Prediction method, program recording medium, prediction system, server, and display device

Abstract

The prediction method includes a step of acquiring (ST1), a step of predicting (ST2), a step of determining (ST4), and a step of processing (ST5). In the step of acquiring (ST1), parameters of the drying operation are acquired. In the step of predicting (ST2), based on the acquired parameters, a drying time required for the drying operation is predicted by a first prediction method. In the step of determining (ST4), in a case where a resumption of the drying operation is detected in a stop of the drying operation, it is determined whether or not the first prediction method performed in the step of predicting (ST2) can be applied. In the step of processing (ST5), in a case where it is determined that the first prediction method can be applied, the first prediction method is continued, and in a case where it is determined that the first prediction method cannot be applied, a prescribed processing is performed.

Description

Technical Field

[0001] This disclosure relates to a method, program recording medium, prediction system, server, and display device for predicting the drying time required for the drying operation of clothing. Background Technology

[0002] Patent Document 1 discloses a drum-type washer-dryer. This drum-type washer-dryer comprises a weight measuring device for measuring the weight information of the laundry, a storage device for storing the weight information, and a control device including a comparison-computation device for comparing and calculating using the weight information. This drum-type washer-dryer determines the end of the drying process by predicting the remaining time of the drying process based on the weight of the laundry over time.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 11-146999 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] This disclosure provides a prediction method that allows the prediction of typical drying time to continue even when the drying operation is stopped.

[0008] Methods used to solve problems

[0009] One aspect of the prediction method disclosed herein includes an acquisition step, a prediction step, a determination step, and a processing step. In the acquisition step, parameters of the drying operation of a washing machine with a drying function are acquired. In the prediction step, based on the parameters acquired in the acquisition step, the drying time required for the drying operation is predicted using a first prediction method. In the determination step, if the restart of the drying operation is detected during a stoppage, it is determined whether the first prediction method of the prediction step can be applied. In the processing step, if it is determined that the first prediction method can be applied, the first prediction method is continued; if it is determined that the first prediction method cannot be applied, a prescribed processing step is performed.

[0010] One aspect of the prediction method disclosed herein includes a prediction step, a determination step, and a processing step. In the prediction step, based on parameters of the drying operation, the drying time required for the drying operation is predicted using a first prediction method. In the determination step, if the restart of the drying operation is detected during a stoppage, it is determined whether the first prediction method of the prediction step can be applied. In the processing step, if it is determined that the first prediction method can be applied, the first prediction method is continued; if it is determined that the first prediction method cannot be applied, a prescribed processing step is performed.

[0011] One aspect of this disclosure involves a program recording medium that records a program that enables one or more processors to execute the aforementioned prediction method.

[0012] One aspect of the prediction system disclosed herein includes an acquisition unit, a prediction unit, a determination unit, and a processing unit. The acquisition unit acquires parameters of the drying operation of a washing machine with a drying function. Based on the parameters acquired by the acquisition unit, the prediction unit predicts the drying time required for the drying operation using a first prediction method. If the determination unit detects the restart of the drying operation during a stop, it determines whether the first prediction method performed by the prediction unit can be applied. If the processing unit determines that the first prediction method can be applied, it continues the first prediction method; if it determines that the first prediction method cannot be applied, it performs a prescribed process.

[0013] One aspect of this disclosure relates to a server equipped with the aforementioned prediction system. This prediction system communicates with the aforementioned washing machine with a drying function via an external network.

[0014] One aspect of the display device disclosed herein includes a communication function and a display function. The communication function is for communicating with the prediction system. The display function is for displaying information to prompt the user when information regarding the execution result of the processing unit is received from the prediction system via the communication function.

[0015] Invention Effects

[0016] According to the prediction method disclosed herein, there are the following advantages: even if the drying operation stops during operation, it is easy to continue the usual method for predicting drying time. Attached Figure Description

[0017] Figure 1 This is a block diagram showing the configuration of a washing machine with a drying function installed in the prediction system of the embodiment.

[0018] Figure 2This is a comparison chart of the first and second prediction methods used to predict drying time.

[0019] Figure 3 This is an illustration of the problem in the comparative example prediction system when the drying operation stops during operation.

[0020] Figure 4 This is an explanatory diagram illustrating the operation of the determination unit and prediction unit of the prediction system in the embodiment.

[0021] Figure 5 This is a flowchart illustrating an example of the operation of the prediction system in an implementation method.

[0022] Figure 6 This is a flowchart illustrating an example of the operation of the determination unit of the prediction system in an implementation method.

[0023] Figure 7 It is a block diagram showing the overall structure including the prediction system of the relevant implementation method variations. Detailed Implementation

[0024] (Based on the understanding that forms the basis of this disclosure)

[0025] First, the inventor's perspective will be explained below.

[0026] For example, the drum-type washer-dryer disclosed in Patent Document 1 has a drying function that performs a drying operation to dry washed clothes. Furthermore, the washer-dryer has a function to predict the drying time required for the drying operation and display the predicted drying time as the remaining time until the end of the drying operation on a display screen. By viewing the remaining time displayed on the screen, the user can roughly determine when the drying operation will end and thus make plans accordingly.

[0027] However, during the drying process, there may be situations where the user pauses the drying operation, such as to check the dryness of the clothes or to add more clothes. However, since the aforementioned function for predicting drying time does not account for pauses, there is a question regarding whether the function for predicting the normal drying time should resume when the pause is lifted and the drying operation restarts.

[0028] Here, if the function to predict the usual drying time is always restarted when the drying operation resumes, the accuracy of the drying time prediction will decrease, and consequently, the accuracy of the remaining time until the end of the drying operation may also decrease. In this case, the drying operation may actually end before or after the remaining time has elapsed, potentially leading to user dissatisfaction and a decrease in user convenience. On the other hand, if the function to predict the drying time is always stopped when the drying operation resumes, the user will not be able to know the remaining time until the end of the drying operation, which may also lead to a decrease in user convenience.

[0029] In view of the above, the inventors created this disclosure.

[0030] The following is a brief reference to the appendix. Figure 1 Each embodiment will be described in detail. However, there are instances where unnecessary details are omitted. For example, detailed descriptions of already well-known matters or repetitive descriptions of substantially the same configurations may be omitted. This is to avoid unnecessarily lengthy descriptions and to facilitate understanding by those skilled in the art.

[0031] Furthermore, the inventors have provided drawings and the following description in order to enable those skilled in the art to fully understand this disclosure, but these are not intended to limit the subject matter of the claims.

[0032] (Implementation Method)

[0033] [1-1. Overall Composition]

[0034] First, use Figure 1 The overall structure, including the prediction system 1 of the implementation method, is described. Figure 1 This is a block diagram showing the configuration of a washing machine 2 (hereinafter referred to as "washing machine 2") equipped with the predictive system 1 of the embodiment and having a drying function. In the embodiment, the predictive system 1 is mounted on the washing machine 2.

[0035] The washing machine 2 includes a function unit 21, an input unit 22, a display unit 23, one or more sensors 24, a washing tub 25, and a heat pump 26. Furthermore, the washing machine 2 also includes a predictive system 1. In this embodiment, the washing machine 2 is, for example, a drum-type washing machine. However, the washing machine 2 is not limited to a drum-type washing machine; for example, it could also be a longitudinal type washing machine.

[0036] The functional unit 21 performs various functions of the washing machine 2. In this embodiment, the functional unit 21 has a washing function that performs a washing operation to wash clothes contained in the washing tub 25, and a drying function that performs a drying operation to dry the clothes contained in the washing tub 25 and after washing. The washing operation washes the clothes by rotating the washing tub 25, rinsing, and / or dehydrating them. The drying operation dries the clothes contained in the washing tub 25 and after washing by supplying dehumidified dry air through the heat pump 26.

[0037] Furthermore, the functional unit 21 also has a display function that displays the predicted washing time (i.e., the remaining time until the end of the washing cycle) and the predicted drying time (i.e., the remaining time until the end of the drying cycle) required for the washing operation on the display unit 23. In this embodiment, the predicted drying time is predicted by the prediction system 1. The predicted washing time may also be predicted by the prediction system 1 or by other systems besides the prediction system 1; however, this will not be described here.

[0038] Function unit 21 performs functions corresponding to the input received by input unit 22. Additionally, function unit 21 can also communicate with the user's information terminal 3, thereby performing functions corresponding to the user's input received by the information terminal 3. Information terminal 3 may include, for example, a smartphone, tablet computer, or desktop or laptop computer. Communication between information terminal 3 and washing machine 2 is, for example, wireless communication following standards such as WiFi (registered trademark) or BLE (Bluetooth Low Energy). However, the communication standard between information terminal 3 and washing machine 2 is not specifically limited.

[0039] The input unit 22 accepts input based on user operation. The input unit 22 may consist of, for example, buttons that accept various types of input. The input unit 22 accepts inputs such as selecting the operation to be performed by the function unit 21, selecting the operation content (e.g., operation mode), starting operation, and pausing operation. Furthermore, if the display unit 23 is a touch panel display, the display unit 23 may also serve as part of the input unit 22.

[0040] Display unit 23 is, for example, a liquid crystal display (LCD) that displays various information about the washing machine 2. For instance, if the machine is in washing mode, display unit 23 displays a string and / or image indicating that it is in washing mode, as well as a string indicating the predicted washing time. Similarly, if the machine is in drying mode, display unit 23 displays a string and / or image indicating that it is in drying mode, as well as a string indicating the predicted drying time. Furthermore, display unit 23 may also include, in addition to an LCD, a light that illuminates or extinguishes according to the information displayed.

[0041] Each sensor 24 detects various states related to the operation of the washing machine 2. In other words, each sensor 24 detects parameters related to the operation of the washing machine 2. For example, one or more sensors 24 may include a weight sensor that detects the weight of the clothes contained in the washing tub 25. The one or more sensors 24 mainly include sensors that detect the state of the washing operation and sensors that detect the state of the drying operation. Further explanation of the sensors that detect the state of the washing operation is omitted here.

[0042] In this embodiment, among the more than one sensor 24, at least a first temperature sensor for detecting the intake temperature and a second temperature sensor for detecting the exhaust temperature are included. Both the first and second temperature sensors are composed of thermistors. The first temperature sensor is, for example, located near the intake port of the heat pump 26. The second temperature sensor is, for example, located near the exhaust port of the heat pump 26. Here, "intake temperature" refers to the temperature of the air drawn into the heat pump 26 from the washing tank 25. Furthermore, "exhaust temperature" refers to the temperature of the air discharged from the heat pump 26 into the washing tank 25.

[0043] [1-2. Prediction System]

[0044] Next, the details of prediction system 1 will be explained. Prediction system 1 is as follows: Figure 1 As shown, the system includes an acquisition unit 11, a prediction unit 12, a detection unit 13, a determination unit 14, a processing unit 15, a prompting unit 16, and a storage unit 17. Alternatively, in some embodiments, the prediction system 1 may include at least the acquisition unit 11, the prediction unit 12, the determination unit 14, and the processing unit 15, but may also omit the detection unit 13, the prompting unit 16, and the storage unit 17.

[0045] The acquisition unit 11 acquires parameters of the drying operation performed by the washing machine 2. The acquisition unit 11 is the execution body of the acquisition step ST1 in the prediction method. In the embodiment, the acquisition unit 11 periodically acquires the detection results (i.e., parameters of the drying operation) of each sensor 24 from the start to the end of the drying operation.

[0046] Here, as already described, among the more than one sensor 24, there are a first temperature sensor and a second temperature sensor. Therefore, the acquisition unit 11 acquires the intake temperature and discharge temperature as parameters for the drying operation. That is, the washing machine 2 has the function of performing drying operation using the heat pump 26. Furthermore, the parameters (for the drying operation) include the temperature of the air drawn from the washing tub 25 of the washing machine 2 into the heat pump 26 and the temperature of the air discharged from the heat pump 26 into the washing tub 25.

[0047] Furthermore, in this embodiment, the acquisition unit 11 acquires not only the inhalation temperature and the discharge temperature, but also information on the drying operation mode and historical information on the drying operation (e.g., the actual time required for the most recent dozens of drying operations). Information on the drying operation mode can be acquired by acquiring the input indicating the operating mode received by the input unit 22 or the information terminal 3. Furthermore, historical information on the drying operation can be acquired by reading it from the storage unit 17.

[0048] The prediction unit 12 predicts the drying time required for drying operation using a first prediction method based on the parameters obtained by the acquisition unit 11 (acquisition step ST1). The prediction unit 12 is the execution body of the prediction step ST2 in the prediction method. Here, as a method for predicting the drying time required for drying operation, there are, for example, a first prediction method using a learned model and a second prediction method using a rule base. In this embodiment, the prediction unit 12 basically uses the first prediction method to predict the drying time. That is, the method of predicting the drying time using the first prediction method is equivalent to a normal method for predicting the drying time.

[0049] In the first prediction method, a learned model that has undergone machine learning to predict drying time is used to predict the drying time. The learned model, for example, is a neural network with a multi-layered structure, and is a model that has undergone machine learning to output a predicted drying time when parameters of the drying operation are input. That is, in the first prediction method, the predicted drying time output from the learned model is obtained by inputting the parameters acquired by the acquisition unit 11 into the learned model. In the first prediction method, the predicted drying time is obtained periodically by periodically inputting the parameters acquired by the acquisition unit 11 (e.g., intake temperature and discharge temperature) into the learned model. In other words, the predicted drying time is updated periodically during drying operation.

[0050] Parameters for drying operation may include, for example, intake temperature, discharge temperature, operating mode, and / or drying operation history. The drying operation history may be representative values ​​of the actual time required for the most recent dozens of drying operations. Representative values ​​may include, for example, the mean, median, and mode. In this implementation, all the parameters listed above are input into the learned model.

[0051] For example, the learned model is machine learning performed through supervised learning using multiple learning datasets. Each learning dataset includes information representing parameters of the drying operation as input data and information representing the actual drying time required as the correct solution data. Furthermore, in this implementation, the drying operation parameters included in each learning dataset are the parameters listed above.

[0052] In the second prediction method, the drying time is primarily predicted based on the intake and exhaust temperatures, according to a rule base. Specifically, in the second prediction method, the moving average of the difference between the intake and exhaust temperatures is monitored periodically (e.g., every 30 to 60 minutes) for a certain period of time (e.g., a few minutes). The degree of dryness of the garment is determined by comparing the change in the moving average over this period of time with a threshold. Furthermore, the predicted drying time is obtained based on the degree of dryness of the garment. That is, for the second prediction method, similar to the first prediction method, the predicted drying time is periodically updated during drying operation based on the periodically obtained intake and exhaust temperatures.

[0053] Figure 2 This is a comparison chart of the first and second prediction methods used to predict drying time. Figure 2 In the diagram, the vertical axis represents the drying time (remaining time), and the horizontal axis represents the elapsed time since the start of the drying operation. Furthermore, in... Figure 2 In the diagram, solid lines represent drying times predicted by the first prediction method, dashed lines represent drying times predicted by the second prediction method, and dashed lines represent the actual drying times. Figure 2 In the diagram, the bends in the solid lines represent the update timing of the predicted drying time based on the first prediction method. Furthermore, in... Figure 2 In the diagram, the circled marker with a single dotted line indicates the update timing of the predicted drying time based on the second prediction method.

[0054] like Figure 2 As shown, for the second prediction method, the predicted drying time deviates significantly from the actual drying time, and the predicted drying time tends to fluctuate considerably upon update. On the other hand, for the first prediction method, the predicted drying time follows the actual drying time less closely, and the predicted drying time does not fluctuate significantly upon update. Thus, although it also depends on the learning level of the learned model, the first prediction method generally has higher accuracy in predicting drying time compared to the second prediction method.

[0055] The detection unit 13 (detection step ST3) detects both the stoppage and restart of the drying operation. The detection unit 13 is the executing entity of detection step ST3 in the prediction method. In this embodiment, the detection unit 13 detects the stoppage of the drying operation by receiving an input indicating a stoppage in the drying operation, received by the input unit 22 or the information terminal 3. Furthermore, the detection unit 13 detects the restart of the drying operation by receiving an input indicating a restartpage in the drying operation, received by the input unit 22 or the information terminal 3.

[0056] If the detection unit 13 (detection step ST3) detects the restart of drying operation, the determination unit 14 (determination step ST4) determines whether the first prediction method performed by the prediction unit 12 (prediction step ST2) can be applied. The determination unit 14 is the execution body of the determination step ST4 in the prediction method.

[0057] Here, if the drying operation is paused during drying, the internal environment of the washing tank 25 may change significantly when the drying operation resumes compared to when it was paused. For example, if the drying operation is paused, the heat pump 26 also stops, causing the internal temperature of the washing tank 25, such as the suction temperature and discharge temperature, to drop or fluctuate. Furthermore, when the drying operation is paused, the internal temperature of the washing tank 25 may also drop or fluctuate because the user opens the lid of the washing tank 25. Consequently, the longer the pause period, the more difficult it is to avoid significant changes in the internal environment of the washing tank 25.

[0058] Thus, if the internal environment of the washing tank 25 changes during the period from the pause to the restart of the drying operation, there is a possibility that the accuracy of the drying time prediction after the restart of the drying operation may decrease. The following uses... Figure 3 Explain the problem. Figure 3 This is an explanatory diagram illustrating the problem in the comparative example's prediction system when drying operation is paused. The comparative example's prediction system differs from the prediction system 1 of the embodiment in that it lacks a determination unit 14. Figure 3 In the diagram, the vertical axis represents the drying time (remaining time), and the horizontal axis represents the elapsed time since the start of the drying operation. Furthermore, in... Figure 3 In the diagram, the solid line represents the drying time predicted by the first prediction method, and the dashed line represents the actual drying time. Furthermore, in... Figure 3 In the diagram, the dotted line represents the drying time predicted by the first prediction method assuming no pause occurs.

[0059] Figure 3 In the example shown, the drying operation is paused at time t1 and restarted at time t2. Furthermore, in... Figure 3In the example shown, the comparative example's prediction system resumes its prediction of the drying time when the drying operation is restarted at time t2. In this case, the prediction unit restarts the prediction of the drying time based on the parameters at time t2, but does not consider the changes in the internal environment of the washing tank 25 between the pause and restart of the drying operation. Therefore, Figure 3 In the example shown, the predicted drying time after time t2 deviates significantly from the actual drying time compared to the case where there was no pause in the drying operation, resulting in a decrease in the accuracy of the drying time prediction.

[0060] Therefore, in this embodiment, the determination unit 14 (determination step ST4) determines whether the first prediction method performed by the prediction unit 12 (prediction step ST2) can be applied based on whether there are parameters in the history of parameters before the drying operation stopped that are similar to the parameters at the restart of the drying operation. That is, in this embodiment, the determination unit 14 determines whether the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation based on whether the internal environment of the washing tank 25 at the restart of the drying operation is similar to the internal environment of the washing tank 25 before the drying operation was stopped.

[0061] Specifically, when the detection unit 13 detects the restart of the drying operation, the determination unit 14 causes the acquisition unit 11 to sequentially acquire the parameters of the drying operation (here, the intake temperature and the discharge temperature) for a certain period of time from the restart of the drying operation. Furthermore, the determination unit 14 reads the history of the drying operation parameters prior to the pause in the drying operation from the storage unit 17 and compares this history with the acquired results (i.e., the intake temperature and discharge temperature over a certain period of time). If, within a certain period of time, the acquired results are included in the history, or if the value obtained by adding a tolerance to the acquired results is included in the history (in other words, if the history contains parameters similar to the acquired results), the determination unit 14 determines that the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation. On the other hand, if this is not the case, the determination unit 14 determines that the first prediction method performed by the prediction unit 12 cannot be applied after the restart of the drying operation.

[0062] Processing unit 15 (processing step ST5) continues the first prediction method performed by prediction unit 12 (prediction step ST2) if determination unit 14 (determination step ST4) determines that the first prediction method performed by prediction unit 12 (prediction step ST2) can be applied; if determination unit 14 (determination step ST4) determines that the first prediction method performed by prediction unit 12 (prediction step ST2) cannot be applied, it performs the prescribed processing. Processing unit 15 is the execution body of processing step ST5 in the prediction method.

[0063] Specifically, when the determination unit 14 determines that the first prediction method performed by the prediction unit 12 can be applied, the processing unit 15 traces back to the acquisition time of a parameter similar to the obtained result in the aforementioned history, and causes the prediction unit 12 to predict the drying time. On the other hand, when the determination unit 14 determines that the first prediction method performed by the prediction unit 12 cannot be applied, the processing unit 15 switches the prediction method used by the prediction unit 12 from the first prediction method to the second prediction method. In this case, the prediction unit 12 predicts the drying time using the second prediction method after the drying operation restarts. That is, in this embodiment, the prescribed processing includes the process of switching from the first prediction method to a second prediction method different from the first prediction method to predict the drying time.

[0064] Furthermore, the prescribed processing is not limited to the process of switching from the first prediction method to the second prediction method, and may also include other processes. For example, the prescribed processing may include, in addition to the process of switching from the first prediction method to the second prediction method, a process informing the user via the prompting unit 16 that the prediction accuracy of the drying time after the restart of the drying operation has decreased. Alternatively, the prescribed processing may involve the prediction unit 12 maintaining the drying time prediction made using the first prediction method, and informing the user via the prompting unit 16 that the prediction accuracy of the drying time after the restart of the drying operation has decreased.

[0065] The following uses Figure 4 Here is an example of the action performed by the determination unit 14 and the prediction unit 12. Figure 4 This is an explanatory diagram illustrating an example of the operation of the determination unit 14 and the prediction unit 12 of the prediction system 1 according to the embodiment. Figure 4 In the diagram, the vertical axis represents the drying time (remaining time), and the horizontal axis represents the elapsed time since the start of the drying operation. Furthermore, in... Figure 4 In the diagram, the solid line represents the drying time predicted by the first prediction method, and the dashed line represents the actual drying time. Furthermore, in... Figure 4 In the diagram, the dotted line represents the drying time predicted by the first prediction method under the assumption that no pauses occur.

[0066] Figure 4 In the example shown, the drying operation is paused at time t1 and restarted at time t2. Furthermore, Figure 4 In the example shown, during a certain period of time from time t2 to time t3, the determination unit 14 causes the acquisition unit 11 to acquire the parameters of the drying operation (here, the intake temperature and the discharge temperature). Figure 4 In the example shown, the parameters of the drying operation at time t2 are similar to those at time t0 before the drying operation is paused (see reference). Figure 4 (as shown by the circular mark). Therefore... Figure 4In the example shown, the determination unit 14 determines that the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation. And... Figure 4 In the example shown, the processing unit 15 backtracks to time t0, causes the prediction unit 12 to predict the drying time, and reflects the prediction result in the drying time after time t3.

[0067] so, Figure 4 In the example shown, even if the drying operation is interrupted, the first prediction method performed by the prediction unit 12 can continue, thereby suppressing the decrease in the prediction accuracy of the drying time after the drying operation resumes.

[0068] The prompting unit 16 (prompting step ST6) prompts the user of the washing machine 2 with information about the execution result of the processing unit 15 (processing step ST5). The prompting unit 16 is the execution body of the prompting step ST6 in the prediction method. Here, the information about the execution result of the processing unit 15 may include, for example, information indicating that the first prediction method continues after the restart of the drying operation, or information indicating that the prediction of the drying time has switched from the first prediction method to the second prediction method after the restart of the drying operation. Furthermore, the information about the execution result of the processing unit 15 may include information indicating that the prediction accuracy of the drying time has decreased after the restart of the drying operation.

[0069] In this embodiment, the prompting unit 16 causes the display unit 23 to display information about the execution result of the processing unit 15 in the form of a string and / or an image, thereby prompting the user with this information. Alternatively, the prompting unit 16 may also send information about the execution result of the processing unit 15 to the information terminal 3, causing the display of the information terminal 3 to show it.

[0070] Alternatively, if the display unit 23 has a lamp, the prompting unit 16 may prompt the user with information about the execution result of the processing unit 15 by indicating the lamp's illumination status. For example, suppose the lamp on the display unit 23 is lit during the drying operation prediction. Furthermore, suppose the information about the execution result of the processing unit 15 indicates that the drying time prediction has switched from the first prediction method to the second prediction method after the restart of the drying operation. In this case, the prompting unit 16 may prompt the user with the above information by turning off the lamp and turning on other lamps, changing the lamp's light color, or flashing the lamp.

[0071] Alternatively, the prompting unit 16 may, for example, output an audio message from a speaker on the washing machine 2 to prompt the user with information about the execution result of the processing unit 15. Furthermore, the prompting unit 16 may combine both display-based and audio-based prompts to prompt the user with information about the execution result of the processing unit 15.

[0072] The storage unit 17 is a storage device that stores information (computer programs, etc.) required by the functional units 21 of the washing machine 2 and the various parts of the prediction system 1 to perform various functions. The storage unit 17 is implemented, for example, by a semiconductor memory, but is not particularly limited and can use a well-known electronic information storage mechanism. The storage unit 17 stores, for example, historical information of washing and drying operations, as well as historical information of the detection results of the sensor 24.

[0073] [2. Action]

[0074] For the actions of prediction system 1 constructed as described above, the following uses... Figure 5 and Figure 6 Please provide an explanation. Figure 5 This is a flowchart illustrating an example of the operation of the prediction system 1 in the implementation method. Figure 6 This is a flowchart illustrating an example of the operation of the determination unit 14 of the prediction system 1 in the embodiment. Hereinafter, we will explain this using the example of the washing machine 2's function unit 21 executing the drying operation upon receiving an operation input from the user instructing the drying operation to begin.

[0075] First, the acquisition unit 11 acquires the parameters for the drying operation (S1) before the drying operation is executed. Processing S1 corresponds to the acquisition step ST1 of the prediction method. Next, the prediction unit 12 predicts the drying time (S2) based on the parameters acquired by the acquisition unit 11. Processing S2 corresponds to the prediction step ST2 of the prediction method. The predicted drying time is displayed on the display unit 23. Thereafter, processes S1 and S2 are repeated periodically until the drying time becomes zero (i.e., the drying operation ends) (S3: No, S4: No), so that the predicted drying time is updated periodically and displayed on the display unit 23. And when the drying time becomes zero (S3: Yes), the operation of the prediction system 1 ends.

[0076] Here, during drying operation (S3: No), the detection unit 13 monitors whether a pause and restart of the drying operation occurs (S4). Process S4 corresponds to the detection step ST3 of the prediction method. Furthermore, when the detection unit 13 detects a pause and restart of the drying operation (S4: Yes), the determination unit 14 determines whether the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation (S5). Process S5 corresponds to the determination step ST4 of the prediction method. Regarding process S5, using... Figure 6 To be discussed later.

[0077] If the determination unit 14 determines that the method is applicable (S6: Yes), the processing unit 15 continues the first prediction method performed by the prediction unit 12 (S7). Conversely, if the determination unit 14 determines that the method is not applicable (S6: No), the processing unit 15 switches the prediction method used by the prediction unit 12 from the first prediction method to the second prediction method (S8). Processing steps S7 and S8 correspond to the prediction method processing step ST5. Furthermore, the prompting unit 16 causes the display unit 23 to display information about the execution result of the processing unit 15, prompting the user (S9). Processing step S9 corresponds to the prediction method prompting step ST6.

[0078] Next, use Figure 6 Here is an example of the operation of process S5, namely the determination unit 14. First, the determination unit 14, for a certain period of time after the restart of the drying operation, causes the acquisition unit 11 to acquire the parameters of the drying operation (S51). Then, the determination unit 14 reads the history of the parameters of the drying operation before the pause of the drying operation from the storage unit 17 and compares the history with the acquisition result (S52). If the history contains parameters similar to the acquisition result (S53: Yes), the determination unit 14 determines that the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation (S54). On the other hand, if the history does not contain parameters similar to the acquisition result (S53: No), the determination unit 14 repeatedly performs processes S51 to S53 until a certain period of time has elapsed (S55: No). Furthermore, if a certain period of time passes (S55: Yes) and the state in the history does not contain parameters similar to the obtained result, the determination unit 14 determines that the first prediction method performed by the prediction unit 12 cannot be applied after the restart of the drying operation (S56).

[0079] [3. Effects, etc.]

[0080] The advantages of the prediction system 1 in the following implementation will be explained.

[0081] As mentioned above, if the drying operation stops midway through, whether the function of predicting the normal drying time is restarted at the beginning of the drying operation or the function of predicting the normal drying time is stopped at the beginning of the drying operation, the user convenience will decrease.

[0082] In response, the prediction system 1 of this embodiment determines whether the first prediction method (i.e., the normal drying time prediction method) performed by the prediction unit 12 can be applied after the drying operation resumes, and decides on the processing that can be taken after the drying operation resumes based on the determination result. Therefore, based on the determination result, even if the drying operation stops during operation, the first prediction method performed by the prediction unit 12 can continue, thereby suppressing the decrease in the prediction accuracy of the drying time after the drying operation resumes. That is, the prediction system 1 of this embodiment does not uniformly determine the processing after the drying operation resumes simply because the drying operation stops during operation, but rather determines the processing that can be taken after the restart based on the situation at the time the drying operation resumes.

[0083] As described above, the prediction system 1 of this embodiment has the advantage of being able to easily continue the method of predicting the normal drying time even if the drying operation stops during operation. Furthermore, compared to the case where the function of predicting the normal drying time is always restarted or the function of predicting the normal drying time is always stopped when the drying operation restarts, the prediction system 1 of this embodiment has the advantage of not easily reducing user convenience.

[0084] [4. Variations]

[0085] As described above, the embodiments are examples of the technology disclosed in this application. However, the technology disclosed herein is not limited to this and can be applied to embodiments with appropriate changes, substitutions, additions, omissions, etc. Furthermore, new embodiments can be formed by combining the constituent elements described in the above embodiments.

[0086] Therefore, the following are examples of variations of the implementation method.

[0087] In the above embodiment, the prediction system 1 is mounted on the washing machine 2, but it is not limited thereto. For example, the prediction system 1 may also be set separately from the washing machine 2. Figure 7 This is a block diagram illustrating the overall structure of the prediction system 1, including variations of the relevant implementation methods. Figure 7 In the example shown, prediction system 1 is mounted on server 4. In other words, server 4 has prediction system 1. Furthermore, in Figure 7 In the example shown, the washing machine 2 also includes a communication unit 27 and a storage unit 28.

[0088] The communication unit 27 communicates with the server 4 via an external network NT1, such as the Internet. Communication between the communication unit 27 and the server 4 can be wireless or wired. Furthermore, the standard for communication between the communication unit 27 and the server 4 is not specifically limited.

[0089] Storage unit 28 is a storage device for information (computer programs, etc.) required by storage function unit 21 to perform various functions. Storage unit 28 is implemented, for example, by a semiconductor memory, but is not particularly limited and can use a well-known electronic information storage mechanism. Storage unit 28 stores, for example, historical information of washing and drying operations and historical information of detection results from sensor 24. That is, storage unit 28 stores data related to washing machine 2 from the data stored in storage unit 17 of prediction system 1.

[0090] Server 4 is located at a remote location, for example, away from the facility where washing machine 2 is installed, and is configured to communicate with washing machine 2 via external network NT1. That is, prediction system 1 communicates with washing machine 2 via external network NT1. Therefore, in this modified example, prediction system 1 performs the functions of each component by sending and receiving data between server 4 and washing machine 2 via external network NT1.

[0091] In the above embodiment, among the more than one sensor 24, a humidity sensor that measures the humidity (relative humidity) of the washing tank 25 may also be included. In this case, the parameter prediction unit 12 for drying operation may predict the drying time by referring not only to the intake temperature and discharge temperature but also to the humidity of the washing tank 25. In this case, it is expected that the prediction accuracy of the drying time will be further improved.

[0092] In the above embodiment, the determination unit 14 can also determine whether the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation based on whether the history contains a pattern of time series changes in the parameters of the drying operation over a certain period of time. Furthermore, the determination unit 14 can also determine whether the first prediction method performed by the prediction unit 12 can be applied after the restart of the drying operation based on whether the history contains representative values ​​(e.g., average, mode, or median) of the parameters of the drying operation over a certain period of time.

[0093] In the above embodiment, the determination unit 14 compares the history of parameters before the drying operation is paused with the parameters at the time the drying operation resumes to determine whether the first prediction method performed by the prediction unit 12 can be applied, but it is not limited to this. For example, the determination unit 14 may also calculate the stop time required from the pause to the resumption of the drying operation based on the detection results of the detection unit 13. Furthermore, the determination unit 14 may compare the calculated stop time with a predetermined time. If the stop time is less than the predetermined time, it is determined that the first prediction method performed by the prediction unit 12 can be applied; if the stop time is more than the predetermined time, it is determined that the first prediction method performed by the prediction unit 12 cannot be applied. This is because if the stop time is short, it can be considered that the internal environment of the washing tank 25 has changed significantly compared to the time of the pause.

[0094] In the above embodiment, the pause of the drying operation occurs based on the user's input to pause, but it is not limited to this. For example, the pause of the drying operation may also occur if the operation of the washing machine 2 stops due to a momentary power outage. In order to deal with such a situation, the detection unit 13 may also monitor the power status of the washing machine 2, thereby detecting the pause and restart of the drying operation.

[0095] In the above embodiment, the prediction system 1 (prediction method) includes a detection unit 13 (detection step ST3), but is not limited thereto. For example, the prediction system 1 (prediction method) may be able to detect the restart of drying operation by the determination unit 14 (determination step ST4), or it may not need to detect the stop of operation during drying operation.

[0096] In the above embodiment, the prediction system 1 targets a washing machine 2 with a drying function, but it is not limited to this. For example, the prediction system 1 can also target a dryer without a washing function, such as a bathroom dryer. In this case, simply replacing "washing machine 2 with a drying function (or washing machine 2)" with "dryer" in the above embodiment is sufficient.

[0097] Furthermore, for example, in the above embodiment, the prediction system 1 is implemented as a single device, but it can also be implemented by multiple devices. When the prediction system 1 is implemented by multiple devices, the constituent elements of the prediction system 1 can be distributed among the multiple devices in any way. That is, this disclosure can be implemented either through cloud computing or through edge computing.

[0098] Furthermore, as in the embodiments described above, all or part of the components of the prediction system 1 of this disclosure may be constructed by dedicated hardware, or may be implemented by executing software programs suitable for each component. Each component may also be implemented by a program execution unit such as a CPU (Central Processing Unit) or processor reading and executing software programs recorded on recording media such as HDD (Hard Disk Drive) or semiconductor memory.

[0099] Furthermore, the predictive system 1 disclosed herein may also consist of one or more electronic circuits. These electronic circuits may be either general-purpose circuits or special-purpose circuits.

[0100] One or more electronic circuits may include semiconductor devices, integrated circuits (ICs), or large-scale integration (LSIs). ICs or LSIs can be integrated onto a single chip or multiple chips. Here, they are referred to as ICs or LSIs, but the terminology may change depending on the degree of integration; they may be called system LSIs, very large-scale integration (VLSIs), or ultra-large-scale integration (ULSIs). Furthermore, post-fabrication programmable gate arrays (FPGAs) can also use LSIs for the same purpose.

[0101] Furthermore, this disclosure, in its entirety or in specific form, can also be implemented by a system, apparatus, method, integrated circuit, or computer program. Alternatively, it can be implemented by a computer-readable, non-transitory recording medium such as an optical disc, HDD, or semiconductor memory storing the computer program. For example, this disclosure can also be implemented as a program for causing a computer to execute the control methods described above. Furthermore, the program can be recorded on a computer-readable, non-transitory recording medium such as a CD-ROM, or distributed via communication paths such as the Internet.

[0102] As described above, embodiments have been illustrated as examples of the technology of this disclosure. To this end, accompanying drawings and detailed descriptions are provided.

[0103] Therefore, the constituent elements described in the accompanying drawings and detailed description include not only those necessary for solving the problem, but also those used to illustrate the above-mentioned technology but not necessarily for solving the problem. Thus, the mere presence of these non-essential constituent elements in the accompanying drawings or detailed description should not automatically lead to the conclusion that these non-essential constituent elements are necessary.

[0104] Furthermore, the above embodiments are used to illustrate the technology of this disclosure, and therefore various changes, substitutions, additions, omissions, etc., can be made within the scope of the claims or their equivalents.

[0105] (Summarize)

[0106] As described above, the prediction method of the embodiment includes an acquisition step ST1, a prediction step ST2, a determination step ST4, and a processing step ST5. In the acquisition step ST1, parameters of the drying operation of the washing machine 2 with a drying function are acquired. In the prediction step ST2, based on the parameters acquired in the acquisition step ST1, the drying time required for the drying operation is predicted using a first prediction method. In the determination step ST4, if the restart of the drying operation is detected during a stop, it is determined whether the first prediction method performed in the prediction step ST2 can be applied. In the processing step ST5, if it is determined that the first prediction method can be applied, the first prediction method continues; if it is determined that the first prediction method cannot be applied, a prescribed processing step is performed.

[0107] Therefore, when the drying operation stops during operation, compared to either always restarting the function of predicting the normal drying time or always stopping the function of predicting the normal drying time when the drying operation restarts, there is an advantage in that it is easier to continue the method of predicting the normal drying time.

[0108] Furthermore, for example, in the determination step ST4, it is determined whether the first prediction method can be applied based on whether there are parameters similar to those at the start of the drying operation in the history of parameters before the drying operation was stopped.

[0109] Therefore, the following advantages are available: by referring to the relatively simple treatment of the internal environment of the washing tank 25, it is easy to determine whether the usual drying time prediction method performed by prediction step ST2 can be applied after the restart of the drying operation.

[0110] Furthermore, for example, the washing machine 2 with a drying function has the function of performing drying operation using a heat pump 26. Parameters include the temperature of the air drawn from the washing tub 25 of the washing machine 2 with the drying function to the heat pump 26 and the temperature of the air discharged from the heat pump 26 to the washing tub 25.

[0111] Therefore, there are the following advantages: it is easy to determine whether the usual drying time prediction method performed by prediction step ST2 can be applied after the restart of the drying operation by using relatively easy-to-measure physical quantities.

[0112] Furthermore, for example in the first prediction method, the drying time is predicted using a learned model that has undergone machine learning to predict the drying time.

[0113] Therefore, compared to predicting drying time through a rule base, it has the advantage of being able to expect improved accuracy in predicting drying time.

[0114] In addition, the specified process, for example, includes switching from the first prediction method to a second prediction method that is different from the first prediction method to predict the drying time.

[0115] Therefore, compared to the situation where the function of predicting the drying time is stopped after the drying operation restarts, it has the advantage of making the user less unhappy.

[0116] In addition, the prediction method may include, for example, a prompting step ST6 that provides the user with information about the execution result of the processing step ST5.

[0117] Therefore, since users can understand what happens after the drying operation restarts, it has the advantage of making users less likely to feel unhappy.

[0118] Furthermore, the prediction method of the implementation includes a prediction step ST2, a determination step ST4, and a processing step ST5. In the prediction step ST2, the drying time required for the drying operation is predicted using a first prediction method based on the parameters of the drying operation. In the determination step ST4, if the restart of the drying operation is detected during a stop, it is determined whether the first prediction method performed in the prediction step ST2 can be applied. In the processing step ST5, if it is determined that the first prediction method can be applied, the first prediction method continues; if it is determined that the first prediction method cannot be applied, a prescribed processing is performed.

[0119] Therefore, when the drying operation stops during operation, compared to either always restarting the function of predicting the normal drying time or always stopping the function of predicting the normal drying time when the drying operation restarts, there is an advantage in that it is easier to continue the method of predicting the normal drying time.

[0120] Furthermore, the implementation program causes one or more processors to execute the above prediction method.

[0121] Therefore, when the drying operation stops during operation, compared to either always restarting the function of predicting the normal drying time or always stopping the function of predicting the normal drying time when the drying operation restarts, there is an advantage in that it is easier to continue the method of predicting the normal drying time.

[0122] Furthermore, the prediction system 1 of the embodiment includes an acquisition unit 11, a prediction unit 12, a determination unit 14, and a processing unit 15. The acquisition unit 11 acquires parameters of the drying operation of the washing machine 2 with a drying function. Based on the parameters acquired by the acquisition unit 11, the prediction unit 12 predicts the drying time required for the drying operation using a first prediction method. The determination unit 14, when detecting the restart of the drying operation during a stop, determines whether the first prediction method performed by the prediction unit 12 can be applied. If it is determined that the first prediction method can be applied, the processing unit 15 continues the first prediction method; if it is determined that the first prediction method cannot be applied, it performs a prescribed process.

[0123] Therefore, when the drying operation stops during operation, compared to either always restarting the function of predicting the normal drying time or always stopping the function of predicting the normal drying time when the drying operation restarts, there is an advantage in that it is easier to continue the method of predicting the normal drying time.

[0124] Furthermore, the server 4 in this embodiment includes the aforementioned prediction system 1. The prediction system 1 communicates with the washing machine 2, which has a drying function, via an external network NT1.

[0125] Therefore, when the drying operation stops during operation, compared to either always restarting the function of predicting the normal drying time or always stopping the function of predicting the normal drying time when the drying operation restarts, there is an advantage in that it is easier to continue the method of predicting the normal drying time.

[0126] Furthermore, the display device in this embodiment has a communication function and a display function. The communication function is the function of communicating with the prediction system 1. The display function is the function of displaying information to prompt the user when information about the execution result of the processing unit 15 is received from the prediction system 1 via the communication function.

[0127] The display device described herein is, for example, the display unit 23 of a washing machine 2 with a drying function or the display of an information terminal 3. Alternatively, the display device may be the display unit of a dryer.

[0128] Industrial availability

[0129] This disclosure can be applied to washing machines and other appliances with drying functions that perform drying operations.

[0130] Label Explanation

[0131] 1 Prediction system; 11 Acquisition unit; 12 Prediction unit; 14 Judgment unit; 15 Processing unit; 2 Washing machine with drying function; 25 Washing tank; 26 Heat pump; 4 Server; NT1 External network; ST1 Acquisition step; ST2 Prediction step; ST4 Judgment step; ST5 Processing step; ST6 Prompt step.

Claims

1. A prediction method, characterized in that, include: The steps involve obtaining the parameters for the drying operation of a washing machine with a drying function; The prediction step involves predicting the drying time required for the drying operation using the first prediction method, based on the parameters obtained in the above-mentioned acquisition step. The determination step involves determining, in the case that the restart of the drying operation is detected during the shutdown of the aforementioned drying operation, whether the first prediction method performed in the aforementioned prediction step can be applied; and The processing steps are as follows: if it is determined that the first prediction method can be applied, continue with the first prediction method; if it is determined that the first prediction method cannot be applied, perform the prescribed processing.

2. The prediction method as described in claim 1, characterized in that, In the above determination step, it is determined whether the first prediction method can be applied based on whether there are parameters in the history of the parameters before the drying operation stops that are the same as or within the error tolerance range of the parameters at the start of the drying operation.

3. The prediction method as described in claim 1 or 2, characterized in that, The aforementioned washing machine with a drying function has the capability to perform the aforementioned drying operation using a heat pump. The parameters mentioned above include the temperature of the air drawn into the heat pump from the washing tub of the washing machine with the drying function, and the temperature of the air discharged from the heat pump into the washing tub.

4. The prediction method as described in claim 1 or 2, characterized in that, In the first prediction method described above, the drying time is predicted using a learned model that has undergone machine learning to predict the drying time.

5. The prediction method as described in claim 4, characterized in that, The aforementioned processing includes switching from the first prediction method to a second prediction method that is different from the first prediction method to predict the drying time.

6. The prediction method as described in claim 1 or 2, characterized in that, It also includes a prompting step that displays information to the user regarding the results of the above processing steps.

7. A prediction method, characterized in that, include: The prediction step involves predicting the drying time required for the aforementioned drying operation based on the parameters of the drying operation using the first prediction method. The determination step involves determining, in the case that the restart of the drying operation is detected during the shutdown of the aforementioned drying operation, whether the first prediction method performed in the aforementioned prediction step can be applied; and The processing steps are as follows: if it is determined that the first prediction method can be applied, continue with the first prediction method; if it is determined that the first prediction method cannot be applied, perform the prescribed processing.

8. A program recording medium, characterized in that, Record a program that causes one or more processors to execute the prediction method according to any one of claims 1, 2, and 7.

9. A prediction system, characterized in that, have: The acquisition department obtains the drying operation parameters of the washing machine with drying function; The prediction unit, based on the parameters obtained by the acquisition unit, predicts the drying time required for the drying operation using the first prediction method. The determination unit determines whether the first prediction method performed by the prediction unit can be applied when the restart of the drying operation is detected during the shutdown of the drying operation. as well as The processing unit continues the first prediction method if it determines that the first prediction method can be applied, and performs the prescribed processing if it determines that the first prediction method cannot be applied.

10. A server, characterized in that, Having the prediction system of claim 9, The aforementioned prediction system communicates with the aforementioned washing machine with drying function via an external network.

11. A display device, characterized in that, have: The system includes a communication function for communicating with the prediction system described in claim 9. as well as The display function, when receiving information about the execution results of the processing unit from the prediction system via the aforementioned communication function, prompts the user by displaying that information.

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