Cleaning parameter recommendation method, electronic equipment and fabric cleaning equipment

Abstract

The embodiment of the invention discloses a cleaning parameter recommendation method, electronic equipment and fabric cleaning equipment, and belongs to the technical field of washing machine control. The cleaning parameter recommendation method comprises the steps of obtaining clothes washing historical data of a user, and analyzing the clothes washing historical data to extract continuous use preference of the user; generating a time sequence weighted frequency statistical model according to the continuous use preference; calculating a current recommendation parameter through the time sequence weighted frequency statistical model; and adjusting the recommendation parameters and generating a recommendation result in combination with external environment data and periodic nursing requirements. The embodiment of the invention ensures that the recommendation parameters better meet the actual requirements of the user. The process not only improves the intelligent level of the washing machine, but also remarkably improves the user experience.

Description

Technical Field

[0001] The present application relates to the technical field of fabric cleaning equipment control, and in particular to a method for recommending cleaning parameters, an electronic device, and a fabric cleaning equipment. Background Art

[0002] With the rapid development of smart homes, washing machines, as an indispensable household appliance, are becoming increasingly intelligent. Traditional washing machines typically require users to manually select washing modes and parameters, a complex operation that struggles to meet the personalized needs of different users. Self-adaptive washing machines, on the other hand, can better adapt to user habits.

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

[0004] The embodiments of the present application provide a method for recommending cleaning parameters, an electronic device, and a fabric cleaning device to at least solve the technical problems in the related art such as the low accuracy of the recommendation system in recognizing user behavior patterns, the failure to distinguish between high-frequency continuous use scenarios and occasional scenarios, the lack of a periodic task reminder mechanism, and the failure to optimize the recommendation logic in combination with external environmental data.

[0005] According to a first aspect of an embodiment of the present application, a method for recommending cleaning parameters is provided, comprising: Obtaining the user's laundry history data and analyzing the laundry history data to extract the user's continuous usage preferences; Generate a time-order weighted frequency statistical model based on continuous usage preference; Calculate the current recommended parameters through the time-series weighted frequency statistical model; Combined with external environmental data and periodic care needs, the recommendation parameters are adjusted and recommendation results are generated.

[0006] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, analyzing laundry history data to extract the user's continuous usage preference includes: Extracting laundry parameter records within a set time period from the laundry history data, where the set time period includes a first time period and a second time period, and the second time period is shorter than the first time period; Calculate the usage frequency of each laundry parameter within a time period; determining a first washing parameter that is most frequently used during a first period of time and a second washing parameter that is most frequently used during a second period of time; When the first washing parameter is different from the second washing parameter, determining an excitation coefficient for the second washing parameter, and taking a product of the excitation coefficient and a usage frequency of the second washing parameter as a weighted usage frequency of the second washing parameter; The highest frequency between 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.

[0007] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, calculating the current recommended parameters using a time-series weighted frequency statistical model includes: Determine the two laundry parameters with the highest priority based on weighted usage frequency; Obtain external environmental data at the current time, including season information, weather information, and temperature and humidity information; Adjust the weights of the two highest priority laundry parameters based on external environmental data; The adjusted weights are combined with the weighted usage frequency, and the current recommended parameters are calculated through a time-series weighted frequency statistical model.

[0008] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, adjusting the recommendation parameters and generating the recommendation results in combination with external environment data and periodic care needs includes: Determine the time threshold for periodic care tasks based on laundry history data; Get the current season information and temperature and humidity information; Determine whether the triggering conditions of the periodic nursing task are currently met; If satisfied, the parameters of the periodic nursing tasks are included in the recommended range; Generate recommendation results based on the parameters and current recommendation parameters.

[0009] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: The recommendation results are presented to the user, and the laundry history data is updated based on user feedback.

[0010] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, after presenting the recommendation results to the user, the method further includes: Display the recommendation results to the user through the display screen; Recording the user's adjustment operation on the recommendation result in the laundry history data, and the adjustment operation record is used to record the user's confirmation or adjustment operation on the recommendation result; The time-series weighted frequency statistical model is recalculated based on the updated laundry history data.

[0011] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining a time threshold for a periodic care task based on laundry history data includes: Calculate the time threshold for drum cleaning based on the user's laundry 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 time for cleaning the drum and the time for washing in a seasonal change are determined according to the time threshold for cleaning the drum and the time threshold for washing in a seasonal change.

[0012] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, adjusting the weights of the two highest-priority laundry parameters according to external environment data includes: Adjust the weights of washing temperature and washing time based on seasonal information; Adjust the weight of whether to enable the drying function based on weather information; Adjust the weights of rinse times and spin speed based on temperature and humidity information.

[0013] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, after calculating the current recommended parameters using a time-series weighted frequency statistical model, the method further includes: Determine seasonal changes in laundry habits based on laundry history data; Adjust the time window length of the time-series weighted frequency statistical model according to changes in laundry habits; Recalculate the current recommended parameters based on the adjusted time window length.

[0014] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, adjusting the time window length of the time-series weighted frequency statistical model according to changes in laundry habits includes: Calculate the average frequency of laundry in different seasons based on laundry history data; Determine the adjustment amount of the time window length based on the average laundry frequency; The adjusted time window length is applied to the time series weighted frequency statistics model.

[0015] The washing parameter recommendation method provided by an embodiment of the present invention first obtains the user's laundry history data, analyzes this data to extract the user's continuous usage preferences, then determines the current recommended parameters based on these preferences. Finally, the recommended parameters are adjusted and generated based on external environmental data and periodic care needs. By deeply analyzing the user's laundry history data to extract the user's continuous usage preferences, the method avoids the influence of occasional operations on the recommendation results. Furthermore, by combining external environmental data (such as season and weather) with periodic care needs, the recommendation logic is further optimized, making the recommended parameters more tailored to the user's actual needs. This process not only enhances the intelligence level of the washing machine but also significantly improves the user experience.

[0016] According to a second aspect of an embodiment of the present application, a cleaning parameter recommendation device is provided, comprising: an acquisition unit, configured to acquire the user's laundry history data and analyze the laundry history data to extract the user's continuous usage preference; a determination unit, configured to generate a time-series weighted frequency statistical model according to the continuous usage preference; The determination unit is further configured to calculate the current recommended parameters through a time-series weighted frequency statistical model; The processing unit is used to combine external environmental data and periodic care needs, adjust recommendation parameters and generate recommendation results.

[0017] 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 method for recommending cleaning parameters of the above-mentioned first aspect or any corresponding embodiment thereof.

[0018] 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 method for recommending cleaning parameters as described in any one of the above items is implemented.

[0019] 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 method for recommending cleaning parameters as described in any one of the above items.

[0020] 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

[0021] Figure 1 This is a flow chart of a recommended method for cleaning parameters provided in an embodiment of the present application; Figure 2 This is a schematic diagram of a specific process of recommending cleaning parameters according to an embodiment of the present application; Figure 3 is a schematic diagram of a process for determining continuous usage preferences provided in an embodiment of the present application; Figure 4 This is a flow chart of adjusting recommended parameters provided in an embodiment of the present application; Figure 5 It is a structural diagram of a device for recommending cleaning parameters 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

[0022] 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.

[0023] 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.

[0024] 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.

[0025] As mentioned in the background, with the rapid development of smart homes, washing machines, as an indispensable household appliance, are becoming increasingly intelligent. Traditional washing machines typically require users to manually select wash modes and parameters, a complex operation that struggles to meet the personalized needs of different users. Self-adaptive washing machines, on the other hand, can better adapt to user habits. Some related products make recommendations by recording basic historical data, but adjust parameters based solely on overall frequency of use, failing to fully consider users' continuous usage preferences and periodic maintenance needs. Furthermore, they fail to distinguish between high-frequency continuous use scenarios and occasional use, which can easily lead to recommendations that deviate from actual needs. Furthermore, periodic tasks such as drum cleaning and seasonal maintenance rely on user memory and lack proactive reminder mechanisms. Finally, external environmental data (such as season and weather) is not incorporated to optimize recommendation logic, leaving room for further improvement in intelligent functionality. With the expansion of the smart home market, consumer demand for intelligent home appliances is increasing.

[0026] Based on this, the present invention provides a method for recommending cleaning parameters. Figure 1 The flowchart of the recommended method for cleaning parameters shown includes the following processing procedures.

[0027] S101: Acquire the user's laundry history data, and analyze the laundry history data to extract the user's continuous usage preference.

[0028] In a specific implementation, the user's laundry history data is first obtained. The data can be obtained from a storage unit stored inside the washing machine or from a cloud server. Then, the laundry parameter records within a set time period are extracted from the laundry history data, and 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 frequencies of the two laundry parameters are weighted. Finally, the one with the higher weighted usage frequency is used as the user's continuous use preference. S102: Determine current recommended parameters based on the continuous usage preference.

[0029] In specific implementation, firstly, a time-series weighted frequency statistical model is generated according to the continuous usage preference, and then the current recommendation parameters are calculated through the time-series weighted frequency statistical model.

[0030] S103: Based on external environmental data and periodic care needs, adjust recommendation parameters and generate recommendation results.

[0031] In specific implementation, the time threshold for periodic care tasks is first determined based on historical laundry data. Then, the seasonal information and temperature and humidity information are used to determine whether the trigger conditions for the periodic care tasks are currently met. If so, the parameters of the periodic care tasks are included in the recommended range, and a recommendation result is generated based on these parameters and the current recommended parameters. If not, the periodic care tasks are not included in the recommended range.

[0032] This embodiment first obtains the user's laundry history data and analyzes it to extract the user's continuous usage preferences. Based on these preferences, current recommendation parameters are determined. Finally, external environmental data and periodic care needs are combined to adjust the recommendation parameters and generate a recommendation result. By deeply analyzing the user's laundry history data and extracting the user's continuous usage preferences, the system avoids the influence of occasional operations on the recommendation results. Furthermore, by combining external environmental data (such as season and weather) with periodic care needs, the recommendation logic is further optimized, making the recommended parameters more tailored to the user's actual needs. This process not only enhances the intelligence level of the washing machine but also significantly improves the user experience.

[0033] 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.

[0034] The present invention provides a method for recommending washing parameters, the core of which is to obtain the user's laundry history data and conduct in-depth analysis thereof, extract the user's continuous use preferences, and generate the final recommendation results in combination with external environmental data and periodic care needs. During implementation, it is first necessary to clarify the hardware composition of the washing machine and its 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 through an internal bus, in which the control panel is responsible for receiving the operating instructions input by the user, the storage module is used to save the laundry history data and the recommended model parameters, the Wi-Fi module is used to obtain external environmental data, the sensor module is used to monitor the operating status of the washing machine, and the display module is used to display the recommendation results to the user. In combination with actual applications, such as Figure 2 As shown, the recommended method for cleaning parameters specifically includes the following processing procedures: S201: Acquire the user's laundry history data, and analyze the laundry history data to extract the user's continuous usage preference.

[0035] In specific implementations, the user's laundry history data is collected to analyze their continuous usage preferences. In practice, the washing machine's control panel records each user's laundry operation, including parameters such as the selected wash mode, wash 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 to form a complete data sequence. To analyze the user's continuous usage preferences, the storage module extracts laundry parameter records for a set time period. For example, the set time period can be the last month or the last three months, and the specific duration can be dynamically adjusted based on the user's laundry frequency. The extracted data is processed by the calculation module to calculate the frequency of use of each laundry parameter within the time period. Based on this, the two laundry parameters with the highest number of consecutive uses are identified, and an incentive coefficient is calculated based on the number of consecutive uses. The incentive coefficient is calculated as follows: the incentive coefficient is equal to the number of consecutive uses divided by the total number of uses, multiplied by a preset weight value. The incentive coefficient is multiplied by the frequency of use to obtain the weighted frequency of use, which is recorded as the user's continuous usage preference.

[0036] Specifically, if 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, serving as a longer-term preference analysis, is longer 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, a compensation coefficient S, also known as an incentive coefficient, is calculated. The usage frequency is then determined by calculating the number of consecutive uses of the laundry parameter. The usage frequency P in the second time period is then multiplied by S to obtain its weighted usage frequency PS. This value is then compared with the highest usage frequency in the first time period, and the higher value is determined as the user's continuous usage preference.

[0037] S202: Generate a time-series weighted frequency statistical model based on the continuous usage preference.

[0038] In specific implementation, a time-weighted frequency statistical model is constructed based on weighted usage frequency. The core of this model is to capture users' short-term and long-term usage habits by dynamically adjusting the length of the time window. For example, in the hot summer season, due to the high frequency of users' laundry, the time window length can be shortened to one week; while in the cold winter season, due to the low frequency of users' laundry, 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 laundry frequency. The specific calculation method is: first calculate the average laundry frequency in different seasons, and then determine the increase or decrease ratio of the time window length based on the frequency. In this way, it can flexibly adapt to the seasonal changes in users' laundry habits, thereby improving the accuracy of recommendations.

[0039] S203: Calculate current recommended parameters using a time-series weighted frequency statistical model.

[0040] In specific implementation, when generating the current recommended parameters, the two washing parameters with the highest priority are first determined based on the weighted usage frequency. These two parameters are usually the washing mode and water temperature setting that best suit the user's habits. Figure 4 As shown, the Wi-Fi module obtains external environmental data at the current time, including seasonal information, weather information, and temperature and humidity information. This data is obtained 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 laundry parameters based on the external environmental data. For example, during the rainy season or humid weather conditions, the recommended weight of the drying function is increased; in cold and dry winter conditions, the weights of the washing temperature and spin speed are appropriately increased. These adjusted weights are combined with the weighted frequency of use to calculate the current recommended parameters.

[0041] S204: Based on the external environment data and periodic care needs, the recommendation parameters are adjusted and a recommendation result is generated.

[0042] During specific implementation, in order to further optimize the recommendation logic, periodic care needs are also combined. The time threshold of the periodic care task is 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 washes clothes three times a week, the time interval for drum cleaning can be set to once a month; if the user washes clothes once a week, the time interval can be extended to once every two months. The time threshold for seasonal washing is determined by combining the temperature and humidity information obtained by the Wi-Fi module and the user's laundry history data. When the seasons change and the temperature difference between morning and evening exceeds the set threshold, the seasonal washing task is triggered. These time thresholds can be stored to provide support for subsequent recommendation logic. When the trigger conditions for the periodic care task are met, the relevant parameters are included in the recommendation range, and the final recommendation results are generated.

[0043] In this step, the recommendation results are displayed to the user through a display module located on the washing machine's control panel, which presents the recommendations using an LCD screen. The user can confirm or adjust the recommendation results through the touch screen. The user's adjustment operations are recorded in the laundry history data and used to update the time-series weighted frequency statistical model. This process forms a closed-loop feedback mechanism, ensuring that the recommendation system can continuously learn and optimize. For example, if the user manually adjusts the recommended washing mode multiple times, the system will recalculate the weighted usage frequency of the mode and increase its priority the next time it recommends. This dynamic adjustment mechanism makes the recommendation results more closely aligned with the user's actual needs.

[0044] In addition, it also includes adaptive adjustments to changes in laundry habits in different seasons. For example, in the hot summer season, users tend to choose the quick wash mode to reduce the risk of bacterial growth caused by the accumulation of clothes; in the cold winter season, users are more concerned about the warmth and comfort of clothes, so they will choose a higher washing temperature and a longer rinsing time. The system analyzes the user's laundry history data to determine the changes in laundry habits in different seasons, and adjusts the time window length of the time series weighted frequency statistics model accordingly. For example, in the hot summer season, the time window length can be shortened to one week; in the cold winter season, the time window length can be extended to two weeks. In this way, the system can better capture the seasonal changes in users' laundry habits, thereby improving the accuracy of recommendations.

[0045] In practice, suppose a user frequently uses the quick and cold wash modes in the summer, but gradually switches to the standard and warm wash modes in the fall. Based on the user's laundry history, the system calculates that the weighted usage frequency of the quick and cold wash modes is higher in the summer, while the weighted usage frequency of the standard and warm wash modes gradually increases in the fall. Combined with external environmental data, the system appropriately increases the recommendation weight of the warm wash mode in the fall and decreases the weight of the quick wash mode. Furthermore, if the system detects that the temperature difference between morning and evening exceeds a set threshold, it triggers a seasonal wash task and adds the relevant parameters to the recommended range. After receiving the recommendation, the user can confirm or adjust it. For example, if the user decides that the recommended warm wash mode does not meet their needs, they can manually switch to the cold wash mode. This adjustment is recorded in the laundry history and reflected in the next recommendation.

[0046] Through the above-described implementation, the present invention implements a method for recommending washing parameters for intelligent washing machines. This method extracts users' consistent usage preferences through in-depth analysis of their laundry history data, and then generates final recommendations based on external environmental data and periodic care needs. The entire process encompasses multiple steps, including data collection, analytical modeling, parameter adjustment, and user feedback, ensuring that recommendations meet users' actual needs while adapting to changes in the external environment. The above is an example of an embodiment of the method according to the present application. The embodiment of the present invention also provides a method and device for recommending cleaning parameters. Figure 5 Schematic diagram of a cleaning parameter recommendation method according to an embodiment of the present invention. Figure 5 , the cleaning parameter recommendation device 700 includes the following modules.

[0047] An acquisition unit 701 is configured to acquire a user's laundry history data and analyze the laundry history data to extract the user's continuous usage preference; A determining unit 702 is configured to generate a time-series weighted frequency statistical model according to the continuous usage preference; The determining unit 702 is further configured to calculate the current recommended parameters using the time-series weighted frequency statistical model; The processing unit 703 is configured to adjust the recommendation parameters and generate a recommendation result based on the external environment data and the periodic nursing needs.

[0048] 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.

[0049] An embodiment of the present application also provides a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the processor executes the steps of the method for recommending cleaning parameters according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0050] The computer program product can be written in any combination of one or more programming languages to write program codes for performing the operations of the embodiments of this specification, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.

[0051] 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 method for recommending cleaning parameters according to various embodiments of the present specification as described in the above “Exemplary Method” section of the present specification.

[0052] An embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a recommended method for cleaning parameters, and the processor is configured to adopt the above-mentioned recommended method for cleaning parameters when executing the recommended method for cleaning parameters.

[0053] Specifically, such as Figure 6 As 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 recommended method for cleaning parameters. The processor 100 is configured to employ the aforementioned method when executing the recommended method for cleaning parameters stored in the memory 500.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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 method for recommending cleaning parameters, characterized in that: The method comprises: Acquiring user's laundry history data, and analyzing the laundry history data to extract the user's continuous usage preference; generating a time-series weighted frequency statistical model according to the continuous usage preference; Calculating current recommended parameters through the time-series weighted frequency statistical model; The recommendation parameters are adjusted and a recommendation result is generated based on the external environmental data and periodic care needs.

2. The method according to claim 1, characterized in that The analyzing the laundry history data to extract the user's continuous usage preference includes: Extracting laundry parameter records within a set time period from the laundry history data, wherein the set time period includes a first time period and a second time period, and the second time period is shorter than the first time period; calculating the frequency of use of each laundry parameter during the time period; Determine a first laundry parameter that is used most frequently during the first time period and a second laundry parameter that is used most frequently during the second time period; When the first laundry parameter is different from the second laundry parameter, determining an excitation coefficient for the second laundry parameter, and using a product of the excitation coefficient and a usage frequency of the second laundry parameter as a weighted usage frequency of the second laundry parameter; The highest frequency between the weighted usage frequency of the second laundry parameter and the usage frequency of the first laundry parameter is used as the weighted usage frequency corresponding to the continuous usage preference of the user.

3. The method according to claim 2, characterized in that The calculating of the current recommended parameters by the time-series weighted frequency statistical model includes: determining two laundry parameters with the highest priority according to the weighted usage frequencies; Obtain external environmental data at the current time, including season information, weather information, and temperature and humidity information; adjusting the weights of the two laundry parameters with the highest priorities according to the external environment data; The adjusted weight is combined with the weighted usage frequency, and the current recommendation parameter is calculated through the time-series weighted frequency statistical model.

4. The method according to claim 1, wherein The step of combining the external environmental data and the periodic care needs, adjusting the recommendation parameters, and generating the recommendation results includes: determining a time threshold for a periodic care task based on the laundry history data; Get the current season information and temperature and humidity information; Determine whether the triggering conditions of the periodic nursing task are currently met; If satisfied, the parameters of the periodic nursing tasks are included in the recommended range; Generate a recommendation result based on the parameters and the current recommendation parameters.

5. The method according to claim 4, characterized in that The method further comprises: The recommendation results are presented to the user, and the laundry history data is updated based on user feedback.

6. The method according to claim 5, characterized in that After presenting the recommendation results to the user, the method further includes: Displaying the recommendation results to the user via a display screen; Recording the user's adjustment operation on the recommendation result into the laundry history data, wherein the adjustment operation record is used to record the user's confirmation or adjustment operation on the recommendation result; The time-series weighted frequency statistical model is recalculated according to the updated laundry history data.

7. The method according to claim 4, characterized in that Determining the time threshold of the periodic care task based on the laundry history data includes: Calculate the time threshold for drum cleaning based on the user's laundry 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 according to the drum cleaning time threshold and the seasonal washing time threshold.

8. The method according to claim 3, characterized in that The adjusting the weights of the two laundry parameters with the highest priorities according to the external environment data includes: Adjusting the weights of washing temperature and washing time according to the seasonal information; adjusting the weight of whether to enable the drying function according to the weather information; The weights of the number of rinses and the spin speed are adjusted according to the temperature and humidity information.

9. The method according to claim 1, characterized in that After calculating the current recommended parameters using the time-series weighted frequency statistical model, the method further includes: determining changes in laundry habits in different seasons based on the laundry history data; Adjusting the time window length of the time-series weighted frequency statistical model according to the change in laundry habits; Recalculate the current recommended parameters based on the adjusted time window length.

10. The method according to claim 9, characterized in that The adjusting the time window length of the time series weighted frequency statistical model according to the change in the laundry habit includes: Calculating the average frequency of laundry in different seasons based on the laundry history data; determining an adjustment range of the time window length based on the average laundry frequency; The adjusted time window length is applied to the time series weighted frequency statistical model.

11. 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 method for recommending cleaning parameters according to any one of claims 1 to 10 by executing the computer instructions.

12. A fabric cleaning device, characterized in that: The invention adopts the method for recommending cleaning parameters according to any one of claims 1 to 10, or has the electronic device according to claim 11.

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