Intelligent washing machine and control method and system thereof

Abstract

The embodiment of the invention provides an intelligent washing machine and a control method and system thereof. The method comprises the steps that state parameters are obtained, wherein the state parameters comprise clothes parameters of clothes to be washed and weather parameters of the place where the washing machine is located; and generating washing program parameters according to the state parameters, the user habit parameters and a preset rule. Clothes parameters of clothes to be washed and weather parameters of a place where the washing machine is located are obtained, washing program parameters are generated according to state parameters, user habit parameters and preset rules, programs are automatically matched according to user habits, and manual selection is not needed; meanwhile, environment, clothes and user data are fused, so that the decision accuracy is improved; the water level, time and temperature are adjusted according to needs, and resource waste is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent household appliances, in particular to an intelligent washing machine and a control method and system thereof. BACKGROUND

[0002] Traditional washing machines usually provide multiple preset washing programs (such as standard, quick wash, heavy duty, wool, drying, etc.), which need to be manually selected by users according to the material, dirtiness and personal experience of the clothes.

[0003] The working mode of the current washing machine has obvious drawbacks: User decision burden: In the face of numerous programs, ordinary users have difficulty making the optimal choice, which may result in poor washing effect or damage to the clothes. Ignoring environmental factors: The existing washing machine program is static and cannot respond to the dynamic changes in the external environment. For example, in humid and rainy weather, users want to perform more thorough dehydration or start the drying function, while in sunny and dry weather, they may want to use a water-saving mode and only perform light dehydration for drying. These all require manual intervention by the user. Unintelligent experience: Although some high-end washing machines are equipped with load sensing, automatic detergent dispensing and other functions, the core program selection still relies on manual operation by the user, and the truly "one-key worry-free" intelligent experience has not been achieved. SUMMARY

[0004] The present application provides an intelligent washing machine and a control method and system thereof to solve the problem that the existing washing machine is generally a static program and cannot respond to dynamic changes in the external environment, and the degree of intelligence is low.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] A control method of an intelligent washing machine, comprising:

[0007] obtaining state parameters, the state parameters including clothes parameters of clothes to be washed and weather parameters of a location of the washing machine;

[0008] generating washing program parameters according to the state parameters, user habit parameters and preset rules.

[0009] Optionally, the clothes parameters include clothes type, clothes material, clothes color, clothes weight, dirtiness and / or dirtiness type.

[0010] And / or, the weather parameters include environmental temperature, environmental humidity, wind speed level, sunshine intensity and air quality index.

[0011] And / or, the user habit parameters include habit use time and habit collection time.

[0012] And / or, the laundry program parameters comprise: water temperature, number of times, rotation speed and time length of the washing stage, water temperature, number of times, rotation speed and time length of the rinsing stage, rotation speed and time length of the dehydration stage and rotation speed and time length of the drying stage.

[0013] Optionally, the generation of the laundry program parameters according to the state parameters, the user habit parameters and the preset rules comprises:

[0014] determining the water temperature of the washing stage and / or the rinsing stage according to the clothes type, the clothes material, the clothes color, the ambient temperature, the dirt type, the ambient humidity and / or the sunshine intensity;

[0015] And / or, determining the rotation speed and / or time length of the washing stage and / or the rinsing stage according to the clothes material, the clothes type, the clothes weight, the dirt degree, the dirt type and / or the air quality parameter;

[0016] And / or, determining the rotation speed and / or time length of the dehydration stage and / or the drying stage according to the ambient temperature, the ambient humidity, the wind speed level, the sunshine intensity;

[0017] And / or, determining the number of times of the washing stage and / or the rinsing stage according to the air quality index;

[0018] And / or, determining the weather parameter at the current time and / or the future preset time period according to the habit use time and / or the habit collection time.

[0019] Optionally, the determination of the number of times of the washing stage and / or the rinsing stage according to the air quality index comprises:

[0020] when the air quality index is greater than or equal to a preset value, increasing the rinsing program by one time or a preset number of times.

[0021] Optionally, the method further comprises:

[0022] receiving feedback of the laundry program from the user;

[0023] adjusting the preset rules according to the feedback.

[0024] Optionally, the user habit parameters are learned by an AI model based on pre-stored historical data;

[0025] And / or, the clothes parameters are detected by a sensor;

[0026] And / or, the weather parameters are collected by a built-in wireless communication module.

[0027] The application further provides a control system of an intelligent washing machine, comprising:

[0028] The parameter acquisition module is configured to acquire state parameters, which include laundry parameters of the laundry to be washed and weather parameters of a location of the washing machine.

[0029] The program generation module has an AI model and is configured to generate washing program parameters based on the AI model, the state parameters and the user habit parameters and preset rules.

[0030] Optionally, the laundry parameters include laundry type, laundry material, laundry color, laundry weight, dirtiness degree and / or dirtiness type.

[0031] And / or, the weather parameters include ambient temperature, ambient humidity, wind speed level, sunshine intensity and air quality index.

[0032] And / or, the user habit parameters include habit using time and habit collection time.

[0033] And / or, the washing program parameters include water temperature, number of times, rotation speed and time length of a washing stage, water temperature, number of times, rotation speed and time length of a rinsing stage, rotation speed and time length of a dehydration stage and rotation speed and time length of a drying stage.

[0034] The application further provides an intelligent washing machine, comprising a processor, a memory and a program stored in the memory and capable of working on the processor, and the processor implements the steps of the method according to any one of the above embodiments when executing the program.

[0035] The application further provides an intelligent washing machine comprising the control system according to any one of the above embodiments.

[0036] The control method of the intelligent washing machine provided in the embodiments of the application comprises: acquiring state parameters, which include laundry parameters of the laundry to be washed and weather parameters of a location of the washing machine; and generating washing program parameters based on the state parameters, user habit parameters and preset rules.

[0037] Compared with the prior art, the intelligent washing machine and the control method and system thereof provided in the embodiments of the application have the following technical effects:

[0038] By acquiring the laundry parameters of the laundry to be washed and the weather parameters of the location of the washing machine, the washing program parameters are generated based on the state parameters, the user habit parameters and preset rules, the program is automatically matched according to the user habit without manual selection, the three types of data, i.e., the environment, the laundry and the user, are fused to improve the decision accuracy, and the water level, the time and the temperature are adjusted as needed to avoid resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0040] Figure 1 A method flow chart of a control method of an intelligent washing machine provided by an embodiment of the application. DETAILED DESCRIPTION

[0041] Embodiments of the application disclose an intelligent washing machine and a control method and system thereof, to solve the problem that existing washing machines are generally static programs and cannot correspond to dynamic changes of external environment and have low intelligent degree.

[0042] In order to make the technical solutions and advantages of the embodiments of the application clearer, the exemplary embodiments of the application are further described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be noted that, in the case of no conflict, the embodiments in the application and the features in the embodiments can be combined with each other.

[0043] Please refer to Figure 1 , Figure 1 A method flow chart of a control method of an intelligent washing machine provided by an embodiment of the application.

[0044] In a specific embodiment, the control method of the intelligent washing machine provided by the application comprises the following steps.

[0045] S11: acquiring state parameters, the state parameters comprising clothes parameters of clothes to be washed and weather parameters of a place where the washing machine is located;

[0046] S12: generating washing program parameters according to the state parameters, user habit parameters and preset rules.

[0047] The laundry parameters of the laundry to be washed can include laundry weight, laundry material, dirtiness, laundry quantity, laundry volume, and whether special items are included; the weather parameters can include ambient temperature, humidity, whether it is raining or overcast, ultraviolet intensity, and wind force, etc.; the user habit parameters are preference data formed by the user in the long-term use process, such as commonly used start time, commonly used washing mode, preferred water temperature, whether to start the drying function, etc.; obtained by analyzing the user's regular use habits in the past preset period; the preset rules can be obtained by machine learning model, rule library or AI model built-in the washing machine; the washing program parameters include washing mode, water temperature, detergent dosage, washing time, rinsing times, whether to dry, etc.; automatically matched with the program according to the user habit, without manual selection; meanwhile, the three types of data of environment, laundry and user are fused to improve the decision accuracy; the water level, time and temperature are adjusted on demand to avoid resource waste. The user does not need to pay attention to the weather or complex parameters, and can start with one key to improve the convenience.

[0048] For example, the washing program includes a washing stage, a rinsing stage, a dehydration stage and a drying stage; the influence of the weather parameters on the washing program is reflected in the water temperature and time length of the washing stage, the time length and rotation speed of the dehydration stage, the time length and rotation speed of the drying stage, etc.; for example, if it is predicted that it will be raining in the future with high humidity, the decision is to extend the dehydration time or start the drying program to the "super dry" state to avoid the clothes producing mold smell due to being unable to dry; if it is predicted that the weather is sunny, dry and the wind speed reaches, the decision is to shorten the dehydration time or only perform low-speed dehydration to keep the clothes moist to dry quickly in the sunlight while saving energy.

[0049] The preset rules can correspond to different laundry parameters and weather parameters and washing program parameters; for example, the corresponding relationship of the laundry parameters, weather parameters and washing program is established by previously counting the washing effect after washing with various washing programs under different washing parameters and weather parameters, there can be multiple washing program parameters corresponding to the same weather parameter and laundry parameter, and there is a difference between the multiple washing program parameters, such as washing time, power consumption and washing effect, the generated washing program can be considered as the one with the best washing effect, the shortest time and the lowest power consumption, which can be pushed to the user, so that the user can freely choose the program that fits him / her. Alternatively, the AI model makes decisions according to the user's habits, and after the execution is completed, the terminal APP can push a report to the user to explain the washing logic this time, and the user scores the washing effect, which will be used as feedback data for optimizing the subsequent machine learning model.

[0050] In an optional embodiment, the laundry parameters include: laundry type, laundry material, laundry color, laundry weight, dirtiness, and / or dirt type;

[0051] And / or, the weather parameters include ambient temperature, ambient humidity, wind speed level, sunshine intensity, air quality index;

[0052] And / or, the user habit parameters include habit use time and habit collection time.

[0053] And / or, the laundry program parameters include water temperature, number of times, rotation speed and duration of the washing stage, water temperature, number of times, rotation speed and duration of the rinsing stage, rotation speed and duration of the dehydration stage, and rotation speed and duration of the drying stage.

[0054] The type of clothes includes underwear, outerwear, sportswear, baby clothes and beddings, etc. Image recognition can be achieved through the setting of sensors to distinguish the washing intensity and mode. The material of clothes includes cotton, chemical fiber, wool, silk and blended fabric, etc. The material of clothes can be recognized through the material sensor and image recognition sensor to decide whether high temperature or strong dehydration can be used. The color of clothes includes dark, light, color and white, etc. which can be obtained through the camera and image recognition algorithm to prevent color mixing. The weight of clothes includes wet or dry weight. The bottom of the clothes is provided with a weighing sensor to accurately control the water level, detergent dosage, etc. The degree of dirtiness includes light, medium and heavy. The turbidity sensor detects the change of water transparency to adjust the washing time and number of times. The degree of dirtiness includes oil stains, sweat stains, blood stains and mud stains, etc. which can be recognized through the spectrum analyzer or AI recognition to select the washing program, such as matching the special washing program.

[0055] The ambient temperature can include the current room temperature and the external temperature. The current room temperature can be detected by the built-in temperature and humidity sensor, and the external temperature can be obtained by connecting the weather API through the WiFi module. The ambient humidity can also be obtained by the built-in temperature and humidity sensor or the weather API connected through the WiFi module. By obtaining the weather parameters at the starting time or in the future preset washing cycle, the washing program parameters can be generated to select the appropriate washing program parameters according to the weather parameters at the end of washing. The wind speed level can also be obtained by connecting the weather API through the WiFi module, such as strong wind weather, which is conducive to drying, so the dehydration rotation speed can be reduced. The solar intensity can be obtained through the light sensor or network, and the dehydration rotation speed can be reduced when there is strong sunlight to achieve natural drying. The air quality parameter can be obtained through the sensor or the weather API connected through the WiFi module. When the air quality is poor, an additional rinsing program can be added to ensure that the allergens and pollutants on the surface of the clothes are completely removed.

[0056] The user habit parameters include habit use time and habit collection time. The habit use time is the time period when the user often starts the washing machine, which can be counted through the historical operation record to realize automatic reservation or avoid peak electricity price. The habit collection time is the time when the user usually takes out the washed clothes, which can be analyzed through the user behavior log. The washing program parameters can be adjusted through the habit collection time to prevent odor or timed drying.

[0057] The water temperature of the washing stage affects the stain removal effect and the clothes protection, the number of times of washing improves the cleanliness, the rotation speed affects the cleaning rate, and the time length is dynamically adjusted according to the dirtiness degree and the dirt type; the water temperature of the rinsing stage can save energy, and warm water can remove bubbles more thoroughly; the amount of detergent increases the number and / or time length of rinsing to ensure that the detergent is completely removed; the rotation speed of the dehydration stage is determined by the clothes material to avoid damage to the clothes due to high rotation speed, and the time length of the dehydration stage is determined according to the weather parameters to ensure sufficient dehydration and avoid wet weight. The time length of the drying stage is adjusted according to the weather parameters, the clothes weight, etc.

[0058] By inputting the clothes parameters, weather parameters, and user habit parameters, the washing, rinsing, dehydration, and drying stage parameters are generated according to the preset rules, and the washing machine completes the washing according to the program.

[0059] In an optional embodiment, the washing program parameters are generated according to the state parameters, user habit parameters, and preset rules, including:

[0060] The water temperature of the washing stage and / or the rinsing stage is determined according to the clothes type, clothes material, clothes color, environmental temperature, environmental humidity, and / or sunshine intensity;

[0061] And / or, the rotation speed and / or time length of the washing stage and / or the rinsing stage is determined according to the clothes material, clothes type, clothes weight, dirtiness degree, dirt type, and / or control quality parameter;

[0062] And / or, the rotation speed and / or time length of the dehydration stage and / or the drying stage is determined according to the environmental temperature, environmental humidity, wind speed level, and sunshine intensity;

[0063] And / or, the number of times of the washing stage and / or the rinsing stage is determined according to the air quality index;

[0064] And / or, the weather parameters of the current time and / or the future preset time period are determined according to the habit use time and / or the habit collection time.

[0065] When the clothes material is wool or silk, cold water is used for washing; when the clothes material is cotton or chemical fiber, the water temperature can be heated to 40-60℃, when the clothes type is sportswear, the water temperature can be heated to above 40℃, and when the clothes type is underwear, warm water can be used for sterilization; when the clothes color is dark or prone to fading, cold water can be used to prevent discoloration; when the environmental temperature is low, the water temperature can be appropriately increased to avoid that cold water cannot be cleaned; when the dirt type is oil stain, long-time soaking is required, and the water temperature is increased; when the environmental humidity is low or there is no sunshine, drying can be required, and the water temperature can be appropriately reduced to save energy. Thus, dynamic water temperature adjustment is realized, and the stain removal effect, clothes protection, and energy consumption optimization are considered.

[0066] When the clothes material is wool or silk, low-speed gentle washing is performed; when the clothes material is cotton, high-speed washing can be performed; when the clothes type is bedding or jeans, strong stirring can be performed, and when the clothes type is underwear, a gentle mode can be adopted; when the clothes weight is large, the washing time is extended and the rotation speed is increased; when the dirtiness is severe, the washing time is increased and the rotation speed is increased; when the dirt type is oil stain, long soaking and high-temperature washing are required; when the dirt type is bloodstain, cold water anti-coagulation treatment is adopted; when the air quality parameter is high, the amount of detergent or the washing time is extended to remove allergens such as pollen in the clothes.

[0067] When the ambient temperature is high, the drying temperature or time can be reduced, and when the ambient humidity is high, the dehydration rotation speed can be increased and the drying time can be extended; when the wind speed level is large, the dehydration rotation speed can be reduced to protect the clothes and achieve natural drying; when the solar radiation intensity is large, drying can be skipped or the drying time can be shortened; thus, when the natural conditions are favorable, mechanical dehydration and drying energy consumption are reduced.

[0068] In an optional embodiment, the number of washing stages and / or rinsing stages is determined according to the air quality index, specifically comprising:

[0069] When the air quality index is greater than or equal to a preset value, one or a preset number of rinsing programs are added.

[0070] When the air quality index AQI is less than 100, normal rinsing can be performed 1-2 times; when the air quality index AQI is greater than or equal to 100, the number of rinsing is increased, such as 2-3 times; when the air quality index AQI is greater than 200, a deep rinsing mode is enabled to ensure no dust residue; when the air quality is poor, the clothes are easy to adsorb particulate matter, and rinsing needs to be strengthened to protect the user's health.

[0071] According to the habit using time, the weather of the period is predicted to determine whether to enable drying; according to the pick-up time, the weather of the pick-up time is predicted, and if it rains, automatic drying is performed to avoid clothes being damp; it can automatically adjust the program start and completion time according to user habits; it combines environmental, clothes and user data to make adaptive decisions; at the same time, it increases rinsing when the air quality index is high to reduce dust residue; it reduces drying energy consumption by using natural conditions, selects appropriate rotation speed and water temperature according to the material to extend the service life of the clothes; it does not need to be manually selected by the user, and automatically recommends the optimal solution; and based on user habits, it predicts the future weather to make decisions in advance.

[0072] Specifically, the above method further comprises:

[0073] receiving user feedback on the washing program;

[0074] adjusting the preset rules according to the feedback.

[0075] Among them, the user evaluates the washing program recommended or executed by the system, realizes man-machine interaction and feedback loop; the system dynamically optimizes the decision logic according to the feedback, realizes self-learning and self-adaptation. Such as "the program is too long", "the water temperature is too high", "it is not clean", "it is too noisy" and so on, which can be obtained through button scoring, voice input and so on; Or the user manually modifies the system recommended program, such as increasing the water temperature, increasing the rinsing; The system automatically records the operation log; Or the user frequently re-washes after a program, the system identifies it as "unsatisfactory" signal; Automatically analyze user modification habits.

[0076] As for the adjustment of weight, when the user adjusts the water temperature many times, the weight of "water temperature recommended value" is increased; Or add a new rule, when the user often selects "90℃ sterilization" when washing sports clothes, add a new rule "sports clothes and sweat stains, recommend high temperature washing"; Or weaken or delete the rule, such as the user skips the drying many times, reduce the triggering frequency of "high humidity-enable drying"; Or threshold optimization, such as the original rule is humidity> 70%, start drying; User turned off many times, adjust to humidity> 85% to enable. It can continuously optimize individualization, break the limitation of static rules; Improve user satisfaction, which constitutes a complete artificial intelligence closed loop system of perception-decision-execution-feedback-optimization.

[0077] In another embodiment, the user habit parameters are obtained by AI model learning based on pre-stored historical data;

[0078] And / or, adopt sensor to detect clothes parameter;

[0079] And / or, adopt built-in wireless communication module to collect weather parameters.

[0080] According to historical data to predict user preference program, pre-stored historical data includes user operation record, environmental data, clothes information and user feedback, stored in ontology or cloud database; AI model can dynamically optimize the recommendation strategy based on user feedback.

[0081] Adopt sensor to detect clothes parameter, such as weighing sensor to detect clothes weight, which can be realized by installing in the bottom damping system; Through RFID reader to detect clothes type, material, washing history; Through camera and image recognition algorithm to detect clothes color, quantity and dirt degree; Through near infrared spectrometer to analyze material composition, through turbidity sensor to detect dirt degree optically; Thus realize full-automatic identification without manual input, improve parameter accuracy, reduce misoperation. Adopt built-in wireless communication module to collect weather parameters, such as WiFi module, real-time collection of temperature, humidity, wind speed, AQI, weather forecast and automatic matching of geographic location, realize environmental perception ability, realize forward-looking decision according to whether it will rain tomorrow or not to decide whether to dry or not, improve the autonomy and intelligent level of the system.

[0082] In a specific embodiment, a user puts a pile of daily clothes into a washing machine, selects the "AI washing" key and leaves. The washing machine automatically weighs and scans the clothes, while networking to obtain weather information: there will be thunder showers this afternoon, and the current humidity is 85%. The system decides to use the standard washing mode, but increases the dehydration speed from the default 1000 rpm to 1200 rpm, and automatically starts the drying program, set to "standard drying". After washing, the user can take out and directly wear or store, completely without worrying about the weather.

[0083] Based on the above control method of the intelligent washing machine, the application also provides a control system of an intelligent washing machine, comprising:

[0084] a parameter acquisition module for acquiring state parameters, including clothes parameters of the clothes to be washed and weather parameters of the place where the washing machine is located;

[0085] a program generation module with an AI model, for generating washing program parameters based on the AI model, state parameters and user habit parameters, and preset rules.

[0086] Among them, the clothes parameters of the clothes to be washed can include clothes weight, clothes material, dirtiness, clothes quantity, clothes volume and whether to contain special items; the weather parameters can include environmental temperature, humidity, whether it is raining or overcast, ultraviolet intensity and wind power, etc.; the user habit parameters are preference data formed by the user in the long-term use process, such as commonly used start time, commonly used washing mode, preferred water temperature, whether to start the drying function, etc.; which are obtained by analyzing the user's regular use habits in the past preset period; the preset rules can be obtained by machine learning model, rule library or AI model built-in the washing machine; the washing program parameters include washing mode, water temperature, detergent dosage, washing time, rinsing times, whether to dry, etc.; which are automatically matched according to the user's habits without manual selection; at the same time, the three types of data of environment, clothes and user are fused to improve the decision accuracy; the water level, time and temperature are adjusted as needed to avoid resource waste. The user does not need to pay attention to the weather or complex parameters, and can start with one key to improve the convenience.

[0087] For example, the washing program includes washing stage, rinsing stage, dehydration stage and drying stage; the influence of weather parameters on the washing program is reflected in the water temperature and duration of the washing stage, the duration and speed of the dehydration stage, the duration and speed of the drying stage, etc.; for example, if it is predicted that it will be raining in the future with high humidity, the decision is to extend the dehydration time or start the drying program to "super dry" state to avoid mold smell caused by clothes that cannot be dried; if it is predicted that the weather is sunny, dry and the wind speed reaches, the decision is to shorten the dehydration time or only perform low-speed dehydration to keep the clothes moist to quickly dry in the sun, while saving energy.

[0088] The preset rule can correspond to different clothes parameters and weather parameters and washing program parameters; for example, the corresponding relationship of clothes parameters, weather parameters and washing program is established by statistically analyzing the washing effect of various washing programs under different washing parameters and weather parameters in advance, the washing program parameters corresponding to the same weather parameter and clothes parameter can be multiple, and there are differences between the multiple washing program parameters, such as washing time, power consumption and washing effect, the generated washing program can be considered as the best washing effect, the shortest time and the lowest power consumption, and the program can be pushed to the user, so that the user can freely select the program suitable for him. Or, the AI model makes a decision according to the user's habits, and after the execution is completed, a report can be pushed to the user through the terminal APP to explain the washing logic, and the user scores the washing effect, which will be used as feedback data for optimizing the subsequent machine learning model.

[0089] Compared with the prior art, the intelligent washing machine and the control method and system thereof provided in the embodiments of the present application have the following technical effects:

[0090] It generates washing program parameters according to state parameters, user habit parameters and preset rules by acquiring clothes parameters of clothes to be washed and weather parameters of the place where the washing machine is located, automatically matches the program according to user habits without manual selection; at the same time, it combines three types of data of environment, clothes and user to improve the decision accuracy; adjusts the water level, time and temperature on demand to avoid resource waste.

[0091] Optionally, the clothes parameters include clothes type, clothes material, clothes color, clothes weight, dirtiness degree and / or dirtiness type.

[0092] And / or, the weather parameters include environmental temperature, environmental humidity, wind speed level, sunshine intensity and air quality index.

[0093] And / or, the user habit parameters include habit using time and habit taking time.

[0094] And / or, the washing program parameters include water temperature, number of times, rotation speed and time length of the washing stage, water temperature, number of times, rotation speed and time length of the rinsing stage, rotation speed and time length of the dehydration stage and rotation speed and time length of the drying stage.

[0095] The present application also provides an intelligent washing machine, which comprises a processor, a memory and a program stored in the memory and capable of working on the processor, and the processor implements the steps of any method of the above-mentioned embodiments when executing the program.

[0096] The present application also provides an intelligent washing machine, which comprises the control system of any one of the above-mentioned embodiments.

[0097] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the present disclosure without departing from the spirit and scope of the application. Accordingly, it is intended that such additions and modifications be included within the scope of the application. It is the following claims, including any amendments thereto, which define the scope of the application.

[0098] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A control method of a smart washing machine, characterized by, The method comprises: obtaining state parameters, the state parameters comprising laundry parameters of laundry to be washed and weather parameters of a location of a washing machine; generating washing program parameters according to the state parameters, user habit parameters and preset rules.

2. The control method of the smart washing machine according to claim 1, characterized in that, The laundry parameters comprise laundry type, laundry material, laundry color, laundry weight, dirtiness degree and / or dirtiness type. The weather parameters comprise ambient temperature, ambient humidity, wind speed level, sunshine intensity and air quality index. The user habit parameters comprise habit use time and habit collection time. The washing program parameters comprise water temperature, number of times, rotation speed and time length of a washing stage, water temperature, number of times, rotation speed and time length of a rinsing stage, rotation speed and time length of a dehydration stage and rotation speed and time length of a drying stage.

3. The control method of the smart washing machine according to claim 1, characterized in that, The generation of the washing program parameters according to the state parameters, user habit parameters and preset rules comprises: determining water temperature of the washing stage and / or the rinsing stage according to the laundry type, the laundry material, the laundry color, the ambient temperature, the dirtiness type, the ambient humidity and / or the sunshine intensity; determining rotation speed and / or time length of the washing stage and / or the rinsing stage according to the laundry material, the laundry type, the laundry weight, the dirtiness degree, the dirtiness type and / or the air quality parameter; determining rotation speed and / or time length of the dehydration stage and / or the drying stage according to the ambient temperature, the ambient humidity, the wind speed level and the sunshine intensity; determining number of times of the washing stage and / or the rinsing stage according to the air quality index; determining the weather parameters at a current time and / or a future preset time period according to the habit use time and / or the habit collection time.

4. The control method of the smart washing machine according to claim 3, characterized in that, The determination of the number of times of the washing stage and / or the rinsing stage according to the air quality index specifically comprises: when the air quality index is greater than or equal to a preset value, increasing a rinsing program by one or a preset number of times.

5. The control method of the intelligent washing machine according to claim 3, characterized in that, The method further comprises: receiving feedback of the washing program from a user; adjusting the preset rules according to the feedback.

6. The control method of the intelligent washing machine according to claim 1, characterized in that, The user habit parameters are learned by an AI model based on pre-stored historical data; The laundry parameters are detected by a sensor. The weather parameters are collected by a built-in wireless communication module.

7. A control system of a smart washing machine, characterized in that, The method comprises: a parameter acquisition module configured to obtain state parameters, the state parameters comprising laundry parameters of laundry to be washed and weather parameters of a location of a washing machine; a program generation module having an AI model and configured to generate washing program parameters based on the AI model, the state parameters and user habit parameters and preset rules.

8. The control system of the smart washing machine according to claim 7, characterized in that, The laundry parameters comprise laundry type, laundry material, laundry color, laundry weight, dirtiness degree and / or dirtiness type. The weather parameters comprise ambient temperature, ambient humidity, wind speed level, sunshine intensity and air quality index. The user habit parameters comprise habit use time and habit collection time. And / or, the laundry program parameters include: water temperature, number of times, rotation speed and time length of the washing stage, water temperature, number of times, rotation speed and time length of the rinsing stage, rotation speed and time length of the dehydration stage and rotation speed and time length of the drying stage.

9. A smart washing machine characterized by, A computer program product comprising a processor, a memory and a program stored on the memory operable on the processor, the processor implementing the steps of the method of any of claims 1-6 when executing the program.

10. A smart washing machine comprising the control system of any of claims 7-8.

Images