Clothes drying control method, device and system and medium

Abstract

The invention relates to the technical field of intelligent household appliances, in particular to a clothes drying control method, device and system and a medium. The method comprises the following steps: receiving current first state information of the washing and drying all-in-one machine uploaded when the washing and drying all-in-one machine is currently in a drying mode and a drying heating pipe is started; according to the first state information, a pre-configured deep learning network model is adopted to analyze the first state information so as to predict and obtain a first predicted air inlet temperature in the cylinder of the washing and drying all-in-one machine at the next moment; when it is determined that the first predicted air inlet temperature is larger than a preset upper limit temperature threshold value, a heating closing signal is sent to the washing and drying all-in-one machine, so that the washing and drying all-in-one machine closes a drying heating pipe according to the heating closing signal, and the drying heating pipe is started according to the temperature, obtained through monitoring, in the barrel or a heating starting signal fed back by the cloud platform; when it is determined that the drying heating pipe is restarted, the second state information currently collected and obtained by the washing and drying all-in-one machine is uploaded to the cloud platform again.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home appliances, and in particular to a clothes drying control method, device, system and medium. Background Art

[0002] At present, when a washer-dryer dries clothes, the drying heating tube is first turned on to heat the drum. When the temperature in the drum reaches a preset initial temperature, the drying heating tube is turned off. When the temperature in the drum drops to a preset minimum temperature, the drying heating tube is turned on again to heat the drum again. This operation is repeated until the clothes are dried.

[0003] However, when drying clothes in this way, although the drying heating tube is turned off based on the temperature in the drum reaching the preset initial temperature, the drying heating tube will continue to emit heat for a short period of time, causing the temperature in the drum to continue to rise, thereby causing the clothes in the drum to be damaged when the temperature is too high.

[0004] Therefore, there is an urgent need for a new clothes drying control method that can effectively dry the clothes while ensuring that the quality of the clothes is not damaged. Summary of the invention

[0005] In order to solve the above problem, that is, when the user uses a washer-dryer to dry clothes, the drying heating tube is turned off when the temperature in the drum reaches a preset initial temperature, but the drying heating tube continues to emit heat for a short period of time afterwards, thereby causing the temperature in the drum to continue to rise, thereby causing the clothes in the drum to be damaged. This application provides a clothes drying control method, device, system and medium.

[0006] In a first aspect, an embodiment of the present application provides a clothes drying control method, the method comprising:

[0007] receiving first current status information of the washer-dryer uploaded when the washer-dryer is currently in a drying mode and a drying heating tube is turned on;

[0008] According to the first state information, a preconfigured deep learning network model is used to analyze the first state information to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment;

[0009] When it is determined that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, a heating shutdown signal is sent to the washer-dryer, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and when it is determined to restart the drying heating tube based on the monitored temperature in the drum or the heating startup signal fed back by the cloud platform, the second status information currently collected and acquired by the washer-dryer is re-uploaded to the cloud platform.

[0010] In the preferred technical solution of the above-mentioned clothes drying control method, it also includes:

[0011] receiving the third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, and analyzing the third state information using the preconfigured deep learning network model according to the third state information, so as to predict and obtain a second predicted air inlet temperature in the drum of the washer-dryer at the next moment;

[0012] When it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer, so that the washer-dryer turns on the drying heating tube according to the heating start signal.

[0013] In the preferred technical solution of the above-mentioned clothes drying control method, the current status information includes one or more of the following combinations:

[0014] The voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed, the motor speed and the drying operation time.

[0015] In the preferred technical solution of the above-mentioned clothes drying control method, obtaining the preconfigured deep learning network model includes:

[0016] Collect and obtain the operating parameters of the washer-dryer to be trained at each moment, wherein the operating parameters at each moment include one or more of the following: the voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed, the motor speed and the drying operation time;

[0017] The voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed and the motor speed corresponding to each moment are used as the initial input data to be trained, and the air inlet temperature corresponding to the next moment of the moment is used as the initial output data to be trained;

[0018] Preprocessing the initial input data to be trained and the initial output data to be trained to obtain final input data to be trained and final output data to be trained;

[0019] The initially configured deep learning network model is trained according to the final input data to be trained and the final output data to be trained, and the deep learning network model after the successful training is used as the preconfigured deep learning network model.

[0020] In the preferred technical solution of the above-mentioned clothes drying control method, the preconfigured deep learning network model is a CNN-BiLSTM model.

[0021] In the preferred technical solution of the above-mentioned clothes drying control method, it includes:

[0022] When monitoring the washer-dryer to enter a drying mode, turning on the drying heating tube, and collecting and acquiring current first state information of the washer-dryer;

[0023] Sending the first state information to a cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment;

[0024] receiving a heating shutoff signal sent by the cloud platform when it determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and shutting down the drying heating tube according to the heating shutoff signal;

[0025] When it is determined to restart the drying heating tube based on the temperature in the drum obtained through monitoring or the heating start signal fed back by the cloud platform based on the current third status information of the washer-dryer uploaded when the washer-dryer turns off the drying heating tube, the second status information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform.

[0026] In the preferred technical solution of the above-mentioned method for controlling clothes drying, when it is determined to restart the drying heating tube according to the temperature in the drum obtained by monitoring or the heating start signal fed back by the cloud platform, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform, including:

[0027] The temperature in the drum is monitored and obtained, and when it is determined that the temperature in the drum is less than a preset lower temperature threshold, the drying heating tube is restarted, and the second status information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform.

[0028] or,

[0029] Uploading the third state information collected by the washer-dryer when the drying heating tube is turned off, so that the cloud platform can analyze the third state information according to the third state information by using the preconfigured deep learning network model to predict and obtain the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and sending the heating start signal to the washer-dryer when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold;

[0030] According to the heating start signal, the drying heating tube is restarted, and the second status information currently collected and acquired by the washer-dryer is re-uploaded to the cloud platform.

[0031] In a second aspect, an embodiment of the present application provides a cloud platform, including:

[0032] A receiving module, used for receiving the first current state information of the washer-dryer uploaded when the washer-dryer is currently in a drying mode and the drying heating tube is turned on;

[0033] a processing module, configured to analyze the first state information using a preconfigured deep learning network model according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at a next moment;

[0034] The processing module is also used to send a heating shutdown signal to the washer-dryer when it is determined that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and re-uploads the second status information currently collected and acquired by the washer-dryer to the cloud platform when it is determined to restart the drying heating tube based on the temperature in the drum obtained by monitoring or the heating startup signal fed back by the cloud platform.

[0035] In a third aspect, an embodiment of the present application provides a washer-dryer, comprising:

[0036] an acquisition module, used to start the drying heating tube when monitoring the washer-dryer entering a drying mode, and collect and acquire the current first state information of the washer-dryer;

[0037] an analysis module, configured to send the first state information to a cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at a next moment;

[0038] The analysis module is further configured to receive a heating shutdown signal sent by the cloud platform when the cloud platform determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and shut down the drying heating tube according to the heating shutdown signal;

[0039] The analysis module is also used to determine when to restart the drying heating tube based on the temperature in the drum obtained through monitoring or the heating start signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, and to re-upload the second state information currently collected and obtained by the washer-dryer to the cloud platform.

[0040] In a fourth aspect, an embodiment of the present application provides a clothing drying control system, comprising: a cloud platform and a washer-dryer, wherein the cloud platform is used to execute a clothing drying control method as described in any one of the first aspects; and the washer-dryer is used to execute a clothing drying control method as described in any one of the first aspects.

[0001] In a fifth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, a clothes drying control method as described in any one of the first aspects is implemented.

[0041] It can be understood by those skilled in the art that the present application provides a clothing drying control method, device, system and medium, and the method specifically comprises: receiving the current first state information of the washer-dryer uploaded when the washer-dryer is currently in drying mode and the drying heating tube is turned on; analyzing the first state information using a preconfigured deep learning network model based on the first state information to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment; when it is determined that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, sending a heating shutdown signal to the washer-dryer so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and when it is determined to restart the drying heating tube based on the monitored temperature in the drum or the heating startup signal fed back by the cloud platform, re-uploading the second state information currently collected and obtained by the washer-dryer to the cloud platform, so that the temperature in the drum does not exceed the preset upper limit temperature threshold, thereby ensuring that the clothes are not scalded. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The following describes a preferred embodiment of a clothes drying control method, device, system and medium of the present application with reference to the accompanying drawings, which are as follows:

[0043] Figure 1 A schematic diagram of the structure of a first embodiment of a clothes drying control system provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the structure of a clothes drying control system provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a flow chart of a first embodiment of a method for controlling clothes drying provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of a flow chart of a second embodiment of a clothes drying control method provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of a flow chart of a third embodiment of a clothes drying control method provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of the CNN-BiLSTM network structure provided in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of a flow chart of a fourth embodiment of a clothes drying control method provided in an embodiment of the present application;

[0050] Figure 8 A schematic diagram of a flow chart of a fifth embodiment of a clothes drying control method provided in an embodiment of the present application;

[0051] Fig. 9 A schematic diagram of the structure of a cloud platform embodiment 1 provided in an embodiment of the present application;

[0052] Fig.10 This is a structural schematic diagram of a first embodiment of a washer-dryer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] First, those skilled in the art should understand that these implementations are only used to explain the technical principles of the present application and are not intended to limit the scope of protection of the present application. Those skilled in the art can make adjustments to them as needed to adapt to specific application scenarios.

[0054] Secondly, it should be noted that in the description of the embodiments of the present application, terms such as "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description, and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present application.

[0055] In addition, it should be noted that in the description of the embodiments of the present application, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] In the prior art, when a user uses a washer-dryer to process clothes, during the drying process, the drying heating tube in the washer-dryer dissipates heat into the drum. When the temperature in the drum reaches a preset upper temperature threshold, the drying heating tube is turned off. However, within several minutes, the drying heating tube continues to dissipate heat, so that the temperature in the drum continues to rise, causing the clothes in the drum to be damaged due to excessive temperature.

[0058] Based on the above problems, the inventive concept of the present application is: how to provide a new clothes drying control method to achieve clothes drying while protecting the clothes from damage.

[0059] Figure 1 This is a structural diagram of a first embodiment of a clothes drying control system provided in the present application, such as Figure 1 As shown, the clothes drying control system mainly includes: a cloud platform 11 and a washer-dryer 12.

[0060] Specifically, the washer-dryer 12 sends the current first state information to the cloud platform 11, so that the cloud platform 11 uses a preconfigured deep learning network model to analyze the first state information based on the current first state information, so as to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer 12 at the next moment. When the cloud platform 11 determines that the first predicted air inlet temperature is greater than the preset upper temperature threshold, it sends a heating off signal to the washer-dryer 12 so that the washer-dryer 12 turns off the drying heating pipe, and when it is determined to restart the drying heating pipe according to the temperature in the drum obtained by monitoring or the heating on signal fed back by the cloud platform 11, it re-uploads the currently acquired second state information to the cloud platform 11.

[0061] In this embodiment, for example, the drying control system may include: a washing and drying machine, a WIFI module, a database, and a cloud server. The structural diagram of the drying control system is as follows: Figure 2 shown.

[0062] Specifically, the washer-dryer 21 sends the current first state information to the database 23 through the WIFI module 22, the cloud platform 24 takes out the current first state information from the database 23, and uses a preconfigured deep learning network model to analyze the first state information to obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment, and when the cloud platform 24 determines that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, the cloud platform 24 sends a heating shutdown signal to the washer-dryer 21 through the WIFI module 22 to make the washer-dryer 21 turn off the drying heating pipe, and when it is determined to restart the drying heating pipe based on the temperature in the drum obtained by detection or the heating start signal fed back by the cloud platform 24, the cloud platform 24 re-uploads the currently collected second state information to the cloud platform 24 through the WIFI module 22.

[0063] The technical solution of the present application is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0064] Figure 3 A schematic diagram of a flow chart of a first embodiment of a clothes drying control method provided in the present application, such as Figure 3 As shown, the execution subject of this embodiment is a cloud platform, and the method includes the following steps:

[0065] Step S301: receiving the first current status information of the washer-dryer uploaded when the washer-dryer is in a drying mode and the drying heating tube is turned on.

[0066] In this embodiment, for example, when the washer-dryer is in drying mode and the drying heating tube is in the on state, the washer-dryer sends the current first state information to the cloud platform via WIFI so that the cloud platform receives the current first state information.

[0067] In this embodiment, the current first state information includes one or more of the following: voltage of the drying heating tube, current of the drying heating tube, air inlet temperature, air outlet temperature, fan speed, motor speed and drying running time.

[0068] Step S302: Based on the first state information, a preconfigured deep learning network model is used to analyze the first state information to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment.

[0069] In this embodiment, a preconfigured deep learning network model is stored in the cloud platform. The cloud platform inputs the first state information into the preconfigured deep learning network model to obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment.

[0070] Step S303: When it is determined that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, a heating shutdown signal is sent to the washer-dryer, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and when it is determined to restart the drying heating tube based on the monitored temperature in the drum or the heating startup signal fed back by the cloud platform, the second status information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform.

[0071] In this embodiment, the washer-dryer turns off the drying heating tube after receiving a heating shutdown signal from the cloud platform, and restarts the drying heating tube when the temperature in the drum obtained through monitoring is less than a preset lower temperature threshold or a heating startup signal fed back by the cloud platform, and sends the second status information currently collected by the washer-dryer to the cloud platform.

[0072] In this embodiment, the current first state information of the washer-dryer uploaded when the washer-dryer is currently in drying mode and the drying heating tube is turned on is received; based on the first state information, the first state information is analyzed using a preconfigured deep learning network model to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment; when it is determined that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, a heating shutdown signal is sent to the washer-dryer, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and when it is determined to restart the drying heating tube according to the monitored temperature in the drum or the heating startup signal fed back by the cloud platform, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform. Compared with the prior art, when the temperature in the drum reaches a preset upper limit temperature threshold, the drying heating tube is turned off, but within several minutes, the drying heating tube continues to dissipate heat, so that the temperature in the drum continues to rise, causing the clothes in the drum to be damaged due to overhigh temperature, the present application adopts a preconfigured deep learning network model to analyze the first state information to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment, and when it is determined that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, a heating shutdown signal is sent to the washer-dryer, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, so that the temperature in the drum does not exceed the upper limit temperature threshold, thereby ensuring that the clothes are not scalded while drying.

[0073] Figure 4 This is a flow chart of a second embodiment of a clothes drying control method provided in the present application. Based on the above embodiment, Figure 4Specifically, in step S303, after the washer-dryer turns off the drying heating tube according to the heating off signal, the method may further include:

[0074] Step S401: Receive the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, and analyze the third state information using a preconfigured deep learning network model based on the third state information to predict and obtain the second predicted air inlet temperature in the drum of the washer-dryer at the next moment.

[0075] In this embodiment, when the drying heating pipe of the washer-dryer is in a closed state, the washer-dryer sends the third state information to the cloud platform, and the cloud platform inputs the third state information into a preconfigured deep learning network model to obtain the second predicted air inlet temperature in the drum of the washer-dryer at the next moment.

[0076] Step S402: When it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer, so that the washer-dryer turns on the drying heating tube according to the heating start signal.

[0077] In this embodiment, the third state information of the washer-dryer uploaded by the washer-dryer when the drying heating pipe is turned off is received, and according to the third state information, the third state information is analyzed by using a preconfigured deep learning network model to predict the second predicted air inlet temperature in the drum of the washer-dryer at the next moment; when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer so that the washer-dryer turns on the drying heating pipe according to the heating start signal. The present application receives the third state information of the washer-dryer uploaded by the washer-dryer when the drying heating pipe is turned off, and the third state information is analyzed by using a preconfigured deep learning network model to predict the second predicted air inlet temperature in the drum of the washer-dryer at the next moment; when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer so that the washer-dryer turns on the drying heating pipe according to the heating start signal, and achieves rapid heating to the upper temperature threshold, thereby improving the accuracy of temperature control.

[0078] Figure 5 This is a flow chart of a third embodiment of a clothes drying control method provided in an embodiment of the present application. Based on the above embodiments, Figure 5 As shown, obtaining a preconfigured deep learning network model includes the following steps:

[0079] Step S501: collecting and acquiring the operating parameters of the washer-dryer to be trained at various times.

[0080] In this embodiment, the operating parameters at each moment include one or more of the following: voltage of the drying heating tube, current of the drying heating tube, air inlet temperature, air outlet temperature, fan speed, motor speed and drying operation time.

[0081] Step S502: The voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed and the motor speed corresponding to each moment are used as the initial input data to be trained, and the air inlet temperature corresponding to the next moment of this moment is used as the initial output data to be trained.

[0082] Step S503: pre-processing the initial input data to be trained and the initial output data to be trained to obtain final input data to be trained and final output data to be trained.

[0083] In this embodiment, for example, the initial input data to be trained and the initial output data to be trained are first normalized from 0 to 1 to obtain preprocessed input data and final output data to be trained, and then the preprocessed input data is input into a preconfigured random forest model to obtain the Gini coefficient of each preprocessed input data, and the data whose Gini coefficient of each preprocessed input data is greater than the Gini coefficient threshold value is screened according to a preset Gini coefficient threshold value to obtain the final input data to be trained.

[0084] Step S504: Train the initially configured deep learning network model according to the final input data to be trained and the final output data to be trained, and use the successfully trained deep learning network model as the preconfigured deep learning network model.

[0085] In this embodiment, for example, the preconfigured deep learning network model may be a CNN-BiLSTM model.

[0086] Figure 6 The schematic diagram of the CNN-BiLSTM network structure provided in the embodiment of the present application is as follows: Figure 6As shown, the network includes: a one-dimensional CNN layer and a two-layer BiLSTM, wherein the one-dimensional CNN includes a convolution layer and a pooling layer, a SELU activation function is used in the convolution layer, and a global maximum pooling is used in the pooling layer, and the BiLSTM includes a forward LSTM and a reverse LSTM, and both the forward LSTM and the reverse LSTM use a tanh activation function. During the training process, a final input data to be trained is first input into the one-dimensional CNN to obtain a one-dimensional feature, and then the one-dimensional feature is input into the BiLSTM to obtain a root mean square error according to a final output data to be trained corresponding to the final input data to be trained, and then the parameters of the CNN-BiLSTM network are updated using the Adam optimizer, and the above process is repeated until the minimum root mean square error is obtained to obtain the CNN-BiLSTM model.

[0087] In the present application, the operating parameters of the washer-dryer to be trained at each moment are collected and acquired; the voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed and the motor speed corresponding to each moment are used as the initial input data to be trained, and the air inlet temperature corresponding to the next moment of this moment is used as the initial output data to be trained; the initial input data to be trained and the initial output data to be trained are preprocessed to obtain the final input data to be trained and the final output data to be trained; according to the final input data to be trained and the final output data to be trained, the initially configured deep learning network model is trained, and the deep learning network model after the successful training is used as the preconfigured deep learning network model. The present application collects the operating parameters of the washer-dryer to be trained at each moment, obtains the initial input data to be trained and the initial output data to be trained and performs preprocessing to obtain the final input data to be trained and the final output data to be trained, and then trains the initially configured deep learning network model according to the final input data to be trained and the final output data to be trained, and uses the successfully trained deep learning network model as the preconfigured deep learning network model to predict the temperature in the drum of the washer-dryer in advance to ensure that the temperature does not exceed the preset upper limit temperature threshold, so that the clothes will not be damaged while drying.

[0088] Figure 7 This is a flow chart of a fourth embodiment of a clothes drying control method provided in the present application, such as Figure 7 As shown, the execution subject of this embodiment is a washer-dryer, and the method includes the following steps:

[0089] Step S701: when monitoring the washer-dryer to enter a drying mode, turning on the drying heating tube, and collecting and acquiring the current first state information of the washer-dryer.

[0090] In this embodiment, when the washer-dryer determines to enter the drying mode, it sends a command to the drying heating tube to turn on the drying heating tube, and collects and obtains the current first state information of the washer-dryer.

[0091] Step S702: Send the first state information to the cloud platform, so that the cloud platform can analyze the first state information based on the first state information using a preconfigured deep learning network model to predict the first predicted air inlet temperature in the drum of the washer-dryer at the next moment.

[0092] Step S703: receiving a heating shutdown signal sent by the cloud platform when it determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and shutting down the drying heating tube according to the heating shutdown signal.

[0093] In this embodiment, for example, when the cloud platform determines that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, it generates a heating shutdown signal and sends the heating shutdown signal to the washer-dryer through the WIFI network. After the washer-dryer receives the heating shutdown signal, it turns off the drying heating tube according to the heating shutdown signal.

[0094] Step S704: When it is determined to restart the drying heating tube based on the temperature in the drum obtained through monitoring or the heating start signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform.

[0095] In this embodiment, if the washer-dryer detects that the temperature inside the drum is lower than the preset lower temperature threshold or the predicted air inlet temperature obtained by the cloud platform is greater than the preset lower temperature threshold, it is determined to restart the heating tube and re-upload the second status information currently collected by the washer-dryer to the cloud platform.

[0096] In this embodiment, when monitoring the washer-dryer to enter the drying mode, the drying heating pipe is turned on, and the current first state information of the washer-dryer is collected and obtained; the first state information is sent to the cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information based on the first state information to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment; a heating shutdown signal is received when the cloud platform determines that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, and the drying heating pipe is turned off according to the heating shutdown signal; when it is determined to restart the drying heating pipe based on the temperature in the drum obtained by monitoring or the heating startup signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the cloud platform when the drying heating pipe is turned off, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform. After turning on the drying heating tube, the present application collects and obtains the current first state information of the washer-dryer, and sends the first state information to the cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information based on the first state information to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment, receives a heating shutdown signal sent by the cloud platform when it determines that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, and turns off the drying heating tube according to the heating shutdown signal; when it is determined to restart the drying heating tube based on the temperature in the drum obtained by monitoring or the heating startup signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform, so as to keep the temperature in the drum of the washer-dryer within a normal range, thereby ensuring that the clothes are not damaged while being dried.

[0097] Figure 8 This is a flow chart of a fifth embodiment of a clothes drying control method provided in an embodiment of the present application. Based on the above embodiments, Figure 8 As shown, specifically, a specific implementation of step S704 is:

[0098] Step S801: Monitor and obtain the temperature in the drum, and when it is determined that the temperature in the drum is lower than the preset lower temperature threshold, restart the drying heating tube, and re-upload the second status information currently collected by the washer-dryer to the cloud platform.

[0099] or,

[0100] The third state information collected by the washer-dryer when the drying heating tube is turned off is uploaded, so that the cloud platform can analyze the third state information based on the third state information by using a preconfigured deep learning network model to predict the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and send a heating start signal to the washer-dryer when it is determined that the second predicted air inlet temperature is lower than the preset lower temperature threshold.

[0101] In this embodiment, for example, the washer-dryer obtains the temperature in the drum every 2 seconds, and when it is determined that the temperature in the drum is less than the preset lower temperature threshold, the drying heating pipe is restarted, and the second state information currently collected by the washer-dryer is sent to the cloud platform. Alternatively, after the washer-dryer turns off the drying heating pipe, it collects and obtains the third state information, and sends the third state information to the cloud platform. After receiving the third state information, the cloud platform predicts and obtains the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer.

[0102] Step S802: Restart the drying heating tube according to the heating start signal, and re-upload the second status information currently collected by the washer-dryer to the cloud platform.

[0103] In this embodiment, the temperature in the drum is monitored and obtained, and when it is determined that the temperature in the drum is less than the preset lower temperature threshold, the drying heating pipe is restarted, and the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform; or, the third state information collected and obtained by the washer-dryer when the drying heating pipe is turned off is uploaded, so that the cloud platform can analyze the third state information based on the third state information using a preconfigured deep learning network model to predict the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer; according to the heating start signal, the drying heating pipe is restarted, and the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform. The present application restarts the drying heating pipe when it is determined that the temperature in the drum is less than a preset lower temperature threshold, or uploads the third state information collected by the washer-dryer when the drying heating pipe is turned off, so that the cloud platform can analyze the third state information based on the third state information and use a preconfigured deep learning network model to predict the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and send a heating start signal to the washer-dryer when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, restart the drying heating pipe according to the heating start signal, and re-upload the second state information currently collected by the washer-dryer to the cloud platform, so as to control the drying temperature within a normal range, so as to dry the clothes while ensuring that the clothes are not damaged.

[0104] Fig. 9 A schematic diagram of the structure of a cloud platform embodiment 1 provided in the present application is shown in FIG. Fig. 9 As shown, the cloud platform includes: a receiving module 91 and a processing module 92.

[0105] Specifically, the receiving module 91 is used to receive the current first state information of the washer-dryer uploaded when the washer-dryer is currently in the drying mode and the drying heating pipe is turned on. The processing module 92 is used to analyze the first state information using a preconfigured deep learning network model based on the first state information to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment. The processing module 92 is also used to send a heating shutdown signal to the washer-dryer when it is determined that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, so that the washer-dryer turns off the drying heating pipe according to the heating shutdown signal, and re-uploads the second state information currently collected and obtained by the washer-dryer to the cloud platform when it is determined to restart the drying heating pipe according to the monitored temperature in the drum or the heating startup signal fed back by the cloud platform.

[0106] In this embodiment, the cloud platform can be used to execute a clothes drying control method provided by any of the above embodiments. The specific implementation method and technical effect are similar and will not be repeated here.

[0107] Fig.10 A structural diagram of a washer-dryer embodiment 1 provided in the present application is shown in FIG. Fig.10 As shown, the washer-dryer includes: an acquisition module 101 and an analysis module 102.

[0108] Specifically, the acquisition module 101 is used to turn on the drying heating pipe when monitoring the washer-dryer to enter the drying mode, and collect and obtain the current first state information of the washer-dryer. The analysis module 102 is used to send the first state information to the cloud platform, so that the cloud platform can analyze the first state information based on the first state information using a preconfigured deep learning network model to predict and obtain the first predicted air inlet temperature in the drum of the washer-dryer at the next moment. The analysis module 102 is also used to receive a heating shutdown signal sent by the cloud platform when it determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and turn off the drying heating pipe according to the heating shutdown signal. The analysis module 102 is also used to re-upload the second state information currently collected and obtained by the washer-dryer to the cloud platform when it determines to restart the drying heating pipe based on the temperature in the drum obtained by monitoring or the heating startup signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating pipe is turned off.

[0109] In this embodiment, the washer-dryer can be used to execute a clothing drying control method provided by any of the above embodiments. The specific implementation method and technical effect are similar and will not be repeated here.

[0110] The present application also provides a computer-readable storage medium on which a computer program is stored. When a processor executes the computer program, the above-mentioned clothes drying control method is implemented.

[0111] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The computer-readable storage medium may be any available medium that can be accessed by a general or special purpose computer.

[0112] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0113] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0116] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0117] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0118] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A clothes drying control method, characterized in that: include: receiving first current status information of the washer-dryer uploaded when the washer-dryer is currently in a drying mode and a drying heating tube is turned on; According to the first state information, a preconfigured deep learning network model is used to analyze the first state information to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment; When it is determined that the first predicted air inlet temperature is greater than the preset upper limit temperature threshold, a heating shutdown signal is sent to the washer-dryer, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and when it is determined to restart the drying heating tube based on the monitored temperature in the drum or the heating startup signal fed back by the cloud platform, the second status information currently collected and acquired by the washer-dryer is re-uploaded to the cloud platform.

2. The method according to claim 1, characterized in that Also includes: receiving the third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, and analyzing the third state information using the preconfigured deep learning network model according to the third state information, so as to predict and obtain a second predicted air inlet temperature in the drum of the washer-dryer at the next moment; When it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold, a heating start signal is sent to the washer-dryer, so that the washer-dryer turns on the drying heating tube according to the heating start signal.

3. The method according to claim 1 or 2, characterized in that: The current status information includes one or a combination of the following: The voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed, the motor speed and the drying operation time.

4. The method according to claim 3, characterized in that Obtaining the preconfigured deep learning network model includes: Collect and obtain the operating parameters of the washer-dryer to be trained at each moment, wherein the operating parameters at each moment include one or more of the following: the voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed, the motor speed and the drying operation time; The voltage of the drying heating tube, the current of the drying heating tube, the air inlet temperature, the air outlet temperature, the fan speed and the motor speed corresponding to each moment are used as the initial input data to be trained, and the air inlet temperature corresponding to the next moment of the moment is used as the initial output data to be trained; Preprocessing the initial input data to be trained and the initial output data to be trained to obtain final input data to be trained and final output data to be trained; The initially configured deep learning network model is trained according to the final input data to be trained and the final output data to be trained, and the deep learning network model after the successful training is used as the preconfigured deep learning network model.

5. The method according to claim 4, characterized in that The preconfigured deep learning network model is a CNN-BiLSTM model.

6. A clothes drying control method, characterized in that: include: When monitoring the washer-dryer to enter a drying mode, turning on the drying heating tube, and collecting and acquiring current first state information of the washer-dryer; Sending the first state information to a cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at the next moment; receiving a heating shutoff signal sent by the cloud platform when it determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and shutting down the drying heating tube according to the heating shutoff signal; When it is determined to restart the drying heating tube based on the temperature in the drum obtained through monitoring or the heating start signal fed back by the cloud platform based on the current third status information of the washer-dryer uploaded when the washer-dryer turns off the drying heating tube, the second status information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform.

7. The method according to claim 6, characterized in that When it is determined to restart the drying heating tube according to the temperature in the drum obtained by monitoring or the heating start signal fed back by the cloud platform, the second state information currently collected and obtained by the washer-dryer is re-uploaded to the cloud platform, including: Monitor and obtain the temperature in the drum, and when it is determined that the temperature in the drum is less than a preset lower temperature threshold, restart the drying and heating tube, and re-upload the second state information currently collected and obtained by the washer-dryer to the cloud platform; or, Uploading the third state information collected by the washer-dryer when the drying heating tube is turned off, so that the cloud platform can analyze the third state information according to the third state information by using the preconfigured deep learning network model to predict and obtain the second predicted air inlet temperature in the drum of the washer-dryer at the next moment, and sending the heating start signal to the washer-dryer when it is determined that the second predicted air inlet temperature is less than the preset lower temperature threshold; According to the heating start signal, the drying heating tube is restarted, and the second status information currently collected and acquired by the washer-dryer is re-uploaded to the cloud platform.

8. A cloud platform, characterized in that: include: A receiving module, used for receiving the first current state information of the washer-dryer uploaded when the washer-dryer is currently in a drying mode and the drying heating tube is turned on; a processing module, configured to analyze the first state information using a preconfigured deep learning network model according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at a next moment; The processing module is also used to send a heating shutdown signal to the washer-dryer when it is determined that the first predicted air inlet temperature is greater than a preset upper limit temperature threshold, so that the washer-dryer turns off the drying heating tube according to the heating shutdown signal, and re-uploads the second status information currently collected and acquired by the washer-dryer to the cloud platform when it is determined to restart the drying heating tube based on the temperature in the drum obtained by monitoring or the heating startup signal fed back by the cloud platform.

9. A washer-dryer, characterized in that: include: an acquisition module, used to start the drying heating tube when monitoring the washer-dryer entering a drying mode, and collect and acquire the current first state information of the washer-dryer; an analysis module, configured to send the first state information to a cloud platform, so that the cloud platform uses a preconfigured deep learning network model to analyze the first state information according to the first state information, so as to predict and obtain a first predicted air inlet temperature in the drum of the washer-dryer at a next moment; The analysis module is further configured to receive a heating shutdown signal sent by the cloud platform when the cloud platform determines that the first predicted air inlet temperature is greater than a preset upper temperature threshold, and shut down the drying heating tube according to the heating shutdown signal; The analysis module is also used to determine when to restart the drying heating tube based on the temperature in the drum obtained through monitoring or the heating start signal fed back by the cloud platform based on the current third state information of the washer-dryer uploaded by the washer-dryer when the drying heating tube is turned off, and to re-upload the second state information currently collected and obtained by the washer-dryer to the cloud platform.

10. A clothes drying control system, comprising: A cloud platform and a washer-dryer, wherein the cloud platform is used to execute a clothes drying control method as described in any one of claims 1 to 5; and the washer-dryer is used to execute a clothes drying control method as described in any one of claims 6 and 7.

11. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a clothes drying control method according to any one of claims 1 to 7 is implemented.

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