects are placed based on the reflectance information.

[0007] A program according to one aspect of the present invention causes one or more processors to execute the prediction method. [Effects of the Invention]

[0008] The prediction system, prediction method, and program of the present invention have the advantage of making it easier to predict the brightness of a space when one or more objects are placed, while reducing the effort required to input information. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating a functional configuration of a prediction system according to an embodiment. [Figure 2] FIG. 2 is a schematic diagram illustrating an example of a space that is a prediction target of the prediction system according to the embodiment. [Figure 3] FIG. 3 is a diagram showing an example of an input screen used in the prediction system according to the embodiment. [Figure 4] FIG. 4 is a flowchart illustrating an example of the operation of the prediction system according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating a first example of use of the prediction system according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating a second example of use of the prediction system according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating a third example of use of the prediction system according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the embodiments will be described in detail with reference to the drawings. Note that the embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection forms, steps, step order, etc. shown in the following embodiments are merely examples and are not intended to limit the present invention. Furthermore, among the components in the following embodiments, components not recited in independent claims will be described as optional components.

[0011] Note that each figure is a schematic diagram and is not necessarily an exact illustration. Therefore, for example, the scales and the like do not necessarily match in each figure. Furthermore, each figure is a schematic diagram in which emphasis, omission, and proportions have been appropriately adjusted to illustrate the present invention, and may differ from the actual shapes, positional relationships, and proportions. Furthermore, in each figure, substantially identical components are assigned the same reference numerals, and duplicate explanations may be omitted or simplified.

[0012] Furthermore, in this specification, terms indicating relationships between elements such as the same, as well as numerical values ​​and numerical ranges, are not expressions that express only the strict meaning, but also expressions that mean that a substantially equivalent range, for example, a difference of about several percent (for example, about 5%) is included. For example, "horizontal direction" means not only a completely horizontal direction but also an error of about several percent, for example, 5%, that occurs during manufacturing or arrangement.

[0013] (Embodiment) A prediction system according to an embodiment will be described below with reference to the drawings. FIG. 1 is a block diagram showing the functional configuration of a prediction system 100 according to an embodiment. The prediction system 100 according to the embodiment is a system for predicting the brightness of a space 2 in which one or more objects 5 are arranged, as shown in FIG. 2. More specifically, the prediction system 100 is a system for predicting the brightness of the space 2 when one or more objects 5 are arranged in the space 2 through a simulation, without actually arranging the one or more objects 5 in the space 2. The prediction system 100 can be used, for example, for a simulation when arranging lighting fixtures 4 (see FIG. 2) in the space 2. The prediction system 100 can also be used, for example, for controlling the dimming rate of the lighting fixtures 4 in the space 2.

[0014] In this embodiment, the prediction system 100 calculates the average luminance of the ceiling surface 31 (see FIG. 2) as the brightness of the space according to the JIS (Japanese Industrial Standards) as 20 cd / m 3 The average luminance of the wall surface 32 (see Figure 2) is 30 cd / m 3 In light of the recommendation that the average luminance of the wall surface 32 of space 2 and the average luminance of the ceiling surface 31 of space 2 be equal to or greater than this (see JIS Z 9125:2023 "Indoor lighting standard"), the brightness of space 2 is predicted to be the average luminance of the wall surface 32 of space 2 and the average luminance of the ceiling surface 31 of space 2.

[0015] The brightness of the space 2 predicted by the prediction system 100 may be the average luminance of the floor surface. Also, the brightness of the space 2 predicted by the prediction system 100 may be a representative value of luminance (such as the mode or median) instead of the average luminance. Also, the brightness of the space 2 predicted by the prediction system 100 is not limited to luminance, and may be another brightness index such as illuminance.

[0016] Furthermore, in the embodiment, the prediction system 100 predicts the brightness of the space 2 on the premise that the shape and size of the space 2 are fixed. Specifically, in the embodiment, the prediction system 100 predicts the brightness of the space 2 on the premise that the shape of the space 2 is a rectangular parallelepiped and the size of the space 2 in a plan view is several meters long by several meters wide (for example, 10 meters long by 7 meters wide).

[0017] Fig. 2 is a schematic diagram showing an example of a space 2 that is a prediction target of the prediction system 100 according to the embodiment. In the example shown in Fig. 2, the space 2 is a conference room that is configured with multiple surfaces 3 (a ceiling surface 31, multiple wall surfaces 32, and a floor surface 33). In the example shown in Fig. 2, the one or more objects 5 include multiple meeting tables, multiple chairs, and multiple people.

[0018] Space 2 is not limited to a conference room, but may be another space within an office. Space 2 is also not limited to a space within an office, but may be a space within an educational facility such as an elementary school, junior high school, high school, or university, a space within a public facility such as a community center or library, or a space within a store or commercial facility. Space 2 may also be a space within a residential facility such as a detached house or apartment building.

[0019] Furthermore, the one or more objects 5 are not limited to a meeting table, chairs, and people, but may also be, for example, a desk, a partition, a shelf, an animal, a plant, etc. In other words, the one or more objects 5 may be any object that exists within the space 2, and may be fixed in the space 2 or may be a moving object that moves over time.

[0020] As shown in Fig. 2, lighting fixtures 4 are installed in space 2. In the example shown in Fig. 2, a plurality of lighting fixtures 4 are installed on ceiling surface 31 of space 2. Note that lighting fixtures 4 are not limited to being installed on ceiling surface 31, but may also be installed on wall surface 32 or floor surface 33, or on one or more objects 5.

[0021] In the example shown in FIG. 2, each lighting fixture 4 is a base light serving as ambient lighting that uniformly illuminates the space 2, and is equipped with a light source having a solid-state light-emitting element such as an LED (Light Emitting Diode). In other words, each lighting fixture 4 has a diffused light distribution characteristic. Note that the solid-state light-emitting element used in each lighting fixture 4 is not limited to an LED, but may be an organic EL (Electro-Luminescence) element or the like. Furthermore, each lighting fixture 4 is not limited to a light source having a solid-state light-emitting element, but may be a fluorescent lamp or the like.

[0022] Each lighting fixture 4 may be a spotlight serving as a task light and equipped with a light source having a solid-state light-emitting element such as an LED. In other words, each lighting fixture 4 may have a concentrated light distribution characteristic. Furthermore, each lighting fixture 4 is not limited to a spotlight, and may be, for example, a stand light, a downlight, or a universal downlight. Furthermore, the multiple lighting fixtures 4 may include one or more base lights and one or more task lights.

[0023] [Prediction System] Next, a prediction system 100 according to an embodiment will be described in detail. In the embodiment, the prediction system 100 is realized by installing a dedicated application on an information processing terminal such as a smartphone owned by a user. The information processing terminal is not limited to a smartphone, but may also be a tablet terminal, a desktop or laptop personal computer, or the like.

[0024] The prediction system 100 may be realized by, for example, a server device. In this case, information may be input to the prediction system 100 by, for example, an information processing terminal that can communicate with the server device via a network such as the Internet.

[0025] 1, the prediction system 100 includes an acquisition unit 11, a determination unit 12, a prediction unit 13, a display unit 14, and a storage unit 15. In the embodiment, the prediction system 100 is required to include at least the acquisition unit 11, the determination unit 12, and the prediction unit 13, and may not include the display unit 14 and the storage unit 15.

[0026] The acquisition unit 11 acquires characteristic information. Here, the characteristic information is information that includes information about the light reflection of one or more objects 5 that can be placed in the space 2, but does not include information about the coordinates of the one or more objects 5 in the space 2. In other words, the characteristic information does not include information that specifically indicates the positions in the space 2 at which the one or more objects 5 are placed.

[0027] Specifically, the characteristic information may include information indicating the type of each of the one or more objects 5, such as a desk, partition, shelf, animal, or plant. The characteristic information may also include information indicating the material of each of the one or more objects 5, such as wood, plastic, or cloth. The characteristic information may also include information indicating the color of each of the one or more objects 5, such as white, gray, or black paint. The characteristic information may also include information indicating the type of surface finish of each of the one or more objects 5, such as matte finish or glossy finish. The characteristic information may also include information indicating the number of the one or more objects 5, or information indicating the size of each of the one or more objects 5, for example.

[0028] Furthermore, the characteristic information may include, for example, information indicating the orientation of each of the one or more placement objects 5. The "information indicating the orientation" here refers to, for example, information indicating whether the orientation along the longitudinal direction of the placement object 5 is along a horizontal plane or a vertical plane. The characteristic information may also include, for example, information indicating the relative positional relationship of each of the one or more placement objects 5 with respect to a surface 3 constituting the space 2. The "information indicating the relative positional relationship with the surface 3" here refers to, for example, information indicating whether the placement object 5 is placed in contact with the surface 3 or whether the placement object 5 is placed away from the surface 3. Note that neither the "information indicating the orientation" nor the "information indicating the relative positional relationship with the surface 3" corresponds to information regarding the coordinates of the one or more placement objects 5 in the space 2.

[0029] 3, the acquiring unit 11 acquires the characteristic information by accepting an input on an input screen 141 displayed on the display unit 14. That is, in the embodiment, the acquiring unit 11 acquires, as the characteristic information, information that the user inputs while looking at the input screen 141.

[0030] 3 is a diagram showing an example of an input screen 141 used in the prediction system 100 according to the embodiment. The display unit 14 may display the input screens 141 of (a), (b), (c), (d), and (e) of FIG. 3 all at once, or may display them sequentially as the user enters data. For example, when the user finishes entering data on the input screen 141 shown in (a) of FIG. 3, the display unit 14 may next display the input screen 141 shown in (b) of FIG. 3.

[0031] The example shown in Fig. 3(a) shows an input screen 141 for inputting the type of the object 5. In the example shown in Fig. 3(a), a total of four icons I1, namely, "Desk," "Shelf (wall-mounted)," "Shelf (other than wall-mounted)," and "Other," are displayed on the input screen 141. When the user inputs to select one of the four icons I1, the acquisition unit 11 acquires, as characteristic information, information indicating the type of the object 5 corresponding to the input.

[0032] When the user inputs to select either the "shelf (wall-mounted)" icon I1 or the "shelf (other than wall-mounted)" icon I1, the acquisition unit 11 further acquires, as characteristic information, information indicating the relative positional relationship of the object 5 with the surface 3. Specifically, when the user inputs to select the "shelf (wall-mounted)" icon I1, the acquisition unit 11 acquires, as characteristic information, information indicating that the object 5 is placed so as to be in contact with the wall surface 32. When the user inputs to select the "shelf (other than wall-mounted)" icon I1, the acquisition unit 11 acquires, as characteristic information, information indicating that the object 5 is placed away from the wall surface 32.

[0033] The example shown in FIG. 3(b) illustrates an input screen 141 for inputting the size of the object 5. The example shown in FIG. 3(b) illustrates the input screen 141 when the user inputs to select "desk" as the type of the object 5. In the example shown in FIG. 3(b), a total of four icons I2, namely, "personal desk," "meeting desk (seats 4)," "meeting desk (seats 6)," and "other," are displayed on the input screen 141. When the user inputs to select one of the four icons I2, the acquisition unit 11 acquires, as characteristic information, information indicating the size of the object 5 according to the input.

[0034] The example shown in Fig. 3(c) shows an input screen 141 for inputting the number of objects 5. In the example shown in Fig. 3(c), an icon I3 representing a numeric keypad is displayed on the input screen 141. When the user inputs a number using the icon I3, the acquisition unit 11 acquires, as characteristic information, information indicating the number of objects 5 corresponding to the input.

[0035] The example shown in Fig. 3(d) represents an input screen 141 for inputting the material of the object 5. In the example shown in Fig. 3(d), a total of four icons I4, namely, "wood," "plastic," "cloth," and "other," are displayed on the input screen 141. When the user performs an input to select one of the four icons I4, the acquisition unit 11 acquires, as characteristic information, information indicating the material of the object 5 according to the input.

[0036] The example shown in Fig. 3(e) represents an input screen 141 for inputting the color of the object 5. In the example shown in Fig. 3(e), a total of 11 colored icons I5 are displayed on the input screen 141. In Fig. 3(e), the color differences between the icons I1 are represented by the density or size of dots. When the user inputs to select one of the colored icons I5, the acquisition unit 11 acquires, as characteristic information, information indicating the color of the object 5 corresponding to the input.

[0037] The determination unit 12 determines reflection information regarding the reflection of light in the space 2 when one or more objects 5 are placed, based on the characteristic information acquired by the acquisition unit 11. In the embodiment, the determination unit 12 determines the reflectance of light on each of the multiple surfaces 3 that make up the space 2 as the reflection information. More specifically, the reflectance of light on each of the multiple surfaces 3 is the reflectance of the interior material that is the material used on each of the multiple surfaces 3 (interior reflectance).

[0038] Specifically, the determination unit 12 determines, as the reflection information, the light reflectance on the ceiling surface 31, the light reflectance on the wall surface 32, and the light reflectance on the floor surface 33. Note that, for example, if there are multiple wall surfaces 32, the light reflectance on the wall surface 32 is the average value of the light reflectance on each of the multiple wall surfaces 32.

[0039] In addition, in the embodiment, the determination unit 12 determines the light reflectance of each of the multiple surfaces 3 by correcting the initial value of the light reflectance of one or more of the multiple surfaces 3 based on the characteristic information. Specifically, in the embodiment, the initial value of the light reflectance of the ceiling surface 31, the initial value of the light reflectance of the wall surfaces 32, and the initial value of the light reflectance of the floor surface 33 in the space 2 in which no object 5 is placed are set in advance. The determination unit 12 then determines the light reflectance of each of the ceiling surface 31, the wall surfaces 32, and the floor surface 33 by multiplying the initial value of the light reflectance of one or more of the ceiling surface 31, the wall surfaces 32, and the floor surface 33 by a correction coefficient determined based on the characteristic information. Note that the light reflectance of a surface not multiplied by the correction coefficient becomes the initial value of the light reflectance of that surface. For example, the determination unit 12 determines the correction coefficient by referring to data indicating the correlation between the characteristic information and the correction coefficient, which is pre-stored in the storage unit 15.

[0040] For example, assume that the size of the space 2 in a plan view is 10 m long by 7 m wide, and that the initial light reflectance of the ceiling surface 31 is 50%, the initial light reflectance of the wall surface 32 is 30%, and the initial light reflectance of the floor surface 33 is 10%. Also assume that the acquisition unit 11 has acquired characteristic information indicating that the type of object 5 is a "desk," the size of the object 5 is a "meeting desk (seating 6)," the number of objects 5 is "three," and the material of the objects 5 is "wood." In this case, the determination unit 12 determines the correction coefficient for the light reflectance of the ceiling surface 31 to be 1.7 based on the characteristic information by referencing the data stored in the memory unit 15. The determination unit 12 then multiplies the initial value of the light reflectance of the ceiling surface 31, 50%, by the determined correction coefficient, 1.7, to determine the light reflectance of the ceiling surface 31 to be 85%.

[0041] In this case, the determination unit 12 determines the light reflectance of the wall surface 32 to be 30% of the initial value, and the light reflectance of the floor surface 33 to be 10% of the initial value. That is, in this case, the characteristic information affects only the correction coefficient for the light reflectance of one surface 3 (here, the ceiling surface 31) among the multiple surfaces 3. In other words, the determination unit 12 determines the light reflectance of one surface 3 among the multiple surfaces 3 by correcting the initial value of the light reflectance of that one surface 3 based on the characteristic information.

[0042] The prediction unit 13 predicts the brightness of the space 2 when one or more objects 5 are placed, based on the reflection information determined by the determination unit 12. In the embodiment, the prediction unit 13 predicts the brightness of the space 2 when one or more objects 5 are placed, based on the light reflectance of each of the multiple surfaces 3. Specifically, the prediction unit 13 receives the light reflectance of the ceiling surface 31, the light reflectance of the wall surfaces 32, and the light reflectance of the floor surface 33 as input, and calculates the average brightness of the wall surfaces 32 of the space 2 and the average brightness of the ceiling surface 31 of the space 2 using an appropriate function.

[0043] The prediction unit 13 may predict the brightness of the space 2 without using a function, by referring to data that is stored in advance in the memory unit 15 and indicates the correlation between the light reflectance of each of the multiple surfaces 3 and the brightness of the space 2. For example, the prediction unit 13 may predict the average luminance of the wall surfaces 32 of the space 2 and the average luminance of the ceiling surface 31 of the space 2 based on the light reflectance of the ceiling surface 31, the light reflectance of the wall surfaces 32, and the light reflectance of the floor surface 33, by referring to the above data stored in the memory unit 15.

[0044] The display unit 14 is, for example, a liquid crystal display or an organic EL display, and displays an input screen 141 for inputting characteristic information, as already described. The display unit 14 also displays, for example, a result screen showing the result of the prediction of the brightness of the space 2 by the prediction unit 13. In the embodiment, the display unit 14 is a touch panel display, and also functions as an input interface that accepts input from the user. Note that the display unit 14 and the input interface may be configured separately.

[0045] The storage unit 15 is a storage device that stores information (computer programs, etc.) necessary for the prediction system 100 to execute various processes. The storage unit 15 is realized by, for example, an HDD (Hard Disk Drive), but may also be realized by a semiconductor memory, and is not particularly limited, and any known means for storing electronic information can be used.

[0046] [Operation] An example of the operation of the prediction system 100 according to the embodiment will be described below with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the operation of the prediction system 100 according to the embodiment.

[0047] First, the acquisition unit 11 acquires characteristic information (S1). In the embodiment, as already described, the acquisition unit 11 acquires characteristic information by accepting input on the input screen 141 (see FIG. 3) displayed on the display unit 14.

[0048] Next, the determination unit 12 determines the reflection information based on the characteristic information acquired by the acquisition unit 11 (S2). In the embodiment, as already described, the determination unit 12 determines the light reflectance of each of the ceiling surface 31, the wall surface 32, and the floor surface 33 as the reflection information by multiplying the initial value of the light reflectance of one or more surfaces among the ceiling surface 31, the wall surface 32, and the floor surface 33 by a correction coefficient determined based on the characteristic information.

[0049] Next, prediction unit 13 predicts the brightness of space 2 based on the reflection information determined by determination unit 12 (S3). In the embodiment, as already described above, prediction unit 13 receives the light reflectance of ceiling surface 31, the light reflectance of wall surface 32, and the light reflectance of floor surface 33 as input, and calculates the average luminance of wall surface 32 of space 2 and the average luminance of ceiling surface 31 of space 2 as the brightness of space 2 using an appropriate function.

[0050] Then, the prediction system 100 displays a result screen showing the prediction result of the brightness of the space 2 by the prediction unit 13 on the display unit 14 (S4). This allows the user to understand how bright the space 2 will be when one or more objects 5 are placed, without actually placing one or more objects 5.

[0051] The following are examples of how the prediction system 100 according to the embodiment can be used. Note that the examples of how the prediction system 100 can be used are not limited to the first to third examples of use listed below.

[0052] [1st usage example] FIG. 5 is an explanatory diagram of a first use example of the prediction system 100 according to the embodiment. (a) of FIG. 5 shows a plan view of a space 2 in which a plurality of lighting fixtures 4 are installed but no object 5 is placed. (a) of FIG. 5 also shows the initial value (50%) of light reflectance on a ceiling surface 31, the initial value (30%) of light reflectance on a wall surface 32, and the initial value (10%) of light reflectance on a floor surface 33 when no object 5 is placed. (a) of FIG. 5 also shows the average luminance (38.4 cd / m ) of a wall surface 32 of the space 2 when no object 5 is placed. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (15.5 cd / m 2 ) is stated.

[0053] FIG. 5(b) shows a plan view of a space 2 in which one or more objects 5 are arranged, and in which a plurality of lighting fixtures 4 are installed. In the example shown in FIG. 5(b), the one or more objects 5 are three wooden desks with a reflectance of 50% (a total of 18 seats). Each desk measures 1.2 m in length, 3.6 m in width, and 0.7 m in height. FIG. 5(b) also shows the average luminance (40.7 cd / m) of a wall surface 32 in the space 2, which is the result of a simulation performed using lighting simulation software, specifying the positions (coordinates) of one or more objects 5 (here, three wooden desks) in the space 2, as well as various parameters such as size and reflectance. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (28.1 cd / m 2 ) is stated.

[0054] Fig. 5(c) shows a plan view of a space 2 in which one or more objects 5 are arranged and in which a plurality of lighting fixtures 4 are installed. In the example shown in Fig. 5(c), the prediction results made by the prediction system 100 are shown based on characteristic information indicating that the type of object 5 is a "desk," the material of the object 5 is "wood," and the number of seats in the object 5 is "18 seats."

[0055] Specifically, in (c) of FIG. 5, the reflectance of light on the ceiling surface 31 (85%), the reflectance of light on the wall surface 32 (30%), and the reflectance of light on the floor surface 33 (10%) are shown as reflection information determined based on the characteristic information. In the example shown in (c) of FIG. 5, the reflectance of light on the ceiling surface 31 is corrected based on the characteristic information. In addition, (c) of FIG. 5 shows the average luminance (39.7 cd / m ) of the wall surface 32 of the space 2, which is the result predicted by the prediction system 100. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (27.2 cd / m 2 ) is stated.

[0056] Here, as described above, the light reflectance on ceiling surface 31 is corrected in the first use example. The reason for this will be explained. In the first use example, object 5 is a "desk" made of "wood," and its light reflectance is higher than the initial value (10%) of light reflectance on floor surface 33. In the first use example, the top surface of object 5 is closer to each of the lighting fixtures 4 installed on ceiling surface 31 than to floor surface 33. For this reason, in the first use example, prediction system 100 determines the correction coefficient corresponding to the light reflectance on ceiling surface 31 to be a value higher than 1, and corrects the light reflectance on ceiling surface 31 by multiplying the determined correction coefficient by the initial value of light reflectance on ceiling surface 31.

[0057] Furthermore, if the light reflectance of each of one or more objects 5 is lower than the light reflectance on the floor surface 33, the prediction system 100 determines the correction coefficient corresponding to the light reflectance on the ceiling surface 31 to be a value lower than 1.

[0058] Furthermore, the prediction system 100 increases or decreases the correction coefficient depending on the number of objects 5. For example, if the light reflectance of the objects 5 is higher than the light reflectance of the floor surface 33, the prediction system 100 determines a higher correction coefficient corresponding to the light reflectance of the ceiling surface 31 as the number of objects 5 increases, and vice versa.

[0059] 5(b) and 5(c), in the first use example, the prediction results by the prediction system 100 are almost the same as the results of a simulation performed by specifying the positions and various parameters of one or more objects 5 in the space 2. In other words, in the first use example, it can be said that the prediction performance of the prediction system 100 for predicting the brightness of the space 2 is almost the same as the prediction performance of the simulation for predicting the brightness of the space 2.

[0060] [Second usage example] Fig. 6 is an explanatory diagram of a second use example of the prediction system 100 according to the embodiment. Fig. 6(a) is the same as Fig. 5(a), and therefore a description thereof will be omitted here.

[0061] FIG. 6(b) shows a plan view of a space 2 in which one or more objects 5, each equipped with a plurality of lighting fixtures 4, are arranged. In the example shown in FIG. 6(b), the one or more objects 5 is a single, tall, gray shelf with a reflectance of 80%. The shelf measures 0.4 m in length, 6 m in width, and 2.7 m in height. The shelf is also arranged so as to be in contact with a wall surface 32. FIG. 6(b) also shows the average luminance (47.2 cd / m) of a wall surface 32 in the space 2, which is the result of a simulation performed using lighting simulation software by specifying the positions (coordinates) of one or more objects 5 (here, a single, tall, gray shelf) in the space 2, as well as various parameters such as size and reflectance. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (17.1 cd / m 2 ) is stated.

[0062] Fig. 6(c) shows a plan view of a space 2 in which one or more objects 5 are arranged and in which a plurality of lighting fixtures 4 are installed. In the example shown in Fig. 6(c), the result predicted by the prediction system 100 is shown based on characteristic information indicating that the type of object 5 is "shelf (wall-mounted)", the color of the object 5 is "gray", and the number of objects 5 is "1".

[0063] Specifically, in (c) of FIG. 6, the reflectance of light on the ceiling surface 31 (50%), the reflectance of light on the wall surface 32 (60%), and the reflectance of light on the floor surface 33 (10%) are shown as reflection information determined based on the characteristic information. In the example shown in (c) of FIG. 6, the reflectance of light on the wall surface 32 is corrected based on the characteristic information. In addition, in (c) of FIG. 6, the average luminance (46.7 cd / m ) of the wall surface 32 in the space 2, which is the result predicted by the prediction system 100, is shown. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (16.2 cd / m 2 ) is stated.

[0064] Here, as described above, the light reflectance on the wall surface 32 is corrected in the second use example, and the reason for this will be explained. In the second use example, the object 5 is a "gray" "shelf," and its light reflectance is higher than the initial value (30%) of the light reflectance on the wall surface 32. In the second use example, the shelf, which is the object 5, is placed so as to be in contact with the wall surface 32. For this reason, in the second use example, the prediction system 100 determines the correction coefficient corresponding to the light reflectance on the wall surface 32 to be a value higher than 1, and corrects the light reflectance on the wall surface 32 by multiplying the determined correction coefficient by the initial value of the light reflectance on the wall surface 32.

[0065] If the light reflectance of the object 5 is equal to or less than the light reflectance of the wall surface 32, the prediction system 100 determines the correction coefficient corresponding to the light reflectance of the wall surface 32 to be a value less than 1.

[0066] Furthermore, the prediction system 100 increases or decreases the correction coefficient depending on the number of placed objects 5. For example, if the light reflectance of the placed objects 5 is higher than the light reflectance of the wall surface 32, the prediction system 100 determines a higher value for the correction coefficient corresponding to the light reflectance of the wall surface 32 as the number of placed objects 5 increases, and vice versa.

[0067] 6(b) and 6(c), in the second use example, the prediction results by the prediction system 100 are almost the same as the results of a simulation performed by specifying the positions and various parameters of one or more objects 5 in the space 2. In other words, in the second use example, it can be said that the prediction performance of the prediction system 100 for the brightness of the space 2 is almost the same as the prediction performance of the simulation for the brightness of the space 2.

[0068] [Third usage example] Fig. 7 is a diagram illustrating a third example of use of the prediction system 100 according to the embodiment. Fig. 7(a) is the same as Fig. 5(a), and therefore a description thereof will be omitted here.

[0069] FIG. 7(b) shows a plan view of a space 2 in which one or more objects 5 are arranged, and in which a plurality of lighting fixtures 4 are installed. In the example shown in FIG. 7(b), the one or more objects 5 are two low, gray shelves with a reflectance of 80%. Each shelf measures 0.4 m in length, 1.5 m in width, and 1.2 m in height. Each shelf is also positioned away from a wall 32. FIG. 7(b) also shows the average luminance (39.3 cd / m) of a wall 32 in the space 2, which is the result of a simulation performed using lighting simulation software, specifying the positions (coordinates) of one or more objects 5 (here, two high, gray shelves) in the space 2, as well as various parameters such as size and reflectance. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (18.9 cd / m 2 ) is stated.

[0070] 7(c) shows a plan view of a space 2 in which one or more objects 5 are arranged and in which a plurality of lighting fixtures 4 are installed. In the example shown in FIG. 7(c), the prediction results made by the prediction system 100 are shown based on characteristic information indicating that the type of object 5 is "shelf (other than wall-mounted)," the color of the object 5 is "gray," and the number of objects 5 is "two."

[0071] Specifically, in (c) of FIG. 7, the reflectance of light on the ceiling surface 31 (55%), the reflectance of light on the wall surface 32 (35%), and the reflectance of light on the floor surface 33 (10%) are shown as reflection information determined based on the characteristic information. In the example shown in (c) of FIG. 7, the reflectance of light on the ceiling surface 31 and the reflectance of light on the wall surface 32 are each corrected based on the characteristic information. In addition, (c) of FIG. 7 shows the average luminance (40.2 cd / m ) of the wall surface 32 of the space 2, which is the result predicted by the prediction system 100. 2 ) and the average luminance of the ceiling surface 31 of the space 2 (19.0 cd / m 2 ) is stated.

[0072] Here, as described above, in the third use example, the light reflectance on the ceiling surface 31 and the light reflectance on the wall surface 32 are corrected. The reason for this will be explained. In the third use example, the object 5 is a gray "shelf," and its light reflectance is higher than the initial value (50%) of the light reflectance on the ceiling surface 31 and the initial value (30%) of the light reflectance on the wall surface 32. In the third use example, the two shelves that are the object 5 are both placed away from the wall surface 32. Therefore, in the third use example, the prediction system 100 determines the correction coefficient corresponding to the light reflectance on the ceiling surface 31 to be a value higher than 1, and corrects the light reflectance on the ceiling surface 31 by multiplying the determined correction coefficient by the initial value of the light reflectance on the ceiling surface 31. In addition, the prediction system 100 determines the correction coefficient corresponding to the light reflectance on the wall surface 32 to be a value higher than 1, and corrects the light reflectance on the wall surface 32 by multiplying the determined correction coefficient by the initial value of the light reflectance on the wall surface 32.

[0073] In addition, if the light reflectance of the object 5 is equal to or lower than the light reflectance on the ceiling surface 31 and equal to or lower than the light reflectance on the wall surface 32, the prediction system 100 determines the correction coefficient corresponding to the light reflectance on the ceiling surface 31 and the correction coefficient corresponding to the light reflectance on the wall surface 32 to be values ​​lower than 1.

[0074] Furthermore, the prediction system 100 increases or decreases the correction coefficient depending on the number of objects 5. For example, if the light reflectance of the objects 5 is higher than the light reflectance of the ceiling surface 31, the prediction system 100 determines a higher correction coefficient corresponding to the light reflectance of the wall surface 32 as the number of objects 5 increases, and a lower correction coefficient corresponding to the light reflectance of the wall surface 32 as the number of objects 5 decreases. The same applies to the correction coefficient corresponding to the light reflectance of the wall surface 32.

[0075] 7(b) and 7(c), in the third use example, the prediction results by the prediction system 100 are almost the same as the results of a simulation performed by specifying the positions and various parameters of one or more objects 5 in the space 2. In other words, in the third use example, it can be said that the prediction performance of the prediction system 100 for predicting the brightness of the space 2 is almost the same as the prediction performance of the simulation for predicting the brightness of the space 2.

[0076] [advantage] The advantages of the prediction system 100 according to the embodiment will be described below. First, the inventor's viewpoint will be described. For example, in a space such as an office, it is important to enhance the impression of the space by taking into consideration the brightness of the space. Here, the brightness of the space is affected by objects placed in the space, so if the influence of the objects is not taken into consideration, the brightness of the space may be brighter or darker than expected.

[0077] However, in order to take into account the effects of placed objects when designing lighting, it is necessary to perform a simulation using lighting simulation software by specifying the spatial position (coordinates) of the placed object, as well as various parameters such as size and reflectance, which is a very time-consuming process.

[0078] For example, even if the plan is for a space to be an office, with a total of 50 individual desks in the work area, the specifications for each individual desk and their location in the office have not yet been determined, and lighting design often needs to be considered before that. In such cases, the designer must consider the specifications and placement of each individual desk, but it is time-consuming to determine the size and reflectivity of each individual desk and the location (coordinates) of each individual desk without first consulting the client. One reason for this time-consuming process is the difficulty of creating and considering design drawings that leave a physical mark, out of consideration for the client.

[0079] The inventors of the present application have therefore discovered a prediction system 100 that can solve the above-mentioned problem. That is, the prediction system 100 according to the embodiment predicts the brightness of the space 2 when one or more objects 5 are placed using characteristic information that includes information about the light reflection of one or more objects 5 that can be placed in the space 2, but does not include information about the coordinates of the one or more objects 5 in the space 2. Therefore, the prediction system 100 according to the embodiment does not need to consider the placement of the one or more objects 5 in the space 2, and therefore has the advantage of easily predicting the brightness of the space 2 while reducing the effort required for inputting information. In other words, the prediction system 100 according to the embodiment can predict the brightness of the space 2 when one or more objects 5 are placed with the same accuracy as the above simulation, without having to specify in detail the placement and specifications of the one or more objects 5 as in the above simulation.

[0080] (Variation) Although the embodiment has been described above, the present invention is not limited to the above embodiment. Modifications of the embodiment will be listed below. The modifications described below may be combined as appropriate.

[0081] In the above embodiment, the acquisition unit 11 acquires the characteristic information by accepting input on the input screen 141 displayed on the display unit 14, but this is not limiting. For example, the acquisition unit 11 may accept input of the product number of the object 5 and acquire the characteristic information corresponding to the product number from an external database. Furthermore, for example, the acquisition unit 11 may recognize the object 5 from a drawing or catalog in which the object 5 is listed using an appropriate image recognition algorithm, and acquire the characteristic information corresponding to the recognized object 5 from an external database.

[0082] In the above embodiment, the prediction system 100 predicts the brightness of the space 2 on the assumption that the shape and size of the space 2 are fixed, but this is not limiting. For example, the acquisition unit 11 may further acquire shape information indicating the shape of the space 2 or size information indicating the size of the space 2. Then, the determination unit 12 may determine the reflectance information further based on the shape information or the size information.

[0083] Furthermore, for example, in the above embodiment, the prediction system 100 is realized by a single device, but it may be realized by multiple devices. When the prediction system 100 is realized by multiple devices, the components of the prediction system 100 may be distributed among the multiple devices in any manner. For example, in the above embodiment, the prediction system 100 may be provided in a server device or in an information terminal installed in a closed space. In other words, the present invention may be realized by cloud computing or edge computing.

[0084] For example, the communication method between the devices in the above-described embodiment is not particularly limited, and a relay device (not shown) may be used in the communication between the devices.

[0085] In the above-described embodiments, each component may be realized by executing a software program suitable for that component, or by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0086] Furthermore, each component may be realized by hardware. For example, each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or each may be a separate circuit. Furthermore, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0087] Furthermore, the general or specific aspects of the present invention may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0088] For example, the present invention may be realized as a prediction method executed by a computer such as the prediction system 100, as a program for causing a computer to execute such a prediction method, or as a computer-readable non-transitory recording medium on which such a program is recorded.

[0089] In addition, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art would think of, or forms realized by arbitrarily combining the components and functions of each embodiment within the scope that does not deviate from the spirit of the present invention.

[0090] (summary) As described above, the prediction system 100 according to the first aspect includes an acquisition unit 11, a determination unit 12, and a prediction unit 13. The acquisition unit 11 acquires characteristic information that includes information about the light reflection of one or more objects 5 that can be placed in the space 2, but does not include information about the coordinates of the one or more objects 5 in the space 2. The determination unit 12 determines reflection information about the light reflection in the space 2 when the one or more objects 5 are placed, based on the characteristic information. The prediction unit 13 predicts the brightness of the space 2 when the one or more objects 5 are placed, based on the reflection information.

[0091] Such a prediction system 100 has the advantage that it is not necessary to consider the placement of one or more objects 5 in the space 2, and therefore it is easy to predict the brightness of the space 2 when one or more objects 5 are placed, while reducing the effort required for inputting information.

[0092] In the prediction system 100 according to the second aspect, in the first aspect, the determination unit 12 determines the light reflectance of each of the multiple surfaces 3 constituting the space 2 as reflection information. The prediction unit 13 predicts the brightness of the space 2 when one or more objects 5 are placed, based on the light reflectance of each of the multiple surfaces 3.

[0093] According to such a prediction system 100, the brightness of the space 2 can be predicted based only on the reflectance of light on each of the multiple surfaces 3, which has the advantage of making it easy to reduce the processing load.

[0094] In addition, in the prediction system 100 according to the third aspect, in the second aspect, the determination unit 12 determines the light reflectance on each of the multiple surfaces 3 by correcting the initial value of the light reflectance on each of one or more surfaces 3 among the multiple surfaces 3 based on the characteristic information.

[0095] According to such a prediction system 100, the brightness of the space 2 can be predicted based only on the reflectance of light on each of the multiple surfaces 3, which has the advantage of making it easy to reduce the processing load.

[0096] In addition, in the prediction system 100 according to the fourth aspect, in the third aspect, the determination unit 12 determines the light reflectance at any one of the multiple surfaces 3 by correcting the initial value of the light reflectance at the one surface 3 based on the characteristic information.

[0097] Such a prediction system 100 has the advantage that the processing load can be easily reduced compared to when the reflectance of light on all surfaces 3 is corrected.

[0098] In addition, in the prediction system 100 according to the fifth aspect, in any one of the first to fourth aspects, the characteristic information includes information indicating the number of one or more placement objects 5, or information indicating the size of each of the one or more placement objects 5.

[0099] According to such a prediction system 100, the brightness of the space 2 is predicted taking into consideration the number or size of one or more objects 5, which has the advantage of making it easier to improve prediction accuracy.

[0100] In addition, in the prediction system 100 according to the sixth aspect, in any one of the first to fifth aspects, the characteristic information includes information indicating the orientation of each of the one or more objects 5.

[0101] According to such a prediction system 100, the brightness of the space 2 is predicted taking into consideration the orientation of each of the one or more objects 5, which has the advantage of easily improving prediction accuracy.

[0102] In addition, in the prediction system 100 according to the seventh aspect, in any one of the first to sixth aspects, the characteristic information includes information indicating the relative positional relationship of each of one or more objects 5 with respect to the surface 3 that constitutes the space 2.

[0103] Such a prediction system 100 predicts the brightness of the space 2 taking into consideration the relative positional relationship between the surface 3 and each of the one or more objects 5, and therefore has the advantage of easily improving prediction accuracy.

[0104] In addition, in the prediction system 100 according to an eighth aspect, in any one of the first to seventh aspects, the acquisition unit 11 further acquires shape information indicating the shape of the space 2 or size information indicating the size of the space 2. The determination unit 12 determines the reflection information further based on the shape information or the size information.

[0105] According to such a prediction system 100, the brightness of the space 2 is predicted taking into consideration not only the one or more objects 5 but also the shape or size of the space 2, which has the advantage of easily improving prediction accuracy.

[0106] In addition, the prediction system 100 according to a ninth aspect is any one of the first to eighth aspects, and further includes a display unit 14 that displays an input screen 141 for inputting characteristic information. The acquisition unit 11 acquires the characteristic information by accepting input on the input screen 141.

[0107] According to such a prediction system 100, the user can obtain characteristic information by inputting the characteristic information while looking at the input screen 141, which has the advantage that the user can easily obtain the characteristic information that he or she desires.

[0108] Furthermore, a prediction method according to a tenth aspect acquires characteristic information that includes information about the light reflection of one or more objects 5 that may be placed in the space 2, but does not include information about the coordinates of the one or more objects 5 in the space 2. The prediction method also determines reflection information about the light reflection in the space 2 when one or more objects 5 are placed, based on the characteristic information. The prediction method also predicts the brightness of the space 2 when one or more objects 5 are placed, based on the reflection information.

[0109] According to this prediction method, there is no need to consider the placement of one or more objects 5 in the space 2, which has the advantage of making it easier to predict the brightness of the space 2 when one or more objects 5 are placed, while reducing the effort required to input information.

[0110] A program according to an eleventh aspect causes one or more processors to execute the prediction method according to the tenth aspect.

[0111] Such a program has the advantage that it is not necessary to consider the placement of one or more objects 5 in space 2, making it easier to predict the brightness of space 2 when one or more objects 5 are placed, while reducing the effort required to input information. [Explanation of symbols]

[0112] 100 Prediction System 11 Acquisition Department 12 Decision Section 13 Prediction Department 14 Display section 141 Input screen 2 space 3 sides 5 Arrangements

Claims

1. an acquisition unit that acquires characteristic information including information about light reflection of one or more objects that can be arranged in a space, but not including information about coordinates of the one or more objects in the space; a determination unit that determines, based on the characteristic information, reflection information regarding reflection of light in the space when the one or more objects are arranged; a prediction unit that predicts the brightness of the space when the one or more objects are arranged based on the reflection information, Prediction system.

2. the determiner determines, as the reflection information, a reflectance of light on each of a plurality of surfaces that form the space; the prediction unit predicts the brightness of the space when the one or more objects are arranged based on the reflectance of the light on each of the plurality of surfaces. The prediction system of claim 1 .

3. the determiner determines the reflectance of light on each of the plurality of surfaces by correcting an initial value of the reflectance of light on each of one or more surfaces among the plurality of surfaces based on the characteristic information. The prediction system of claim 2 .

4. the determiner determines the reflectance of the light on any one of the surfaces by correcting an initial value of the reflectance of the light on the one surface among the plurality of surfaces based on the characteristic information. The prediction system of claim 3 .

5. The characteristic information includes information indicating the number of the one or more placement objects, or information indicating the size of each of the one or more placement objects. The prediction system according to any one of claims 1 to 4.

6. the characteristic information includes information indicating an orientation of each of the one or more placement objects; The prediction system according to any one of claims 1 to 4.

7. the characteristic information includes information indicating a relative positional relationship between each of the one or more objects and a surface that constitutes the space; The prediction system according to any one of claims 1 to 4.

8. the acquisition unit further acquires shape information indicating a shape of the space or size information indicating a size of the space, The determination unit determines the reflection information further based on the shape information or the size information. The prediction system according to any one of claims 1 to 4.

9. a display unit that displays an input screen for inputting the characteristic information; the acquisition unit acquires the characteristic information by accepting input on the input screen. The prediction system according to any one of claims 1 to 4.

10. Acquire characteristic information that includes information about light reflection of one or more objects that can be arranged in a space, but does not include information about coordinates of the one or more objects in the space; determining reflection information regarding reflection of light in the space when the one or more objects are arranged based on the characteristic information; predicting the brightness of the space when the one or more objects are placed based on the reflection information; Forecasting methods.

11. one or more processors, Executing the prediction method according to claim 10, program.

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