Video tutorial configuration method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a video tutorial configuration method and device, electronic equipment and storage medium, relates to the technical field of computers, in particular to the technical field of big data and intelligent recommendation. The specific implementation scheme is: obtaining the use frequency and use duration of each function and each sub-function in the client by the user in the preset period; filtering the to-be-recommended function and the to-be-recommended sub-function based on the use frequency and the use duration of each function and each sub-function; and issuing the video tutorial of the to-be-recommended function and the video tutorial of the to-be-recommended sub-function to the client. In this way, the issued video tutorial can meet the use requirements of each user.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to the field of big data and intelligent recommendation technology. Background Technology

[0002] As applications (APPs) are constantly updated and iterated, their complexity increases and their functions become more numerous.

[0003] The server can send video tutorials for the app to the client so that users can quickly learn how to use the various functions included in the app through the video tutorials. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for configuring video tutorials.

[0005] Firstly, this disclosure provides a method for configuring video tutorials, including:

[0006] Get the number of times and duration of user usage of each function and sub-function in the client within a preset period;

[0007] Based on the number of times and duration of use of each function and each sub-function, filter the functions and sub-functions to be recommended.

[0008] The video tutorials for the feature to be recommended and the video tutorials for the sub-features to be recommended are sent to the client.

[0009] Secondly, this disclosure provides a video tutorial configuration device, including:

[0010] The acquisition module is used to acquire the number of times and duration of user usage of each function and sub-function in the client within a preset period.

[0011] The filtering module is used to filter recommended functions and sub-functions based on the number of times and duration of use of each function and each sub-function.

[0012] The sending module is used to send video tutorials for the recommended function and the recommended sub-functions to the client.

[0013] Thirdly, this disclosure provides an electronic device, including:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0017] Fourthly, this disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in the first aspect.

[0018] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0021] Figure 1 This is a flowchart of a video tutorial configuration method provided in an embodiment of this disclosure;

[0022] Figure 2 This is a flowchart of another video tutorial configuration method provided in this embodiment of the disclosure;

[0023] Figure 3 This is a flowchart illustrating yet another video tutorial configuration method provided in this disclosure.

[0024] Figure 4 This is an exemplary schematic diagram of a video tutorial configuration method provided in an embodiment of this disclosure;

[0025] Figure 5 This is a schematic diagram of the structure of a video tutorial configuration device provided in an embodiment of this disclosure;

[0026] Figure 6 This is a block diagram of an electronic device used to implement the video tutorial configuration method of the embodiments of this disclosure. Detailed Implementation

[0027] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0028] Currently, video tutorials for various apps are pre-made by operators based on the app's functions. The content of the video tutorials distributed by the server to different users' clients is exactly the same. However, because different users have different focuses when using the app's functions, and different users have different levels of familiarity with each function, the video tutorials distributed by the server to the client may not be what the user needs and may fail to meet the user's usage requirements.

[0029] Take video editing apps as an example. Many users use these apps daily, but their understanding of the app's functions varies. For instance, a recommended video tutorial might not meet the professional needs of a user familiar with video editing. Conversely, a user with no video editing experience might not learn how to use the app's functions even with the recommended tutorials. Furthermore, a user might need to learn a specific function within the app, but the tutorials provided by the server might not include that function.

[0030] To address the aforementioned issues, this disclosure provides a video tutorial configuration method. This method is applied to a server, which can be the backend server of an app. Figure 1 As shown, the method includes:

[0031] S101. Obtain the number of times and duration of use of each function and sub-function in the client by the user within a preset period.

[0032] The preset period can be set based on experience. For example, the preset period can be 1 day.

[0033] In the embodiments of this application, each function and sub-function has its own corresponding display page. The display page corresponding to the function includes a jump component for the sub-function. Optionally, it may also include other function keys that do not have a jump function.

[0034] For example, when a user clicks a button for a certain function, the client will jump to the display page corresponding to that function; when a user clicks the jump component for a sub-function displayed on that display page, the client will jump to the display page corresponding to that sub-function.

[0035] As an example, the client is an editing tool client, with the main function being subtitles. Sub-functions include entrance and exit animations under the subtitle function. Other function keys for this function may include bolding and italicizing the font. When the user clicks on a subtitle, the client displays the corresponding subtitle function's page, which includes jump components for entrance and exit animations, as well as bolding and italicizing function keys.

[0036] When a user clicks the jump component for the exit animation, the client will redirect to the corresponding display page for the exit animation; when a user clicks the jump component for the entrance animation, the client will redirect to the corresponding display page for the entrance animation. Users can select exit animation effects on the exit animation display page, or they can select entrance animation effects on the entrance animation display page.

[0037] When a user clicks on bold and italic fonts, the subtitles in the client will be displayed in bold and italic fonts, respectively.

[0038] Taking the entrance animation sub-function as an example, the number of times the corresponding display page is displayed within a preset period is the number of times the entrance animation sub-function is used, and the duration of the display page being displayed within the preset period is the usage duration of the entrance animation sub-function.

[0039] S102. Based on the number of times and duration of use of each function and each sub-function, filter the functions and sub-functions to be recommended.

[0040] Understandably, the frequency and duration of use of functions and sub-functions can reflect the user's understanding of the functions and sub-functions and the user's actual usage needs for the functions and sub-functions.

[0041] For example, if a user uses function A the most times in a day, it indicates that the user has a high demand for function A recently; if a user uses function B for the longest time in a day, it indicates that the user may have a high demand for function B, but the user may not have a deep understanding of function B and cannot use it proficiently, resulting in a longer usage time.

[0042] S103. Send video tutorials for the features to be recommended and the sub-features to be recommended to the client.

[0043] The tutorial management platform stores video tutorials for each function and sub-function in the client. The server can search for video tutorials for the functions and sub-functions to be recommended from the tutorial management platform.

[0044] The tutorial management platform is a platform for operators to create and upload video tutorials. The video tutorials can be stored in the cloud database or local database of the tutorial management platform.

[0045] Using the above method, the server can obtain the number of times and duration of user usage of each function and sub-function in the client within a preset period. The number of times and duration of usage can, to some extent, reflect the user's understanding of the functions and sub-functions, as well as the user's usage needs for each function and sub-function in the application. Therefore, the functions and sub-functions selected by the server based on the number of times and duration of usage for each function and sub-function are more likely to be the functions and sub-functions that the user wants to learn about. The server then sends video tutorials for the recommended functions and sub-functions to the client, achieving accurate recommendations of video tutorials that meet the user's usage needs.

[0046] The server can determine the usage frequency and duration of each function and sub-function based on the data points reported by the client. For example... Figure 2 As shown, the above-mentioned S101, obtaining the number of times and duration of use of each function and sub-function in the client by the user within a preset period, is specifically implemented as follows:

[0047] S1011. Obtain the function tracking data and sub-function tracking data reported by the client within a preset period.

[0048] Specifically, when a user closes the display page corresponding to a function, the client will send function tracking data to the server; when a user closes the display page corresponding to a sub-function, the client will send sub-function tracking data to the server.

[0049] Function tracking data includes the function tags of the functions used by the user and the duration of each use. Sub-function tracking data includes the sub-function tags of the sub-functions used by the user and the duration of each use.

[0050] As an example, the function label for the subtitle function is "01", the label for the entrance animation sub-function within the subtitle function is "001", and the label for the exit animation sub-function within the subtitle function is "002".

[0051] In one implementation, S1012 can be executed directly after S1011.

[0052] In another implementation, after S1011, the server can further determine whether the acquired functional data points have reached a preset threshold. If not, S1011 continues to be executed.

[0053] If the number of function tracking data entries obtained is less than the preset threshold, then the function tracking data and sub-function tracking data reported by the client in each period before the preset period are obtained sequentially. After obtaining the function tracking data and sub-function tracking data reported in each period, it is determined whether the total number of function tracking data entries obtained has reached the preset threshold, until the total number of entries reaches the preset threshold.

[0054] The preset threshold for the number of items can be set based on experience.

[0055] Understandably, the more feature tracking data entries, the more accurately it reflects user needs and understanding of the features. If the number of feature tracking data entries within a preset period is less than a preset threshold, it indicates that the number of entries is too small, and insufficient data cannot accurately reflect user needs and understanding. Therefore, the server can sequentially obtain feature tracking data and sub-feature tracking data reported by the client in each period prior to the preset period until the total number reaches the preset threshold. In this way, the server can obtain sufficiently rich feature tracking data and sub-feature tracking data, enabling it to more accurately determine the recommended features and sub-features that match the user's needs.

[0056] As an example, with a preset threshold of 100 records, assuming the client reports 60 function tracking data records within a preset period, the server retrieves function tracking data and sub-function tracking data from the previous period. If the previous period contained 20 function tracking data records, and the server's total number of records is still less than 100, the server continues to retrieve function tracking data and sub-function tracking data from the next preceding period. If 40 records are retrieved this time, the server's total number of records is 120, reaching the 100 threshold. The server then stops retrieving function tracking data and sub-function tracking data and uses the 120 records and the sub-function tracking data from the three periods mentioned above to proceed to the next step.

[0057] S1012. Based on the obtained function tracking data and sub-function tracking data, calculate the number of times each function is used and the duration of use, as well as the number of times each sub-function is used and the duration of use.

[0058] Specifically, the server will use the number of function point data entries with the same function tag as the usage count of the function corresponding to that function tag. For example, if the function tag of all 5 function point data entries is "01", then the subtitle function can be determined to have been used 5 times.

[0059] The sum of the single-use durations of the function data points with the same function tag is taken as the usage duration of the function corresponding to that function tag. For example, if the single-use durations of the five function data points with the function tag "01" are 1 minute, 5 minutes, 3 minutes, 10 minutes, and 1 minute respectively, then the usage duration of the subtitle function can be determined to be 20 minutes.

[0060] The number of sub-function data entries with the same sub-function label is taken as the number of times the corresponding sub-function is used.

[0061] The sum of the single usage durations of the sub-function data points with the same sub-function label is used as the usage duration of the sub-function corresponding to that sub-function label.

[0062] This allows for accurate determination of the usage count and duration of each function, as well as the usage count and duration of each sub-function. Consequently, the server can more accurately determine the recommended functions and sub-functions based on the usage count and duration.

[0063] Using the above method, the usage frequency and duration of each function, as well as the usage frequency and duration of each sub-function, calculated based on the function tracking data and sub-function tracking data, can realistically reflect users' usage of each function and sub-function, and thus accurately reflect users' usage needs for each function and sub-function. In this way, the server can mine user usage needs for each function and sub-function based on the tracking data, so as to accurately determine the functions and sub-functions that meet user needs.

[0064] In some embodiments of this application, the server can also perform data cleaning on the function tracking data and sub-function tracking data reported by the client, and delete erroneous sub-function tracking data.

[0065] The function tracking data includes the usage time range of the function this time, and the sub-function tracking data includes the usage time range of the sub-function this time and the function tag of the function to which the sub-function belongs. The usage time range can be represented by the start usage timestamp and the end usage timestamp.

[0066] The sub-function tracking data can include a tag obtained by concatenating the function tag and the sub-function tag. As an example, if the function tag of the subtitle function is "01", the tag of the entrance animation sub-function of the subtitle function is "001", and the tag of the exit animation sub-function of the subtitle function is "002", then the tag included in the entrance animation sub-function tracking data can be "01_001", and the tag included in the exit animation sub-function tracking data can be "01_002".

[0067] Based on this, before S1012, which calculates the usage frequency and duration of each function and each sub-function based on the acquired function tracking data and sub-function tracking data, the method further includes:

[0068] For each sub-function's data points, determine whether there exists a usage time range for the function's data points that includes the usage time range for that sub-function's data points. If not, delete the sub-function's data points.

[0069] For each sub-function tracking data, the server can obtain the corresponding function tag from the sub-function tracking data, then search for function tracking data that includes the function tag from the obtained function tracking data, and determine whether the usage time interval included in the searched function tracking data includes the usage time interval of the sub-function tracking data.

[0070] It is understandable that users can only use the sub-functions included in a function while using a certain function. If the usage time interval of the sub-function's data points is not within the usage time interval of the function's data points to which the sub-function belongs, the server can determine the sub-function's data points as erroneous data and delete the erroneous data.

[0071] As an example, the preset cycle is 1 day. Within one day, the client reports a total of 4 function point data entries for the subtitle function. The usage time intervals for the subtitle function's function point data are 01:00 to 01:05, 01:40 to 01:42, 01:59 to 02:05, and 03:00 to 03:05. The usage time interval for the entrance animation sub-function's function point data is 01:20 to 01:21. If the usage time interval of the entrance animation sub-function's function point data does not fall within the usage time interval of any of the above-mentioned subtitle function's function point data, then the sub-function point data for the entrance animation sub-function will be deleted.

[0072] Using the above method, the function tracking data includes the usage time interval of the function at this time, and the sub-function tracking data includes the usage time interval of the sub-function at this time. Since users can only further use the sub-functions within a function while using the function, the usage time interval of the sub-function at this time should be within the usage time interval of the function at this time. If the usage time interval of the sub-function at this time does not belong to the usage time interval of the function including the sub-function at this time, then the sub-function tracking data is determined to be erroneous data, and the server deletes the erroneous data, ensuring the accuracy and validity of the tracking data.

[0073] In some embodiments of this disclosure, such as Figure 3As shown, the above S102, which filters the functions and sub-functions to be recommended based on the number of times and duration of use of each function and each sub-function, can be implemented as follows:

[0074] S1021. Select the first preset number of functions according to the order of the number of times each function is used, from most to least.

[0075] S1022. Select the second-to-last preset number of functions according to the order of usage time from longest to shortest.

[0076] S1023. Select the chosen functions as functions to be recommended.

[0077] It is understandable that the first preset number of functions selected in S1021 and the second preset number of functions selected in S1022 may have the same functions. Therefore, the server can perform deduplication on the first preset number of functions and the second preset number of functions, and use the deduplicated functions as the functions to be recommended.

[0078] As an example, the first preset number of functions selected in S1021 above includes: function A, function B, and function C; the second preset number of functions selected in S1022 above includes: function A, function B, function D, and function E. After deduplication, the functions to be recommended are: function A, function B, function C, function D, and function E.

[0079] S1024. Based on the number of times and duration of use of each sub-function, select the sub-functions to be recommended from the sub-functions included in the recommended function.

[0080] This step can be implemented in the following two ways:

[0081] Implementation Method 1: For each feature to be recommended, obtain the usage count and usage duration of each sub-feature included in that feature. Sub-features with a usage count greater than a preset threshold or a usage duration greater than a preset threshold are selected as recommended sub-features.

[0082] The preset number of times threshold and the preset duration threshold can be set based on experience.

[0083] Sub-functions used more than a preset threshold number of times are likely to be frequently used by users. Sub-functions used for a longer period of time than a preset threshold number of times may be unfamiliar to users, or they may be frequently used. Sub-functions used more than the preset threshold number of times and those used for a longer period of time are designated as sub-functions to be recommended. This approach ensures that the recommended sub-functions better meet user needs.

[0084] Implementation Method 2: For each feature to be recommended, obtain the usage count and usage duration of each sub-feature included in that feature. Filter out sub-features whose usage count exceeds a preset threshold or whose usage duration exceeds a preset threshold. If the number of filtered sub-features is greater than a third preset number, select the third preset number of sub-features from the filtered sub-features as the recommended sub-features. If the number of filtered sub-features is less than or equal to the third preset number, then select the filtered sub-features as the recommended sub-features.

[0085] The third preset quantity can be set based on experience, for example, the third preset quantity can be 5.

[0086] In this way, a suitable number of sub-functions that best meet user needs can be selected for a function to be recommended, which can avoid pushing too many video tutorials for a single function to users and save users' viewing time.

[0087] Using the above method, the server obtains a first preset number of functions based on their usage frequency, from highest to lowest. Since higher usage frequency indicates a greater likelihood that a function is frequently used by the user, this method can filter frequently used functions as potential recommendations. Furthermore, the server obtains a second preset number of functions based on their usage time, from highest to lowest. Since longer usage time indicates a greater likelihood that a function is not yet mastered by the user, this method can filter out functions that users are not yet proficient in as potential recommendations. Then, from these potential recommendations, sub-functions are selected for recommendation, accurately identifying frequently used but not yet mastered sub-functions, making subsequent video tutorials more relevant to user needs.

[0088] In some embodiments of this application, the above-mentioned S102, which filters the functions and sub-functions to be recommended based on the number of times and duration of use of each function and each sub-function, can also be implemented as follows:

[0089] The acquired function tracking data and sub-function tracking data are input into a pre-trained artificial intelligence (AI) model. This allows the AI ​​model to filter and recommend functions and sub-functions based on the frequency and duration of use of each function and sub-function. The model then outputs the function tags for the recommended functions and sub-functions.

[0090] In the process of using AI models to determine the function labels of the functions to be recommended and the function labels of the sub-functions to be recommended, the function labels of the functions to be recommended and the function labels of the sub-functions to be recommended output by the AI ​​model can be corrected manually according to actual needs. The AI ​​model can then be iteratively updated based on the results of the manual correction to improve its accuracy.

[0091] By using the above method, a pre-trained AI model is used to obtain the functions and sub-functions to be recommended. The server can quickly obtain the functions and sub-functions to be recommended based on the data points and the AI ​​model, thereby improving the efficiency and accuracy of the server in determining the functions and sub-functions to be recommended.

[0092] After identifying the features and sub-features to be recommended, the server needs to determine the corresponding video tutorials. Since the app is constantly being updated and iterated, the tutorial management platform pre-stores video tutorials for each feature and sub-feature in each version of the app. The server can first determine the client's version number and use the video tutorial set corresponding to that version number as the designated video tutorial set. Then, it selects video tutorials from this designated set. Thus, before the server determines the video tutorials for the features and sub-features to be recommended, it first determines the client's version number, allowing the server to select the corresponding video tutorials from the set corresponding to that version. Compared to existing technologies, this eliminates the need for complex version control by operations personnel, such as recording the features of each feature in each version and creating separate tutorial videos for each version of the app. It also narrows the scope of video tutorials for the server to select features and sub-features, reducing the workload for both the server and operations personnel, lowering maintenance costs, and increasing flexibility.

[0093] After determining the function and sub-function to be recommended, the server can obtain multiple professional-level video tutorials corresponding to the function and sub-function. Before step S103, which sends the video tutorials for the function and sub-function to the client, the method further includes:

[0094] Step A: Determine the first professional level based on the usage time and / or number of times the feature to be recommended is used, and search for video tutorials of the first professional level corresponding to the feature to be recommended from the specified video tutorial set.

[0095] The tutorial management platform pre-stores multiple professional-level video tutorials for each function and multiple professional-level video tutorials for each sub-function. The server can distribute video tutorials of different professional levels to different users.

[0096] An increase in user time spent using the feature or its sub-features indicates a deeper understanding and increased proficiency in its use. The server can then provide users with more professional video tutorials.

[0097] There are three ways to determine the first professional level:

[0098] Method 1: Based on the preset correspondence between usage frequency range and professional level, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the first professional level.

[0099] Each professional level corresponds to a usage frequency range. For example, professional level 1 corresponds to a usage frequency range of 0-20 times, professional level 2 corresponds to a usage frequency range of 21-40 times, and professional level 3 corresponds to a usage frequency range of 41-60 times. The professional level of professional levels 1, 2, and 3 decreases sequentially. The preset correspondence between usage frequency ranges and professional levels can be pre-set based on experience.

[0100] Method 2: Based on the preset correspondence between usage time intervals and professional levels, the professional level corresponding to the usage time interval to which the recommended function belongs is taken as the first professional level.

[0101] Each professional level corresponds to a usage time range. For example, professional level 1 corresponds to a usage time range of 0-10 minutes, professional level 2 corresponds to a usage time range of 10-20 minutes, and professional level 3 corresponds to a usage time range of 20-30 minutes. This embodiment of the disclosure does not limit the preset correspondence between usage time ranges and professional levels.

[0102] Method 3: Based on the preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the feature to be recommended is designated as the third professional level. Based on the preset correspondence between usage duration ranges and professional levels, the professional level corresponding to the usage duration range of the feature to be recommended is designated as the fourth professional level. One professional level is selected from the third and fourth professional levels as the first professional level.

[0103] There are three methods for the server to select a professional level from the third and fourth professional levels as the first professional level:

[0104] Method 1: Select the higher professional level from the third and fourth professional levels as the first professional level.

[0105] Method 2: Select the lower professional level from the third and fourth professional levels as the first professional level.

[0106] Method 3: Randomly select one professional level from the third and fourth professional levels as the first professional level.

[0107] The server determines the professional level of a recommended feature based on usage frequency and duration, or by combining both factors, and then determines the corresponding video tutorials. Compared to the server directly providing each client with video tutorials of a fixed professional level, this method allows the server to provide users with more precise video tutorials that match their professional level, accurately identifying user needs and improving the user experience.

[0108] Step B: Determine the second professional level based on the usage duration and / or number of times the sub-function to be recommended is used, and search for video tutorials of the second professional level corresponding to the sub-function to be recommended from the specified video tutorial set.

[0109] There are three ways to determine a second professional level:

[0110] Method 1: Based on the preset correspondence between usage frequency range and professional level, the professional level corresponding to the usage frequency range of the sub-function to be recommended is used as the second professional level.

[0111] Method 2: Based on the preset correspondence between usage time intervals and professional levels, the professional level corresponding to the usage time interval to which the sub-function to be recommended belongs is taken as the second professional level.

[0112] Method 3: Based on the preset correspondence between usage frequency range and professional level, the professional level corresponding to the usage frequency range of the sub-function to be recommended is taken as the fifth professional level.

[0113] Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is taken as the sixth professional level.

[0114] Choose one of the fifth and sixth professional levels as the second professional level.

[0115] There are three methods for the server to select a professional level from the fifth and sixth professional levels as the second professional level:

[0116] Method 1: Select the higher professional level from the fifth and sixth professional levels as the second professional level.

[0117] Method 2: Select the lower professional level from the fifth and sixth professional levels as the second professional level.

[0118] Method 3: Randomly select one professional level from the fifth and sixth professional levels as the second professional level.

[0119] The server can determine the professional level of a sub-function to be recommended based on usage frequency and duration, or it can make a comprehensive judgment based on both factors, thereby determining the corresponding video tutorials. Compared to the server directly providing video tutorials of a fixed professional level to each client, this method allows the server to provide users with more precise video tutorials, accurately identify user needs, and improve the user experience.

[0120] Using the above method, the server can determine the first professional level of a recommended function based on its usage duration and frequency. This first professional level indicates the difficulty level of the video tutorial content. Usage duration and frequency reflect the user's understanding of the recommended function, allowing the server to more accurately determine the difficulty level of the video tutorials for that function when sending them to the user. Similarly, the server can determine the second professional level of a recommended sub-function based on its usage duration and frequency, and then determine the corresponding video tutorials for that level. In this way, the server can accurately send video tutorials of varying difficulty levels based on the user's understanding of the recommended function and its sub-functions, thus meeting the needs of different users.

[0121] In some embodiments of this disclosure, before S103, sending the video tutorials for the recommended function and the video tutorials for the recommended sub-functions to the client, the method further includes:

[0122] For each feature to be recommended, the video tutorial for that feature and the video tutorials for its sub-features to be recommended are combined into a single video tutorial.

[0123] Accordingly, S103, sending video tutorials for the features to be recommended and their sub-features to be recommended to the client, can be implemented as follows:

[0124] Send the stitched video tutorial for each feature to be recommended to the client.

[0125] Using the above method, the server combines the video tutorials for each feature to be recommended into a single video tutorial and sends it to the client. This allows users to understand how to use a feature and its multiple sub-features through a single video tutorial, making it easier for users to view the video tutorials for each feature to be recommended.

[0126] In some embodiments of this disclosure, such as Figure 4 As shown, Figure 4An exemplary schematic diagram of a video tutorial configuration method provided in this disclosure embodiment specifically includes:

[0127] S401, Default Function Configuration Video Tutorial, provides an overall introduction to the functions for first-time users.

[0128] The default function video tutorial is configured by the server for the client. When a user uses the client for the first time, the client plays the default function video tutorial.

[0129] S402. The client reports and statistics the number of times each function and sub-function is used and the duration of use.

[0130] The server can use the function tracking data and sub-function tracking data reported by the client to count the number of times each function is used and the duration of use, as well as the number of times each sub-function is used and the duration of use.

[0131] S403. The server analyzes the function usage of each user every day.

[0132] In this embodiment, the preset period is 1 day. The server performs statistical analysis on the function tracking data and sub-function tracking data of each user every day to determine the usage of each user's functions and sub-functions. The usage data includes the number of times used and the duration of use. The specific statistical method is described in the relevant description in the above embodiments, and will not be repeated here.

[0133] S404. The server analyzes the validity of the data and determines the top function and sub-functions.

[0134] The server ensures the validity of the data points reported by the client by deleting erroneous sub-function data points. The specific method for the server to delete erroneous sub-function data points is described in the above embodiments and will not be repeated here.

[0135] Based on ensuring data validity, the server sorts each function and its sub-functions to determine the top-ranked functions and sub-functions. These top-ranked functions and sub-functions (i.e., the top functions and sub-functions) are the functions and sub-functions to be recommended in the above embodiment. The specific method used by the server to determine the functions and sub-functions to be recommended is described in the above embodiment and will not be repeated here.

[0136] S405. The server searches the tutorial management platform based on the names of the functions and sub-functions.

[0137] The name of a function is the function label in the function point data of the above embodiments, and the name of a sub-function is the sub-function label in the sub-function point data of the above embodiments.

[0138] The tutorial management platform includes video tutorials for each function and sub-function, and each video tutorial corresponds to a tag. The server can match the function tag or sub-function tag with the video tutorial tag. If the matching results are different, step S406 is executed; if the matching results are the same, step S408 is executed.

[0139] S406, Search failed.

[0140] S407. Feedback includes the addition of corresponding video tutorials.

[0141] If the server matches the function tag or sub-function tag with the video tutorial tag, and the matching results are all different, it indicates that the tutorial management platform does not contain video tutorials for that function or sub-function. In this case, the server can remind the operations staff to add video tutorials for that function or sub-function.

[0142] S408, Search successful.

[0143] S409. Combine video tutorials from most to least frequently used ones, based on usage patterns.

[0144] The server determines the professional level of video tutorials to be distributed based on the number of times and / or the duration of use, from most to least. The specific method for the server to determine the professional level is described in the above embodiments and will not be repeated here.

[0145] After the server determines the video tutorials, it will combine video tutorials with the same function tags into a single video tutorial.

[0146] S410. Distribute the combined video tutorials.

[0147] S411: A reminder is displayed within the client and played when the user clicks on it.

[0148] After receiving the video tutorial, the client prompts the user to watch it, which can be done in two ways:

[0149] Method 1: When a user launches the client, a pop-up window displays video tutorials. The pop-up window includes multiple video tutorials sent by the server. Users can click on the video tutorials in the pop-up window to trigger the client to play the selected video tutorial.

[0150] Method 2: When a user opens a certain function, if the function is related to the video tutorials sent by the server, the client displays a help button on the display page, and the user can click the help button to query the video tutorials sent by the server.

[0151] Using the above method, the server can analyze user needs and understanding of various functions and sub-functions within the client based on the tracking data uploaded by the client. This client-uploaded tracking data reflects frequently used functions or sub-functions, indicating user habits. The server then determines user needs and understanding of each function and sub-function based on these habits, and subsequently provides different video tutorials for different users, more accurately helping each user improve their understanding. Furthermore, each function and sub-function corresponds to video tutorials at different professional levels, and the server can provide tutorials at varying levels based on user behavior, further precisely meeting the diverse needs of different users. In summary, this method allows for more efficient, comprehensive, and detailed recommendation of video tutorials that meet user needs.

[0152] Based on the same concept, embodiments of this disclosure provide a video tutorial configuration device, such as... Figure 5 As shown, the device includes:

[0153] The acquisition module 501 is used to acquire the number of times and duration of use of each function and sub-function in the client by the user within a preset period.

[0154] The filtering module 502 is used to filter recommended functions and sub-functions based on the number of times each function and each sub-function is used and the duration of use.

[0155] The sending module 503 is used to send video tutorials for the features to be recommended and video tutorials for the sub-features to be recommended to the client.

[0156] Optional, the filtering module 502 is specifically used for:

[0157] Select the first preset number of functions according to their usage frequency from most to least.

[0158] Select the first two preset number of functions according to their usage time from longest to shortest.

[0159] The selected features will be designated as features to be recommended.

[0160] Based on the number of times and duration of use of each sub-function, select the sub-functions to be recommended from the sub-functions included in the recommended function.

[0161] Optional, the filtering module 502 is specifically used for:

[0162] For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature;

[0163] Sub-functions that are used more than a preset threshold number of times or whose usage duration exceeds a preset threshold duration will be designated as sub-functions to be recommended.

[0164] Optional, the filtering module 502 is specifically used for:

[0165] For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature.

[0166] Filter out sub-functions that are used more than a preset number of times or for a longer duration than a preset duration threshold.

[0167] If the number of selected sub-functions is greater than the third preset number, then select the third preset number of sub-functions from the selected sub-functions as the sub-functions to be recommended.

[0168] If the number of selected sub-functions is less than or equal to the third preset number, then the selected sub-functions will be used as sub-functions to be recommended.

[0169] Optionally, the device may also include a lookup module;

[0170] The search module is used to determine the first professional level based on the usage duration and / or number of times the feature to be recommended is used, and to search for video tutorials of the first professional level corresponding to the feature to be recommended from a specified set of video tutorials.

[0171] The search module is also used to determine the second professional level based on the usage duration and / or number of times the sub-function to be recommended is used, and to search for video tutorials of the second professional level corresponding to the sub-function to be recommended from the specified video tutorial set.

[0172] Optional, the search module, specifically used for:

[0173] Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the first professional level; or...

[0174] Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the function to be recommended is taken as the first professional level; or...

[0175] Based on the preset correspondence between usage frequency range and professional level, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the third professional level;

[0176] Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval to which the recommended function belongs is taken as the fourth professional level.

[0177] Choose one of the third and fourth professional levels as the first professional level.

[0178] The search module is specifically used for:

[0179] Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the sub-function to be recommended is taken as the second professional level; or,

[0180] Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is used as the second professional level; or...

[0181] Based on the preset correspondence between usage frequency range and professional level, the professional level corresponding to the usage frequency range of the sub-function to be recommended is taken as the fifth professional level.

[0182] Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval to which the recommended sub-function belongs is taken as the sixth professional level.

[0183] Choose one of the fifth and sixth professional levels as the second professional level.

[0184] Optionally, the device may also include a determining module and a specifying module:

[0185] The determination module is used to determine the client's version number.

[0186] The specified module is used to select the video tutorial set corresponding to the client's version number as the specified video tutorial set.

[0187] Optionally, the device also includes a splicing module:

[0188] The splicing module is used to splice the video tutorials of each feature to be recommended with the video tutorials of the sub-features to be recommended that are included in the feature to be recommended into a single video tutorial.

[0189] The sending module is specifically used for:

[0190] Send the stitched video tutorial for each feature to be recommended to the client.

[0191] Optionally, module 501 is used to obtain:

[0192] Obtain the function tracking data and sub-function tracking data reported by the client within a preset period. The function tracking data includes the function tag of the function used by the user and the duration of each use. The sub-function tracking data includes the sub-function tag of the sub-function used by the user and the duration of each use.

[0193] Based on the obtained function tracking data and sub-function tracking data, calculate the number of times each function is used and the duration of use, as well as the number of times each sub-function is used and the duration of use.

[0194] Optionally, the device may also include a determination module:

[0195] The acquisition module 501 is also used to acquire, in turn, the functional point data and sub-functional point data reported by the client in each period before the preset period if the number of functional point data entries is less than the preset number threshold.

[0196] The judgment module is used to determine whether the total number of function point data entries has reached a preset threshold after acquiring the function point data and sub-function point data reported in each cycle, until the total number of entries reaches the preset threshold.

[0197] Optionally, module 501 is used to obtain:

[0198] The number of data entries with the same function tag is taken as the number of times the function corresponding to that function tag is used.

[0199] The sum of the single-use durations of the functional data points with the same functional label is used as the usage duration of the corresponding functional label.

[0200] The number of sub-function data entries with the same sub-function label is taken as the number of times the corresponding sub-function is used.

[0201] The sum of the single usage durations of the sub-function data points with the same sub-function label is used as the usage duration of the sub-function corresponding to that sub-function label.

[0202] Optionally, the function tracking data also includes the usage time range of the function this time, and the sub-function tracking data also includes the usage time range of the sub-function this time and the function label of the function to which the sub-function belongs; the device also includes a deletion module:

[0203] The deletion module is used to determine whether there is a usage time range for the function's data points that includes the usage time range of the sub-function's data points for each sub-function's data points. If not, the data points for that sub-function are deleted.

[0204] Optional, the filtering module 502 is specifically used for:

[0205] The acquired function tracking data and sub-function tracking data are input into a pre-trained AI model so that the AI ​​model can filter functions and sub-functions to be recommended based on the number of times and duration of use of each function and each sub-function.

[0206] Obtain the function tags of the functions to be recommended and the function tags of the sub-functions to be recommended from the output of the AI ​​model.

[0207] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0208] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0209] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0210] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0211] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0212] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the video tutorial configuration method. For example, in some embodiments, the video tutorial configuration method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the video tutorial configuration method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the video tutorial configuration method by any other suitable means (e.g., by means of firmware).

[0213] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0214] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0215] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0216] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0217] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0218] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0219] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0220] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A video tutorial configuration method, applied to the server side, including: The system acquires function tracking data and sub-function tracking data reported by the client within a preset period. The function tracking data includes the function tag of the function used by the user, the duration of a single use, and the time interval during which the function was used. The sub-function tracking data includes the sub-function tag of the sub-function used by the user, the duration of a single use, the time interval during which the sub-function was used, and the function tag of the function to which the sub-function belongs. When the user closes the display page corresponding to a function, the client is triggered to send function tracking data to the server. When the user closes the display page corresponding to a sub-function, the client is triggered to send sub-function tracking data to the server. If the usage time interval of the sub-function's data points is not within the usage time interval of the data points of the function to which the sub-function belongs, the server will determine the sub-function's data points as erroneous data and delete the erroneous data. The number of data entries with the same function tag is taken as the number of times the function corresponding to that function tag is used. The sum of the single usage durations of the acquired function tag data with the same function tag is used as the usage duration of the function corresponding to that function tag. The number of sub-function data entries with the same sub-function label is taken as the number of times the corresponding sub-function is used. The sum of the single usage durations of the sub-function data points with the same sub-function label is taken as the usage duration of the sub-function corresponding to that sub-function label. Based on the number of times and duration of use of each function and each sub-function, filter the functions and sub-functions to be recommended. Send video tutorials for the recommended function and the recommended sub-functions to the client. The process of filtering recommended functions and sub-functions based on the number of times and duration of use of each function and sub-function includes: Select the first preset number of functions according to their usage frequency from most to least frequent; Select the first two preset number of functions according to their usage time from longest to shortest. The selected features will be designated as features to be recommended. Based on the number of times and duration of use of each sub-function, sub-functions to be recommended are selected from the sub-functions included in the recommended function.

2. The method according to claim 1, wherein, The selection of recommended sub-functions from the sub-functions included in the recommended function, based on the usage frequency and duration of each sub-function, includes: For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature; Sub-functions that are used more than a preset threshold number of times or whose usage duration exceeds a preset threshold duration will be designated as sub-functions to be recommended.

3. The method according to claim 1, wherein, The selection of recommended sub-functions from the sub-functions included in the recommended function, based on the usage frequency and duration of each sub-function, includes: For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature; Filter out sub-functions that are used more than a preset number of times or for a longer duration than a preset duration threshold; If the number of selected sub-functions is greater than the third preset number, then select the third preset number of sub-functions from the selected sub-functions as the sub-functions to be recommended. If the number of selected sub-functions is less than or equal to the third preset number, then the selected sub-functions will be used as sub-functions to be recommended.

4. The method according to claim 1, before sending the video tutorials for the recommended function and the video tutorials for the recommended sub-functions to the client, the method further includes: A first professional level is determined based on the usage duration and / or number of times the recommended function is used, and video tutorials corresponding to the first professional level of the recommended function are searched from a specified video tutorial set. The second professional level is determined based on the usage duration and / or number of times the sub-function to be recommended is used, and video tutorials corresponding to the second professional level of the sub-function to be recommended are searched from the specified video tutorial set.

5. The method according to claim 4, wherein, The determination of the first professional level based on the usage duration and / or frequency of the recommended function includes: Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the first professional level; or... Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the function to be recommended is taken as the first professional level; or... Based on the preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the third professional level. Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the function to be recommended is used as the fourth professional level. Choose one of the third and fourth professional levels as the first professional level; The determination of the second professional level based on the usage duration and / or frequency of the sub-function to be recommended includes: Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the sub-function to be recommended is taken as the second professional level; or, Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is used as the second professional level; or... Based on the preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the sub-function to be recommended is designated as the fifth professional level. Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is designated as the sixth professional level. Choose one of the fifth and sixth professional levels as the second professional level.

6. The method according to claim 4 or 5, before determining the first professional level based on the usage duration and / or number of uses of the function to be recommended, the method further includes: Determine the version number of the client; The video tutorial set corresponding to the client's version number is used as the designated video tutorial set.

7. The method according to claim 1 or 4, before sending the video tutorials for the recommended function and the video tutorials for the recommended sub-functions to the client, the method further includes: For each feature to be recommended, the video tutorial for that feature and the video tutorials for its sub-features to be recommended are combined into a single video tutorial. The step of sending video tutorials for the recommended function and the recommended sub-functions to the client includes: Send the spliced ​​video tutorial corresponding to each feature to be recommended to the client.

8. The method according to claim 1, wherein, Before calculating the usage frequency and duration of each function, and the usage frequency and duration of each sub-function, based on the acquired function tracking data and sub-function tracking data, the method further includes: If the number of function point data entries obtained is less than the preset number threshold, then the function point data and sub-function point data reported by the client in each period before the preset period are obtained in sequence. After acquiring the function tracking data and sub-function tracking data reported within each cycle, it is determined whether the total number of acquired function tracking data entries has reached the preset threshold number, until the total number of entries reaches the preset threshold number.

9. The method according to claim 1, wherein, The process of filtering recommended functions and sub-functions based on the frequency and duration of use of each function and sub-function includes: The acquired function tracking data and sub-function tracking data are input into a pre-trained AI model so that the AI ​​model can filter functions and sub-functions to be recommended based on the number of times and duration of use of each function and each sub-function. Obtain the function tags of the functions to be recommended and the function tags of the sub-functions to be recommended, output by the AI ​​model.

10. A video tutorial configuration device, applied on a server side, comprising: The acquisition module is used to acquire function tracking data and sub-function tracking data reported by the client within a preset period. The function tracking data includes the function tag of the function used by the user, the duration of a single use, and the time interval during which the function was used. The sub-function tracking data includes the sub-function tag of the sub-function used by the user, the duration of a single use, the time interval during which the sub-function was used, and the function tag of the function to which the sub-function belongs. When the user closes the display page corresponding to a function, the client is triggered to send function tracking data to the server. When the user closes the display page corresponding to a sub-function, the client is triggered to send sub-function tracking data to the server. The deletion module is used to determine the sub-function's data as erroneous if the usage time interval of the sub-function's data points is not within the usage time interval of the function's data points to which the sub-function belongs. The erroneous data will then be deleted. The acquisition module is further configured to take the number of function point data entries with the same function tag as the number of times the function corresponding to the function tag is used; and take the sum of the single usage durations of the function point data entries with the same function tag as the usage duration of the function corresponding to the function tag. The number of sub-function data entries with the same sub-function tag is taken as the number of times the sub-function corresponding to that sub-function tag is used; the sum of the single usage durations of the sub-function data entries with the same sub-function tag is taken as the usage duration of the sub-function corresponding to that sub-function tag. The filtering module is used to filter recommended functions and sub-functions based on the number of times and duration of use of each function and each sub-function. The sending module is used to send video tutorials for the recommended function and the recommended sub-functions to the client. The filtering module is specifically used for: Select the first preset number of functions according to their usage frequency from most to least frequent; Select the first two preset number of functions according to their usage time from longest to shortest. The selected features will be designated as features to be recommended. Based on the number of times and duration of use of each sub-function, sub-functions to be recommended are selected from the sub-functions included in the recommended function.

11. The apparatus according to claim 10, wherein, The filtering module is specifically used for: For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature; Sub-functions that are used more than a preset threshold number of times or whose usage duration exceeds a preset threshold duration will be designated as sub-functions to be recommended.

12. The apparatus according to claim 10, wherein, The filtering module is specifically used for: For each feature to be recommended, obtain the number of times and duration of use of each sub-feature included in that feature; Filter out sub-functions that are used more than a preset number of times or for a longer duration than a preset duration threshold; If the number of selected sub-functions is greater than the third preset number, then select the third preset number of sub-functions from the selected sub-functions as the sub-functions to be recommended. If the number of selected sub-functions is less than or equal to the third preset number, then the selected sub-functions will be used as sub-functions to be recommended.

13. The apparatus of claim 10, further comprising a search module; The search module is used to determine a first professional level based on the usage duration and / or number of times the function to be recommended is used, and to search for video tutorials of the first professional level corresponding to the function to be recommended from a specified video tutorial set. The search module is also used to determine the second professional level based on the usage duration and / or number of times the sub-function to be recommended is used, and to search for video tutorials of the second professional level corresponding to the sub-function to be recommended from the specified video tutorial set.

14. The apparatus according to claim 13, wherein, The search module is specifically used for: Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the first professional level; or... Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the function to be recommended is taken as the first professional level; or... Based on the preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the function to be recommended is taken as the third professional level. Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the function to be recommended is used as the fourth professional level. Choose one of the third and fourth professional levels as the first professional level; The search module is specifically used for: Based on a preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the sub-function to be recommended is taken as the second professional level; or, Based on a preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is used as the second professional level; or... Based on the preset correspondence between usage frequency ranges and professional levels, the professional level corresponding to the usage frequency range of the sub-function to be recommended is designated as the fifth professional level. Based on the preset correspondence between usage duration intervals and professional levels, the professional level corresponding to the usage duration interval of the sub-function to be recommended is designated as the sixth professional level. Choose one of the fifth and sixth professional levels as the second professional level.

15. The apparatus according to claim 13 or 14, further comprising a determining module and a designating module: The determining module is used to determine the version number of the client; The designated module is used to select the video tutorial set corresponding to the version number of the client as the designated video tutorial set.

16. The apparatus according to claim 10 or 13, further comprising a splicing module: The splicing module is used to splice the video tutorial of each function to be recommended with the video tutorials of the sub-functions to be recommended included in the function to be recommended into a single video tutorial. The sending module is specifically used for: Send the spliced ​​video tutorial corresponding to each feature to be recommended to the client.

17. The apparatus according to claim 10, wherein, The device also includes a judgment module: The acquisition module is also used to acquire, in turn, the functional point data and sub-functional point data reported by the client in each period before the preset period if the number of functional point data acquired is less than a preset number threshold. The judgment module is used to determine whether the total number of function point data entries obtained reaches the preset number threshold after acquiring the function point data and sub-function point data reported in each cycle, until the total number of entries reaches the preset number threshold.

18. The apparatus according to claim 10, wherein, The filtering module is specifically used for: The acquired function tracking data and sub-function tracking data are input into a pre-trained AI model so that the AI ​​model can filter functions and sub-functions to be recommended based on the number of times and duration of use of each function and each sub-function. Obtain the function tags of the functions to be recommended and the function tags of the sub-functions to be recommended, output by the AI ​​model.

19. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

21. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-9.

Images