Clothing processing system

Abstract

To provide a washing machine that finishes operation punctually at a reservation set time.SOLUTION: A washing machine system of the disclosure consists of a washing machine 100, a service server 260, and a communication terminal 270, which can bidirectionally communicate via a communication network. A model computing part 263 to create and update an operation time prediction model from a data base 262 in which operation data of the washing machine 100 is stored is provided in the washing machine system. During reserved operation of the washing machine 100, the washing machine starts operation at the time obtained by subtracting a time obtained by adding a predetermined time to a predicted operation time estimated from the prediction model from the reservation time, and finishes the operation punctually at the reservation time by changing control so as to extend an operation time when the predicted operation time at a predetermined timing after starting the operation is shorter than a residual time until the reservation time.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to Clothing processing system this.

Background Art

[0002] Patent Document 1 discloses a washing machine system that can start a washing operation from outside by remote control from a mobile phone so that the washing ends at a time convenient for the user.

[0003] Patent Document 2 discloses a clothes dryer that can preset the end time of a drying operation, perform a short preliminary operation immediately after the presetting, estimate the required drying time from the operation result, and start the drying operation at least by the estimated required drying time backward from the end time.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] The present disclosure provides a clothes processing apparatus, a clothes processing apparatus system, and a clothes processing method that end an operation according to a reserved time.

Means for Solving the Problems

[0007] The garment processing system in this disclosure comprises a garment processing device and a server, wherein the garment processing device includes a communication unit that communicates with the server via a communication network, and transmits operation data including information on actual operating time to the server via the communication unit, and the server includes a server communication unit that communicates with the garment processing device via a communication network, a database that stores the operation data received from the garment processing device, and a model calculation unit that creates an operation time prediction model based on the operation data stored in the database. A prediction calculation unit that calculates a predicted operating time from the operating conditions of the garment processing device based on the aforementioned operating time prediction model, Equipped with, The garment processing apparatus comprises an actuator, control means for controlling the actuator and executing the operation, end time setting receiving means for receiving the setting of the end time of the operation, and start time setting means for setting the start time of the operation, wherein the control means is configured to extend the operation time by controlling the actuator based on the predicted operation time, and the start time setting means sets the start time as the time obtained by subtracting the predicted operation time and a predetermined buffer time from the end time received by the end time setting receiving means. The communication unit, in the operation of the clothing processing device, The control means When an operation extension control is performed to extend the operating time by changing the control of the actuator, information indicating that the operation extension control has been performed is sent to the server, and the model calculation unit does not reflect the operation data from the time the operation extension control was performed in the operation time prediction model. [Effects of the Invention]

[0009] In this disclosure Clothing processing system The train can be scheduled to end operation at the reserved time. [Brief explanation of the drawing]

[0010] [Figure 1] Perspective view of the washing machine in Embodiment 1 [Figure 2] Cross-sectional view of the washing machine in Embodiment 1 [Figure 3] Block diagram of the washing machine system in Embodiment 1 [Figure 4] Time chart from scheduled operation to start of operation of the washing machine in Embodiment 1 [Figure 5] Time chart of the washing machine after the start of operation in Embodiment 1 [Figure 6] Diagram showing the remaining operating time of the washing machine system when the AI ​​prediction function is turned ON, after the washing machine has started operating in Embodiment 1. [Figure 7] Diagram showing the estimated end time when the AI ​​prediction function of the washing machine system is turned ON, after the washing machine has started operating in Embodiment 1. [Figure 8] AI prediction function termination indicator diagram for Embodiment 1, which notifies the termination of the AI ​​prediction function of the washing machine system when the washing machine operation is temporarily paused. [Figure 9] Diagram showing the remaining operating time of the washing machine system when the AI ​​prediction function is turned OFF, after the washing machine has started operating in Embodiment 1. [Modes for carrying out the invention]

[0011] (Knowledge that forms the basis of this disclosure) At the time the inventors conceived this disclosure, washing machines or washer-dryers sometimes had discrepancies between the initially expected end time and the actual end time, because the operating time varied depending on the type and condition of the laundry, or the ambient temperature. Therefore, in the case of washing machines or washer-dryers with a reservation function, there was a discrepancy between the end time set when the reservation function was configured and the actual end time. Furthermore, even in the case of washing machines or washer-dryers that can be remotely controlled from a mobile phone to start the washing operation while away from home, as in Patent Document 1, there was a discrepancy between the end time expected by the user and the actual end time. In addition, technologies that predict the operating time in advance, such as in Patent Document 2, had low accuracy in predicting the operating time, and there was a discrepancy between the initially predicted end time and the actual end time.

[0012] If the washing machine finishes earlier than the scheduled or expected completion time, the clothes may be left in the washing tub for an extended period, potentially causing wrinkles. On the other hand, if the washing machine finishes later than the scheduled or expected completion time, the user must wait until the machine has finished.

[0013] Under these circumstances, the inventors discovered the challenge of preventing errors between the initially anticipated end time and the actual end time of operation, and in order to solve this problem, they arrived at the subject matter of this disclosure.

[0014] Hereinafter, embodiments will be described in detail with reference to the drawings. However, a more detailed description than necessary may be omitted. For example, a detailed description of well-known matters or a redundant description of substantially the same configuration may be omitted. This is to avoid making the following description overly redundant and to facilitate the understanding of those skilled in the art.

[0015] Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0016] (Embodiment 1) Hereinafter, embodiments will be described with reference to FIGS. 1 to 6. In the first embodiment, as an example of a clothing treatment apparatus, a washing machine 100, which is a drum-type washing and drying machine, will be described. As an example of a clothing treatment apparatus system, a washing machine system including the washing machine 100, a service server 260, a communication terminal 270, etc. will be described.

[0017] [1-1. Configuration] [1-1-1. Configuration of the washing machine] FIG. 1 is a perspective view of the washing machine in the first embodiment of the present invention, and FIG. 2 is a longitudinal sectional view of the washing machine in the first embodiment of the present invention.

[0018] In FIGS. 1 and 2, inside a housing 101, which is the outer shell of the washing machine 100, a bottomed cylindrical water tank 105 is elastically vibration-proof supported by a plurality of suspensions (not shown) and dampers 163. Inside the water tank 105, a bottomed cylindrical drum 106 is rotatably disposed. A plurality of baffles 106a are provided on the inner wall surface of the drum 106. By rotating the drum 106 at a low speed, the baffles 106a can hook the clothing and give a stirring action such as lifting it upward and dropping it. A plurality of through small holes 106b are formed on the circumferential surface of the drum 106. At the bottom of the water tank 105, a tank rotation motor 138 (see FIG. 3) for rotationally driving the drum 106 is disposed.

[0019] The front of the housing 101 has a clothing input / output opening 103 for loading and unloading clothes. A lid 102 is provided on the front of the housing 101. The lid 102 covers the clothing input / output opening 103 in a way that allows it to be opened and closed. The lid 102 is locked / unlocked by a lid lock (not shown). The lid 102 can be opened when the lid lock is unlocked. By opening the lid 102, clothes can be loaded into the drum 106 from the clothing input / output opening 103.

[0020] An operation display unit 104 is provided on the upper part of the lid 102, which includes a washing machine operation unit 104b (see Figure 3) for operating the washing machine and a washing machine display unit 104a (see Figure 3) for displaying the operating status.

[0021] As shown in Figure 1, a small, openable lid 114 is provided on the top of the housing 101. When the small lid 114 is opened, a detergent tank 117, a fabric softener tank 126, and a detergent case 115 are revealed.

[0022] Above the water tank 105 of the housing 101, an automatic liquid dispensing device 109 including a pump unit 111 is provided. The automatic liquid dispensing device 109 uses the pump unit 111 to draw liquid stored in the detergent tank 117 and the fabric softener tank 126 and supplies it to the water tank 105. The detergent case 115 supplies a single dose of detergent, manually dispensed, to the water tank 105.

[0023] The washing machine 100 is equipped with actuators such as a drum rotating motor 138 that rotates the drum 106, a water supply 110 that introduces tap water into the washing machine 100, a drain valve 112 that drains the water from the water tank 105, and a drying unit 130 that dries the air. The housing 101 is equipped with control means 222 (see Figure 3) that controls the actuators and sequentially executes a series of processes such as washing, rinsing, spinning, and drying.

[0024] The control means 222 includes a drying completion determination unit 222a (see Figure 3), a sequence control unit 222b (see Figure 3), a fabric quantity determination unit 222c (see Figure 3), and a liquid input amount calculation unit (not shown).

[0025] As described later, the drying completion determination unit 222a determines the end of drying based on the difference between the temperature detected by the first temperature detection means 133 (see Figure 3) and the temperature detected by the second temperature detection means 134 (see Figure 3). To make a judgment.

[0026] The sequence control unit 222b controls the aforementioned actuator.

[0027] The fabric load determination unit 222c classifies laundry up to 10 kg into approximately 10 stages based on the torque current value when the drum rotation motor 138 is rotated at a constant speed, for example, and determines the amount of water to be used for washing. The liquid agent amount calculation unit calculates the amount of detergent and fabric softener to be added from the fabric load detected by the fabric load determination unit. A foam detection means 136 is provided in the water tank 105 to detect the amount of foam generated.

[0028] The water supply unit 110 is equipped with solenoid valves, a first water supply valve 110a (see Figure 3) and a second water supply valve 110b (see Figure 3). The first water supply valve 110a and the second water supply valve 110b are configured to open and close their respective pathways, and when open, they allow tap water introduced into the water supply unit 110 to flow into the pathways.

[0029] The drain valve 112 is located in the middle of the drain hose 113 connected to the bottom of the water tank 105. By opening and closing it, it controls the drainage, and when opened, it releases the water in the water tank 105 to the outside of the machine via the drain hose 113.

[0030] The drying unit 130 consists of a PTC heater 131, a blower 132, and a filter 137, and is built into the top of the washing machine. Air heated by the PTC heater 131 is blown into the drum 106 by the blower 132 and exhausted through the filter 137 to an exhaust port 135 located on the upper rear surface of the washing machine. The airflow at this time is as shown by the dashed arrows in Figures 1 and 2, and the airflow path is provided with a first temperature detection means 133 for detecting the temperature before heating by the PTC heater 131 and a second temperature detection means 134 for detecting the temperature immediately after passing through the drum 106.

[0031] [1-1-2, Washing Machine System Configuration] Figure 3 is a block diagram showing the configuration of the washing machine system in Embodiment 1 of the present invention.

[0032] As shown in Figure 3, the washing machine system of this embodiment is configured so that the washing machine 100, the service server 260, and the communication terminal 270 can communicate with each other via a gateway system 250 and a public communication network 251.

[0033] The service server 260 is connected to the washing machine 100 via the public communication network 251 and the gateway system 250 by the server communication unit 261, enabling communication between them.

[0034] The washing machine 100 is connected to the service server 260 via the gateway system 250 and the public communication network 251 by the washing machine communication unit 232.

[0035] The communication terminal 270 is a smartphone with a dedicated application installed, and is connected to the service server 260 via the public communication network 251 by the terminal communication unit 271. The communication terminal 270 is equipped with a terminal display unit 272 that is configured to display images of the dedicated application.

[0036] With this configuration, the service server 260 sends requests to the washing machine 100, sends data to the communication terminal 270, receives data from the washing machine 100, and receives requests from the communication terminal 270.

[0037] The service server 260 consists of a server computer and includes a server communication unit 261, a database 262, a model calculation unit 263, and a prediction calculation unit 264.

[0038] The service server 260 stores the operation data received from the washing machine 100 in the database 262, and the model calculation unit 263 creates and updates an operation time prediction model based on the data stored in the database 262. Artificial intelligence (AI) technologies such as machine learning are applied to the creation and updating of the operation time prediction model, and machine learning is applied to generate clusters for the data in the database 262.

[0039] Although only one washing machine 100 and one communication terminal 270 are shown in Figure 3, in reality, multiple washing machines and multiple communication terminals are connected to the service server 260 via the public communication network 251. However, the service server 260 manages the unique identification ID assigned to each washing machine and the ID of the communication terminal, and controls communication so that, for example, a washing machine operation request from an arbitrary communication terminal is not sent to an unspecified number of washing machines connected to the service server 260.

[0040] [1-2. Operation] The operation and function of the washing machine in the above configuration are shown below with reference to Figures 4 and 5.

[0041] The operation of the washing machine 100 includes a 'washing process' in which clothes are soaked in washing water and the drum 106 is rotated to remove dirt, a 'rinsing process' in which clothes soaked in detergent water are rinsed with water, a 'spinning process' in which water-soaked clothes are dehydrated, and a 'drying process' in which hot air is supplied to the drum 106 to dry the clothes inside the drum 106.

[0042] Before doing laundry, the user pours detergent solution into the detergent tank 117 and fabric softener solution into the fabric softener tank 126. At that time, the user can operate the operation display unit 104 to select and register the brands of detergent and / or fabric softener to be used, and the registered brands are stored in the memory unit (not shown) of the control means 222.

[0043] When doing laundry, the user opens the lid 102 and puts clothes into the drum 106 through the clothes input / output opening 103. Next, they operate the operation display unit 104 to turn on the power switch and set various operating conditions such as the course, reservation time, wash, rinse, spin, and dry.

[0044] [1-2-1. Reservation operation] As shown in Figure 4, the user can set a scheduled operation for the washing machine 100 by operating the operation display unit 104 provided on the washing machine 100 or the communication terminal 270 (S101). When the user inputs the time they want the operation to end from the operation display unit 104 or the communication terminal 270, the washing machine 100 transmits information on the various set operating conditions (course, wash time, number of rinses, spin-drying time, drying mode, and scheduled time) to the service server 260 (S102). Figure 4 shows an example where the user sets a scheduled operation for "23:00", sets the end time to "9:00" the next day, and sets the operating condition to "standard course".

[0045] Next, the fabric quantity determination process (S103), described later, is executed, and the detected amount of clothing (fabric quantity) is sent to the service server 260 (S104).

[0046] The following describes the operation when the AI ​​prediction function is ON. In this embodiment, the AI ​​prediction function is a function that calculates the predicted driving time based on information about driving conditions and driving status, and notifies the remaining driving time or the end time of driving.

[0047] Based on the received information regarding operating conditions and status (course, wash time, number of rinses, spin-drying time, drying mode, amount of fabric), the service server 260 uses the predictive calculation unit 264 to calculate the predicted operating time for each of the washing, rinsing, spin-drying, and drying processes (S105), and transmits the calculated predicted operating time to the washing machine 100 and the communication terminal 270 (S106). In addition to the predicted operating time for each process (same data), the service server 260 also transmits information on the course and reservation time to the communication terminal 270 (S106).

[0048] The washing machine 100 calculates the start time of operation by subtracting the time obtained by adding the "allowance time" described later from the received predicted operation time from the reservation time (S107), and waits until that time, displaying the start time of operation and the reservation time on the operation display unit 104 (S108). Figure 4 shows that the reservation time set as the end time "9:00" and the calculated start time of operation "6:00" are displayed in S108. At the same time, the scheduled end times for each process of washing, rinsing, spinning, and drying are calculated by "start time of operation" + "predicted operation time for each process" + "allowance time for each process (described later)".

[0049] Meanwhile, the communication terminal 270 similarly calculates the start time of operation and displays the start time of operation and the reservation time (S108).

[0050] "Allowance time" is calculated by allocating 5 minutes, 5 minutes, 10 minutes, and 60 minutes to each of the washing, rinsing, spinning, and drying processes, respectively, and adding these allocated times together according to the settings for each process. For example, if all processes of washing, rinsing, spinning, and drying are performed, it would be 5 + 5 + 10 + 60 = 80 minutes, while if only the washing process is performed, it would be 5 minutes.

[0051] When the scheduled start time arrives, the washing machine begins its "washing cycle." The washing machine 100 sends data to the service server 260 indicating that it has started the cycle (S201). The service server 260 then sends data to the communication terminal 270 indicating that it has started the cycle (S201).

[0052] [1-2-2. Washing cycle] The following describes the washing machine operation when the AI ​​prediction function is turned ON.

[0053] During a washing cycle, the sequence control unit 222b of the control means 222 controls the sequential execution of the fabric load determination process, the washing process, the rinsing process, and the spin-drying process. However, if the aforementioned scheduled operation is performed, the fabric load determination process is omitted, and the process starts from the washing process.

[0054] In the fabric quantity determination step (S103), the amount of laundry (fabric) in the drum 106 is detected by measuring the torque current value when the drum rotating motor 138 is repeatedly rotated in the forward and reverse directions at a constant rotation speed. After the fabric quantity determination step is completed, the washing step is performed.

[0055] In the washing process, first, the pump unit 111 is activated to discharge an appropriate amount of detergent solution from the detergent tank 117 into the water channel. Then, the first water supply valve 110a is opened, and an amount of tap water corresponding to the amount of cloth is supplied into the drum 106, simultaneously supplying the detergent solution discharged into the water channel together with the tap water into the water tank 105.

[0056] After water supply is complete, the drum rotation motor is driven alternately in forward and reverse directions at a predetermined cycle for a predetermined time, rotating the drum 106 and agitating the laundry inside the drum 106. At this time, the foam detection means 136 detects the amount of foam generated in the water tank 105. If there is a lot of foam, it is assumed that there is little sebum on the laundry, and the washing time is shortened, or a defoaming operation such as draining / refilling is inserted midway through to adjust the amount of foam.

[0057] As shown in Figure 5, during the washing process, a foam test is performed on the service server 260 3 minutes after the water supply is complete. Simultaneously with transmitting data from the intelligence device 136 (S202), a request for estimated operating time is sent. Upon receiving this request, the service server 260 uses the prediction calculation unit 264 to recalculate the predicted operating time for each of the washing, rinsing, spinning, and drying processes based on the foam generation status and the time required for water supply (S203), and sends this to the washing machine 100 (S204). Figure 5 shows that the data transmission in S204 occurred at "6:05".

[0058] The control means 222 compares the scheduled end time of the washing process (start time + predicted washing time after fabric load determination + buffer time) with the predicted end time of the washing process calculated from the current predicted operating time (start time + predicted washing process operating time). If it appears that the washing process will end 3 minutes or more earlier than the initially scheduled end time, it reduces the stirring frequency (lengthens the stirring cycle) to extend the operating time of the washing process (S205). Figure 5 shows that the re-estimated end time of the washing process is "6:16", the initial end time of the washing process is "6:20", and there is a difference of 4 minutes between the re-estimated end time of the washing process and the initial end time of the washing process.

[0059] The stirring cycle and the extended washing time are calculated according to Equations 1 and 2 below.

[0060]

number

[0061]

number

[0062] By following equations 1 and 2 above, it is possible to extend the washing time without changing the mechanical force applied to the laundry.

[0063] After the washing process is complete, the rinsing process is performed.

[0064] In the rinsing process, first, the drain valve 112 is opened to drain the water from the tank 105, and then the tank rotating motor 138 is rotated at high speed to perform dewatering.

[0065] After the dewatering is complete, the pump unit 111 is activated to discharge an appropriate amount of fabric softener liquid from the fabric softener tank 126 into the water channel. Then, the second water supply valve 110b is opened to supply the fabric softener liquid discharged into the water channel, along with a predetermined amount of tap water, into the water tank 105.

[0066] After water supply is complete, the drum rotating motor 138 is driven alternately in forward and reverse directions at a predetermined cycle for a predetermined time, thereby rotating the drum 106 and agitating the laundry inside the drum 106.

[0067] After the first spin cycle of the rinse process is completed, the service server 260 receives the time taken for draining and spinning, and simultaneously receives a request for estimated operating time. Based on the time taken for draining and spinning, the service server 260 uses the prediction calculation unit 264 to calculate the estimated operating time for each of the rinse, spinning, and drying processes again, and sends this to the washing machine 100.

[0068] At this time, the control means 222 compares the initially scheduled end time of the rinsing process (rinsing process start time + predicted washing time after fabric load determination + buffer time) with the predicted end time of the rinsing process calculated from the current predicted operating time (rinsing process start time + predicted rinsing process operating time). If it appears that the rinsing process will end 3 minutes or more earlier than the initially scheduled end time, it initiates a "standby process" before the next water supply. Insert the following: In other words, if it is determined that the difference between the initially scheduled end time of the rinsing process and the predicted end time of the rinsing process calculated from the current predicted operating time is greater than or equal to a predetermined value (for example, 3 minutes), the operation extension control, the "standby process," is executed. In the "standby" process, the tank rotating motor 138 is rotated once in the forward / reverse direction every minute. The time of the standby process is calculated according to formula 3 below.

[0069]

number

[0070] Once the rinsing process is complete, the dewatering process is performed. In the dewatering process, the drain valve 112 is opened to drain the water from the tank 105, and then the tank rotating motor 138 is rotated at high speed to perform dewatering.

[0071] If the setting is "No Drying," the washing cycle is completed when the spin cycle is finished. After the washing cycle is completed, a "loosening" process is performed in which the drum rotation motor rotates once every minute in the forward and reverse directions until the scheduled time. After the "loosening" process is completed, the system sends a message to the service server 260 indicating that the operation is complete, along with operation data (course, amount of fabric, actual operating time for each wash, rinse, and spin cycle, data sent when requesting estimated operating time for each cycle, and whether or not operating time extension control was performed for each cycle), and then automatically shuts off the power to complete the series of operations.

[0072] The service server 260 saves the received data to the database 262 and inputs it into the model calculation unit 263 to update the operating time prediction model. However, process data for which operating time extension control has been performed is not used to update the operating time prediction model.

[0073] While the washing cycle is in progress, the screen of the communication terminal 270 displays the remaining operating time display 301 when the AI ​​prediction function is ON, as shown in Figure 6. The remaining operating time display 301 when the AI ​​prediction function is ON includes an indication 305 that the AI ​​prediction function is ON, a display 306 of the remaining operating time based on the predicted operating time calculated by the prediction calculation unit 264, a display 307 of the predicted operating time for the currently ongoing cycle, and a display 308 indicating the operating conditions.

[0074] By operating the communication terminal 270, the remaining operating time display 301 when the AI ​​prediction function is ON and the scheduled end time display 302 when the AI ​​prediction function is ON can be switched and displayed. The scheduled end time display 302 when the AI ​​prediction function is ON, shown in Figure 7, includes an indication 305 that the AI ​​prediction function is ON, an indication 309 that shows the scheduled end time based on the predicted operating time calculated by the prediction calculation unit 264, an indication 307 that shows the predicted operating time for the currently ongoing journey, and an indication 308 that shows the operating conditions. Note that when the AI ​​prediction function is OFF, the scheduled end time may be delayed, so in this embodiment, the scheduled end time display 302 when the AI ​​prediction function is ON can only be displayed when the AI ​​prediction function is ON.

[0075] If the setting is "with drying," the "loosening" process will be performed from the end of the dewatering process until the scheduled start time of the drying process, and then the "drying process" described below will be performed.

[0076] [1-2-3. Drying operation] The drying operation will be explained below.

[0077] During the drying operation, the sequence control unit 222b of the control means 222 drives the blower 132 and the PTC heater 131 to blow hot air into the drum 106.

[0078] At this time, the drum 106 rotates in forward and reverse directions at predetermined intervals to prevent the laundry from getting tangled and to allow drying air to circulate throughout.

[0079] The determination of the end of the drying operation is made using the first temperature detection means 133 and the second temperature detection means 134.

[0080] The drying completion determination unit 222a determines that drying is complete when the difference between the temperature detected by the second temperature detection means 134 and the temperature detected by the first temperature detection means 133 reaches 15°C. After determining that drying is complete, the PTC heater 131 is turned off and a cooling operation is performed for 5 minutes, after which the rotation of the blower 132 and drum 106 is stopped to complete the drying operation.

[0081] The PTC heater 131 is set to be 20°C above the ambient temperature when fully powered on. While the laundry in the drum 106 is damp, the air heated by the PTC heater 131 is cooled by the moisture as it passes through the drum 106. As the amount of moisture in the clothes decreases, the amount of heat removed decreases, and the temperature of the air that has passed through the drum 106 approaches the temperature heated by the PTC heater 131. That is, the temperature difference between the temperature of the first temperature detection means 133, which is approximately the ambient temperature, and the temperature of the air that has passed through the drum 106, as measured by the second temperature detection means 134, approaches 20°C. In this embodiment, when the temperature difference between the temperature of the first temperature detection means 133 and the temperature of the second temperature detection means 134 reaches 15°C, it is determined that the amount of moisture in the laundry has decreased sufficiently, and the drying process is considered complete.

[0082] The following explanation is based on Figure 5. Figure 5 shows that the drying process started at "6:55". Five minutes after the start of the drying process, or when the difference between the temperature of the second temperature detection means 134 and the temperature of the first temperature detection means 133 reaches 10°C, the temperature of the second temperature detection means 134 at the start of the drying process and the current temperature are transmitted to the service server 260 (S301). Simultaneously, a request for operation time estimation is sent.

[0083] Based on the received data, the service server 260 calculates the predicted operating time using the prediction calculation unit 264 (S302), and transmits the calculated predicted operating time to the washing machine 100 and the communication terminal 270 (S303). Figure 5 shows that the data transmission in S303 took place at "7:00".

[0084] The washing machine 100 compares the predicted end time of operation (drying process start time + predicted drying process operation time), calculated from the received predicted operation time, with the reserved time. If the difference is, for example, 5 minutes or more, it performs operation extension control (S304). Figure 5 shows that the predicted end time of operation is "8:15", the reserved time is "9:00", and the difference between the predicted end time of operation and the reserved time is 45 minutes.

[0085] In washing machines or washer-dryers, there are many uncertain factors that affect the operating time, such as changes in the water supply capacity from the tap, uneven distribution or tangling of clothes, and clogging of the drainage and drying paths, making it impossible to accurately estimate the operating time. Therefore, in this embodiment, the machine starts operating from a time that takes into account the extension of the operating time due to uncertain factors, based on the predicted operating time set at the time of reservation. After a certain amount of time has passed since the start of operation, that is, when the uncertain factors that cause the extension of the operating time have been narrowed down to some extent, the machine controls the machine to extend the end time, thereby ending the operation at the reserved time.

[0086] The operation time extension control involves at least one of the following: reducing the frequency of agitation of the laundry, lowering the drying temperature, or adding a waiting period. Reducing the frequency of agitation of the laundry reduces the mechanical force applied to the clothes per unit time, making it possible to adjust the operation time to the extended side without causing problems such as fabric damage.

[0087] During the "wash," "rinse," "spin," and "dry" cycles of a washer-dryer, the most active time The "drying" process is the most difficult to estimate. Therefore, by detecting the temperature and humidity of the drying air using a drying temperature detection means after the drying process has started, and then adjusting the operating time based on a highly accurate estimate of the "drying" process, the error between the scheduled end time and the scheduled start time can be reduced.

[0088] In this embodiment, the operation extension control reduces the amount of current supplied to the PTC heater 131 and lengthens the stirring cycle. That is, if the difference between the predicted end time and the reserved time is greater than or equal to a predetermined value (e.g., 5 minutes), the degree of heating in the drying operation is reduced and the interval between forward and reverse operation is lengthened. Alternatively, the amount of current supplied to the PTC heater 131 may be reduced without lengthening the stirring cycle.

[0089] The stirring cycle and the amount of current supplied to the PTC heater 131 are calculated according to equations 4 and 5 below.

[0090]

number

[0091]

number

[0092] After the drying cycle is complete, the washing machine 100 sends a message to the service server 260 indicating that the operation is complete and includes operation data (course, amount of fabric, actual operating time for each wash, rinse, spin, and dry cycle, data sent when requesting an estimated operating time for each cycle, and whether or not operating time extension control was used for each cycle) (S305), and then automatically shuts off the power to complete the series of operations.

[0093] The service server 260 saves the received data to the database 262 and inputs it into the model calculation unit 263 to update the operating time prediction model. Then, the service server 260 sends information about the end of operation to the communication terminal 270 (S306).

[0094] The operating data input to the model calculation unit 263 includes the amount of laundry detected by the laundry amount detection means installed in the washing machine, and the temperature gradient at the start of the drying process detected by the drying temperature detection means installed in the drying air passage. This allows factors that have a significant impact on the operating time of the washer-dryer to be input into the operating time prediction model, thereby improving the accuracy of the operating time estimation.

[0095] The service server 260 does not use the operation data of processes where operation time extension control has been performed to update the operation time prediction model. This prevents the degradation of the operation time prediction model that would occur if the data from when operation time extension control was performed, i.e., data from irregular operation, were used to update the operation time prediction model.

[0096] As described above, the accuracy of driving time estimation can be continuously improved by learning using big data stored in database 262. In addition, the start time is set to take into account a buffer time to account for any extensions in driving time due to uncertain factors, and the end time of driving can be extended by, for example, adding a waiting process, once a certain amount of time has passed since the start of driving, i.e., when the uncertain factors that cause extensions in driving time have been narrowed down to some extent. By controlling the machine in the direction of operation, it is possible to terminate the operation at the scheduled time. Figure 5 shows that the drying process was completed and the operation terminated at the scheduled time of "9:00".

[0097] [1-2-4. Temporarily suspending operations] When the washing machine 100 receives a pause command via the washing machine control panel 104b during a wash or wash-dry cycle, it pauses the operation and unlocks the lid 102. The user can open the lid 102 to open the clothes loading / unloading opening 103 and remove or add laundry through the clothes loading / unloading opening 103.

[0098] If the lid 102 is opened during a wash-and-dry cycle, uncertain factors may arise, such as a delay in the completion time of the current cycle, an increase or decrease in the amount of fabric, or a decrease in the air temperature inside the water tub 105. Therefore, the AI ​​prediction function in this embodiment terminates when the operation is temporarily paused, and the washing machine 100 or the washing machine display unit 104a displays the AI ​​prediction function termination indicator 303 shown in Figure 8.

[0099] The AI ​​prediction function termination display 303 can be switched to the remaining operating time display 304 when the AI ​​prediction function is OFF, as shown in Figure 9, by user operation. The remaining operating time display 304 when the AI ​​prediction function is OFF does not include the display 305 indicating that the AI ​​prediction function is ON, thus indicating to the user that the AI ​​prediction function is OFF. This suppresses the display of predicted operating time that may have errors due to uncertain factors, and prevents the user from being informed of an incorrect remaining operating time or scheduled end time.

[0100] In this embodiment, the service server 260 does not use process data from operations that have been temporarily suspended to update the operating time prediction model. This prevents uncertainties arising from temporary suspensions from being reflected in the updates of the operating time prediction model, thereby improving the accuracy of the operating time prediction model.

[0101] [1-3. Effects, etc.] As described above, in this embodiment, the washing machine 100 includes a control means 222 that controls actuators such as a drum rotating motor 138, a water supply unit 110, a drain valve 112, a drying unit 130, etc., to sequentially execute the washing, rinsing, spinning, and drying processes; an operation display unit 104 or a washing machine communication unit 232 that accepts the setting of the end time of the operation; and a control means 222 that sets the start time of the operation and stores predicted operating time information calculated by an operating time prediction model. The control means 222 is configured to set the start time as the time obtained by subtracting the predicted operating time and a predetermined buffer time from the end time accepted by the operation display unit 104 or the washing machine communication unit 232, and to be able to perform operation extension control to extend the operating time by changing the control of the actuator in any of the washing, rinsing, spinning, or drying processes.

[0102] As a result, by subtracting a predetermined time from the operating time based on the end time received by the operation display unit 104 or the washing machine communication unit 232, the operation can be started at the scheduled end time, whether the operating time is longer than expected or shorter than the predetermined buffer time.

[0103] Furthermore, as in this embodiment, the operation extension control may include at least one of the following: reducing the frequency of agitating the laundry, lowering the drying temperature, and providing a waiting period.

[0104] This allows for extended operation control without significantly affecting the condition of the laundry.

[0105] Furthermore, as in this embodiment, the timing for starting the operation extension control is the start of the drying process. This may also occur when a predetermined time has elapsed and / or when the temperature detected by the drying temperature detection means reaches a predetermined temperature.

[0106] This allows for indirect estimation of the moisture content and drying speed of clothing based on the behavior of the drying temperature detection means within a predetermined time after the start of the drying process. This enables highly accurate estimation of the drying process duration and subsequent adjustment of the duration, thereby reducing the error between the scheduled end time and the scheduled start time.

[0107] In this embodiment, the washing machine system comprises a washing machine 100 and a service server 260. The washing machine 100 includes a washing machine communication unit 232 that communicates with the service server 260 via a communication network and transmits operation data, including information on the actual operating time, to the service server 260 via the washing machine communication unit 232. The service server 260 includes a server communication unit 261 that communicates with the washing machine 100 via a communication network, a database 262 that stores operation data received from multiple washing machines 100, and a model calculation unit 263 that creates an operation time prediction model based on the operation data stored in the database 262.

[0108] Furthermore, as in this embodiment, the service server 260 may include a prediction calculation unit 264 that calculates a predicted operating time from the operating conditions of the washing machine 100 based on an operating time prediction model, and the server communication unit 261 may transmit the calculated predicted operating time to the washing machine 100.

[0109] Furthermore, as in this embodiment, when the washing machine 100 performs operation extension control, the washing machine communication unit 232 sends information to the service server 260 indicating that operation extension control has been performed, and the model calculation unit 263 does not need to reflect the operation data from the cycle in which operation extension control was performed in the operation time prediction model.

[0110] The garment processing method in this embodiment includes the steps of: receiving the operating conditions and the end time of operation for the washing machine 100; calculating the predicted operating time using an operating time prediction model based on the operating conditions; setting the time obtained by subtracting the predicted operating time and a predetermined buffer time from the end time as the start time of operation; and extending the operating time by changing the control of the actuator during the operation of the washing machine 100.

[0111] (Embodiment 2) A key feature of Embodiment 2 is that the communication terminal 270 can select whether or not to extend the operation before implementing the operation extension control. The other configurations and operations are the same as those of Embodiment 1, and their explanation will be omitted.

[0112] [2-1. Operation] In the drying process of Embodiment 1, when the washing machine 100 sends a request for estimated operating time to the service server 260, the service server 260 calculates the predicted operating time using the prediction calculation unit 264 based on the received data, and transmits the calculated predicted operating time to the washing machine 100 and the communication terminal 270. The washing machine 100 compares the predicted end time of operation (drying process start time + predicted drying process operating time) calculated from the received predicted operating time with the reservation time. If the difference is 5 minutes or more, the terminal display unit 272 displays the estimated end time of operation and indicates that the operation is expected to end earlier than the reservation time, and also displays a confirmation screen asking whether to extend the operation until the reservation time.

[0113] The communication terminal 270 sends the user's selection result from the confirmation screen to the service server 260, and the service server 260 sends that selection result to the washing machine 100.

[0114] If the received selection result is "extend", the washing machine 100 will perform the operation as described in Embodiment 1. It performs operation extension control.

[0115] If the user selects "Do not extend," both the communication terminal 270 and the washing machine 100 will change the displayed reservation time (end time of operation) to the current time + estimated remaining operating time and continue operation.

[0116] [2-2. Effects, etc.] With these configurations, for example, if a user has set a reservation time to coincide with their expected return time from being out, but ends up returning earlier than planned, the system can complete the cycle in the shortest possible time, providing a user-friendly washing machine system.

[0117] (Embodiment 3) The distinguishing feature of Embodiment 3 is that the buffer time is changed according to the maturity level of the model. The other configurations and operations are the same as those of Embodiment 1, and their explanation will be omitted.

[0118] [3-1. Operation] The service server 260 calculates the standard deviation of the difference between the estimated operating time for each process immediately following the fabric load determination process and the actual operating time, based on the past n operating data of the washing machine 100 for which the operating time is predicted, assuming a normal distribution. It then transmits +2σ of this standard deviation to the washing machine 100 and the communication terminal 270. The past n processes refer to the total number of operating cycles of all washing machines 100 that send data to the server. In calculating the start time of operation in Embodiment 1, the washing machine 100 and the communication terminal 270 calculate the start time of operation by using the received +2σ value for each process as "allowance time".

[0119] With these configurations, as the operating time prediction model matures—that is, as operating data is accumulated and the prediction model is updated—the error between the predicted operating time at the start of operation and the actual operating time decreases, allowing for a reduction in the "buffer time" added to the predicted operating time when calculating the start time of operation at the time of reservation setting.

[0120] Because the operating time prediction model is constantly updated, as the amount of data in the database increases, the accuracy of the operating time estimation improves, including uncertain factors that affect operating time, such as changes in water supply capacity from the tap, uneven distribution or tangling of clothes, and clogging of drainage and drying paths. Therefore, by shortening the "buffer time" that is added to account for extensions in operating time due to uncertain factors, it is possible to complete the operation on time according to the reservation without unnecessarily extending the operating time.

[0121] [3-2. Effects, etc.] In this embodiment, the washing machine 100 shortens the buffer time as the number of cycles of the washing machine 100 increases.

[0122] As the accuracy of driving time estimation improves, the "buffer time" factored in to account for delays due to uncertainties can be shortened, allowing the train to finish its journey on schedule without unnecessarily extending the driving time.

[0123] (Other embodiments) As described above, Embodiments 1 to 3 have been explained as examples of the technology disclosed in this application. However, the technology in this disclosure is not limited thereto and can be applied to embodiments that have been modified, replaced, added, or omitted. Furthermore, it is possible to create new embodiments by combining the components described in Embodiments 1 to 3 above.

[0124] Therefore, other embodiments are illustrated below.

[0125] In embodiments 1 to 3, a drum-type washing machine was described as an example of a garment processing device. However, the garment processing device only needs to be capable of performing a washing cycle or a wash-and-dry cycle, so for example, a top-loading washing machine may also be used.

[0126] In embodiments 1 to 3, the washing machine communication unit 232 was assumed to be able to communicate with the gateway system 250 using wireless LAN in order to remotely control the washing machine from outside the home. However, the present invention is not limited to this, and the washing machine communication unit 232 and the gateway system 250 may communicate using short-range wireless communication.

[0127] In embodiments 1 to 3, a communication terminal 270, which is a smartphone with a dedicated application installed, was described as an example of a communication terminal. The communication terminal is not limited to a smartphone, as it only needs to be able to communicate with the clothing processing device and / or the server. The communication terminal may also be, for example, an in-vehicle device or other home appliance connected to a home network.

[0128] In embodiments 1 to 3, a PTC heater 131 was described as an example of a heating means. The heating means is not limited to the PTC heater 131, as any means that heats air is acceptable, and other heaters or heat pumps may be used.

[0129] Embodiments 1 to 3 describe washing and drying operations when the AI ​​predictive operation function is ON. The technology in this disclosure is not limited to the AI ​​predictive operation function in Embodiments 1 to 3. For example, if the predicted operation time is not calculated, the operation data may be input to the model calculation unit to update the operation time prediction model. For example, if the operation data is not input to the model calculation unit to update the operation time prediction model, the predicted operation time may be calculated.

[0130] Embodiments 1 to 3 described margin time and operation extension control. However, the technology in this disclosure is not limited to those that include margin time and operation extension control, and may not include margin time and operation extension control.

[0131] In Embodiment 3, the "+2σ" calculated using past n operation data of the washing machine 100 and the "margin time" based on "+2σ" were explained. The margin time in Embodiment 3 is not limited to Embodiment 3, as it only needs to be shortened as the number of operation data input to the operation time prediction model increases. The number of operation data may be the total number of operations for a single washing machine 100. The number of operation data may not include the number of operation data that were not reflected in the operation time prediction model.

[0132] Since the above embodiments are for illustrative purposes only, various modifications, substitutions, additions, omissions, etc., can be made within the scope of the claims or their equivalents. [Industrial applicability]

[0133] As described above, the washing machine system according to the present invention is applicable to washing machines that can perform scheduled operation. Specifically, this disclosure is applicable to drum-type washing machines, top-loading washing machines, and the like. [Explanation of symbols]

[0134] 100 Washing Machines 101 cabinets 102 Lid 103 Clothes input / output opening 104 Operation display section 104a Washing machine display unit 104b Washing machine control panel 105 Aquariums 106 Drums 106a Baffle 109 Automatic liquid dispensing device 110 Water dispenser 110a First water supply valve 110b Second water supply valve 111 Pump Unit 112 Drain valve 113 Drain hose 114 Small lid 115 Detergent Case 117 Detergent tank 126 Fabric softener tank 130 Drying Unit 131 PTC heater 132 Blower 133 First temperature detection means 134 Second temperature detection means 135 Exhaust port 136 Bubble detection means 137 Filters 138 Tank Rotating Motor 163 Damper 222 Control means 222a Drying completion determination unit 222b Sequence Control Unit 222c Cloth amount determination section 232 Washing Machine Communications Department 250 Gateway Systems 251 Public telecommunications network 260 Service Servers 261 Server Communication Unit 262 Databases 263 Model Calculation Unit 264 Prediction Calculation Unit 270 Communication terminals 271 Terminal Communication Section 272 Terminal display unit 301 Display of remaining driving time when AI prediction function is ON 302 Display of estimated end time when AI prediction function is ON 303 AI prediction function termination message 304 Display of remaining driving time when AI prediction function is OFF 305 Display indicating that the AI ​​prediction function is ON. 306 Display of remaining driving time based on predicted driving time 307 Display of estimated travel time for the currently ongoing journey. 308 Display indicating operating conditions 309 Display showing the estimated end time based on the predicted operating time.

Claims

1. It comprises a garment processing device and a server, The garment processing device includes a communication unit that communicates with the server via a communication network, and transmits operation data, including information on the actual operating time, to the server via the communication unit. The aforementioned server, A server communication unit that communicates with the garment processing device via a communication network, A database that stores the operation data received from the garment processing device, A model calculation unit that creates an operating time prediction model based on the operating data stored in the database, The system includes a prediction calculation unit that calculates a predicted operating time from the operating conditions of the garment processing device based on the aforementioned operating time prediction model, The garment processing apparatus is Actuator and Control means for controlling the actuator and performing the operation, An end time setting reception means for receiving the setting of the end time of the aforementioned operation, The system includes a start time setting means for setting the start time of the aforementioned operation, The control means is configured to extend the operating time by controlling the actuator based on the predicted operating time. The start time setting means sets the start time as the time obtained by subtracting the predicted operating time and a predetermined buffer time from the end time received by the end time setting receiving means. When the control means performs an operation extension control to extend the operating time by changing the control of the actuator during the operation of the garment processing device, the communication unit transmits information to the server indicating that the operation extension control has been performed. The model calculation unit does not reflect the operation data from the time the operation extension control was performed in the operation time prediction model. Garment processing system.

2. The garment processing apparatus according to claim 1, characterized in that the predetermined buffer time is shortened as the number of operating data reflected in the operating time prediction model increases.

Images