Air conditioning navigation system and air conditioning navigation method

Abstract

This air conditioning navigation system (100) for navigating seating for air conditioning states suited to an air conditioning preference of a user using a facility having air-conditioned spaces in which air conditioners (110) are installed, comprises: a mobile terminal (30) that transmits user characteristic information (321) that indicates the characteristics pertaining to the air conditioning preferences of a user; and a cloud server (20) that, on the basis of the user characteristic information (321) acquired from the mobile terminal (30) and air conditioning characteristic information (211) that indicates the characteristics of air conditioning in the air-conditioned spaces, generates suitable seating information (221) that indicates a seat suited to the air conditioning preferences of the user, and transmits the suitable seating information (221) to the mobile terminal (30).

Description

[Technical Field]

[0001] The present disclosure relates to an air conditioning navigation system that navigates to a seat with an air conditioning state that matches a user's air conditioning preferences, and Sky This invention relates to a navigation method. [Background technology]

[0002] In recent years, the number of offices with free address systems, where employees are not assigned fixed seats and can work freely, has been increasing. It is also common for users to choose their own seats when using facilities such as libraries and cafeterias.

[0003] In offices, libraries, and cafeterias, the indoor air-conditioning environment is not uniform, with some areas being exposed to warm or cool air and others being far from the air conditioner outlet and therefore less effective. It is up to the user whether they prefer a seat with high air-conditioning effectiveness or a seat with low air-conditioning effectiveness. For example, users who are sensitive to cold often prefer to use a seat with low air-conditioning effectiveness when the air conditioner is running, while users who are sensitive to heat often prefer to use a seat with high air-conditioning effectiveness when the air conditioner is running.

[0004] However, it is difficult for a user to determine which seats have a high air conditioning effect and which seats have a low air conditioning effect before sitting down.

[0005] Patent Document 1 discloses an air conditioning navigation system that notifies users who use a free address office of information about seats that will provide the air conditioning environment they desire. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-133175 Summary of the Invention [Problem to be solved by the invention]

[0007] However, the air conditioning navigation system disclosed in Patent Document 1 has the problem that if a user uses multiple offices, the air conditioning navigation cannot be used unless the user's air conditioning preference information is registered in the air conditioning navigation system installed in each office.In other words, the air conditioning navigation system disclosed in Patent Document 1 cannot obtain suitable seat information that indicates seats with air conditioning environments that match the user's air conditioning preferences in multiple facilities unless information about air conditioning preferences is registered for each facility.

[0008] The present disclosure has been made in consideration of the above, and aims to provide an air conditioning navigation system that can obtain suitable seat information indicating seats with air conditioning environments that match a user's air conditioning preferences in multiple facilities, without having to register information regarding air conditioning preferences for each facility. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems and achieve the objectives, the air conditioning navigation system according to the present disclosure is an air conditioning navigation system that navigates a user who uses a facility having an air-conditioned space where an air conditioner is installed to a seat with an air conditioning state that matches the air conditioning preferences, and includes a user terminal that transmits user characteristic information that indicates characteristics related to the user's air conditioning preferences. The air conditioning navigation system includes an information processing device that generates suitable seat information that indicates a seat that matches the user's air conditioning preferences based on the user characteristic information acquired from the user terminal and air conditioning characteristic information that indicates the air conditioning characteristics of the air-conditioned space, and transmits the suitable seat information to the user terminal. [Effects of the Invention]

[0010] The air conditioning navigation system according to the present disclosure has the advantage of being able to obtain suitable seat information indicating seats with air conditioning environments that match the user's air conditioning preferences at multiple facilities without having to register information regarding air conditioning preferences for each facility. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing the configuration of an air conditioning navigation system according to a first embodiment. [Figure 2] 1 is a flowchart showing the flow of operations of the air conditioning navigation system according to the first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of a user characteristic information input screen of the air conditioning navigation system according to the first embodiment; [Figure 4] FIG. 10 is a diagram showing an example of a navigation target facility selection screen of the air conditioning navigation system according to the first embodiment; [Figure 5] FIG. 10 is a diagram showing an example of display of suitable seat information in the air conditioning navigation system according to the first embodiment. [Figure 6] 10 is a flowchart showing the flow of a suitable seat information output process performed by a processing unit of a cloud server of the air conditioning navigation system according to the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of the configuration of a temperature preference point calculation table of the air conditioning navigation system according to the first embodiment; [Figure 8] FIG. 10 is a diagram showing an example of the configuration of a wind-contact allowable point calculation table for the air-conditioning navigation system according to the first embodiment; [Figure 9] FIG. 1 is a diagram showing an example of a floor plan map of the air conditioning navigation system according to the first embodiment. [Figure 10] FIG. 1 is a diagram showing an example of an indoor air conditioner layout map of the air conditioning navigation system according to Embodiment 1. [Figure 11] FIG. 1 is a diagram showing an example of a wind contact map of the air conditioning navigation system according to the first embodiment; [Figure 12] FIG. 1 is a diagram showing an example of a temperature distribution map of the air conditioning navigation system according to the first embodiment. [Figure 13] FIG. 1 is a diagram showing an example of a temperature point map of the air conditioning navigation system according to the first embodiment. [Figure 14] FIG. 10 is a diagram showing an example of a wind contact point map of the air conditioning navigation system according to the first embodiment. [Figure 15]FIG. 10 is a diagram showing an example of suitable seat information of the air conditioning navigation system according to the first embodiment; [Figure 16] FIG. 10 is a diagram showing the configuration of an air conditioning navigation system according to a second embodiment. [Figure 17] 10 is a flowchart showing the operation of the air conditioning navigation system according to the second embodiment. [Figure 18] 10 is a flowchart showing the flow of a suitable seat information output process performed by a processing unit of a cloud server of an air conditioning navigation system according to a second embodiment. [Figure 19] FIG. 10 is a diagram showing the configuration of an air conditioning navigation system according to a third embodiment. [Figure 20] 10 is a flowchart showing the flow of operations of the air conditioning navigation system according to the third embodiment. [Figure 21] FIG. 10 is a diagram showing the configuration of an air conditioning navigation system according to a fourth embodiment. [Figure 22] 10 is a flowchart showing the flow of a suitable seat information output process performed by a processing unit of a cloud server of an air conditioning navigation system according to a fourth embodiment. [Figure 23] FIG. 10 is a diagram showing the configuration of a determination unit of an air conditioning navigation system according to a fourth embodiment. [Figure 24] FIG. 10 is a diagram showing an example of the configuration of a learning device provided in an air conditioning navigation system according to a fourth embodiment. [Figure 25] FIG. 10 is a diagram for explaining a neural network used by the learning device of the air conditioning navigation system according to the fourth embodiment to generate a trained model. [Figure 26] 10 is a flowchart showing an example of the operation of the learning device of the air conditioning navigation system according to the fourth embodiment. [Figure 27] FIG. 10 is a diagram showing an example of the configuration of an inference device of an air conditioning navigation system according to a fourth embodiment. [Figure 28] 10 is a flowchart showing an example of the operation of the inference device provided in the air conditioning navigation system according to the fourth embodiment. [Figure 29] FIG. 10 is a diagram showing the configuration of an air conditioning navigation system according to a fifth embodiment. [Figure 30]10 is a flowchart showing the flow of a process for correcting user characteristic information performed by a processing unit of a mobile terminal in an air conditioning navigation system according to Embodiment 5. [Figure 31] FIG. 10 is a diagram showing an example of the hardware configuration of a cloud server of an air conditioning navigation system according to any one of the first to fifth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0012] The air conditioning navigation system according to the embodiment will be described below. Mu and The air conditioning navigation method will be described in detail with reference to the drawings.

[0013] Embodiment 1 1 is a diagram showing the configuration of an air conditioning navigation system according to Embodiment 1. Air conditioning navigation system 100 includes facility air conditioning equipment 10, a cloud server 20, and a mobile terminal 30.

[0014] The facility air conditioning equipment 10 includes a plurality of air conditioners 110 and an air conditioning control device 120 that controls each of the air conditioners 110. The air conditioners 110 and the air conditioning control device 120 are connected to a cloud server 20. The connection between the air conditioners 110 and the air conditioning control device 120 and the cloud server 20 is realized, for example, via the Internet.

[0015] The air conditioner 110 transmits temperature distribution information 111 to the cloud server 20. The temperature distribution information 111 is the temperature value for each area when the air-conditioned space is subdivided. The temperature distribution information 111 can be acquired by a thermal image sensor attached to the air conditioner 110, or by placing multiple temperature sensors in the space and acquiring the information via communication, but the acquisition method and data format are not important.

[0016] The air conditioner 110 transmits setting information 119 to the air conditioning control device 120. The setting information 119 includes a room temperature setting, a wind direction setting, and a wind speed setting.

[0017] The air conditioning control device 120 transmits air conditioning control information 121 to the cloud server 20. The air conditioning control information 121 is information indicating the operating status of each device that makes up the in-facility air conditioning equipment 10 and the impact of that operation on the air-conditioned space. The air conditioning control information 121 includes at least one of the room temperature setting of the air conditioner 110, the air direction setting of the air conditioner 110, and the air speed setting of the air conditioner 110.

[0018] Cloud server 20, which is an information processing device, is a server communicably connected to facility air conditioning equipment 10 and mobile terminal 30. Cloud server 20 includes a storage unit 210 that stores information, and a processing unit 220 that performs arithmetic processing using the information stored in storage unit 210.

[0019] The storage unit 210 of the cloud server 20 holds air conditioning characteristic information 211. The air conditioning characteristic information 211 includes spatial structure information 212, temperature distribution information 111, and air conditioning control information 121. The spatial structure information 212 includes the floor plan of the air conditioned space and the installation positions of the air conditioners 110 in the air conditioned space.

[0020] The cloud server 20 receives the suitable seat acquisition request 311 and the user characteristic information 321 from the mobile terminal 30, and performs calculations in the processing unit 220 using the received information and the air conditioning characteristic information 211 stored in the storage unit 210, and outputs the suitable seat information 221. The cloud server 20 also transmits the suitable seat information 221 to the mobile terminal 30.

[0021] The mobile terminal 30 is a user terminal operated by a user. The mobile terminal 30 includes an input unit 310, a storage unit 320, a display unit 330, and a processing unit 350. The mobile terminal 30 is capable of communicating with the cloud server 20. The connection between the mobile terminal 30 and the cloud server 20 is realized, for example, via the Internet. The mobile terminal 30 is configured, for example, by a smartphone and an application installed on the smartphone, but may have a configuration different from that illustrated. Also, although the user terminal is configured as a mobile terminal 30 in this example, the user terminal does not necessarily have to be portable. For example, the user terminal may be a desktop computer terminal.

[0022] The mobile terminal 30 holds user characteristic information 321 that indicates the user's characteristics related to air conditioning preferences. The user characteristic information 321 includes at least one of the user's age, the user's sex, the user's temperature preference, the user's wind preference, and the user's physical condition. The user's temperature preference is information that indicates whether the user is sensitive to heat or cold. The user's wind preference is information that indicates the user's preference for wind.

[0023] The input unit 310 of the mobile terminal 30 accepts input operations by the user. At least one piece of information constituting the user characteristic information 321 stored in the storage unit 320 is input by the user through the input unit 310. In addition, the user of the mobile terminal 30 can transmit the suitable seat acquisition request 311 and the user characteristic information 321 to the cloud server 20 by operating the input unit 310.

[0024] The mobile terminal 30 displays the suitable seat information 221 received from the cloud server 20 on the display unit 330 .

[0025] Next, the operation of the air conditioning navigation system 100 pertaining to Embodiment 1 will be described. FIG. 2 is a flowchart showing the flow of operation of the air conditioning navigation system pertaining to Embodiment 1. Prior to executing the operation shown in FIG. 2, spatial structure information 212, such as the floor plan of the air-conditioned space, is saved in the memory unit 210 of the cloud server 20 by operation by the administrator of the air conditioner 110. Furthermore, the air conditioning control device 120 sends air-conditioning control information 121 to the cloud server 20 every time the control state of the air conditioner 110 changes, and stores the air-conditioning control information 121 in the memory unit 210 of the cloud server 20. Furthermore, the air conditioner 110 periodically acquires temperature distribution information 111 of the air-conditioned space using a thermal image sensor, sends it to the cloud server 20, and stores the temperature distribution information 111 in the memory unit 210 of the cloud server 20.

[0026] In step S1, the mobile terminal 30 accepts an input operation of user characteristic information 321 via the input unit 310 and stores the user characteristic information 321 in the storage unit 320. FIG. 3 is a diagram showing an example of a user characteristic information input screen of the air conditioning navigation system according to Embodiment 1. The user characteristic information input screen 400 includes an age input field 401, a gender input field 402, a cooling room temperature preference input field 403, a heating room temperature preference input field 404, and a wind tolerance input field 405. By pressing a set button 406 after entering information in each input field, the user characteristic information 321 is stored in the storage unit 320. Furthermore, by pressing a cancel button 407, the user can cancel the input of the user characteristic information 321.

[0027] In step S2, the mobile terminal 30 accepts an operation on the input unit 310 to select a navigation target facility and an operation to request acquisition of suitable seat information 221. FIG. 4 is a diagram showing an example of a navigation target facility selection screen of the air conditioning navigation system according to embodiment 1. The navigation target facility selection screen 410 includes an input from map button 411, an address search button 412, a facility name search button 413, an address input field 414, and a facility name input field 415. The navigation target facility selection screen 410 also includes a physical condition input field 416, a basic information setting button 417, a number of seats input field 418, and a suitable seat display button 419. The user's physical condition is included in the user characteristic information 321, but since this information changes from day to day, a physical condition input field is provided on the navigation target facility selection screen 410 so that the user's physical condition can be set at the same time as selecting a navigation target facility. Note that the physical condition input field 416 may be provided on the user characteristic information input screen 400 shown in FIG. 3. The user characteristic information 321 includes the user's age, sex, temperature preference, and wind preference, which are set on the user characteristic information input screen 400 shown in FIG. 3, as well as the user's physical condition, which is set on the navigation target facility selection screen 410 shown in FIG. 4.

[0028] By pressing the map input button 411, the user can select a facility to be navigated from the map. Furthermore, by inputting the address of the facility to be navigated in the address input field 414 and then pressing the address search button 412, the user can select the facility located at the address input in the address input field 414 as the facility to be navigated. Furthermore, by inputting the facility name in the facility name input field 415 and then pressing the facility name search button 413, the user can select the facility whose name has been input in the facility name input field 415 as the facility to be navigated.

[0029] In step S3, the mobile terminal 30 transmits the user characteristic information 321 and the identifier 322 of the facility to be navigated to the cloud server 20 together with the suitable seat acquisition request 311. When the suitable seat display button 419 is pressed with the facility to be navigated selected, the user characteristic information 321 and the identifier 322 of the facility to be navigated to the cloud server 20 together with the suitable seat acquisition request 311.

[0030] In step S4, the cloud server 20 reads out the air conditioning characteristic information 211 of the facility indicated by the identifier 322 of the facility to be navigated from the storage unit 210, and generates suitable seat information 221 indicating a seat that suits the user's air conditioning preferences based on the air conditioning characteristic information 211 of the facility to be navigated and the received user characteristic information 321.

[0031] In step S5, the cloud server 20 transmits the suitable seat information 221 to the mobile terminal 30 in response to the suitable seat acquisition request 311.

[0032] In step S6, the mobile terminal 30 displays the suitable seat information 221 received from the cloud server 20 on the display unit 330. FIG. 5 is a diagram showing an example of the display of suitable seat information in the air conditioning navigation system according to embodiment 1. The suitable seat information 221 includes a facility map 421 and a suitable seat list 422. The facility map 421 is a floor plan of the facility to be navigated, showing the layout of tables and seats. The suitable seat list 422 displays a list of information about seats in the facility to be navigated that are suitable for the user's air conditioning preferences. The mobile terminal 30 may also display information used to output the suitable seat information 221 on the display unit 330, such as a wind pressure map 453 shown in FIG. 11 and a temperature distribution map 454 shown in FIG. 12.

[0033] FIG. 6 is a flowchart showing the flow of the suitable seat information output process performed by the processing unit of the cloud server of the air conditioning navigation system according to Embodiment 1. In step S201, the processing unit 220 calculates temperature preference points. FIG. 7 is a diagram showing an example of the configuration of a temperature preference point calculation table for the air conditioning navigation system according to Embodiment 1. The processing unit 220 calculates temperature preference points based on the temperature preference point calculation table 430 and the received user characteristic information 321. The temperature preference point calculation table 430 includes a correction value table 431 that associates answers to questions about age, gender, preference for cooled room temperature, preference for heated room temperature, and physical condition with temperature preference correction values, and a conversion table 432 that associates the sum of the temperature preference correction values ​​based on the answers to each question with temperature preference points. The correction value table 431 defines separate temperature preference correction values ​​for cooling and heating. The temperature preference points can be calculated by referring to the correction value table 431, obtaining and adding up the temperature preference correction values ​​based on the answers to each question, and then referring to the conversion table 432 to convert the sum of the temperature preference correction values ​​into temperature preference points.

[0034] Next, in step S202, the processing unit 220 calculates the wind resistance tolerance point. Fig. 8 is a diagram showing an example of the configuration of a wind resistance tolerance point calculation table for the air conditioning navigation system according to embodiment 1. The processing unit 220 calculates the wind resistance tolerance point based on the wind resistance tolerance point calculation table 440 and the received user characteristic information 321. The wind resistance tolerance point calculation table 440 is a table that associates the degree to which the user is bothered by the wind from the air conditioner with the wind resistance tolerance point.

[0035] In step S203, the processing unit 220 acquires the air conditioning characteristic information 211 of the facility to be navigated from the storage unit 210. The air conditioning characteristic information 211 includes, for example, a floor plan map 451, an indoor air conditioner layout map 452, a wind exposure map 453, and a temperature distribution map 454.

[0036] 9 is a diagram showing an example of a floor plan map of the air conditioning navigation system according to Embodiment 1. Floor plan map 451 associates the position of tables in a room with the number of seats.

[0037] 10 is a diagram showing an example of an indoor air conditioner layout map of the air conditioning navigation system according to Embodiment 1. The indoor air conditioner layout map 452 associates the positions of the air conditioners 110 in the room with information indicating the characteristics of the air conditioners 110. The information indicating the characteristics of the air conditioners 110 includes the number of air outlets and the direction correction angle.

[0038] 11 is a diagram showing an example of a wind contact map of the air conditioning navigation system according to Embodiment 1. Wind contact map 453 associates positions in a room with wind contact points at each position. The wind contact points are numerical representations of the strength of the wind contact from the air conditioner 110, with larger point values ​​indicating stronger wind contact from the air conditioner 110.

[0039] 12 is a diagram showing an example of a temperature distribution map of the air conditioning navigation system according to Embodiment 1. Temperature distribution map 454 associates positions in a room with temperature points at each position. The temperature points are numerical values ​​obtained by dividing the temperature at that position into temperature ranges, with larger point values ​​indicating higher temperature ranges.

[0040] In step S204, the processing unit 220 overlays the floor plan map 451 and the temperature distribution map 454 to create a temperature point map 455 that shows the temperature points for each seat. Figure 13 is a diagram showing an example of the temperature point map of the air conditioning navigation system according to Embodiment 1. The temperature point map 455 shows the temperature points at the positions where each seat in the room is installed.

[0041] In step S205, the processing unit 220 overlays the floor plan map 451 and the wind exposure map 453 to create a wind exposure point map 456 that shows the wind exposure point for each seat. Figure 14 is a diagram showing an example of the wind exposure point map of the air conditioning navigation system according to Embodiment 1. The wind exposure point map 456 shows the wind exposure points at the positions where each seat in the room is installed.

[0042] In step S206, the processing unit 220 extracts seats whose temperature preference points match in the temperature point map 455 and whose wind exposure point is equal to or lower than the wind exposure tolerance point in the wind exposure point map 456, and generates suitable seat information 221. Fig. 15 is a diagram showing an example of suitable seat information of the air conditioning navigation system according to embodiment 1.

[0043] In the air conditioning navigation system 100 according to the first embodiment, for example, if a 30-year-old female user who is in normal physical condition and who is sensitive to heat and wants to avoid the wind from the air conditioner visits a restaurant in the summer when the air conditioner is operating, the temperature preference point calculation table 430 shown in Fig. 7 calculates the temperature preference point to be 4. Furthermore, the wind resistance tolerance point calculation table 440 shown in Fig. 8 calculates the wind resistance tolerance point to be 0. Here, if the user wishes to use a table with four seats at a restaurant for which the floor plan map 451 shown in Fig. 9, the indoor air conditioner layout map 452 shown in Fig. 10, the wind resistance map 453 shown in Fig. 11, and the temperature distribution map 454 shown in Fig. 12 are set as the air conditioning characteristics information 211, the suitable seats are G11, G14, G15, G17, G21, J11, J24, M11, M20, M21, P11, P12, P18, P20, and Q4. As shown in FIG. 5, display unit 330 of mobile terminal 30 displays suitable seat information 221 including a seating layout in a restaurant and a list of seats that suit the temperature preference of the user.

[0044] The air conditioning navigation system 100 according to the first embodiment can obtain suitable seat information 221 that indicates seats with air conditioning environments that match the user's air conditioning preferences at multiple facilities, without the need to register user characteristic information 321, which is information about the user's air conditioning preferences, for each facility. By displaying the suitable seat information 221 on the mobile terminal 30, the air conditioning navigation system 100 according to the first embodiment can easily recognize where the seats that match the user's temperature preferences are located, even in a facility that the user is using for the first time.

[0045] Embodiment 2 16 is a diagram showing the configuration of an air conditioning navigation system according to Embodiment 2. Air conditioning navigation system 100 according to Embodiment 2 differs from air conditioning navigation system 100 according to Embodiment 1 in that storage unit 210 of cloud server 20 stores past temperature distribution information 213 and past air conditioning control information 214.

[0046] The past temperature distribution information 213 is stored for a certain period at predetermined intervals going back in time to the values ​​of the temperature distribution information 111. For example, the value of the temperature distribution information 111 for each hour is stored for one year.

[0047] Past air conditioning control information 214 is stored for a fixed period at predetermined intervals going back in time to the values ​​of air conditioning control information 121. For example, hourly values ​​of air conditioning control information 121 are stored for one year.

[0048] Figure 17 is a flowchart showing the operation of the air conditioning navigation system according to embodiment 2. Prior to executing the operation shown in Figure 17, spatial structure information 212, such as the floor plan of the air-conditioned space, is saved in the storage unit 210 of the cloud server 20. Furthermore, the air conditioning control device 120 sends air-conditioning control information 121 to the cloud server 20 every time the control state of the air conditioner 110 changes, and stores the air-conditioning control information 121 in the storage unit 210 of the cloud server 20. Furthermore, the air conditioner 110 periodically acquires temperature distribution information 111 of the air-conditioned space using a thermal image sensor, sends this information to the cloud server 20, and stores the temperature distribution information 111 in the storage unit 210 of the cloud server 20. Furthermore, when the cloud server 20 receives air-conditioning control information 121 from the air-conditioning control device 120, it saves the air-conditioning control information 121 it already holds in the storage unit 210 as past air-conditioning control information 214, and then stores the received air-conditioning control information 121 in the storage unit 210. In addition, when the cloud server 20 receives the temperature distribution information 111 from the air conditioner 110, it saves the temperature distribution information 111 that it already holds in the memory unit 210 as past temperature distribution information 213, and then stores the received temperature distribution information 111 in the memory unit 210.

[0049] The process of step S11 is the same as the process of step S1 in the air conditioning navigation system 100 according to the first embodiment.

[0050] In step S12, the mobile terminal 30 accepts, on the input unit 310, an operation to select a facility to be navigated, an operation to select a date and time of use, and an operation to request acquisition of suitable seat information 221.

[0051] In step S13, the mobile terminal 30 transmits the user characteristic information 321, the date and time of use, and the identifier 322 of the facility to be navigated to, together with the suitable seat acquisition request 311, to the cloud server 20.

[0052] In step S14, the cloud server 20 generates suitable seat information 221 for the date and time of use based on the air conditioning characteristic information 211 of the facility to be navigated and the received user characteristic information 321.

[0053] The processing in steps S15 and S16 is the same as the processing in steps S5 and S6 in the air conditioning navigation system 100 according to the first embodiment.

[0054] 18 is a flowchart showing the flow of the suitable seat information output process by the processing unit of the cloud server of the air conditioning navigation system according to Embodiment 2. The air conditioning navigation system 100 according to Embodiment 2 predicts future air conditioning states using past temperature distribution information 213 and past air conditioning control information 214, and uses this to calculate suitable seat information 221.

[0055] The processing in steps S211 and S212 is the same as the processing in steps S201 and S202 in the air conditioning navigation system 100 according to the first embodiment.

[0056] In step S213, the processing unit 220 acquires the spatial structure information 212, past temperature distribution information 213, and past air-conditioning control information 214 of the target facility from the storage unit 210, and extracts from this data that is predicted to match the air-conditioning state at the user-specified date and time. For example, if the user-specified time is 11:00 on August 6th and the current time is 15:00 on August 5th, one method would be to simply apply the temperature distribution information 111 and air-conditioning control information 121 recorded at 11:00 on August 5th, the same time as the user-specified time, as predicted data, but the prediction method is not critical.

[0057] The processes in steps S214, S215, and S216 are the same as the processes in steps S204, S205, and S206 in the air conditioning navigation system 100 according to the first embodiment.

[0058] In the above explanation, the processing unit 220 calculates the air conditioning characteristic information 211 at a future time point based on the past temperature distribution information 213 and the past air conditioning control information 214. However, if a control schedule for the air conditioner 110 is stored in the storage unit 210 of the cloud server 20, the processing unit 220 may calculate the air conditioning characteristic information 211 at a future time point based on the control schedule stored in the storage unit 210. Furthermore, the processing unit 220 may calculate the air conditioning characteristic information 211 at a future time point based on the control schedule stored in the storage unit 210 in addition to the past temperature distribution information 213 and the past air conditioning control information 214.

[0059] The air conditioning navigation system 100 according to the second embodiment allows the user to specify a future date and time to obtain a suitable seat. Therefore, if the user is unable to secure a seat that suits their air conditioning preferences at the specified time, the user can take measures such as changing the facility they are using.

[0060] Embodiment 3 19 is a diagram showing the configuration of an air conditioning navigation system according to embodiment 3. This system differs from air conditioning navigation system 100 according to embodiment 2 in that mobile terminal 30 accepts input operations on input unit 310 for inputting post-use impressions 340, transmits post-use impressions 340 to cloud server 20, and is equipped with processing unit 350 that analyzes post-use impressions 340 and performs processing to correct the value of user characteristic information 321. The air conditioning navigation system 100 according to embodiment 3 differs from air conditioning navigation system 100 according to embodiment 2 in that memory unit 210 of cloud server 20 stores air conditioning requests 230.

[0061] The post-use impressions 340 are impressions of users after using the facility based on the suitable seat information 221. The post-use impressions 340 include at least one of dissatisfaction with the room temperature and dissatisfaction with the ventilation. The air conditioning requests 230 are data summarizing the post-use impressions 340 of users who use the air conditioning navigation system 100.

[0062] 20 is a flowchart showing the operational flow of the air conditioning navigation system according to Embodiment 3. The processing from step S21 to step S26 is the same as the processing from step S1 to step S6 of the air conditioning navigation system 100 according to Embodiment 1. In step S27, the mobile terminal 30 accepts an input operation on the input unit 310 for inputting impressions 340 after use.

[0063] In step S28, the mobile terminal 30 analyzes the post-use impression 340 in the processing unit 350 and corrects the user characteristic information 321 so that suitable seats that meet the user's preferences can be presented from the next time onwards. For example, in the air conditioning navigation system 100 that uses the temperature preference point calculation table 430 shown in Fig. 7, if the impression that the room was cold is input in the summer when air conditioning is in operation, one method of correcting the user characteristic information 321 is to subtract 1 from the cooling temperature preference correction value for the air-conditioned room temperature preference, but the method of correcting the user characteristic information 321 is not limited.

[0064] In step S29, the mobile terminal 30 transmits the identifier 322 of the facility to be navigated, the seat used, and the post-use feedback 340 to the cloud server 20.

[0065] In step S30, the cloud server 20 updates the air conditioning request 230 to reflect the post-use feedback 340 received from the mobile terminal 30. For example, one method is to divide the air-conditioned space into five areas, and then update the air conditioning request 230 by aggregating the feedback of the most recent 20 users belonging to each area using a moving average, but the method for updating the air conditioning request 230 is not critical.

[0066] In step S31, the cloud server 20 notifies the air conditioner manager of the updated content of the air conditioning request 230 by means of e-mail or the like.

[0067] The air conditioning navigation system 100 according to the third embodiment corrects the user characteristic information 321 based on the post-use impressions 340 entered by the user, and can therefore recommend to the user a seat that better suits the user's air conditioning preferences.

[0068] Embodiment 4 21 is a diagram showing the configuration of an air conditioning navigation system according to Embodiment 4. Air conditioning navigation system 100 according to Embodiment 4 differs from air conditioning navigation system 100 according to Embodiment 2 in that a trained model 240 for estimating and correcting air conditioning characteristic information 211 is stored in storage unit 210 of cloud server 20, and processing unit 220 includes determination unit 52.

[0069] In the air conditioning navigation system 100 according to the first to third embodiments, suitable seat positions are selected using the air conditioning characteristic information 211 received from the air conditioner 110 and the air conditioning control device 120, and the air conditioning characteristic information 211 set by the air conditioner manager, but the air conditioning navigation system 100 according to the fourth embodiment selects suitable seat positions using machine learning.

[0070] In the air conditioning navigation system 100 according to the fourth embodiment, the determination unit 52 receives as input the floor plan of the space to be air-conditioned, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature, and estimates or corrects the air conditioning characteristic information 211 using the trained model 240, and outputs suitable seat information 221.

[0071] 22 is a flowchart showing the flow of a suitable seat information output process by a processing unit of a cloud server of an air conditioning navigation system according to Embodiment 4. In Embodiment 4, the processing unit 220 estimates or corrects the air conditioning characteristic information 211 using the trained model 240, and uses it to calculate the suitable seat information 221.

[0072] The processing of steps S221 and S222 is the same as the processing of steps S201 and S202 of the air conditioning navigation system 100 according to embodiment 1. In step S223, the processing unit 220 acquires the air conditioning characteristic information 211 of the target facility and the trained model 240 from the memory unit 210, and estimates or corrects the air conditioning characteristic information 211 using the trained model 240, which infers the wind exposure and indoor temperature distribution at each position in the room, using as input the floor plan of the air-conditioned space, the installation positions of the air conditioners 110, the operating status of the air conditioners 110, and the outside air temperature.

[0073] For example, one method is to use a floor plan map 451 of the target facility shown in Figure 9 and an indoor air conditioner layout map 452 shown in Figure 10 as input, estimate the wind exposure and indoor temperature distribution at each position in the room using the trained model 240, and automatically generate a wind exposure map 453 shown in Figure 11 and a temperature distribution map 454 shown in Figure 12 for use in subsequent calculations, but any estimation method or correction method is acceptable. The learning operation of the trained model 240 by the determination unit 52 and the inference operation using the trained model 240 will be described later.

[0074] The processes in steps S224, S225, and S226 are the same as the processes in steps S204, S205, and S206 in the air conditioning navigation system 100 according to the first embodiment.

[0075] 23 is a diagram showing the configuration of a determination unit of an air conditioning navigation system according to Embodiment 4. The determination unit 52 includes a learning device 60 and an inference device 61.

[0076] Fig. 24 is a diagram showing an example configuration of a learning device provided in the air conditioning navigation system according to embodiment 4. The learning device 60 includes a data acquisition unit 601 and a model generation unit 602. The trained model storage unit 250 shown in Fig. 24 is the part of the storage unit 210 shown in Fig. 21 that stores the trained model 240.

[0077] The data acquisition unit 601 acquires learning data including the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature. In other words, the learning data includes the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature.

[0078] The model generation unit 602 uses the learning data output from the data acquisition unit 601 to learn the relationship between the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature, and the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space, and the temperature distribution, which is the temperature values ​​for each region when the air-conditioned space is subdivided. In other words, the model generation unit 602 generates a trained model 240 for inferring the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each region when the air-conditioned space is subdivided, from the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature.

[0079] Here, the learning data is data that correlates the floor plan of the air-conditioned space, the installation location of the air conditioner 110, the operating status of the air conditioner 110, and the outside air temperature with the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided.

[0080] The learning device 60 is used to learn the relationship between the layout of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature, and the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space, and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, but it may also be configured to operate as a device separate from the cloud server 20, for example, connected to the cloud server 20 via a network.

[0081] The learning algorithm used by the model generation unit 602 can be a known algorithm such as supervised learning, unsupervised learning, reinforcement learning, etc. As an example, a case where a neural network is applied will be described.

[0082] The model generation unit 602 learns, for example, by so-called supervised learning in accordance with a neural network model, the relationship between the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, the outside air temperature, the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space, and the temperature distribution, which is the temperature value for each area when the air-conditioned space is subdivided. Here, supervised learning refers to a method of providing the learning device 60 with data pairs of input and labels, which are the results, to learn the features of the learning data and infer the results from the input.

[0083] The neural network is composed of an input layer 71 consisting of a plurality of neurons, an intermediate layer 72 consisting of a plurality of neurons, and an output layer 73 consisting of a plurality of neurons. The intermediate layer 72, which is a hidden layer, may be one layer, or two or more layers.

[0084] Fig. 25 is a diagram illustrating a neural network used by the learning device of the air conditioning navigation system according to embodiment 4 to generate a trained model. For example, in a three-layer neural network such as the one shown in Fig. 25, when multiple inputs are input to neurons X1, X2, and X3 in input layer 71, the input values ​​are multiplied by weights w11, w12, w13, w14, w15, and w16 before being input to neurons Y1 and Y2 in intermediate layer 72. The results are then further multiplied by weights w21, w22, w23, w24, w25, and w26 before being output from neurons Z1, Z2, and Z3 in output layer 73. This output result varies depending on the values ​​of weights w11, w12, w13, w14, w15, and w16 and weights w21, w22, w23, w24, w25, and w26.

[0085] The neural network used in the model generation unit 602 of the learning device 60 according to the fourth embodiment learns, by so-called supervised learning, the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, in accordance with learning data created based on a combination of the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature, which are acquired by the data acquisition unit 601, and the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided.

[0086] That is, the neural network learns by inputting the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating status of the air conditioner 110, and the outside temperature into the input layer 71, and adjusting the weights w11, w12, w13, w14, w15, w16 and weights w21, w22, w23, w24, w25, w26 so that the results output from the output layer 73 approximate the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided.

[0087] The model generation unit 602 generates and outputs the trained model 240 by executing the above-described learning.

[0088] The trained model storage unit 250 stores the trained model 240 output from the model generation unit 602.

[0089] Next, the operation of the learning device 60 to generate the trained model 240 will be described with reference to Fig. 26. Fig. 26 is a flowchart showing an example of the operation of the learning device of the air conditioning navigation system according to the fourth embodiment.

[0090] In step S111, the learning device 60 acquires learning data. Specifically, the data acquisition unit 601 acquires learning data including the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, the outside air temperature, the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space, and the temperature distribution, which is the temperature value for each region when the air-conditioned space is subdivided. Note that the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature are acquired simultaneously with the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided. However, it is sufficient if the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature are acquired in association with the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided. The floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature, the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, may each be acquired at different times.

[0091] In step S112, the learning device 60 performs a learning process. Specifically, the model generation unit 602 learns the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the temperature distribution, which is the temperature values ​​for each region when the air-conditioned space is subdivided, at each position in the air-conditioned space corresponding to the floor plan, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the temperature distribution, which is the temperature values ​​for each region when the air-conditioned space is subdivided, by so-called supervised learning, in accordance with learning data including the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature acquired by the data acquisition unit 601, and the temperature distribution, which is the temperature values ​​for each region when the air-conditioned space is subdivided, and generates a learned model 240.

[0092] In step S113, the learning device 60 stores the trained model 240 in the trained model storage unit 250. Specifically, the model generation unit 602 outputs the trained model 240 generated in step S112, and the trained model storage unit 250 stores the trained model 240.

[0093] In this embodiment, the case where supervised learning is applied to the learning algorithm used by the model generation unit 602 of the learning device 60 has been described, but the present invention is not limited to this. As for the learning algorithm, reinforcement learning, unsupervised learning, semi-supervised learning, or the like can also be applied in addition to supervised learning.

[0094] Furthermore, the learning algorithm used in the model generation unit 602 can be deep learning, which learns to extract the features themselves, or machine learning can be performed according to other known methods, such as genetic programming, functional logic programming, or support vector machines.

[0095] Fig. 27 is a diagram showing an example configuration of an inference device of an air conditioning navigation system according to embodiment 4. The inference device 61 includes a data acquisition unit 611 and an inference unit 612. The trained model storage unit 250 shown in Fig. 27 is the same as the trained model storage unit 250 shown in Fig. 24.

[0096] The data acquisition unit 611 acquires the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature. The floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature acquired by the data acquisition unit 611 are similar to the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature acquired by the data acquisition unit 601 of the learning device 60.

[0097] The inference unit 612 uses the learned model 240 stored in the learned model memory unit 250 to infer the strength of the wind from the air conditioner 110 at each position in the air conditioned space corresponding to the floor plan, the installation location of the air conditioner 110, the operating state of the air conditioner 110, and the outside air temperature, and the temperature distribution, which is the temperature values ​​for each area when the air conditioned space is subdivided. That is, the inference unit 612 inputs the floor plan of the air-conditioned space, the installation location of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature acquired by the data acquisition unit 611 into the trained model 240, and infers the floor plan of the air-conditioned space, the installation location of the air conditioner 110, the operating state of the air conditioner 110, and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, at each position in the air-conditioned space corresponding to the outside temperature, and acquires as inference results the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, and the strength of the wind from the air conditioner 110 at each position in the air-conditioned space inferred from the floor plan of the air-conditioned space, the installation location of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature.

[0098] In this embodiment, it has been described that the trained model 240 generated by the learning device 60 in the cloud server 20 constituting the air conditioning navigation system 100 is used to output the strength of the wind blowing from the air conditioner 110 at each position in the air conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air conditioned space is subdivided. However, it is also possible to obtain a trained model 240 generated outside the air conditioning navigation system 100 and use this trained model 240 to output the strength of the wind blowing from the air conditioner 110 at each position in the air conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air conditioned space is subdivided.

[0099] 28 is a flowchart showing an example of the operation of the inference device provided in the air conditioning navigation system according to Embodiment 4. In step S121, the inference device 61 acquires data used to infer the air conditioning characteristic information 211. Specifically, the data acquisition unit 611 acquires the floor plan of the air-conditioned space, the installation positions of the air conditioners 110, the operating status of the air conditioners 110, and the outside temperature.

[0100] In step S122, the inference device 61 inputs the data acquired in step S121 into the learned model 240 stored in the learned model storage unit 250, and obtains an inference result. That is, the inference unit 612 inputs the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the outside temperature acquired by the data acquisition unit 611 into the learned model 240 stored in the learned model storage unit 250, and accordingly acquires the floor plan of the air-conditioned space, the installation position of the air conditioner 110, the operating state of the air conditioner 110, and the temperature distribution, which is the temperature value for each region when the air-conditioned space is subdivided, output from the learned model 240.

[0101] In step S123, the inference device 61 outputs the inference result.

[0102] The air conditioning navigation system 100 according to the fourth embodiment selects suitable seat positions using air conditioning characteristic information 211, which uses machine learning to infer the strength of the wind blowing from the air conditioner 110 at each position in the air conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air conditioned space is subdivided, and can therefore guide the user to a seat that suits the user's temperature preferences.

[0103] In the above explanation, an example was given of a configuration in which machine learning is used to infer the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space and the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided, but the configuration may also be such that machine learning is used to infer either the strength of the wind blowing from the air conditioner 110 at each position in the air-conditioned space or the temperature distribution, which is the temperature values ​​for each area when the air-conditioned space is subdivided.

[0104] Embodiment 5. 29 is a diagram showing the configuration of an air conditioning navigation system according to embodiment 5. The air conditioning navigation system 100 according to embodiment 5 differs from the air conditioning navigation system 100 according to embodiment 3 in that the memory unit 210 of the cloud server 20 stores a trained model 241 for correcting user characteristic information 321, the memory unit 320 of the mobile terminal 30 stores suitable seat information 221, and the processing unit 350 of the mobile terminal 30 includes a determination unit 62. The determination unit 62 is similar to the determination unit 52 of the air conditioning navigation system 100 according to embodiment 4, and includes a learning device 60 and an inference device 61.

[0105] FIG. 30 is a flowchart showing the flow of the user characteristic information correction process performed by the processing unit of the mobile terminal of the air conditioning navigation system according to embodiment 5. The air conditioning navigation system 100 according to embodiment 5 corrects the user characteristic information 321 using a trained model 241. In the air conditioning navigation system 100 according to embodiment 5, the learning data is data that associates the post-use impressions 340 with the correction value of the user characteristic information 321. In the learning device 60 of the air conditioning navigation system 100 according to embodiment 5, the model generation unit 602 uses the learning data output from the data acquisition unit 601 to learn the relationship between the post-use impressions 340 and the correction value of the user characteristic information 321. In other words, the model generation unit 602 generates a trained model 241 for inferring the correction value of the user characteristic information 321 from the post-use impressions 340. Furthermore, in the inference device 61 of the air conditioning navigation system 100 according to embodiment 5, the inference unit 612 uses the learned model 241 stored in the learned model storage unit 250 to infer a correction value of the user characteristic information 321 corresponding to the post-use impressions 340.

[0106] In step S301, the processing unit 350 of the mobile terminal 30 acquires, from the cloud server 20, the air conditioning characteristic information 211 of the facility that is the subject of the post-use feedback 340.

[0107] In step S302, the processing unit 350 of the mobile terminal 30 acquires the trained model 241 from the cloud server 20 based on the air conditioning characteristic information 211 and the suitable seat information 221 stored in the memory unit 320. In step S303, the processing unit 350 of the mobile terminal 30 calculates a correction value for the user characteristic information 321 using the post-use impressions 340 and the trained model 241. In step S304, the processing unit 350 of the mobile terminal 30 applies the correction value to the user characteristic information 321 and stores the corrected user characteristic information 321 in the memory unit 320.

[0108] The air conditioning navigation system 100 according to the fifth embodiment corrects the user characteristic information 321 using a correction value based on machine learning, and is therefore able to guide the user to a seat that suits the user's temperature preferences.

[0109] Next, the hardware configuration of the cloud server 20 and mobile terminal 30 that make up the air conditioning navigation system 100 will be described. Fig. 31 is a diagram showing an example of the hardware configuration of the cloud server of an air conditioning navigation system according to any of Embodiments 1 to 5. As shown in Fig. 31, the cloud server 20 is a computer system including a processor 101, a memory 102, a storage device 103, and an interface circuit 104. The processor 101, the memory 102, the storage device 103, and the interface circuit 104 can send and receive data to and from each other via a bus 105.

[0110] The processor 101 executes the functions of the processing unit 220 by reading and executing an operating system (OS) and processing programs stored in the storage device 103. Note that part or all of the processing unit 220 may be configured with hardware such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array). In other words, the processing circuit that realizes part or all of the processing unit 220 may be dedicated hardware.

[0111] The processor 101 can also read the OS and processing program from one or more storage media including a magnetic disk, a USB (Universal Serial Bus) memory, an optical disk, a compact disk, and a DVD (Digital Versatile Disc) via an interface not shown, store them in the storage device 103, and execute them.

[0112] Furthermore, cloud server 20 may be realized by a plurality of servers connected to each other. When cloud server 20 includes a plurality of servers, the processes executed by cloud server 20 can be regarded as a single virtual server by each of the plurality of servers executing the processes.

[0113] Similarly, the mobile terminal 30 is a computer system including a processor 101, a memory 102, a storage device 103, and an interface circuit 104. The storage unit 320 is realized by the memory 102 and the storage device 103. The processor 101 executes the functions of the processing unit 350 by reading and executing the OS and processing programs stored in the storage device 103. Note that a part or all of the processing unit 350 may be configured with hardware such as an ASIC and an FPGA. In other words, the processing circuit that realizes a part or all of the processing unit 350 may be dedicated hardware.

[0114] The configurations shown in the above embodiments are merely examples of the content, and may be combined with other known technologies, or parts of the configurations may be omitted or modified without departing from the spirit of the invention. [Explanation of symbols]

[0115] 10 In-facility air conditioning equipment, 20 Cloud server, 30 Mobile terminal, 52, 62 Judgment unit, 60 Learning device, 61 Inference device, 71 Input layer, 72 Intermediate layer, 73 Output layer, 100 Air conditioning navigation system, 101 Processor, 102 Memory, 103 Storage device, 104 Interface circuit, 105 Bus, 110 Air conditioner, 111 Temperature distribution information, 119 Setting information, 120 Air conditioning control device, 121 Air conditioning control information, 210, 320 Memory unit, 211 Air conditioning characteristic information, 212 Spatial structure information, 213 Past temperature distribution information, 214 Past air conditioning control information, 220, 350 Processing unit, 221 Suitable seat information, 230 Air conditioning request, 240, 241 Trained model, 250 Trained model memory unit, 310 Input unit, 311 Compatible seat acquisition request, 321 user characteristic information, 322 identifier, 330 display unit, 340 post-use impressions, 400 user characteristic information input screen, 401 age input field, 402 gender input field, 403 cooling room temperature preference input field, 404 heating room temperature preference input field, 405 wind tolerance input field, 406 setting button, 407 cancel button, 410 navigation target facility selection screen, 411 input from map button, 412 address search button, 413 facility name search button, 414 address input field, 415 facility name input field, 416 physical condition input field, 417 basic information setting button, 418 number of seats input field, 419 compatible seat display button, 421 facility map, 422 compatible seat list, 430 temperature preference point calculation table, 431 correction value table, 432 conversion table, 440 Wind exposure tolerance point calculation table, 451 floor plan map, 452 indoor air conditioner placement map, 453 wind exposure map, 454 temperature distribution map, 455 temperature point map, 456 wind exposure point map, 601, 611 data acquisition unit, 602 model generation unit, 612 inference unit.

Claims

1. An air conditioning navigation system that navigates a seat with an air conditioning state that matches the air conditioning preferences of a user who uses a facility having an air-conditioned space where an air conditioner is installed, a user terminal that transmits user characteristic information indicating characteristics related to the user's air conditioning preferences and an identifier of the facility that is a navigation target; an information processing device that generates suitable seat information indicating seats that suit the user's air conditioning preferences based on the user characteristic information acquired from the user terminal and air conditioning characteristic information that indicates the air conditioning characteristics of the air-conditioned space of the facility indicated by the identifier, and transmits the suitable seat information to the user terminal.

2. The air conditioning navigation system according to claim 1, wherein the user terminal includes an input unit that accepts input operations by the user, and when an operation to input the user characteristic information is performed on the input unit, the user characteristic information is transmitted to the information processing device.

3. The air conditioning navigation system according to claim 2, wherein the user terminal corrects the user characteristic information based on post-use impressions, which are impressions of the user who used the facility based on the suitable seat information, input to the input unit.

4. the user terminal transmits the feedback after use to the information processing device; The air conditioning navigation system according to claim 3 , wherein the information processing device notifies a manager of the air conditioner of the post-use feedback.

5. The air conditioning navigation system according to claim 3, wherein the user terminal infers a correction value for the user characteristic information from the post-use impressions using a trained model for inferring a correction value for the user characteristic information from the post-use impressions.

6. 2. The air conditioning navigation system according to claim 1, wherein the air conditioning characteristic information includes at least one of the floor plan of the air conditioned space, the installation position of the air conditioner in the air conditioned space, the operating state of the air conditioner, and a temperature distribution which is the temperature values ​​for each area when the air conditioned space is subdivided.

7. The air conditioning navigation system of claim 6, wherein the information processing device infers at least one of the wind direction and the temperature distribution from the floor plan of the air conditioned space, the installation position of the air conditioner in the air conditioned space, the operating state of the air conditioner, and the outside temperature using a trained model for inferring the strength of the wind direction from the air conditioner at each position in the air conditioned space and the temperature distribution from the floor plan of the air conditioned space, the installation position of the air conditioner in the air conditioned space, the operating state of the air conditioner, and the outside temperature.

8. The air conditioning navigation system according to claim 1 , wherein the user characteristic information includes at least one of the user's age, the user's sex, the user's temperature preference, the user's physical condition, and the user's wind preference.

9. The air conditioning navigation system according to any one of claims 1 to 8, wherein the information processing device calculates the air conditioning characteristic information at a future time point based on at least one of the control schedule of the air conditioner, past control information of the air conditioner, and past temperature distribution in the air-conditioned space, and generates the suitable seat information at the future time point using the air conditioning characteristic information at the future time point.

10. An air conditioning navigation method for navigating a seat with an air conditioning state that matches the air conditioning preferences of a user who uses a facility having an air-conditioned space in which an air conditioner is installed, comprising: a step in which the user terminal outputs user characteristic information indicating characteristics related to the user's air conditioning preferences and an identifier of the facility to be navigated; an information processing device that has received the user characteristic information and the identifier generates suitable seat information that indicates a seat that is suitable for the air conditioning preference of the user in the facility indicated by the identifier, based on the user characteristic information acquired from the user terminal and air conditioning characteristic information that indicates the air conditioning characteristics of the air-conditioned space; a step of the information processing device transmitting the suitable seat information to the user terminal; and a step in which the user terminal displays the suitable seat information.

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