JP2021039576A - Cleaning load management device, cleaning load management system, cleaning load management method, and program - Google Patents

Abstract

To provide a cleaning load management device, a cleaning load management system, a cleaning load management method, and a program capable of contributing to the creation of an appropriate cleaning plan obtained by taking the degree of dirtiness of a room into account.SOLUTION: A cleaning load management device 1 includes: an operation history acquisition unit 111 for acquiring operation history information showing respective operation histories of an air conditioner, a lighting device, and a bathroom ventilation fan; a cleaning load acquisition unit 112 for acquiring load result information showing a cleaning load required for guest room cleaning; a coefficient determination unit 115 for determining a neural network coefficient of a neural network for acquiring a cleaning load on the basis of the operation history information and the load result information; and a neural network calculation unit 114 for calculating a cleaning load in cleaning a room from the operation history information in a cleaning load prediction object period corresponding to a period when a user stay in the guest room by using the neural network with a weighting coefficient determined by the coefficient determination unit 115.SELECTED DRAWING: Figure 7

Description

The present invention relates to a cleaning load management device, a cleaning load management system, a cleaning load management method and a program.

In accommodation facilities such as hotels and inns, it is necessary for the cleaning staff to efficiently clean the guest rooms when the guests are not in the guest rooms. On the other hand, multiple data tables that manage the work status of the cleaning staff who clean multiple rooms, the check-out time of the guests staying in each room, the cleaning status of each room, and the cleaning work time of each room. A keyboard that gives instructions to create a cleaning plan for the accommodation facility, and when a cleaning plan creation instruction is given, the room to be cleaned first and the cleaning start time within the range of each individual cleaning staff at work by referring to the data table. For all the rooms where the guest did not stay and have not been cleaned, the point value for determining the cleaning order is calculated, and the cleaning plan for each room for each cleaning staff is calculated based on the calculated point value. An accommodation facility cleaning plan creation system equipped with a hard disk device that stores a cleaning plan program to be created has been proposed (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2003-67509

However, the accommodation facility cleaning planning system described in Patent Document 1 does not consider that the degree of dirtiness of each room changes depending on how the guests in each room spend their time. Therefore, for example, some rooms are excessively used. If it is dirty, it takes time to clean the room, and there is a risk that the cleaning will not be carried out according to the created cleaning plan.

The present invention has been made in view of the above reasons, and provides a cleaning load management device, a cleaning load management system, a cleaning load management method, and a program that can contribute to the creation of an appropriate cleaning plan in consideration of the degree of dirt in the room. The purpose is.

In order to achieve the above object, the cleaning load management device according to the present invention
An operation history acquisition unit that acquires operation history information indicating the operation history of at least one device installed in the room, and an operation history acquisition unit.
A cleaning load acquisition unit that acquires load record information indicating the cleaning load, which is the workload required to clean the room, and a cleaning load acquisition unit.
A coefficient determination unit that determines the neural network coefficient of the neural network for obtaining the cleaning load when the user cleans the room after staying in the room based on the operation history information and the load record information. ,
Using the neural network whose neural network coefficient is determined by the coefficient determining unit, the operation history information in the cleaning load prediction target period including at least a part of the period during which the user stayed in the room is used to obtain the room. It is provided with a neural network calculation unit that obtains a cleaning load when cleaning is performed.

According to the present invention, the neural network calculation unit uses a neural network in which the neural network coefficient is determined by the coefficient determination unit to determine the cleaning load when cleaning the room from the operation history information in the cleaning load prediction target period. Ask. This makes it possible to estimate the cleaning load according to the usage status of the user's room and the degree of dirtiness of the room after the user stays in the room, so it is necessary to clean the room and restore it to its original state. The optimum cleaning work time can be calculated and reflected in the cleaning schedule of the cleaning worker. Therefore, the wasted time in the cleaning schedule can be reduced, and the efficiency of the cleaning work can be improved.

Schematic configuration diagram of the cleaning load management system according to the first embodiment of the present invention. A block diagram showing a hardware configuration of an operating device and an air conditioner according to the first embodiment. Functional block diagram of the operating device and the air conditioner according to the first embodiment A block diagram showing a hardware configuration of a terminal device according to the first embodiment. Functional block diagram of the terminal device according to the first embodiment A block diagram showing a hardware configuration of the cleaning load management device according to the first embodiment. Functional block diagram of the cleaning load management device according to the first embodiment The figure which shows an example of the information stored in the operation history storage part which concerns on Embodiment 1. The figure which shows an example of the information stored in the cleaning load storage part which concerns on Embodiment 1. The figure for demonstrating an example of the relationship between the load record information and the operation history which concerns on Embodiment 1. The figure which shows an example of the information stored in the guest room information storage part which concerns on Embodiment 1. Explanatory drawing of neural network used in cleaning load management apparatus which concerns on Embodiment 1. The figure which shows an example of the information stored in the worker information storage part which concerns on Embodiment 1. (A) is an operation explanatory diagram when determining the weight coefficient of the neural network of the cleaning load management device according to the first embodiment, and (B) is the cleaning showing the predicted cleaning load by the cleaning load management device according to the first embodiment. Operation explanatory diagram when outputting load information A sequence diagram showing an example of the operation of the cleaning load management system according to the first embodiment. The figure which shows an example of the operation screen image displayed on the display part of the terminal apparatus which concerns on Embodiment 1. A sequence diagram showing an example of the operation of the cleaning load management system according to the first embodiment. (A) is a diagram showing an example of an operation screen image displayed on the display unit of the terminal device according to the first embodiment, and (B) is an operation displayed on the display unit of the terminal device according to the first embodiment. Diagram showing another example of the screen image A flowchart showing an example of the flow of operation history information / environmental performance information management processing executed by the operating device according to the first embodiment. A flowchart showing an example of a flow of terminal control processing executed by the operating device according to the first embodiment. A flowchart showing an example of the flow of the cleaning load management process executed by the cleaning load management device according to the first embodiment. A flowchart showing an example of the flow of the coefficient determination process executed by the cleaning load management device according to the first embodiment. Schematic configuration diagram of the cleaning load management system according to the second embodiment of the present invention. Functional block diagram of the cleaning load management device according to the second embodiment Block diagram showing the hardware configuration of the cloud server according to the second embodiment Functional block diagram of the cloud server according to the second embodiment A sequence diagram showing an example of the operation of the cleaning load management system according to the second embodiment. A sequence diagram showing an example of the operation of the cleaning load management system according to the second embodiment. A sequence diagram showing an example of the operation of the cleaning load management system according to the second embodiment. A flowchart showing an example of the flow of the cleaning load management process executed by the cleaning load management device according to the second embodiment. A flowchart showing an example of the flow of the coefficient information generation process executed by the cloud server according to the second embodiment. The figure which shows an example of the operation screen image displayed on the display part of the terminal device which concerns on a modification.

(Embodiment 1)
Hereinafter, the cleaning load management device according to the embodiment of the present invention will be described with reference to the drawings. The cleaning load management device according to the present embodiment uses a neural network to check the operation history of at least one device installed in the room during the target period, and the cleaning load of the cleaning worker when cleaning the room. Is what you want. This cleaning load management device includes an operation history acquisition unit that acquires operation history information that indicates the operation history of at least one device installed in the room, and a load record that indicates the cleaning load that is the workload required for cleaning the room. It includes a cleaning load acquisition unit for acquiring information, a coefficient determination unit, and a neural network calculation unit. Here, the coefficient determining unit is a neural network for obtaining the cleaning load when cleaning the room after the user has stayed for a preset target period based on the acquired operation history information and load record information. Determine the neural network coefficients of the network. In addition, the neural network calculation unit obtains the cleaning load when cleaning the room from the operation history information in the target period by using the neural network in which the neural network coefficient is determined by the coefficient determination unit.

As shown in FIG. 1, for example, the cleaning load management system according to the present embodiment includes a cleaning load management device 1 installed in the accommodation facility H and a terminal device 2 possessed by a cleaning worker who cleans the accommodation facility. And the operation device 4 installed in each guest room RA and RB in the accommodation facility H. Further, the cleaning load management system includes a customer server 10 capable of communicating with the cleaning load management device 1 via the wide area network NW1. The wide area network NW1 is, for example, the Internet. Further, an air conditioner 5, a lighting fixture 6, and a bathroom ventilation fan 7 are installed in each of the guest rooms RA and RB of the accommodation facility H. The user can operate the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 by using the operating device 4. Here, the operating device 4 and the air conditioner 5 may be connected by wire, for example, and the operating device 4, the lighting fixture 6, and the bathroom ventilation fan 7 may be wirelessly connected, for example. Further, the operating device 4 may be wirelessly connectable to, for example, a mobile terminal (not shown) possessed by a user of the guest room RA or RB. In this case, when the user operates the mobile terminal, operation information indicating the operation contents for the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7 is transmitted from the mobile terminal to the operation device 4, and the operation device 4 sends the mobile terminal. The control information corresponding to the operation information received from may be transmitted to the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7.

The cleaning load management device and each air conditioner 5 are connected to a local network NW2 laid in the accommodation facility H. The local network NW2 is a wired or wireless LAN (Local Area Network) conforming to a communication standard such as Ethernet (registered trademark). Further, the local network NW2 is connected to the wide area network NW1 via the router 91 and the data circuit-terminating equipment 92. The router 91 is, for example, a broadband router, and the data circuit-terminating equipment 92 has functions such as a model and a gateway. Further, a wireless communication device 8 installed in the accommodation facility H is connected to the local network NW2, and the terminal device 2 can communicate with the cleaning load management device 1 via the wireless communication device 8. .. The wireless communication device 8 functions as, for example, a wireless LAN access point, and executes communication with the terminal device 2 by, for example, a communication method conforming to the wireless LAN standard of IEEE802.11ac.

The customer server 10 is a server provided with a large-capacity storage, and manages information indicating the reservation status of the users of each room in the accommodation facility H, information indicating the stay status, and the like.

As shown in FIG. 2, for example, the operation device 4 includes a CPU (Central Processing Unit) 401, a main storage unit 402, an auxiliary storage unit 403, a display unit 404, an input unit 405, a device communication interface 407, and the like. A bus 409 that connects them to each other is provided. The main storage unit 402 is a volatile memory such as a RAM (Random Access Memory), and is used as a work area of the CPU 401. The auxiliary storage unit 403 is a non-volatile memory such as a semiconductor flash memory, and stores a program for the CPU 401 to execute various processes. The display unit 404 is a display device such as a liquid crystal display. The input unit 405 is a touch pad arranged on the display unit 404, a button switch arranged on a part of the housing of the operating device 4, and the like. The device communication interface 407 is for communicating with the air conditioner 5, and is, for example, a wired communication interface. The air conditioner 5 has a control unit 510 for controlling the operation of a compressor (not shown) or a blower fan (not shown), for example, as shown in FIG. Further, the air conditioner 5 measures the humidity of the room RA (RB) in which the air conditioner 5 is installed and the temperature sensor (not shown) that detects the temperature of the room RA (RB) in which the air conditioner 5 is installed. It is equipped with a humidity sensor (not shown) to detect. The control unit 510 includes a CPU 501, a main storage unit 502, an auxiliary storage unit 503, a local communication interface 506, a device communication interface 507, and a bus 509 connecting these to each other. The main storage unit 502 is used as a work area of the CPU 501, and the auxiliary storage unit 503 stores programs for the CPU 501 to execute various processes. The local communication interface 506 is, for example, a wired communication interface or a wireless module, and is connected to the local network NW2. The device communication interface 507 is for communicating with the operating device 4, and is, for example, a wired communication interface.

The CPU 401 reads the program stored in the auxiliary storage unit 403 into the main storage unit 402 and executes it, so that the reception unit 411, the control information generation unit 412, the display control unit 413, and the control information transmission are executed, for example, as shown in FIG. It functions as a unit 414, an operation history acquisition unit 415, an operation history transmission unit 416, an environment information acquisition unit 417, an environment record transmission unit 418, and a timekeeping unit 419. Further, as shown in FIG. 3, the auxiliary storage unit 403 shown in FIG. 2 includes a control information storage unit 431 that stores the control information generated by the control information generation unit 412, an operation history storage unit 432, and a display unit 404. It has an image storage unit 433 for storing image information indicating an operation screen image to be displayed on the screen, and an environmental performance storage unit 434. The operation history storage unit 432 is a combination of control information transmitted from the operation device 4 to the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7, and date and time information indicating the date and time when the control information is transmitted. Store operation history information. The environmental record storage unit 434 indicates environmental information indicating environmental parameters such as temperature and humidity of the guest room RA (RB) in which the air conditioner 5 is installed acquired from the air conditioner 5, and the date and time when the environmental information is acquired. Stores environmental performance information that is a combination of date and time information.

The reception unit 411 receives operation information indicating the operation content of the operation performed on the input unit 405 by the user of the guest room RA (RB). The control information generation unit 412 generates control information of the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7 corresponding to the operation information received by the reception unit 411, and stores the generated control information in the control information storage unit 431. .. The display control unit 413 acquires image information corresponding to the operation information received by the reception unit 411 from the image storage unit 433, and causes the display unit 404 to display the operation screen image corresponding to the acquired image information. The control information transmission unit 414 transmits the control information stored in the control information storage unit 431 to the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7. Here, the control information transmission unit 414 transmits, for example, when the control information stored in the control information storage unit 431 is updated, the updated control information is transmitted to the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7. When the control information transmission unit 414 transmits the control information to the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7, the operation history acquisition unit 415 sends the transmitted control information to the date and time indicating the date and time when the control information is transmitted. It is stored in the operation history storage unit 432 in association with the information.

When the operation history transmission unit 416 receives the teacher information request information described later from the cleaning load management device 1 via the air conditioner 5, the operation history storage unit 432 stores the past preset teacher information target period. All of the operation history information included in the above is transmitted to the cleaning load management device 1 via the air conditioner 5. Here, the teacher information target period is set to, for example, a period from the latest time in the guest room RA (RB) to the latest time from the time going back to the past by a period of one week to one year. For example, it is assumed that the teacher information target period is set to the period from the time when the teacher information is targeted to the past by one year from the latest time to the latest time. In this case, when the operation history storage unit 432 stores only the operation history information from the time retroactive to the latest time point by one week from the latest time point, the operation history transmission unit 416 is the operation history storage unit 432. Sends all the operation history information stored in.

Further, when the operation history transmission unit 416 receives the operation history request information described later from the cleaning load management device 1 via the air conditioner 5, the operation history storage unit 432 stores the operation history storage unit 432 within a preset load determination target period. The operation history information in the above is transmitted to the cleaning load management device 1 via the air conditioner 5. Here, the load determination target period is set to, for example, the period from the check-in to the check-out by the user who has recently stayed in the guest room RA (RB).

The environmental information acquisition unit 417 air-conditions the environmental requirement information that requests the air conditioner 5 to transmit the environmental information indicating the environmental parameters such as the temperature and humidity of the guest room RA (RB) in which the air conditioner 5 is installed. Environmental information is acquired from the air conditioner 5 by transmitting it to the machine 5. Then, the environmental information acquisition unit 417 stores the combination of the acquired environmental information and the date and time information indicating the acquisition date and time of the environmental information in the environmental record storage unit 434 as the environmental record information. When the environmental performance transmission unit 418 receives the teacher information request information from the cleaning load management device 1 via the air conditioner 5, the environmental performance storage unit 434 stores the environmental performance information included in the above-mentioned teacher information target period. All of the above are transmitted to the cleaning load management device 1 via the air conditioner 5. For example, as described above, it is assumed that the teacher information target period is set to the period from the latest point in time to the latest point in time, which is one year back in the past. In this case, when the environmental record storage unit 434 stores only the operation history information from the time retroactive to the latest point by one week from the latest point, the environmental record transmission unit 418 is the environmental record storage unit 434. Sends all the operation history information stored in. Further, when the environmental record transmission unit 418 receives the environmental record request information described later from the cleaning load management device 1 via the air conditioner 5, the environment record storage unit 434 stores the environment within the above-mentioned load determination target period. The actual information is transmitted to the cleaning load management device 1 via the air conditioner 5.

Based on the time information output from the software timer (not shown), the timekeeping unit 419 determines whether or not the actual acquisition time for acquiring the operation history information and the environmental actual information has arrived. The achievement acquisition time is set so as to arrive every 10 minutes, for example. When the actual acquisition time arrives, the timekeeping unit 419 outputs the actual acquisition time notification information to the operation history acquisition unit 415 and the environmental information acquisition unit 417.

Returning to FIG. 2, the CPU 501 of the air conditioner 5 reads the program stored in the auxiliary storage unit 503 into the main storage unit 502 and executes the program. It functions as a generation unit 512 and a relay unit 513. The operation control unit 511 controls the operation of the compressor and the blower fan based on the control information received from the operation device 4. The environmental information generation unit 512 generates environmental information indicating environmental parameters such as temperature detected by the temperature sensor and humidity detected by the humidity sensor, and transmits the generated environmental information to the operating device 4. When the relay unit 513 receives the teacher information request information or the operation history request information and the environmental performance request information from the cleaning load management device 1, the relay unit 513 transfers the received teacher information request information or the operation history request information and the environmental performance request information to the operation device 4. Send to. When the relay unit 513 receives the operation history information and the environmental record information from the operation device 4, the relay unit 513 transmits the received operation history information and the environmental record information to the cleaning load management device 1.

The terminal device 2 is, for example, a mobile information terminal or the like, and as shown in FIG. 4, the CPU 201, the main storage unit 202, the auxiliary storage unit 203, the display unit 204, the input unit 205, the wireless module 206, and the like. It includes a bus 209 that connects them to each other. The main storage unit 202 is used as a work area of the CPU 201, and the auxiliary storage unit 203 stores a program for the CPU 201 to execute various processes. The display unit 204 is a display device such as a liquid crystal display. The input unit 205 is a touch pad arranged so as to overlap the display unit 204. The wireless module 206 is for communicating with the wireless communication device 8, and executes communication by, for example, a communication method conforming to the wireless LAN communication standard of IEEE802.11ac.

By reading the program stored in the auxiliary storage unit 203 into the main storage unit 202 and executing the CPU 201, for example, as shown in FIG. 5, the reception unit 211, the display control unit 212, the load record acquisition unit 213, and the load record transmission It functions as unit 214 and schedule acquisition unit 215. Further, as shown in FIG. 5, the auxiliary storage unit 203 shown in FIG. 4 includes a schedule storage unit 231 for storing schedule information indicating a daily cleaning schedule of a cleaning worker, a load record storage unit 232, and a display unit. It has an image storage unit 233 for storing image information indicating an operation screen image to be displayed on the 204. The load record storage unit 232 stores load record information indicating the record of the cleaning load of the cleaning worker who possesses the terminal device 2.

The reception unit 211 receives operation information indicating the operation content of the operation performed by the cleaning worker on the input unit 205. The display control unit 212 acquires image information corresponding to the operation information received by the reception unit 211 from the image storage unit 233, and causes the display unit 204 to display the operation screen image corresponding to the acquired image information. The load record acquisition unit 213 acquires load record information indicating the cleaning load record based on the operation information received by the reception unit 211, and stores the acquired load record information in the load record storage unit 232. The load record transmission unit 214 transmits the load record information stored in the load record storage unit 232 to the cleaning load management device 1 according to the operation information received by the reception unit 211. The schedule acquisition unit 215 acquires the schedule information from the cleaning load management device 1 by transmitting the schedule request information requesting the cleaning load management device 1 to transmit the schedule information according to the operation information received by the reception unit 211. To do. Then, the schedule acquisition unit 215 stores the acquired schedule information in the schedule storage unit 231. At this time, the display control unit 212 causes the display unit 204 to display an operation screen image for notifying the cleaning operator of the cleaning schedule indicated by the schedule information stored in the schedule storage unit 231.

As shown in FIG. 6, for example, the cleaning load management device 1 includes a CPU 101, a main storage unit 102, an auxiliary storage unit 103, a local communication interface 104, and a bus 109 that connects them to each other. The CPU 101 is, for example, a multi-core processor. The main storage unit 102 is used as a work area of the CPU 101, and the auxiliary storage unit 103 stores a program for the CPU 101 to execute various processes. The local communication interface 104 is, for example, a wired communication interface or a wireless module, and is connected to the local network NW2.

By reading the program stored in the auxiliary storage unit 103 into the main storage unit 102 and executing the CPU 101, as shown in FIG. 7, the operation history acquisition unit 111, the cleaning load acquisition unit 112, the guest room information acquisition unit 113, and the environment It functions as an achievement acquisition unit 119, a neural network calculation unit 114, a coefficient determination unit 115, a coefficient setting unit 116, a schedule generation unit 117, a schedule transmission unit 118, and a timekeeping unit 120. Further, as shown in FIG. 7, the auxiliary storage unit 103 shown in FIG. 6 includes an operation history storage unit 131, a cleaning load storage unit 132, a guest room information storage unit 133, an environmental performance storage unit 138, and a neural network storage. It has a unit 134, a predictive load storage unit 135, a schedule storage unit 136, and a worker information storage unit 137. As shown in FIG. 8, for example, the operation history storage unit 131 stores information indicating the operating states of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 in association with the date and time information. In the example shown in FIG. 8, the operation history storage unit 131 includes operation information indicating an on / off state, operation mode information indicating an operation mode such as cooling and ventilation, and air volume information indicating a set air volume for the air conditioner 5. The set temperature information indicating the set temperature and the set temperature information are stored in association with the date and time information. Further, the operation history storage unit 131 stores the operation information indicating the on / off state of the lighting fixtures 6 installed in the bedroom and the bathroom of the guest room RA (RB) in association with the date and time information. Further, the operation history storage unit 131 stores the operation information indicating the on / off state and the air volume information indicating the set air volume of the bathroom ventilation fan 7 in association with the date and time information.

In the case of the example shown in FIG. 8, after the user checks in on 5/28, at 17:00, the air conditioner 5 installed in the bedroom is operated in the operation mode “cooling”, the air volume “strong”, and the set temperature “ It is turned on in the state of being set to "25 ° C.", and the lighting fixture 6 is turned on. Next, at 20:00 on May 28, the user changes the operation mode of the air conditioner 5 in the bedroom to "ventilation" and turns on the bathroom ventilation fan 7 installed in the bathroom with an air volume of "weak". At the same time, the lighting fixture 6 installed in the bathroom is turned on. After that, at 0:00 on May 29, the user turns off the air conditioner 5 and the lighting fixture 6 installed in the bedroom and turns off the bathroom ventilation fan 7. Next, at 6:00 on May 29, the user set the air conditioner 5 installed in the bedroom to the operation mode "cooling", the air volume "weak", and the set temperature "27 ° C". It is turned on, the lighting fixture 6 installed in the bedroom is turned on, and the bathroom ventilation fan 7 is turned on with a "strong" air volume. Subsequently, at 6:10 on May 29, the user changed the settings of the air conditioner 5 installed in the bedroom to the operation mode "air purification", the air volume "strong", and the set temperature "25 ° C". ing. After that, at 7:35 before check-out on May 29, the user changed the operation mode of the air conditioner 5 installed in the bedroom to "ventilation" and turned off the lighting equipment installed in the bedroom. The air volume of the bathroom ventilation fan 7 is changed to "weak".

As shown in FIG. 9, for example, the cleaning load storage unit 132 stores the load record information including the work time index information indicating the index of the length of the cleaning work time and the work load index information representing the index of the load of the cleaning work. , The room identification information and the work day information indicating the date when the cleaning work was performed are stored in association with each other. In the example shown in FIG. 9, the work time index information shows a numerical value when the length of the work time required for the cleaning work is expressed in five stages, and the smaller the value, the longer the work time. Further, the work load index information shows a numerical value when the load applied to the cleaning worker in the cleaning work is expressed in five stages, and the smaller the numerical value is, the larger the work load is. Here, the relationship between the work time index and the work load index included in the load record information and the operation history of each of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 in the cabin RA (RB) will be described. For example, as shown in FIG. 10, the deodorizing air-cleaning button of the air conditioner 5 is used, the ventilation function of the air conditioner 5 is used at a low air volume, the number of operations of the air conditioner 5 is small, and the bathroom ventilation fan 7 is operated. When the air volume is "weak", the on-time of the lighting equipment 6 is short, the number of passengers in the guest room RA (RB) is small, and the number of children included in the users is small, the working time index and the workload index are It tends to grow. On the other hand, instead of using the deodorizing air-cleaning button of the air conditioner 5, the ventilation function of the air conditioner 5 is used with the air volume "strong", the air conditioner 5 is operated many times, and the bathroom ventilation fan 7 is operated with the air volume "strong". When the lighting fixture 6 is used for a long time, the number of guest room RA (RB) users is large, and the number of children included in the users is large, the working time index and the workload index tend to be small. ..

As shown in FIG. 11, for example, the guest room information storage unit 133 includes room type information indicating the type of each of the plurality of rooms in the accommodation facility H, room area information indicating the size of the room, and a user staying in the room. It stores room information including guest room information indicating the type of room and the number of users staying in the room. Further, the guest room information storage unit 133 stores the guest room information in association with the room identification information for identifying the room. Room type information includes a single room with a single bed, a double room with a double bed, a twin room with two single beds, a king room with two double beds, and three single beds. This is information that identifies each of the installed triple room and the suite room where two double beds are installed and the bedroom and the living room are separated. In addition, the number of guests information includes information indicating "adult" and "child" as the type of user. In the example shown in FIG. 11, the room identification information, that is, the room having the room numbers "301" and "302" is a single room, and the room having the room numbers "303" and "304" is a double bedroom. It indicates that the rooms with room numbers "305" and "306" are twin rooms, and the rooms with room numbers "501" and "502" are suite rooms. Two double beds are provided to separate the guest room from the living room.

Returning to FIG. 7, the environmental record storage unit 138 uses the environmental record information indicating the actual values of the past environmental parameters such as temperature and humidity in each room (for example, guest rooms RA and RB) in the accommodation facility H as the date and time information. I remember it in association with it.

The neural network storage unit 134 stores information about a neural network having a predetermined number of nodes and layers used by the neural network calculation unit 114. Specifically, the neural network storage unit 134 stores information indicating the number of nodes, the number of layers, the weighting coefficient corresponding to each node, and the activation function of the neural network. This neural network is for obtaining the cleaning load when cleaning the guest room RA (RB) after the user stays for a preset target period. This neural network has an input layer L10, a hidden layer L20, and an output layer L30 as shown in FIG. The input layer L10 inputs the numerical values indicated by the operation history information, the environmental performance information, and the guest room information to the hidden layer L20.

The hidden layer L20 includes N (N is a positive integer) including a set number of M [j] nodes x [j, i] (1 ≦ i ≦ M [j], M [j] is a positive integer). ) Consists of layers. That is, the hidden layer L20 has a structure in which each node row is connected to each other. Here, the output y [j, i] of each node x [j, i] is represented by the relational expression of the following equation (1).
Here, W [j, i, k] indicates a weighting coefficient, and f (*) indicates an activation function. This weighting coefficient W [j, i, k] corresponds to the neural network coefficient that determines the structure of the neural network described above. Further, as the activation function, a non-linear function such as a sigmoid function, a ramp function, a step function, or a softmax function is used. The hidden layer L20 is the sum of the information input to the nodes obtained by multiplying the output of each node in the previous layer by a weighting coefficient. Then, the output of the activation function with the sum as an argument is transmitted to the next layer. The output layer L30 outputs the output y [j, i] from the final layer of the hidden layer L20 as it is. Then, the neural network storage unit 134 stores information indicating the structure of the neural network represented by the equation (1) and information indicating the weighting coefficient W [j, i, k] of the neural network. Further, the neural network storage unit 134 stores initial weight coefficient information indicating an initial value of the weight coefficient of the neural network.

Returning to FIG. 7, the predicted load storage unit 135 stores the cleaning load information indicating the predicted value of the cleaning load calculated by the neural network calculation unit 114.

As shown in FIG. 13, for example, the worker information storage unit 137 stores information indicating the work history of each cleaning worker and work day information indicating the work day in association with the worker identification information. The information indicating the work history includes service period information indicating the length of service at the accommodation facility H currently working and information on the number of years of experience before working indicating the period of experience before working at the accommodation facility H currently working. Including.

Returning to FIG. 7, the schedule storage unit 136 stores the schedule information of each cleaning worker generated by the schedule generation unit 117.

By transmitting the operation history request information to the operation device 4, the operation history acquisition unit 111 transmits the operation history of each of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 installed in the guest room RA and RB of the accommodation facility H. Acquire the operation history information to be shown. Here, the operation history request information is information for requesting the operation device 4 to transmit the operation history information of each of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 in the cleaning load prediction target period. The environmental performance acquisition unit 119 acquires the environmental performance information indicating the environmental performance of the guest rooms RA and RB by transmitting the environmental performance request information to the operation device 4. Here, the environmental performance request information is information for requesting the operating equipment to transmit the environmental performance information of the guest room RA (RB) in which the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 are installed. .. The guest room information acquisition unit 113 acquires guest room information from the customer server 10 by transmitting the guest room information request information requesting the customer server 10 to transmit the guest room information to the customer server 10. The cleaning load acquisition unit 112 acquires load record information indicating the cleaning load, which is the work load required for cleaning the guest rooms RA and RB of the accommodation facility H, from the terminal device 2.

The coefficient determination unit 115 determines the weight coefficient of the neural network for obtaining the cleaning load described above based on the operation history information, the environmental record information, the guest room information, and the load record information. First, the coefficient setting unit 116 acquires initial weighting coefficient information indicating the initial weighting coefficient from the neural network storage unit 134, and sets the acquired initial weighting coefficient as the weighting coefficient of the neural network. Next, the coefficient determination unit 115 acquires the cleaning load calculated by the neural network calculation unit 114 using the neural network, for example, as shown in FIG. 14 (A). Here, the neural network calculation unit 114 calculates the cleaning load using the neural network based on the operation history information DAT1, the environmental performance information DAT2, and the guest room information DAT3, which are teacher information. Subsequently, the coefficient determining unit 115 acquires the load actual information DAT4 which is the teacher information stored in the cleaning load storage unit 132, and the cleaning load indicated by the acquired load actual information DAT4 and the cleaning load calculated by using the neural network. Calculate the error with. Then, the coefficient determination unit 115 determines the weight coefficient of the neural network by the error backpropagation method (backpropagation) based on the calculated error. Here, the coefficient determination unit 115 determines the weighting coefficient using, for example, an autoencoder. Then, the coefficient determination unit 115 stores the weight coefficient information indicating the determined weight coefficient in the neural network storage unit 134.

The coefficient setting unit 116 sets the weighting coefficient determined by the coefficient determining unit 115 as the weighting coefficient of the neural network. The neural network calculation unit 114 cleans the guest room RA (RB) from the operation history information, the environmental performance information, and the guest room information in the cleaning load prediction target period by using the neural network whose weight coefficient is determined by the coefficient determination unit 115. Obtain the cleaning load that indicates the predicted cleaning load when performing. Here, as shown in FIG. 14 (B), the coefficient setting unit 116 uses the weight coefficient determined by the coefficient determining unit 115 stored in the neural network storage unit 134 in the neural network weight used by the neural network calculation unit 114. Set to coefficient. Then, the neural network calculation unit 114 uses a neural network in which a weighting coefficient is set by the coefficient setting unit 116 from the operation history information DAM1, the environmental performance information DAC1 and the guest room information DAR1 in the cleaning load prediction target period, and predicts the cleaning load. Is calculated, and the cleaning load information indicating the calculated predicted cleaning load is output. The neural network calculation unit 114 stores the predicted load information indicating the cleaning load indicating the obtained predicted cleaning load in the predicted load storage unit 135.

Returning to FIG. 7, the schedule generation unit 117 is based on the cleaning load obtained by the neural network calculation unit 114 stored in the prediction load storage unit 135 and the worker information stored in the worker information storage unit 137. Generates schedule information that indicates the cleaning schedule of the cleaning worker. Here, the schedule generation unit 117 calculates the cleaning work time allocated to the cleaning worker from the obtained cleaning load, for example, by using the relational expression of the following formula (2).
(Cleaning work time) = (Cleaning load) x (C1 x (service period) + C2 x (experience period))
... Equation (2)
Here, C1 and C2 are preset coefficients. Then, the schedule generation unit 117 refers to the worker information storage unit 137, and in each guest room according to the service period and the experience period of each of the cleaning workers who are working on the day when the cleaning work of the guest rooms RA and RB is performed. Calculate the cleaning work time in RA and RB. Then, the schedule generation unit 117 indicates the calculated cleaning work time of each guest room RA, RB, and the usage status information indicating whether or not the user of the room (for example, guest room RA, RB) in the accommodation facility H is staying in the room. And, based on, the schedule information is generated. The schedule generation unit 117 stores the generated schedule information in the schedule storage unit 136. The schedule transmission unit 118 transmits the schedule information stored in the schedule storage unit 136 to the operation device 4.

The timekeeping unit 120 determines whether or not the coefficient update time for updating the weight coefficient of the neural network stored in the neural network storage unit 134 has arrived based on the time information output from the software timer (not shown). .. The coefficient update time is set so as to arrive, for example, every day. When the coefficient update time arrives, the timekeeping unit 120 outputs the coefficient update time arrival notification information to the operation history acquisition unit 111, the environmental performance acquisition unit 119, the guest room information acquisition unit 113, and the coefficient determination unit 115.

Next, the operation of the cleaning load management system according to the present embodiment will be described with reference to FIGS. 15 and 18. First, as shown in FIG. 15, when the preset performance information acquisition time arrives, the operation device 4 acquires the control information from the control information storage unit 431 and stores it in the operation history storage unit 432 (step S1). ), Environmental information is acquired from the air conditioner 5 and stored in the environmental record storage unit 434 (step S2). After that, the operating device 4 repeatedly executes the processes of steps S1 and S2 every time the performance information acquisition time comes.

Further, after the cleaning worker of the accommodation facility H has cleaned the guest room RA (RB), the terminal device is displayed in a state where the operation screen image for inputting the questionnaire regarding the cleaning load is displayed on the display unit 204 of the terminal device 2. It is assumed that the operation of inputting to the questionnaire is performed via the input unit 205 of 2. Here, in the terminal device 2, the operation screen image GA1 for inputting the questionnaire, as shown in FIG. 16, for example, is displayed on the display unit 204. The operation screen image GA1 contains an area M1 for displaying the guest room number and the cleaning date of the cleaned guest room RA (RB), a message MQ1 indicating the question content of the questionnaire regarding the cleaning time, and the question content of the questionnaire regarding the load of cleaning work. Includes the message MQ2 and. Further, the operation screen image GA1 includes touch areas BA1 and BA2 for answering the question contents of each questionnaire with five-step indexes, and areas MA1 and MA2 for displaying the answer contents. Then, for example, the cleaning worker cleans the guest room RA (RB) after the user of the guest room RA (RB) checks out, and then displays the operation screen image GA1 on the display unit 204 of the terminal device 2. In the state, the questionnaire is answered by touching the part corresponding to the index value to be answered displayed in the touch areas BA1 and BA2 in the input unit 205. Note that FIG. 16 shows a case where the guest room RA (RB) where the cleaning work was performed has the guest room number “403” and the cleaning date is “May 29”. Further, the message MQ1 is set to "Is the cleaning work time long or short?", And the message MQ2 is set to "Is the cleaning work difficult?".

Returning to FIG. 15, when the cleaning worker performs an operation of inputting to the questionnaire, the terminal device 2 receives operation information indicating the content of the input operation to the questionnaire (step S3). Next, the terminal device 2 generates load record information indicating the result of the cleaning load corresponding to the operation information indicating the received operation content and stores it in the load record storage unit 232 (step S4). Subsequently, when the terminal device 2 receives an operation for transmitting the questionnaire result after performing the input operation to the questionnaire of the cleaning worker, the load record information stored in the load record storage unit 232 is stored in the terminal device 2. Is transmitted to the cleaning load management device 1 (step S5). On the other hand, when the cleaning load management device 1 receives the load record information, the cleaning load management device 1 stores the received load record information in the cleaning load storage unit 132 (step S6). After that, each time the cleaning worker inputs the questionnaire, the series of processes from steps S3 to S6 described above are repeatedly executed.

After that, it is assumed that the coefficient update time for updating the weight coefficient of the neural network used by the preset cleaning load management device 1 has arrived. In this case, the teacher information request information requesting the operation device 4 to transmit the operation history information and the environmental performance information as the teacher information is transmitted from the cleaning load management device 1 to the operation device 4 (step S7). On the other hand, when the operating device 4 receives the teacher information request information from the cleaning load management device 1, the operation history information and the environmental performance information are transmitted from the operating device 4 to the cleaning load management device 1 (step). S8). Here, the operation history information is the operation history information of each of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 operated by the operation device 4, and the environmental performance information is the air conditioner operated by the operation device 4. This is environmental performance information showing actual values such as temperature and humidity of the guest room RA (RB) in which 5th grade is installed.

Next, the teacher information request information requesting the customer server 10 to transmit the guest room information as the teacher information is transmitted from the cleaning load management device 1 to the customer server 10 (step S9). On the other hand, when the customer server 10 receives the teacher information request information from the cleaning load management device 1, the customer server 10 identifies the guest room information corresponding to the received teacher information request information (step S10). Subsequently, the specified guest room information is transmitted from the customer server 10 to the cleaning load management device 1 (step S11). On the other hand, the cleaning load management device 1 performs a coefficient determination process for determining the weight coefficient of the neural network based on the received operation history information, environmental record information, guest room information, and load record information stored in the cleaning load storage unit 132. Execute (step S12). The cleaning load management device 1 stores the weight coefficient information indicating the weight coefficient of the neural network determined by executing the coefficient determination process in the neural network storage unit 134. After that, every time the coefficient update time comes, the series of processes from steps S7 to S12 described above are repeatedly executed.

After that, as shown in FIG. 17, it is assumed that the cleaning worker of the accommodation facility H performs an operation on the terminal device 2 to update the daily cleaning schedule of the cleaning worker. In this case, the terminal device 2 receives the operation information indicating the content of the operation for the cleaning worker to update the cleaning schedule (step S13). Next, the schedule request information requesting the cleaning load management device 1 to transmit the schedule information is transmitted from the terminal device 2 to the cleaning load management device 1 (step S14). On the other hand, when the cleaning load management device 1 receives the schedule request information, the above-mentioned operation history request information and the environmental performance request information are transmitted from the cleaning load management device 1 to the operating device 4 (step S15). On the other hand, when the operating device 4 receives the operation history request information and the environmental performance request information, the operation history information and the environmental performance information in the cleaning load prediction target period are transmitted from the operating device 4 to the cleaning load management device 1 in response to the reception. Is transmitted to (step S16). Subsequently, the above-mentioned guest room information request information is transmitted from the cleaning load management device 1 to the customer server 10 (step S17). On the other hand, when the customer server 10 receives the guest room information request information from the cleaning load management device 1, the customer server 10 identifies the guest room information corresponding to the received guest room information request information (step S18). Subsequently, the specified guest room information is transmitted from the customer server 10 to the cleaning load management device 1 (step S19). On the other hand, the cleaning load management device 1 calculates the predicted cleaning load using the neural network based on the received operation history information, environmental performance information, and guest room information (step S20). The cleaning load management device 1 stores the predicted load information indicating the calculated predicted cleaning load in the predicted load storage unit 135.

After that, the cleaning load management device 1 shows the cleaning schedule of the cleaning worker based on the predicted load information stored in the predicted load storage unit 135 and the worker information stored in the worker information storage unit 137. Is generated (step S21). Here, the cleaning load management device 1 stores the generated schedule information in the schedule storage unit 136. Next, the generated schedule information is transmitted from the cleaning load management device 1 to the terminal device 2 (step S22). On the other hand, when the terminal device 2 receives the schedule information, the terminal device 2 causes the display unit 204 to display an operation screen image for notifying the cleaning operator of the cleaning schedule indicated by the received schedule information (step S23).

Here, the terminal device 2 causes the display unit 204 to display the operation screen images GA2 and GA3 for notifying the cleaning operator of the cleaning schedule as shown in FIG. 18A or FIG. 18B, for example. As shown in FIG. 18A, the operation screen image GA2 for notifying the cleaning schedule of XX days displayed on the display unit 204 of the terminal device 2 possessed by the cleaning operator is from 9:00 to 9:30. It is set to clean the guest room (room) XXX, clean the guest room (room) YYYY from 9:30 to 9:45, and clean the guest room (room) ZZZZ from 9:45 to 10:30. Here, the cleaning time of each room corresponds to the cleaning time required to return each room to the original state, and reflects the cleaning load obtained by the neural network calculation unit 114. In this way, the display unit 204 of the terminal device 2 displays the cleaning schedule in which the cleaning time of each room is set to the optimum time according to the degree of dirt, so that the work efficiency of the cleaning worker is improved. Waste of cleaning work is reduced. Further, as shown in FIG. 18B, the operation screen image GA3 for notifying the cleaning schedule of □ month □ day displayed on the display unit 204 of the terminal device 2 possessed by the cleaning worker is from 9:00 to 9 It is set to clean the guest room (room) XXX from 9:450 to 10:15, clean the guest room (room) YYYY from 9:450 to 10:15, and clean the guest room (room) ZZZZ from 10:15 to 10:30. There is. The cleaning time of each room shown in FIG. 18B is different from the cleaning time of each room shown in FIG. 18A. This reflects that the degree of dirtiness in each room differs depending on how the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 of the users staying in each room are used.

Next, the operation history information / environmental performance information management process executed by the operation device 4 according to the present embodiment will be described with reference to FIG. This device status information transmission process is started, for example, when the power is turned on to the operating device 4. Further, in the operation device 4, in parallel with the operation history information / environmental performance information management process, the control information generation unit 412 generates control information according to the operation information received by the reception unit 411, and the control information transmission unit 414. However, a process of controlling the operation of the air conditioner 5, the lighting equipment 6 or the bathroom ventilation fan 7 by transmitting the generated control information to the air conditioner 5, the lighting equipment 6 or the bathroom ventilation fan 7 is also executed.

First, the timekeeping unit 419 determines whether or not the preset result acquisition time has arrived (step S401). When it is determined by the timekeeping unit 419 that the actual acquisition time has not yet arrived (step S401: No), the process of step S405 described later is executed. On the other hand, when the timekeeping unit 419 determines that the actual acquisition time has arrived (step S401: Yes), the operation history acquisition unit 415 indicates the control information from the control information storage unit 431 and the date and time when the control information is transmitted. Obtained with date and time information. Then, the operation history acquisition unit 415 stores the combination of the acquired control information and the date and time information in the operation history storage unit 432 as the operation history information (step S402). Next, the environmental information acquisition unit 417 transmits the above-mentioned environmental requirement information to the air conditioner 5 (step S403). As a result, the environmental information acquisition unit 417 acquires the environmental information from the air conditioner 5, and stores the acquired environmental information and the date and time information indicating the date and time when the environmental information was acquired as the environmental record information. It is stored in the unit 434 (step S404).

Subsequently, the operation history transmission unit 416 and the environment result transmission unit 418 determine whether or not the above-mentioned teacher information request information has been received from the cleaning load management device 1 (step S405). When the operation history transmission unit 416 and the environment result transmission unit 418 determine that the teacher information request information has not been received (step S405: No), the process of step S408 described later is executed. On the other hand, it is assumed that the operation history transmission unit 416 and the environment result transmission unit 418 have determined that the teacher information request information has been received (step S405: Yes). In this case, the operation history transmission unit 416 transmits the operation history information within the above-mentioned teacher information target period stored in the operation history storage unit 432 to the cleaning load management device 1 (step S406), and the environment record transmission unit 418 transmits the environmental result transmission unit 418. The environmental record information within the above-mentioned teacher information target period stored in the environmental record storage unit 434 is transmitted to the cleaning load management device 1 (step S407).

After that, the operation history transmission unit 416 determines whether or not the above-mentioned operation history request information has been received from the cleaning load management device 1 (step S408). When the operation history transmission unit 416 determines that the operation history request information has not been received (step S408: No), the process of step S410 described later is executed. On the other hand, when the operation history transmission unit 416 determines that the operation history request information has been received (step S408: Yes), the operation history storage unit 432 manages the operation history information within the above-mentioned cleaning load prediction target period stored in the operation history storage unit 432. It is transmitted to the device 1 (step S409). Next, the environmental performance transmission unit 418 determines whether or not the above-mentioned environmental performance request information has been received from the cleaning load management device 1 (step S410). When the environmental record transmission unit 418 determines that the environmental record request information has not been received (step S410: No), the process of step S401 is executed again. On the other hand, when the environmental performance transmission unit 418 determines that the environmental performance request information has been received (step S410: Yes), the environmental performance storage unit 434 manages the environmental performance information within the above-mentioned cleaning load prediction target period stored in the cleaning load management. It is transmitted to the device 1 (step S411). Subsequently, the process of step S401 is executed again.

Next, the terminal control process executed by the terminal device 2 according to the present embodiment will be described with reference to FIG. This terminal control process is started, for example, when the power is turned on to the terminal device 2. First, the reception unit 211 determines whether or not the cleaning worker has received the operation information corresponding to the questionnaire input operation by performing the operation of inputting the cleaning worker into the input unit 205 in the above-mentioned questionnaire regarding the cleaning time and cleaning load. (Step S201). At this time, the display control unit 212 acquires, for example, image information indicating the operation screen image GA1 shown in FIG. 16 from the image storage unit 233, and causes the display unit 204 to display the operation screen image GA1 indicated by the acquired image information. .. Here, if the reception unit 211 determines that the operation information corresponding to the questionnaire input operation is not received (step S201: No), the process of step S203 described later is executed. On the other hand, when the reception unit 211 determines that the operation information corresponding to the questionnaire input operation has been received (step S201: Yes), the load record acquisition unit 213 has the load record based on the operation information corresponding to the received questionnaire input operation. Information is generated and stored in the load record storage unit 232 (step S202).

Next, whether or not the reception unit 211 has received the operation information corresponding to the questionnaire transmission operation by performing an operation for the cleaning worker to transmit the answer to the questionnaire to the cleaning load management device 1 to the input unit 205. (Step S203). Here, if the reception unit 211 determines that the operation information corresponding to the questionnaire transmission operation is not received (step S203: No), the process of step S205 described later is executed. On the other hand, when it is determined that the reception unit 211 has received the operation information corresponding to the questionnaire transmission operation (step S203: Yes), the load record transmission unit 214 cleans the load record information stored in the load record storage unit 232. It is transmitted to 1 (step S204).

Subsequently, the reception unit 211 has received the operation information corresponding to the schedule update operation by the cleaning worker performing an operation for updating the cleaning schedule stored in the schedule storage unit 231 to the input unit 205. (Step S205). Here, if the reception unit 211 determines that the operation information corresponding to the schedule update operation is not received (step S205: No), the process of step S201 is executed again. On the other hand, it is assumed that the reception unit 211 has determined that the operation information corresponding to the schedule update operation has been received (step S205: Yes). In this case, the schedule acquisition unit 215 acquires the schedule information from the cleaning load management device 1 by transmitting the schedule request information requesting the cleaning load management device 1 to transmit the schedule information (step S206). S207). At this time, the schedule acquisition unit 215 stores the acquired schedule information in the schedule storage unit 231. After that, the display control unit 212 displays an operation screen image for notifying the cleaning operator of the cleaning schedule, for example, as shown in FIGS. 18A and 18B, based on the schedule information stored in the schedule storage unit 231. It is displayed on the display unit 204 (step S208). Then, the process of step S201 is executed again.

Next, the cleaning load management process executed by the cleaning load management device 1 according to the present embodiment will be described with reference to FIGS. 21 and 22. This cleaning load management process is started, for example, when the power is turned on to the cleaning load management device 1. First, the cleaning load acquisition unit 112 determines whether or not the load record information has been received from the terminal device 2 (step S101). When it is determined that the cleaning load acquisition unit 112 has not received the load record information (step S101: No), the process of step S103, which will be described later, is executed. On the other hand, when the cleaning load acquisition unit 112 determines that the load record information has been received (step S101: Yes), the cleaning load acquisition unit 112 stores the received load record information in the cleaning load storage unit 132 (step S102).

Next, the timekeeping unit 120 determines whether or not the preset coefficient update time has arrived (step S103). When the timekeeping unit 120 determines that the coefficient update time has not yet arrived (step S103: No), the process of step S109, which will be described later, is executed. On the other hand, when the timekeeping unit 120 determines that the coefficient update time has arrived (step S103: Yes), the operation history acquisition unit 111 and the environmental performance acquisition unit 119 respectively transfer the above-mentioned teacher information request information to the operation device 4. Transmit (step S104). As a result, the operation history acquisition unit 111 acquires the operation history information within the above-mentioned teacher information target period from the operation device 4, and the environmental record acquisition unit 119 acquires the environmental record information within the teacher information target period from the operation device 4. Acquire (step S105). Here, the operation history acquisition unit 111 stores the acquired operation history information in the operation history storage unit 131, and the environmental record acquisition unit 119 stores the acquired environmental record information in the environmental record storage unit 138. Subsequently, the guest room information acquisition unit 113 acquires the guest room information within the teacher information target period from the customer server 10 by transmitting the teacher information request information to the customer server 10 (step S106) (step S107). Here, the guest room information acquisition unit 113 stores the acquired guest room information in the guest room information storage unit 133. After that, the coefficient determination process is executed (step S108).

Here, the details of the coefficient determination process will be described in detail with reference to FIG. 22. First, the neural network calculation unit 114 acquires the operation history information from the operation history storage unit 131, the environmental performance information from the environment performance storage unit 138, and the guest room information from the guest room information storage unit. Further, the neural network calculation unit 114 acquires load actual information from the cleaning load storage unit 132 (step S301). Here, the operation history information, the environmental performance information, the guest room information, and the load performance information are all information in the above-mentioned teacher information target period. Next, the coefficient setting unit 116 acquires the initial weighting coefficient information indicating the initial weighting coefficient or the weighting coefficient information indicating the most recently used weighting coefficient from the neural network storage unit 134, and the weighting coefficient indicated by the acquired weighting coefficient information. Is set in the weighting coefficient of the neural network (step S302). Subsequently, the neural network calculation unit 114 calculates the predicted cleaning load from the acquired operation history information, environmental performance information, and guest room information using the neural network in which the weighting coefficient is set by the coefficient setting unit 116 (step S303). ). After that, the coefficient determining unit 115 calculates an error between the calculated predicted cleaning load and the cleaning load indicated by the load actual information (step S304). Next, the coefficient determination unit 115 newly determines the weight coefficient of the neural network by the error backpropagation method (backpropagation) based on the calculated error (step S305). Then, the coefficient determining unit 115 stores the determined weighting coefficient in the neural network storage unit 134 (step S306).

Returning to FIG. 21, the schedule generation unit 117 then determines whether or not the schedule update request information has been received from the terminal device 2 (step S109). When the schedule generation unit 117 determines that the schedule update request information has not been received from the terminal device 2 (step S109: No), the process of step S101 is executed again. On the other hand, it is assumed that the schedule generation unit 117 determines that the schedule update request information has been received from the terminal device 2 (step S109: Yes). In this case, the operation history acquisition unit 111 transmits the above-mentioned operation history request information to the operation device 4, and the environment record acquisition unit 119 transmits the above-mentioned environment record request information to the operation device 4 (step S110). As a result, the operation history acquisition unit 111 acquires the operation history information within the cleaning load prediction target period described above from the operation device 4, and the environmental record acquisition unit 119 acquires the environmental record from the operation device 4 within the cleaning load prediction target period. Acquire information (step S111). Here, the operation history acquisition unit 111 stores the acquired operation history information in the operation history storage unit 131, and the environmental record acquisition unit 119 stores the acquired environmental record information in the environmental record storage unit 138. Subsequently, the guest room information acquisition unit 113 acquires guest room information within the cleaning load prediction target period from the customer server 10 by transmitting the guest room information request information to the customer server 10 (step S112) (step S113). Here, the guest room information acquisition unit 113 stores the acquired guest room information in the guest room information storage unit 133.

After that, the neural network calculation unit 114 calculates the predicted cleaning load from the acquired operation history information, environmental performance information, and guest room information using the neural network in which the weighting coefficient is set by the coefficient setting unit 116 (step S114). .. Here, the neural network calculation unit 114 stores the cleaning load information indicating the calculated predicted cleaning load in the predicted load storage unit 135.

Next, the schedule generation unit 117 is a cleaning worker based on the cleaning load obtained by the neural network calculation unit 114 stored in the predictive load storage unit 135 and the worker information stored in the worker information storage unit 137. Generates schedule information indicating the cleaning schedule of (step S115). Here, the schedule generation unit 117 stores the generated schedule information in the schedule storage unit 136. Subsequently, the schedule transmission unit 118 transmits the schedule information stored in the schedule storage unit 136 to the terminal device 2 (step S116). After that, the process of step S101 is executed again.

As described above, according to the cleaning load management device 1 according to the present embodiment, the neural network calculation unit 114 uses the neural network in which the weighting coefficient is determined by the coefficient determination unit 115, and the cleaning load prediction target period. The cleaning load when cleaning the guest room RA (RB) is obtained from the operation history information, the environmental performance information, and the guest room information in. This makes it possible to estimate the cleaning load according to the usage status of the guest room RA (RB) of the user and the degree of dirtiness of the room after the user stays in the guest room RA (RB). The optimum cleaning work time required for cleaning and returning to the original state can be calculated and reflected in the cleaning schedule of the cleaning worker. Therefore, the wasted time in the cleaning schedule can be reduced, and the efficiency of the cleaning work can be improved.

Further, the guest room information acquisition unit 113 according to the present embodiment determines the size of the guest rooms RA and RB, the types of users staying in the guest rooms RA and RB, that is, the types of adults and children, and the guest rooms RA and RB. Acquire room information including the number of guests who stayed. As a result, the neural network calculation unit 114 calculates the cleaning load based on the size of the guest rooms RA and RB, the types of users staying in the guest rooms RA and RB, and the number of users staying in the guest rooms RA and RB. be able to. Therefore, the accuracy of the calculated cleaning load is improved, and an appropriate cleaning schedule can be created accordingly.

Further, the schedule generation unit 117 according to the present embodiment generates schedule information based on the usage status information indicating whether or not the user is staying in the guest rooms RA and RB. As a result, the cleaning schedule is created in consideration of the time zone during which the user is staying and the guest rooms RA and RB cannot be cleaned, so that the wasted time zone in the cleaning schedule is reduced.

(Embodiment 2)
The cleaning load management system according to the present embodiment includes a cleaning load management device installed in each of a plurality of accommodation facilities, and a cloud server capable of communicating with the plurality of cleaning load management devices via a wide area network. Then, in the cloud server, the neural network coefficient of the neural network for obtaining the cleaning load when cleaning the room used in each cleaning load management device is determined.

As shown in FIG. 23, for example, the cleaning load management system according to the present embodiment includes a cleaning load management device 2001 installed in each of a plurality of accommodation facilities H1, H2, H3, ..., A terminal device 2, and a terminal device 2. It includes an operating device 4. Further, the cleaning load management system includes a cleaning load management device 1, a cloud server 2100 capable of communicating via a wide area network NW1, and a customer server 10. In FIG. 23, the same components as those in the first embodiment are designated by the same reference numerals as those in FIG.

The cleaning load management device 2001 has the same hardware configuration as the cleaning load management device 1 described in the first embodiment, and as shown in FIG. 6, the CPU 101, the main storage unit 102, and the auxiliary storage unit 103 , A local communication interface 104 and a bus 109 that connects them to each other. Then, the CPU 101 reads the program stored in the auxiliary storage unit 103 into the main storage unit 102 and executes it, so that the operation history acquisition unit 111, the guest room information acquisition unit 113, and the environmental result acquisition unit 119 are as shown in FIG. , Neural network calculation unit 114, teacher information transmission unit 2122, coefficient acquisition unit 2121, coefficient setting unit 116, schedule generation unit 117, schedule transmission unit 118, and timing unit 2120. In FIG. 24, the same reference numerals as those in FIG. 7 are attached to the same configurations as those in the first embodiment. Further, as shown in FIG. 24, the auxiliary storage unit 103 shown in FIG. 6 includes an operation history storage unit 131, a guest room information storage unit 133, an environmental performance storage unit 138, a neural network storage unit 134, and a predictive load storage unit. It has a unit 135, a schedule storage unit 136, and a worker information storage unit 137.

The teacher information transmission unit 2122 acquires the operation history information within the teacher information target period from the operation history storage unit 131, and acquires the environmental record information within the teacher information target period from the environmental record storage unit 138. Then, the teacher information transmission unit 2122 transmits the acquired operation history information and the environmental performance information to the cloud server 2100 as teacher information for determining the weighting coefficient of the neural network for obtaining the cleaning load.

The coefficient acquisition unit 2121 acquires coefficient information indicating the weighting coefficient of the neural network determined by the cloud server 2100 from the cloud server 2100. Then, the coefficient acquisition unit 2121 stores the weight coefficient information included in the acquired coefficient information in the neural network storage unit 134.

The timekeeping unit 2120 determines whether or not the coefficient update time for updating the weight coefficient of the neural network stored in the neural network storage unit 134 has arrived based on the time information output from the software timer (not shown). .. When the coefficient update time arrives, the timekeeping unit 2120 outputs the coefficient update time arrival notification information to the operation history acquisition unit 111, the environmental record acquisition unit 119, the guest room information acquisition unit 113, and the teacher information transmission unit 2122.

As shown in FIG. 25, for example, the cloud server 2100 includes a CPU 1001, a main storage unit 1002, an auxiliary storage unit 1003, a wide area communication interface 1004, and a bus 1009 that connects them to each other. The CPU 1001 is, for example, a multi-core processor. The main storage unit 1002 is used as a work area of the CPU 101, and the auxiliary storage unit 1003 stores a program for the CPU 1001 to execute various processes. The wide area communication interface 1004 is connected to the wide area network NW1 and can communicate with the cleaning load management device 2001 installed in each of a plurality of accommodation facilities H1, H2, H3, ..., via the wide area network NW1. ..

As shown in FIG. 26, the CPU 1001 reads the program stored in the auxiliary storage unit 1003 into the main storage unit 1002 and executes it, so that the teacher information acquisition unit 2111, the cleaning load acquisition unit 2118, the guest room information acquisition unit 2112, and the neural It functions as a network calculation unit 2113, a coefficient determination unit 2115, a coefficient setting unit 2114, a coefficient information generation unit 2116, and a coefficient information transmission unit 2117. Further, as shown in FIG. 26, the auxiliary storage unit 1003 shown in FIG. 25 includes a teacher information storage unit 2131, a cleaning load storage unit 2134, a guest room information storage unit 2132, and a neural network storage unit 2133. The teacher information storage unit 2131 provides information indicating the operating status of each of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 installed in the accommodation facilities H1, H2, H3, ..., Date and time information, and the accommodation facility. The accommodation facility identification information and the room identification information that identify H1, H2, H3, ... Are stored in association with each other.

In addition, the teacher information storage unit 2131 provides environmental performance information indicating past performance values of environmental parameters such as temperature and humidity in each room (for example, guest room RA, RB) of accommodation facilities H1, H2, H3, and so on. , Date and time information, room identification information, and accommodation facility identification information are stored in association with each other. The cleaning load storage unit 2134 performs load record information including work time index information indicating an index of the length of cleaning work time and workload index information indicating an index of the load of cleaning work, room identification information, and cleaning work. The work day information indicating the date of implementation and the accommodation facility identification information are stored in association with each other.

The guest room information storage unit 2132 includes accommodation facility identification information, room type information indicating the type of each of the plurality of rooms in the accommodation facility H, room area information indicating the size of the room, and the type of user staying in the room. The guest room information including the number of guests who stayed in the room and the number of guests who stayed in the room is stored.

The neural network storage unit 2133 stores information about the same neural network as the neural network used in the cleaning load management device 2001 installed in each accommodation facility H1, H2, H3, .... Specifically, the neural network storage unit 2133 stores weight coefficient information indicating the weight coefficient of the neural network.

The teacher information acquisition unit 2111 acquires teacher information including operation history information and environmental performance information from the cleaning load management device 2001 installed in each of the accommodation facilities H1, H2, H3, .... Here, the operation history information is information indicating the operation history of each of the air conditioners 5, the lighting fixtures 6, and the bathroom ventilation fan 7 installed in the guest rooms RA and RB of the accommodation facilities H1, H2, H3, ... .. In addition, the environmental performance information is information indicating the environmental performance of each guest room RA, RB of the accommodation facilities H1, H2, H3, .... The guest room information acquisition unit 2112 acquires guest room information from the customer server 10 by transmitting the teacher information request information to the customer server 10. The guest room information acquisition unit 2112 stores the acquired guest room information in the guest room information storage unit 2132. The cleaning load acquisition unit 2118 acquires load record information indicating the cleaning load, which is the work load required for cleaning the guest rooms RA and RB of the accommodation facility H, from the terminal device 2. The cleaning load acquisition unit 2118 stores the acquired load record information in the cleaning load storage unit 2134.

The coefficient determination unit 2115 determines the weight coefficient of the neural network for obtaining the cleaning load described above based on the operation history information, the environmental record information, the guest room information, and the load record information. First, the coefficient setting unit 2114 acquires the initial weighting coefficient information from the neural network storage unit 2133, and sets the initial weighting coefficient indicated by the acquired initial weighting coefficient information as the weighting coefficient of the neural network. Next, the coefficient determination unit 2115 acquires the cleaning load calculated by the neural network calculation unit 2113 using the neural network. Here, the neural network calculation unit 2113 calculates the cleaning load using the neural network based on the operation history information, the environmental performance information, and the guest room information which are the teacher information. Subsequently, the coefficient determining unit 2115 acquires the load actual information which is the teacher information stored in the cleaning load storage unit 2134, and the cleaning load indicated by the acquired load actual information and the cleaning load calculated by using the neural network. Calculate the error. Then, the coefficient determination unit 2115 determines the weight coefficient of the neural network by the error backpropagation method (backpropagation) based on the calculated error. Further, the coefficient determination unit 2115 stores the weight coefficient information indicating the determined weight coefficient in the neural network storage unit 2133.

The coefficient information generation unit 2116 acquires the weight coefficient information indicating the weight coefficient determined by the coefficient determination unit 2115 from the neural network storage unit 2133, and generates the coefficient information including the acquired weight coefficient information. The coefficient information transmission unit 2117 transmits the coefficient information generated by the coefficient information generation unit 2116 to the cleaning load management device 2001.

Next, the operation of the cleaning load management system according to the present embodiment will be described with reference to FIGS. 27 to 29. First, as shown in FIG. 27, the operating device 4 executes the processes of steps S21 and S22 when the preset performance information acquisition time arrives. The processing of steps S21 and S22 is the same as the processing of steps S1 and S2 described in the first embodiment. After that, the operating device 4 repeatedly executes the processes of steps S21 and S22 each time the performance information acquisition time comes. Further, after the cleaning worker of the accommodation facility H has cleaned the guest room RA (RB), the terminal device is displayed in a state where the operation screen image for inputting the questionnaire regarding the cleaning load is displayed on the display unit 204 of the terminal device 2. It is assumed that the operation of inputting to the questionnaire is performed via the input unit 205 of 2. In this case, the terminal device 2 receives the operation information indicating the content of the input operation to the questionnaire (step S23). Next, the terminal device 2 generates load record information indicating the result of the cleaning load corresponding to the operation information indicating the received operation content and stores it in the load record storage unit 232 (step S24). Subsequently, when the terminal device 2 receives an operation for transmitting the questionnaire result after performing the input operation to the questionnaire of the cleaning worker, the load record information stored in the load record storage unit 232 is stored in the terminal device 2. Is transmitted to the cloud server 2100 (step S25). On the other hand, when the cloud server 2100 receives the load record information, the cloud server 2100 stores the received load record information in the teacher information storage unit 2131 (step S26). After that, each time the cleaning worker inputs the questionnaire, the series of processes from steps S23 to S26 described above are repeatedly executed.

After that, it is assumed that the coefficient update time for updating the weight coefficient of the neural network used by the preset cleaning load management device 1 has arrived. In this case, the cleaning load management device 2001 transmits the teacher information request information requesting the operation device 4 to transmit the operation history information and the environmental performance information as the teacher information (step S27). On the other hand, when the operating device 4 receives the teacher information request information, the operation history information and the environmental performance information are transmitted from the operating device 4 to the cleaning load management device 2001 (step S28). Here, the operation history information and the environmental performance information are the same as the operation history information and the environmental performance information described in the first embodiment.

Next, as shown in FIG. 28, teacher information including the operation history information and the environmental performance information acquired by the cleaning load management device 2001 from the operation device 4 is transmitted from the cleaning load management device 2001 to the cloud server 2100 (step). S29). Here, when the cloud server 2100 receives the teacher information, the teacher information storage unit 2131 stores the operation history information and the environmental performance information included in the received teacher information. Subsequently, the teacher information request information requesting the customer server 10 to transmit the guest room information as the teacher information is transmitted from the cloud server 2100 to the customer server 10 (step S30). On the other hand, when the customer server 10 receives the teacher information request information from the cloud server 2100, the customer server 10 identifies the guest room information corresponding to the received teacher information request information (step S31). Subsequently, the specified guest room information is transmitted from the customer server 10 to the cloud server 2100 (step S32). On the other hand, the cloud server 2100 executes a coefficient determination process for determining the weight coefficient of the neural network based on the received guest room information, the operation history information stored in the teacher information storage unit 2131, the environment record information, and the load record information. (Step S33). The cloud server 2100 stores the weight coefficient information indicating the weight coefficient of the neural network determined by executing the coefficient determination process in the neural network storage unit 2133. After that, the coefficient information including the weighting coefficient information stored in the neural network storage unit 2133 is transmitted from the cloud server 2100 to the cleaning load management device 2001 (step S34). On the other hand, when the cleaning load management device 2001 receives the coefficient information, the neural network storage unit 134 stores the weighting coefficient included in the received coefficient information to update the weighting coefficient information (step S35). After that, each time the coefficient update time comes, the series of processes from steps S27 to S35 described above are repeatedly executed.

Next, it is assumed that the cleaning worker of the accommodation facility H performs an operation on the terminal device 2 to update the daily cleaning schedule of the cleaning worker. In this case, the terminal device 2 receives the operation information indicating the content of the operation for the cleaning worker to update the cleaning schedule (step S36). Subsequently, the schedule request information is transmitted from the terminal device 2 to the cleaning load management device 2001 (step S37). On the other hand, when the cleaning load management device 2001 receives the schedule request information, the above-mentioned operation history request information and the environmental performance request information are transmitted from the cleaning load management device 2001 to the operating device 4 (step S38). On the other hand, when the operating device 4 receives the operation history request information and the environmental performance request information, the operation history information and the environmental performance information in the cleaning load prediction target period are transmitted from the operating device 4 to the cleaning load management device 1 in response to the reception. Is transmitted to (step S39). After that, as shown in FIG. 29, the above-mentioned guest room information request information is transmitted from the cleaning load management device 2001 to the customer server 10 (step S40).

On the other hand, when the customer server 10 receives the guest room information request information from the cleaning load management device 2001, the customer server 10 identifies the guest room information corresponding to the received guest room information request information (step S41). Next, the identified guest room information is transmitted from the customer server 10 to the cleaning load management device 2001 (step S42). On the other hand, the cleaning load management device 2001 calculates the predicted cleaning load using the neural network based on the received operation history information, environmental performance information, and guest room information (step S43). After that, the cleaning load management device 2001 shows the schedule information indicating the cleaning schedule of the cleaning worker based on the predicted load information stored in the predicted load storage unit 135 and the worker information stored in the worker information storage unit 137. Is generated (step S44). Next, the generated schedule information is transmitted from the cleaning load management device 2001 to the terminal device 2 (step S45). On the other hand, when the terminal device 2 receives the schedule information, the terminal device 2 causes the display unit 204 to display an operation screen image for notifying the cleaning operator of the cleaning schedule indicated by the received schedule information (step S46).

Next, the cleaning load management process executed by the cleaning load management device 2001 according to the present embodiment will be described with reference to FIG. First, the timekeeping unit 2120 determines whether or not the preset coefficient update time has arrived (step S2101). When the timekeeping unit 2120 determines that the coefficient update time has not yet arrived (step S2101: No), the process of step S2106, which will be described later, is executed. On the other hand, when the timekeeping unit 2120 determines that the coefficient update time has arrived (step S2101: Yes), the operation history acquisition unit 111 and the environmental performance acquisition unit 119 respectively transfer the above-mentioned teacher information request information to the operation device 4. Transmit (step S2102). As a result, the operation history acquisition unit 111 acquires the operation history information within the above-mentioned teacher information target period from the operation device 4, and the environmental record acquisition unit 119 acquires the environmental record information within the teacher information target period from the operation device 4. Acquire (step S2103). Next, the teacher information transmission unit 2122 transmits the operation history information acquired by the operation history acquisition unit 111 and the environment record acquisition unit 119, and the teacher information including the environment record information to the cloud server 2100 (step S2104). Subsequently, the coefficient acquisition unit 2121 acquires the coefficient information from the cloud server 2100, and stores the weight coefficient information included in the acquired coefficient information in the neural network storage unit 134 (step S2105).

After that, the schedule generation unit 117 determines whether or not the schedule update request information has been received from the terminal device 2 (step S2106). When the schedule generation unit 117 determines that the schedule update request information has not been received from the terminal device 2 (step S2106: No), the process of step S2101 is executed again. On the other hand, it is assumed that the schedule generation unit 117 determines that the schedule update request information has been received from the terminal device 2 (step S2106: Yes). In this case, the operation history acquisition unit 111 transmits the above-mentioned operation history request information to the operation device 4, and the environment record acquisition unit 119 transmits the above-mentioned environment record request information to the operation device 4 (step S2107). As a result, the operation history acquisition unit 111 acquires the operation history information within the cleaning load prediction target period described above from the operation device 4, and the environmental record acquisition unit 119 acquires the environmental record from the operation device 4 within the cleaning load prediction target period. Acquire information (step S2108). Subsequently, the guest room information acquisition unit 113 acquires guest room information within the cleaning load prediction target period from the customer server 10 by transmitting the guest room information request information to the customer server 10 (step S2109) (step S2110).

Next, the neural network calculation unit 114 calculates the predicted cleaning load from the acquired operation history information, environmental performance information, and guest room information using the neural network in which the weighting coefficient is set by the coefficient setting unit 116 (step S2111). ). Subsequently, the schedule generation unit 117 provides schedule information indicating the cleaning schedule of the cleaning worker based on the cleaning load obtained by the neural network calculation unit 114 and the worker information stored in the worker information storage unit 137. Generate (step S2112). After that, the schedule transmission unit 118 transmits the generated schedule information to the terminal device 2 (step S2113). After that, the process of step S2101 is executed again.

Next, the coefficient information generation process executed by the cloud server 2100 according to the present embodiment will be described with reference to FIG. 31. First, the cleaning load acquisition unit 2118 determines whether or not the load record information has been received from the terminal device 2 (step S21001). When it is determined that the cleaning load acquisition unit 112 has not received the load record information (step S21001: No), the process of step S21003 to be described later is executed. On the other hand, when the cleaning load acquisition unit 112 determines that the load record information has been received (step S21001: Yes), the cleaning load acquisition unit 112 stores the received load record information in the cleaning load storage unit 132 (step S21002).

Next, the teacher information acquisition unit 2111 determines whether or not the teacher information has been received from the cleaning load management device 2001 (step S21003). When the teacher information acquisition unit 2111 determines that the teacher information has not been received (step S21003: No), the process of step S21001 is executed again. On the other hand, when the teacher information acquisition unit 2111 determines that the teacher information has been received (step S21003: Yes), the teacher information storage unit stores the operation history information and the environmental performance information within the teacher information target period included in the received teacher information. Store in 2131. Subsequently, the guest room information acquisition unit 2112 acquires the guest room information within the teacher information target period from the customer server 10 by transmitting the teacher information request information to the customer server 10 (step S21004) (step S21005). Here, the guest room information acquisition unit 2112 stores the acquired guest room information in the guest room information storage unit 2132. After that, the coefficient determination process is executed (step S21006). This coefficient determination process is the same as the coefficient determination process described with reference to FIG. 22 in the first embodiment. Here, the coefficient determination unit 2115 stores the weight coefficient information indicating the determined weight coefficient in the neural network storage unit 2133.

Next, the coefficient information generation unit 2116 generates coefficient information including the weight coefficient information stored in the neural network storage unit 2133 (step S21007). Subsequently, the coefficient information transmission unit 2117 transmits the generated coefficient information to the cleaning load management device 2001 (step S21008). After that, the process of step S21001 is executed again.

As described above, according to the control system according to the present embodiment, the cloud server 2100 determines the weighting coefficient of the neural network used in the cleaning load management device 2001. This has the advantage that the processing load in the cleaning load management device 2001 is reduced.

Although each embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiments. For example, as shown in FIG. 32, when the operating device 4 displays the language selection button image BU1 for selecting the language of the message (for example, messages M31, M32) together with the message on the display unit 404, the input unit 405 Language selection button The operation history information may include language information indicating the language selected by touching the portion corresponding to the image BU1. In the example shown in FIG. 32, the language selection button image BU1 is arranged at the lower right of the display unit 404. Here, when the language of the message displayed on the display unit 404 is Japanese, the language selection button image BU1 is displayed as "Eng". Then, when the part corresponding to the language selection button image BU1 displayed as "Eng" in the input unit 405 is touched, the language of the message displayed on the display unit 404 is switched to English. At this time, the language selection button image BU1 is displayed as "Chi". Then, when the part corresponding to the language selection button image BU1 displayed as "Chi" on the input unit 405 is touched, the language of the message displayed on the display unit 404 is switched to Chinese. In this way, by touching the part corresponding to the language selection button image BU1 in the input unit 405, the language of the message displayed on the display unit 404 is changed to "Japanese"-> "English"-> "Chinese". → ・ ・ ・ ・ → You can switch in the order of “Japanese”. Then, the operation device 4 may transmit information indicating the history of the operation of touching the portion corresponding to the language selection button image BU1 in the input unit 405 to the cleaning load management device 1 as the operation history information.

Here, the influence of the language selection of the guest room RA and RB users on the cleaning work amount will be described. The usage, usage time, and frequency of use of the air conditioner 5, the lighting fixture 6, and the bathroom ventilation fan 7 installed in the guest rooms RA and RB tend to differ depending on the lifestyle or national character of the user. For example, Westerners tend to use only the shower, not the bathtub. In this case, since the time the user stays in the bathroom is shortened, the degree of dirtiness of the bathroom is reduced, so that the cleaning load when cleaning the inside of the bathroom is reduced. On the other hand, Japanese people tend to use bathtubs as well as showers. In this case, since the user stays in the bathroom for a long time, the degree of dirtiness of the bathroom tends to be larger than that of Westerners. Therefore, the cleaning load when cleaning the bathroom becomes large. Then, by adding information indicating the history of the operation of touching the portion corresponding to the language selection button image BU1 in the input unit 405 in which the language selected in the operation device 4 is reflected to the operation history information, the guest rooms RA and RB Since the lifestyle or national character of the user can be reflected in the predicted cleaning load, the accuracy of the predicted cleaning load can be improved accordingly.

In each embodiment, the neural network calculation unit 114 uses the operation history information in the period from the beginning of the cleaning load prediction target period to the past time point by a preset margin period from the end of the cleaning load prediction target period. The cleaning load when cleaning RA and RB may be obtained. For example, when the cleaning load prediction target period is set from the user's check-in time to the check-out time, the neural network calculation unit 114 checks out from the beginning of the cleaning load prediction target period. The cleaning load may be calculated based on the operation history information and the environmental record information up to the time when the time is 10 minutes back from the time of the cleaning.

According to this configuration, the cleaning schedule can be created before the user actually checks out the accommodation facility H, so that the work efficiency of the cleaning worker can be improved accordingly.

In each embodiment, an example in which the environmental performance acquisition unit 119 acquires environmental information indicating environmental parameters such as temperature and humidity at the place where the air conditioner 5 is installed has been described. However, the type of environmental information is not limited to this, and indicates environmental parameters such as the illuminance of the place where the lighting fixture 6 is installed, the temperature, humidity, and the illuminance of the bathroom where the bathroom ventilation fan 7 is installed. It may be the one that acquires environmental information. In this case, for example, if the bathroom ventilation fan 7 has a temperature sensor and a humidity sensor, and the environmental performance acquisition unit 119 acquires environmental information indicating the temperature and humidity of the bathroom detected by the temperature sensor and the humidity sensor. Good. Further, the lighting equipment 6 may have an illuminance sensor, and the environmental performance acquisition unit 119 may acquire environmental information indicating the illuminance of the bedroom or bathroom detected by the illuminance sensor.

For example, when the air conditioner 5 is frequently turned on and off while the change in the indoor temperature detected by the temperature sensor and the humidity sensor is small, the amount of activity of the user tends to be large, and the user uses it. The weighting coefficient of the neural network is determined so that the cleaning load of the guest rooms RA and RB is large. On the other hand, when the room temperature detected by the temperature sensor and the humidity sensor is in a temperature range where the user can comfortably spend, for example, between 18 ° C. and 25 ° C., the user turns off the air conditioner 5. If the illuminance in the room detected by the illuminance sensor is, for example, between 50 lux and 150 lux, and the user does not turn on the lighting fixture 6, the user is in the guest room RA or RB. There is a tendency for the user to be highly conscious of reducing the energy consumption of the air conditioner, and the weighting coefficient of the neural network is determined so that the cleaning load of the guest room RA and RB used by the user is reduced.

Alternatively, in each embodiment, the cleaning load when the neural network calculation unit 114 uses the neural network to clean the guest room RA (RB) from only the operation history information in the cleaning load prediction target period and the guest room information. It may be the one that asks for.

In the second embodiment, the neural network calculation unit 114 adds the above-mentioned operation history information, environmental performance information, and the like to the area information indicating the area where the accommodation facilities H1, H2, H3, ... Are present, and the accommodation facility. The cleaning load may be calculated based on at least one of the management entity identification information that identifies the management entity that manages H1, H2, H3, .... Here, the area information indicates, for example, a region-specific area identification number assigned to each area, and the management entity identification information is, for example, unique to each management entity of accommodation facilities H1, H2, H3, ... It may indicate the management entity identification number of. In this case, the cleaning load management device 2001 may include at least one of a regional information acquisition unit that acquires regional information from the customer server 10 and a management entity identification information acquisition unit that acquires management entity identification information. Good. Further, the cloud server 2100 may also include at least one of a regional information acquisition unit that acquires regional information from the customer server 10 and a management entity identification information acquisition unit that acquires management entity identification information. Then, the coefficient determination unit 2115 may determine the weight coefficient of the neural network based on the area information acquired from the customer server 10 and the management entity identification information.

In each embodiment, the schedule generation unit 117 determines the required cleaning load for the guest rooms RA and RB and the service period and the experience period of each of the cleaning workers who are working on the day when the cleaning work of the guest rooms RA and RB is performed. An example of calculating the cleaning work time in each of the guest rooms RA and RB based on the length has been described. However, the present invention is not limited to this, and for example, each guest room RA, RB is based on the gender, age, and number of guest rooms in charge of each cleaning worker who is working on the day when the schedule generation unit 117 cleans the guest rooms RA and RB. The cleaning work time in the above may be calculated.

In the cleaning load management devices 1 and 2001 according to each embodiment, the guest room information acquisition units 113 and 2112 acquire the time when the users of the guest rooms RA and RB actually check in and the time when they check out, and the guest room information. It may be stored in the storage units 133 and 2132. Here, for example, when the time for operating the air conditioner 5, the lighting fixture 6, or the bathroom ventilation fan 7 of the guest rooms RA and RB is shorter than the length of stay in the guest rooms RA and RB, the user is in the guest rooms RA and RB. The weighting coefficient is determined so that the cleaning load of the guest rooms RA and RB used by the user is reduced.

In each embodiment, the cleaning load management devices 1 and 2001 include a seasonal information generation unit (not shown) that generates seasonal information indicating the season to which the date and time indicated by the date and time information belongs based on the date and time information. You may. Here, for example, when the date and time indicated by the date and time information is included in March to June, the seasonal information generation unit assigns the seasonal information "10" indicating "spring", and the date and time indicated by the date and time information is July or If it is included in August, seasonal information "20" indicating "summer" may be added. Further, for example, when the date and time indicated by the date and time information is included in September to November, the seasonal information generation unit assigns the seasonal information "30" indicating "autumn", and the date and time indicated by the date and time information is December or 2 If it is included in the month, seasonal information "40" indicating "winter" may be added. In this case, the neural network calculation unit 114 may calculate the cleaning load from the seasonal information together with the operation history information, the environmental performance information, and the guest room information.

Further, various functions of the cleaning load management devices 1, 2001 and the cloud server 2100 according to the present invention may be realized by software, firmware, or a combination of software and firmware. In this case, the software or firmware is written as a program, and the program can be read by a computer such as a flexible disc, a CD-ROM (Compact Disc Read Only Memory), a DVD (Digital Versatile Disc), and an MO (Magneto-Optical Disc). A computer capable of realizing each of the above-mentioned functions may be configured by storing and distributing the program in a recording medium, reading the program into a computer, and installing the program. Then, when each function is realized by sharing the OS (Operating System) and the application, or by cooperating with the OS and the application, only the part other than the OS may be stored in the recording medium.

Further, it is also possible to superimpose each program on the carrier wave and distribute it via the network. For example, the program may be posted on a bulletin board system (BBS, Bulletin Board System) on the network and distributed via the network. Then, by starting these programs and executing them in the same manner as other application programs under the control of the OS, the above-mentioned processing may be executed.

The present invention is suitable for a system for creating a cleaning schedule for each room of an accommodation facility.

1,2001,3001 Cleaning load management device, 2 terminal device, 4 operation device, 5 air conditioner, 6 lighting equipment, 7 bathroom ventilation fan, 8 wireless communication device, 10 customer server, 91 router, 92 data circuit-terminating equipment, 101 , 201,401,501,1001 CPU, 102,202,402,502,1002 Main memory, 103,203,403,503,1001 Auxiliary storage, 104 Local communication interface, 105,305 Out-of-home communication interface, 109 , 209,409,509,1009 Bus, 111,415 Operation history acquisition unit, 112 Cleaning load acquisition unit, 113,2112 Room information acquisition unit, 114, 2113 Neural network calculation unit, 115,2115 Coefficient determination unit, 116,2114 Coefficient setting unit, 117 schedule generation unit, 118 schedule transmission unit, 119 environment record acquisition unit, 131,432 operation history storage unit, 132 cleaning load storage unit, 133,2132 guest room information storage unit, 134,2133 neural network storage unit, 135 Predictive load storage, 136,231 Schedule storage, 137 Worker information storage, 138,434 Environmental performance storage, 204,404 Display, 205,405 Input, 206 Wireless module, 211,411 Reception, 212,413 Display control unit, 213 load record acquisition unit, 214 load record transmission unit, 215 schedule acquisition unit, 232 load record storage unit, 233,433 image storage unit, 407,507 device communication interface, 412 control information generation unit, 414 Control information transmission unit, 416 Operation history transmission unit, 417 Environmental information acquisition unit, 418 Environmental performance transmission unit, 431 Control information storage unit, 506 Local communication interface, 511 Operation control unit, 512 Environmental information generation unit, 513 Relay unit, 2111 Teacher information acquisition unit, 2116 coefficient information generation unit, 2117 coefficient information transmission unit, 2131 teacher information storage unit, BA1, BA2 touch area, GA1, GA2, GA3, GA4 operation screen image, H, H1, H2, H3 accommodation facility , M1, MA1, MA2 area, MQ1, MQ2 message, NW1 wide area network, NW2 local network, RA, RB guest room

Claims (10)

An operation history acquisition unit that acquires operation history information indicating the operation history of at least one device installed in the room, and an operation history acquisition unit.
A cleaning load acquisition unit that acquires load record information indicating the cleaning load, which is the workload required to clean the room, and a cleaning load acquisition unit.
A coefficient determination unit that determines the neural network coefficient of the neural network for obtaining the cleaning load when the user cleans the room after staying in the room based on the operation history information and the load record information. ,
Using the neural network whose neural network coefficient is determined by the coefficient determining unit, the operation history information in the cleaning load prediction target period including at least a part of the period during which the user stayed in the room is used to obtain the room. It is equipped with a neural network calculation unit that calculates the cleaning load when cleaning.
Cleaning load management device. It is further equipped with an environmental performance acquisition department that acquires environmental performance information that indicates the performance of the room environment.
The coefficient determination unit determines the neural network coefficient based on the environmental performance information together with the operation history information and the load performance information.
The neural network calculation unit uses the neural network whose coefficient is determined by the coefficient determination unit to clean the room from the operation history information and the environmental performance information in the cleaning load prediction target period. Find the cleaning load when doing,
The cleaning load management device according to claim 1. Further provided with a guest room information acquisition unit for acquiring guest room information including at least one of the size of the room, the type of user staying in the room, and the number of users staying in the room.
The coefficient determination unit determines the neural network coefficient based on the guest room information together with the operation history information and the load record information.
The neural network calculation unit cleans the room from the operation history information and the guest room information in the cleaning load prediction target period by using the neural network in which the neural network coefficient is determined by the coefficient determination unit. Find the cleaning load when
The cleaning load management device according to claim 1 or 2. A schedule generation unit that generates schedule information indicating a cleaning schedule of the room based on the cleaning load obtained by the neural network calculation unit is further provided.
The cleaning load management device according to any one of claims 1 to 3. The schedule generation unit generates the schedule information based on the usage status information indicating whether or not the user is staying in the room.
The cleaning load management device according to claim 4. The neural network calculation unit cleans the room from the operation history information in the period from the beginning of the cleaning load prediction target period to the past time point by a preset margin period from the end of the cleaning load prediction target period. Find the cleaning load when doing,
The cleaning load management device according to any one of claims 1 to 5. An operation history acquisition unit that acquires operation history information indicating the operation history of at least one device installed in the room, and an operation history acquisition unit.
A neural network for determining the degree of the cleaning load required for cleaning the room with respect to the operation history of the device as the degree of the workload required for cleaning the room after the user of the room stays in the room. The coefficient acquisition unit that acquires the neural network coefficient, and
Using the neural network set to the neural network coefficient acquired by the coefficient acquisition unit, the room is based on the operation history information in the cleaning load prediction target period including at least a part of the period in which the user stayed in the room. It is equipped with a neural network calculation unit that calculates the cleaning load when cleaning.
Cleaning load management device. An operation history acquisition unit that acquires operation history information indicating the operation history of at least one device installed in the room, and an operation history acquisition unit.
A cleaning load acquisition unit that acquires load record information indicating the cleaning load, which is the workload required to clean the room, and a cleaning load acquisition unit.
A coefficient determination unit that determines the neural network coefficient of the neural network for obtaining the cleaning load when the user cleans the room after staying in the room based on the operation history information and the load record information. ,
Using the neural network whose neural network coefficient is determined by the coefficient determining unit, the operation history information in the cleaning load prediction target period including at least a part of the period during which the user stayed in the room is used to obtain the room. It is equipped with a neural network calculation unit that calculates the cleaning load when cleaning.
Cleaning load management system. A step to acquire operation history information indicating the operation history of at least one device installed in the room, and
The step of acquiring load record information indicating the cleaning load, which is the workload required for cleaning the room, and
Based on the operation history information and the load record information, a step of determining the neural network coefficient of the neural network for obtaining the cleaning load when the user cleans the room after staying in the room, and
When cleaning the room from the operation history information in the cleaning load prediction target period including at least a part of the period in which the user stayed in the room using the neural network in which the neural network coefficient is determined. Including steps to determine the cleaning load,
Cleaning load management method. Computer,
An operation history acquisition unit that acquires operation history information indicating the operation history of at least one device installed in a room.
Cleaning load acquisition unit that acquires load record information indicating the cleaning load, which is the workload required to clean the room.
A coefficient determination unit that determines the neural network coefficient of the neural network for obtaining the cleaning load when the user cleans the room after staying in the room based on the operation history information and the load record information.
Using the neural network whose neural network coefficient is determined by the coefficient determining unit, the operation history information in the cleaning load prediction target period including at least a part of the period during which the user stayed in the room is used to obtain the room. Neural network calculation unit that calculates the cleaning load when cleaning,
A program to function as.