Nursing control method of washing machine, electronic equipment and laundry equipment

Abstract

The embodiment of the invention discloses a nursing control method of a washing machine, electronic equipment and clothes washing equipment, and belongs to the technical field of washing machine control. The method comprises the steps that the washing type and clothes state information of washed clothes are obtained, and the clothes state information comprises the clothes weight of the clothes; inputting the clothes taking interval time of the user, the clothes weight and the washing type into a nursing starting time calculation model to obtain nursing starting time; according to the clothes state information, the nursing operation duration and nursing parameters of clothes nursing are determined; and performing nursing operation at the nursing starting time according to the nursing operation duration and the nursing parameters. According to the embodiment of the invention, the nursing parameters, the nursing duration and the starting time can be dynamically adjusted by using the clothes state information, and precise and personalized nursing control is realized. The possibility that the clothes generate peculiar smell in the inner barrel of the washing machine is effectively reduced, the intelligent level and sanitation performance of the equipment are remarkably improved, and the use experience of a user is improved.

Description

Technical Field

[0001] The present application relates to the technical field of washing machine control, and in particular to a washing machine care control method, electronic equipment, and laundry equipment. Background Art

[0002] With the rapid development of modern social economy and the significant improvement of residents' living standards, the gradual improvement of the smart home ecosystem has made washing machines an efficient, convenient and intelligent household appliance. Their functions and performance are continuously upgraded, and they have become one of the indispensable smart appliances for modern families.

[0003] However, in actual washing machine usage, users often don't hang clothes out to dry immediately after washing. This leaves clothes in the drum, which can easily cause odors due to residual moisture, ambient temperature and humidity, and bacterial growth. This problem is particularly pronounced in humid or high-temperature environments. Summary of the Invention

[0004] The embodiments of the present application provide a washing machine care control method, an electronic device, and a laundry appliance to at least solve the technical problem in the related art of odor caused by factors such as residual moisture, environmental temperature and humidity conditions, and bacterial growth after washing clothes.

[0005] According to a first aspect of an embodiment of the present application, a washing machine care control method is provided, comprising: Acquiring washing type and clothing status information of the laundry after washing, wherein the clothing status information at least includes the clothing weight of the laundry; The user's clothing collection interval, clothing weight, and washing type are input into the nursing start time calculation model to obtain the nursing start time. The user's clothing collection interval is determined based on the user's historical clothing collection time data; Determine the running time and parameters of clothing care according to clothing status information; Execute nursing operations at the nursing start time based on the nursing running time and nursing parameters.

[0006] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the clothing status information includes temperature, humidity, and degree of dirtiness of the clothing. Then, based on the clothing status information, determining the care operation duration and care parameters for clothing care includes: Determine the duration of care operation based on the degree of soiling; Determine care parameters based on temperature and humidity.

[0007] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the nursing start time calculation model is trained in the following manner: Obtain model input data, which includes historical clothing weight, historical washing type, and historical clothing collection time data; Determine the optimal nursing start interval time recorded corresponding to the model input data as the training label of the model input data; Using the model input data as input, the training labels are compared with the target nursing start time output by the nursing start time calculation model. The nursing initiation time calculation model is trained based on the difference between the training label and the target nursing initiation time.

[0008] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the degree of soiling is determined based on the pH value of the clothing. When the pH value of the clothing is in a first pH range, the degree of soiling is determined to be a first degree. When the pH value of the clothing is in a second pH range, the degree of soiling is determined to be a second degree. When the pH value of the clothing is in a third pH range, the degree of soiling is determined to be a third degree. The degrees of soiling corresponding to the first, second, and third degrees decrease in sequence. Determining the duration of the care operation according to the degree of soiling includes: When the degree of dirtiness is the first degree, the nursing operation duration is determined to be the first duration; When the dirtiness level is the second level, determining the nursing operation duration to be the second duration; When the degree of dirtiness is the third degree, the nursing operation duration is determined to be the third duration, wherein the first duration is greater than the second duration, and the second duration is greater than the third duration.

[0009] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining the nursing parameters according to temperature and humidity includes: Sort clothes according to temperature and humidity; Determine nursing parameters based on the classification results and the preset nursing parameter comparison table.

[0010] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the humidity is clothing humidity, and the clothing is classified according to temperature and humidity, including: Get the ambient humidity of the environment surrounding the clothes; Determining the humidity type of the clothes according to the humidity of the clothes, where the humidity type includes at least a high humidity type, a moderate humidity type, and a low humidity type; Determine the ambient temperature and humidity type according to the ambient humidity and temperature, the ambient temperature and humidity type including at least a high temperature and high humidity type, a high temperature and low humidity type, a low temperature and high humidity type, a low temperature and low humidity type, and a moderate type; Classify clothes based on humidity type and ambient temperature and humidity type.

[0011] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining the humidity type of the clothes according to the humidity of the clothes includes: If the clothing humidity is greater than or equal to the first clothing humidity threshold, determining the humidity type as a high humidity type; If the clothing humidity is greater than the second clothing humidity threshold and less than the first clothing humidity threshold, determining the humidity type as the moderate humidity type; If the clothing humidity is less than or equal to the second clothing humidity threshold, the humidity type is determined to be a low humidity type.

[0012] In combination with the first aspect, in an optional implementation of an embodiment of the present application, the first clothing humidity threshold is 60%, and the second clothing humidity threshold is 45%.

[0013] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining nursing parameters based on the classification results and a preset nursing parameter comparison table includes: According to the classification results, the corresponding nursing temperature, fan speed and fan on duration are queried from the preset nursing parameter comparison table; The nursing temperature, the fan speed and the fan on-time are determined as nursing parameters.

[0014] In combination with the first aspect, in an optional implementation of the embodiment of the present application, after performing the nursing operation at the nursing start time according to the nursing running time and the nursing parameters, the method further includes: During the care process, the humidity changes of clothes are monitored in real time, and the care operation is stopped when the humidity of clothes drops below the preset threshold; When it is determined that the user needs to take out the clothes or needs to stop the care operation, the care operation is immediately terminated and the washing machine door is unlocked.

[0015] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, obtaining the washing type and clothing status information of the clothes after washing, where the clothing status information includes the clothing weight of the clothes, includes: Determine whether the clothes in the drum are taken out after the washing process is completed; When the laundry is not taken out, the washing type and laundry status information of the laundry are obtained.

[0016] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the user's clothing removal interval, clothing weight, and washing type are input into a nursing start time calculation model to obtain the nursing start time, including: Determine whether to remove clothes after the washing process based on their weight; When the clothes are not taken out, the user's clothing taking interval, clothing weight and washing type are input into the care start time calculation model to obtain the care start time.

[0017] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: When performing care operations, obtaining target clothing status information; Adjust care parameters according to target clothing status information.

[0018] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: During the nursing operation, detect environmental information; When the preset time is left before the end of nursing care, determine whether to extend the nursing time based on environmental information and adjust the nursing parameters accordingly; If confirmed, extend the nursing time and continue the nursing procedure according to the adjusted preset nursing parameters.

[0019] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: During the care process, the humidity information of the clothes in the barrel is detected multiple times at certain time intervals; When the humidity information detected multiple times indicates that the nursing effect does not reach the preset effect; Adjust care parameters and extend care duration.

[0020] In the washing machine care control method provided by an embodiment of the present invention, the method first obtains clothing status information after washing. Based on the clothing status information, the method then determines the clothing care start time, care run duration, and care parameters. Finally, based on the care run duration and care parameters, the care operation is executed at the care start time. This solution utilizes clothing status information to dynamically adjust care parameters, care duration, and start time, achieving precise and personalized care control. This not only effectively reduces the possibility of clothing odors in the washing machine drum, but also significantly improves the device's intelligence and sanitation performance, enhancing the user experience.

[0021] According to a second aspect of an embodiment of the present application, a care control device for a washing machine is provided, comprising: an acquiring unit, configured to acquire washing type and clothing status information of the laundry after washing, wherein the clothing status information includes the clothing weight; A first determining unit is configured to input the user's clothing-picking interval, clothing weight, and washing type into a nursing start time calculation model to obtain a nursing start time, wherein the user's clothing-picking interval is determined based on the user's historical clothing-picking time data; A second determining unit is used to determine the care running time and care parameters for the clothing care according to the clothing status information; The processing unit is used to perform nursing operations at the nursing start time according to the nursing running time and nursing parameters.

[0022] According to the third aspect of the embodiments of the present application, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the computer instructions to thereby execute the washing machine care control method of the above-mentioned first aspect or any corresponding embodiment thereof.

[0023] According to the fourth aspect of the embodiments of the present application, the embodiments of this specification provide a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the care control method for the washing machine as described in any one of the above items is implemented.

[0024] According to the fifth aspect of the embodiments of the present application, the embodiments of this specification provide a computer program product or computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the care control method of the washing machine as described in any one of the above items.

[0025] The technical effects obtained in the above-mentioned second to fifth aspects are similar to the technical effects obtained by the corresponding technical means in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 1 is a flow chart of a washing machine care control method provided in an embodiment of the present application; Figure 2 1 is a schematic diagram of a specific flow chart of a washing machine care control method provided in an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of the nursing start time calculation model provided in an embodiment of the present application; Figure 4 This is a schematic diagram of the classification results provided by the embodiment of the present application; Figure 5 1 is a schematic structural diagram of a washing machine care control device provided in an embodiment of the present application; Figure 6 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0028] It should be understood that the "plurality" mentioned herein refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0029] In addition, the terms "comprises" and "having" and any variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, product or apparatus.

[0030] As mentioned in the background technology, with the rapid development of modern social economy and the significant improvement of residents' living standards, the gradual improvement of the smart home ecosystem has made washing machines an efficient, convenient and intelligent household appliance. Their functions and performance are continuously upgraded, and they have become one of the indispensable smart appliances in modern families.

[0031] However, in actual washing machine usage, users often don't hang clothes out to dry immediately after washing. This leaves clothes in the drum, which can easily cause odors due to residual moisture, ambient temperature and humidity, and bacterial growth. This problem is particularly pronounced in humid or high-temperature environments.

[0032] Therefore, the market urgently needs an innovative solution to achieve precise control of care parameters, thereby effectively reducing the risk of odor in the inner drum after washing clothes, and improving user experience and the hygiene performance of the equipment.

[0033] Based on this, the embodiment of the present application provides a care control method for a washing machine, referring to Figure 1 The flowchart of the washing machine care control method shown in FIG. 1 includes the following processing steps.

[0034] S101: Acquire clothing status information after washing.

[0035] In specific implementation, after the washing machine performs a series of processes such as cleaning and drying on the clothes, the user may not be able to take out the processed clothes in time. At this time, the clothing status information of the clothes is obtained. The clothing status information can include a variety of contents, such as the type and weight of the clothes, the effect after washing, and other clothing status information of the clothes themselves, and can also include environmental clothing status information of the environment in which the clothes are located, such as ambient temperature and humidity, etc., to provide data support for subsequent decisions on detailed parameters for care.

[0036] It should be noted that the acquisition of clothing status information can be started when the method is to be executed, or it can be acquired first and then the data can be called when the method is executed, or the average value can be obtained over a period of time. The specific acquisition method is not limited in the embodiments of this disclosure.

[0037] Before executing this step, you can also determine whether the clothes have been taken out. If they have been taken out, there is no need to continue with the subsequent steps. Of course, you can also determine whether the clothes have been taken out based on the weight of the clothes in the clothing status information. For example, when the weight of the clothes currently weighed is less than the judgment threshold used to determine whether the clothes exist, it is considered that the clothes have been taken out. S102: Determine the clothing care start time, care running time and care parameters for clothing care according to the clothing status information.

[0038] During specific implementation, according to the clothing status information determined in the above steps, the various parameters corresponding to clothing care are determined separately, including care start time, care running time and care parameters. For each decision, one or more data in the clothing status information may be required. S103: Execute nursing operations at the nursing start time according to the nursing running time and nursing parameters.

[0039] During specific implementation, after determining the nursing start time, nursing operation duration and nursing parameters, nursing operations are performed at the nursing start time according to the nursing operation duration and nursing parameters, thereby reducing the possibility of odor generation in the inner drum.

[0040] This embodiment first obtains post-wash laundry status information. Based on this information, the system then determines the start time, run duration, and parameters for the laundry care program. Finally, based on the run duration and parameters, the laundry care program is executed at the start time. This solution leverages clothing status information to dynamically adjust the parameters, duration, and start time of the laundry care program, enabling precise and personalized laundry care control. This not only effectively reduces the risk of laundry odors in the washing machine drum, but also significantly enhances the device's intelligence and sanitation, improving the user experience.

[0041] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The steps shown in the relevant flow charts can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flow charts, in some cases, the steps shown or described can be executed in an order different from that shown here. In other words, the order of steps described in the foregoing embodiments is only an example, and reasonable adjustment of the order of steps based on the content of the embodiments of the present application is also within the scope of protection of the embodiments of the present application.

[0042] like Figure 2 As shown, the nursing control method of the washing machine specifically includes the following processing steps: S201: Acquire clothing status information after washing.

[0043] In this embodiment, clothing care can specifically include operations such as heating and ventilation. The clothing status information includes the washing type, clothing weight, degree of dirtiness, temperature, and humidity of the clothing. The temperature is the ambient temperature, and the humidity can include the ambient humidity and clothing humidity. The overall structure of the washing machine in this embodiment may include an inner drum, a humidity sensor, a temperature and humidity sensor, a fan, and a heating device.

[0044] In practice, the inner drum of a washing machine holds clothes and serves as the primary storage space after washing. A humidity sensor is mounted on the inner drum wall, in direct contact with the clothes to measure humidity. Temperature and humidity sensors are located on the top and sides of the washing machine's outer shell, respectively, to collect ambient temperature and humidity. A fan and heater are located inside the washing machine, near the inner drum, to regulate air flow and temperature within the drum according to set parameters.

[0045] During actual operation, the humidity level of the clothing is first measured using a humidity sensor. This humidity sensor can utilize a highly sensitive capacitive humidity detection element, capable of sensing the amount of residual moisture on the clothing surface in real time. Specifically, the humidity sensor is mounted at a specific location on the inner drum wall, ensuring stable contact with the clothing without affecting its normal rotation. Simultaneously, temperature and humidity sensors located on the top and sides of the washing machine's outer casing operate synchronously to measure the current ambient temperature and humidity. These sensors are preferably secured to the outer casing with screws and protected by a waterproof seal to prevent external moisture from interfering with their measurement accuracy.

[0046] S202: Determine the care start time according to the weight of the clothes and the washing type.

[0047] During specific implementation, the user's historical clothing-picking time data is first obtained, and the user's clothing-picking interval is determined based on this. Then, the user's clothing-picking interval, clothing weight, and washing type are input into the care start time calculation model to obtain the care start time. The user's clothing-picking interval can be the average of the user's historical clothing-picking time, or it can be obtained through other calculation methods to best reflect the user's regular clothing-picking habits, such as the median value, etc. The embodiments of the present disclosure do not limit this.

[0048] Historical data on user laundry removal times can be recorded in the washing machine's built-in memory or retrieved via the cloud. This data is used to calculate the user's average laundry removal interval. A pre-trained care start-time calculation model is then used to calculate the care start time, taking into account laundry weight and wash type. For example, a shorter care start time is set for lighter laundry and the quick wash mode, while a longer care start time is set for heavier laundry and the deep wash mode. This ensures that the care function is neither initiated too early, wasting resources, nor too late, impacting effectiveness.

[0049] For the calculation model of nursing start time, a deep neural network can be used, and its structure diagram is as follows: Figure 3 As shown in the figure, by analyzing input parameters such as clothing weight, washing type, and the user's usual clothing removal time, the activation interval of the care mode is intelligently optimized. The neural network model has powerful nonlinear modeling and learning capabilities, and can effectively capture the complex relationship between input and output parameters. By training the neural network model, the activation interval of the care mode can be dynamically adjusted, thereby improving care effectiveness and reducing resource consumption. The model is trained as follows: First, the model input data is obtained. Specifically, input data such as laundry weight, wash type, and the user's usual laundry removal time are collected. The corresponding optimal start-up and maintenance interval is recorded as an output parameter. The wash type is determined by the user's selected program. The laundry weight is obtained by weighing the washing machine. If this is the first use, meaning there is no historical laundry removal time, a preset start-up and maintenance interval is determined based solely on the wash type and laundry weight. This interval can be determined through multiple experiments. When the laundry weight is fixed, the time at which bacteria begin to grow on laundry varies depending on the wash type, resulting in differences in the start-up and maintenance interval. Similarly, when the wash type is fixed, the start-up and maintenance interval varies for different laundry weights. The optimal start-up and maintenance interval is determined by comprehensively considering the correlation between wash type and laundry weight. When the user removes the laundry, the time interval between the end of the wash cycle and the time the laundry is removed is recorded as the user's historical laundry removal interval. Subsequent optimal start-up and maintenance intervals also need to take into account the user's laundry removal habits to avoid long waits for laundry. The average of the user's historical laundry removal intervals is input into the model, and the optimal start-up and maintenance interval is determined based on the wash type and laundry weight.

[0050] In one example, the relationship between the preset start-up care intervals for different types and weights of clothing is shown in the following table:

[0051] After cleaning and preprocessing the input data, the model is trained. Specifically, the optimal nursing start interval corresponding to the model input data is used as the training label for the model input data. The training label is then compared with the target nursing start time output by the nursing start time calculation model using the model input data as input. Finally, the nursing start time calculation model is trained based on the difference between the training label and the target nursing start time. During the model training process, the data on wash type, clothing weight, and the average interval between users' historical laundry retrievals were all derived from the parameter collection phase of the previous design. This phase involved extensive repetitive experiments and systematic data recording to comprehensively capture and quantify the relationship between laundry weight, wash type, and retrieval time and the optimal interval under different conditions. Through these experiments and data recording, a precise mapping between retrieval time and clothing weight and the optimal interval was established, providing a solid data foundation for subsequent model training, ensuring that the model can more accurately set the optimal interval.

[0052] In practical applications, the model uses real-time data on clothing weight and user retrieval times, combined with historical operational data for learning and reasoning, to output the optimal interval for engaging care mode. This method adaptively adjusts care mode activation times based on varying clothing weights, user-selected wash types, and dynamic changes in user retrieval habits, significantly improving care effectiveness while optimizing resource efficiency.

[0053] In one example, if the user selects wash type X for this laundry session and the laundry weight is g, these two parameters are input into the model. The model then calculates the optimal start-up and maintenance interval α based on the user's historical average laundry removal interval t and the current X and g. When the user takes the laundry out, the time of this user's removal is recorded and input into the model for subsequent laundry sessions. The model then continues learning to calculate the optimal start-up and maintenance interval for the next wash session. The output is shown in the following table:

[0054] S203: Determine the duration of the care operation according to the degree of dirtiness.

[0055] During specific implementation, in this step, the duration of the care operation is determined by the degree of dirtiness of the clothes. As the washing process is completed, the degree of dirtiness of the clothes is affected by many factors, including the amount of detergent used, the weight and material of the clothes, and the washing program and degree of cleanliness selected by the user. The degree of dirtiness of the clothes will also show significant differences. Specifically, it can be judged by the clarity of the water or a picture containing the surface of the clothes, or by the pH value of the water. In this embodiment, the degree of dirtiness of the clothes is determined by the pH value. Specifically, a pH sensor is installed in the drainage system, and the pH value is collected in the last dehydration stage. The measured value is compared and analyzed with the preset standard range to determine the specific running time of the care program. Specifically, when the pH value of the clothes is in the first pH range, the degree of dirtiness is determined to be the first degree; when the pH value of the clothes is in the second pH range, the degree of dirtiness is determined to be the second degree; when the pH value of the clothes is in the third pH range, the degree of dirtiness is determined to be the third degree, wherein the degrees of dirtiness corresponding to the first, second, and third degrees decrease in sequence; correspondingly, when the degree of dirtiness is the first degree, the nursing operation duration is determined to be the first duration; when the degree of dirtiness is the second degree, the nursing operation duration is determined to be the second duration; when the degree of dirtiness is the third degree, the nursing operation duration is determined to be the third duration, wherein the first duration is greater than the second duration, and the second duration is greater than the third duration. It should be noted that the specific values of the first pH range, the second pH range, the third pH range, and the first duration, the second duration, and the third duration can be set as needed, and are not limited in the embodiments of the present disclosure.

[0056] In one example, since the pH of laundry after a normal wash is typically in the neutral range of 6-8, this pH range indicates high cleanliness and a low likelihood of bacterial growth. However, when the pH exceeds this range, it may indicate varying degrees of soiling. For example, a pH of 10 may indicate detergent residue on the surface of the clothing, while a pH of 5.5 may indicate bacterial growth or other contaminants. Therefore, based on the pH measurement results, the care run duration is intelligently adjusted: the first pH range is 4.5-5.5 or 9.6-11.5, corresponding to a first duration of 60 minutes to ensure adequate cleaning and sterilization. The second pH range is 5.6-6.5 or 7.6-9.5, indicating moderate soiling, and the second duration is 45 minutes. The third pH range is 6.6-7.5, indicating less soiling, and the third duration is 30 minutes.

[0057] S204: Determine care parameters based on temperature and humidity.

[0058] In specific implementation, the clothes are first classified according to temperature and humidity, and then the care parameters are determined based on the classification results and the preset care parameter comparison table.

[0059] Humidity includes ambient humidity and clothing humidity, and temperature refers to ambient temperature. In specific classification, the humidity type of the clothing is determined according to the humidity of the clothing. The humidity type includes at least high humidity type, moderate humidity type and low humidity type. The ambient temperature and humidity type is determined according to the ambient humidity and temperature. The ambient temperature and humidity type includes at least high temperature and high humidity type, high temperature and low humidity type, low temperature and high humidity type, low temperature and low humidity type and moderate type. Finally, the clothing is classified based on the humidity type of the clothing and the ambient temperature and humidity type.

[0060] Regarding humidity type, if the clothing humidity is greater than or equal to a first clothing humidity threshold, the humidity type is determined to be high humidity. If the clothing humidity is greater than a second clothing humidity threshold and less than the first clothing humidity threshold, the humidity type is determined to be moderate humidity. If the clothing humidity is less than or equal to the second clothing humidity threshold, the humidity type is determined to be low humidity. The values of the first clothing humidity threshold and the second clothing humidity threshold can be set as needed and are not limited in the present embodiment. In one example, the first clothing humidity threshold and the second clothing humidity threshold are 60 and 45, respectively. That is, if the clothing humidity is greater than 60%, it is determined to be high humidity; if it is between 45% and 60%, it is determined to be moderate humidity; and if it is less than 45%, it is determined to be low humidity. This classification process is completed by a built-in algorithm, ensuring accurate and reliable classification results. Subsequently, the ambient temperature and humidity are compared and analyzed with the preset range, and the environment is divided into five types: high temperature and high humidity, high temperature and low humidity, low temperature and high humidity, low temperature and low humidity, and moderate humidity. For example, when the ambient temperature is greater than 25°C and the humidity is greater than 60%, it is considered a high-temperature, high-humidity environment; when the ambient temperature is greater than 25°C and the humidity is less than 30%, it is considered a high-temperature, low-humidity environment. This hierarchical classification provides a basis for setting subsequent care parameters.

[0061] Finally, the humidity type and ambient temperature and humidity type are combined to determine the classification result. Based on the classification result, the corresponding care temperature and fan speed are then searched from the preset care parameter comparison table and determined as the care parameters. The ambient temperature and humidity, as well as the humidity of the clothing, are key factors in determining clothing care parameters (temperature, speed, and fan duration). In high-temperature and high-humidity environments, higher temperatures are required to enhance the sterilization effect, and the fan duration and speed are increased to remove moisture. In low-temperature and low-humidity environments, lower temperatures and speeds are required to protect the clothing material. High-humidity clothing requires higher care temperatures and longer fan durations to dry quickly and prevent mold. Low-humidity clothing can use milder care conditions.

[0062] In one example, there are categories a to o, which are classified as follows: Figure 4 The corresponding heating temperature, speed and duration of fan on are shown in the following table.

[0063]

[0064] S205: Execute nursing operations at the nursing start time according to the nursing running time and nursing parameters.

[0065] During specific implementation, after determining the nursing start time, nursing operation duration and nursing parameters, nursing operations are performed at the nursing start time according to the nursing operation duration and nursing parameters, thereby reducing the possibility of odor generation in the inner drum. In this step, you can also monitor the changes in clothing humidity in real time during the care process, and stop the care operation when the clothing humidity drops below a preset threshold. When it is determined that the user needs to take out the clothes or needs to stop the care operation, immediately terminate the care operation and unlock the washing machine door.

[0066] In one example, after a washing machine completes a normal wash cycle, a highly sensitive capacitive sensing element directly contacts the surface of the clothing, detecting a humidity level of 55%. This determines the clothing to be moderately wet. Simultaneously, temperature and humidity sensors located on the top and sides of the washing machine's housing work synchronously, collecting data indicating an ambient temperature of 30°C and a humidity of 70%, respectively, and classifying the current environment as high temperature and high humidity.

[0067] At the same time, we further optimized the nursing start time by combining the data of users' clothing removal habits. After analysis and calculation, we found that the average clothing removal interval for users was 1 hour, and the nursing start time was 30 minutes.

[0068] When the care start time arrives, the corresponding parameters are queried from the preset care parameter comparison table. For moderately wet clothes in a high temperature and high humidity environment, the system sets the care temperature to 45°C, the fan speed to 600 rpm, and the fan on duration to 20 minutes. These parameters are transmitted to the heating device and fan through the output interface of the control system, driving them to operate according to the set values. The heating device is located inside the washing machine near the inner drum, receives the control signal through the power cord and heats the air in the inner drum; the fan adjusts the air flow in the inner drum according to the set speed and duration, thereby accelerating the drying of clothes and inhibiting bacterial growth.

[0069] During the care operation, the humidity sensor sends data to the control system at regular intervals, monitoring changes in clothing humidity in real time. If the humidity drops below 40%, the control system automatically stops the care operation, avoiding energy waste and excessive care. Furthermore, a user interface is located above the front panel of the washing machine, allowing users to interrupt the care operation at any time with the touch of a button. When the user presses the stop button, the control system immediately terminates the care operation and unlocks the washing machine door, ensuring flexible access to clothing.

[0070] During this step, specific care details can be adjusted based on the execution. In one possible implementation, the care operation is not static once initiated. During the care operation, the system continuously or periodically acquires target clothing status information. This target clothing status information may include one or more key parameters such as clothing moisture, inner drum temperature, ambient temperature, and ambient humidity, monitored in real time during the care process. This acquisition relies on the washing machine's built-in sensor network (e.g., clothing moisture sensor, inner drum temperature sensor, and ambient temperature and humidity sensor). The control system compares and analyzes this real-time acquired target clothing status information with the expected care path or preset thresholds set based on the initial clothing status information when the care program is initiated. If the actual status deviates from expectations (e.g., clothing moisture decreases significantly slower than expected, or a sudden change in ambient temperature and humidity results in reduced care efficiency), the system dynamically adjusts the ongoing care parameters based on pre-set adjustment rules or algorithms. These adjustments may involve the care temperature (e.g., increasing or decreasing heating power), the fan speed (e.g., increasing or decreasing ventilation intensity), and the fan on-time (dynamically allocating fan operating periods within the total care duration). This dynamic adjustment mechanism ensures that the nursing process can flexibly respond to changes in the actual operating environment, always moving towards the optimal nursing effect (such as efficient dehumidification and odor prevention), and avoiding waste of resources or insufficient care.

[0071] In another possible implementation, to ensure that clothing is ideally dry and hygienic by the end of the treatment, the system performs a critical assessment near the end of the treatment. Specifically, the control system continuously monitors environmental information, primarily ambient temperature and humidity. When the system detects that only a preset time (e.g., 5 or 10 minutes) remains until the end of the scheduled treatment duration, it triggers an environmental assessment. The system compares the detected environmental information (particularly humidity) with preset thresholds or conditions. For example, if the humidity is significantly above a certain threshold (e.g., 70%), this indicates that the external environment is extremely humid. If clothing is not removed promptly after the treatment, there is a high risk of rapid moisture absorption and odor generation within the remaining time. In this case, the system determines that the treatment duration needs to be extended. The length of the extension can be determined by a preset algorithm or a lookup table based on the degree of humidity exceeding the standard. Furthermore, to account for the need for an extended treatment, the system adjusts treatment parameters accordingly. For example, it may increase the fan speed to enhance ventilation and dehumidification capabilities, or optimize the fan start and stop strategy for the remaining (including extended) time. These adjusted parameters constitute a new set of preset treatment parameters. The system then continues the care process according to the extended duration and adjusted preset care parameters until the new end time. This mechanism effectively addresses the risks associated with sudden changes in the environment during the later stages of care, ensuring that the care effect is not disrupted by adverse environmental factors.

[0072] In another possible implementation, in order to finely control the nursing process and ensure that the expected drying effect is achieved, during the execution of the nursing operation, the system will detect the humidity information of the clothes in the drum multiple times at preset fixed time intervals (for example, every 10 minutes, 15 minutes) through the humidity sensor in the inner drum. Each detection obtains an instantaneous clothing humidity value. The system will analyze this series of humidity detection values obtained in chronological order to determine whether the actual nursing effect has achieved the expected effect. The "characterization that the nursing effect has not achieved the preset effect" can be defined in a variety of ways, such as: (1) at a specific time point, the clothing humidity does not drop below the target humidity threshold expected at that time point; (2) the rate of decrease of the clothing humidity (i.e., the percentage of humidity decrease per unit time) is lower than the preset minimum acceptable rate threshold; (3) several consecutive detections show that the humidity decrease is stagnant or changes very little. When the system determines that the nursing effect has not achieved the preset effect based on the humidity information detected multiple times (for example, the clothing humidity decreases too slowly), it will immediately take remedial measures. The measures include adjusting the nursing parameters, such as increasing the nursing temperature to accelerate water evaporation and increasing the fan speed to enhance air circulation to remove moisture. At the same time, considering that the initially set care operation time may not be enough to achieve the target dryness of the clothes, the system will extend the care time. The length of the extension can be determined based on the difference between the current humidity and the target humidity and the expected effect of the adjusted care parameters. Through this closed-loop control of periodic monitoring, effect evaluation, and dynamic adjustment of parameters and duration, the system can effectively overcome the problem of insufficient care caused by deviations in initial state assessment, environmental interference, or differences in clothing characteristics, significantly improving the reliability and ultimate effect of care.

[0073] This solution uses user data on clothing retrieval habits and clothing status as input, combined with a machine learning algorithm to optimize the start-up time of the care process. This avoids the instability of care start-up caused by traditional methods due to failure to obtain user location or weather data. Furthermore, by monitoring clothing and ambient temperature and humidity data in real time, clothing is scientifically classified. Based on this classification, care parameters (such as temperature, speed, and fan duration) are dynamically adjusted, achieving precise and personalized care control. This process not only effectively reduces the possibility of clothing odors in the washing machine drum, but also significantly improves the device's intelligence and hygienic performance.

[0074] The above is an example of the method embodiment according to the present application. The embodiment of the present invention also provides a care control method device for a washing machine. Figure 5 Schematic diagram of a washing machine care control method according to an embodiment of the present invention. Figure 5 , the care control device 700 of the washing machine includes the following modules.

[0075] An acquiring unit 701 is configured to acquire washing type and clothing status information of the laundry after washing, wherein the clothing status information includes the clothing weight of the laundry; The first determining unit 702 is configured to input the user's clothing collection interval, clothing weight, and washing type into a nursing start time calculation model to obtain a nursing start time, wherein the user's clothing collection interval is determined based on the user's historical clothing collection time data; The second determining unit 703 is used to determine the care running time and care parameters for the clothing care according to the clothing status information; The processing unit 704 is configured to execute the nursing operation at the nursing start time according to the nursing running time and the nursing parameters.

[0076] The above describes the device embodiments of the present application. For detailed descriptions of the specific execution processes of data, terms, nouns, steps, technical issues and effects, alternative methods and combinations, please refer to the descriptions in the method embodiments, which will not be repeated here.

[0077] An embodiment of the present application also provides a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the washing machine care control method according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0078] The computer program product can be written in any combination of one or more programming languages to write program codes for executing the operations of the embodiments of this specification, and the programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as "C" language or similar programming languages.

[0079] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program is used by a processor to execute the steps of the washing machine care control method according to various embodiments of the present specification described in the above "Exemplary Method" section of the present specification.

[0080] An embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a washing machine care control method, and the processor is used to adopt the above-mentioned washing machine care control method when executing the washing machine care control method.

[0081] Specifically, such as Figure 6As shown, the electronic device includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400, and a memory 500. The communication bus 200 is configured to enable communication between these components. The user interface 300 may include a display screen, and the external communication interface 400 may include a standard wired interface and a wireless interface. The memory 500 stores a method for controlling the care of a washing machine. The processor 100 is configured to employ the method when executing the method stored in the memory 500.

[0082] The descriptions of the computer program product, computer-readable storage medium, and electronic device described above are similar to the descriptions of the method embodiments described above and have similar beneficial effects as the method embodiments. For technical details not disclosed in the computer program product, computer-readable storage medium, and electronic device of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0083] The sequence of the serial numbers or introduction of the embodiments of this application is for description only and does not represent the superiority or inferiority of the embodiments.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, 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 through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0085] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0086] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0087] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transient storage medium. It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the scene data of the current frame in the three-dimensional virtual scene, the client's device information, and the scene interaction information involved in the embodiments of this application are all obtained with full authorization.

[0088] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A washing machine care control method, characterized in that: The method comprises: Acquiring washing type and clothing status information of the clothing after washing, wherein the clothing status information at least includes clothing weight of the clothing; Inputting the user's clothing-picking interval, the clothing weight, and the washing type into a nursing start time calculation model to obtain the nursing start time, wherein the user's clothing-picking interval is determined based on the user's historical clothing-picking time data; Determining a care operation duration and care parameters for the clothing care according to the clothing status information; Performing a nursing operation at the nursing start time according to the nursing running time and the nursing parameters.

2. The method according to claim 1, characterized in that The clothing status information includes temperature, humidity, and degree of dirtiness of the clothing. Then, determining the care operation duration and care parameters for the clothing care based on the clothing status information includes: Determining the duration of the nursing operation according to the degree of dirtiness; The care parameters are determined according to the temperature and the humidity.

3. The method according to claim 1, characterized in that The nursing start time calculation model is trained in the following way: Obtaining model input data, wherein the model input data includes historical clothing weight, historical washing type, and historical clothing collection time data; Determining the optimal nursing start interval time recorded corresponding to the model input data as a training label of the model input data; Taking the model input data as input, comparing the training label with the target nursing start time outputted by the nursing start time calculation model; The nursing start time calculation model is trained based on the difference between the training label and the target nursing start time.

4. The method according to claim 2, characterized in that The degree of soiling is determined based on the pH value of the clothing. When the pH value of the clothing is in a first pH range, the degree of soiling is determined to be a first degree. When the pH value of the clothing is in a second pH range, the degree of soiling is determined to be a second degree. When the pH value of the clothing is in a third pH range, the degree of soiling is determined to be a third degree. The degrees of soiling corresponding to the first, second, and third degrees decrease in sequence. Determining the duration of the nursing operation according to the degree of soiling includes: When the dirtiness level is the first level, determining the nursing operation duration to be the first duration; When the dirtiness level is the second level, determining the nursing operation duration to be a second duration; When the dirtiness level is the third level, the nursing operation duration is determined to be a third duration, wherein the first duration is greater than the second duration, and the second duration is greater than the third duration.

5. The method according to claim 2, characterized in that Determining the nursing parameters according to the temperature and the humidity includes: classifying the clothes according to the temperature and the humidity; The nursing parameters are determined based on the classification results and the preset nursing parameter comparison table.

6. The method according to claim 5, characterized in that The humidity is clothing humidity, and the classifying of the clothing according to the temperature and the humidity includes: Acquire the ambient humidity of the environment surrounding the clothing; determining a humidity type of the clothes according to the clothes humidity, wherein the humidity type includes at least a high humidity type, a moderate humidity type, and a low humidity type; Determining an ambient temperature and humidity type according to the ambient humidity and the temperature, the ambient temperature and humidity type including at least a high temperature and high humidity type, a high temperature and low humidity type, a low temperature and high humidity type, a low temperature and low humidity type, and a moderate type; The clothes are classified in combination with the humidity type and the ambient temperature and humidity type.

7. The method according to claim 6, characterized in that The determining the humidity type of the clothes according to the clothes humidity includes: If the clothing humidity is greater than or equal to a first clothing humidity threshold, determining that the humidity type is a high humidity type; If the clothing humidity is greater than a second clothing humidity threshold and less than the first clothing humidity threshold, determining that the humidity type is a moderate humidity type; If the clothing humidity is less than or equal to the second clothing humidity threshold, the humidity type is determined to be a low humidity type.

8. The method according to claim 7, characterized in that The first clothing humidity threshold is 60%, and the second clothing humidity threshold is 45%.

9. The method according to claim 5, characterized in that Determining the nursing parameters based on the classification results and the preset nursing parameter comparison table includes: According to the classification result, the corresponding nursing temperature, fan speed and fan on duration are searched from the preset nursing parameter comparison table; The nursing temperature, the fan speed and the fan on-time are determined as the nursing parameters.

10. The method according to claim 1, characterized in that After performing the nursing operation at the nursing start time according to the nursing running time and the nursing parameters, the method further includes: During the care process, the humidity change of the clothes is monitored in real time, and the care operation is stopped when the humidity of the clothes drops below a preset threshold; When it is determined that the user needs to take out the clothes or needs to stop the care operation, the care operation is immediately terminated and the washing machine door is unlocked.

11. The method according to claim 1, wherein The obtaining of the washing type and clothing status information of the clothing after washing, wherein the clothing status information includes the clothing weight of the clothing, comprises: Determine whether the clothes in the drum are taken out after the washing process is completed; When the clothes are not taken out, the washing type of the clothes and the clothes status information are obtained.

12. The method according to claim 1, characterized in that The step of inputting the interval time between users taking out clothes, the weight of the clothes, and the washing type into a nursing start time calculation model to obtain the nursing start time includes: determining whether to take out the clothes after the washing process is completed according to the weight of the clothes; When the clothes are not taken out, the interval time between the user taking out the clothes, the weight of the clothes and the washing type are input into the care start time calculation model to obtain the care start time.

13. The method according to claim 1, wherein The method further comprises: When performing the care operation, obtaining target clothing status information; The care parameters are adjusted according to the target clothing status information.

14. The method according to claim 1, wherein The method further comprises: During the nursing operation, detecting environmental information; When the preset time is left before the end of the nursing, determine whether to extend the nursing operation time according to the environmental information and adjust the nursing parameters accordingly; If confirmed, extend the nursing time and continue the nursing procedure according to the adjusted preset nursing parameters.

15. The method according to claim 1, wherein The method further comprises: During the care process, the humidity information of the clothes in the barrel is detected multiple times at certain time intervals; When the humidity information detected multiple times indicates that the nursing effect does not reach the preset effect; Adjust care parameters and extend care duration.

16. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the washing machine care control method according to any one of claims 1 to 15 by executing the computer instructions.

17. A laundry device, characterized in that: It adopts the washing machine care control method according to any one of claims 1 to 15, or has the electronic device according to claim 16.

Images