Clothes washing reminding method, device and system and computer readable storage medium

Abstract

The embodiment of the invention provides a clothes washing reminding method, device and system and a computer readable storage medium, and the method comprises the steps: inputting a plurality of features collected by a sampling module arranged on a washing machine into corresponding estimation models according to the respective corresponding feature types, and obtaining estimation results outputted by the estimation models, the estimation result is used for describing whether clothes are suitable for washing; wherein the estimation models corresponding to different feature types are obtained by training a to-be-trained model based on a plurality of training sample sets, and each training sample set comprises a plurality of feature samples of the same type, which influence the clothes washing habits of the user; and generating clothes washing reminding information based on the plurality of obtained estimation results, and sending the clothes washing reminding information to a user terminal. According to the technical scheme, a user can be helped to quickly make a decision about whether to wash clothes or not, so that the use comfort of the washing machine is improved.

Description

Technical Field

[0001] This application relates to the technical field of big data processing. Specifically, it relates to a laundry reminder method, device, system, and computer-readable storage medium. Background Art

[0002] In order to improve the quality of life and reduce the burden of housework, various household devices have emerged, bringing a lot of convenience to people's lives.

[0003] During the use of some household devices, especially during the use of washing machines, people need to judge whether it is suitable to do laundry based on multiple characteristics such as the quantity of laundry to be changed, the temperature and humidity of the laundry to be dried; however, with the rapid development of the times, people's life rhythm is getting faster and faster, and the things people need to think about every day are also increasing. The judgment process before making a laundry decision often brings trouble to people, thus affecting the comfort of using the washing machine. Summary of the Invention

[0004] To solve the above technical problems, embodiments of this application provide a laundry reminder method, device, system, and computer-readable storage medium.

[0005] According to one aspect of the embodiments of this application, a laundry reminder method is provided, including: inputting multiple characteristics collected by a sampling module provided on a washing machine into corresponding estimation models respectively according to their respective corresponding characteristic types, and obtaining estimation results respectively output by each estimation model, where the estimation results are used to describe whether it is suitable to do laundry; among them, the estimation models corresponding to different characteristic types are obtained by training a model to be trained based on multiple training sample sets, and each training sample set contains multiple characteristic samples of the same type that affect the user's laundry habits; generating a laundry reminder message based on the obtained multiple estimation results, and sending the laundry reminder message to a user terminal.

[0006] In the above embodiment, different types of estimation models are obtained by training a model to be trained based on multiple training sample sets. Based on the trained estimation models, multiple characteristics collected by the sampling module are input into the corresponding estimation models respectively according to the corresponding types, and the estimation results of whether it is suitable to do laundry for each characteristic type are obtained respectively, and a laundry reminder message is generated based on the estimation results and sent to the user terminal; thereby realizing the estimation of the user's laundry habits according to different characteristic types, and generating corresponding laundry reminder messages according to the estimation results, so as to help the user quickly make a decision on whether to do laundry, thereby improving the comfort of using the washing machine.

[0007] In an embodiment of the present application, multiple training sample sets are updated according to multiple features periodically collected by the sampling module, and each type of corresponding estimation model is updated and trained based on the updated multiple training sample sets.

[0008] In the above embodiment, multiple training sample sets are updated based on multiple features periodically collected. As the number of training sample sets increases, the reliability of the corresponding estimation model becomes stronger and stronger, and the estimation results obtained according to the estimation model become more and more accurate, making the laundry reminder information more in line with the user's laundry habits, thereby improving the user's dependence on the laundry reminder information and further improving the use comfort of the washing machine.

[0009] In an embodiment of the present application, generating laundry reminder information based on the obtained multiple estimation results includes: calculating the proportion of the first quantity corresponding to suitable laundry in the multiple estimation results; determining whether the proportion of the first quantity is greater than a preset proportion threshold. If so, generate laundry reminder information for describing suitable laundry, otherwise generate laundry reminder information for describing unsuitable laundry.

[0010] In the above embodiment, by calculating the proportion of the first quantity and determining the proportion threshold, different conditions for suitable and unsuitable laundry are clearly distinguished, thereby guiding the user to make corresponding laundry decisions.

[0011] In an embodiment of the present application, determine the preset proportion interval that the proportion of the first quantity conforms to; obtain the recommended star rating pre-associated with the preset proportion interval; add the recommended star rating to the laundry reminder information to obtain the laundry reminder information including the recommended star rating.

[0012] In the above embodiment, through the setting of the recommended star rating, when the user terminal receives the laundry reminder information, it directly reflects the degree of suitability for laundry according to the corresponding recommended star rating, making the laundry reminder information more intuitive, thereby reducing the understanding difficulty of the laundry reminder information and improving the user's use comfort.

[0013] In an embodiment of the present application, generating laundry reminder information based on the obtained multiple estimation results includes: obtaining the estimation results output by the estimation models corresponding to the feature types belonging to the same feature dimension, and calculating the proportion of the second quantity corresponding to suitable laundry in the multiple estimation results corresponding to the same feature dimension; determining the target feature dimension with the largest proportion of the second quantity, and determining the target type corresponding to suitable laundry in the estimation results of the target feature dimension; generating the laundry reminder information according to the push text mapped by the target type.

[0014] In the above embodiments, during use, through the specific text description in the laundry reminder information, the specific characteristic types used to describe whether it is suitable for laundry and the corresponding status are intuitively reflected, which makes a certain explanation for the estimation result of whether it is suitable for laundry or not. At the same time, it is convenient for users to verify the true status of the specific characteristics, thereby improving the accuracy of the user's laundry decision and further enhancing the comfort of using the washing machine.

[0015] In an embodiment of the present application, the sending of the laundry reminder information to the user terminal includes: periodically sending the generated laundry reminder information to the user terminal according to the period of feature collection by the sampling module.

[0016] In the above embodiments, the user can receive laundry reminder information indicating whether it is suitable for laundry or not at the time nodes of a fixed cycle, and quickly make a decision on whether to do laundry based on the received laundry reminder information, without the need to actively send the collected laundry characteristics and actively receive the laundry reminder information, thereby improving the convenience of the laundry reminder method.

[0017] According to one aspect of the embodiments of the present application, an electronic device is provided, including: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the laundry reminder method as described in the above embodiments.

[0018] According to one aspect of the embodiments of the present application, a laundry reminder system is provided, including: a washing machine, on which a sampling module is provided, and the sampling module is used to periodically collect characteristics affecting the user's laundry habits;

[0019] A user terminal;

[0020] A laundry reminder device for executing the method according to any one of claims 1-7 to send laundry reminder information to the user terminal.

[0021] In an embodiment of the present application, a communication module is further provided on the washing machine for sending the characteristics collected by the sampling module to the user terminal, so as to report the characteristics to the laundry reminder device through the user terminal;

[0022] Alternatively, the communication module is used to directly report the characteristics collected by the sampling module to the laundry reminder device.

[0023] According to one aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a processor of a computer, cause the computer to execute the laundry reminder method as described in the above embodiments.

[0024] In the technical solution of the embodiment of the present application, different types of estimation models are obtained by training a model to be trained with multiple training sample sets, and the trained estimation models are continuously updated based on multiple features collected by the sampling module. The multiple features collected by the sampling module are respectively input into the corresponding estimation models according to the corresponding types, and the estimation results indicating whether each feature type is suitable or not suitable for laundry are obtained respectively. Based on all the estimation results, the proportion results of being suitable and not suitable for laundry are calculated respectively, and it is determined whether it is suitable for laundry according to the proportion results, and the corresponding laundry reminder information is generated and sent to the user terminal; thereby realizing the estimation of the user's laundry habits according to different feature types, and generating the corresponding laundry reminder information according to the estimation results, so as to help the user quickly make a decision on whether to do laundry.

[0025] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings according to these drawings without creative efforts. In the drawings:

[0027] Figure 1 is a schematic diagram of the architecture of an implementation environment of a laundry reminder system related to the present application.

[0028] Figure 2 is a flowchart of a laundry reminder method shown in an exemplary embodiment of the present application.

[0029] Figure 3 is a schematic diagram of a table corresponding to different feature types shown in an exemplary embodiment of the present application.

[0030] Figure 4 is a flowchart of the training process of an estimation model shown in an exemplary embodiment of the present application.

[0031] Figure 5 is a flowchart of determining whether it is suitable for laundry shown in an exemplary embodiment of the present application.

[0032] Figure 6 is a flowchart of determining a recommended star rating shown in an exemplary embodiment of the present application.

[0033] Figure 7 is a flowchart of determining push text shown in an exemplary embodiment of the present application.

[0034] Figure 8 It is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present application. Detailed implementation manners

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0036] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will recognize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0037] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the content and operations / steps, nor do they necessarily have to be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0039] It should be noted that: "a plurality" as mentioned herein means two or more. " / " describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0040] Figure 1 It is a schematic architecture diagram of an exemplary laundry reminder system. As Figure 1 shown, the laundry reminder system includes: a washing machine 100, on which a sampling module is provided, and the sampling module is used to periodically collect features affecting the user's laundry habits; a user terminal 101; and a laundry reminder device 101 for sending laundry reminder information to the user terminal 101.

[0041] Among them, a communication module is further provided on the washing machine 100, which is used to send the features collected by the sampling module to the user terminal 101, so as to report the features to the laundry reminder device 102 through the user terminal 101;

[0042] Alternatively, the communication module is used to directly report the features collected by the sampling module to the laundry reminder device 102.

[0043] It should be understood that Figure 1 is only a schematic diagram of the architecture of an exemplary laundry reminder system, and does not represent a limitation on the architecture of the laundry reminder system. In an actual application scenario, the laundry reminder system may include components different from the Figure 1 shown architecture, such as including more or fewer components than the Figure 1 shown architecture, which is not limited here.

[0044] The technical solution of the embodiment of the present application proposes a laundry reminder method, as specifically shown in Figure 2 . This method can be executed by the laundry reminder device 101. Of course, it can also be executed by other devices in the laundry reminder system, which is not limited here. This method at least includes steps S110 to S120, which are introduced in detail as follows:

[0045] In step S110, multiple features collected by the sampling module provided on the washing machine 100 are respectively input into the corresponding estimation models according to their respective corresponding feature types, and the estimation results respectively output by each estimation model are obtained. The estimation results are used to describe whether it is suitable for laundry; among them, the estimation models corresponding to different feature types are obtained by training the model to be trained based on multiple training sample sets, and each training sample set contains multiple feature samples of the same type that affect the user's laundry habits.

[0046] Specifically, as shown in Figure 3 , an example is used for illustration. The multiple feature types collected by the sampling module at least include Figure 3 the multiple feature types corresponding to the secondary indicators in Figure 3 , but are not limited to the multiple feature types exemplified in

[0047] It should be noted that the model to be trained in this application can be trained based on the random forest model in the statistical model. The random forest model is an optimization based on the traditional decision tree algorithm. By stacking multiple decision tree classifiers into a strong classifier, the classification purpose is finally achieved through the voting results of multiple classifiers, which is also the generation method of multiple estimation models in this application.

[0048] At the same time, combine the multiple features collected each time and send them to the laundry reminder device 101 to obtain a training sample set. For the generation of the estimation model, it can be the training sample set collected each time the user uses the washing machine 100 within one year. Input all the training sample sets within one year into the model to be trained for training. Of course, it can also be accumulated according to the number of training sample sets. When the preset number threshold is reached, input the accumulated multiple training sample sets into the model to be trained for training, so as to generate the estimation model.

[0049] To improve the reliability of the estimation model, for example, after the training sample set meets the conditions, this application also verifies through a preset training sample set, such as the estimation model generated by five training sample sets, to judge whether the estimation result is accurate. When its accuracy is higher than a certain threshold, such as higher than 50% or 80%, it is considered that the estimation result is accurate. If it does not reach the corresponding threshold, a certain amount of training sample sets need to be added for updated training to improve the reliability of the estimation model.

[0050] It should be noted that, as Figure 4 shown, if the collected training sample set is insufficient and cannot meet the training of the estimation model, that is, the estimation model still cannot reach the corresponding accuracy, at this time, the laundry reminder information can be pushed according to experience. For example, on a sunny day, according to the empirical data of most people's mobile phones, it will be considered a feature suitable for laundry, and the laundry reminder device 102 will send the preset laundry reminder information to the user terminal 101, so as to send a relatively appropriate laundry reminder information to tide over the laundry information push in the early stage of model training.

[0051] Based on the training results of the estimation model, input the multiple collected features into the corresponding estimation models according to their respective corresponding feature types. For example, input the temperature value into the estimation model corresponding to the temperature feature type. After being judged by the model, output the estimation result of whether the corresponding temperature value is suitable for laundry or not; and so on, through different estimation models, respectively output the corresponding estimation results.

[0052] In step S120, generate laundry reminder information based on the obtained multiple estimation results, and send the laundry reminder information to the user terminal 101.

[0053] Specifically refer to Figure 5 ,Figure 5 is a flowchart showing the determination of suitability for laundry according to another exemplary embodiment. As Figure 5 shown, in Figure 2 step S120 of the embodiment shown, generating laundry reminder information based on the obtained multiple estimation results includes at least steps S210 to S220, which are introduced in detail as follows:

[0054] In step S210, calculate the proportion of the first quantity corresponding to the suitability for laundry among the multiple estimation results.

[0055] Specifically, obtain the estimation results of each estimation model respectively, and conduct statistics on the estimation results. For example, if there are 10 total estimation models and 3 estimation models with the estimation result of being suitable for laundry, then the statistical result of being suitable for laundry is 3 out of 10, that is, the proportion of the first quantity is 3 out of 10; if there are 5 or more estimation models with the estimation result of being suitable for laundry, then the statistical result of being suitable for laundry is 5 out of 10 or more, that is, the proportion of the first quantity is 5 out of 10 or more.

[0056] In step S220, determine whether the proportion of the first quantity is greater than a preset proportion threshold. If so, generate laundry reminder information for describing the suitability for laundry; otherwise, generate laundry reminder information for describing the unsuitability for laundry.

[0057] Based on the above example, if the proportion threshold is 4 out of 10, when the proportion of the first quantity is 3 out of 10, at this time, generate laundry reminder information for the unsuitability for laundry; when the proportion of the first quantity is 5 out of 10 or more, at this time, generate laundry reminder information for the suitability for laundry. Of course, the boundary of the specific proportion threshold for the suitability and unsuitability for laundry can be determined according to the actual usage scenario.

[0058] In the embodiment of the present application, different types of estimation models are obtained by training the model to be trained with multiple training sample sets. Based on the trained estimation models, multiple features collected by the sampling module are respectively input into the corresponding estimation models according to the corresponding types, and the estimation results of whether each feature type is suitable or unsuitable for laundry are obtained respectively. And based on the estimation results, statistics are conducted, and corresponding laundry reminder information is generated according to the preset boundary threshold and sent to the user terminal 101; thereby realizing the estimation of the user's laundry habits according to different feature types, and generating corresponding laundry reminder information according to the estimation results, so as to help the user quickly make a decision on whether to do laundry.

[0059] In some embodiments, sending the laundry reminder information to the user terminal 101 includes: sending the generated laundry reminder information to the user terminal 101 periodically according to the period of feature collection by the sampling module.

[0060] Specifically, the sampling period is set so that the laundry reminder device 101 can send laundry reminder information to the user terminal 101 according to a fixed period, and the sampling period of the data feature can be one day or other time. Taking one day as an example, the time node corresponding to each day is selected, and the washing machine 100 collects multiple feature data and sends them to the laundry reminder device 101, and then the laundry reminder device 101 sends the laundry reminder information to the user terminal 101.

[0061] Through the above implementation, the user can receive laundry reminder information whether it is suitable or not to wash clothes at a fixed periodic time node, and the user can quickly make a decision whether to wash clothes according to the corresponding laundry reminder information.

[0062] In some embodiments, multiple training sample sets are updated according to multiple features periodically collected by the sampling module, and estimation models corresponding to each type are updated and trained based on the updated multiple training sample sets.

[0063] Specifically, each time the laundry reminder device 101 receives multiple features sent by the washing machine 100, the collected multiple features are used as a new training sample set, combined with multiple training sample sets used for training the estimation model, and then the combined multiple training sample sets are used to retrain the model to be trained to obtain a new estimation model, thereby realizing the update of the estimation model. Therefore, the estimation model of the present application can be re-updated with each laundry reminder.

[0064] Through the above implementation, based on the continuous increase of training sample sets, the reliability of the estimation model becomes stronger and stronger, and the estimation results output by the estimation model become more and more accurate, so that the laundry reminder information is more and more in line with the user's laundry habits, thereby improving the user's dependence on use.

[0065] In some embodiments, Figure 6 As shown, in order to improve the reminder effect of the laundry reminder information, the laundry reminder information is generated based on the obtained multiple estimation results, and at least steps S310 to S330 are included, which are described in detail as follows:

[0066] In step S310, it is determined that the first quantity ratio meets the preset ratio interval.

[0067] Specifically, different proportion intervals are set in advance according to the total number of feature types. For example, if there are 10 feature types in total, the total proportion interval is divided into five parts, among which 1 / 10 to 2 / 10 is one interval, 2 / 10 to 4 / 10 is one interval, and so on.

[0068] In step S320, the recommended star rating pre-associated with the preset proportion interval is obtained.

[0069] Exemplarily, based on the above-mentioned proportion intervals, recommended star ratings for the corresponding intervals are set. For example, for the proportion interval described as 1 / 10 to 2 / 10, since the statistical proportion suitable for laundry is relatively low, the corresponding proportion interval is set to one star. Another example is the proportion interval described as 8 / 10 to 1, since the statistical proportion suitable for laundry is relatively high, the corresponding proportion interval is set to five stars. For the corresponding recommended star ratings of other proportion intervals, they increase gradually star by star according to the proportion intervals from low to high. Of course, for the recommended star ratings, according to the user's usage habits, the proportion intervals suitable for laundry can also be described in other forms.

[0070] In step S330, the recommended star rating is added to the laundry reminder information to obtain the laundry reminder information including the recommended star rating.

[0071] Through the above implementation manner, after the user terminal 101 receives the laundry reminder information, through the content of the recommended star rating, it guides the user to make an initial judgment, thereby reducing the understanding difficulty of the laundry reminder information, making the laundry reminder information more intuitive, and improving the user's usage comfort.

[0072] In some embodiments, as Figure 7 shown, in order to further improve the reminder effect of the laundry reminder information, generating the laundry reminder information based on the obtained multiple estimation results at least further includes steps S410 to S430, which are introduced in detail as follows:

[0073] In step S410, the estimation results output by the estimation models corresponding to the feature types belonging to the same feature dimension are obtained, and the second quantity proportion corresponding to the multiple estimation results corresponding to the same feature dimension and suitable for laundry is calculated.

[0074] During the use process, after the proportions corresponding to all feature types are statistically counted, then the different feature dimensions are statistically counted. Taking the first quantity proportion calculated based on the above embodiments as an example, while calculating the first quantity proportion, the second quantity proportion of the feature types belonging to the same feature dimension is calculated. For example, if the estimation results of temperature and humidity are both suitable for laundry, and temperature and humidity belong to the same feature dimension, that is, the weather dimension, therefore, in the first quantity proportion, the corresponding proportions of temperature and humidity are used to calculate the second quantity proportion corresponding to the weather dimension.

[0075] In step S420, the target feature dimension with the largest second quantity proportion is determined, and the target type corresponding to the estimation result suitable for laundry in the target feature dimension is determined.

[0076] After calculating the second quantity ratios for multiple feature dimensions respectively, determine the maximum value of the estimated results for suitable laundry among the respective second quantity ratios, and identify the feature dimension corresponding to the maximum second quantity ratio as the most influential factor on laundry habits.

[0077] In step S430, generate a laundry reminder message according to the push text mapped by the target type.

[0078] Specifically, when the most influential factor on laundry habits is the weather dimension, determine the feature types with estimated results of suitable for laundry within the weather dimension, and combine the literal descriptions of the specific features in the corresponding feature types into the laundry reminder message.

[0079] Illustratively, for each feature type, relevant literal descriptions suitable for laundry are preset respectively. For example, for the pollution degree feature, the literal descriptions suitable for laundry are "excellent air quality", "good air quality", and "mild pollution". Therefore, when the estimated result of the pollution degree feature is suitable for laundry, select the corresponding literal description according to the specific pollution degree feature and add it to the laundry reminder message.

[0080] It should be noted that when the laundry reminder message indicates not suitable for laundry, corresponding literal descriptions can also be set and added to the laundry reminder message.

[0081] Through the above implementation manners, based on the specific literal descriptions in the laundry reminder message, it is possible to explain the results of suitable for laundry or not suitable for laundry, and at the same time, it can more intuitively reflect the corresponding states of different features, and is convenient for users to verify the true states of specific features, improving the accuracy of users' laundry decisions.

[0082] The embodiment of the present application also provides an electronic device, including a processor and a memory. Among them, computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the laundry reminder method as described above is implemented.

[0083] Figure 8 Shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiment of the present application.

[0084] It should be noted that Figure 8 The computer system 800 of the electronic device shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present application.

[0085] Such as Figure 8As shown, computer system 800 includes a Central Processing Unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 802 or the program loaded from the storage section 808 into the Random Access Memory (RAM) 803, such as executing the method described in the above embodiments. In the RAM 803, various programs and data required for system operation are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An Input / Output (I / O) interface 805 is also connected to the bus 804.

[0086] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc. and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0087] Specifically, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the Central Processing Unit (CPU) 801, various functions defined in the system of the present application are executed.

[0088] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0090] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not constitute a limitation to the unit itself in some cases.

[0091] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0092] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0093] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in the form of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0094] After considering the specification and practicing the embodiments disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include well-known knowledge or conventional technical means in the technical field not disclosed in the present application.

[0095] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A laundry reminder method, characterized in that, it includes: Inputting multiple features collected by a sampling module set on a washing machine into corresponding estimation models respectively according to their respective corresponding feature types, and obtaining estimation results respectively output by each estimation model, where the estimation results are used to describe whether it is suitable for doing laundry; among them, the estimation models corresponding to different feature types are obtained by training a model to be trained based on multiple training sample sets, and each training sample set contains multiple feature samples of the same type that affect the user's laundry habits; Generating a laundry reminder message based on the obtained multiple estimation results, and sending the laundry reminder message to a user terminal.

2. The method according to claim 1, characterized in that, the method further includes: Updating the multiple training sample sets according to multiple features periodically collected by the sampling module, and performing updated training on the estimation models corresponding to each type based on the updated multiple training sample sets.

3. The method according to claim 1, characterized in that, the generating a laundry reminder message based on the obtained multiple estimation results includes: Calculating the proportion of the first quantity corresponding to suitable laundry in the multiple estimation results; Judging whether the proportion of the first quantity is greater than a preset proportion threshold. If so, generating a laundry reminder message for describing suitable laundry, otherwise generating a laundry reminder message for describing unsuitable laundry.

4. The method according to claim 3, characterized in that, the method further includes: Determining the preset proportion interval that the proportion of the first quantity conforms to; Obtaining the recommended star rating pre-associated with the preset proportion interval; Adding the recommended star rating to the laundry reminder message to obtain a laundry reminder message including the recommended star rating.

5. The method according to claim 1, characterized in that, the generating a laundry reminder message based on the obtained multiple estimation results includes: Obtaining the estimation results output by the estimation models corresponding to the feature types belonging to the same feature dimension, and calculating the proportion of the second quantity corresponding to suitable laundry in the multiple estimation results corresponding to the same feature dimension; Determining the target feature dimension with the largest proportion of the second quantity, and determining the target type in the target feature dimension whose estimation result corresponds to suitable laundry; Generating the laundry reminder message according to the push text mapped by the target type.

6. The method according to any one of claims 1-5, characterized in that, the sending the laundry reminder message to a user terminal includes: According to the period of feature collection by the sampling module, sending the generated laundry reminder message to the user terminal periodically.

7. A laundry reminder device, characterized in that, it includes: One or more processors; A memory for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the laundry reminder device to implement the method according to any one of claims 1-7.

8. A laundry reminder system, characterized in that, it includes: A washing machine, on which a sampling module is set, and the sampling module is used to periodically collect features that affect the user's laundry habits; A user terminal; A laundry reminder device, which is used to execute the method described in any one of claims 1-7 to send a laundry reminder message to the user terminal.

9. The system according to claim 8, wherein, a communication module is further provided on the washing machine, which is used to send the characteristics collected by the sampling module to the user terminal, so as to report the characteristics to the laundry reminder device through the user terminal; alternatively, the communication module is used to directly report the characteristics collected by the sampling module to the laundry reminder device.

10. A computer-readable storage medium, wherein, computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method described in any one of claims 1-7.

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