Drying time prediction method, drying time prediction system, and dryer

Abstract

This drying time prediction method comprises: acquiring operation conditions when starting the operation of a first dryer (10); acquiring a first actual-performance operation time for a first operation on the basis of each of one or more first operations performed by the first dryer (10) in the past; acquiring operation completion time information relating to the completion time of the operation based on the starting of the operation, said operation completion time information being outputted from a trained model (245) by inputting, into the trained model (245), the operation conditions and first actual-performance information based on the first actual-performance operation time; and outputting first information based on the operation completion time information.

Description

[Technical field]

[0001] The present disclosure relates to predicting drying times. [Background technology]

[0002] Techniques for making predictions regarding future events are known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-85914 A Summary of the Invention [Problem to be solved by the invention]

[0004] It is desirable to display the remaining drying time of the drying operation to a user who uses a dryer or the like to dry clothes or the like.

[0005] Therefore, an object of the present disclosure is to provide a drying time prediction method for predicting drying time. [Means for solving the problem]

[0006] A drying time prediction method according to one embodiment of the present disclosure acquires operating conditions when a first dryer starts operating, acquires a first actual operating time for each of one or more first operations that the first dryer has performed in the past, acquires operation end time information relating to the end time of operation due to the start of the operation output from the trained model by inputting the operating conditions and first actual information based on the first actual operating time into a trained model, and outputs first information based on the operation end time information.

[0007] A drying time prediction system according to one embodiment of the present disclosure includes an operating condition acquisition unit that acquires operating conditions when starting operation of a first dryer, a first actual operating time acquisition unit that acquires a first actual operating time of a first operation for each of one or more first operations that the first dryer has performed in the past, a trained model that, when inputted with the operating conditions and first actual information based on the first actual operating time, outputs operation end time information relating to the end time of the operation due to the start of the operation, and an output unit that outputs first information based on the operation end time information.

[0008] A dryer according to one embodiment of the present disclosure is a first dryer that utilizes the drying time prediction system, and includes a providing unit that provides the operating conditions, the sensing information, and the first actual operating time to the drying time prediction system, an acquiring unit that acquires the first information from the drying time prediction system, and a display unit that displays related to the first information. Effect of the Invention

[0009] According to the drying time prediction method and the like according to one aspect of the present disclosure, the drying time can be predicted. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram showing the relationship between the remaining drying time displayed by a conventional method and the actual remaining drying time. [Diagram 2] FIG. 2 is a schematic diagram showing the relationship between the remaining drying time displayed by a method using a trained model and the actual remaining drying time. [Diagram 3] FIG. 3 is a block diagram showing an example of a configuration of a drying time prediction system according to an embodiment. [Figure 4] FIG. 4 is a block diagram showing an example of a configuration of a dryer according to an embodiment. [Diagram 5] FIG. 5 is a block diagram illustrating an example of a configuration of a server device according to the embodiment. [Figure 6]FIG. 6 is a schematic diagram showing an example of how the standardization unit according to the embodiment calculates the standardized actual operation time. [Figure 7] FIG. 7 is a block diagram illustrating an example of a configuration of the smartphone according to the embodiment. [Figure 8] FIG. 8 is a flowchart of the drying time prediction process according to the embodiment. [Figure 9] FIG. 9 is a flowchart of the standardization process according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] (How one aspect of the present disclosure was achieved) Conventionally, the remaining drying time in a dryer or the like is displayed by displaying the remaining drying time at the start of the drying operation, which is a default value predetermined for each amount of laundry plus a correction value, and then counting down the remaining drying time as time passes.

[0012] Conventionally, in order to display the remaining drying time with high accuracy, the degree of dryness is judged at fixed points during the drying process (for example, every 10 minutes) using sensing information related to the status of the dryer, and the displayed remaining drying time is adjusted according to the judgment results.

[0013] FIG. 1 is a schematic diagram showing the relationship between the remaining drying time displayed by a conventional method and the actual remaining drying time.

[0014] As shown in Figure 1, when displaying the remaining drying time using the conventional method, problems such as the displayed remaining drying time changing discontinuously or the countdown of the displayed remaining drying time stopping for a certain period of time can occur. These problems can confuse the user who is viewing the displayed remaining drying time.

[0015] In view of such problems, the inventors conducted experiments and studies in order to realize a drying time prediction method etc. that predicts the drying time more accurately than before. As a result, the inventors discovered that it is possible to predict the drying time more accurately than before by using a learned model that is trained in advance to output the remaining drying time when inputting the operating conditions when starting the operation of a dryer etc., sensing information related to the state of the dryer during operation after the start of the operation, and the actual operating time of the dryer that has been operated in the past.

[0016] FIG. 2 is a schematic diagram showing the relationship between the remaining drying time displayed by the method using the above-mentioned trained model and the actual remaining drying time.

[0017] As can be seen from Figures 1 and 2, by predicting drying time using a method that utilizes the above-mentioned trained model, it is possible to display the remaining drying time with greater accuracy than before.

[0018] Based on this finding, the inventors further conducted intensive experiments and studies, and came up with the following drying time prediction method, etc.

[0019] A drying time prediction method according to one embodiment of the present disclosure acquires operating conditions when a first dryer starts operating, acquires a first actual operating time for each of one or more first operations that the first dryer has performed in the past, acquires operation end time information relating to the end time of operation due to the start of the operation output from the trained model by inputting the operating conditions and first actual information based on the first actual operating time into a trained model, and outputs first information based on the operation end time information.

[0020] According to the above-mentioned drying time prediction method, when the operating conditions at the start of operation of the first dryer and the first actual operating time of the operation previously performed by the first dryer are input in advance, the drying time can be predicted using a learned model trained to output the end time of the operation from the start of that operation.

[0021] Furthermore, in the process of acquiring sensing information related to the state of the first dryer during operation by the operation and acquiring the operation end time information, the operation end time information output from the trained model may be acquired by inputting the sensing information in addition to the operating conditions and the first actual information into the trained model.

[0022] As a result, the operation end time information output from the trained model will also reflect the contents of the sensing information, making it possible to predict the drying time with greater accuracy.

[0023] Furthermore, for each of the one or more first operations, first actual operating conditions at the start of the first operation are obtained in association with the first actual operating time, and for each of one or more second operations previously performed by one or more second dryers, a second actual operating time of the second operation and second actual operating conditions at the start of the second operation linked to the second actual operating time are obtained, and for each of the first actual operating times, a standardized actual operating time is calculated by standardizing the first actual operating time based on a distribution of the second actual operating times linked to the second actual operating conditions that match the first actual operating conditions linked to the first actual operating time, and the first actual information may be based on the standardized actual operating time calculated for each of the first actual operating times.

[0024] As a result, the standardized actual operation time reflects the tendency for the drying time of the first dryer to increase or decrease in the past operation when viewed on the distribution of the drying times of the past operations of one or more second dryers under each actual operating condition, which makes it possible to predict the drying time with greater accuracy.

[0025] Furthermore, an average value of the standardized actual operation time for each of the first actual operation times may be calculated, and the first performance information may be based on the average value.

[0026] As a result, the average value reflects the above-mentioned tendency toward longer or shorter drying times under all actual operating conditions, and therefore the predicted drying time can reflect the above-mentioned tendency toward longer or shorter drying times under all actual operating conditions.

[0027] In addition, the first operation may be an operation performed by the first dryer during a predetermined period in the past, and the second operation may be an operation performed by the one or more second dryers during the predetermined period in the past.

[0028] In general, the type of material fed into a dryer tends not to change significantly over a period of time, which allows for more accurate prediction of the drying time.

[0029] In addition, the operating conditions and the first actual operating conditions may include an amount of laundry to be loaded into the first dryer and an operating mode of the first dryer, which are set by a user of the first dryer, and the second actual operating conditions may include an amount of laundry to be loaded into the one or more second dryers and an operating mode of the one or more second dryers, which are set by a user of each of the one or more second dryers.

[0030] This makes it possible to predict the drying time according to the amount of laundry to be put into the dryer and the operation mode of the dryer, both set by the user.

[0031] Moreover, the first dryer and the one or more second dryers may be of the same model.

[0032] This allows the drying time to be predicted with greater accuracy.

[0033] A drying time prediction system according to one embodiment of the present disclosure includes an operating condition acquisition unit that acquires operating conditions when starting operation of a first dryer, a first actual operating time acquisition unit that acquires a first actual operating time of a first operation for each of one or more first operations that the first dryer has performed in the past, a trained model that, when inputted with the operating conditions and first actual information based on the first actual operating time, outputs operation end time information relating to the end time of the operation due to the start of the operation, and an output unit that outputs first information based on the operation end time information.

[0034] According to the above-mentioned drying time prediction system, when the operating conditions at the start of operation of the first dryer and the first actual operating time of the operation previously performed by the first dryer are input in advance, the drying time can be predicted using a learned model that has been trained to output the end time of the operation based on the start of that operation.

[0035] A dryer according to one embodiment of the present disclosure is a first dryer that utilizes the drying time prediction system, and includes a providing unit that provides the operating conditions and the first actual operating time to the drying time prediction system, an acquiring unit that acquires the first information from the drying time prediction system, and a display unit that displays related to the first information.

[0036] The dryer obtains the first information from the drying time prediction system, and is thus able to predict the drying time.

[0037] Hereinafter, specific examples of a drying time prediction method, a drying time prediction system, and a dryer according to one embodiment of the present disclosure will be described with reference to the drawings. Each embodiment shown here shows one specific example of the present disclosure. Therefore, the numerical values, shapes, components, the arrangement and connection form of the components, steps (processes), and the order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure. In addition, each figure is a schematic diagram and is not necessarily a precise illustration.

[0038] In addition, the comprehensive or specific aspects of the present disclosure may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium.

[0039] (Embodiment) [1. Configuration] FIG. 3 is a block diagram showing an example of a configuration of a drying time prediction system according to an embodiment.

[0040] As shown in FIG. 3, a drying time prediction system 1 according to the embodiment includes a plurality of dryers 10, a server device 20, a plurality of smartphones 30, and a network 40.

[0041] 3, the multiple dryers 10 include dryer 10A, dryer 10B, dryer 10M, etc. In the following, dryer 10A, dryer 10B, dryer 10M, etc. will be simply referred to as dryer 10, except when each individual dryer is explicitly distinguished from the others. Here, the multiple dryers 10 will be described as being of the same model, but the multiple dryers 10 are not necessarily limited to being of the same model.

[0042] 3, the multiple smartphones 30 include a smartphone 30A, a smartphone 30B, a smartphone 30N, etc. In the following, the smartphones 30A, 30B, 30N, etc. will be simply referred to as smartphones 30, except when each individual smartphone is explicitly distinguished from the others.

[0043] The network 40 relays communication between devices connected to the network 40. The devices connected to the network 40 include a plurality of dryers 10, a server device 20, and a plurality of smartphones 30.

[0044] The network 40 may be, for example, the Internet, a cellular network, or a LAN (Local Area Network).

[0045] FIG. 4 is a block diagram showing an example of the configuration of the dryer 10.

[0046] The dryer 10 is connected to a network 40 and has a function of drying clothes, etc. The dryer 10 may be, for example, a washer-dryer that further has a function of washing clothes, etc.

[0047] As shown in FIG. 4, the dryer 10 includes a communication unit 100, an input receiving unit 110, an operation control unit 120, a drying unit 130, a past data accumulation unit 140, a sensing unit 150, a providing unit 160, an acquisition unit 170, and a display unit 180.

[0048] The communication unit 100 is connected to a network 40, and communicates with external devices connected to the network 40 via the network 40. The external devices include a server device 20 and a plurality of smartphones 30.

[0049] The communication unit 100 is realized, for example, by having an input / output interface (not shown), and a processor (not shown) provided in the dryer 10 executing a program stored in a memory (not shown) provided in the dryer 10 to control the input / output interface.

[0050] The drying section 130 includes a drying chamber (not shown) and dries clothes and the like placed in the drying chamber.

[0051] The drying section 130, for example, includes a rotary drum-type drying chamber (not shown), a heater (not shown), and a blower fan (not shown), and dries the clothes and other items placed inside the drying chamber by rotating the rotary drum-type drying chamber to agitate the clothes and other items placed inside, and by sending air warmed by the heater into the rotary drum-type drying chamber with the blower fan.

[0052] The drying section 130 further includes sensors for sensing the state of the drying chamber and / or the state of the clothes etc. put into the drying chamber. The sensors include, for example, a thermometer for sensing the discharge temperature of the air blown into the drying chamber, a thermometer for sensing the intake temperature of the air sent out from the drying chamber to the outside, an angular velocity meter for sensing the rotation speed of the drying chamber, an accelerometer for sensing the imbalance of the weight of the drying chamber, a motor ammeter for sensing the amount of fabric such as clothes put into the drying chamber, etc.

[0053] The input receiving unit 110 receives input from a user who uses the dryer 10. The input received by the input receiving unit 110 includes operating conditions when starting the operation of the dryer 10. The operating conditions include, for example, the amount of laundry, such as clothes, to be put into the dryer 10 and the operating mode of the dryer 10. Note that, although the input receiving unit 110 is described here as receiving the amount of laundry input by the user, the input receiving unit 110 may receive, for example, the amount of laundry acquired by the sensing unit 150 from the sensing unit 150 described below.

[0054] The input reception unit 110 includes, for example, a touch panel (not shown), and is realized by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10 to control the touch panel.

[0055] The operation control unit 120 controls the operation of the drying unit 130 based on the input received by the input receiving unit 110, thereby operating the dryer 10.

[0056] The operation control unit 120 is realized, for example, by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10.

[0057] The past data storage unit 140 stores the actual operating conditions and actual operating time for each operation of the dryer 10 that has been performed in the past, in association with each other.

[0058] The past data accumulation unit 140 includes, for example, a hard disk (not shown), and is realized by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10 to control the hard disk.

[0059] The sensing unit 150 controls the sensors included in the drying unit 130 to obtain sensing information related to the state of the dryer 10 during operation. The sensing information includes, for example, the discharge temperature, the suction temperature, the rotation speed of the drying chamber, and imbalance in the drying chamber. Furthermore, when starting operation of the dryer 10, the sensing unit 150 may control the sensors included in the drying unit 130 to obtain the amount of fabric such as clothes placed in the drying chamber.

[0060] The sensing unit 150 repeatedly acquires sensing information during operation of the dryer 10. For example, the sensing unit 150 acquires sensing information every minute during a period from when the operation of the dryer 10 is started to when it is ended.

[0061] The sensing unit 150 is realized, for example, by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10.

[0062] The providing unit 160 provides the driving conditions accepted by the input accepting unit 110, the mutually linked actual driving conditions and actual driving times stored in the past data accumulation unit 140, and the sensing information acquired by the sensing unit 150 to the server device 20 via the communication unit 100. More specifically, the providing unit 160 provides the driving conditions to the server device 20 every time the input accepting unit 110 accepts the driving conditions, transmits the mutually linked actual driving information and actual driving times to the server device 20 every time the past data accumulation unit 140 updates the mutually linked actual driving information and actual driving times, and provides the sensing information to the server device 20 every time the sensing unit 150 acquires the sensing information.

[0063] The providing unit 160 is realized, for example, by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10.

[0064] The acquiring unit 170 acquires first information (described later) indicating the remaining drying time from the server device 20 via the communication unit 100. More specifically, the acquiring unit 170 acquires the first information every time the first information is transmitted from the server device 20.

[0065] The acquisition unit 170 is realized, for example, by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10.

[0066] Display unit 180 performs display related to the first information acquired by acquisition unit 170. More specifically, every time first information is acquired by acquisition unit 170, display unit 180 displays the remaining drying time indicated by the first information.

[0067] The display unit 180 includes, for example, a touch panel (not shown), and is realized by a processor included in the dryer 10 executing a program stored in a memory included in the dryer 10 to control the touch panel.

[0068] FIG. 5 is a block diagram showing an example of the configuration of the server device 20. As shown in FIG.

[0069] The server device 20 is connected to the network 40 and has a function of predicting the remaining drying time of the dryer 10 that is operating to dry clothes or the like.

[0070] 5, the server device 20 includes a communication unit 200, a past data accumulation unit 205, a sensing information acquisition unit 210, an operating condition acquisition unit 215, a first actual operating time acquisition unit 220, a second actual operating time acquisition unit 225, a standardization unit 230, a representative value calculation unit 235, a prediction unit 240, and an output unit 250. Here, the prediction unit 240 includes a trained model 245.

[0071] The communication unit 200 is connected to a network 40, and communicates with external devices connected to the network 40 via the network 40. The external devices include a plurality of dryers 10 and a plurality of smartphones 30.

[0072] The communication unit 200, for example, has an input / output interface (not shown), and is realized by a processor (not shown) provided in the server device 20 executing a program stored in a memory (not shown) provided in the server device 20 to control the input / output interface.

[0073] The sensing information acquiring unit 210 acquires the sensing information provided from the providing unit 160 via the communication unit 200. Every time the sensing information is provided from the providing unit 160, the sensing information acquiring unit 210 acquires the sensing information.

[0074] The sensing information acquisition unit 210 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0075] The operating condition acquisition unit 215 acquires the operating conditions provided from the provision unit 160 via the communication unit 200. Every time the operating conditions are provided from the provision unit 160, the operating condition acquisition unit 215 acquires the operating conditions.

[0076] The operating condition acquisition unit 215 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0077] The past data accumulation unit 205 acquires the mutually linked actual operating conditions and actual operating times provided from the provision unit 160 via the communication unit 200, and stores the acquired mutually linked actual operating conditions and actual operating times. Every time the mutually linked actual operating conditions and actual operating times are provided from the provision unit 160, the past data accumulation unit 205 acquires and stores the mutually linked actual operating conditions and actual operating times.

[0078] That is, the past data storage unit 205 stores the actual operating conditions and actual operating times of each of the operations performed in the past by all the dryers 10 constituting the drying time prediction system 1 in association with each other.

[0079] The past data accumulation unit 205 includes, for example, a hard disk (not shown), and is realized by a processor included in the server device 20 executing a program stored in a memory included in the server device 20 to control the hard disk.

[0080] The first actual operating time acquisition unit 220 acquires, from the actual operating conditions and actual operating times that are linked to each other and that are stored by the past data accumulation unit 205, actual operating conditions (hereinafter also referred to as "first actual operating conditions") and actual operating time (hereinafter also referred to as "first actual operating time") that are linked to each other and that were generated by operation performed by one dryer 10 (hereinafter also referred to as "first dryer 10") during a specified period in the past.

[0081] Here, the predetermined period may be, for example, a period from the present to a date and time a predetermined number of days in the past, or a period in the same season as the present season (for example, if the present is April, and April is spring, then the period from the present to the past spring (for example, March, April, and May)). In other words, the predetermined period may be a period during which the types of materials fed into the dryer 10 tend not to change significantly.

[0082] The first actual operating time acquisition unit 220 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0083] The second actual operation time acquisition unit 225 acquires actual operation conditions (hereinafter also referred to as "second actual operation conditions") and actual operation times (hereinafter also referred to as "second actual operation times") linked to each other by operation performed by one or more arbitrary dryers 10 (hereinafter also referred to as "one or more second dryers 10") from the actual operation conditions and actual operation times linked to each other stored by the past data accumulation unit 205. Here, the one or more second dryers 10 will be described as including the first dryer 10, that is, the one or more second dryers 10 will be described as including all dryers 10, but the one or more second dryers 10 may not include the first dryer 10.

[0084] The second actual operating time acquisition unit 225 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0085] The standardization unit 230 calculates a standardized actual operating time by standardizing the first actual operating time based on the distribution of second actual operating conditions linked to second actual operating conditions that match the first actual operating conditions linked to the first actual operating time, for each first actual operating time.

[0086] Here, it is assumed that the standardization unit 230 calculates the standardized actual operating time for each of the first actual operating times using the following (Equation 1) as an example that is not necessarily limited to this.

[0087] n = (t-mean(Tc)) / std(Tc) (Equation 1)

[0088] In (Equation 1), n ​​is the standardized actual operating time, t is the first actual operating time, Tc is a set of second actual operating times linked to second operating conditions that match the first operating conditions linked to the first actual operating time, mean(Tc) is the average value of all second actual operating times included in the set Tc, and std(Tc) is the variance of all second actual operating times included in the set Tc.

[0089] As shown in (Equation 1), when the first actual operating time matches the mean(Tc), that is, when the first actual operating time matches the average value of the actual operating times of operations performed in all dryers 10 under the same actual operating conditions, the standardized actual operating time is 0.

[0090] Furthermore, when the first actual operating time is greater than mean(Tc), i.e., when the first actual operating time is greater than the average value of the actual operating times of operations performed in all dryers 10 under the same actual operating conditions, i.e., when the first actual operating time tends to be longer compared to the actual operating times of operations performed in all dryers 10 under the same actual operating conditions, the standardized actual operating time will be a positive value.

[0091] Furthermore, when the first actual operating time is smaller than mean(Tc), i.e., when the first actual operating time is smaller than the average value of the actual operating times of operations performed in all dryers 10 under the same actual operating conditions, i.e., when the first actual operating time tends to be shorter compared to the actual operating times of operations performed in all dryers 10 under the same actual operating conditions, the standardized actual operating time will be a negative value.

[0092] In this way, the standardized actual operating time calculated by the standardization unit 230 reflects the tendency for the first actual operating time to become longer or shorter when viewed on the distribution of second actual operations linked to second actual operating conditions that match the first actual operating conditions linked to the first actual operating time.

[0093] FIG. 6 is a schematic diagram showing an example of how the standardization unit 230 calculates the standardized actual operation time.

[0094] In Figure 6, the upper graph is a graph in which the first actual operating time for each operation performed in the first dryer 10 is plotted with the date and time of the operation on the horizontal axis and the first actual operating time on the vertical axis, and the lower graph is a graph in which the standardized actual operating time for each operation performed in the first dryer 10 is plotted with the date and time of the operation on the horizontal axis and the actual operating time on the vertical axis.

[0095] In the upper graph, first actual operation time 601 to first actual operation time 606 are first actual operation times in operations performed in first dryer 10 within a predetermined period of time.

[0096] Here, the first actual operating time 605 is the actual operating time of the operation performed under the actual operating condition of condition A (e.g., laundry amount is 2 kg, and the operating mode is the fast drying mode), the first actual operating time 602 and the first actual operating time 604 are the actual operating time of the operation performed under the actual operating condition of condition B (e.g., laundry amount is 2 kg, and the operating mode is the standard drying mode), the first actual operating time 601 and the first actual operating time 603 are the actual operating time of the operation performed under the actual operating condition of condition C (e.g., laundry amount is 3 kg, and the operating mode is the fast drying mode), and the first actual operating time 606 is the actual operating time of the operation performed under the actual operating condition of condition D (e.g., laundry amount is 3 kg, and the operating mode is the standard drying mode).

[0097] The standardization unit 230 standardizes the first actual driving time 605 based on the distribution of the second actual driving time linked to the actual driving condition that satisfies the condition A to calculate a standardized actual driving time 655, standardizes the first actual driving time 602 and the first actual driving time 604 based on the distribution of the second actual driving time linked to the actual driving condition that satisfies the condition B to calculate a standardized actual driving time 652 and a standardized actual driving time 654, standardizes the first actual driving time 601 and the first actual driving time 603 based on the distribution of the second actual driving time linked to the actual driving condition that satisfies the condition C to calculate a standardized actual driving time 651 and a standardized actual driving time 653, and standardizes the first actual driving time 606 based on the distribution of the second actual driving time linked to the actual driving condition that satisfies the condition D to calculate a standardized actual driving time 656.

[0098] 6, standardized actual operation time 651 to standardized actual operation time 654 and standardized actual operation time 656 are smaller than 0. Therefore, it can be seen that first actual operation time 601 to first actual operation time 604 and first actual operation time 606 tend to be shorter than the actual operation time of operation performed in all dryers 10 under the same actual operating conditions. Also, standardized actual operation time 655 is larger than 0. Therefore, it can be seen that first actual operation time 605 tends to be longer than the actual operation time of operation performed in all dryers 10 under the same actual operating conditions.

[0099] Returning to FIG. 5 again, the explanation of the server device 20 will be continued.

[0100] The standardization unit 230 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0101] The representative value calculation unit 235 calculates a representative value of the standardized actual operation times calculated by the standardization unit 230 .

[0102] Here, the representative value of the standardized actual operating time calculated by the representative value calculation unit 235 is described as being the average value of the standardized actual operating time. That is, the representative value calculation unit 235 is described as calculating the average value of the standardized actual operating time calculated by the standardization unit 230. However, the representative value of the standardized actual operating time calculated by the representative value calculation unit 235 does not necessarily need to be limited to the example of being the average value of the standardized actual operating time. For example, the representative value of the standardized actual operating time calculated by the representative value calculation unit 235 may be the maximum value, minimum value, median value, or mode value of the standardized actual operating time.

[0103] The average value of the standardized actual operating time calculated by the representative value calculation unit 235 reflects the tendency for the actual operating time to increase or decrease under all actual operating conditions for operation performed by the first dryer 10 over a specified period in the past.

[0104] The representative value calculation unit 235 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0105] The trained model 245 is a machine learning model that is pre-trained to output operation end time information related to the end time of an operation started under the operation conditions when the operation conditions, the sensing information, and the average value of the standardized actual operation time are input. Here, the operation end time information is described as the remaining drying time until the operation started by the operation is ended.

[0106] The trained model 245 is realized, for example, by a neural network constructed on the server device 20 when a processor included in the server device 20 executes a program stored in a memory included in the server device 20.

[0107] The learned model 245 is created, for example, by training a machine learning model using operating conditions, sensing information, and the average value of the standardized actual operating time as input data, and the remaining drying time that is previously created based on actual measured values ​​obtained by actually operating the dryer 10 as correct answer data.

[0108] The prediction unit 240 acquires driving end time information output from the trained model 245 by inputting the driving conditions acquired by the driving condition acquisition unit 215, the sensing information acquired by the sensing information acquisition unit 210, and the average value calculated by the representative value calculation unit 235 to the trained model 245. More specifically, the prediction unit 240 acquires driving end time information output from the trained model 245 by inputting the driving conditions, the sensing information, and the average value to the trained model 245 every time the sensing information is acquired by the sensing information acquisition unit 210.

[0109] Then, each time the prediction unit 240 acquires the operation end time information, it calculates first information indicating the remaining drying time of the operation for drying clothes or the like performed by the first dryer 10 based on the acquired operation end time information.

[0110] The prediction unit 240 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0111] The output unit 250 outputs the first information calculated by the prediction unit 240 to the first dryer 10 and the smartphone 30 linked to the first dryer 10 via the communication unit 200. More specifically, every time the first information is calculated by the prediction unit 240, the output unit 250 transmits the first information to the first dryer 10 and the smartphone 30 linked to the first dryer 10 via the communication unit 200.

[0112] The output unit 250 is realized, for example, by a processor included in the server device 20 executing a program stored in a memory included in the server device 20.

[0113] FIG. 7 is a block diagram showing an example of the configuration of the smartphone 30. As shown in FIG.

[0114] The smartphone 30 is connected to the network 40, is linked to at least one of the multiple dryers 10, and has a function of displaying information transmitted from the server device 20.

[0115] As shown in FIG. 7, the smartphone 30 includes a communication unit 300, an acquisition unit 310, and a display unit 320.

[0116] The communication unit 300 is connected to a network 40, and communicates with external devices connected to the network 40 via the network 40. The external devices include a plurality of dryers 10 and a server device 20.

[0117] The communication unit 300, for example, has an input / output interface (not shown), and is realized by a processor (not shown) of the smartphone 30 executing a program stored in a memory (not shown) of the smartphone 30 to control the input / output interface.

[0118] The acquisition unit 310 acquires the first information from the server device 20 via the communication unit 300. More specifically, the acquisition unit 310 acquires the first information every time the first information is transmitted from the server device 20.

[0119] The acquisition unit 310 is realized, for example, by a processor included in the smartphone 30 executing a program stored in a memory included in the smartphone 30.

[0120] Display unit 320 performs display related to the first information acquired by acquisition unit 310. More specifically, every time first information is acquired by acquisition unit 310, display unit 320 displays the remaining drying time indicated by the first information.

[0121] The display unit 320 includes, for example, a touch panel (not shown), and is realized by a processor included in the smartphone 30 executing a program stored in a memory included in the smartphone 30 to control the touch panel.

[0122] [2. Operation] Hereinafter, the operation of the drying time prediction system 1 having the above configuration will be described.

[0123] The drying time prediction system 1 executes a drying time prediction process for predicting the drying time of the first dryer 10 during a drying operation for drying loaded clothes or the like.

[0124] FIG. 8 is a flowchart of the drying time prediction process.

[0125] The drying time prediction process starts when the drying operation of the first dryer 10 is started.

[0126] When the drying operation is started in the first dryer 10, the providing unit 160 of the first dryer 10 provides the operating conditions accepted by the input accepting unit 110 of the first dryer 10 to the server device 20. Then, the operating condition acquiring unit 215 acquires the operating conditions (step S5).

[0127] When the operating condition acquisition unit 215 acquires the operating conditions, the first actual operating time acquisition unit 220 checks whether or not there are first actual operating conditions and first actual operating times that are linked to each other and that are the result of operation performed by the first dryer 10 during a specified period in the past, among the linked actual operating conditions and actual operating times stored by the past data accumulation unit 205 (step S10).

[0128] In the processing of step S10, if corresponding first actual operating conditions and first actual operating times exist (step S10: Yes), the drying time prediction system 1 performs a standardization process to standardize the first actual operating times for each first actual operating time based on the distribution of second actual operations linked to second actual operating conditions that match the first actual operating conditions linked to the first actual operating times (step S15).

[0129] FIG. 9 is a flowchart of the standardization process.

[0130] When the standardization process is started, the first actual operation time acquisition unit 220 acquires first actual operation conditions and first actual operation times that are linked to each other and that are obtained by operation performed by the first dryer 10 during a predetermined period in the past from among the actual operation conditions and actual operation times that are linked to each other and stored by the past data accumulation unit 205 (step S110). In addition, the second actual operation time acquisition unit 225 acquires second actual operation conditions and second actual operation times that are linked to each other and that are obtained by operation performed by one or more second dryers 10 during a predetermined period in the past from among the actual operation conditions and actual operation times that are linked to each other and stored by the past data accumulation unit 205.

[0131] When the process of step S110 is completed, or when there is an unselected first actual operating time in the process of step S170 described below (step S170: Yes), the first actual operating time acquisition unit 220 selects one unselected first actual operating time (step S120). Here, the unselected first actual operating time refers to a first actual operating time that has not yet been selected in the loop process formed by the processes of steps S120 to S170: Yes, among the first actual operating times acquired by the process of step S110.

[0132] When one unselected first actual driving time is selected, the standardization unit 230 checks whether or not there is a second actual driving time linked to a second actual driving condition that matches the first actual driving condition linked to one unselected first actual driving time among the second actual driving times acquired by the second actual driving time acquisition unit 225 (step S130).

[0133] In the processing of step S130, if a corresponding second actual driving time exists (step S130: Yes), the standardization unit 230 acquires the corresponding second actual driving time (step S140), and calculates the standardized actual driving time by standardizing the selected one of the first actual driving times based on the distribution of the corresponding second actual driving time (step S150).

[0134] In the process of step S130, if there is no corresponding second actual operating time (step S130: No), the standardization unit 230 calculates a predetermined default value as the standardized actual operating time (step S160).

[0135] When the process of step S150 is completed, or when the process of step S160 is completed, the first actual driving time acquisition unit 220 checks whether or not there is an unselected first actual driving time (step S170).

[0136] In the process of step S170, if there is an unselected first actual operation time (step S170: Yes), the drying time prediction system 1 proceeds to the process of step S120.

[0137] In the process of step S170, if there is no unselected first actual operation time (step S170: No), the drying time prediction system 1 ends the standardization process.

[0138] Returning to FIG. 8, the explanation of the drying time prediction process will be continued.

[0139] When the standardization process is completed, the representative value calculation unit 235 calculates the average value of the standardized actual operation times calculated in the standardization process (step S20).

[0140] In the process of step S10, if there are no corresponding first actual operating conditions and first actual operating times (step S10: No), the representative value calculation unit 235 calculates a predetermined default value as the average value (step S25).

[0141] When the processing of step S20 is completed, or when the processing of step S25 is completed, the sensing information acquisition unit 210 checks whether sensing information has been provided by the providing unit 160 of the first dryer 10 (step S30).

[0142] In the process of step S30, if the sensing information has not been provided (step S30: No), the sensing information acquisition unit 210 waits until the sensing information is provided (step S30: No is repeated).

[0143] If sensing information is provided in the processing of step S30 (step S30: Yes), or if new sensing information is provided in the processing of step S60 described below (step S60: Yes), the sensing information acquisition unit 210 acquires the sensing information (step S35).

[0144] When the sensing information acquisition unit 210 acquires the sensing information, the prediction unit 240 inputs the driving conditions acquired by the processing of step S5, the average value calculated by the processing of step S20, and the sensing information acquired by the processing of step S35 into the trained model 245 (step S40), and acquires the driving end time information output from the trained model 245 (step S45).

[0145] When the operation end time information is acquired, the prediction unit 240 calculates first information indicating the remaining drying time of the operation for drying clothes or the like performed by the first dryer 10 based on the acquired operation end time information. Then, the output unit 250 outputs the first information indicating the remaining drying time to the first dryer 10 and the smartphone 30 linked to the first dryer 10 (step S50).

[0146] When the output unit 250 outputs the first information, the acquisition unit 170 of the first dryer 10 acquires the first information, and the display unit 180 of the first dryer 10 displays the remaining drying time indicated by the first information (step S55). In addition, the acquisition unit 310 of the smartphone 30 linked to the first dryer 10 acquires the first information, and the display unit 320 of the smartphone 30 linked to the first dryer 10 displays the remaining drying time indicated by the first information.

[0147] Meanwhile, the sensing information acquisition unit 210 checks whether new sensing information has been provided by the providing unit 160 of the first dryer 10 (Step S60).

[0148] If new sensing information is not provided in the processing of step S60 (step S60: No), the sensing information acquisition unit 210 waits until new sensing information is provided (repeats step S60: No and step S65: No) as long as the operation of the first dryer 10 has not ended (step S65: No).

[0149] In the process of step S60, if new sensing information has been provided (step S60: Yes), the drying time prediction system 1 proceeds to the process of step S35.

[0150] If the operation of the first dryer 10 is completed while waiting for new sensing information to be provided (step S65: Yes), the drying time prediction system 1 ends the drying time prediction process.

[0151] [3. Consideration] As shown in the upper graph of Fig. 6, in general, the first dryer 10 often performs operations under various actual operating conditions during a given period of time in the past. In addition, in general, when the actual operating conditions differ, the actual operating time of the operation started under the actual operating conditions differs. For this reason, simply calculating an average value of the actual operating times of the operations performed by the first dryer 10 during a given period of time in the past may not adequately reflect the tendency of the first dryer 10 to be longer or shorter during the given period of time in the past.

[0152] 6, the number of times that the first dryer 10 operates under one actual operating condition during a given period in the past is generally limited, and therefore there is a possibility that the operation will not be performed a statistically valid number of times. For this reason, simply calculating an average value of the actual operating times of the operation under one actual operating condition performed by the first dryer 10 during a given period in the past may not adequately reflect the tendency toward longer or shorter operation times performed by the first dryer 10 during the given period in the past.

[0153] In contrast, according to the drying time prediction system 1 having the above-described configuration, as described above, the average value of the standardized actual operating time calculated by the representative value calculation unit 235 reflects the tendency for the actual operating time to become longer or shorter under all actual operating conditions of operation performed by the first dryer 10 during a specified period in the past.

[0154] Therefore, the drying time prediction system 1 can predict the drying time with higher accuracy than ever before.

[0155] (supplement) Although the drying time prediction system according to one aspect of the present disclosure has been described based on the embodiment, the present disclosure is not limited to this embodiment. As long as it does not deviate from the spirit of the present disclosure, various modifications conceived by a person skilled in the art to this embodiment and forms constructed by combining components in different embodiments may also be included within the scope of one or more aspects of the present disclosure.

[0156] (1) In the embodiment, the prediction unit 240 has been described as being included in the server device 20. However, as long as the same function as that of the drying time prediction system 1 can be realized, the prediction unit 240 does not necessarily need to be limited to the example of the configuration included in the server device 20. For example, the prediction unit 240 may be configured to be included in one of the multiple dryers 10, or may be configured to be included in one of the multiple smartphones 30.

[0157] Similarly, as long as the same functions as those of the drying time prediction system 1 can be realized, at least a part of the components of the server device 20 may be, for example, a configuration provided in at least one of the multiple dryers 10, or may be, for example, a configuration provided in at least one of the multiple smartphones 30. At least a part of the components of the dryer 10 may be, for example, a configuration provided in the server device 20, or may be, for example, a configuration provided in at least one of the multiple smartphones 30. At least a part of the components of the smartphone 30 may be, for example, a configuration provided in the server device 20, or may be, for example, a configuration provided in at least one of the multiple dryers 10.

[0158] (2) In the embodiment, the multiple dryers 10 have been described as being included in the drying time prediction system 1. In contrast to this, as another configuration example, a configuration in which the multiple dryers 10 are external devices of the drying time prediction system 1 is also conceivable. In this case, each of the multiple dryers 10 provides the drying time prediction system 1 with the operating conditions accepted by the input accepting unit 110, the associated actual operating conditions and actual operating times stored in the past data accumulation unit 140, and the sensing information acquired by the sensing unit 150, and acquires first information indicating the remaining drying time from the drying time prediction system 1.

[0159] (3) In the embodiment, the multiple smartphones 30 are described as being included in the drying time prediction system 1. However, the multiple smartphones 30 do not have to be limited to the example as essential components of the drying time prediction system 1. As another configuration example, a configuration in which the multiple smartphones 30 are not included in the drying time prediction system 1 is also conceivable.

[0160] (4) In the embodiment, the learned model 245 is described as a machine learning model that is trained in advance to output driving end time information related to the end time of driving started under the driving conditions when the driving conditions, the sensing information, and the average value of the standardized actual driving time are input. In contrast, as another configuration example, the learned model 245 may be a machine learning model that is trained in advance to output driving end time information related to the end time of driving started under the driving conditions when the driving conditions, the sensing information, and the standardized actual driving time are input. In this case, the prediction unit 240 inputs the driving conditions acquired by the driving condition acquisition unit 215, the sensing information acquired by the sensing information acquisition unit 210, and the standardized actual driving time calculated by the standardization unit 230 to the learned model 245, thereby acquiring driving end time information output from the learned model 245. As yet another example of the configuration, the learned model 245 may be a machine learning model that is pre-trained to output driving end time information related to the end time of driving that started under the driving conditions when the driving conditions and the standardized actual driving time are input. In this case, the prediction unit 240 inputs the driving conditions acquired by the driving condition acquisition unit 215 and the standardized actual driving time calculated by the standardization unit 230 into the learned model 245, thereby acquiring driving end time information output from the learned model 245.

[0161] (5) In the embodiment, the prediction unit 240 has been described as including one trained model 245. In contrast, as another configuration example, the prediction unit 240 may be configured to include a plurality of trained models 245. For example, the prediction unit 240 may be configured to include a plurality of trained models 245 that are trained in advance according to the elapsed time since the dryer 10 started operating. In this case, the prediction unit 240 selects a trained model 245 according to the elapsed time since the first dryer 10 started operating from among the plurality of trained models 245, and inputs the operating conditions acquired by the operating condition acquisition unit 215, the sensing information acquired by the sensing information acquisition unit 210, and the average value calculated by the representative value calculation unit 235 to the selected trained model 245, thereby acquiring operation end time information output from the trained model 245.

[0162] (6) The present disclosure can be realized not only as a system or device, but also as a method in which the processing means constituting the system or device are steps, as a program that causes a computer to execute those steps, as a computer-readable recording medium such as a CD-ROM on which the program is recorded, or as information, data, or signals indicating the program. Furthermore, those programs, information, data, and signals may be distributed via a communication network such as the Internet. [Industrial Applicability]

[0163] The present disclosure is widely applicable to systems, devices, etc. for predicting drying time. [Explanation of symbols]

[0164] 1 Drying time prediction system 10, 10A, 10B, 10M dryer 20 Server device 30, 30A, 30B, 30N Smartphones 40 Network 100, 200, 300 Communication Department 110 Input reception section 120 Operation control unit 130 Drying section 140, 205 Past Data Storage Section 150 Sensing section 160 Provision Department 170, 310 Acquisition Department 180, 320 display 210 Sensing information acquisition unit 215 Operation condition acquisition unit 220 First actual operation time acquisition unit 225 Second Actual Operation Time Acquisition Unit 230 Standardization Department 235 Representative value calculation section 240 Prediction Department 245 trained models 250 Output section 601, 602, 603, 604, 605, 606 First actual operation time 651, 652, 653, 654, 655, 656 Standardized Actual Operation Time

Claims

1. Acquire operating conditions when starting operation of the first dryer; acquiring sensing information related to a state of the first dryer during operation by the operation; acquiring a first actual operation time of each of one or more first operations previously performed by the first dryer; In order to cause a trained model to output driving end time information relating to the end time of driving due to the start of the driving, the driving conditions, first actual information based on the first actual driving time, and the sensing information are input to the trained model to acquire the driving end time information output from the trained model; moreover, For each of the one or more first operations, a first actual operating condition at the start of the first operation is acquired in association with the first actual operating time; For each of one or more second operations previously performed by one or more second dryers, a second actual operation time of the second operation and second actual operation conditions associated with the second actual operation time when starting the second operation are acquired; calculating a standardized actual operation time by standardizing the first actual operation time for each of the first actual operation times based on a distribution of the second actual operation times linked to the second actual operation conditions that coincide with the first actual operation conditions linked to the first actual operation times, in order to reflect a trend toward a lengthening or shortening of actual operation times under all actual operation conditions of operations previously performed by the first dryer; The first actual operation information is based on the standardized actual operation time calculated for each of the first actual operation times, Outputting first information based on the operation end time information Drying time prediction method.

2. Furthermore, an average value of the standardized actual operation time for each of the first actual operation times is calculated, The first performance information is based on the average value. The drying time prediction method according to claim 1 .

3. the first operation is an operation that the first dryer performed during a predetermined period in the past; The second operation is an operation that the one or more second dryers performed during the predetermined period in the past. The drying time prediction method according to claim 1 or 2.

4. The operating conditions and the first actual operating conditions include an input laundry amount to be input into the first dryer and an operating mode of the first dryer, both set by a user of the first dryer; The second actual operating conditions include a load laundry amount to be loaded into the one or more second dryers and an operating mode of the one or more second dryers, the load laundry amount being set by a user of each of the one or more second dryers. The drying time prediction method according to any one of claims 1 to 3.

5. The first dryer and the one or more second dryers are of the same model. The drying time prediction method according to any one of claims 1 to 4.

6. an operating condition acquisition unit that acquires operating conditions when starting an operation of the first dryer; a sensing information acquisition unit that acquires sensing information related to a state of the first dryer during operation by the operation; a first actual operation time acquisition unit that acquires a first actual operation time of each of one or more first operations that have been previously performed by the first dryer; a trained model that outputs operation end time information related to an end time of operation due to the start of the operation when the operation conditions, first actual information based on the first actual operation time, and the sensing information are input; and an output unit that outputs first information based on the operation end time information, The first actual operation time acquisition unit further acquires, for each of the one or more first operations, a first actual operation condition at the start of the first operation in association with the first actual operation time; moreover, a second actual operation time acquisition unit that acquires, for each of one or more second operations previously performed by one or more second dryers, a second actual operation time of the second operation and a second actual operation condition at the start of the second operation that is linked to the second actual operation time; a standardization unit that calculates a standardized actual operation time by standardizing the first actual operation time for each of the first actual operation times based on a distribution of the second actual operation times linked to the second actual operation conditions that coincide with the first actual operation conditions linked to the first actual operation times, in order to reflect a trend of lengthening or shortening the actual operation time under all actual operation conditions of operations previously performed by the first dryer, The first performance information is based on the standardized performance operation time calculated for each of the first performance operation times. Drying time prediction system.

7. 7. The first dryer utilizing the drying time prediction system of claim 6, a providing unit that provides the operating conditions and the first actual operating time to the drying time prediction system; An acquisition unit that acquires the first information from the drying time prediction system; A display unit that displays the first information. dryer.

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