Patent Publication Number: US-2023160590-A1

Title: Data collection apparatus, remote control system, and data collection method

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a U.S. national stage application of International Patent Application No. PCT/JP2020/017931 filed on Apr. 27, 2020, the disclosure of which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to a data collection apparatus, a remote control system, a data collection method, and a program. 
     BACKGROUND 
     Services are known for control, maintenance, or the like of, through a remote operation, a facility device system including facility devices arranged in a real-estate property such as an office building and a commerce facility. In such services, data is collected from systems to which the service is provided, and processing such as control and maintenance is executed by utilizing an analysis result of the collected data. For example, Patent Literature 1 discloses a technique in which (i) a centralized management center that collects operation information of air conditioners determines whether maintenance is necessary based on the operation information collected thereby and (ii) a service provider performs maintenance of the air conditioners based on information that is received from the centralized management center and that relates to necessity of maintenance. 
     Patent Literature 
     Patent Literature 1: Unexamined Japanese Patent Application Publication No. 2001-357113 
     In order to provide a high-quality service with respect of control, maintenance, or the like, collecting from multiple systems a wide variety of types of data of operations of the systems, device models of facility devices, or the like, is important. Collecting a wide variety of types of data comprehensively and analyzing the wide variety of types of data enable more advanced control, a more accurate malfunction diagnosis, or the like. However, conventional techniques such as the aforementioned technique are not devised for efficiently collecting a wide variety of types of data. 
     SUMMARY 
     The present disclosure is made in view of the aforementioned circumstances, and an objective of the present disclosure is to provide a data collection apparatus, a remote control system, a data collection method, and a program that enable efficiently collecting a wide variety of types of data from a facility device system. 
     To achieve the aforementioned objective, a data collection apparatus according to the present disclosure includes collection means for collecting operation data indicating an operation status of a facility device system, storage means for storing the operation data, evaluation means for calculating an evaluation value indicating an evaluation of the collected operation data based on a comparison between the collected operation data and the stored operation data, determination means for determining compensation for provision of the collected operation data based on the evaluation value, and presentation means for presenting the determined compensation to a provider of the collected operation data. 
     According to the present disclosure, a data collection apparatus, a remote control system, a data collection method, and a program that enable efficiently collecting a wide variety of types of data from a facility device system can be provided. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram of a remote control system according to Embodiment 1; 
         FIG.  2    is a block diagram illustrating hardware configuration of a data collection apparatus and a control apparatus according to Embodiment 1; 
         FIG.  3    is a block diagram illustrating functional configuration of the data collection apparatus and the control apparatus according to Embodiment 1; 
         FIG.  4    illustrates an example of operation data according to Embodiment 1; 
         FIG.  5    illustrates an example of a distribution of operating times according to Embodiment 1; 
         FIG.  6    illustrates an example of utilization data according to Embodiment 1; 
         FIG.  7    illustrates an example of an image including information relating to remote control according to Embodiment 1; 
         FIG.  8    is a flowchart of data collection processing executed by the data collection apparatus according to Embodiment 1; 
         FIG.  9    is a flowchart of control processing executed by the control apparatus according to Embodiment 1; 
         FIG.  10    is a block diagram illustrating functional configuration of a data collection apparatus and a control apparatus according to Embodiment 2; 
         FIG.  11    illustrates an example of additional data according to Embodiment 2; and 
         FIG.  12    is a flowchart of data collection processing executed by the data collection apparatus according to Embodiment 2. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiment 1 
     A remote control system  1  according to Embodiment 1 is a system that performs remote control of a facility device system constructed in a real-estate property. The facility device system is, for example, an air conditioning system that controls air conditioners, an illumination system that controls illumination devices, or a system that controls elevators. In the explanation below, the facility device system is taken to be an air conditioning system. 
     A manager of the remote control system  1  is taken to be a provider of a service for air conditioners such as automatic control, maintenance, or the like of the air conditioners, and is taken to have a contract for providing the service with an owner or a manager of an air conditioning system  300 . 
     As illustrated in  FIG.  1   , the remote control system  1  includes a data collection apparatus  100  that collects operation data of facility device systems and a control apparatus  200  that performs remote control of facility devices based on the collected operation data. The data collection apparatus  100  and the control apparatus  200  are mutually connected via a non-illustrated network. 
     The data collection apparatus  100  is connected to a plurality of the air conditioning systems  300  via a network. The air conditioning systems  300  are taken to be arranged in mutually different real-estate properties. Each of the real-estate properties is, for example, an office, a commerce facility, or an apartment house. The data collection apparatus  100  collects the operation data from the air conditioning systems  300 . 
     The control apparatus  200  performs remote control of the air conditioning systems  300  based on the operation data collected by the data collection apparatus  100 . The remote control is, for example, automatic control of air conditioners included in the air conditioning systems  300 , or a malfunction diagnosis of the air conditioning systems  300 . 
     Each of the air conditioning systems  300  includes an air conditioner  310 , a system controller  320 , and a connection terminal  330 . 
     The air conditioner  310  includes one or more indoor units  311  and one or more outdoor units  312 . The one or more indoor units  311  are arranged to the interior of an air-conditioning target space whereas the one or more outdoor units  312  are arranged to the exterior of the air-conditioning target space. Each of the one or more indoor units  311  and the one or more outdoor units  312  includes a sensor for measuring a temperature, a pressure, or the like. 
     The system controller  320  is a device that controls operation of the air conditioner  310 . The system controller  320  is communicable with the one or more indoor units  311  and the one or more outdoor units  312  via wireless communication or wired communication. The system controller  320  acquires the operation data that includes values set for the one or more indoor units  311  and the one or more outdoor units  312 , values measured by the sensors included in the one or more indoor units  311  and the one or more outdoor units  312 , and the like. 
     The connection terminal  330  is connected to the data collection apparatus  100  via a network  400 . Further, the connection terminal  330  is communicable with the system controller  320  via wireless communication or wired communication. The connection terminal  330  acquires the operation data from the system controller  320  and transmits the acquired operation data to the data collection apparatus  100 . 
     The network  400  is a wireless or wired communication network such as the Internet, an intranet, an extranet, a local area network (LAN), a virtual private network (VPN), and a telephone network. 
     Next, hardware configuration of the data collection apparatus  100  and the control apparatus  200  is described with reference to  FIG.  2   . The data collection apparatus  100  includes a processor  11  that executes processing for collecting data, a main storage  12  that is used by the processor  11  as a work area, an auxiliary storage  13  that stores various types of data used for processing by the processor  11 , a communicator  14  for communication with an external device, an inputter  15  that acquires inputted information, an outputter  16  that presents various types of information, and a real time clock (RTC)  17  that performs clocking. The main storage  12 , the auxiliary storage  13 , the communicator  14 , the inputter  15 , the outputter  16 , and the RTC  17 , are each connected to the processor  11  via a bus  18 . 
     The processor  11  includes a central processing unit (CPU). The processor  11  executes a program stored in the auxiliary storage  13 , thereby achieving various functions of the data collection apparatus  100 . 
     The main storage  12  includes a random access memory (RAM). The program is loaded from the auxiliary storage  13  into the main storage  12 . The main storage  12  is used by the processor  11  as a work area. 
     The auxiliary storage  13  includes a nonvolatile memory such as an electrically erasable programmable read-only memory (EEPROM). The auxiliary storage  13  stores the program and various types of data used for processing by the processor  11 . The auxiliary storage  13  supplies, in accordance with instructions from the processor  11 , to the processor  11  data to be used by the processor  11 , and stores data supplied from the processor  11 . 
     The communicator  14  includes a network interface circuit for communication with an external device. The communicator  14  receives a signal from the external device and outputs to the processor  11  data indicated by the signal. Further, the communicator  14  transmits to the external device a signal indicating data outputted from the processor  11 . 
     The inputter  15  includes an input device such as input keys and a pointing device. The inputter  15  acquires information inputted by a user of the data collection apparatus  100  and sends the acquired information to the processor  11 . 
     The outputter  16  includes an output device such as a liquid crystal display (LCD) and a speaker. The outputter  16  may be included in a touch screen that is formed integrally with a pointing device included in the inputter  15 . The outputter  16  presents various types of information to the user in accordance with instructions from the processor  11 . 
     The RTC  17  is a device for clocking that includes an oscillator circuit achieved by a crystal oscillator. The RTC  17  has, for example, a built-in battery, to continue clocking even during power-off of the data collection apparatus  100 . 
     Next, hardware configuration of the control apparatus  200  is described with reference to  FIG.  2   . The control apparatus  200  includes a processor  21  that executes remote control processing, a main storage  22  that is used by the processor  21  as a work area, an auxiliary storage  23  that stores various types of data used for processing by the processor  21 , a communicator  24  for communication with an external device, an inputter  25  that acquires inputted information, an outputter  26  that presents various types of information, and an RTC  27  that performs clocking. The main storage  22 , the auxiliary storage  23 , the communicator  24 , the inputter  25 , the outputter  26 , and the RTC  27  are each connected to the processor  21  via a bus  28 . 
     The processor  21 , the main storage  22 , the auxiliary storage  23 , the communicator  24 , the inputter  25 , the outputter  26 , and the RTC  27  have respective functions that are similar to those of the processor  11 , the main storage  12 , the auxiliary storage  13 , the communicator  14 , the inputter  15 , the outputter  16 , and the RTC  17 . 
     Next, functions of the data collection apparatus  100  and the control apparatus  200  are described with reference to  FIG.  3   . First, the functions of the data collection apparatus  100  are described. The data collection apparatus  100  functionally includes a collector  101  that collects the operation data from each of the air conditioning systems  300 , an operation data storage  102  that stores the operation data, an evaluator  103  that calculates an evaluation value indicating an evaluation of the operation data, a determiner  104  that determines compensation for provision of the operation data based on the evaluation value, and a presenter  105  that presents the determined compensation. 
     The collector  101  collects the operation data indicating an operation status of the air conditioning system  300 . The collector  101  is achieved by cooperation between the processor  11  and the communicator  14 . The collector  101  is an example of collection means. 
     The operation data is data indicating the operation status of the air conditioning system  300 . The operation data includes dynamic data that changes over time and static data that remains static over time. The dynamic data is, for example, information relating to (i) a remote-controller set value such as a temperature setting, a wind direction, and a wind velocity, (ii) a value of the sensor included in the air conditioner  310 , and (iii) an operating time, an operation mode, presence/absence of abnormality, and content of control of the air conditioner  310 . The static data is, for example, information of a device model of the air conditioner  310  and information of a system scale of the air conditioning system  300 . The information of the system scale is, for example, information of a number of the indoor units  311  included in the air conditioning system  300  and a number of the outdoor units  312  included in the air conditioning system  300 . In the present embodiment, the dynamic data included in the operation data is taken to be time-series data at one-minute intervals. 
     The collector  101  collects the operation data from each of the air conditioning systems  300 , for example, periodically at one-hour intervals. That is to say, the collector  101  collects, together every one hour, the operation data including the time-series data at one-minute intervals. The intervals at which the collection is performed are freely determined by a manager of the data collection apparatus  100 . The static data included in the operation data is not necessarily collected periodically and may be collected just at predetermined timings. For example, the collector  101  may collect the static data at a time of startup of the air conditioner  310 . 
     The collector  101  transmits the collected operation data to the control apparatus  200  that is described later. Further, the collector  101  registers the collected operation data in the operation data storage  102  after the evaluator  103  that is described later calculates the evaluation value for the collected operation data. 
     The operation data storage  102  stores the operation data of each of the air conditioning systems  300  to which a service of remote control is provided by the remote control system  1 . The operation data storage  102  is achieved by the auxiliary storage  13 . The operation data storage  102  is an example of storage means. 
       FIG.  4    illustrates an example of the operation data stored in the operation data storage  102 . The table of  FIG.  4    is taken to indicate, for example, the operation data collected from the air conditioning system  300  constructed in a real-estate property A. The air conditioning system  300  constructed in the real-estate property A is taken to be assigned the air conditioning system ID “ 0001 ”. The operation data storage  102  stores, for each of the air conditioning systems  300 , a table of the operation data as illustrated in  FIG.  4   . In the table of  FIG.  4   , a device model of the air conditioner  310  included in an air conditioning system  300 , a system scale of the air conditioning system  300 , date and time of measurement of the dynamic data of the operation data, a temperature setting for the air conditioner  310 , a temperature sensor value measured by a temperature sensor of the one or more indoor units  311 , a pressure sensor value measured by a pressure sensor of the one or more outdoor units  312 , a used function used in the air conditioning system  300 , an abnormality code indicating content of an abnormality occurring in the air conditioning system  300 , an operation mode of the air conditioner  310 , and an electric power consumption amount of the air conditioner  310 , are registered in association with one another. 
     The temperature sensor value is, for example, an inlet temperature that is a temperature of air drawn through an inlet of the one or more the indoor units  311 . The pressure sensor value is, for example, a value of a pressure of a refrigerant flowing through piping of the one or more outdoor units  312 . 
     The used function is a function relating to control of the air conditioner  310  of the air conditioning system  300  and is a function that enables achievement of energy conservation. For example, “normal” indicates a control state in which no special control for achievement of energy conservation is performed. The “function A” indicates, for example, evaporation temperature control that is control of a temperature in an evaporator for suppressing electric power consumption in response to approach to a set temperature. The “function B” indicates, for example, rotation control that is control in which multiple indoor units  311  are regarded as one group and operations of the indoor units of the group are sequentially switched such that at least one indoor unit of the indoor units of the group operates to suppress electric power consumption by, for example, stopping operation or performing ventilation. 
     The abnormality code is a code associated with content of an abnormality that may possibly occur in the air conditioning system  300 . Here “absent” as the abnormality code indicates that no abnormality occurs in the air conditioning system  300 . The “code A” indicates, for example, leakage of refrigerant in the one or more outdoor units  312 . 
     The electric power consumption amount indicates an amount of electric power consumed by the air conditioner  310  during a predetermined unit period. In the present embodiment, the unit period is one minute, and the electric power consumption amount in the present embodiment is an electric power amount measured in one minute immediately before the date and time of measurement of the operation data. 
     The record in the first line of the table of  FIG.  4    indicates that the device model of the air conditioner  310  included in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” is “A11-1”, this air conditioning system  300  includes “twelve indoor units and two outdoor units”, the dynamic data of the operation data has been measured at “8:00 on Jul. 1, 2019”, the temperature setting for the air conditioner  310  is “26° C.”, the temperature sensor value is “26.5° C.”, the pressure sensor value is “12.0 Pa”, the used function is “function A”, the operation mode is “cooling”, presence/absence of an abnormality is “absent”, and the electric power consumption amount is “0.1 kWh”. 
     The evaluator  103  of  FIG.  3    calculates the evaluation value indicating an evaluation of the collected operation data based on a comparison between the collected operation data and the stored operation data. The evaluator  103  is achieved by the processor  11 . The evaluator  103  is an example of evaluation means. 
     The evaluation of the operation data is freely determined by the manager of the remote control system  1 , for example, by the determination method “the evaluation of the operation data is higher as a degree to which the manager desires to acquire the operation data” is higher. In the present embodiment, (i) as rarity of operation data is higher, that is, as an amount of the operation data stored in the operation data storage  102  is smaller, a degree to which the manager desires to acquire operation data of the same type as the operation data is taken to be higher, and (ii) such operation data is taken to have a high evaluation. For example, a distribution of the operation data is obtained with respect to a predetermined evaluation item, and operation data belonging to the same class are regarded as being of the same type. 
     The evaluator  103  obtains a distribution of the stored operation data with respect to the predetermined evaluation item, and calculates for the collected operation data an evaluation value that is higher as an amount of the operation data included in the class of the obtained distribution to which the collected operation data belongs is smaller. The amount of the operation data is represented by, for example, a data amount such as a number of samples or bytes. Hereinafter, the amount of the stored operation data is called the “accumulation amount”, and the accumulation amount is represented by the number of samples. 
     A method performed by the evaluator  103  for calculating the evaluation value is specifically described. The evaluator  103  firstly obtains, for the collected operation data, values with respect to predetermined evaluation items. The evaluation items are, for example, the operating time, the device model, the system scale, the abnormality code, and the used function. 
     If the collector  101  is taken to have collected from the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” the operation data from 8:00 to 9:00 on Mar. 7, 2020, the evaluator  103  obtains, with respect to the evaluation item “operating time”, an accumulated operating time from the date and time of construction of the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” to 9:00 on Mar. 7, 2020. With respect to the evaluation item “device model”, the evaluator  103  obtains a device model number indicating a device model of the air conditioner  310  included in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”. With respect to the evaluation item “system scale”, the evaluator  103  obtains a number of the one or more indoor units  311  included in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” and a number of the one or more outdoor units  312  included in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”. With respect to the evaluation item “abnormality code”, the evaluator  103  obtains an abnormality code included in the operation data from 8:00 to 9:00 on Mar. 7, 2020. With respect to the evaluation item “used function”, the evaluator  103  obtains a value of a used function included in the operation data from 8:00 to 9:00 on Mar. 7, 2020. 
     Then the evaluator  103  obtains distributions of the operation data of the other air conditioning systems  300  stored in the operation data storage  102  with respect to the evaluation items. For example, with respect to the evaluation item “operating time”, the evaluator  103  obtains operating times of the other air conditioning systems  300  and obtains a histogram as illustrated in  FIG.  5    in which classes are set based on the operating time. In the histogram of  FIG.  5   , one class is set for every 5,000 hour period. If the operating time obtained for the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” is taken to be “6,000 hours”, the evaluator  103  (i) determines that the operating time of the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” belongs to the class of 5,000-10,000 hours in the histogram of  FIG.  5   , that is, the rectangle marked with diagonal lines in  FIG.  5   , and (ii) obtains a sample number X 1  that is a number of samples belonging to the class of 5,000-10,000 hours. Further, the evaluator  103  obtains a sample number Y 1  that is a number of samples included in all of the classes in the histogram of  FIG.  5   . Similarly, for each of the other evaluation items, the evaluator  103  obtains a histogram and then obtains (i) a sample number Xi (i is 1˜n, where n is a number of the evaluation items) that is a number of samples in the class to which the collected operation data belongs and (ii) a sample number Yi that is a number of samples in all classes. 
     Then the evaluator  103  calculates, for each of the evaluation items, an evaluation value i based on Formula 1 below. 
       Evaluation value  i= 1.0− Xi/Yi    (Formula 1)
 
     The evaluation value obtained by Formula 1 above is closer to 1 as the number of samples in the class to which the collected operation data belongs is smaller. That is to say, the evaluation value obtained for the collected operation data is higher as the accumulation amount of the operation data that can be deemed as being of the same type as the collected operation data is smaller. 
     The determiner  104  determines, based on the evaluation values, compensation for provision of the collected operation data. The determiner  104  is achieved by the processor  11 . The determiner  104  is an example of determination means. 
     The compensation is a reward given for a provider of the collected operation data as a reward for the provision. The provider of the operation data is typically an owner of the air conditioning system  300  that receives a remote control service. In the explanation below, the compensation is taken to be an evaluation point that can be used to discount a service utilization charge for the remote control system. The evaluation point is, for example, an average value of the evaluation values i obtained for the evaluation items. Upon determining the evaluation point based on the average value of the evaluation values i, the determiner  104  registers the determined point in a table stored in a utilization data storage  202  of the control apparatus  200  that is described later. 
     The presenter  105  presents the determined compensation to the provider of the collected operation data. The presenter  105  is achieved by the processor  11 . The presenter  105  is an example of presentation means. 
     If, for example, the operation data from 8:00 to 9:00 on Mar. 7, 2020, is collected from the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”, the presenter  105  sends, to the connection terminal  330  in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”, a message including a value of the evaluation point. 
     Next, functions of the control apparatus  200  are described with reference to  FIG.  3   . The control apparatus  200  includes a controller  201  that executes remote control, a utilization data storage  202  that stores information relating to utilization of the remote control, and a notifier  203  that sends notification of information relating to the remote control. 
     The controller  201  executes remote control of the air conditioning systems  300  based on the collected operation data. The controller  201  is achieved by cooperation between the processor  21  and the communicator  24 . The controller  201  is an example of control means. 
     The controller  201  includes a control executor  211  that executes control of the air conditioners  310  of the air conditioning systems  300 , and a malfunction diagnoser  212  that performs a malfunction diagnosis of the air conditioning systems  300 . The control executor  211 , for example, predicts a demand electric power of the next day based on the operation data stored in the operation data storage  102  and executes control for electric-load leveling based on the predicted demand electric power and the collected operation data. The malfunction diagnoser  212 , for example, diagnoses that a sign of malfunction is found in response to a high similarity between (i) the collected operation data and (ii) the operation data that is from other real-estate properties in which a malfunction occurs previously and that is data before occurrence of the malfunction. 
     Further, the controller  201  registers, in the utilization data storage  202 , information of a time at which the service of the remote control is provided. 
     The utilization data storage  202  stores utilization data relating to the service for the multiple air conditioning systems  300 . The utilization data storage  202  is achieved by the auxiliary storage  23 . 
     The utilization data is data indicating statuses of utilization of the service by the air conditioning systems  300 . The utilization data includes, for example, a utilization time of the service, a utilization charge for the service, and the evaluation point. 
       FIG.  6    illustrates an example of the utilization data stored in the utilization data storage  202 . In the table of  FIG.  6   , an air conditioning system ID for identification of the air conditioning system  300 , a utilization time that is a time spent to utilize the service, a charge required for utilization of the service for the utilization time, and the evaluation point obtained for the operation data collected in the utilization time are registered in association with one another. The evaluation point is a value registered by the determiner  104 . 
     The first line of the table of  FIG.  6    indicates that the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” has utilized the remote control service from 8:00 on Mar. 7, 2020 to 9:00 on Mar. 7, 2020, the charge for utilization of the service during this period is “10 yen”, and the evaluation point given for provision of the operation data collected during this period is “0.4”. 
       FIG.  7    illustrates an example of the information relating to the remote control of which notification is sent by the notifier  203 . Upon the notifier  203  transmitting the information relating to the remote control to, for example, the connection terminal  330  of the air conditioning system  300  from which the operation data is collected, the image  500  of  FIG.  7    is displayed on a screen included in the system controller  320 . 
     The information relating to the remote control is, for example, information relating to the content of control executed by the control executor  211  and a result of the malfunction diagnosis. Further, the information relating to the remote control includes a value of cost that is required for execution of the remote control and that is obtained with reference to compensation for provision of data. The value of cost that is required for execution of the remote control and that is obtained with reference to compensation is, for example, a value of a billing amount obtained by deducting the evaluation point from the service utilization charge. The notifier  203  obtains the billing amount with reference to the items of the utilization charge and the evaluation point in the table of  FIG.  6    that is stored in the utilization data storage  202 . The billing amount is taken to be obtained by, for example, subtracting, from a utilization charge per the utilization time, a value obtained by multiplying the evaluation point by a predetermined price. For example, in the case in which the predetermined price is “10 yen”, the billing amount for the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” for the period “from 8:00 on Mar. 7, 2020 to 9:00 on Mar. 7, 2020” is obtained by “10 (yen)−0.4×10 (yen)=6 (yen)”. 
       FIG.  7    illustrates an example of the information relating to the remote control of which notification is sent by the notifier  203 . Upon the notifier  203  transmitting the information relating to the remote control to, for example, the connection terminal  330  of the air conditioning system  300  from which the operation data is collected, the image  500  of  FIG.  7    is displayed on a screen included in the system controller  320 . 
     Next, processing executed by the data collection apparatus  100  and the control apparatus  200  of the remote control system  1  according to the present embodiment is described with reference to flowcharts. 
     First, data collection processing executed by the data collection apparatus  100  is described with reference to the flowchart of  FIG.  8   . The data collection apparatus  100  executes, for example, the data collection processing of  FIG.  8    in a one-hour period. 
     The collector  101  collects the operation data from the air conditioning system  300  (step S 101 ). The evaluator  103  obtains a value of the collected operation data with respect to a predetermined evaluation item (step S 102 ). Then the evaluator  103  obtains a distribution of the operation data of the air conditioning system  300  stored in the operation data storage  102  with respect to the evaluation item (step S 103 ). Then the evaluator  103  calculates an evaluation value based on a class of the distribution of the stored operation data with respect to the evaluation item to which the value of the collected operation data with respect to the evaluation item belongs (step S 104 ). 
     The determiner  104  determines an evaluation point based on the evaluation value (step S 105 ). The presenter  105  presents the determined point to the provider of the collected operation data (step S 106 ). 
     Next, control processing executed by the control apparatus  200  is described with reference to the flowchart of  FIG.  9   . The data control apparatus  200  executes, for example, the control processing of  FIG.  9    in a one-hour period upon ending of the data collection processing by the data collection apparatus  100 . 
     The control executor  211  transmits, based on the collected operation data, to the air conditioning system  300 , information indicating content of control in the next period (step S 201 ). The malfunction diagnoser  212  performs a malfunction diagnosis of the collected operation data (step S 202 ). 
     The notifier  203  obtains the billing amount per the utilization time based on the utilization data stored in the utilization data storage  202  (step S 203 ). Then the notifier  203  sends, to the provider of the operation data, notification of the information relating to the remote control and including the billing amount (step S 204 ). 
     According to the present embodiment, compensation for provision of the collected operation data is determined based on an evaluation of the collected operation data, and the determined compensation is presented to the provider of the collected operation data. This enables prompting of the provision of the operation data, enabling prompt collection of a wide variety of types of data. Further, high compensation is determined for data that the manager of the remote control system desires to acquire whereas low compensation is determined for data for which acquisition is not as much desired by the manager, thereby enabling cost that is lower than in a case in which fixed compensation is given for any data collection. Thus, according to the present embodiment, collection of a wide variety of types of data that is efficient in view of time and cost can be performed. 
     For example, in a case in which data is evaluated to have an evaluation that is higher as the accumulation amount of the operation data in the remote control system is smaller, high compensation is paid for provision of data during a period shortly after the start of operation of the remote control system, since the amount of data accumulated in the remoted control system is small during this period. In contrast, after the amount of accumulated data is large through operation by the remote control system for a long period of time after the start of operation, low compensation is paid for provision of data. This configuration enables prompting a customer who enjoys a service of the remote control system to provide data during a period shortly after the start of operation of the remote control system, thus enabling prompt collection of data. Further, the customer who utilizes the remote control system can utilize the service at a low price during the period shortly after start of operation of the remote control system. 
     Furthermore, according to the aforementioned embodiment, in a case in which, for example, the manager of the remote control system desires to acquire the operation data of a particular device model, a formula is settable by which the evaluation value is calculated such that a high evaluation value is obtained for the operation data of this device model. For example, in a case in which acquisition of the operation data from an air conditioning system including an air conditioner of a device model for a cold weather region is desired, high compensation is given for provision of such operation data. Although an amount of data collectable from a cold weather region is small since a number of air conditioning systems in such a region is small due to sparse population, giving high compensation enables prompting a customer to provide data, enabling collecting a larger amount of data promptly. 
     Furthermore, according to the present embodiment, compensation for provision of data collected on a large scale can be automatically calculated and presented, thereby eliminating the need for analyzing the collected data by humans, and accordingly enabling low operational cost. 
     Furthermore, a wide variety of types of data is collectable in the remote control system according to the present embodiment, and thus using such types of data enables improving quality of service. Further, collecting a wide variety of types of data by using the data collection apparatus according to the present embodiment enables performing a high-accuracy market analysis based on the collected data, enabling development of a product having a high added value for customers. 
     Embodiment 2 
     A remote control system  1  according to Embodiment 2 is a system that collects additional data in addition to the operation data. 
     The remote control system  1  according to Embodiment 2 includes a data collection apparatus  100  and a control apparatus  200 . The control apparatus  200  of Embodiment 2 has functions that are similar to those of the control apparatus  200  of Embodiment 1. The data collection apparatus  100  of Embodiment 2 has functions described below, in addition to the functions of the data collection apparatus  100  of Embodiment 1. 
     The functions of the data collection apparatus  100  of the present embodiment are described with reference to  FIG.  10   . The data collection apparatus  100  functionally includes a collector  101  that collects the operation data from each of the air conditioning systems  300 , an operation data storage  102  that stores the operation data, an evaluator  103  that calculates an evaluation value indicating an evaluation of the operation data, a determiner  104  that determines compensation for provision of the operation data based on the evaluation value, a presenter  105  that presents the determined compensation, a receiver  106  that receives the additional data, and a classifier  107  that classifies the operation data. 
     The receiver  106  receives input of the additional data. The receiver  106  is achieved by cooperation between the processor  11  and the communicator  14 . The receiver  106  is an example of receiving means. 
     The additional data is data to be associated with the operation data and is data that cannot be collected automatically from a facility device system as a remote control target, that is, cannot be collected in the manner of collecting the operation data. That is to say, the additional data is data that is unavailable without being input from the exterior. The input from the exterior may be performed manually or automatically. The additional data is, for example, an installation location of the air conditioner  310 , building information of a building in which the air conditioning system  300  is constructed, and a use application of the air conditioning system  300 . Examples of the installation location include an arrangement of indoor units within a floor, and an installation location of outdoor units. The installation location of the outdoor units is, for example, a location such as a roof, a veranda, and the ground. The building information includes an age, a size, an insulation performance, a piping length, a location and orientation of a window, or the like. The building information may be building information modeling (BIM) data. The use application is, for example, an item indicating an office, a shop, a hospital, or the like. 
     The receiver  106 , upon receiving the input of the additional data, registers the additional data in the operation data storage  102  in association with the operation data. For example, in a case of receiving input of the building information of a real-estate property in which the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” is constructed, the receiver  106  registers the inputted building information in the operation data storage  102  in association with the operation data of the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”. 
       FIG.  11    illustrates an example of the additional data stored in the operation data storage  102 . In the table of  FIG.  11   , an air conditioning system ID for identification of an air conditioning system  300 , the installation location of the air conditioner  310 , the building information of a real-estate property in which the air conditioning system  300  is constructed, and the use application of the real-estate property in which the air conditioning system  300  is constructed are registered in association with one another. In this example, the installation location of the air conditioner  310  is taken to be the installation location of the one or more outdoor units  312 , and the building information is taken to be the age. The additional data is associated with the operation data of  FIG.  4    using the air conditioning system ID as a key. 
     For example, the first line of the table of  FIG.  11    indicates that the one or more outdoor units  312  of the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” is installed on the “roof”, the age of the real-estate property in which the air conditioning system  300  is constructed is “one year”, and the use application of this real-estate property is the “office”. 
     The classifier  107  classifies the operation data stored in the operation data storage  102  into one or more groups based on the values the operation data stored in the operation data storage  102 . The classifier  107  performs the classification upon the determiner  104  determining that the additional data is added to the collected operation data. The classifier  107  is achieved by the processor  11 . The classifier  107  is an example of classification means. 
     For example, clustering that is an example of unsupervised learning is employed to perform the classification of the operation data. Hereinafter, classification by employment of k-means clustering is described. Clusters obtained through the classification are taken to be the aforementioned “groups”. First, the operation data stored in the operation data storage  102  is divided into data pieces that each are a data piece per unit time. Thereafter, evaluation items used for the classification are determined. In the present embodiment, the unit time is taken to be one hour, and the classification is performed based on the temperature sensor value and the pressure sensor value. Since the operation data is data measured every one minute, each of the data pieces obtained through the dividing by the unit time is representable by a multidimensional vector having dimensions of 60 (a number of pieces of measured data)×2 (the temperature sensor value and the pressure sensor value). 
     For example, in the case in which the operation data storage  102  stores the operation data of the air conditioning system  300  corresponding to the air conditioning system ID “ 0002 ” for 1,000 hours, the air conditioning system  300  corresponding to the air conditioning system ID “ 0002 ” has 1,000 multidimensional vectors. As described above, the air conditioning system  300  has multiple multidimensional vectors for the operation data. The classifier  107  performs clustering of all of multidimensional vectors that all of the air conditioning systems  300  have and that are stored in the operation data storage  102 . 
     Then the determiner  104  determines, from among the one or more groups obtained through the classification, a group to which the collected operation data belongs. 
     For example, the determiner  104  (i) calculates a distance between each of vectors of centroids of k clusters obtained by the k-means method and a vector indicating the collected operation data and (ii) determines a cluster having a centroid of which vector defines the shortest distance from the vector indicating the collected operation data to be the cluster to which the collected operation data belongs. 
     The determiner  104  obtains, for the pieces of the operation data included in the cluster determined to be the cluster to which the collected operation data belongs, an appending rate that is a rate of appending of the additional data. The appending rate is a ratio, to all of the pieces of the operation data included in the determined cluster, of pieces of the operation data to which the additional data is appended. 
     Then the determiner  104  corrects the compensation based on the appending rate, more specifically, obtains a corrected evaluation point based on Formula 2 below. 
       Corrected evaluation point=1.0−Appending rate×(1.0−Evaluation point)   (Formula 2)
 
     The corrected evaluation point obtained by Formula 2 above is closer to the evaluation point determined based on the accumulation amount of the operation data as the appending rate is higher, that is, as a rate of appending of the additional data to the pieces of the operation data included in the cluster to which the collected operation data belongs is higher. In contrast, the corrected evaluation point is higher as the rate of appending of the additional data to the pieces of the operation data included in the cluster to which the collected operation data belongs is lower. For example, the evaluation point corrected by Formula (2) above is, in the case in which the appending rate is 100%, a value that is the same as the evaluation point determined based on the accumulation amount of the operation data. In contrast, in the case in which the appending rate is 0%, the evaluation point is 1. 
     The presenter  105  presents the corrected evaluation point to the provider of the collected operation data. For example, in a case in which (i) the operation data from 8:00 to 9:00 on Mar. 7, 2020, is collected from the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ” and (ii) the additional data is appended to the collected operation data, the presenter  105  sends, to the connection terminal  330  in the air conditioning system  300  corresponding to the air conditioning system ID “ 0001 ”, a message including a value of the corrected evaluation point. 
     Next, data collection processing executed by the data collection apparatus  100  of the present embodiment is described with reference to the flowchart of  FIG.  12   . The data collection apparatus  100  executes, for example, the data collection processing of  FIG.  12    in a one-hour period. 
     The processing in steps S 301 -S 305  is similar to the processing of steps S 101 -S 105  in the flowchart of  FIG.  8   . 
     The determiner  104  determines whether the additional data is appended to the collected operation data (step S 306 ). Upon the determiner  104  determining that the additional data is appended to the collected operation data (YES in step S 306 ), the classifier  107  classifies the operation data stored in the operation data storage  102  into multiple clusters based on the values of the operation data (step S 307 ). Conversely, upon the determiner  104  determining that the additional data is not appended to the collected operation data (NO in step S 306 ), the processing proceeds to step S 310  and the presenter  105  presents the evaluation point obtained in step S 305  (step S 310 ). 
     Upon the operation data stored in the operation data storage  102  being classified into multiple clusters in step S 307 , the determiner  104  determines, based on a value of the collected operation data, from among the clusters obtained through the classification, a cluster to which the collected operation data belongs (step S 308 ). The determiner  104  obtains the appending rate that is a rate of addition of the additional data to the operation data included in the determined cluster, and corrects the evaluation point based on the appending rate (step S 309 ). Then the presenter  105  presents the corrected evaluation point (step S 310 ). 
     According to the present embodiment, upon the collected operation data being operation data that belongs to a group in which a ratio of the operation data to which the additional data is appended is low and being operation data to which the additional data is appended, high compensation is determined for provision of the collected operation data. This configuration enables prompting appending of the additional data, enabling efficient collection of the additional data that is difficult to acquire automatically from the system. 
     Modified Examples 
     The aforementioned embodiments of the present disclosure may be modified and applied in various manners. 
     Although the data collection apparatus  100  and the control apparatus  200  of the aforementioned embodiments are taken to be connected via a network, this configuration is not limiting. One apparatus may double as the data collection apparatus  100  and the control apparatus  200 . Further, a part or all of the elements included in the data collection apparatus  100  and the control apparatus  200  may be provided on a cloud server. 
     Although the evaluator  103  of the aforementioned embodiments obtains a distribution of the operation data with respect to each single evaluation item such as “operating time”, such a distribution may be obtained with respect to a combination of multiple evaluation items. For example, a histogram may be obtained in which classes are set based on an item that is a combination of the “device model” and the “abnormality code”. Further, for example, the accumulation amount of the operation data in an air conditioning system constructed in a general office is large whereas the accumulation amount of the operation data in an air conditioning system constructed in a general hospital ward is small. In such a case, creating a class representing a characteristic of hospital wards by combining multiple evaluation items enables efficiently collecting the operation data from hospital wards. 
     Furthermore, although the compensation is taken to be the evaluation point that can be used to discount a service utilization charge in the explanation in the aforementioned embodiments, the compensation is not limited thereto and may be, for example, cash, a ticket for a free on-site service in a case of malfunction, or a right to enjoy a more expensive remote control service. 
     Furthermore, although the evaluation point is corrected based only on the appending rate of the additional data only in the aforementioned embodiment, this configuration is not limiting, and a correction amount of the evaluation point may be changed according to a type of the additional data. For example, the correction amount is changed based on a utility value of the additional data, a degree of difficulty in acquiring the additional data, or the like. Specifically, for the operation data to which data that affects efficiency of an air conditioner, such as data relating to an insulation performance of a real-estate property, is appended, correction is performed such as heightening of the value of the evaluation point. Additionally, for the operation data to which data that changes depending on a status of use of a real-estate property, such as data of a number of residents in the real-estate property and a number of uses of the real-estate property, is appended, correction is performed such as heightening of the value of the evaluation point. This configuration enables defining the compensation according to a type of the additional data, enabling cost of compensation for the additional data that is lower than in a case in which fixed compensation is paid for any additional data. 
     Furthermore, although the data collection processing of  FIG.  8    is executed in a one-hour period and the control processing of  FIG.  9    is executed in a one-hour period upon ending of the data collection processing in the aforementioned embodiments, this configuration is not limiting. For example, each of the data collection processing of  FIG.  8    and the control processing of  FIG.  9    may be executed periodically at a predetermined timing. 
     Furthermore, the order of steps in the control processing executed by the control apparatus  200  in the aforementioned embodiments is not limited to the order of  FIG.  9   . For example, the order of step S 201  and step S 202  is reversible. 
     Furthermore, applying to an existing personal computer or to an existing information terminal device an operation program defining operations of the data collection apparatus  100  and the control apparatus  200  according to the aforementioned embodiments can allow the personal computer or the information terminal device to function as the data collection apparatus  100  and the control apparatus  200  according to the aforementioned embodiments. 
     Furthermore, such a program may be distributed by any method, for example, may be distributed by storing the program in a computer-readable recording medium, such as a compact disk read only memory (CD-ROM), a digital versatile disk (DVD) and a memory card, or may be distributed via a communication network such as the Internet. 
     The foregoing describes some example embodiments for explanatory purposes. Although the foregoing discussion has presented specific embodiments, persons skilled in the art will recognize that changes may be made in form and detail without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. This detailed description, therefore, is not to be taken in a limiting sense, and the scope of the invention is defined only by the included claims, along with the full range of equivalents to which such claims are entitled. 
     INDUSTRIAL APPLICABILITY 
     According to the present disclosure, a data collection apparatus, a remote control system, a data collection method, and a program that enable efficiently collecting a wide variety of types of data from a facility device system can be provided.