Seat recommendation device, seat recommendation method, and seat recommendation program

Abstract

The disclosed technology makes it possible to recommend seats that achieve both proximity between users and comfort in temperature.　This seat recommendation device inputs user-related data and seat-related data. The user-related data at least specifies predetermined related users related to a user. The seat-related data includes temperature information related to users and seats. The seat recommendation device includes a recommended seat calculation unit that outputs recommendation data indicating recommended seats that achieve both proximity between the users and comfort in temperature.

Description

Seat recommendation device, seat recommendation method, and seat recommendation program

[0001] The disclosed technology relates to a seat recommendation device, a seat recommendation method, and a seat recommendation program.

[0002] In some offices, it is desirable to arrange colleagues who work in a related field close to each other in order to promote communication and thereby increase productivity.

[0003] From a similar perspective, optimization of seating arrangements has been studied for pair programming classes, taking into account the academic ability of students and the proximity of friends, and it is possible to calculate seating arrangements in an office using a similar method (see Non-Patent Document 1).

[0004] On the other hand, because each worker has a different preferred illuminance, an illuminance sensor is installed at each seat and seating arrangements are calculated to match the pre-registered preferred illuminance of each worker (see Non-Patent Document 2).From a similar perspective, it is also possible to grasp or estimate the spatial temperature distribution from sensors in office air conditioning and calculate seating arrangements that match the preferred temperature in a similar way.

[0005] Satoshi Suzuki, Multiple pair programming practice using optimization of pair formation and seating arrangement, Information Processing Society of Japan Research Report. Computer and Education Research Report 2014 (7), 1-8, 2014-11-29 Information Processing Society of Japan, Kazuhiro Miwa et al., Study on seating arrangement method based on worker's preferred illuminance in intelligent lighting, Information Science and Technology Forum 2015

[0006] However, even if a seating arrangement is calculated so that colleagues who should be encouraged to interact are placed close to each other, as in Non-Patent Document 1, there may be cases where the temperature at the seat differs greatly from the temperature preferred by the worker. Conversely, even if a seating arrangement is calculated based on the temperature preferred by the worker, as in Non-Patent Document 2, there may be cases where colleagues who should be encouraged to interact are placed far apart.

[0007] Furthermore, conventional technology assumes that users will sit according to the calculated seating arrangement. However, when considering applications such as recommending seats to workers, only a limited number of workers actually sit as recommended, which can result in cases where the intended improvement is not achieved and the seating arrangement is reversed. Specifically, there can be cases where a worker moves as recommended, but a colleague they want to work with does not move, resulting in them moving farther away, or where they try to move to a seat as recommended, but the seat remains occupied.

[0008] The disclosed technology has been made in consideration of the above points, and aims to provide a seat recommendation device, a seat recommendation method, and a seat recommendation program that are capable of recommending seats that balance proximity between users and thermal comfort.

[0009] A first aspect of the present disclosure is a seat recommendation device that receives user-related data and seat-related data as input, where the user-related data defines at least predetermined related users related to the user, and the seat-related data defines temperature information related to the user and the seat, and includes a recommended seat calculation unit that outputs recommendation data indicating recommended seats that achieve both proximity between users and thermal comfort.

[0010] A second aspect of the present disclosure is a seat recommendation method in which user-related data and seat-related data are input, the user-related data defines at least predetermined related users related to the user, and the seat-related data defines temperature information related to the user and the seat, and a computer executes a process to output recommendation data indicating recommended seats that achieve both proximity between users and thermal comfort.

[0011] The disclosed technology makes it possible to recommend seats that balance proximity between users and thermal comfort.

[0012] FIG. 1 is a block diagram showing the configuration of a seat recommendation device of this embodiment. FIG. 2 is a block diagram showing the hardware configuration of the seat recommendation device. FIG. 3 is a block diagram showing the functional configuration of a recommended seat calculation unit. FIG. 4 is an example of relationship data between workers. FIG. 5 is an example of desired temperature data for workers. FIG. 6 is an example of current location data for workers. FIG. 7 is an example of seat master data. FIG. 8 is an example of spatial temperature distribution data. FIG. 9 is an example of recommendation candidate data. FIG. 10 is an example of distance calculation result data between related parties. FIG. 11 is an example of calculated recommendation data. FIG. 12 is a flowchart showing the flow of seat recommendation processing by the seat recommendation device. FIG. 13 is a flowchart showing details of the recommended seat calculation processing by the recommended seat calculation unit. FIG. 14 is an example of the display result of recommendation data.

[0013] An example of an embodiment of the disclosed technology will be described below with reference to the drawings. Note that the same reference numerals are used to designate identical or equivalent components and parts in each drawing. Also, the dimensional proportions in the drawings are exaggerated for the sake of explanation and may differ from the actual proportions.

[0014] The configuration of this embodiment will be described below. FIG. 1 is a block diagram showing the configuration of a seat recommendation device of this embodiment. The seat recommendation device 100 includes an office worker-related data storage unit 102, a seat-related data storage unit 104, a startup unit 110, a recommended seat calculation unit 112, a recommended data storage unit 114, and a display unit 116. In the following description, an example of a user will be described as an office worker, but the present disclosure is applicable to users in general who reside in spaces other than offices. The office worker-related data is an example of "user-related data" in this disclosure.

[0015] Fig. 2 is a block diagram showing the hardware configuration of the seat recommendation device 100. As shown in Fig. 2, the seat recommendation device 100 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display interface (I / F) 16, and a communication interface (I / F) 17. Each component is connected to each other via a bus 19 so as to be able to communicate with each other.

[0016] The CPU 11 is a central processing unit that executes various programs and controls each part. That is, the CPU 11 reads programs from the ROM 12 or the storage 14 and executes the programs using the RAM 13 as a work area. The CPU 11 controls the above components and performs various arithmetic processing in accordance with the programs stored in the ROM 12 or the storage 14. In this embodiment, a seat recommendation program is stored in the ROM 12 or the storage 14.

[0017] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured by a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data.

[0018] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to input various types of information.

[0019] The display interface 16 is, for example, a liquid crystal display, and displays various information. The display interface 16 may be a touch panel type and function as the input unit 15.

[0020] The communication interface 17 is an interface for communicating with other devices such as terminals, etc. For this communication, for example, a wired communication standard such as Ethernet (registered trademark) or FDDI, or a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark) is used.

[0021] The functional components of the seat recommendation device 100 will be described below. Each functional component is realized by the CPU 11 reading out a seat recommendation program stored in the ROM 12 or storage 14, expanding it in the RAM 13, and executing it. The worker-related data storage unit 102 stores, as worker-related data, relationship data between workers, current seat data of the workers, and desired temperature data of the workers. The seat-related data storage unit 104 stores, as seat-related data, spatial temperature distribution data and seat master data. Examples of how each data is stored will be described later.

[0022] The startup unit 110 starts processing of the recommended seat calculation unit 112 at a specified date and time. The recommended seat calculation unit 112 receives various data from the worker-related data storage unit 102 and the seat-related data storage unit 104, and outputs the calculated recommended seat as recommendation data to the recommended data storage unit 114. The display unit 116 is capable of displaying information on the user interface of a specified device. The display unit 116 receives recommendation data from the recommendation data storage unit 114, and displays the recommended seats of the recommendation data, as well as the seat positions of related users, reference information (temperature conditions at each seat), etc.

[0023] Next, a description will be given of the recommended seat calculation unit 112. The recommended seat calculation unit 112 has a function of calculating recommendation data that achieves both proximity between users and thermal comfort.

[0024] 3 is a block diagram showing the functional configuration of the recommended seat calculation unit 112. The recommended seat calculation unit 112 includes a data acquisition unit 210, a combining unit 220, a temperature difference calculation unit 222, a travel distance calculation unit 224, a recommended candidate generation unit 226, a related-party distance calculation unit 228, an optimization calculation unit 230, and an output unit 232. The data acquisition unit 210 also includes (1) a relationship data acquisition unit 310, (2) a current seat data acquisition unit 312, (3) a desired temperature data acquisition unit 314, (4) a temperature distribution data acquisition unit 316, and (5) a seat master data acquisition unit 318. The data acquisition unit 210 in the recommended seat calculation unit 112 acquires relationship data between workers, current seat data of the workers, and desired temperature data of the workers from the worker-related data storage unit 102 using the respective units (1) to (3). On the other hand, the units (4) and (5) acquire spatial temperature distribution data and seat master data from the seat-related data storage unit 104, respectively.

[0025] The recommended seat calculation unit 112 performs processing using the various data acquired by the data acquisition unit 210. The acquired current seat data of the worker, desired temperature data of the worker, spatial temperature distribution data, and seat master data are combined by the combination unit 220. Then, after processing by the temperature difference calculation unit 222, travel distance calculation unit 224, and recommendation candidate generation unit 226, they become input to the related party distance calculation unit 228 and optimization calculation unit 230. Meanwhile, relationship data between workers becomes input to the related party distance calculation unit 228, and recommendation result data is output to the recommendation data storage unit 114 via the optimization calculation unit 230 and output unit 232. Details of the processing of each unit of the recommended seat calculation unit 112 will be described later in the flow description.

[0026] FIG. 4 is an example of relationship data between workers. For each "worker ID," the "worker ID" of the "related person of the worker" who is a colleague that the worker with that ID wants to work nearby is stored. Alternatively, the "worker ID" of a colleague that the company recommends working nearby is stored. In other words, the IDs of related people for each user are stored. Note that related people associated with workers as shown in FIG. 4 are an example of predetermined related users in the present disclosure. Also, while an example is shown in which "related people of the worker" are stored for each "worker ID," this is not limited to this, and related people may be associated with some workers.

[0027] FIG. 5 shows an example of desired temperature data for an operator. A preferred temperature that is comfortable for the operator is stored for each "operator ID." The temperature may be obtained by input by the operator, or the preferred temperature may be estimated based on the average temperature at a seating position where the operator sits frequently. The desired temperature data is an example of the "user's preferred temperature" in the present disclosure.

[0028] Figure 6 shows an example of current location data for an office worker. The location of the office worker is acquired at regular intervals for each "office worker ID" and stored. As shown in the example, the office can be divided into a mesh and the location recorded as a "mesh ID." However, any method of recording, such as recording as two-dimensional coordinate information, is also acceptable.

[0029] Figure 7 is an example of seat master data. When dividing the office into meshes and recording them, the X and Y coordinates that represent the position of the mesh and the number of people that can be accommodated within that mesh are recorded for each "mesh ID" as in the example. For the X and Y coordinates, a specific reference mesh may be set, and relative coordinate information indicating how many meshes away from that mesh on the X and Y axes, respectively, the position is recorded. The number of people that can be seated or working within that mesh is recorded as the number of people that can be seated or working within that mesh.

[0030] 8 shows an example of spatial temperature distribution data. When recording data by dividing an office into meshes, the temperature measured, predicted, or estimated in each mesh is recorded as the "reproduced temperature" for each "mesh ID" as shown in the example. The humidity distribution data is an example of the "spatial temperature distribution in a space including seats" of the present disclosure.

[0031] 9 shows an example of recommendation candidate data. For each "worker ID" and "mesh ID," the number of people that can be accommodated in that mesh, the temperature difference between the reproduced temperature of that mesh and the preferred temperature of the worker, and the travel distance from that mesh to the mesh where the worker is currently located are recorded. The travel distance can be measured using any scale, such as Euclidean distance or Manhattan distance.

[0032] 10 shows an example of distance calculation result data between related parties, which is output by the related party distance calculation unit 228. All combinations of "office worker ID", "related parties of the office worker" in FIG. 4 linked to the "office worker ID", "mesh ID" that is a recommended candidate for the former, and "mesh ID" that is a recommended candidate for the latter are recorded. In addition, for each combination, the distance between the former and latter meshes is recorded as the "distance between related parties".

[0033] 11 shows an example of calculated recommendation data. The recommendation data records a "mesh ID" recommended for each "worker ID."

[0034] Next, we will explain the operation of the seat recommendation device 100. Fig. 12 is a flowchart showing the flow of seat recommendation processing by the seat recommendation device 100. The seat recommendation processing is performed by the CPU 11 reading out a seat recommendation program from the ROM 12 or the storage 14, expanding it into the RAM 13, and executing it.

[0035] In step S100, the CPU 11 functions as the starting unit 110 and starts the processing of the recommended seat calculation unit 112.

[0036] In step S102, the CPU 11 executes the processing as the recommended seat calculation unit 112, and outputs the recommendation data obtained as a result of the processing to the recommendation data storage unit 114.

[0037] In step S104, the CPU 11 inputs the recommendation data from the recommendation data storage unit 114 to the display unit 116, and displays it in a predetermined display mode.

[0038] The recommended seat calculation process in step S102 will now be described with reference to a flowchart of FIG.

[0039] In step S200, the CPU 11 functions as the data acquisition unit 210 to acquire various data from the worker-related data storage unit 102 and the seat-related data storage unit 104.

[0040] In step S202, the CPU 11 functions as the integrating unit 212 to combine the current seat data of the worker, the desired temperature data of the worker, the spatial temperature distribution data, and the seat master data.

[0041] In step S204, the CPU 11 functions as the temperature difference calculation unit 222 to calculate the temperature difference between the reproduced temperature of the mesh and the preferred temperature of the worker for the combination of the worker and the mesh.

[0042] In step S206, the CPU 11 functions as the movement distance calculation unit 224 to calculate the movement distance from the mesh of the combination to the mesh where the worker is currently located.

[0043] In step S208, the CPU 11 functions as the recommended candidate generating unit 226 to extract records for combinations in which the calculated temperature difference or travel distance is below a preset threshold, and generates recommended candidate data.

[0044] In step S210, the CPU 11 functions as the related person distance calculation unit 228 to combine the recommendation candidate data with the relationship data between the workers, and generate related person distance calculation result data.

[0045] In step S212, the CPU 11, as the optimization calculation unit 230, performs optimization calculation for the combination based on the recommendation candidate data and the distance calculation result data between related parties, and obtains recommendation data indicating recommended seats. In the optimization calculation in this step, optimization is performed using an objective function, and the details of the optimization will be described later.

[0046] In step S214, the CPU 11 functions as the output unit 232 to output the calculated recommendation data to the recommendation data storage unit 114, where it is stored.

[0047] The objective function and constraint equations used in the optimization calculation in step S212 are described below. The objective function and constraints are used to balance proximity between users and thermal comfort for the recommendation data. In the optimization calculation, a combinatorial optimization calculation for recommended seats is performed using an objective function that represents the quality of user seating arrangements and a constraint on the capacity defined for each unit that grasps the seating situation, such as a mesh. For example, the proximity between users is used as an indicator for the objective function that represents the quality of seating arrangements. By using the probability indicating which seat a user will be seated in in the objective function related to the proximity between users and the constraint related to the capacity for each mesh, the proximity between colleagues and the number of people seated can be treated as expected values, and a seating arrangement that takes into account users who do not sit as recommended can be determined.

[0048] In the optimization calculation, the objective function of the following formula (1-1) is used. This objective function minimizes the sum of the expected travel distance per related person calculated for each user. Note that the objective function is not limited to the expected travel distance, and any objective function that expresses the proximity between related people may be used, and an objective function that maximizes the expected travel distance may be set depending on the expression method. ... (1-1)

[0049] Each symbol in the formula will be explained below. i: user number, j: mesh number, k: colleague's user number, l: colleague's mesh number. The user numbers i and k are the numbers of the "worker ID". The mesh number is the number of the "mesh ID". Also, X ｉ，ｊ : Variable for whether or not recommendations are implemented (X ｉ，ｊ = {0, 1}), C ｉ : A set of user numbers of colleagues that user i wants to work with (C ｉ ≠φ), P ｉｊ : Probability that user i is in mesh j, d ｉ，ｊ : the moving distance between mesh j and l. ｉｊ is expressed by the auxiliary formula (1-2) below, and represents the probability divided into cases depending on whether or not the recommendation is implemented. ｉｊ is an example of "a probability representing which seat the user is sitting in" in the present disclosure. ... (1-2)

[0050] Concerning the constraints, the first to fourth constraints shown in the following equations (2-1) to (2-4) will be explained. The constraint shown in the following equation (2-1) indicates that the expected number of people in each mesh must be within the capacity of that mesh (first constraint). Note that N ｊ : The number of people that mesh j can accommodate. ... (2-1)

[0051] The constraint shown in the following equation (2-2) indicates that the mean absolute error between the desired temperature and the reproduced temperature at the recommended destination is within a predetermined range (second constraint). ｉ，ｊ is the absolute value of the deviation between the desired temperature of user i and the reproduced temperature of mesh j. α is the threshold value of the mean absolute error of the temperature, and may be a value different from the threshold value related to the temperature difference described in step S208 above. ... (2-2)

[0052] The constraint shown in the following equation (2-3) indicates that the average movement distance from the user's current location to the recommended destination must be within a certain range (third constraint). θ is the movement distance threshold. j' is the current mesh number. ...(2-3) The constraint shown in the following equation (2-4) is a constraint that the number of recommended destinations per user is one. ｉ，ｊ ), the constraint that a unique mesh j is recommended for each user i is assigned. ... (2-4)

[0053] The above is an explanation of each equation used in the optimization calculation. For example, (the second constraint) is a constraint related to thermal comfort, (the third constraint) is a constraint related to the travel distance from the user's current location, and (the fourth constraint) is a constraint that each user's recommended destination is unique. By calculating a combination of recommended seats that minimizes the objective function related to proximity between colleagues under these constraints, it is possible to obtain a combination of seating arrangements that increases proximity between colleagues while maintaining a certain level of thermal comfort for workers. Calculation methods include, but are not limited to, branch-and-cut algorithms, simulated annealing, tabu search, genetic algorithms, particle swarm optimization, greedy algorithms, and quantum annealing. While the example described above deals with the proximity between users as an objective function and thermal comfort as a constraint, the present invention is not limited to this. For example, the proximity between users may be treated as a constraint and thermal comfort as an objective function, or both the proximity between users and thermal comfort may be treated as objective functions.

[0054] FIG. 14 is an example of the display result of recommendation data. The recommendation data shown in the example is intended to be displayed on a smartphone or tablet device. As display information, for example, (d1) the location of a recommended seat is displayed on a layout image of an office. As additional information, (d2) the user's current location in the office and (d3) the location of related parties (seat locations of related users) may be displayed. Furthermore, as reference information, (d4) the user's preferred temperature, (d5) search / setting of related users, etc. may be displayed. Furthermore, in the example of (d4), an interface is provided that allows a user to request a temperature change in the space where the user is located. Furthermore, as reference information, the temperature status of each seat may be represented on the layout image using a heat map. The location of related parties is an example of the "seat locations of related users." This is an example of reference information.

[0055] As described above, the seat recommendation device 100 of this embodiment makes it possible to recommend seats that satisfy both proximity between users and thermal comfort.

[0056] In the above embodiment, the seat recommendation process executed by the CPU by reading the software (program) may be executed by various processors other than the CPU. Examples of processors in this case include dedicated electrical circuits, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs) whose circuit configuration can be changed after manufacture, graphics processing units (GPUs), and application-specific integrated circuits (ASICs), which are processors having a circuit configuration specifically designed to execute specific processes. The seat recommendation process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, a combination of a CPU and an FPGA, etc.). Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit that combines circuit elements such as semiconductor elements.

[0057] In the above embodiment, the seat recommendation program is pre-stored (installed) in the storage 14, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0058] The following additional notes are provided regarding the above-described embodiments.

[0059] (Supplementary Item 1) A seat recommendation device comprising: a memory; and at least one processor connected to the memory, wherein the processor receives user-related data and seat-related data; the user-related data defines at least predetermined related users related to the user; and the seat-related data defines temperature information related to the user and the seat; and the processor is configured to output recommendation data indicating recommended seats that achieve both proximity between users and thermal comfort.

[0060] (Supplementary Item 2) A non-transitory storage medium storing a program executable by a computer to perform seat recommendation processing, wherein user-related data and seat-related data are input, the user-related data defining at least predetermined related users related to the user, and the seat-related data defining temperature information related to the user and the seat, and the non-transitory storage medium outputs recommendation data indicating recommended seats that achieve both proximity between users and thermal comfort.

Claims

1. A seat recommendation device including: a recommended seat calculation unit that receives user-related data and seat-related data as input, the user-related data defining at least a predetermined related user related to the user, the seat-related data defining temperature information related to the user and the seat, and outputs recommendation data indicating a recommended seat that achieves both proximity between users and thermal comfort.

2. The seat recommendation device according to claim 1, wherein the recommended seat calculation unit calculates the recommended seat using the user's preferred temperature and the spatial temperature distribution of the space including the seat as the seat-related data.

3. The seat recommendation device of claim 1, wherein the recommended seat calculation unit performs a combinatorial optimization calculation of the recommended seats using an objective function that represents the good seating arrangement among users and constraints related to the capacity of the seating arrangement that are determined for each unit that grasps the seating situation, and calculates the recommended seats.

4. The seat recommendation device of claim 3, wherein the recommended seat calculation unit applies at least one of the proximity between the users and the thermal comfort in the objective function, and calculates the recommended seat using a probability representing which seat the user is seated in.

5. The seat recommendation device of claim 1, further comprising a display unit capable of displaying information on a user interface of a specified terminal, the display unit displaying at least one of reference information including the seat position of the related user and the temperature conditions of each seat together with the recommended seat.

6. A seat recommendation method in which a computer executes a process in which user-related data and seat-related data are input, the user-related data defines at least a predetermined related user related to the user, and the seat-related data defines temperature information related to the user and the seat, and recommendation data indicating a recommended seat that achieves both proximity between users and thermal comfort is output.

7. A seat recommendation program for causing a computer to function as each part of the seat recommendation device according to any one of claims 1 to 5.

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