Air conditioning system, method for controlling the air conditioning system, and program

Abstract

This disclosure provides an air conditioning system that can prevent the setting of an inappropriate temperature in the air conditioning unit. [Solution] The air conditioning system in this disclosure includes a learning unit that learns data related to user operations to change the set temperature, a determination unit that automatically determines the optimal set temperature based on the learning results of the learning unit, and a setting unit that sets the set temperature determined by the determination unit to the air conditioning device, wherein the learning unit changes the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period required for the air conditioning device to a different pattern from the learning pattern for periods other than the target period.

Description

Technical Field

[0006] ,

[0001] The present disclosure relates to an air conditioning system, a control method for the air conditioning system, and a program.

Background Art

[0002] Conventionally, a technique for setting the set temperature of an air conditioner to suppress the energy consumption of the air conditioner is known. For example, Patent Document 1 discloses an air conditioner that determines a set temperature from the outside air temperature detected by an outside air temperature sensor according to a control parameter for determining the set temperature based on the outside air temperature, and sets the determined set temperature.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present disclosure provides an air conditioning system, a control method for the air conditioning system, and a program that can suppress setting an inappropriate set temperature for the air conditioner.

Means for Solving the Problems

[0005] The air conditioning system in the present disclosure includes a learning unit that learns data related to a change operation of a set temperature by a user, a determination unit that automatically determines an optimal set temperature based on the learning result of the learning unit, and a setting unit that sets the set temperature determined by the determination unit to the air conditioner. The learning unit changes a learning pattern for a target period including either or both of a failure period of the air conditioner and a maintenance required period of the air conditioner to a pattern different from a learning pattern for a period other than the target period.

[0006] Furthermore, the control method for an air conditioning system in this disclosure is a control method for an air conditioning system that air-conditions a space to be air-conditioned using an air conditioning device, the method learning data related to user operations to change the set temperature, automatically determining the optimal set temperature based on the learning results, setting the automatically determined set temperature to the air conditioning device, and in the learning of the data, changing the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period required for the air conditioning device to a different pattern from the learning pattern for periods other than the target period.

[0007] Furthermore, the program in this disclosure causes the computer of an air conditioning system that air-conditions a space to be air-conditioned by an air conditioning device to function as a learning unit that learns data related to user operations to change the set temperature, a determination unit that automatically determines the optimal set temperature based on the learning results of the learning unit, and a setting unit that sets the set temperature determined by the determination unit to the air conditioning device. The learning unit changes the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period required for the air conditioning device to a different pattern from the learning pattern for periods other than the target period. [Effects of the Invention]

[0008] The air conditioning system, control method for the air conditioning system, and program described herein can prevent the air conditioning system from setting an inappropriate temperature. [Brief explanation of the drawing]

[0009] [Figure 1] Diagram showing the configuration of the air conditioning system in Embodiment 1 [Figure 2] Diagram showing the configuration of the terminal device in Embodiment 1 [Figure 3] Diagram showing the configuration of the management server in Embodiment 1. [Figure 4] A diagram showing an example of records held by the management DB in Embodiment 1. [Figure 5] A diagram showing an example of change history data in Embodiment 1. [Figure 6] Flowchart showing the operation of the management server in Embodiment 1 [Figure 7] Flowchart showing the operation of the learning unit in Embodiment 1 [Figure 8] Flowchart showing the operation of the learning unit in Embodiment 1 [Figure 9] Flowchart showing the operation of the learning unit in Embodiment 2 [Figure 10] A diagram showing an example of updating change history data in Embodiment 2. [Figure 11] A diagram showing an example of updating change history data in Embodiment 2. [Figure 12] Flowchart showing the operation of the learning unit in Embodiment 2 [Figure 13] A diagram showing an example of updating change history data in Embodiment 2. [Figure 14] A diagram showing an example of updating change history data in Embodiment 2. [Figure 15] Flowchart showing the operation of the learning unit in Embodiment 3 [Figure 16] Flowchart showing the operation of the learning unit in Embodiment 3 [Modes for carrying out the invention]

[0010] (Knowledge and other information that formed the basis of this disclosure) When the inventors came up with the present disclosure, there was a technology for automatically setting the set temperature of an air conditioner in order to suppress the energy consumption of the air conditioner. Conventionally, in this technology, data related to the change of the set temperature is collected from the air conditioner, the collected data is learned, the set temperature is automatically determined based on the learning result, and the determined set temperature is set in the air conditioner. However, conventionally, regardless of whether the air conditioner is in a failure state or not, and regardless of whether maintenance is required for the air conditioner, the collected data is used for learning. Generally, when the air conditioner is in a failure state or when maintenance is required for the air conditioner, a different usage pattern from normal occurs in the air conditioner. Therefore, the inventors have discovered that conventionally, there is a case where learning is performed using data unsuitable for learning, and there is a possibility that an inappropriate set temperature is set in the air conditioner. In order to solve this problem, the inventors have come to constitute the subject of the present disclosure. Therefore, the present disclosure provides an air conditioning system, a control method of the air conditioning system, and a program that can suppress setting an inappropriate set temperature in an air conditioner.

[0011] Hereinafter, embodiments will be described in detail with reference to the drawings. However, there may be cases where more detailed explanations than necessary are omitted. For example, there may be cases where detailed explanations of already well-known matters or duplicate explanations for substantially the same configurations are omitted. Note that the attached drawings and the following description are provided for those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0012] (Embodiment 1) [1-1. Configuration] [1-1-1. Configuration of the air conditioning system] FIG. 1 is a diagram showing the configuration of an air conditioning system 1000. The air conditioning system 1000 is a system that air-conditions an air-conditioned space S by an air conditioner 1. The air-conditioned space S is a space possessed by a facility H.

[0013] The air conditioning system 1000 of this embodiment is a system that sets the set temperature to the air conditioning device 1.

[0014] The air conditioning system 1000 includes an air conditioning unit 1. In Figure 1, the air conditioning system 1000 includes four or more air conditioning units 1. However, the number of air conditioning units 1 included in the air conditioning system 1000 is not limited to four or more, and may be less than four. The air conditioning unit 1 includes an indoor unit 11 and an outdoor unit 12, and the indoor unit 11 and the outdoor unit 12 perform air conditioning operation to air condition the air-conditioned space S in which the indoor unit 11 is installed. The air conditioning unit 1 is connected to a network NW and communicates with devices connected to the network NW. The network NW is a communication network consisting of a public telephone network, a dedicated line, the internet, or other communication networks.

[0015] The air conditioning system 1000 includes a terminal device 2. The terminal device 2 is used by the maintenance worker P who maintains the air conditioning unit 1. The terminal device 2 shown in Figure 1 is a laptop computer, but it may also be a tablet computer, a desktop computer, or a smartphone. The terminal device 2 is connected to a network NW.

[0016] Terminal device 2 connects to the network NW and communicates with the management server 3, which will be described later. In this embodiment, terminal device 2 receives a specification from the maintenance person P for the period during which maintenance was required for the air conditioner 1 (hereinafter referred to as the "maintenance required period"), and sends maintenance required period data D1 indicating the received maintenance required period to the management server 3. The maintenance required period data D1 contains the air conditioner ID (Identification), which is the identification information of the air conditioner 1. When maintenance person P cleans the air conditioner 1 during an inspection, etc., they input the most recent predetermined period as the maintenance required period to terminal device 2, depending on the degree of dirt on the air conditioner 1. For example, suppose the current inspection is at the end of November and the previous inspection was at the end of August. Also, suppose that maintenance person P cleaned the fan and filter during the current inspection, and maintenance person P determined that the maintenance required period was from September to November. In this case, maintenance person P inputs September to November as the maintenance required period to terminal device 2. Maintenance worker P may also input only November as the required maintenance period, depending on the degree of soiling of air conditioning unit 1.

[0017] The air conditioning system 1000 includes a management server 3 that manages the air conditioning unit 1. The management server 3 is connected to a network NW and processes information with the air conditioning unit 1 and terminal device 2 as clients.

[0018] The air conditioning system 1000 is equipped with a weather server 4. The weather server 4 is a server device that provides weather data. The weather data provided by the weather server includes forecast values ​​for the outside temperature of facility H. The forecast value for the outside temperature of facility H can be any forecast value corresponding to facility H. In other words, the forecast value for the outside temperature of facility H can be the forecast value for the outside temperature of the address of facility H, or it can be the forecast value for the outside temperature of the area where facility H is located, based on the address or postal code of facility H.

[0019] In each diagram, the management server 3 and the weather server 4 are represented by a single block. However, this does not necessarily mean that the management server 3 and the weather server 4 are composed of a single device. For example, the management server 3 and the weather server 4 may consist of multiple server devices with different processing functions, or they may be composed of the same server device.

[0020] [1-1-2. Configuration of the air conditioning system] Referring to Figure 1, the configuration of the air conditioning system 1 will be described. The air conditioning system 1 comprises an indoor unit 11, an outdoor unit 12, a remote control 13, and a communication device 14. The number of indoor units 11 and outdoor units 12 in the air conditioning system 1 may be multiple.

[0021] The indoor unit 11 and the outdoor unit 12 are connected by refrigerant piping and control wiring. Thus, in the air conditioning system 1, the indoor unit 11 and the outdoor unit 12 constitute a refrigerant cycle.

[0022] The remote control 13 is installed on a wall or the like in the air-conditioned space S. The remote control 13 receives requests from the user of the air conditioning unit 1 to set at least the set temperature of the air conditioning unit 1.

[0023] The communication device 14 is connected to the network NW and communicates with the management server 3. The communication device 14 also controls various parts of the air conditioning unit 1. Whenever the set temperature of the air conditioning unit 1 is changed, the communication device 14 sends operation data D2 to the management server 3. Operation data D2 is an example of "data related to the operation of changing the set temperature".

[0024] Operation data D2 indicates that an operation to change the set temperature has been received. Operation data D2 records the air conditioner ID, the date and time of the change, the set temperature before the change, and the set temperature after the change. The change date and time is the date and time when the set temperature of air conditioner 1 was changed. The pre-change setting temperature is the pre-change setting temperature for air conditioning unit 1. The changed set temperature is the set temperature of the air conditioning unit 1 after the change.

[0025] The communication device 14 transmits failure data D3 to the management server 3 when the air conditioner 1 malfunctions and issues an error code. In this embodiment, a failure of the air conditioner 1 does not include a failure in communication with the management server 3. Also, in this embodiment, a failure refers to the state of the air conditioner 1 corresponding to the occurrence of an error code; if no error code is generated, it is not considered a failure. Failure data D3 is data indicating that a failure has occurred, and the air conditioner ID is recorded on it.

[0026] When the air conditioning unit 1 is repaired and the issuance of error codes stops, the communication device 14 sends fault completion data D4 to the management server 3. Fault completion data D4 is data indicating that the fault has been resolved and contains the air conditioner ID.

[0027] The communication device 14 receives setting data D5 from the management server 3. Setting data D5 is data that instructs the setting of the set temperature, and the set temperature to be set in the air conditioner 1 is recorded. The communication device 14 operates the air conditioner 1 at the set temperature recorded in the received setting data D5.

[0028] [1-1-3. Terminal Device Configuration] Figure 2 shows the configuration of terminal device 2. Terminal device 2 comprises a terminal control device 20, a terminal communication unit 21, a display unit 22, and an input unit 23.

[0029] The terminal control device 20 is a device that controls various parts of the terminal device 2. The terminal control device 20 includes a terminal processor 200 such as a CPU (Central Processing Unit), a terminal memory 210, and an interface circuit. Other devices and sensors of the terminal device 2 are connected to this interface circuit.

[0030] The terminal memory 210 is a memory that stores programs and data. The terminal memory 210 stores the control program 211 and data to be processed by the terminal processor 200. The terminal memory 210 has a non-volatile storage area. In addition, the terminal memory 210 has a volatile storage area that constitutes the work area of ​​the terminal processor 200. The terminal memory 210 is composed of, for example, ROM (Read Only Memory) and RAM (Random Access Memory).

[0031] The terminal communication unit 21 is equipped with communication hardware such as communication circuits that conform to a predetermined communication standard, and communicates with each device connected to the network NW.

[0032] The display unit 22 is equipped with a display composed of elements such as liquid crystal and LED (Light Emitting Diode). The display unit 22 displays various information according to the control of the terminal control device 20.

[0033] The input unit 23 is equipped with an interface circuit for connecting to devices such as operation switches, touch input panels, mice, and keyboards, and detects the input operations of maintenance worker P and outputs the detection results to the terminal processor 200.

[0034] The terminal processor 200 functions as a display control unit 201, a reception unit 202, and a terminal communication control unit 203 by reading and executing a control program 211 stored in the terminal memory 210.

[0035] The display control unit 201 causes the display unit 23 to display a specification screen. The specification screen is a screen for specifying the maintenance period.

[0036] The reception unit 202 receives the specification of the maintenance period via the input unit 23 through the maintenance screen displayed by the display control unit 201. The reception unit 202 also receives the input of the air conditioner ID of the air conditioning system 1 to be maintained.

[0037] The terminal communication control unit 203 communicates with the management server 3 via the terminal communication unit 21. When the reception unit 202 receives the specification of the maintenance period and the input of the air conditioner ID, the terminal communication control unit 203 sends maintenance period data D1, which indicates the maintenance period received by the reception unit 202, to the management server 3. This maintenance period data D1 contains the air conditioner ID received by the reception unit 202.

[0038] [1-1-4. Management Server Configuration] Figure 3 shows the configuration of the management server 3. The management server 3 comprises a server control device 30 and a server communication unit 31. The server control device 30 is an example of a "computer".

[0039] The server control device 30 includes a server processor 300 such as a CPU, server memory 310, and interface circuits to which other devices and sensors are connected.

[0040] The server memory 310 is a memory that stores programs and data. The server memory 310 stores the control program 311, the management database 312, and data processed by the server processor 300. The server memory 310 has a non-volatile storage area. Alternatively, the server memory 310 may also have a volatile storage area that constitutes the work area of ​​the server processor 300. The server memory 310 is composed of, for example, ROM or RAM. Control program 311 is an example of a "program".

[0041] The management DB312 is a database that manages information and data. The management DB312 has one record R for each air conditioning unit 1.

[0042] Figure 4 shows an example of a record R in the management DB312. Each record R contains the air conditioner ID, fault information, communication information, location of facility H, current set temperature, change history data D6, first stored data, and second stored data.

[0043] The fault information pertains to a malfunction in the air conditioning system 1. The fault information records whether a malfunction occurred in the air conditioning system 1, and, if so, the date and time the malfunction began. The communication information is information for communicating with the air conditioning device 1, and is, for example, the address information of the communication device 14. The location of facility H is the location of facility H where the air conditioning system 1 is installed, and is, for example, the address of facility H. The currently set temperature is the temperature set in air conditioning unit 1.

[0044] Change history data D6 is data showing the change history of the set temperature of the air conditioner 1. As will become clear later, the change history shown in change history data D6 is the learning result of the learning unit 305. Figure 5 shows an example of change history data D6.

[0045] Change history data D6 records time periods in one-hour increments from 0:00 to 23:59. Additionally, change history data D6 records multiple outside temperatures in 1°C increments.

[0046] The change history data D6 records one collected data D7 for each time period and one outside temperature. Collected data D7 is data that collects various data within a predetermined range of the set temperature. The set temperature to be automatically set in the air conditioner 1 is determined from the predetermined range of the set temperature indicated by the collected data D7.

[0047] The collected data D7 records the set temperatures of multiple air conditioners 1 in 1°C increments within a predetermined range. In the example in Figure 5, the collected data D7 records 23°C, 24°C, 25°C, 26°C, and 27°C. In other words, in the example in Figure 5, the predetermined range of set temperatures shown in the collected data D7 is from 23°C to 27°C.

[0048] The collected data D7 records the number of actual events, the number of changes, and the probability of change for each recorded set temperature. In the example in Figure 5, the collected data D7 records the number of actual events, the number of changes, and the probability of change for each of the following temperatures: 23°C, 24°C, 25°C, 26°C, and 27°C. The "number of occurrences" indicates the number of times the corresponding set temperature was set in the air conditioning unit 1. The number of changes indicates the number of times the temperature setting has been changed from one setting to another. The change probability indicates the probability that the temperature was changed from a corresponding set temperature to another set temperature. The change probability is calculated by dividing the number of corresponding changes by the number of actual changes.

[0049] The first stored data is the data that stores the set temperature-related data D8. The set temperature-related data D8 is data related to the set temperature set in the air conditioner 1, and records the time period, outside temperature, and the set temperature set in the air conditioner 1.

[0050] The second stored data is the data that stores the operation data D2. The operation data D2 stored in the second storage data is linked to the outside temperature.

[0051] The server communication unit 31 is equipped with hardware such as a communication circuit that conforms to a predetermined communication standard, and communicates with the air conditioning unit 1, the terminal device 2, and the weather server 4 in accordance with the control of the server control device 30.

[0052] The server processor 300 functions as a server communication control unit 301, acquisition unit 302, collection unit 303, update unit 304, learning unit 305, determination unit 306, and setting unit 307 by reading and executing the control program 311 stored in the server memory 310.

[0053] [1-1-4-1. Server Communication Control Unit] The server communication control unit 301 communicates with the air conditioning unit 1, the terminal device 2, and the weather server 4 via the server communication unit 31.

[0054] [1-1-4-2. Acquisition section] The acquisition unit 302 acquires the outside air temperature. The acquisition unit 302 selects one record R as the target of processing and acquires the outside air temperature based on the record R being processed.

[0055] More specifically, the acquisition unit 302 outputs request information to the server communication control unit 301 based on the record R to be processed. The request information is information requesting the outside temperature for the time period including the current time, and it records the location of the facility H included in the record R to be processed and the time period to be requested. When the server communication control unit 301 receives the request information from the acquisition unit 302, it transmits the received request information to the weather server 4. The server communication control unit 301 then receives multiple weather data from the weather server 4 corresponding to the transmitted request information. The acquisition unit 302 calculates the average of the forecast values ​​of the weather data received by the server communication control unit 301 and acquires the calculated average value as the outside temperature.

[0056] [1-1-4-3. Collection Department] The collection unit 303 collects the operation data D2 received by the server communication control unit 301. The collection unit 303 collects the operation data D2 in the following manner.

[0057] The collection unit 303 identifies record R from the management DB 312 based on the air conditioner ID recorded in the operation data D2 received by the server communication control unit 301. Next, the collection unit 303 instructs the acquisition unit 302 to acquire the outside air temperature based on the identified record R. Then, the collection unit 303 associates the outside air temperature acquired by the acquisition unit 302 with the operation data D2 received by the server communication control unit 301 and stores it in the second stored data of the identified record R.

[0058] [1-1-4-4. Update section] The update unit 304 updates the current set temperature of record R. When the server communication control unit 301 receives operation data D2, the update unit 304 identifies record R from the management DB 312 based on the air conditioner ID recorded in the received operation data D2. Next, the update unit 304 updates the current set temperature of the identified record R to the changed set temperature recorded in the received operation data D2.

[0059] The update unit 304 updates the fault information of record R. When the server communication control unit 301 receives fault occurrence data D3, the update unit 304 identifies record R from the management DB 312 based on the air conditioner ID recorded in the received fault occurrence data D3. Next, the update unit 304 updates the fault information of the identified record R to information indicating that a fault has occurred. When the server communication control unit 301 receives the failure termination data D4, the update unit 304 identifies record R from the management DB 312 based on the air conditioner ID recorded in the received failure termination data D4. Next, the update unit 304 updates the failure information of the identified record R to information indicating that no failure occurred.

[0060] [1-1-4-5. Learning Department] The learning unit 305 learns operation data D2 by updating change history data D6.

[0061] The following provides a detailed explanation of the learning content in Learning Unit 305. The learning unit 305 updates the actual count recorded in the collected data D7 every hour at L (an integer from 0 to 59) minutes. The learning unit 305 updates the actual count for each record R.

[0062] More specifically, when L minutes have passed in an hour, the learning unit 305 first causes the acquisition unit 302 to acquire the outside temperature based on the record R to be processed. Next, the learning unit 305 generates setting temperature-related data D8, which includes the time period including the current time, the outside temperature acquired by the acquisition unit 302, and the current setting temperature recorded in the record R to be processed. Then, the learning unit 305 stores the generated setting temperature-related data D8 in the first stored data recorded in the record R to be processed. Furthermore, the learning unit 305 refers to the change history data D6 of the record R to be processed and identifies the collected data D7 corresponding to the time period including the current time and the outside temperature acquired by the acquisition unit 302. Next, the learning unit 305 increments the number of actual values ​​recorded in the identified collected data D7 that corresponds to the current set temperature recorded in the record R to be processed. The learning unit 305 also updates the change probability corresponding to the incremented number of actual values ​​according to the number of actual values ​​after the increment.

[0063] Figure 5 will be used to explain how to update the actual figures. Let's take an example where the current time when updating the performance count is in the 2 o'clock hour, and the outside temperature acquired by the acquisition unit 302 is 21°C. In this example, as shown in Figure 5, the learning unit 305 identifies the collected data D7 corresponding to the time period "2 o'clock hour" and the outside temperature "21°C" from the change history data D6 recorded in the record R to be processed.

[0064] If the current set temperature recorded in the record R to be processed is 25°C, the learning unit 305 increments the number of actual occurrences corresponding to the set temperature "25°C" from "4" to "5" among the actual occurrences recorded in the identified collected data D7. In conjunction with this increment, the learning unit 305 also updates the change probability corresponding to the set temperature "25°C" from "3 / 4" to "3 / 5".

[0065] The learning unit 305 updates the change history data D6 based on the operation data D2 collected by the collection unit 303. The learning unit 305 identifies record R from the management DB 312 based on the air conditioner ID recorded in the collected operation data D2. Next, the learning unit 305 causes the acquisition unit 302 to acquire the outside air temperature based on the identified record R. Next, the learning unit 305 identifies the collected data D7 corresponding to the time period including the current time and the acquired outside air temperature from the change history data D6 of the identified record R. Next, the learning unit 305 refers to the collected operation data D2 and increments the number of changes recorded in the identified collected data D7 that corresponds to the pre-change setting temperature recorded in the collected operation data D2. The learning unit 305 also updates the change probability corresponding to the incremented number of changes to reflect the change probability after the increment.

[0066] Figure 5 will be used to explain how to update the number of changes. Let's take an example where the modification date and time of operation data D2 is in the 2 o'clock hour, and the outside temperature acquired by the acquisition unit 302 is 21°C. In this example, as shown in Figure 5, the learning unit 305 identifies the collected data D2 corresponding to the time period "2 o'clock hour" and the outside temperature "21°C" from the change history data D6 recorded in the record R to be processed.

[0067] If the pre-change setting temperature recorded in operation data D2 is 23°C, the learning unit 305 increments the number of changes corresponding to the setting temperature "23°C" from "0" to "1" among the number of changes recorded in the identified collected data D7. In addition, along with the update of the number of changes, the learning unit 305 updates the change probability corresponding to the setting temperature "23°C" from "0 / 3" to "1 / 3".

[0068] [1-1-4-6. Decision Section] The determination unit 305 automatically determines the optimal set temperature based on the learning results of the learning unit 305. In other words, the determination unit 305 automatically determines the optimal set temperature based on the change history shown in the change history data D6. This determination will be explained when Figure 6 is described.

[0069] [1-1-4-7. Settings Section] The setting unit 307 automatically sets a temperature that can suppress the energy consumption of the air conditioner 1. Details of the setting unit 307 will be explained later with reference to the flowchart.

[0070] [1-2. Operation] Next, the operation of each part of the air conditioning system 1000 according to Embodiment 1 will be described. First, we will explain the operation of management server 3 in relation to the automatic setting of the set temperature. Figure 6 is a flowchart showing the operation of the management server 3.

[0071] The flowchart in Figure 6 is a flowchart that starts at K minutes past every hour. Here, K is an integer from 0 to 59, for example, 0. Furthermore, the flowchart in Figure 6 is a flowchart performed for each air conditioning unit 1. In other words, the flowchart in Figure 6 is an operation performed for each record R stored in the management DB 312.

[0072] The acquisition unit 302 acquires the ambient temperature based on the record R to be processed (step SA1).

[0073] Next, the setting unit 307 identifies the collection data D7 to be processed from the change history data D6 recorded in the record R to be processed (step SA2). More specifically, the setting unit 307 identifies the collection data D7 corresponding to the time period including the current time and the outside temperature obtained in step SA1 from the change history data D6 recorded in the record R to be processed.

[0074] Next, the determination unit 306 performs a determination process (step SA3). The decision process is the process of automatically determining the optimal set temperature for the air conditioning unit 1. In the decision process, the collected data D7 identified in step SA2 is the target of processing. In the determination process, the determination unit 306 refers to the collected data D7 identified in step SA2 and determines the set temperature to be set in the air conditioner 1, which corresponds to a change probability of 0.1 (10% in percentage terms) or less than a predetermined threshold (for example, 0.1). To explain using Figure 5, in Figure 5, the change probabilities corresponding to 25°C, 26°C, and 27°C all exceed the predetermined threshold of 10%, while the change probabilities corresponding to 23°C and 24°C are all 0%. In this case, the determination unit 306 determines 24°C as the optimal set temperature because it is the highest temperature that does not exceed the predetermined threshold of 10%. Note that the predetermined threshold being 10% is merely an example, and the predetermined threshold does not have to be 10%.

[0075] By performing this determination process, the determination unit 306 can determine the set temperature to be set on the air conditioner 1 based on the number of times the set temperature of the air conditioner 1 has been changed, thereby determining the set temperature of the air conditioner 1 to a temperature that is unlikely to cause discomfort to the user. Therefore, the determination unit 306 can determine the set temperature to be set on the air conditioner 1 to a temperature that can reduce the number of times the set temperature is changed. Thus, the determination unit 306 can determine the set temperature to be set on the air conditioner 1 to a temperature that can suppress the energy consumption of the air conditioner 1.

[0076] When the determination process is performed, the setting unit 307 sets the set temperature of the air conditioner 1 to the set temperature determined in step SA3 (step SA4).

[0077] Step SA4 will be described in detail. The setting unit 307 generates setting data D5. Next, the setting unit 307 outputs the generated setting data D5 and the communication information recorded in the record R to be processed to the server communication control unit 301. The generated setting data D5 contains the set temperature determined in the determination process. Based on the communication information received from the setting unit 307, the server communication control unit 301 transmits the setting data D5 received from the setting unit 307 to the communication device 14.

[0078] Next, the operation of the learning unit 305 in this embodiment will be described. When the air conditioning unit 1 is malfunctioning or requires maintenance, it will be used in a manner different from normal. An example of this unusual usage is that the set temperature may be significantly changed due to poor air conditioning performance. Therefore, during periods of malfunction or maintenance, the learning unit 305 may learn the change history using operation data D2 that is unsuitable for learning. To address this, the learning unit 305 in this embodiment performs the operations shown in Figures 7 and 8.

[0079] In the following explanation, the period that includes either or both of the period of failure and the period requiring maintenance is referred to as the "coverage period."

[0080] Figure 7 is a flowchart showing the operation of the learning unit 305. Furthermore, the flowchart in Figure 7 is a flowchart performed for each air conditioning unit 1. In other words, the flowchart in Figure 7 is an operation performed for each record R stored in the management DB 312.

[0081] The learning unit 305 determines whether or not it is time to update the change history data D6 (step SB1).

[0082] Step SB1 will be described in detail. The learning unit 305 makes a positive determination in step SB1 when it is time to update the performance count. Furthermore, the learning unit 305 makes a positive determination in step SB1 when it collects operation data D2 containing the same air conditioner ID as the record R being processed.

[0083] If the learning unit 305 determines that it is not yet time to update the change history data D6 (step SB1: NO), it terminates this process.

[0084] On the other hand, if the learning unit 305 determines that it is time to update the change history data D6 (step SB1: YES), it determines whether or not a malfunction has occurred in the air conditioning unit 1 (step SB2). The processing in step SB2 is performed by referring to the malfunction information of the record R to be processed.

[0085] If the learning unit 305 determines that a malfunction has occurred in the air conditioning unit 1 (step SB2: YES), it terminates this process without updating the change history data D6. Note that if the learning unit 305 makes a positive determination in step SB2, it does not increment the actual count, but it does generate and store the set temperature-related data D8.

[0086] On the other hand, if the learning unit 305 determines that no malfunction has occurred in the air conditioning unit 1 (step SB2: NO), it updates the change history data D6 (step SB3).

[0087] As explained above with reference to Figure 7, the learning unit 305 does not update the change history data D6 during the failure period, and therefore does not learn the operation data D2 for the failure period.

[0088] Figure 8 is a flowchart showing the operation of the learning unit 305. The operation shown in Figure 8 is performed in parallel with the operation shown in Figure 7.

[0089] The learning unit 305 determines whether the server communication control unit 301 has received the maintenance required period data D1 (step SC1).

[0090] The learning unit 305 terminates this process if it determines that the server communication control unit 301 has not received the maintenance period data D1 (step SC1: NO).

[0091] If the learning unit 305 determines that the server communication control unit 301 has received the maintenance required period data D1 (step SC1: YES), it identifies record R from the management DB 312 (step SC2). In step SC2, the learning unit 305 identifies record R from the management DB 312 based on the air conditioner ID recorded in the maintenance required period data D1.

[0092] The learning unit 305 retrieves all of the set temperature-related data D8 stored during the maintenance period indicated by the received maintenance period data D1 from the first stored data of the identified record R (step SC3). Each of the set temperature-related data D8 is associated with a date and time stored in the first stored data, and the retrieval in step SC3 is performed based on this associated date and time.

[0093] The learning unit 305 retrieves all of the operation data D2 collected during the maintenance period indicated by the received maintenance period data D1 from the second stored data of the identified record R (step SC4). The retrieval in step SC4 is performed based on the modification date and time recorded in the operation data D2.

[0094] The learning unit 305 updates the change history data D6 of the identified record R based on the set temperature-related data D8 acquired in step SC3 and the operation data D2 acquired in step SC4 (step SC5).

[0095] Step SC5 will be described in detail. The learning unit 305 decrements the actual number recorded in the change history data D6 for each acquired set temperature-related data D8, and updates the change probability in accordance with the decremented actual number.

[0096] Using Figure 5, we will explain the decrement of the actual number. For example, suppose the set temperature-related data D8 records a time period of around 2:00, an outside temperature of 21°C, and a set temperature of 26°C. In this case, the learning unit 305 identifies the collected data D7 corresponding to the time period "around 2:00" and the outside temperature "30°C". Next, the learning unit 305 decrements the number of actual events recorded in the identified collected data D7 that corresponds to the set temperature "26°C" from "3" to "2". In addition, along with the update of the number of actual events, the learning unit 305 updates the change probability corresponding to the set temperature "26°C" from "2 / 3" to "2 / 2".

[0097] Furthermore, step SC5 will be described in detail. The learning unit 305 decrements the number of changes recorded in the change history data D6 for each acquired operation data D2, and updates the change probability in accordance with the decremented number of changes.

[0098] Using Figure 5, we will explain the decrement of the number of changes. For example, suppose operation data D2 records the change date and time, which is 02:12, and the pre-change set temperature, which is 21°C. Also, suppose that the outside temperature, which is 25°C, is associated with this operation data D2. In this case, the learning unit 305 identifies collected data D7 that corresponds to the time period "2 o'clock hour" and the outside temperature "21°C," and decrements the number of changes corresponding to the set temperature "25°C" from "3" to "2" among the number of changes recorded in the identified collected data D7. The learning unit 305 also updates the change probability corresponding to the set temperature "25°C" in the identified collected data D7 from "3 / 4" to "2 / 4."

[0099] As explained above with reference to Figure 8, the learning unit 305 removes the data for the maintenance period from the change history data D6 by performing various decrement operations on the change history data D6. As a result, the learning unit 305 does not learn the operation data D2 for the maintenance period.

[0100] As described above, the learning unit 305 changes the learning pattern for the target period to a different pattern from the learning pattern for periods other than the target period. In other words, it learns the operation data D2 for periods other than the target period, but does not learn the operation data D2 for the target period. This prevents the learning of operation data D2 that is unsuitable for learning.

[0101] [1-3. Effects, etc.] As described above, the air conditioning system 1000, which air-conditions the air-conditioned space S using the air conditioning device 1, includes a learning unit 305 that learns operation data D2. The air conditioning system 1000 also includes a determination unit 305 that automatically determines the optimal set temperature based on the learning results of the learning unit 305. The air conditioning system 1000 also includes a setting unit 307 that sets the set temperature automatically determined by the determination unit 305 to the air conditioning device 1. In learning the change history, the learning unit 305 changes the learning pattern for the target period to a different pattern from the learning pattern for periods other than the target period.

[0102] According to this, by changing the learning pattern, it becomes possible to suppress the learning of unsuitable operational data D2. Therefore, it becomes possible to suppress inappropriate learning results, and thus suppress the setting of an inappropriate temperature in the air conditioner 1.

[0103] Learning unit 305 does not perform any learning during the target period.

[0104] This prevents the learning of unsuitable operational data D2. Therefore, it is possible to further suppress the occurrence of inappropriate learning results, and thus further suppress the automatic setting of an inappropriate temperature to the air conditioner 1.

[0105] In the control method for the air conditioning system 1000, operation data D2 is learned, and based on the learning results, the optimal set temperature is automatically determined and set to the air conditioning device 1. Furthermore, in the control method for the air conditioning system 1000, the learning pattern for the target period is changed to a different pattern from the learning pattern for periods other than the target period when learning operation data D2.

[0106] According to this, it will produce the same effect as the air conditioning system 1000 described above.

[0107] The control program 311 causes the server control device 30 to function as a learning unit 305, a decision unit 306, and a setting unit 307. In learning operation data D2, the learning unit 305 changes the learning pattern for the target period to a different pattern from the learning pattern for periods other than the target period.

[0108] According to this, it will produce the same effect as the air conditioning system 1000 described above.

[0109] (Embodiment 2) Next, Embodiment 2 will be described. The description of Embodiment 2 will mainly focus on the differences from Embodiment 1.

[0110] [2-1. Structure] The configuration of each part of the air conditioning system 1000 in Embodiment 2 is the same as in Embodiment 1.

[0111] [2-2. Operation] Next, the operation of the air conditioning system 1000 in Embodiment 2 will be described. In comparison with Embodiment 1, the learning unit 305 of Embodiment 2 performs the operation shown in Figure 9.

[0112] Figure 9 is a flowchart showing the operation of the learning unit 305. The flowchart in Figure 9 shows the operations performed on the record R stored in the management DB312, specifically the record R indicating that a failure has occurred.

[0113] The learning unit 305 determines whether the failure of the air conditioning unit 1 has ended (step SD1). The processing in step SD1 is performed by referring to the failure information in record R.

[0114] If the learning unit 305 determines that the malfunction of the air conditioning unit 1 has not been resolved (step SD1: NO), it terminates this process.

[0115] On the other hand, if the learning unit 305 determines that the failure of the air conditioner 1 has ended (step SD1: YES), it retrieves all of the set temperature-related data D8 stored during the failure period from the first stored data of the record R to be processed (step SD2). Each of the set temperature-related data D8 is associated with a date and time stored in the first stored data, and the retrieval in step SD2 is performed based on this associated date and time. Since the failure information includes the failure start date, the learning unit 305 can determine the failure period.

[0116] The learning unit 305 obtains the operation data D2 collected during the failure period from the second stored data recorded in record R (step SD3). The acquisition in step SD3 is performed based on the modification date and time recorded in the operation data D2.

[0117] The learning unit 305 updates the change history data D6 recorded in record R based on the acquired set temperature-related data D8 and the acquired operation data D2 (step SD4).

[0118] Step SD4 will be described in detail. The learning unit 305 refers to the acquired set temperature-related data D8 and calculates the number of times the corresponding set temperature has been set in the air conditioner 1 for each combination of time period, outside temperature, and set temperature. For example, suppose there are 10 sets of set temperature-related data D8 in the acquired set temperature-related data D8, each containing the time period "11 o'clock hour", outside temperature "30°C", and set temperature "27°C". In this example, the learning unit 305 calculates that the number of times the corresponding set temperature has been set in the air conditioner 1 for the combination of time period "11 o'clock hour", outside temperature "30°C", and set temperature "27°C" is 10.

[0119] Next, the learning unit 305 calculates the number of occurrences for each combination of time period, outside temperature, and set temperature, and then determines a number of occurrences corresponding to a predetermined ratio of the calculated number of occurrences. Then, based on the determined number of occurrences, the learning unit 305 increases the actual number recorded in the change history data D6 and updates the change probability corresponding to the increased actual number.

[0120] Here, we will explain a specific example of step SD4 with reference to Figure 10. Figure 10 shows an example of updating change history data D6.

[0121] Figure 10 illustrates an example of increasing the number of actual results for the combination of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C". Furthermore, in the explanation of Figure 10, an example is given in which, for the set of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C", the number of times the corresponding set temperature is set in the air conditioner 1 is calculated to be 100 times, and the number of times equivalent to 10% of the calculated 100 times is then determined. Note that this 10% is merely one example of a predetermined percentage, and the predetermined percentage may be other percentages such as 50%.

[0122] In the case of Figure 10, the learning unit 305 identifies the collected data D7 corresponding to the time period "14:00-25:30" and the outside temperature "34°C". Also in the case of Figure 10, the learning unit 305 changes the number of recorded events corresponding to the set temperature "25°C" from "76" to "86", increasing it by "10". This "10" is a value that is 10% lower than the calculated 100 times. In addition, the learning unit 305 updates the change probability corresponding to the set temperature "25°C" in the identified collected data D7 from "56 / 76" to "56 / 86".

[0123] Furthermore, Step SD4 will be described in detail. The learning unit 305 refers to the acquired operation data D2 and calculates the number of times the set temperature has been changed for each combination of time period, outside temperature, and pre-change set temperature. For example, suppose there are 10 pieces of acquired operation data D2 in which the outside temperature "30℃" is associated, and the change date and time "11 o'clock hour" and the pre-change set temperature "27℃" are recorded. In this example, the learning unit 305 calculates that the number of times the set temperature has been changed is 10 for the combination of time period "11 o'clock hour", outside temperature "30℃", and pre-change set temperature "27℃".

[0124] Next, the learning unit 305 calculates the number of changes for each set of time period, outside temperature, and changed set temperature, and then determines the number of changes corresponding to a predetermined percentage of the calculated number of changes. Then, based on the determined number of changes, the learning unit 305 increases the number of changes recorded in the change history data D6 and updates the change probability corresponding to the increased number of changes.

[0125] Here, we will explain a specific example of step SD4 with reference to Figure 11. Figure 11 shows an example of updating change history data D6.

[0126] Figure 11 illustrates an example of increasing the number of changes for the combination of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C". Furthermore, in the explanation of Figure 11, an example is given in which the number of changes to the set temperature is calculated as 20 for the set time "14:00-25:00", outside temperature "34°C", and set temperature "25°C", and the number of changes equivalent to 10% of the calculated 20 is then determined.

[0127] In the case of Figure 11, the learning unit 305 identifies the collected data D7 corresponding to the time period "14:00-24:30" and the outside temperature "34°C". Also in the case of Figure 11, the learning unit 305 changes the number of changes corresponding to the set temperature "25°C" among the number of changes recorded in the identified collected data D7 from "56" to "58", which is an increase of "2". This "2" is a value that is 10% lower than the calculated 20. In addition, the learning unit 305 updates the change probability corresponding to the set temperature "25°C" in the identified collected data D7 from "56 / 86" to "58 / 86".

[0128] In comparison with Embodiment 1, the learning unit 305 of Embodiment 2 performs the operation shown in Figure 12. Figure 12 is a flowchart showing the operation of the learning unit 305. In Figure 12, the same reference numerals are used for the same steps as in Figure 8. Furthermore, in the explanation of Figure 12, explanations of the same steps as in Figure 8 are omitted where appropriate.

[0129] The learning unit 305 updates the change history data D6 of the identified record R based on the acquired set temperature-related data D8 and the acquired operation data D2 (step SE1).

[0130] Step SE1 will be described in detail. The learning unit 305 refers to the acquired set temperature-related data D8 and calculates the number of times the corresponding set temperature has been set in the air conditioner 1 for each combination of time period, outside air temperature, and set temperature. Next, the learning unit 305 calculates the number of times corresponding to a predetermined percentage of the calculated number of times for each combination of time period, outside air temperature, and set temperature. Then, based on the number of times calculated, the learning unit 305 decreases the actual number recorded in the change history data D6 and updates the change probability to correspond to the decreased actual number.

[0131] Here, we will explain a specific example of Step SE1 with reference to Figure 13. Figure 13 shows an example of updating change history data D6.

[0132] Figure 13 illustrates an example of reducing the number of actual results for the combination of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C". Furthermore, in the explanation of Figure 13, an example is given in which, for the set of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C", the number of times the corresponding set temperature is set in the air conditioner 1 is calculated as 100 times, and the number equivalent to 10% of the calculated 100 times is then determined.

[0133] In the case of Figure 13, the learning unit 305 identifies the collected data D7 corresponding to the time period "14:00-25:30" and the outside temperature "34°C". Also in the case of Figure 13, the learning unit 305 reduces the number of actual occurrences corresponding to the set temperature "25°C" from "176" to "86", which is a decrease of "90". This "90" is the difference between the number of times before setting the temperature 10% lower and the number of times after setting the temperature 10% lower. In addition, the learning unit 305 updates the change probability corresponding to the set temperature "25°C" in the identified collected data D7 from "56 / 176" to "56 / 86".

[0134] Furthermore, we will elaborate on step SE1. The learning unit 305 refers to the acquired operation data D2 and calculates the number of changes to the set temperature for each set of time period, outside temperature, and pre-change set temperature. Next, after calculating the number of changes for each set of time period, outside temperature, and pre-change set temperature, the learning unit 305 determines the number of changes corresponding to a predetermined percentage of the calculated number of changes. Then, based on the determined number of changes, the learning unit 305 decreases the number of changes recorded in the change history data D6 and updates the change probability to correspond to the decreased number of changes.

[0135] Here, we will explain a specific example of Step SE1 with reference to Figure 14. Figure 14 shows an example of updating change history data D6.

[0136] Figure 14 illustrates an example of reducing the number of changes for the combination of time period "14:00-25:00", outside temperature "34°C", and set temperature "25°C". Furthermore, in the explanation of Figure 14, an example is given in which the number of changes to the set temperature is calculated as 20 for the set time "14:00-25:00", outside temperature "34°C", and set temperature "25°C", and the number of changes equivalent to 10% of the calculated 20 is then determined.

[0137] In the case of Figure 14, the learning unit 305 identifies the collected data D7 corresponding to the time period "14:00-25:30" and the outside temperature "34°C". Also in the case of Figure 14, the learning unit 305 reduces the number of changes corresponding to the set temperature "25°C" among the number of changes recorded in the identified collected data D7 from "76" to "58", which is a decrease of "18". This "18" is the difference between the number of changes before setting the temperature 10% lower and the number of changes after setting it 10% lower. In addition, the learning unit 305 updates the change probability corresponding to the set temperature "25°C" in the identified collected data D7 from "76 / 86" to "58 / 86".

[0138] As described above, the learning unit 305 of this embodiment reflects in the learning results a set temperature change count lower than the number of set temperature changes obtained from the operation data D2 for the target period. In other words, the learning unit 305 makes the number of set temperature changes to be reflected in the learning results lower than the number of operation data D2 collected for the target period.

[0139] [2-3. Effects, etc.] As explained above, the learning result, which is the history of set temperature changes, includes the number of times the set temperature has been changed. In learning, the learning unit 305 reflects the number of set temperature changes obtained from the operation data D2 in the learning result for periods other than the target period. For the target period, the learning unit 305 reflects in the learning result a set temperature change count that is set lower than the number of set temperature changes obtained from the operation data D2.

[0140] According to this approach, for the target period, the number of changes to the set temperature reflected in the learning results is reduced, thereby suppressing the learning of unsuitable operational data D2. In addition, a portion of the operational data D2 from the target period is reflected in the learning results. Therefore, after the target period ends, it is possible to suppress the setting of inappropriate temperatures due to a small number of samples of operational data D2 reflected in the learning results.

[0141] (Embodiment 3) Next, Embodiment 3 will be described. The description of Embodiment 3 will mainly explain the differences from Embodiments 1 and 2.

[0142] [3-1. Structure] In Embodiment 2, the configuration of each part of the air conditioning system 1000 differs from that of Embodiments 1 and 2 in terms of the information recorded in record R. In Embodiment 3, if the air conditioning unit 1 has a history of repairs, the corresponding record R records the date of the last repair. Also in Embodiment 3, if the air conditioning unit 1 has a history of maintenance, the corresponding record R records the date of the last maintenance.

[0143] [3-2. Operation] Next, the operation of the air conditioning system 1000 in Embodiment 3 will be described. In comparison with embodiments 1 and 2, the learning unit 305 of embodiment 3 performs the operation shown in Figure 15.

[0144] Figure 15 is a flowchart showing the operation of the learning unit 305. The flowchart in Figure 15 shows the operations performed on the record R stored in the management DB 312, specifically the record R indicating that a failure has occurred.

[0145] In Figure 15, the same reference numerals are used for the same steps as in Figure 9. Furthermore, in the explanation of Figure 15, steps identical to those in Figure 9 are omitted as appropriate.

[0146] If the learning unit 305 determines that the malfunction of the air conditioning unit 1 has ended (step SD1: YES), it performs the first adjustment process (step SF1).

[0147] The first adjustment process is the process of adjusting the learning results. In the first adjustment process, if the air conditioner 1 to be processed has a repair history, the learning unit 305 does not include the learning results from the date of the last repair until the end of the failure period in the learning results from the end of the failure period onward. Specifically, the learning unit 305 obtains the set temperature-related data D8 and operation data D2 stored from the date of the last repair until the end of the failure period from the second stored data of record R. Then, the learning unit 305 updates the change history data D6 in the same manner as in step SD4. Furthermore, in the first adjustment process, if the air conditioning unit 1 being processed has no repair history, the learning unit 305 clears the number of repairs, the number of changes, and the probability of changes included in the change history data D6. In other words, the learning unit 305 does not include learning results from before the end of the failure period in the learning results from after the end of the failure period.

[0148] In comparison with Embodiment 1, the learning unit 305 of Embodiment 3 performs the operations shown in Figure 16.

[0149] Figure 16 is a flowchart showing the operation of the learning unit 305. In Figure 16, the same reference numerals are used for the same steps as in Figure 12. Furthermore, in the explanation of Figure 16, steps that are the same as in Figure 12 are omitted where appropriate.

[0150] If the learning unit 305 determines that the server communication control unit 301 has received the maintenance required period data D1 (step SG1: YES), it performs a second adjustment process (step SG2).

[0151] The second adjustment process is the process of adjusting the learning results. In the second adjustment process, if the air conditioning unit 1 to be processed has a maintenance history, the learning unit 305 does not include the change history from the date of the last maintenance to the end of the maintenance period in the learning results from the end of the maintenance period onward. The learning unit 305 obtains the set temperature-related data D8 and operation data D2 stored from the date of the last maintenance to the end of the maintenance period from the second stored data of record R identified in step SC2. Then, the learning unit 305 updates the change history data D6 in the same manner as in step SE1. In the second adjustment process, if the air conditioning unit 1 being processed has no history of maintenance, the learning unit 305 clears the number of maintenance records, the number of changes, and the change probability recorded in the change history data D6. In other words, the learning unit 305 does not include in the learning results from the time the maintenance period ends that it has learned any change history prior to the end of the maintenance period.

[0152] [3-3. Effects, etc.] As explained above, when the target period ends, the learning unit 305 does not include learning results prior to the end of the target period in the learning results from that point onward.

[0153] At the end of the target period, the filters and fans of air conditioner 1 may have been cleaned. Therefore, assuming that air conditioner 1 has been completely replaced, the learning results from before the end of the target period are not included in the learning results from the end of the target period onward. This allows the system to learn the operation data D2 from the time the target period ends, using the operation data D2 from the replaced air conditioner 1. Thus, it is possible to prevent setting an inappropriate temperature after the end of the target period.

[0154] If the air conditioning unit 1 has a history of repairs, the learning unit 305 will not include the learning results from the date of the last repair until the end of the failure period in the learning results from the end of the failure period onward. Also, if the air conditioning unit 1 has a history of maintenance, the learning unit 305 will not include the learning results from the date of the last maintenance until the end of the required maintenance period in the learning results from the end of the required maintenance period onward.

[0155] As a result, after the target period ends, the operation data D2 of the air conditioner 1 can be properly learned as the updated operation data D2 of the air conditioner 1. Therefore, it is possible to properly prevent setting an inappropriate temperature after the target period ends.

[0156] If the air conditioner 1 has no history of repairs, the learning unit 305 will not include in the learning results learned before the end of the failure period the learning results learned after the end of the failure period. Also, if the air conditioner 1 has no history of maintenance, the learning unit 305 will not include in the learning results learned before the end of the maintenance requirement period the learning results learned after the end of the maintenance requirement period.

[0157] This allows the system to properly learn the operation data D2 of the air conditioner 1 as the newly updated operation data D2 after the target period ends. Therefore, it is possible to properly prevent setting an inappropriate temperature after the target period ends.

[0158] (Embodiment 4) Next, Embodiment 4 will be described. The description of Embodiment 4 will mainly focus on the differences from Embodiment 3.

[0159] [4-1. Structure] The configuration of each part of the air conditioning system 1000 in Embodiment 4 is the same as in Embodiment 3.

[0160] [4-2. Operation] Next, the operation of the air conditioning system 1000 in Embodiment 4 will be described. Compared to Embodiment 3, Embodiment 4 differs in its first and second adjustment processes.

[0161] In the first adjustment process of Embodiment 3, the change history learned prior to the end of the failure period is not included at all in the learning results after the end of the failure period. In the first adjustment process of Embodiment 4, the actual count and change count described in Embodiment 2 are updated for the change history learned prior to the end of the failure period. Then, in the first adjustment process of Embodiment 4, the change history for which the actual count and change count described in Embodiment 2 have been updated is included in the learning results after the end of the failure period. In other words, in the first adjustment process of Embodiment 4, a portion of the data prior to the end of the failure period is included in the learning results after the end of the failure period.

[0162] In the second adjustment process of Embodiment 4, the change history learned prior to the end of the maintenance period is not included at all in the learning results after the end of the maintenance period. In the second adjustment process of Embodiment 4, the number of actual changes and the number of modifications described in Embodiment 2 are updated for the learning results prior to the end of the maintenance period. Then, in the second adjustment process of Embodiment 4, the learning results with the updated number of actual changes and the number of modifications described in Embodiment 2 are included in the learning results after the end of the maintenance period.

[0163] [4-3. Effects, etc.] Embodiment 4 achieves the same effects as Embodiment 3. Furthermore, Embodiment 4 can suppress the setting of inappropriate temperatures that may occur after the target period has ended, due to a small number of data samples reflected in the learning results.

[0164] (Other embodiments) As described above, embodiments 1, 2, 3, and 4 have been explained as examples disclosed in this application. However, the technology in this disclosure is not limited thereto and can be applied to embodiments that have been modified, replaced, added, or omitted. Furthermore, it is possible to create new embodiments by combining the components described in embodiments 1, 2, 3, and 4. Therefore, other embodiments are described below as examples.

[0165] In the embodiments described above, a malfunction is defined as the occurrence of an error code. However, a malfunction may be considered even if no error code is generated. For example, even if no error code is generated, a malfunction may be considered if the time required for the room temperature or intake temperature to reach the target set temperature exceeds a predetermined time. This predetermined time is the time it would take to reach the target set temperature if there were no malfunction. Also, for example, even if no error code is generated, a malfunction may be considered if the room temperature or intake temperature has not reached the target set temperature after a predetermined time has elapsed. Furthermore, for example, if a system that diagnoses malfunctions from the operating data of the air conditioner 1 determines that a malfunction has occurred, a malfunction may be considered even if no error code is generated.

[0166] In the embodiment described above, if the air conditioner 1 has a history of repairs, the learning results from the date of the last repair until the end of the failure period are not included in the learning results from the end of the failure period onward. In other embodiments, the timing of the end of the most recent failure period may be identified, and the learning results up to that identified timing may not be included in the learning results from the end of the failure period onward.

[0167] In the embodiment described above, if the air conditioning unit 1 has a history of maintenance, the learning results from the date of the last maintenance until the end of the maintenance period are not included in the learning results from the end of the maintenance period. In other embodiments, the timing of the end of the most recent maintenance period may be identified, and the learning results up to that identified timing may not be included in the learning results from the end of the maintenance period.

[0168] In the embodiment described above, an example was given in which the maintenance worker P specifies the required maintenance period using the terminal device 2. In other embodiments, the maintenance worker P may specify the required maintenance period using the remote control 13. In this case, the remote control 13 is equipped with a display, operation keys, etc.

[0169] In the embodiment described above, the parameter for determining the set temperature to be set in the air conditioning system 1 includes the outside air temperature. In other embodiments, the parameter may include, instead of or in conjunction with the outside air temperature, the outside air humidity, the amount of solar radiation in a predetermined area including the location of facility H, the amount of precipitation in a predetermined area including the location of facility H, etc.

[0170] Figure 5 illustrates a predetermined range indicated by the collected data D7. However, this predetermined range shown in Figure 5 is merely an example and could be the range of settable temperatures that the air conditioner 1 can set, or it could be the range within the settable temperature range that the user is expected to set.

[0171] In the embodiment described above, the multiple set temperatures included in the predetermined range indicated by the collected data D7 are set temperatures in 1°C increments. In other embodiments, the increment of the set temperatures included in the predetermined range is not limited to 1°C, but may be, for example, 0.5°C increments.

[0172] In other embodiments, at least one of the functions of the acquisition unit 302, collection unit 303, update unit 304, learning unit 305, determination unit 306, and setting unit 307 may be executed not by the server control device 30, but by a control device that controls each part of the terminal device 2 or a control device that controls each part of the air conditioning system 1 (for example, a communication device 14). In these other embodiments, the control device that controls each part of the terminal device 2 or the control device that controls each part of the air conditioning system 1 corresponds to a "computer". Also in these other embodiments, a program for realizing at least one of the functions of the acquisition unit 302, collection unit 303, update unit 304, learning unit 305, determination unit 306, and setting unit 307, which is executed by a control device that controls each part of the terminal device 2 or the control device that controls each part of the air conditioning system 1, corresponds to a "program". Furthermore, if the control device that controls each part of the air conditioning unit 1 functions as the setting unit 307, the setting unit 307 sets the set temperature for the air conditioning unit 1 by controlling each part of the air conditioning unit 1. In other words, in this case, the setting unit 307 does not generate setting data D5.

[0173] In the embodiment described above, the change history data D6 is configured to store change history for each pair of time zone and ambient temperature. In other embodiments, instead of storing change history for each pair of time zone and ambient temperature, the server memory 310 may store change history for each set temperature for each time zone. In this other embodiment, the setting unit 307 determines the set temperature based on the change history corresponding to the time zone.

[0174] In the embodiment described above, the set temperature of the air conditioner 1 is automatically determined during the malfunction period, and the determined set temperature is set to the air conditioner 1. In other embodiments, it is not necessary to automatically determine the set temperature of the air conditioner 1 or to set the set temperature to the air conditioner 1 during the malfunction period. In this other embodiment, the air conditioner 1 operates according to the settings of the remote control 13.

[0175] The terminal processor 200 and the server processor 300 may consist of a single processor or multiple processors. These processors may also be hardware programmed to implement the corresponding functional units. That is, these processors may consist of, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0176] The configurations of the terminal device 2 and management server 3 shown in Figures 2 and 3 are examples, and the specific implementation is not particularly limited. In other words, it is not necessarily required that hardware corresponding to each part be implemented individually; it is also possible to configure the system so that a single processor executes programs to realize the functions of each part. Furthermore, some of the functions realized by software in the above-described embodiment may be implemented by hardware, or some of the functions realized by hardware may be implemented by software.

[0177] The operational step units shown in Figures 6, 7, 8, 9, 12, 15, and 16 are divided according to the main processing content to facilitate understanding of the operation, and the operation is not limited by the way the processing units are divided or the names of the processing units. Depending on the processing content, it may be further divided into more step units. Alternatively, it may be divided so that one step unit includes even more processing. Furthermore, the order of the steps may be changed as appropriate, as long as it does not hinder the intent of this disclosure.

[0178] Since the embodiments described above are for illustrative purposes of the technology described herein, various modifications, substitutions, additions, omissions, etc., can be made within the claims or their equivalents.

[0179] (Note) Based on the above description of embodiments, the following technologies are disclosed.

[0180] (Technology 1) An air conditioning system that air-conditions a space using an air conditioning device, comprising: a learning unit that learns data related to user operations to change the set temperature; a determination unit that automatically determines the optimal set temperature based on the learning results of the learning unit; and a setting unit that sets the set temperature automatically determined by the determination unit to the air conditioning device, wherein the learning unit changes the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period of the air conditioning device to a pattern different from the learning pattern for periods other than the target period. According to this, by changing the learning pattern, it becomes possible to suppress the learning of data that is unsuitable for learning. Therefore, it becomes possible to suppress inappropriate learning results, and thus it becomes possible to suppress setting an inappropriate temperature for the air conditioner.

[0181] (Technology 2) The aforementioned learning unit does not learn for the aforementioned target period, as described in Technical 1 of the air conditioning system. This approach helps prevent the system from learning from unsuitable data. Therefore, it can further reduce the likelihood of inappropriate learning results, thus reducing the chance of setting an inappropriate temperature on the air conditioning system.

[0182] (Technology 3) The air conditioning system according to Technology 1, wherein the learning results include the number of times the set temperature is changed, and the learning unit, in learning, reflects the number of times the set temperature is changed obtained from the data in the learning results for periods other than the target period, and reflects in the learning results a number of times the set temperature is changed that is set lower than the number of times the set temperature is changed obtained from the data for the target period. According to this approach, by reducing the number of temperature setting changes included in the learning results for the target period, it is possible to suppress the learning of unsuitable data. In addition, only a portion of the data from the target period is used for learning. Therefore, it is possible to suppress the creation of inappropriate settings due to a small number of data samples reflected in the learning results after the target period has ended.

[0183] (Technology 4) The air conditioning system according to Technology 1 or Technology 2, wherein, when the target period ends, the learning unit does not include the learning results prior to the end of the target period in the learning results from the end of the target period onward. According to this, after the target period ends, the data related to the change operation can be learned as data from the newly replaced air conditioning system. Therefore, it is possible to prevent setting an inappropriate temperature after the target period ends.

[0184] (Technology 5) The air conditioning system according to Technical 4, wherein the learning unit, if the air conditioning system has a history of repairs, does not include the learning results from the date of the last repair to the end of the failure period in the learning results from the end of the failure period onward, and if the air conditioning system has a history of maintenance, does not include the learning results from the date of the last maintenance to the end of the required maintenance period in the learning results from the end of the required maintenance period onward. According to this, after the target period ends, the data related to the change operation can be properly learned as data for the newly updated air conditioning system. Therefore, it is possible to properly prevent setting an inappropriate temperature after the target period ends.

[0185] (Technology 6) The air conditioning system according to Technology 4 or Technology 5, wherein the learning unit, if there is no record of repairs to the air conditioning system, does not include in the learning results learned before the end of the failure period in the learning results after the end of the failure period, and if there is no record of maintenance to the air conditioning system, does not include in the learning results learned before the end of the maintenance requirement period in the learning results after the end of the maintenance requirement period. According to this, it will produce the same effect as the air conditioning system of Technology 5.

[0186] (Technology 7) A control method for an air conditioning system that air-conditions a space using an air conditioning device, comprising: learning data related to user operations to change the set temperature; automatically determining the optimal set temperature based on the learning results; setting the automatically determined set temperature to the air conditioning device; and, in learning the data, changing the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period required for the air conditioning device to a pattern different from the learning pattern for periods other than the target period. According to this, it will produce the same effect as the air conditioning system of Technology 1.

[0187] (Technology 8) A computer for an air conditioning system that air-conditions a space using an air conditioning device is configured to function as follows: a learning unit that learns data related to user operations to change the set temperature; a determination unit that automatically determines the optimal set temperature based on the learning results of the learning unit; and a setting unit that sets the set temperature determined by the determination unit to the air conditioning device, wherein the learning unit is programmed to change the learning pattern for a target period that includes either or both of the failure period of the air conditioning device and the maintenance period of the air conditioning device to a different pattern from the learning pattern for periods other than the target period. According to this, it will produce the same effect as the air conditioning system of Technology 1. [Industrial applicability]

[0188] As described above, the air conditioning system, the control method for the air conditioning system, and the program according to the present invention can be used for setting the set temperature of an air conditioning device. [Explanation of symbols]

[0189] 1. Air conditioning system 2 Terminal devices 3. Management Server 4 Weather Server 11 Indoor unit 12 Outdoor unit 13 Remote control 14. Communication equipment 20 Terminal control device 21 Terminal Communication Section 22 Display section 23 Input section 30 Server control unit (computer) 31 Server Communication Section 200 terminal processors 201 Display Control Unit Room 202, Reception Department 203 Terminal Communication Control Unit 210 Terminal memory 211 Control Program 300 server processors 301 Server Communication Control Unit 302 Acquisition Department 303 Collection Department 304 Update Department Room 305, Learning Department 306 Decision Section 307 Settings Section 310 Server Memory 311 Control program (program) 312 Management DB 1000 Air Conditioning Systems D1 Maintenance Requirement Period Data D2 Operation data (data related to the operation of changing the set temperature) D3 Failure Occurrence Data D4 Failure termination data D5 Configuration Data D6 Change History Data D7 Collected Data D8 Set temperature related data H Facility NW Network P Maintenance Person S Air conditioned space

Claims

1. An air conditioning system that provides air conditioning to a space using an air conditioning device, A learning unit that learns data related to user operations to change the set temperature, Based on the learning results of the learning unit, a determination unit automatically determines the optimal set temperature, The system comprises a setting unit that sets the set temperature automatically determined by the determination unit to the air conditioner, The aforementioned learning unit, The learning pattern for the target period, which includes either or both the failure period of the air conditioning system and the maintenance period required for the air conditioning system, is changed to a different pattern from the learning pattern for periods other than the target period. Air conditioning system.

2. The aforementioned learning unit, For the aforementioned period, no learning will be conducted. The air conditioning system according to claim 1.

3. The learning results include the number of times the set temperature was changed. The aforementioned learning unit, In learning, For periods other than the aforementioned target period, The number of times the set temperature is changed, obtained from the aforementioned data, is reflected in the learning results. For the aforementioned period, The number of set temperature changes set lower than the number of set temperature changes obtained from the aforementioned data is reflected in the learning results. The air conditioning system according to claim 1.

4. The aforementioned learning unit, When the aforementioned target period ends, the learning results prior to the end of the aforementioned target period will not be included in the learning results from the end of the aforementioned target period onward. The air conditioning system according to claim 1 or 2.

5. The aforementioned learning unit, If the air conditioning system has a history of repairs, the learning results from the date of the last repair to the end of the failure period will not be included in the learning results from the end of the failure period onward. If the air conditioning system has a history of maintenance, the learning results from the date of the last maintenance to the end of the maintenance period will not be included in the learning results from the end of the maintenance period onward. The air conditioning system according to claim 4.

6. The aforementioned learning unit, If there is no record of repairs for the aforementioned air conditioning system, All learning results learned prior to the end of the aforementioned failure period are not included in the learning results after the end of the aforementioned failure period. If there is no record of maintenance for the aforementioned air conditioning system, All learning results learned prior to the end of the maintenance period will not be included in the learning results obtained after the end of the maintenance period. The air conditioning system according to claim 4.

7. A control method for an air conditioning system that air-conditions a space using an air conditioning device, The system learns data related to user operations that change the set temperature. Based on the learning results, the optimal temperature setting is automatically determined. The automatically determined set temperature is set in the air conditioning system. In the data learning process, the learning pattern for the target period, which includes either or both of the failure period of the air conditioner and the maintenance period required for the air conditioner, is changed to a different pattern from the learning pattern for periods other than the target period. A method for controlling an air conditioning system.

8. The computer for an air conditioning system that uses an air conditioning device to air-condition a space is A learning unit that learns data related to user operations to change the set temperature, Based on the learning results of the learning unit, a determination unit automatically determines the optimal set temperature, The aforementioned determination unit functions as a setting unit that sets the set temperature determined by the determination unit to the air conditioning unit. The aforementioned learning unit, The learning pattern for the target period, which includes either or both the failure period of the air conditioning system and the maintenance period required for the air conditioning system, is changed to a different pattern from the learning pattern for periods other than the target period. program.

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