Parameter recommendation method for clothing processing equipment, electronic equipment and clothing processing equipment

Abstract

The present application relates to the field of clothing processing, and more specifically, to a parameter recommendation method for clothing processing equipment, an electronic device, and a clothing processing equipment. The method includes, in response to a setting operation for a parameter value of the clothing processing equipment, obtaining the set parameters and obtaining a target setting sequence based on the setting sequence of the set parameters, filtering a recommended parameter group from historical usage data of the clothing processing equipment based on the target setting sequence, and recommending parameters in the recommended parameter group that are different from the set parameters as recommended parameters and values ​​corresponding to the recommended parameters as recommended values. This method achieves the purpose of identifying user identities using the target setting sequence and improves the accuracy of parameter recommendations.

Description

Technical Field

[0001] The present application relates to the field of clothing processing, and in particular to a parameter recommendation method for clothing processing equipment, an electronic device, and a clothing processing equipment. Background Art

[0002] When using a clothes treatment device, users need to select operating parameters according to their needs, such as operating mode, operating time, operating speed, etc. When there are too many operating parameters to select, it undoubtedly increases the user's burden and easily reduces the user experience of the clothes treatment device.

[0003] In order to solve the problem of heavy burden on users when selecting operating parameters, current clothing processing equipment will automatically recommend some operating parameters to users to reduce the user's selection work and thus reduce the user's burden.

[0004] However, current operating parameter recommendation methods recommend parameters based on their frequency of use, recommending the most frequently used parameters to users. However, different users often choose different operating parameters, resulting in low accuracy in current operating parameter recommendation methods. Summary of the Invention

[0005] Embodiments of the present application provide a parameter recommendation method for a clothing processing device, an electronic device, and a clothing processing device, so as to at least solve the technical problem of low accuracy in parameter recommendation.

[0006] According to a first aspect of an embodiment of the present application, a parameter recommendation method for a laundry processing device is provided, the method comprising:

[0007] In response to a setting operation for a value of a parameter of a laundry treatment device, obtaining the set parameters and obtaining a target setting order according to a setting order of the set parameters, wherein the set parameters include the parameters having values ​​set based on the setting operation;

[0008] filtering a recommended parameter group from historical usage data of the laundry treatment device according to the target setting sequence;

[0009] Parameters in the recommended parameter group that are different from the set parameters are recommended as recommended parameters, and values ​​corresponding to the recommended parameters are recommended as recommended values.

[0010] With this embodiment, the target setting order can reflect the user's parameter setting habits, that is, which parameter to set first and which parameter to set next, so that the user's identity can be identified according to the target setting order, and the recommended parameter group screened according to the target setting order is more likely to meet the user's parameter setting habits, so that the recommended parameters recommended to the user are more likely to be the parameters used by the user before, achieving the purpose of identifying the user's identity by using the target setting order and improving the accuracy of parameter recommendation.

[0011] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the historical usage data includes multiple parameter groups, each of which includes historical setting parameters having a historical setting order and historical values ​​corresponding to each of the historical setting parameters;

[0012] The step of screening out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence includes:

[0013] determining whether the historical setting sequence of each parameter group includes the target setting sequence;

[0014] If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

[0015] With this implementation, each parameter group includes a historical setting sequence, and the target setting sequence is searched in the historical setting sequence to obtain a recommended parameter group, which is convenient and fast and helps to reduce the amount of calculation.

[0016] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the set parameter, the value corresponding to the set parameter, the historical setting parameter, and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameters to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical setting parameters to form a second character string;

[0017] The determining whether the historical setting sequence of each parameter group includes the target setting sequence includes:

[0018] Determine whether the second character string contains the first character string.

[0019] Using this implementation method, the set parameters, values, historical setting parameters and historical values ​​are all represented by characters. On the one hand, the characters take up less space, which helps to reduce memory usage. On the other hand, the process of determining whether the target device set sequence is included in the historical setting sequence is changed to determining whether the second string contains the first string. Because the comparison between strings is more convenient, it helps to improve efficiency.

[0020] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining whether the second character string contains the first character string includes:

[0021] Searching for the first character of the first string in the second string, and if the first character of the first string exists in the second string, numbering the first characters in the order in which they appear in the second string to obtain n positions of the first characters, where n is a positive integer;

[0022] Comparing the characters after the first character in the second character string one by one to see if they are identical to the corresponding characters in the first character string;

[0023] If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string;

[0024] If any character after the first first character in the second character string is different from the corresponding character in the first character string, then compare the characters after the second first character position one by one to see if they are the same as the corresponding characters in the first character string, until the character after the last first character position is compared with the corresponding characters in the first character string;

[0025] If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

[0026] By adopting this implementation method, the position of the first character in the second character string is first determined, and then the search is performed according to the position, which is conducive to reducing the number of searches and improving the search efficiency, thereby improving the parameter recommendation efficiency.

[0027] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, determining whether the second character string contains the first character string includes:

[0028] The first character string and the second character string are compared using a preset character string matching algorithm to determine whether the second character string contains the first character string.

[0029] By adopting this implementation method, the string matching algorithm can reduce the number of searches, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0030] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes:

[0031] Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string;

[0032] The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

[0033] With this implementation, the fewer the number of changes, the higher the similarity of the corresponding recommended parameter groups, which is conducive to arranging the recommended parameters and recommended values ​​of the recommended parameter group with the highest similarity in the front, thereby improving the user experience.

[0034] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the recommending the parameter in the recommended parameter group that is different from the set parameter as the recommended parameter and the value corresponding to the recommended parameter as the recommended value includes:

[0035] The recommendation is made after converting the recommendation parameter from the corresponding character to the corresponding text and converting the recommendation value from the corresponding character to the corresponding number according to a preset character replacement table.

[0036] By adopting this implementation method, characters are replaced with words and numbers, which are easier for users to understand and help improve the user experience.

[0037] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, before filtering out the recommended parameter group from the historical usage data of the laundry processing device according to the target setting order, the method further includes:

[0038] determining a standby operation mode of the laundry processing device according to the set parameters;

[0039] Determining parameters to be set according to the waiting operation mode;

[0040] Comparing the parameters to be set with the set parameters to obtain the parameters to be recommended;

[0041] If the parameters to be recommended include the parameters to be recommended that are multi-character, performing the step of filtering out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence;

[0042] If the parameters to be recommended are all single-character parameters to be recommended, the parameter group including the to-be-run mode is determined from the historical usage data, and the parameter group with the most usage times is recommended.

[0043] With this implementation, for a single character, recommendations are made directly based on the number of times it is used, which helps improve recommendation efficiency.

[0044] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, before responding to the setting operation of the parameter value of the laundry processing device, the method further includes:

[0045] If the laundry processing device is in a parameter setting state, determining whether the setting operation is performed;

[0046] If the setting operation has not been performed, the parameter group that has been used the most times in the historical usage data is recommended.

[0047] With this implementation, when no setting operation is performed, the parameter group that is used most frequently in the historical usage data is directly recommended to the user. For users who always use the same set of parameters, this is beneficial to improving the accuracy and efficiency of parameter recommendations.

[0048] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, before filtering out the recommended parameter group from the historical usage data of the laundry processing device according to the target setting order, the method further includes:

[0049] Obtaining at least one identification parameter corresponding to the set parameter, including a key pressing area, a key pressing intensity, a time interval between two adjacent keys being triggered, and a total setting time of the set parameter;

[0050] determining a user identity based on the identification parameters and the target setting sequence;

[0051] Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothes processing device, the current usage of the clothes processing device, and the user identity.

[0052] This implementation utilizes identification parameters and target setting order to determine user identity, helping to improve the accuracy of user identification. After determining the user's identity, different parameter recommendation strategies are used based on historical usage, current usage, and user identity. This allows for more comprehensive considerations during the parameter recommendation process, thereby improving the accuracy of parameter recommendations.

[0053] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the parameter recommendation is performed using different parameter recommendation strategies based on the historical usage of the clothing processing device, the current day usage of the clothing processing device, and the user identity, including:

[0054] If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: performing the step of filtering a recommended parameter group from the historical usage data of the laundry treatment device according to the target setting sequence;

[0055] And / or, if the historical usage indicates that the clothes processing device has been used multiple times within a preset time period, and the usage on the current day and the user identity indicate that the same user has used the clothes processing device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time as a recommended parameter group from the historical usage data of the clothes processing device;

[0056] And / or, if the historical usage indicates that the clothing processing device has been used multiple times within a preset time period, and the usage on that day and the user identity indicate that multiple users have used the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

[0057] Using this implementation method, three parameter recommendation strategies can be set, so that during the parameter recommendation process, three different situations can be distinguished based on historical usage, number of uses on the day and user identity, so that corresponding parameter recommendation strategies can be adopted for each situation, thereby improving the accuracy and flexibility of parameter recommendation.

[0058] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes:

[0059] Determining whether the percentage of single usage times of the laundry processing device within all the time periods in the historical usage exceeds a preset percentage threshold;

[0060] If yes, determining that the historical usage indicates that the laundry treatment device is used only once within a preset time period;

[0061] Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

[0062] With this implementation, the percentage calculation method is used to determine whether the number of times the clothing processing device is used as represented by the historical usage is single or multiple times. The calculation is simple and helps to reduce the occupation of computing resources.

[0063] According to a second aspect of an embodiment of the present application, there is provided an electronic device, the electronic device including a memory and a processor;

[0064] The memory is used to store computer programs;

[0065] The processor is configured to execute the computer program to implement the steps of the method described above.

[0066] According to a third aspect of an embodiment of the present application, a clothing processing device is provided, comprising the electronic device described above or using the method described above to perform parameter recommendations.

[0067] In combination with the third aspect, in an optional implementation of the embodiment of the present application, the clothing processing device includes at least one of a washing machine, a dryer, a washer-dryer and a clothing care machine.

[0068] The technical effects obtained in the above-mentioned second to third 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

[0069] Figure 1 This is a flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application;

[0070] Figure 2 is another flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application;

[0071] Figure 3 is a character replacement table in a parameter recommendation method for a clothing processing device provided in an embodiment of the present application;

[0072] Figure 4 This is an application flow chart of a parameter recommendation method for a clothing processing device provided in an embodiment of the present application;

[0073] Figure 5 This is a schematic diagram of parameter sorting in a washing machine provided in an embodiment of the present application;

[0074] Figure 6 This is a schematic diagram of the KMP algorithm matching process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0075] 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.

[0076] It should be understood that the “plurality” mentioned herein refers to two or more than two. 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 can 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 limit certain different

[0077] 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.

[0078] First, the terms involved in the embodiments of the present application are introduced.

[0079] Parameters: These are features that require a value to be set before or during operation of the laundry treatment device. For example, the operating mode, operating time, temperature, number of times, scheduled time, and speed can all be considered parameters. After certain parameters have corresponding values ​​set, derived parameters may appear. For example, when the operating mode is set to wash mode, parameters such as wash time and wash temperature may also appear. All of these features can be considered parameters. In other words, there are multiple parameters in this embodiment, and any item in the laundry treatment device that can be set to a corresponding value can be considered a parameter.

[0080] Value: Corresponding to the parameter, different parameters have different values. For example, for the operating mode parameter, its value is usually expressed in Chinese characters, such as washing mode, dehydration mode, drying mode, etc. For the operating time parameter, its value is usually expressed in time, such as 20min, 50min, etc. For the temperature parameter, its value is usually expressed in numbers, such as 40, 50, etc., and the default unit is degrees Celsius. In other words, when the clothing processing device is running, each function or operating condition is based on the value of the corresponding parameter. When the user sets the clothing processing device, the value of each parameter is entered.

[0081] When using a clothes treatment device, users need to select operating parameters according to their needs, such as operating mode, operating time, operating speed, etc. When there are too many operating parameters to select, it undoubtedly increases the user's burden and easily reduces the user experience of the clothes treatment device.

[0082] In order to solve the problem of heavy burden on users when selecting operating parameters, current clothing processing equipment will automatically recommend some operating parameters to users to reduce the user's selection work and thus reduce the user's burden.

[0083] However, current operating parameter recommendation methods recommend parameters based on their frequency of use, recommending the most frequently used parameters to users. However, different users often choose different operating parameters, resulting in low accuracy in current operating parameter recommendation methods.

[0084] Based on this, the embodiment of the present application provides a parameter recommendation method for a clothes processing device, referring to Figure 1 The flowchart of the parameter recommendation method for a clothes processing device shown in FIG. 1 includes the following processing steps.

[0085] S100 . In response to a setting operation for parameter values ​​of a laundry treatment device, the set parameters are obtained and a target setting sequence is obtained according to a setting sequence of the set parameters.

[0086] The set parameters include the parameters whose values ​​are set based on the setting operation.

[0087] A setting operation refers to an operation related to the setting of parameter values. The setting operation can be triggered by the user, for example, the user sets the value of one or several parameters by pressing a button on the clothing processing device. This setting process is the setting operation. The setting operation can also be triggered by a controller. For example, after the user starts the clothing processing device, sets the value of one or several parameters on the mobile phone, the mobile phone transmits the user-set value to the clothing processing device. When the clothing processing device receives the data transmitted by the mobile phone, it is deemed that a setting operation has occurred.

[0088] Based on the setting operation, it is possible to determine which parameters have been set, and thus the parameters with set values ​​are regarded as set parameters. Correspondingly, parameters without set values ​​can be understood as unset parameters. It should be noted that different operating modes of the clothing processing device require different parameters to be set, and the number of parameters that need to be set is also different. If the set parameters include the operating mode parameter, it is possible to determine which parameters are still unset based on the value corresponding to the operating mode, and thus obtain the unset parameters. In other embodiments, all parameters of the clothing processing device except the set parameters can also be determined as unset parameters.

[0089] Regarding the target setting order, it should be noted that when users set up the laundry treatment device, they don't directly set parameters, but rather set values. However, each set value corresponds to a parameter. Therefore, by monitoring the setting order of the values, the setting order of the set parameters can be obtained, and thus the target setting order can be obtained. For ease of understanding, the target setting order is as follows: operation mode, number of times, temperature. In other words, the user first sets the value of the operation mode, and then sets the number of times and temperature values ​​in sequence.

[0090] S102: Filtering a recommended parameter group from historical usage data of the laundry processing device according to the target setting sequence.

[0091] It should be noted that historical usage data refers to data related to the previous operation of the clothing treatment device. The historical usage data includes setting data of parameters and their values. Therefore, the target setting sequence can be used to find the setting data containing the target setting sequence in the historical usage data, thereby obtaining a recommended parameter set.

[0092] In other words, each time the laundry treatment device is operated, it records the parameters set by the user, the corresponding values ​​for each parameter, and the order in which the parameters were set. This data is stored as parameter groups and becomes historical usage data. Once a setting operation is completed and a target setting order is determined, the device can search the historical usage data for related or similar parameter groups based on the target setting order and use these as recommended parameter groups.

[0093] S104: Recommend parameters in the recommended parameter group that are different from the set parameters as recommended parameters and recommend values ​​corresponding to the recommended parameters as recommended values.

[0094] That is, the recommended parameter group includes multiple parameters, each of which has a corresponding value. Parameters that are identical to the set parameters are first filtered out, and then the parameters in the recommended parameter group that differ from the set parameters are obtained. The different parameters are then used as recommended parameters, and the values ​​corresponding to the different parameters are then recommended to the user.

[0095] With this embodiment, the target setting order can reflect the user's parameter setting habits, that is, which parameter to set first and which parameter to set next, so that the user's identity can be identified according to the target setting order, and the recommended parameter group screened according to the target setting order is more likely to meet the user's parameter setting habits, so that the recommended parameters recommended to the user are more likely to be the parameters used by the user before, achieving the purpose of identifying the user's identity by using the target setting order and improving the accuracy of parameter recommendation.

[0096] In a possible embodiment of the present application, the historical usage data includes a plurality of parameter groups, each of which includes historical setting parameters having a historical setting sequence and historical values ​​corresponding to each of the historical setting parameters.

[0097] The step of screening out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence includes:

[0098] determining whether the historical setting sequence of each parameter group includes the target setting sequence;

[0099] If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

[0100] It should be noted that if there are a large number of parameters to be set, for example, 10, only 4 of them may be set, leaving 6 unset. Therefore, the target setting sequence only has the setting sequence of 4 parameters, while the historical setting sequence has the setting sequence of 10 parameters. Therefore, the length of the historical setting sequence is longer than the target setting sequence.

[0101] Based on this, when screening recommended parameter groups, it is necessary to determine whether the historical setting sequence contains the target setting sequence, rather than whether it is equivalent to the target setting sequence. It should be noted that inclusion means that there is a sequence in the historical setting sequence that is exactly the same as the target setting sequence and is continuous. For example, if the target setting sequence is 1234, if the historical setting sequence is 15234, it is not considered that the historical setting sequence contains the target setting sequence, because 1234 in the historical setting sequence does not appear continuously.

[0102] With this implementation, each parameter group includes a historical setting sequence, and the target setting sequence is searched in the historical setting sequence to obtain a recommended parameter group, which is convenient and fast and helps to reduce the amount of calculation.

[0103] Optionally, in an implementation of this embodiment, the set parameter, the value corresponding to the set parameter, the historical set parameter, and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameters to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical set parameters to form a second character string;

[0104] The determining whether the historical setting sequence of each parameter group includes the target setting sequence includes:

[0105] Determine whether the second character string contains the first character string.

[0106] The characters may be letters, numbers, symbols, or Chinese characters, and the embodiments do not specifically limit this. It should be noted that the character structures and character types corresponding to different parameters and values ​​should be fixed, so that the characters corresponding to the same parameters are the same, and the characters corresponding to the same values ​​of the same parameters should also be the same. For example, if the character corresponding to the operating mode is 1, then whether in the first character string or the second character string, the 1 that appears represents the operating mode and has a unique meaning.

[0107] It should be noted that for the same parameter, the same value should correspond to the same characters, but in the case of different parameters, the characters used can be repeated. For example, for the operating mode, a represents the mixed mode and b represents the quick wash mode, and the characters for different values ​​cannot be repeated. However, the characters corresponding to the temperature parameter can still use a and b. For example, a represents 30 degrees Celsius and b represents 40 degrees Celsius. Although the characters are the same, because they correspond to different parameters, the meaning of the characters can be distinguished, and there is no restriction on the characters corresponding to the values ​​under different parameters.

[0108] Using this implementation method, the set parameters, values, historical setting parameters and historical values ​​are all represented by characters. On the one hand, the characters take up less space, which helps to reduce memory usage. On the other hand, the process of determining whether the target device set sequence is included in the historical setting sequence is changed to determining whether the second string contains the first string. Because the comparison between strings is more convenient, it helps to improve efficiency.

[0109] Optionally, in an implementation of this embodiment, determining whether the second character string contains the first character string includes:

[0110] Searching for the first character of the first string in the second string, and if the first character of the first string exists in the second string, numbering the first characters in the order in which they appear in the second string to obtain n positions of the first characters, where n is a positive integer;

[0111] Comparing the characters after the first character in the second character string one by one to see if they are identical to the corresponding characters in the first character string;

[0112] If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string;

[0113] If any character after the first first character in the second character string is different from the corresponding character in the first character string, then compare the characters after the second first character position one by one to see if they are the same as the corresponding characters in the first character string, until the character after the last first character position is compared with the corresponding characters in the first character string;

[0114] If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

[0115] For ease of understanding, let's say the first string is 1234, and its first character is 1. Then, search for 1 in the second string and number the 1s in the order they appear, obtaining the positions of multiple 1s. For example, if the second string is 135481058412348513, we can obtain the positions of the four first characters. These four positions are numbered in the order they appear, obtaining positions 1 through 4. Then, we start comparing from the first position. The comparison process is as follows: compare the second character in the first string with the first character after the first position in the second string, the third character in the first string with the second character after the first position in the second string, and so on. If all are the same, the comparison ends and it is determined that the second string contains the first string.

[0116] If there is a single character that is different, the comparison is performed at the second position until the fourth position is completed. For example, if the first character after the first position is 3, which is different from the 2 in the first string, the comparison is performed at the second position. The first character after the second position is 0, which is different from the 2 in the first string, and the comparison is performed at the third position. The first character after the third position is 2, which is the same as the 2 in the first string. The comparison is performed to see if the second character after the third position is 3. If so, the comparison is performed to see if the third character after the third position is 4. If so, the first character after the third position is determined to be 4, and the first character after the third position is determined to be 4.

[0117] By adopting this implementation method, the position of the first character in the second character string is first determined, and then the search is performed according to the position, which is conducive to reducing the number of searches and improving the search efficiency, thereby improving the parameter recommendation efficiency.

[0118] Optionally, in an implementation of this embodiment, determining whether the second character string contains the first character string includes:

[0119] The first character string and the second character string are compared using a preset character string matching algorithm to determine whether the second character string contains the first character string.

[0120] The string matching algorithm includes the KMP (Knuth-Morris-Pratt) algorithm.

[0121] By adopting this implementation method, the string matching algorithm can reduce the number of searches, which is beneficial to reducing the number of searches and improving search efficiency, thereby helping to improve parameter recommendation efficiency.

[0122] Optionally, in an implementation of this embodiment, the method further includes:

[0123] Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string;

[0124] The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

[0125] That is, each second character string corresponds to a number of changes, and the recommended parameter group with the least or most changes is arranged at the top for recommendation.

[0126] With this implementation, the fewer the number of changes, the higher the similarity of the corresponding recommended parameter groups, which is conducive to arranging the recommended parameters and recommended values ​​of the recommended parameter group with the highest similarity in the front, thereby improving the user experience.

[0127] Optionally, in an implementation of this embodiment, the recommending the parameter in the recommended parameter group that is different from the set parameter as the recommended parameter and the value corresponding to the recommended parameter as the recommended value includes:

[0128] The recommendation is made after converting the recommendation parameter from the corresponding character to the corresponding text and converting the recommendation value from the corresponding character to the corresponding number according to a preset character replacement table.

[0129] After the set parameters and corresponding values ​​are determined, they are replaced according to the character replacement table, so that the set parameters, the target setting sequence and the values ​​corresponding to the set parameters are all represented by corresponding characters.

[0130] Before making recommendations, characters are replaced with text, numbers, or symbols using a character replacement table to facilitate user viewing and understanding.

[0131] By adopting this implementation method, characters are replaced with words and numbers, which are easier for users to understand and help improve the user experience.

[0132] Optionally, in an implementation of this embodiment, before filtering out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting order, the method further includes:

[0133] determining a standby operation mode of the laundry processing device according to the set parameters;

[0134] Determining parameters to be set according to the waiting operation mode;

[0135] Comparing the parameters to be set with the set parameters to obtain the parameters to be recommended;

[0136] If the parameters to be recommended include the parameters to be recommended that are multi-character, performing the step of filtering out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence;

[0137] If the parameters to be recommended are all single-character parameters to be recommended, the parameter group including the to-be-run mode is determined from the historical usage data, and the parameter group with the most usage times is recommended.

[0138] In other words, in some application scenarios, the proposed mode is first determined. Because different proposed modes require different parameter settings, once the proposed mode is determined, the parameters to be set can be determined. The parameters to be set are then compared with the parameters to be set. If the parameters to be set are the same as the parameters to be set and the number of parameters is not greater than the number of parameters to be set, no recommendation is made. Otherwise, a recommendation is made.

[0139] When recommending, we first determine whether the parameter to be recommended contains multiple characters. Only if it contains multiple characters, we filter the recommended parameter group according to the target setting order.

[0140] The difference between multi-bit characters and single characters lies in the number of characters. For example, 0000 is a 4-bit character, while 0 is a single character. As for which parameters in the recommended parameters are multi-bit characters, you can check the preset character replacement table.

[0141] With this implementation, for a single character, recommendations are made directly based on the number of times it is used, which helps improve recommendation efficiency.

[0142] Optionally, in an implementation of this embodiment, before responding to the setting operation of the parameter value of the laundry treatment device, the method further includes:

[0143] If the laundry processing device is in a parameter setting state, determining whether the setting operation is performed;

[0144] If the setting operation has not been performed, the parameter group that has been used the most times in the historical usage data is recommended.

[0145] With this implementation, when no setting operation is performed, the parameter group that is used most frequently in the historical usage data is directly recommended to the user. For users who always use the same set of parameters, this is beneficial to improving the accuracy and efficiency of parameter recommendations.

[0146] Optionally, in an implementation of this embodiment, before filtering out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting order, the method further includes:

[0147] At least one identification parameter corresponding to the set parameter is obtained, including a key pressing area, a key pressing strength, a time interval between two adjacent keys being triggered, and a total setting time of the set parameter.

[0148] The laundry processing device includes a button for a user to input parameter values. The button can be a physical mechanical button or a virtual button on a touch screen, which is not specifically limited in this embodiment. When the user enters the value of each parameter through the button, parameters such as the button pressing area, the button pressing force, the time interval between two adjacent button activations, and the total setting time of the set parameter are simultaneously collected to obtain the identification parameter.

[0149] The button pressing area and the button pressing intensity can be collected by setting corresponding sensors. Preferably, the button pressing area is detected by a pressure sensor, and the button pressing intensity is detected by a pressure sensor.

[0150] It should be noted that the key pressing area, key pressing strength, time interval between two adjacent keys being triggered and the total setting time of the set parameters corresponding to different users may be different, so the user identity can be identified using the identification parameters.

[0151] The user identity is determined based on the identification parameters and the target setting sequence.

[0152] After obtaining the identification parameters, the user's identity is determined from two aspects in combination with the target setting sequence, thereby improving the accuracy of the user's identity. Specifically, in one embodiment, the identification parameters and target setting sequence can be continuously collected while the user uses the clothing processing device. After the amount of data reaches a certain level, multiple users can be distinguished, thereby generating corresponding user identities. In another embodiment, the user identity is input by the user or identified by fingerprint recognition, face recognition, or other technologies. After obtaining the user identity, the user's identification parameters and target setting sequence are collected and used as reference data. When it is necessary to identify the user's identity, the collected identification parameters and target setting sequence can be compared with the reference data.

[0153] Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothes processing device, the current usage of the clothes processing device, and the user identity.

[0154] Different clothing processing devices have different historical usage, and their usage on the same day may also be different. Among them, historical usage is used to determine the number of times the clothing processing device is used each day, and the usage on the same day is used to determine the number of times the clothing processing device is used on the same day and the identity of the user at each use. Based on this, a variety of different situations can be divided, for example, divided into three situations. One is that the clothing processing device was only used once a day in the past. This situation proves that all the clothes in the family are washed together; the second is that the clothing processing device was used twice or more a day in the past and the user who used the clothing processing device on the same day was the same person. This situation proves that the person responsible for washing clothes in the family is fixed, but the clothes may be different, such as washing clothes that are not easy to dry in the morning and washing clothes that are easy to dry in the evening; the third is that the clothing processing device was used twice or more a day in the past and the users who used the clothing processing device on the same day were different people. This situation proves that everyone in the family is responsible for washing their own clothes.

[0155] Based on the above classification, different parameter recommendation strategies can be used to recommend parameters, so that the recommended parameters are more in line with the user's actual needs and usage habits.

[0156] This implementation utilizes identification parameters and target setting order to determine user identity, helping to improve the accuracy of user identification. After determining the user's identity, different parameter recommendation strategies are used based on historical usage, current usage, and user identity. This allows for more comprehensive considerations during the parameter recommendation process, thereby improving the accuracy of parameter recommendations.

[0157] Optionally, in an optional implementation of the embodiment of the present application, the parameter recommendation is performed using different parameter recommendation strategies based on the historical usage of the clothing processing device, the current usage of the clothing processing device, and the user identity, including:

[0158] If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: performing the step of filtering a recommended parameter group from the historical usage data of the laundry treatment device according to the target setting sequence;

[0159] And / or, if the historical usage indicates that the clothes processing device has been used multiple times within a preset time period, and the usage on the current day and the user identity indicate that the same user has used the clothes processing device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time as a recommended parameter group from the historical usage data of the clothes processing device;

[0160] And / or, if the historical usage indicates that the clothing processing device has been used multiple times within a preset time period, and the usage on that day and the user identity indicate that multiple users have used the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

[0161] The time period is one day, but may also be other time lengths, which is not specifically limited in this embodiment.

[0162] If the clothing processing device is used only once, the steps in the aforementioned method, namely step S102 and subsequent steps, are executed, and are not described in detail in this embodiment.

[0163] If the clothing processing device is used multiple times and the same user uses the clothing processing device multiple times on the same day, the parameter group corresponding to the user is searched in the historical usage data based on the current moment (that is, the current time). It should be noted that the user's identity can be identified, proving that the user has a certain laundry routine, so the historical usage data also stores data on the user washing clothes multiple times in a day. For ease of understanding, for example, the current moment is 7 o'clock, and the user identity is user A, then the parameter group corresponding to user A is first filtered out from the historical usage data, and then the parameter group used by the user before and after 7 o'clock is filtered out as the recommended parameter group. Among them, the fluctuation range before and after 7 o'clock can be preset, for example, 1 hour before and after, that is, 6-8 o'clock.

[0164] It should also be noted that after obtaining the recommended parameter group, the above step S104 is executed.

[0165] If the laundry treatment device is used multiple times and by different users on the same day, that is, different users have used the laundry treatment device at least once, the laundry type is obtained using the laundry type recognition technology. Then, the parameter group corresponding to the user identity is filtered from the historical usage data. A second screening is performed based on the laundry type in the parameter group to obtain the parameter group that matches the laundry type currently being processed. Finally, a third screening is performed based on the target set order. Preferably, the third screening process is the same as step S102 above and will not be repeated here.

[0166] Using this implementation method, three parameter recommendation strategies can be set, so that during the parameter recommendation process, three different situations can be distinguished based on historical usage, number of uses on the day and user identity, so that corresponding parameter recommendation strategies can be adopted for each situation, thereby improving the accuracy and flexibility of parameter recommendation.

[0167] Optionally, in an optional implementation of the embodiment of the present application, the method further includes:

[0168] Determining whether the percentage of single usage times of the laundry processing device within all the time periods in the historical usage exceeds a preset percentage threshold;

[0169] If yes, determining that the historical usage indicates that the laundry treatment device is used only once within a preset time period;

[0170] Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

[0171] The percentage threshold is set according to actual needs, and may be 100% or 90%, which is not specifically limited in this embodiment.

[0172] With this implementation, the percentage calculation method is used to determine whether the number of times the clothing processing device is used as represented by the historical usage is single or multiple times. The calculation is simple and helps to reduce the occupation of computing resources.

[0173] For ease of understanding, Figure 2As shown, three criteria during user usage are used to identify the user: the area of ​​the button pressed, the pressure applied, the interval between pressing the same button and pressing different buttons, the total time spent entering multiple parameters, and a KMP match for the key press sequence. After identifying the user, the system determines whether the washing machine has been used more than once per day for 90% of the total number of days used, based on the washing machine's historical data. If the washing machine is used only once a day, or if multiple customers wash multiple times, the system recommends appropriate washing parameters and programs based on the user's KMP algorithm. If a single customer selects different washing parameters multiple times per day, then if the number of washes selected during a fixed time period exceeds 10%, parameter recommendations are made based on the parameters selected during that time period. If the same customer consistently selects the same or similar programs, parameter and program recommendations are made based on clothing identification.

[0174] An embodiment of the present application further provides an electronic device, the electronic device comprising a memory and a processor;

[0175] The memory is used to store computer programs;

[0176] The processor is configured to execute the computer program to implement the steps of the method described above.

[0177] According to a third aspect of an embodiment of the present application, a clothing processing device is provided, comprising the electronic device described above or using the method described above to perform parameter recommendations.

[0178] An embodiment of the present application further provides a clothing processing device, comprising the electronic device described above or using the method described above to perform parameter recommendations.

[0179] Optionally, in an implementation of this embodiment, the clothing processing device includes at least one of a washing machine, a drying machine, a washer-dryer and a clothing care machine.

[0180] For ease of understanding, Figure 3-6 As shown, the parameter recommendation method provided in this application can quickly recommend parameter-related solutions to users based on the washing machine usage habits of different users of the same washing machine. The more parameters the user selects, the closer the recommended washing machine-related parameters are to the user's usage habits based on the washing machine's usage record data, thereby improving the user's operating experience of using the washing machine.

[0181] Specifically, the recommendation of different user parameters for a single washing machine is based on the selection and processing of user data differences based on the historical data of the washing machine's usage pattern and frequency accumulation, and complex parameters are matched and recommended based on the KMP algorithm.

[0182] (Note: In computer programming languages, numbers (0-9), letters (a-z / A-Z), and punctuation marks (!@, etc.) all represent specific characters. Furthermore, uppercase and lowercase English letters represent different characters. For example, a and A represent different characters in a computer.)

[0183] like Figure 3 As shown, this application hierarchizes and characterizes the modes and washing parameters of the washing machine to facilitate data processing and parameter recommendations based on user habits. The modes of the washing machine are always set to the first level and are represented by characters such as a~z, A~Z. Each mode is pre-defined to correspond to a specific character. Since the mode is the first level, its letter representation is arranged in the first place. Other parameters are characterized as the second level. However, due to the large amount of data, some parameters may not be fully represented by a single character, so multiple characters are introduced, such as Figure 3 The reservation time, dehydration speed, drying time, etc., or other complex washing machine parameters are characterized using multiple characters such as aaaa / aaab.

[0184] like Figure 4 As shown, after the user turns on the machine, parameter recommendations are made based on whether the user has selected a mode and parameter. (1) If the user has not selected any parameter, the recommendation is made based on the mode with the most usage times recorded for the washing machine and the parameter with the most usage times corresponding to the mode. (2) If the user selects a mode or a parameter, the first-level corresponding characters of the mode are pre-generated, and the second-level parameters are generated according to the order of user selection, and the corresponding parameter character sorting and the corresponding sorting code are generated. If the characters are not selected or partially selected, the recommendation is made based on the number of times the user has used the characters; for example, if the user selects the following in sequence: Figure 3 For example, if the user enters "Super Clean" (m), rinses 5 times (f), temperature 30°C (f), and a reservation time of 130 minutes (0130), but does not select "15" for drying time (KPM algorithm recommended character codes 0015 / 0115 / 0150 / 0155), the parameter character order is 1436, and the alternative recommendation code is 8257. The corresponding order code is "mff0130 + alternative recommended character." If the alternative recommended parameter contains multiple characters, the KMP algorithm uses a multi-character matching recommendation. For example, if the user enters "15" for drying time but does not select a specific parameter, the KMP algorithm recommends the character codes 0015 / 0115 / 0150 / 0155 based on the user's input. The recommendation is then sorted and recommended based on the user's historical usage history. The parameter recommendation process ends when the user selects all parameters and starts the washing machine.

[0185] like Figure 5As shown in the figure, the process of users selecting input parameters is a process of parameter sorting and selection, and also a process of making corresponding recommendations based on the differences in habits of each user. The mode is the first level, and the second-level parameters sort the user parameters in the order in which the users select the parameters. The remaining input and unselected parameters are matched and limited based on the previously selected modes and parameters. After the limitation, the second half of the undetermined parameters are sorted and recommended. For complex parameters represented by multiple characters, they are matched and recommended through the KMP algorithm. Other single parameters are recommended based on the current mode and selected parameters.

[0186] like Figure 6 As shown, in the KMP matching process of multiple character parameters, if the user partially inputs parameters representing characters abcac, character matching recommendations for the corresponding parameters are made based on the representative characters input by the user, and the characters with the highest familiarity are screened out for parameter recommendation. The highest familiarity is measured by the number of comparisons. For example, if the first set of parameters are compared 38 times, the second set of parameters are compared 36 times, and the third set of parameters are compared 45 times, then the second set of parameters has the highest familiarity and is the recommended parameter. In the matching process, the input representative characters are pre-generated into the next array of the KMP algorithm. In the comparison process, character movement matching is performed according to the next array of the KMP algorithm, and the corresponding number of comparisons is counted to obtain the parameter recommendation with the highest familiarity.

[0187] Based on the differences in usage habits of different users of the same washing machine, the most similar usage parameter plans are recommended to different users to improve the user operation experience.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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 parameter recommendation method for a clothes processing device, characterized in that: The method comprises: In response to a setting operation for a value of a parameter of a laundry treatment device, obtaining the set parameters and obtaining a target setting order according to a setting order of the set parameters, wherein the set parameters include the parameters having values ​​set based on the setting operation; filtering a recommended parameter group from historical usage data of the laundry treatment device according to the target setting sequence; recommending a parameter in the recommended parameter group that is different from the set parameter as a recommended parameter and a value corresponding to the recommended parameter as a recommended value; The historical usage data includes a plurality of parameter groups, each of which includes historical setting parameters having a historical setting sequence and historical values ​​corresponding to each of the historical setting parameters; The step of screening out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence includes: determining whether the historical setting sequence of each parameter group includes the target setting sequence; If the historical setting sequence includes the target setting sequence, the corresponding parameter group is used as the recommended parameter group.

2. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: The set parameter, the value corresponding to the set parameter, the historical set parameter, and the historical value are represented by characters, the target setting sequence is formed by arranging the characters corresponding to the set parameters to form a first character string, and the historical setting sequence is formed by arranging the characters corresponding to the historical set parameters to form a second character string; The determining whether the historical setting sequence of each parameter group includes the target setting sequence includes: Determine whether the second character string contains the first character string.

3. The parameter recommendation method for a clothes processing device according to claim 2, characterized in that: The determining whether the second character string contains the first character string includes: Searching for the first character of the first string in the second string, and if the first character of the first string exists in the second string, numbering the first characters in the order in which they appear in the second string to obtain n positions of the first characters, where n is a positive integer; Comparing the characters after the first character in the second character string one by one to see if they are identical to the corresponding characters in the first character string; If the characters after the first character in the second character string are all the same as the corresponding characters in the first character string, it is determined that the second character string contains the first character string; If any character after the first first character in the second character string is different from the corresponding character in the first character string, then compare the characters after the second first character position one by one to see if they are the same as the corresponding characters in the first character string, until the character after the last first character position is compared with the corresponding characters in the first character string; If any character after the last first character in the second character string is different from the corresponding character in the first character string, it is determined that the second character string does not contain the first character string.

4. The parameter recommendation method for a clothes processing device according to claim 2, characterized in that: The determining whether the second character string contains the first character string includes: The first character string and the second character string are compared using a preset character string matching algorithm to determine whether the second character string contains the first character string.

5. The parameter recommendation method for a clothes processing device according to claim 3, characterized in that: The method further comprises: Whenever the position of the nth first character is changed to the position of the next first character, the number of changes is accumulated to obtain the number of changes corresponding to each second character string; The corresponding recommended parameter groups are arranged according to the number of changes, so as to recommend the recommended parameters and the recommended values ​​according to the arrangement order of the recommended parameter groups.

6. The parameter recommendation method for a clothes processing device according to claim 2, characterized in that: The step of taking a parameter in the recommended parameter group that is different from the set parameter as a recommended parameter and recommending a value corresponding to the recommended parameter as a recommended value includes: The recommendation is made after converting the recommendation parameter from the corresponding character to the corresponding text and converting the recommendation value from the corresponding character to the corresponding number according to a preset character replacement table.

7. The parameter recommendation method for a clothes processing device according to claim 2, characterized in that: Before filtering out a recommended parameter group from the historical usage data of the laundry treatment device according to the target setting sequence, the method further includes: determining a standby operation mode of the laundry processing device according to the set parameters; Determining parameters to be set according to the waiting operation mode; Comparing the parameters to be set with the set parameters to obtain the parameters to be recommended; If the parameters to be recommended include the parameters to be recommended that are multi-character, performing the step of filtering out a recommended parameter group from the historical usage data of the laundry processing device according to the target setting sequence; If the parameters to be recommended are all single-character parameters to be recommended, the parameter group including the to-be-run mode is determined from the historical usage data, and the parameter group with the most usage times is recommended.

8. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: Before the step of responding to the setting operation of the parameter value for the laundry treatment device, the method further includes: If the laundry processing device is in a parameter setting state, determining whether the setting operation is performed; If the setting operation has not been performed, the parameter group that has been used the most times in the historical usage data is recommended.

9. The parameter recommendation method for a clothes processing device according to claim 1, characterized in that: Before filtering out a recommended parameter group from the historical usage data of the laundry treatment device according to the target setting sequence, the method further includes: Obtaining at least one identification parameter corresponding to the set parameter, including a key pressing area, a key pressing intensity, a time interval between two adjacent keys being triggered, and a total setting time of the set parameter; determining a user identity based on the identification parameters and the target setting sequence; Different parameter recommendation strategies are used to recommend parameters according to the historical usage of the clothes processing device, the current usage of the clothes processing device, and the user identity.

10. The parameter recommendation method for a clothes processing device according to claim 9, characterized in that: The parameter recommendation is performed using different parameter recommendation strategies according to the historical usage of the laundry processing device, the current usage of the laundry processing device, and the user identity, including: If the historical usage indicates that the laundry treatment device is used once within a preset time period, the parameter recommendation strategy adopted is: performing the step of filtering a recommended parameter group from the historical usage data of the laundry treatment device according to the target setting sequence; And / or, if the historical usage indicates that the clothes processing device has been used multiple times within a preset time period, and the usage on the current day and the user identity indicate that the same user has used the clothes processing device multiple times, the parameter recommendation strategy adopted is: recommending a parameter group corresponding to the user identity and the current time as a recommended parameter group from the historical usage data of the clothes processing device; And / or, if the historical usage indicates that the clothing processing device has been used multiple times within a preset time period, and the usage on that day and the user identity indicate that multiple users have used the clothing processing device multiple times, the parameter recommendation strategy adopted is: based on the target setting order and clothing type, a recommended parameter group is filtered out from the historical usage data of the clothing processing device for recommendation.

11. The parameter recommendation method for a clothes processing device according to claim 10, characterized in that: The method further comprises: Determining whether the percentage of single usage times of the laundry processing device within all the time periods in the historical usage exceeds a preset percentage threshold; If yes, determining that the historical usage indicates that the laundry treatment device is used only once within a preset time period; Otherwise, it is determined that the historical usage indicates that the laundry treatment device has been used multiple times within a preset time period.

12. An electronic device, characterized in that: The electronic device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1 to 11.

13. A clothes processing device, characterized in that: The electronic device according to claim 12 or the method according to any one of claims 1 to 11 is used for parameter recommendation.

14. The clothes treating device according to claim 13, characterized in that: The clothing processing device includes at least one of a washing machine, a drying machine, a washer-dryer and a clothing care machine.

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