Method of recommending cleaning parameters, electronic device and fabric cleaning device

Abstract

This application discloses a method for recommending washing parameters, an electronic device, and a fabric cleaning device, belonging to the field of washing machine control technology. The method for recommending washing parameters includes: acquiring a user's washing history data; analyzing the washing history data to extract the user's continuous usage preferences; generating a time-weighted frequency statistical model based on the continuous usage preferences; calculating the current recommended parameters using the time-weighted frequency statistical model; and adjusting the recommended parameters by combining external environmental data and periodic care needs to generate a recommendation result. This application ensures that the recommended parameters better match the user's actual needs. This process not only improves the intelligence level of the washing machine but also significantly enhances the user experience.

Description

Technical Field

[0001] This application relates to the field of fabric cleaning equipment control technology, and more specifically, to a method for recommending cleaning parameters, electronic equipment, and fabric cleaning equipment. Background Technology

[0002] With the rapid development of smart homes, washing machines, as an indispensable household appliance, are also gradually improving their level of intelligence. Traditional washing machines usually require users to manually select washing modes and parameters, which is complicated to operate and difficult to meet the personalized needs of different users. Washing machines with adaptive capabilities can better adapt to user habits.

[0003] However, in related technologies, recommendation systems have low accuracy in recognizing user behavior patterns, fail to distinguish between high-frequency continuous usage scenarios and occasional scenarios, lack periodic task reminder mechanisms, and fail to optimize recommendation logic by incorporating external environmental data. Summary of the Invention

[0004] This application provides a method for recommending cleaning parameters, an electronic device, and a fabric cleaning device to at least solve the technical problems in related technologies, such as low accuracy in recognizing user behavior patterns, failure to distinguish between high-frequency continuous use scenarios and occasional scenarios, lack of periodic task reminder mechanisms, and failure to optimize recommendation logic by combining external environmental data.

[0005] According to a first aspect of the embodiments of this application, a method for recommending cleaning parameters is provided, comprising:

[0006] Acquire users' laundry history data and analyze the laundry history data to extract users' continuous usage preferences;

[0007] Based on continuous usage preferences, a time-weighted frequency statistical model is generated;

[0008] The current recommendation parameters are calculated using a time-weighted frequency statistical model.

[0009] By combining external environmental data and periodic nursing needs, the recommended parameters are adjusted and recommended results are generated.

[0010] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, analyzing laundry history data to extract the user's continuous usage preferences includes:

[0011] Extract laundry parameter records within a set time period from laundry history data. The set time period includes a first time period and a second time period, and the duration of the second time period is shorter than that of the first time period.

[0012] Calculate the usage frequency of each washing parameter within a time period;

[0013] Determine the first washing parameter that is used most frequently in the first time period and the second washing parameter that is used most frequently in the second time period.

[0014] When the first washing parameter is different from the second washing parameter, an excitation coefficient is determined for the second washing parameter, and the product of the excitation coefficient and the usage frequency of the second washing parameter is used as the weighted usage frequency of the second washing parameter.

[0015] The highest frequency among the weighted usage frequency of the second washing parameter and the usage frequency of the first washing parameter is taken as the weighted usage frequency corresponding to the user's continuous usage preference.

[0016] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the current recommended parameters are calculated using a time-weighted frequency statistical model, including:

[0017] Based on weighted usage frequency, determine the two washing parameters with the highest priority;

[0018] Obtain external environmental data at the current time, including seasonal information, weather information, and temperature and humidity information;

[0019] Adjust the weights of the two highest priority washing parameters based on external environmental data;

[0020] The adjusted weights are combined with the weighted usage frequency, and the current recommendation parameters are calculated using a time-weighted frequency statistical model.

[0021] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, adjusting the recommendation parameters and generating recommendation results by combining external environmental data and periodic care needs includes:

[0022] Based on laundry history data, determine the time threshold for periodic care tasks;

[0023] Obtain current seasonal information and temperature and humidity information;

[0024] Determine whether the triggering conditions for a periodic nursing task are currently met;

[0025] If the conditions are met, the parameters of periodic nursing tasks will be included in the recommended range.

[0026] Based on the parameters and the current recommended parameters, generate recommendation results.

[0027] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes:

[0028] The recommended results are displayed to users, and the laundry history data is updated based on user feedback.

[0029] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, after displaying the recommendation results to the user, the method further includes:

[0030] The recommendation results are displayed to the user on the screen;

[0031] User adjustments to the recommended results are recorded in the laundry history data. The adjustment record is used to record the user's confirmation or adjustment of the recommended results.

[0032] Based on the updated laundry history data, the time-weighted frequency statistical model was recalculated.

[0033] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, determining the time threshold for periodic care tasks based on laundry history data includes:

[0034] Calculate the drum cleaning time threshold based on the user's washing frequency;

[0035] Obtain local temperature and humidity information, and combine it with the user's laundry history data to determine the time threshold for seasonal washing;

[0036] Determine the drum cleaning time and seasonal washing time based on the time thresholds for drum cleaning and seasonal washing.

[0037] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, adjusting the weights of the two highest priority washing parameters based on external environmental data includes:

[0038] Adjust the weighting of washing temperature and washing time based on seasonal information;

[0039] Adjust the weight of whether to enable the drying function based on weather information;

[0040] Adjust the weights of the number of rinses and the dehydration speed based on temperature and humidity information.

[0041] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, after calculating the current recommendation parameters using a time-weighted frequency statistical model, the method further includes:

[0042] Based on historical laundry data, determine changes in laundry habits in different seasons;

[0043] Adjust the time window length of the time-weighted frequency statistical model according to changes in laundry habits;

[0044] The current recommended parameters are recalculated based on the adjusted time window length.

[0045] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, adjusting the time window length of the time-weighted frequency statistical model according to changes in laundry habits includes:

[0046] Calculate the average laundry frequency in different seasons based on historical laundry data;

[0047] The adjustment range for the time window length is determined based on the average washing frequency.

[0048] The adjusted time window length is applied to the time-weighted frequency statistics model.

[0049] The method for recommending cleaning parameters provided in this invention first acquires the user's laundry history data, analyzes the data to extract the user's continuous usage preferences, then determines the current recommended parameters based on these preferences, and finally adjusts the recommended parameters and generates a recommendation result by combining external environmental data and periodic care needs. Through in-depth analysis of the user's laundry history data, the continuous usage preferences are extracted, avoiding the impact of occasional operations on the recommendation results. Furthermore, by combining external environmental data (such as season and weather) and periodic care needs, the recommendation logic is further optimized, making the recommended parameters more closely aligned with the user's actual needs. This process not only improves the intelligence level of the washing machine but also significantly enhances the user experience.

[0050] According to a second aspect of the embodiments of this application, a device for recommending cleaning parameters is provided, comprising:

[0051] The acquisition unit is used to acquire the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preferences.

[0052] The unit is defined to generate a time-weighted frequency statistical model based on continuous usage preferences;

[0053] The unit is also used to calculate the current recommendation parameters using a time-weighted frequency statistical model;

[0054] The processing unit is used to combine external environmental data and periodic care needs to adjust recommended parameters and generate recommended results.

[0055] According to a third aspect of the embodiments of this application, the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the recommended method for cleaning parameters of the first aspect or any corresponding embodiment described above.

[0056] According to a fourth aspect of the embodiments of this application, this specification provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the recommended method for cleaning parameters as described in any of the preceding claims.

[0057] According to a fifth aspect of the embodiments of this application, this specification provides a computer program product or computer program, the computer program product including a computer program stored in a computer-readable storage medium; a processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program to implement a recommended method for cleaning parameters as described in any of the preceding claims.

[0058] The technical effects achieved by the second to fifth aspects mentioned above are similar to those achieved by the corresponding technical means in the first aspect, and will not be repeated here. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the recommended method for cleaning parameters provided in the embodiments of this application;

[0060] Figure 2 This is a schematic diagram of the specific process of the recommended method for cleaning parameters provided in the embodiments of this application;

[0061] Figure 3 This is a schematic diagram of the process for determining continuous usage preferences provided in an embodiment of this application;

[0062] Figure 4 This is a flowchart illustrating the process of adjusting recommended parameters provided in an embodiment of this application;

[0063] Figure 5 This is a schematic diagram of the structure of the recommended device for cleaning parameters provided in the embodiments of this application;

[0064] Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0065] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0066] It should be understood that "multiple" as mentioned herein refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply that they are different.

[0067] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0068] As mentioned in the background technology, with the rapid development of smart homes, washing machines, as an indispensable home appliance, are also gradually improving their intelligence level. Traditional washing machines usually require users to manually select washing modes and parameters, which is complex and difficult to meet the personalized needs of different users. Washing machines with adaptive capabilities can better adapt to user habits. In related technologies, some products make recommendations based on recorded basic historical data, but only adjust parameters according to the total usage frequency, without fully considering users' continuous usage preferences and periodic care needs. At the same time, high-frequency continuous usage scenarios are not distinguished from occasional scenarios, which can easily lead to recommendations that deviate from actual needs. In addition, periodic tasks such as drum cleaning and seasonal care rely on user memory and lack proactive reminder mechanisms. Finally, the recommendation logic is not optimized by combining external environmental data (such as season and weather), and the level of intelligence needs to be further improved. With the expansion of the smart home market, consumers' demand for intelligent home appliances is increasing.

[0069] Based on this, embodiments of this application provide a method for recommending cleaning parameters, referring to... Figure 1 The flowchart shown illustrates the recommended method for cleaning parameters, which includes the following processing steps.

[0070] S101: Obtain the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preferences.

[0071] In practice, the user's laundry history data is first obtained. This data can be obtained from the storage unit inside the washing machine or from the cloud server. Then, laundry parameter records within a set time period are extracted from the laundry history data. The usage frequency of each laundry parameter within the time period is calculated. Then, within the preset time period, the two laundry parameters with the most consecutive uses are identified, and the usage frequency of the two laundry parameters is weighted. Finally, the one with the higher weighted usage frequency is taken as the user's continuous usage preference.

[0072] S102: Determine the current recommended parameters based on continuous usage preferences.

[0073] In practice, a time-weighted frequency statistical model is first generated based on continuous usage preferences, and then the current recommendation parameters are calculated using the time-weighted frequency statistical model.

[0074] S103: Combine external environmental data and periodic nursing needs to adjust recommended parameters and generate recommended results.

[0075] In practice, the time threshold for periodic care tasks is first determined based on historical laundry data. Then, by using seasonal and temperature / humidity information, it is determined whether the triggering conditions for periodic care tasks are met. If so, the parameters for periodic care tasks are included in the recommendation range, and a recommendation result is generated based on these parameters and the current recommended parameters. If the conditions are not met, the tasks are not included in the recommendation range.

[0076] This embodiment first acquires the user's laundry history data, analyzes it to extract the user's continuous usage preferences, then determines the current recommended parameters based on these preferences, and finally adjusts the recommended parameters and generates a recommendation result by combining external environmental data and periodic care needs. Through in-depth analysis of the user's laundry history data, the continuous usage preferences are extracted, avoiding the impact of occasional operations on the recommendation results. Furthermore, by combining external environmental data (such as season and weather) and periodic care needs, the recommendation logic is further optimized, making the recommended parameters more closely aligned with the user's actual needs. This process not only improves the washing machine's intelligence level but also significantly enhances the user experience.

[0077] In the above embodiments of this application, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The steps illustrated in the related flowcharts can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here. In other words, the order of steps described in the foregoing embodiments is merely an example. Reasonable adjustments to the order of steps based on the content of the embodiments of this application are also within the protection scope of the embodiments of this application.

[0078] This invention provides a method for recommending washing parameters. Its core lies in acquiring and deeply analyzing the user's historical washing data to extract continuous usage preferences, and then combining this with external environmental data and periodic care needs to generate a final recommendation result. In implementation, it is first necessary to clarify the hardware components of the washing machine and their relationship with the data processing module. The washing machine includes a control panel, a storage module, a Wi-Fi module, a sensor module, and a display module. These modules transmit data via an internal bus. The control panel receives user input commands, the storage module stores historical washing data and recommendation model parameters, the Wi-Fi module acquires external environmental data, the sensor module monitors the washing machine's operating status, and the display module presents the recommendation results to the user. Then, in conjunction with practical applications, such as... Figure 2 As shown, the recommended method for cleaning parameters specifically includes the following processing steps:

[0079] S201: Obtain the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preferences.

[0080] In practice, the system analyzes users' continuous usage preferences by acquiring their laundry history data. In actual operation, the washing machine's control panel records each washing operation, including parameters such as the selected washing mode, washing time, water temperature, and spin speed, and stores this data in a storage module. The laundry history data in the storage module is arranged chronologically, forming a complete data sequence. To analyze users' continuous usage preferences, laundry parameter records within a set time period are extracted from the storage module. For example, the set time period could be the most recent month or the most recent three months, with the specific duration dynamically adjusted according to the user's laundry frequency. The extracted data is processed by a calculation module to calculate the usage frequency of each laundry parameter within that time period. Based on this, the two laundry parameters with the most consecutive uses are further identified, and an incentive coefficient is calculated based on the number of consecutive uses. The incentive coefficient is calculated as follows: the incentive coefficient equals the number of consecutive uses divided by the total number of uses, multiplied by a preset weight value. Multiplying the incentive coefficient by the usage frequency yields a weighted usage frequency, which is recorded as the user's continuous usage preference.

[0081] Specifically, such as Figure 3 As shown, the preset time period includes two time periods: the first time period and the second time period. The first time period, being a longer-term preference analysis, has a longer duration than the second time period. For example, T1 can be set to one or two weeks to capture recent changes in user habits. For the usage frequency obtained in the second time period, its compensation coefficient S, also known as the incentive coefficient, needs to be calculated. Then, by calculating the number of consecutive uses of this laundry parameter, its usage frequency is determined. The usage frequency P in the second time period is then multiplied by S to obtain its weighted usage frequency PS. This value is compared with the highest usage frequency in the first time period, and the higher value is determined as the user's continuous usage preference.

[0082] S202: Generate a time-weighted frequency statistical model based on continuous usage preferences.

[0083] In practice, a time-weighted frequency statistical model is constructed based on weighted usage frequency. The core of this model lies in capturing users' short-term and long-term usage habits by dynamically adjusting the time window length. For example, during the hot summer months, when users wash clothes more frequently, the time window length can be shortened to one week; while during the cold winter months, when users wash clothes less frequently, the time window length can be extended to two weeks. The adjustment range of the time window length is determined by the user's average washing frequency. Specifically, the calculation method involves first calculating the average washing frequency for different seasons, and then determining the increase or decrease ratio of the time window length based on the frequency. This approach allows for flexible adaptation to changes in users' seasonal washing habits, thereby improving the accuracy of recommendations.

[0084] S203: Calculate the current recommended parameters using a time-weighted frequency statistical model.

[0085] In practice, during the generation of the current recommended parameters, the two highest-priority washing parameters are first determined based on weighted usage frequency. These two parameters are typically the washing mode and water temperature setting that best suit user habits. Subsequently, as... Figure 4 As shown, the external environmental data for the current time point is obtained via the Wi-Fi module, including seasonal information, weather information, and temperature and humidity information. This data is acquired from the internet by the Wi-Fi module and transmitted to the calculation module. The calculation module adjusts the weights of the two highest priority washing parameters based on the external environmental data. For example, during the rainy season or in humid weather conditions, the recommended weight of the drying function is increased; in cold and dry winter conditions, the weights of washing temperature and spin-drying speed are appropriately increased. The adjusted weights are combined with the weighted usage frequency to calculate the current recommended parameters.

[0086] S204: Combine external environmental data and periodic nursing needs to adjust recommended parameters and generate recommended results.

[0087] In practical implementation, to further optimize the recommendation logic, periodic care needs are also considered. The time thresholds for periodic care tasks are determined by the user's laundry frequency and external environmental data. For example, the time threshold for drum cleaning is calculated based on the user's laundry frequency. If the user does laundry three times a week, the interval for drum cleaning can be set to once a month; if the user does laundry once a week, the interval can be extended to once every two months. The time threshold for seasonal washing is determined by combining temperature and humidity information obtained from the Wi-Fi module and the user's historical laundry data. When the season changes and the temperature difference between day and night exceeds the set threshold, the seasonal washing task is triggered. These time thresholds can be stored to support subsequent recommendation logic. When the triggering conditions for periodic care tasks are met, relevant parameters are included in the recommendation scope, and the final recommendation results are generated.

[0088] In this step, the recommendations are displayed to the user via a screen on the washing machine's control panel. The user can confirm or adjust the recommendations using the touchscreen. These adjustments are recorded in the washing history data and used to update the time-weighted frequency statistical model. This process creates a closed-loop feedback mechanism, ensuring the recommendation system continuously learns and optimizes. For example, if the user manually adjusts the recommended washing mode multiple times, the system will recalculate the weighted usage frequency of that mode and increase its priority in future recommendations. This dynamic adjustment mechanism makes the recommendations more closely reflect the user's actual needs.

[0089] Furthermore, the system includes adaptive adjustments to adapt to changes in laundry habits across different seasons. For example, in the hot summer months, users tend to choose quick wash cycles to reduce the risk of bacterial growth from piled-up clothes; while in the cold winter months, users prioritize warmth and comfort, thus opting for higher wash temperatures and longer rinse times. The system analyzes users' historical laundry data to determine these seasonal changes and adjusts the time window length of the time-weighted frequency statistical model accordingly. For instance, the time window can be shortened to one week in the hot summer months and extended to two weeks in the cold winter months. This approach allows the system to better capture seasonal changes in users' laundry habits, thereby improving the accuracy of recommendations.

[0090] In practical applications, suppose a user frequently uses the quick wash and cold water wash modes in the summer, and gradually switches to the standard wash and warm water wash modes in the fall. Based on the user's laundry history data, the system calculates the weighted usage frequency of the quick wash and cold water wash modes in summer, while the weighted usage frequency of the standard wash and warm water wash modes gradually increases in autumn. Combining this with external environmental data, the system will appropriately increase the recommended weight of the warm water wash mode in autumn, while decreasing the recommended weight of the quick wash mode. Furthermore, if the system detects that the temperature difference between day and night exceeds a set threshold, it will trigger a seasonal laundry task and include relevant parameters in the recommendations. After receiving the recommendations, the user can choose to confirm or adjust them. For example, if the user believes that the recommended warm water wash mode does not meet their actual needs, they can manually switch to the cold water wash mode. This adjustment will be recorded in the laundry history data and reflected in the next recommendation.

[0091] Through the above embodiments, this invention provides a method for recommending cleaning parameters for an intelligent washing machine. This method extracts the user's continuous usage preferences through in-depth analysis of the user's historical washing data and combines this with external environmental data and periodic care needs to generate a final recommendation result. The entire process encompasses multiple stages, including data collection, analysis and modeling, parameter adjustment, and user feedback, ensuring that the recommendation result not only meets the user's actual needs but also adapts to changes in the external environment.

[0092] The above examples illustrate the method embodiments according to this application. The present invention also provides a method and apparatus for recommending cleaning parameters. Figure 5 This is a schematic diagram of an apparatus for recommending cleaning parameters according to an embodiment of the present invention. (Refer to...) Figure 5 The recommended cleaning parameter device 700 includes the following modules.

[0093] The acquisition unit 701 is used to acquire the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preferences.

[0094] The determining unit 702 is used to generate a time-weighted frequency statistical model based on the continuous usage preference;

[0095] The determining unit 702 is also used to calculate the current recommended parameters using the time-weighted frequency statistical model;

[0096] The processing unit 703 is used to combine external environmental data and periodic care needs to adjust recommended parameters and generate recommended results.

[0097] The above describes the device embodiments of this application. For detailed descriptions of data, terms, nouns, specific execution processes of steps, technical problems and effects, alternative methods and combinations, please refer to the description in the method embodiments, which will not be repeated here.

[0098] This application also provides a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the recommended method of cleaning parameters according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0099] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments in this specification. The programming languages ​​include object-oriented programming languages ​​such as Java, C++, etc., as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0100] This application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the recommended method of cleaning parameters according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0101] This application also provides an electronic device, including a memory and a processor. The memory stores a recommended method for cleaning parameters, and the processor is used to employ the recommended method for cleaning parameters when executing the recommended method.

[0102] 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 standard wired and wireless interfaces. The memory 500 stores recommended methods for cleaning parameters. The processor 100 is used to employ the recommended methods for cleaning parameters stored in the memory 500.

[0103] The descriptions of the above computer program products, computer-readable storage media, and electronic devices are similar to those of the above method embodiments, and have similar beneficial effects. For any technical details not disclosed in the computer program products, computer-readable storage media, and electronic devices of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0104] The sequence numbers or order of description of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0106] The units described as separate components may or may not be physically separate. 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as 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 this 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0109] 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 related 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 3D virtual scene involved in the embodiments of this application, the client's device information, and the scene interaction information are all obtained with full authorization.

[0110] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for recommending cleaning parameters, characterized in that, The method includes: Acquire the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preferences; Based on the continuous usage preferences, a time-weighted frequency statistical model is generated; The current recommendation parameters are calculated using the time-weighted frequency statistical model. By combining external environmental data and periodic nursing needs, the recommended parameters are adjusted and recommended results are generated; The step of analyzing the laundry history data to extract the user's continuous usage preferences includes: The laundry parameter records within a set time period are extracted from the laundry history data. The set time period includes a first time period and a second time period, and the duration of the second time period is shorter than that of the first time period. Calculate the usage frequency of each washing parameter within the set time period; Determine the first washing parameter that is used most frequently during the first time period and the second washing parameter that is used most frequently during the second time period; When the first washing parameter is different from the second washing parameter, an excitation coefficient is determined for the second washing parameter, and the product of the excitation coefficient and the usage frequency of the second washing parameter is used as the weighted usage frequency of the second washing parameter. The highest frequency among the weighted usage frequency of the second laundry parameter and the usage frequency of the first laundry parameter is taken as the weighted usage frequency corresponding to the user's continuous usage preference.

2. The method according to claim 1, characterized in that, The calculation of the current recommendation parameters using the time-weighted frequency statistical model includes: Based on the weighted usage frequency, determine the two washing parameters with the highest priority; Obtain external environmental data at the current time, including seasonal information, weather information, and temperature and humidity information; Based on the external environment data, adjust the weights of the two highest priority washing parameters; The adjusted weights are combined with the weighted usage frequency, and the current recommendation parameters are calculated using the time-series weighted frequency statistical model.

3. The method according to claim 1, characterized in that, The process of combining external environmental data and periodic care needs to adjust the recommended parameters and generate recommended results includes: Based on the laundry history data, determine the time threshold for periodic care tasks; Obtain current seasonal information and temperature and humidity information; Determine whether the triggering conditions for a periodic nursing task are currently met; If the conditions are met, the parameters of periodic nursing tasks will be included in the recommended range. Based on the parameters and the current recommendation parameters, a recommendation result is generated.

4. The method according to claim 3, characterized in that, The method further includes: The recommended results are displayed to the user, and the laundry history data is updated based on user feedback.

5. The method according to claim 4, characterized in that, After displaying the recommendation results to the user, the process also includes: The recommendation results are displayed to the user on a screen; The user's adjustment of the recommendation results is recorded in the laundry history data. The adjustment operation record is used to record the user's confirmation or adjustment of the recommendation results. The time-weighted frequency statistical model is recalculated based on the updated laundry history data.

6. The method according to claim 3, characterized in that, The step of determining the time threshold for periodic care tasks based on the laundry history data includes: Calculate the drum cleaning time threshold based on the user's washing frequency; Obtain local temperature and humidity information, and combine it with the user's laundry history data to determine the time threshold for seasonal washing; The drum cleaning time and the seasonal washing time are determined based on the drum cleaning time threshold and the seasonal washing time threshold.

7. The method according to claim 2, characterized in that, The step of adjusting the weights of the two highest-priority washing parameters based on the external environment data includes: Adjust the weights of washing temperature and washing time based on the seasonal information. Based on the weather information, adjust the weight of whether to enable the drying function; The weights of the number of rinses and the dehydration speed are adjusted based on the temperature and humidity information.

8. The method according to claim 1, characterized in that, After calculating the current recommendation parameters using the time-weighted frequency statistical model, the process further includes: Based on the aforementioned laundry history data, determine the changes in laundry habits in different seasons; Adjust the time window length of the time-weighted frequency statistical model according to the changes in laundry habits; The current recommended parameters are recalculated based on the adjusted time window length.

9. The method according to claim 8, characterized in that, The step of adjusting the time window length of the time-weighted frequency statistical model according to the changes in laundry habits includes: Based on the laundry history data, calculate the average laundry frequency in different seasons; Based on the average washing frequency, determine the adjustment range for the time window length; The adjusted time window length is applied to the time-weighted frequency statistics model.

10. An electronic device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the recommended method for the cleaning parameters of any one of claims 1 to 9.

11. A fabric cleaning device, characterized in that, It employs the recommended method using the cleaning parameters described in any one of claims 1 to 9, or has the electronic device described in claim 10.

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