Program recommendation method, electronic equipment, clothes treatment equipment and storage medium

Abstract

The invention relates to the technical field of smart home, particularly provides a program recommendation method, electronic equipment, clothes processing equipment and a storage medium, and aims to solve the technical problem that an existing program recommendation mode is relatively single and is difficult to meet user requirements. In order to achieve the purpose, the program recommendation method comprises the steps that use evaluation of a user is obtained; obtaining at least one keyword based on each use evaluation; analyzing the plurality of use evaluations to obtain an optimal program corresponding to each keyword; and recommending the best program based on the keywords selected by the user. Through the implementation mode, the use evaluation of the user can be collected for big data analysis, the optimal program corresponding to each keyword is obtained and recommended to the user, the problem that the program recommendation mode is single is solved, the recommendation result is more accurate, different requirements of the user can be met, and the use experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a program recommendation method, electronic equipment, clothing processing equipment and storage medium. Background Art

[0002] At present, there are various programs for clothes processing equipment. When using, the more accurate the program selected by the user is, the better the washing and care effect will be, but it is often difficult for the user to select the most accurate program.

[0003] Most of the existing program recommendation methods make recommendations based on the material, type, degree of dirtiness, etc. of the clothes. The recommendation methods are relatively simple and difficult to meet the needs of users.

[0004] Accordingly, the art needs a new program recommendation solution to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present invention is proposed to provide a program recommendation method, electronic device, clothing processing device and storage medium to solve or at least partially solve the technical problem that the existing program recommendation method is relatively simple and difficult to meet user needs.

[0006] In a first aspect, a program recommendation method is provided, the method comprising:

[0007] Get user reviews;

[0008] acquiring at least one keyword based on each of the usage evaluations;

[0009] Analyze the plurality of usage evaluations to obtain the best program corresponding to each keyword;

[0010] The optimal program is recommended based on the keyword selected by the user.

[0011] In a technical solution of the above program recommendation method, obtaining the user's usage evaluation includes:

[0012] Get at least one tag selected by the user;

[0013] and / or,

[0014] Get the text content entered by the user.

[0015] In a technical solution of the above program recommendation method, obtaining at least one keyword based on each of the usage evaluations includes:

[0016] If the obtained usage evaluation is at least one tag selected by the user, using the tag as the keyword;

[0017] and / or,

[0018] If the obtained usage evaluation is text content input by the user, the text content is analyzed to obtain at least one of the keywords.

[0019] In a technical solution of the above program recommendation method, analyzing the plurality of usage evaluations to obtain the best program corresponding to each keyword includes:

[0020] When the usage evaluations obtained reach a preset number, the programs corresponding to each keyword are sorted according to the number of times they are used;

[0021] The program with the highest number of uses is used as the best program corresponding to the keyword.

[0022] In a technical solution of the above program recommendation method, the recommending the best program based on the keyword selected by the user includes:

[0023] In response to starting the laundry treatment device, acquiring the keyword selected by the user;

[0024] The corresponding optimal program is recommended based on the keyword.

[0025] In a technical solution of the above program recommendation method, the method further includes:

[0026] After each program is completed, a cleaning report is generated;

[0027] The washing and care report includes at least one of water usage information, electricity usage information, washing and care parameters and washing and care duration.

[0028] In a second aspect, the present invention provides a program recommendation device, the device comprising:

[0029] A first acquisition module, which is configured to acquire a user's usage evaluation;

[0030] A second acquisition module, configured to acquire at least one keyword based on each of the usage evaluations;

[0031] An analysis module, configured to analyze the plurality of usage evaluations to obtain the best program corresponding to each keyword;

[0032] A recommendation module is configured to recommend the optimal program based on the keyword selected by the user.

[0033] In a third aspect, an electronic device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the program recommendation method described in any one of the technical solutions of the above-mentioned program recommendation method.

[0034] In a fourth aspect, a clothes processing device is provided, which includes a clothes processing device body and an electronic device as described in the technical solution of the above electronic device.

[0035] In a fifth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the program recommendation method described in any one of the technical solutions of the above-mentioned program recommendation method.

[0036] The above one or more technical solutions of the present invention have at least one or more of the following Beneficial effects:

[0037] In implementing the technical solution of the present invention, firstly, the user's usage evaluation is obtained, at least one keyword is obtained based on each usage evaluation, and multiple usage evaluations are analyzed to obtain the best program corresponding to each keyword, and the best program is recommended based on the keyword selected by the user. Through the above implementation, the user's usage evaluation can be collected for big data analysis, the best program corresponding to each keyword can be obtained and recommended to the user, which solves the problem of a single program recommendation method, and the recommendation result is more accurate, which can meet the different needs of users and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0039] Figure 1 is a flowchart of main steps of a program recommendation method according to an embodiment of the present invention;

[0040] Figure 2 It is a flowchart diagram of main steps of analyzing multiple usage evaluations to obtain the best program corresponding to each keyword according to an embodiment of the present invention;

[0041] Figure 3 is a flowchart of the main steps of recommending the best program based on the keyword selected by the user according to one embodiment of the present invention;

[0042] Figure 4is a main structural block diagram of a program recommendation device according to an embodiment of the present invention;

[0043] Figure 5 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present invention.

[0044] List of reference numerals:

[0045] 401: first acquisition module; 402: second acquisition module; 403: analysis module; 404: recommendation module; 501: processor; 502: storage device. DETAILED DESCRIPTION

[0046] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0047] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0048] As described in the background art, currently, there are various programs for clothes processing equipment. When using, the more accurate the program selected by the user, the better the washing and care effect, but it is often difficult for the user to select the most accurate program.

[0049] Most of the existing program recommendation methods make recommendations based on the material, type, degree of dirtiness, etc. of the clothes. The recommendation methods are relatively simple and difficult to meet the needs of users.

[0050] Accordingly, a new program recommendation scheme is needed in this field.

[0051] In order to solve the above problems, the present invention provides a program recommendation method, an electronic device, a clothing processing device and a storage medium.

[0052] See attached Figure 1 , Figure 1 is a schematic flow chart of the main steps of a program recommendation method according to an embodiment of the present invention, which is applied to a clothing processing device, such as Figure 1 As shown, the program recommendation method in the embodiment of the present invention mainly includes the following steps S101 to S104.

[0053] Step S101: Obtain user evaluation.

[0054] Step S102: obtaining at least one keyword based on each usage evaluation.

[0055] Step S103: Analyze multiple usage evaluations to obtain the best program corresponding to each keyword.

[0056] Step S104: Recommending the best program based on the keyword selected by the user.

[0057] Based on the method described in steps S101 to S104 above, users' usage evaluations can be collected for big data analysis, and the best program corresponding to each keyword can be obtained and recommended to users, which solves the problem of a single program recommendation method. The recommendation results are more accurate, can meet the different needs of users, and improve the user experience.

[0058] The above steps S101 to S104 are further explained below.

[0059] In some implementations of the above step S101, a washing and care report may be generated after each program is executed.

[0060] The washing and care report includes at least one of water usage information, electricity usage information, washing and care parameters and washing and care duration.

[0061] Specifically, after each execution of the program of the clothing processing device is completed, a washing and care report of this washing and care will be generated. The user can view the water consumption information, electricity consumption information, washing and care parameters and washing and care time of this washing and care in the washing and care report.

[0062] Users can evaluate the washing and care program based on the washing and care report and user experience, such as the noise level, whether the clothes are cleaned cleanly, whether the clothes are dried to an appropriate degree, etc.

[0063] In some embodiments, the user evaluation method may be to select a label. Specifically, a label list may be pre-set to provide some labels for the user to select, such as water saving, power saving, clean washing, quiet, fast, etc. The user may also customize the label list, add, delete or modify the labels in the list, and perform personalized settings. The label selected by the user may be one or more, which is not limited here.

[0064] Furthermore, the user can select a corresponding label as a usage evaluation of the current execution of the program according to the cleaning and care report or his / her own usage experience.

[0065] In some embodiments, the user evaluation may be performed by inputting text content. Specifically, the user may directly input text evaluation in the evaluation box according to the washing and care report or his / her own use experience as the use evaluation of the current execution of the program.

[0066] In some implementations, the user evaluation may be performed by selecting a tag and inputting text content at the same time. Furthermore, the tag selected by the user and the text content input may be used together as the usage evaluation of the current execution of the program.

[0067] After each program is executed, the user's usage evaluation is obtained through various means, and user feedback can be collected in a timely manner.

[0068] The above is the description of step S101 , and the following is a further description of step S102 .

[0069] In some implementations of the above step S102, if the obtained usage evaluation is at least one tag selected by the user, the tag is used as a keyword.

[0070] Taking a washing machine as an example, after a washing program is completed, if the user selects the "water saving" label, then "water saving" will be the keyword for evaluating the execution of this program; if the user selects the "power saving" and "quiet" labels, then "power saving" and "quiet" will be the keywords for evaluating the execution of this program.

[0071] In some implementations of the above step S102, if the obtained usage evaluation is text content input by the user, the text content is analyzed to obtain at least one keyword.

[0072] Taking a washing machine as an example, after a washing program is executed, the user enters the text content "the washing speed is very fast and the washing is very clean" in the evaluation box. Then the text content will be analyzed to obtain content such as "fast speed" and "clean washing" as keywords for the evaluation of this execution of the program.

[0073] The text content may be analyzed by the following method to obtain at least one keyword:

[0074] (1) Method based on word cloud analysis: Word cloud analysis is to generate a text image combination analysis based on the frequency of keywords in a text. The larger the font size in the image, the higher the frequency of the corresponding vocabulary. The text with a higher frequency is the keyword.

[0075] (2) Word frequency-based method: Count the frequency of each word in the text, then sort by frequency and select words with higher frequency as keywords.

[0076] (3) TF-IDF-based method: TF-IDF (Term Frequency-Inverse Document Frequency) is a commonly used keyword extraction method. TF-IDF evaluates the importance of a word in a text by calculating the frequency of occurrence of a word in a text (TF) and the inverse document frequency (IDF) of the word in the entire corpus to obtain keywords.

[0077] (4) Methods based on the TextRank algorithm: TextRank is a graph-based sorting algorithm that represents text as a graph, where words are nodes and the relationships between words are edges. TextRank calculates the weight of nodes based on the connection relationship between nodes and the importance of the nodes themselves, thereby finding keywords.

[0078] (5) Topic model-based method: Topic models can identify key topics in texts and extract keywords related to these topics by modeling the potential topics of texts.

[0079] (6) Machine learning-based methods: Machine learning algorithms, such as support vector machines (SVM) or decision trees, can be used to train a model that can automatically extract keywords from text.

[0080] It should be pointed out that the above method for analyzing text content and obtaining keywords is only a schematic illustration. In actual applications, technicians in this field can choose the above method or other methods to extract keywords from text content according to specific tasks and needs, and there is no limitation here.

[0081] In some implementations, if the obtained usage evaluation includes both the tag selected by the user and the text content input, both the tag and the text extraction content are used as keywords for the usage evaluation of the current execution of the program.

[0082] Furthermore, after obtaining the user's usage evaluation and keywords for this execution program, the specific parameters and keywords of this execution program can be uploaded to the cloud platform. The cloud platform will store the usage evaluation data uploaded by each user each time to perform subsequent operations.

[0083] The above is the description of step S102 , and the following is a further description of step S103 .

[0084] In some implementations of the above step S103, see the attached Figure 2 , Figure 2 1 is a flow chart of the main steps of analyzing multiple usage evaluations to obtain the best program corresponding to each keyword according to an embodiment of the present invention. Figure 2 As shown, step S103 mainly includes the following steps S1031 to S1032.

[0085] Step S1031: When the acquired usage evaluations reach a preset number, the programs corresponding to each keyword are sorted according to the number of times they are used.

[0086] Collecting user evaluations is an ongoing process. When the number of evaluations obtained reaches a preset number, such as 2,000, the evaluations can be analyzed.

[0087] In some implementations, when the laundry processing device is a washing machine, the obtained usage evaluation may be as shown in Table 1 below.

[0088] Table 1 Keywords program Use times Save water Procedure 1 900 times Power saving Procedure 2 500 times Save water Procedure 3 1000 times Wash clean Procedure 4 800 times Power saving Procedure 5 600 times Wash clean Procedure 6 550 times Save water Procedure 7 700 times ... ... ...

[0089] Among them, the keywords used for evaluation mainly include "save water", "save electricity", "wash cleanly", etc.

[0090] Furthermore, the usage evaluation may be analyzed. Specifically, the programs corresponding to each keyword may be sorted according to the number of times they are used.

[0091] The obtained usage evaluation can be shown in Table 2 below.

[0092] Table 2

[0093] As shown in Table 2, the programs corresponding to the keyword "water saving" are sorted by the number of times they are used: program 3, program 1, program 7; the programs corresponding to the keyword "power saving" are sorted by the number of times they are used: program 5, program 2; the programs corresponding to the keyword "wash cleanly" are sorted by the number of times they are used: program 4, program 6.

[0094] In some embodiments, when the clothing processing device is a dryer, if 500 usage evaluations from different users are obtained, where the programs corresponding to the keyword "quiet" include program 1, program 2, and program 4, program 1 is used 100 times, program 2 is used 200 times, and program 4 is used 90 times, then the programs corresponding to the keyword "quiet" are sorted according to the number of times they are used: program 2, program 4, program 1.

[0095] Step S1032: The program that is used most times is used as the best program corresponding to the keyword.

[0096] Specifically, taking the content described in step S1031 as an example, when the washing machine executes the program, the best program corresponding to the keyword "water saving" is program 3, the best program corresponding to "power saving" is program 5, and the best program corresponding to "clean washing" is program 4; when the dryer executes the program, the best program corresponding to the keyword "quiet" is program 2.

[0097] Through the cloud platform, each user's usage evaluation after running each program is collected. When the usage evaluation reaches a preset number, statistical analysis is performed, and the programs corresponding to each keyword are sorted. In this way, the recognized best program corresponding to each keyword can be obtained.

[0098] The above is the description of step S103 , and the following is a further description of step S104 .

[0099] In some implementations of the above step S104, see the attached Figure 3 , Figure 3 FIG. 1 is a flow chart of the main steps of recommending the best program based on the keywords selected by the user according to an embodiment of the present invention. Figure 3 As shown, step S104 mainly includes the following steps S1041 to S1042.

[0100] Step S1041: In response to starting the laundry treatment device, obtaining a keyword selected by the user.

[0101] Step S1042: Recommend the corresponding optimal program based on the keyword.

[0102] In some embodiments, when the clothing processing device is a washing machine, if the keyword selected by the user is "water saving" and the best program corresponding to the keyword "water saving" is program 3, program 3 is recommended to the user; if the keyword selected by the user is "wash clean" and the best program corresponding to the keyword "wash clean" is program 4, program 4 is recommended to the user.

[0103] When the clothes processing device is a dryer, if the keyword selected by the user is "quiet", and the best program corresponding to the keyword "quiet" is program 2, program 2 is recommended to the user.

[0104] In some embodiments, if the clothing processing device is a multifunctional device, such as a device including washing, speed washing, single dehydration, drying, care and other functions, when the clothing processing device is started, the user can first select the function to be executed, and then select a keyword to obtain the best program.

[0105] For example, if the user selects the quick wash function of the clothes processing device and selects the keyword "power saving", the best program corresponding to the keyword "power saving" in the quick wash function is recommended to the user.

[0106] Based on the program recommendation method provided by the present invention, users' usage evaluations can be collected for big data analysis, and the best program corresponding to each keyword can be obtained and recommended to users, which solves the problem of a single program recommendation method. In addition, the recommendation results are more accurate, can meet the different needs of users, and improve the user experience.

[0107] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0108] Furthermore, the present invention also provides a device.

[0109] See attached Figure 4 , Figure 4 FIG. 1 is a main structural block diagram of a program recommendation device according to an embodiment of the present invention. Figure 4 As shown, the program recommendation device in the embodiment of the present invention mainly includes a first acquisition module 401 , a second acquisition module 402 , an analysis module 403 and a recommendation module 404 .

[0110] In some embodiments, the first acquisition module 401 may be configured to acquire a user's usage evaluation. The second acquisition module 402 may be configured to acquire at least one keyword based on the usage evaluation. The analysis module 403 may be configured to analyze the usage evaluation and acquire the best program corresponding to each keyword. The recommendation module 404 may be configured to recommend the best program based on the keyword selected by the user. In one implementation, the description of the specific implementation function can be found in steps S101 to S104.

[0111] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0112] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0113] The above program recommends devices for execution Figure 1 The program recommendation method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related instructions of the program recommendation device can refer to the contents described in the embodiment of the program recommendation method, which will not be repeated here.

[0114] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0115] Furthermore, the present invention also provides an electronic device. Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the main structure of an electronic device according to an embodiment of the present invention. Figure 5 As shown, the electronic device in the embodiment of the present invention mainly includes a processor 501 and a storage device 502. The storage device 502 can be configured to store a program for executing the program recommendation method of the above method embodiment, and the processor 501 can be configured to execute the program in the storage device 502, which includes but is not limited to the program for executing the program recommendation method of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present invention is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

[0116] In some possible implementations of the present invention, the electronic device may include multiple processors 501 and multiple storage devices 502. The program for executing the program recommendation method of the above method embodiment may be divided into multiple subprograms, and each subprogram may be loaded and run by the processor 501 to execute different steps of the program recommendation method of the above method embodiment. Specifically, each subprogram may be stored in different storage devices 502, and each processor 501 may be configured to execute the program in one or more storage devices 502 to jointly implement the program recommendation method of the above method embodiment, that is, each processor 501 executes different steps of the program recommendation method of the above method embodiment to jointly implement the program recommendation method of the above method embodiment.

[0117] The above-mentioned multiple processors 501 may be processors deployed on the same device. For example, the above-mentioned electronic device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors 501 may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors 501 may also be processors deployed on different devices. For example, the above-mentioned electronic device may be a server cluster, and the above-mentioned multiple processors 501 may be processors on different servers in the server cluster.

[0118] Furthermore, the present invention also provides a clothes processing device. In an embodiment of a clothes processing device according to the present invention, the clothes processing device may include a clothes processing device body and the electronic device described in the above electronic device embodiment.

[0119] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the program recommendation method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned program recommendation method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0120] It should be noted that the user information (including but not limited to user device information, user personal information, object information corresponding to clothing processing equipment usage data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, clothing processing equipment usage data, etc.) involved in the embodiments of the present disclosure are all information and data authorized by the user or fully authorized by all parties.

[0121] The data acquisition, collection and other actions involved in the embodiments of the present disclosure are all performed after authorization by the user or the object or after full authorization by all parties.

[0122] The relevant user personal information that may be involved in the embodiments of the present disclosure is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, and based on reasonable purposes of business scenarios, to process personal information that users actively provide during the use of products / services or that is generated due to the use of products / services, as well as personal information obtained with the user's authorization.

[0123] So far, the technical solution of the present invention has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A program recommendation method, characterized in that: The method comprises: Get user reviews; acquiring at least one keyword based on each of the usage evaluations; Analyze the plurality of usage evaluations to obtain the best program corresponding to each keyword; The optimal program is recommended based on the keyword selected by the user.

2. The program recommendation method according to claim 1, characterized in that: The obtaining of the user's usage evaluation comprises: Get at least one tag selected by the user; and / or, Get the text content entered by the user.

3. The program recommendation method according to claim 2, characterized in that: The acquiring at least one keyword based on each of the usage evaluations comprises: If the obtained usage evaluation is at least one tag selected by the user, using the tag as the keyword; and / or, If the obtained usage evaluation is text content input by the user, the text content is analyzed to obtain at least one of the keywords.

4. The program recommendation method according to claim 1, characterized in that: The analyzing the plurality of usage evaluations to obtain the best program corresponding to each keyword comprises: When the usage evaluations obtained reach a preset number, the programs corresponding to each keyword are sorted according to the number of times they are used; The program with the highest number of uses is used as the best program corresponding to the keyword.

5. The program recommendation method according to claim 4, characterized in that: The recommending the best program based on the keyword selected by the user comprises: In response to starting the laundry treatment device, acquiring the keyword selected by the user; The corresponding optimal program is recommended based on the keyword.

6. The program recommendation method according to claim 1, characterized in that: The method further comprises: After each program is completed, a cleaning report is generated; The washing and care report includes at least one of water usage information, electricity usage information, washing and care parameters and washing and care duration.

7. A program recommendation device, characterized in that: The device comprises: A first acquisition module, which is configured to acquire a user's usage evaluation; A second acquisition module, configured to acquire at least one keyword based on each of the usage evaluations; An analysis module, configured to analyze the plurality of usage evaluations to obtain the best program corresponding to each keyword; A recommendation module is configured to recommend the optimal program based on the keyword selected by the user.

8. An electronic device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the program recommendation method according to any one of claims 1 to 6.

9. A clothes processing device, characterized in that: The laundry processing device comprises a laundry processing device body and the electronic device according to claim 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the program recommendation method according to any one of claims 1 to 6.

Images