Electronic equipment control method and device, electronic equipment and storage medium

Abstract

The invention provides an electronic equipment control method and device, electronic equipment and a storage medium, and the method comprises the steps: controlling the electronic equipment to pop up an authorization request interface to a user, and obtaining the authorization of the user for the use record collection of a knowledge card module; after acquiring authorization of a user to use record acquisition of the knowledge card modules, controlling an electronic equipment background system to acquire use records of the user for the knowledge card modules, and counting the opening times and the playing completion rate of the knowledge card modules according to the use records; determining the use popularity value of each knowledge card module according to the opening frequency and the complete playing rate of each knowledge card module; and according to the use heat value of each knowledge card module, updating the sequence of the knowledge card elements corresponding to each knowledge card module in an electronic equipment interface display area. According to the method, the knowledge card element sorting is dynamically adjusted based on the user use record, effective optimization of knowledge card layout is realized, and the user use experience is improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a control method, apparatus, electronic device, and storage medium for an electronic device. Background Technology

[0002] As a learning aid device integrating image recognition, knowledge learning, and intelligent push functions, the learning aid uses a knowledge card system in its front-end software interface as one of its core interactive carriers. This knowledge card system presents different types of learning content to users through categorized card elements.

[0003] Currently, the knowledge card system of learning machines generally adopts a fixed layout mechanism in the front-end software interface design. The relative positions of various knowledge card elements in the display area are preset and fixed in the long term, making it impossible to adapt to the actual user habits and meet the differentiated usage needs of users. Summary of the Invention

[0004] In view of this, this application provides a control method for an electronic device that dynamically adjusts the sorting of knowledge card elements based on user usage records, thereby achieving adaptive optimization of the knowledge card layout and improving the user experience.

[0005] In a first aspect, this application provides a control method for an electronic device, the method comprising the following steps:

[0006] Control the electronic device to display an authorization request interface to the user and obtain the user's authorization for the collection of usage records of the knowledge card module;

[0007] After obtaining authorization from the user to collect usage records of the knowledge card module, the system controls the electronic device's backend system to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records.

[0008] The usage popularity value of each knowledge card module is determined based on the number of times it is opened and the completion rate.

[0009] Based on the usage popularity value of each knowledge card module, update the sorting of the knowledge card elements corresponding to each knowledge card module in the display area of ​​the electronic device interface.

[0010] Optionally, the knowledge card module includes at least two of the following: a science popularization card module, a Chinese character card module, an idiom card module, a word card module, and a story card module;

[0011] The usage record includes at least one of the following: timestamp, operation record, duration of time spent on the interface, and interface interaction record.

[0012] Optionally, the step of calculating the number of times each knowledge card module is opened and the completion rate based on the usage records includes:

[0013] Obtain the preset time domain analysis range;

[0014] Based on the timestamp, select valid records that fall within the time domain analysis range from the operation records;

[0015] The number of valid records corresponding to each knowledge card module is counted as the number of times that knowledge card module is opened.

[0016] Optionally, the step of calculating the number of times each knowledge card module is opened and the completion rate based on the usage records further includes:

[0017] The preset interface interaction logic configuration information library is invoked, which stores the completion judgment rules corresponding to each knowledge card module.

[0018] Based on the interface interaction records of each knowledge card module, determine the total number of interface interactions corresponding to each knowledge card module;

[0019] For each knowledge card module, analyze its interface interaction records according to its completion judgment rules, and count the number of records that meet the completion conditions;

[0020] The completion rate of each knowledge card module is calculated based on the number of records that meet the completion criteria and the total number of interface interactions.

[0021] Optionally, the operation record includes at least one of the following: a click operation, a swipe operation, a close operation, or a return operation for the knowledge card module;

[0022] The interface interaction record includes at least one of the following: function trigger record within the card or task completion status.

[0023] Optionally, determining the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate includes calculating the usage popularity value according to a calculation formula, wherein the calculation formula is:

[0024] Popularity value = number of opens × α + completion rate × β × k, where α is the weighting coefficient of the number of opens, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantification conversion coefficient of the completion rate.

[0025] Optionally, updating the sorting of knowledge card elements corresponding to each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module is triggered by at least one of the following triggering conditions:

[0026] Responds when the user ends the current session on any knowledge card module;

[0027] Based on a pre-defined periodic update strategy;

[0028] The cumulative increase in the number of times any knowledge card module is opened reaches a preset threshold.

[0029] Secondly, this application provides a control device for an electronic device, the control device for the electronic device comprising:

[0030] The authorization management module is used to control electronic devices to pop up an authorization request interface for users and obtain user authorization for the collection of knowledge card module usage records;

[0031] The record acquisition module, after obtaining authorization from the user to collect the usage records of the knowledge card module, controls the background system of the electronic device to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records;

[0032] The popularity calculation module determines the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate.

[0033] The sorting update module updates the sorting of the knowledge card elements corresponding to each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module.

[0034] Thirdly, this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the control method of the electronic device provided in the embodiments of the present invention.

[0035] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the control method of the electronic device provided in the embodiments of the present invention.

[0036] This application's implementation uses the number of times the app is opened and the completion rate as core indicators to comprehensively calculate the usage popularity of each knowledge card module on the electronic device, accurately capturing user preferences. Based on the usage popularity of each knowledge card module, the sorting of knowledge card elements is dynamically updated, allowing frequently used or deep learning knowledge card modules to be automatically displayed first. This effectively reduces the number of times and time spent searching in the scrollable list, significantly improving the efficiency of frequently used functions and ensuring the continuity of the user's learning process. The control method of the electronic device allows the layout of knowledge card elements to be dynamically and in real-time adjusted according to the user's actual usage records. For example, during exam review, subject-related knowledge cards will be automatically placed first due to increased usage popularity; during extended learning scenarios such as holidays, science and story cards will be prioritized due to increased usage popularity. This solves the problem of insufficient flexibility in fixed layout mechanisms, effectively improving the intelligence of the electronic device and effectively adapting to the differentiated usage habits and core needs of users of different ages. This makes the electronic device more targeted and practical in its interaction design, thereby effectively improving the intelligence and user experience of the electronic device. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the learning machine provided in the embodiments of this application;

[0039] Figure 2 This is a schematic diagram of the knowledge card application interface of the learning machine provided in this application embodiment;

[0040] Figure 3 This is a schematic diagram of the knowledge card application interface of the learning machine provided in another embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the image acquisition interface of the learning camera provided in the embodiments of this application;

[0042] Figure 5 This is a schematic diagram of the initial interface of the knowledge card application provided in Embodiment 1 of this application;

[0043] Figure 6 This is a schematic diagram of the application interface of the idiom knowledge card provided in Embodiment 1 of this application;

[0044] Figure 7This is a schematic diagram of the application interface of the word knowledge card provided in Embodiment 1 of this application;

[0045] Figure 8 This is a schematic diagram of the updated interface of the knowledge card elements provided in Embodiment 2 of this application;

[0046] Figure 9 This is another schematic diagram of the updated knowledge card elements provided in Embodiment 2 of this application;

[0047] Figure 10 This is a flowchart of a control method for an electronic device provided in an embodiment of this application;

[0048] Figure 11 This is a flowchart of a control method for an electronic device provided in another embodiment of this application;

[0049] Figure 12 This is a flowchart of a control method for an electronic device provided in another embodiment of this application;

[0050] Figure 13 This is a schematic diagram of the structure of the control device of the electronic device provided in the embodiments of this application;

[0051] Figure 14 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0054] In this document, references to "embodiment" or "implementation" mean that a particular feature, structure, or characteristic described in connection with an embodiment or implementation may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0055] Please see Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 10 , Figure 1 This is a schematic diagram of the learning machine provided in the embodiments of this application. Figure 2 This is a schematic diagram of the knowledge card application interface of the learning machine provided in this application embodiment. Figure 3 This is a schematic diagram of the knowledge card application interface of the learning machine provided in another embodiment of this application. Figure 4 This is a schematic diagram of the image acquisition interface of the learning camera provided in the embodiments of this application. Figure 10 This is a flowchart of a control method for an electronic device provided in an embodiment of this application. The control method for the electronic device includes the following steps:

[0056] S101. Control the electronic device to display an authorization request interface to the user and obtain the user's authorization for the collection of knowledge card module usage records.

[0057] The electronic device can be a learning device 100, which is an intelligent educational device integrating image acquisition, intelligent recognition, and assisted learning functions. Its hardware includes an image acquisition module for acquiring image information of the object to be recognized and a main control module for data processing. Its front-end software integrates an image recognition engine and a knowledge service unit. The front-end software of the learning device 100 is an application running on its hardware, possessing functions such as interface display, user interaction, and command transmission. It can receive user operations through a touch screen 110, physical buttons, etc. The hardware of the learning device 100 typically integrates components such as a processor, memory, touch screen 110, microphone, and speaker, supporting multiple interaction methods such as voice interaction and touch interaction. The image recognition function is implemented by acquiring images of text, objects, or scenes taken by the user through the image acquisition module. The image recognition engine then preprocesses, extracts, and matches the images, ultimately outputting recognition results containing object categories and key information, providing data support for subsequent learning assistance functions. The knowledge card system is a collection of auxiliary learning units associated with the image recognition results of the Learning Machine 100. The system includes multiple knowledge card modules, each corresponding to a specific type of focused learning content. The trigger condition for activation is that the Learning Machine 100 completes image recognition and outputs the results. For example, specifically, after the image recognition process terminates, the right side of the Learning Machine 100's front-end interactive interface automatically triggers the display of knowledge card elements 120, forming a column layout with the left-side recognition result display area, thus achieving a linked presentation of recognition results and auxiliary knowledge.

[0058] The aforementioned knowledge card module can be understood as an independent functional unit in the Learning Machine 100 that carries a specific learning topic. It integrates data storage, interaction rules, and content services related to the topic and is the core software component for realizing knowledge presentation and user interaction.

[0059] The aforementioned knowledge card element 120 can be understood as the visual presentation carrier of the knowledge card module on the front-end interface of the learning machine 100. It is an interface element that users can perceive and operate, used to trigger the function call of the corresponding knowledge card module and display the core identification information of the corresponding knowledge card module.

[0060] Optionally, the knowledge card system includes, but is not limited to, modules for popular science knowledge cards, Chinese character knowledge cards, idiom knowledge cards, vocabulary knowledge cards, example sentence knowledge cards, and story knowledge cards. The specific number and type of these knowledge card modules can be adjusted according to the product positioning of the Paixueji 100, and this application does not impose a unique limitation on them.

[0061] The aforementioned popular science knowledge card module focuses on natural science and humanities common sense, integrating graphic explanations, dynamic demonstrations and fun Q&A functions, and can intelligently push related knowledge content based on the identified object.

[0062] The aforementioned Chinese character knowledge card module is designed to support the entire process of learning Chinese characters, including dynamic stroke demonstrations, pinyin annotations, radical decomposition, word formation expansion, and writing practice guidance. It also supports accurate matching and triggering of rare and easily confused characters in the identified text.

[0063] The aforementioned idiom knowledge card module uses idioms as the core unit and includes explanations, etymological backgrounds, parts of speech, comparisons of synonyms and antonyms, and scenario-based application examples. It can identify idioms in text and automatically trigger in-depth analysis.

[0064] The aforementioned vocabulary knowledge card module focuses on foreign language vocabulary learning, integrating functions such as standard pronunciation playback, phonetic symbol annotation, part-of-speech classification, word meaning analysis, fixed collocations, and example sentence demonstrations. It can match and identify foreign language words or the corresponding foreign language names of objects in text.

[0065] The above-mentioned example sentence knowledge card module serves as a link between vocabulary and grammar, providing a matching function with the "Chinese characters," "idioms," and "words" modules. It includes complete sentence structure display, component analysis, tone marking, and substitution exercises to enhance language application skills.

[0066] The aforementioned story knowledge card module integrates audio and text content, covering enlightenment stories, fables and fairy tales, and subject-related stories, such as interesting stories and stories of historical figures. It supports audio playback control and the extraction and annotation of key knowledge points.

[0067] Please see Figure 5 , Figure 6 and Figure 7 , Figure 5 This is a schematic diagram of the initial interface of the knowledge card application provided in Embodiment 1 of this application. Figure 6 This is a schematic diagram of the application interface of the idiom knowledge card provided in Embodiment 1 of this application. Figure 7This is a schematic diagram of the application interface for the word knowledge card provided in Embodiment 1 of this application. In Embodiment 1 of this application, the user takes and collects an image of the target object, a packaging box. The image recognition engine of the learning machine 100 preprocesses the image, extracts and matches features, and outputs a recognition result containing key information about the target object, "packaging box". Based on the recognition result of the target image, the user can further call the relevant functions of the knowledge card module. For example, after the user clicks on the idiom knowledge card element 120, that is, after the user clicks on the virtual function icon corresponding to the idiom knowledge card module, the idiom knowledge card module is called and outputs relevant idioms about "packaging box", such as "all-encompassing". As another example, after the user clicks on the word knowledge card element 120, that is, after the user clicks on the virtual function icon corresponding to the word knowledge card module, the word knowledge card module is called and outputs relevant English words about "packaging box", such as "BOX".

[0068] Preferably, the knowledge card elements 120 corresponding to each knowledge card module are presented on the display interface of the learning machine 100 in the form of a scrollable list. The scrollable list can extend vertically or horizontally along the interface, and the knowledge card elements 120 corresponding to each knowledge card module are arranged in a preset initial order. Users can scroll the scrollable list by touching to browse the knowledge card elements 120, and can also click on the knowledge card elements 120 to select them and invoke the functions of the knowledge card module.

[0069] Please refer to it again. Figure 2 and Figure 3 ,in Figure 3 It can be understood as Figure 2 The diagram illustrates the interface after scrolling down the knowledge card element list provided. In this embodiment, within the limited touchscreen space of the learning machine 100, multiple types of knowledge card elements 120 can be effectively presented, avoiding interface congestion caused by an increase in the number of cards, resolving the technical contradiction between carrying multiple contents and displaying on a small screen, and optimizing the utilization of interface space. Furthermore, the scrollable list format adapts to users' touch interaction habits, making it especially suitable for younger users and quick operation scenarios. Users can browse and access all knowledge card elements 120 without switching interfaces, effectively reducing interface jumps compared to paginated displays and improving operational convenience. In addition, in the display interface of the learning machine 100, the scrollable card list and the image recognition result area form a column. While browsing the image recognition results, users can easily switch knowledge cards by scrolling to obtain auxiliary knowledge, achieving a seamless interaction between recognition and learning.

[0070] In this embodiment, the usage records of the learning machine 100 involve users' personal usage habits data. Therefore, explicit user authorization must be obtained before data collection to ensure user privacy and security. The authorization request interface can be understood as a pop-up window of the learning machine 100 front-end software or a pop-up window of the parent control terminal associated with the learning machine 100. The interface includes authorization description text, an "Agree" button, and a "Deny" button. The authorization description text must clearly inform the user of the purpose, scope, and usage method of data collection, such as "To optimize the display order of knowledge cards, it is necessary to collect data such as your card opening and usage completion status. You can revoke the authorization at any time in the settings." Preferably, to avoid young users having a vague understanding of the concept of authorization, the authorization request interface can be understood as a pop-up window of the parent control terminal associated with the learning machine 100, and data collection is carried out after parental authorization.

[0071] Optionally, when the user clicks the "Agree" button, the Paixueji 100 marks the authorization status as "Authorized," and the usage record collection operation can be performed normally afterwards; when the user clicks the "Deny" button, the authorization status is marked as "Unauthorized," the Paixueji 100 maintains the default fixed layout of the knowledge card module, and the authorization option is only provided again when the user actively enters the settings interface, so as to avoid frequent pop-up windows interfering with the user.

[0072] S102. After obtaining the user's authorization to collect the usage records of the knowledge card module, control the background system of the electronic device to collect the user's usage records for each knowledge card module, and count the number of times each knowledge card module is opened and the completion rate based on the usage records.

[0073] In this embodiment, the backend system of the aforementioned learning card machine 100 can be understood as a backend service unit responsible for data collection, processing, and storage. It interacts with the frontend software, and the operation of the backend system does not affect the normal display and operation response of the frontend interface. After user authorization, the backend system starts the collection process, captures the user's operation behavior on each knowledge card module in real time, and generates standardized usage records.

[0074] S103. Determine the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate.

[0075] In this embodiment of the application, the number of times the knowledge card module is opened and the completion rate can be understood as core indicators representing the user's preference for the knowledge card module. The number of times the knowledge card module is opened reflects the frequency of use of the knowledge card module, and the completion rate reflects the attractiveness of the knowledge card module's content and the user's learning depth.

[0076] Optionally, after obtaining the number of times each knowledge card module is opened and its completion rate, these two indicators can be standardized to eliminate differences in magnitude before calculating the usage popularity value. In a specific embodiment of this application, the formula for calculating the usage popularity value of each knowledge card module is: Usage Popularity Value = Number of Opens × α + Completion Rate × β × k, where α is the weighting coefficient of the number of opens, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantization conversion coefficient of the completion rate.

[0077] Where α is the weighting coefficient for the number of times the content is opened, and β is the weighting coefficient for the completion rate, α+β=1. In an optional implementation of this application, α∈[0.3,0.5], β∈[0.5,0.7], and the weighting coefficients have multiple configuration methods. Specifically, the first configuration method for the weighting coefficients is the system default configuration, where α=0.4 and β=0.6. The second configuration method for the weighting coefficients is a scenario-based adaptation configuration, which is automatically adjusted according to the user's age group. For example, for users aged 3-6, the weighting coefficients can be: α=0.3, β=0.7, with the weighting coefficients focusing more on the completion rate and reflecting the user's acceptance of the content; for primary school students aged 7-12, the weighting coefficients can be: α=0.4, β=0.6, with the weighting coefficients balancing the frequency and depth of the user's use of the knowledge card module; for middle school students aged 13-16, the weighting coefficients can be: α=0.5, β=0.5, with the weighting coefficients focusing more on the number of times the content is opened and reflecting the user's learning needs. The third way to configure the weighting coefficients is by manual configuration by the user. Users can customize the weighting coefficient configuration through the settings interface of the Paixueji100, either locally or in the cloud.

[0078] Optionally, for a single knowledge card module, the normalized value of the number of times it is opened = the number of times this knowledge card module is opened / the maximum number of times all knowledge card modules are opened. If the number of times all knowledge card modules are opened is 0, then the normalized value is 0. The normalized value of the completion rate = completion rate / 100%, converting the percentage completion rate into a value between 0 and 1.

[0079] S104. Based on the usage popularity value of each knowledge card module, update the sorting of the knowledge card elements 120 corresponding to each knowledge card module in the electronic device interface display area.

[0080] In one specific embodiment of this application, a 7-year-old primary school student is used as the statistical subject, and the statistical time domain is set to the most recent 7 days. This period coincides with the user's after-school review scenario. After completing homework using the Learning Machine 100 each day, the student will use the knowledge card module for reinforcement exercises. The effective usage data for each knowledge card module is as follows: the science module was opened 5 times and completed 3 times, with a completion rate of 60%; the Chinese character module was opened 12 times and completed 10 times, with a completion rate of 83.3%; the idiom module was opened 20 times and completed 18 times, with a completion rate of 90%; the vocabulary module was opened 18 times and completed 15 times, with a completion rate of 83.3%; the example sentence module was opened 10 times and completed 8 times, with a completion rate of 80%; and the story module was opened 6 times and completed 5 times, with a completion rate of 83.3%.

[0081] Furthermore, in this embodiment, if the learning machine 100 adopts the system default weighting coefficients, i.e., α=0.4 and β=0.6, its standardized values ​​are calculated as follows: the standardized value of the number of times the science popularization module is opened is 5 / 20, i.e., the standardized value of the number of times the science popularization module is opened is 0.25, and the standardized value of the completion rate is 0.6; the standardized value of the number of times the Chinese character module is opened is 12 / 20, i.e., the standardized value of the number of times the Chinese character module is opened is 0.6, and the standardized value of the completion rate is approximately 0.833; the standardized value of the number of times the idiom module is opened is 20 / 20, i.e., the standardized value of the idiom module is approximately 0.833. The normalized value for the number of opens is 1.0, and the normalized value for the completion rate is 0.9. The normalized value for the number of opens for the word module is 18 / 20, which means the normalized value for the number of opens for the word module is 0.9, and the normalized value for the completion rate is approximately 0.833. The normalized value for the number of opens for the example sentence module is 10 / 20, which means the normalized value for the number of opens for the example sentence module is 0.5, and the normalized value for the completion rate is 0.8. The normalized value for the number of opens for the story module is 6 / 20, which means the normalized value for the number of opens for the story module is 0.3, and the normalized value for the completion rate is approximately 0.833.

[0082] Furthermore, in this embodiment, the usage popularity values ​​are calculated as follows: the usage popularity value of the science popularization module is 0.4×0.25+0.6×0.6=0.460; the usage popularity value of the Chinese character module is 0.4×0.6+0.6×0.833≈0.740; the usage popularity value of the idiom module is 0.4×1.0+0.6×0.9=0.940; the usage popularity value of the word module is 0.4×0.9+0.6×0.833≈0.860; the usage popularity value of the example sentence module is 0.4×0.5+0.6×0.8=0.2+0.48=0.680; and the usage popularity value of the story module is 0.4×0.3+0.6×0.833≈0.620. The usage popularity of each knowledge card module is ranked as follows: Idiom module (0.940) > Word module (0.860) > Chinese character module (0.740) > Example sentence module (0.680) > Story module (0.620) > Popular science module (0.460).

[0083] Please see Figure 8 and Figure 9 , Figure 8 This is a schematic diagram of the updated interface of the knowledge card elements provided in Embodiment 2 of this application. Figure 9 This is another schematic diagram of the updated interface provided in Embodiment 2 of this application for the knowledge card elements. Figure 9 It can be understood as Figure 8 The diagram shows the interface after scrolling down the list of knowledge card elements provided in the image. In this embodiment, based on the usage popularity value of each knowledge card module, the sorting of the knowledge card elements 120 corresponding to each knowledge card module in the display area of ​​the learning machine 100 interface is updated. That is, the display priority order of the knowledge card elements 120 is as follows: idiom module, word module, Chinese character module, example sentence module, story module, and science module, which is highly consistent with the core needs of the user for after-school review.

[0084] In this embodiment, the sorting rule for knowledge card elements 120 is based on the usage popularity value of the knowledge card modules. Specifically, the display elements corresponding to each knowledge card module are sorted in descending order of usage popularity value. If multiple knowledge card modules have the same popularity value or a difference of less than or equal to 0.01, they are sorted according to their default sorting priority. For example, the default priority can be preset as: Popular Science > Chinese Characters > Idioms > Words > Example Sentences > Stories. Furthermore, if a knowledge card module has no valid usage records, i.e., its popularity value is zero, it is arranged after knowledge card modules with popularity values ​​according to the default priority.

[0085] In summary, this application's implementation uses the number of times the app is opened and the completion rate as core indicators to comprehensively calculate the usage popularity of each knowledge card module in the learning machine 100, accurately capturing user preferences. Based on the usage popularity of each knowledge card module, the sorting of knowledge card elements 120 is dynamically updated, allowing frequently used or deep learning knowledge card modules to be automatically displayed first. This effectively reduces the number of times and time spent searching in the scrollable list, significantly improving the efficiency of frequently used functions and ensuring the continuity of the user's learning process. The control method allows the layout of the knowledge card elements 120 to be dynamically and in real-time adjusted according to the user's actual usage records. For example, during exam review, subject-specific knowledge cards will automatically be prioritized due to increased usage; during extended learning scenarios such as holidays, science and story cards will be prioritized due to increased usage, solving the problem of insufficient flexibility in fixed layout mechanisms. This effectively enhances the intelligence of the learning device 100 and can effectively adapt to the differentiated usage habits and core needs of users of different age groups, making the learning device 100 more targeted and practical in its interaction design. This effectively enhances the intelligence of the learning device 100, making the layout of the knowledge card elements 120 in the learning device 100 intelligently associated with the user's usage habits, thereby improving the user experience and ease of use.

[0086] In one optional embodiment of this application, the step of determining the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate of each knowledge card module further includes a personalized optimization mechanism based on a user profile tag system. Specifically, firstly, based on user authorization, basic information and usage behavior data are collected to construct a user profile tag system that includes learning preference dimensions and interaction habit dimensions. The learning preference dimension includes, but is not limited to, tags such as literacy enlightenment or idiom accumulation, while the interaction habit dimension includes, but is not limited to, tags such as short, frequent, and fast operations or deep immersive operations. The weight of each tag is dynamically updated based on the user's behavior data over the past 30 days.

[0087] Optionally, the backend system of the learning machine 100 is pre-configured with a mapping rule library of tags and weights, used to automatically adjust the weight coefficients α and β in the popularity value calculation formula according to the user profile tags. For example, for users with the tag combination of "3-6 years old + story preference + short frequency operation", the system automatically configures α=0.2 and β=0.8 to reduce the impact of accidental touch operation on the ranking by increasing the completion rate weight; for users with the tag combination of "7-10 years old + exam preparation + efficient operation", α=0.5 and β=0.5 are configured to balance usage frequency and learning depth; for users with the tag combination of "11-13 years old + science exploration + in-depth operation", α=0.3 and β=0.7 are configured to focus on the quality of knowledge absorption.

[0088] Optionally, the backend system of the learning card reader 100 also includes a weight self-learning module. When it detects that a user manually adjusts the sorting of knowledge cards, it automatically triggers a fine-tuning of the α / β coefficients, optimizing the weight configuration towards the user's manual preferences. The weight self-learning module operates on a 7-day adaptation cycle, continuously learning user behavior preferences to ensure the dynamic accuracy of the weight configuration and the system's adaptive capabilities.

[0089] In this embodiment, by dynamically adjusting weight coefficients based on user profiles, the limitations of fixed weight calculations can be overcome. This allows for precise adaptation to different user groups, effectively improving the match between the calculated popularity value of the knowledge card module and the user's actual learning needs. For example, reducing the weight coefficient for accidental touches by younger users effectively suppresses their interference with the ranking results. Simultaneously, increasing the weight coefficient for completion rate ensures that content they genuinely prefer is consistently prioritized, thereby reducing the impact of invalid operations on ranking accuracy. Furthermore, the weight self-learning module can automatically optimize weight configuration based on the user's manual adjustment behavior, thereby reducing the frequency of user intervention and improving user convenience.

[0090] Optionally, the knowledge card module includes at least two of the following: a science popularization card module, a Chinese character card module, an idiom card module, a word card module, and a story card module. The usage record includes at least one of the following: timestamp, operation record, interface dwell time, and interface interaction record.

[0091] The timestamps mentioned above can be understood as recording the precise time when an operation occurred, and can be used to filter usage data within a specific time range.

[0092] The above operation log can be understood as the user's core operations on the knowledge card module, which can be used to determine the module's open, closed, and other states.

[0093] The aforementioned interface dwell time can be understood as the continuous time a user stays on a particular card module interface, which can be used to help determine the effectiveness of usage.

[0094] The above interface interaction records can be understood as the user's detailed interaction behavior within the card module, used to determine whether the module learning task has been completed.

[0095] In this embodiment, the timestamp provides a temporal basis for data analysis, enabling the system to perform effective record filtering based on a specific time domain, ensuring the timeliness of statistical results. The operation records and the duration of screen time are key criteria for determining the validity of open counts and filtering out erroneous operations. This eliminates invalid data such as accidental clicks or short-term exits, making the statistics on completion rate and open counts more accurate, thus more realistically reflecting users' actual needs for each knowledge card module. Furthermore, the interface interaction records are the core data source for accurately determining whether a session has been completed, making subsequent statistics on open counts and completion rates more accurate and reliable, ensuring the credibility of usage popularity values ​​and the rationality of ranking results.

[0096] Please see Figure 11 , Figure 11 This is a flowchart of a control method for an electronic device according to another embodiment of this application. The step of statistically analyzing the number of times each knowledge card module is opened and the completion rate based on the usage records includes the following steps:

[0097] S1031. Obtain the preset time domain analysis range.

[0098] In this embodiment of the application, the time domain analysis range can be understood as the time interval used to filter valid usage records.

[0099] Optionally, the time domain analysis range is a configurable parameter, and it includes multiple configuration methods. The time domain analysis range can be configured using the system default setting, with the initial default time domain being the most recent 7 days, adapting to the usage habits of most users. Alternatively, the time domain analysis range can be configured by the user; the user can select a preset time range through the Paixueji 100's settings interface or input a custom start and end time through the date selector. The time domain analysis range can also be configured for intelligent adaptation; the Paixueji 100's backend system dynamically adjusts based on the user's average daily usage frequency over the past 30 days. If the average daily usage frequency is ≥5 times, the time domain analysis range is set to the most recent 3 days to ensure rapid response to recent habit changes; if 1 time ≤ average daily usage frequency < 5 times, the time domain analysis range is set to the most recent 7 days; if the average daily usage frequency is < 1 time, the time domain analysis range is set to the most recent 30 days to avoid distortion in popularity calculations due to insufficient records.

[0100] S1032. Based on the timestamp, select valid records from the operation records that fall within the time domain analysis range.

[0101] In one optional embodiment of this application, to ensure the accuracy of the statistics on the number of times the knowledge card module is opened, the operation records need to be screened for validity, excluding invalid records such as erroneous operations. For example, the criterion for determining a valid record can be that the user stays on the interface for more than or equal to 2 seconds after clicking to open the knowledge card module. If the user closes the knowledge card module immediately after clicking, that is, the user stays on the interface for less than 2 seconds, it is considered an erroneous operation, and the record is invalid and not included in the statistics.

[0102] S1033. Count the number of valid records corresponding to each knowledge card module, and use this count as the number of times the knowledge card module is opened.

[0103] In one optional embodiment of this application, to avoid statistical bias caused by repeated clicks within a short period of time, multiple opening operations of the same knowledge card module by the same user within a short period of time are only counted as a single valid opening. For example, multiple opening operations of the same knowledge card module by the same user within 2 minutes are only counted as 1 valid opening.

[0104] In this embodiment, the control method of the learning machine 100 first defines the time domain analysis scope, and then filters valid data from the operation records based on timestamps, which can accurately focus on users' recent preferences. For example, if the most recent 7 days are set as the time domain, the valid records of the story knowledge card module frequently used by children aged 3-6 in the past week will be fully counted, avoiding interference from low-frequency records from several months ago, making the statistical results more in line with real-time needs and improving the accuracy of popularity assessment. At the same time, the valid records are dynamically updated over time, which enables the sorting of knowledge card elements 120 to be adjusted in real time, solving the problem that a fixed layout cannot adapt to stage-specific needs. In addition, filtering valid records by timestamps can eliminate redundant data outside the time domain, avoiding the resource consumption caused by full record statistics. Furthermore, whether it is fun knowledge cards frequently used by young users or subject-based knowledge cards frequently used by school-age users, they can all be placed in priority display positions through accurate statistics, allowing users of different ages and with different needs to quickly find the target knowledge cards, effectively improving the ease of operation of the learning machine 100 and adapting to the diverse usage scenarios of users.

[0105] In one optional embodiment of this application, the step of filtering out valid records within the time domain analysis range from the operation records based on the timestamp further includes an abnormal behavior identification mechanism, which achieves accurate filtering of invalid usage records through multi-dimensional feature extraction and rule determination.

[0106] Specifically, the abnormal behavior recognition model constructs multiple feature extraction dimensions, including but not limited to operation rhythm features, interaction depth features, and environmental association features. The operation rhythm feature is used to identify abnormal continuous operation behaviors, such as clicking at intervals less than or equal to 0.5 seconds, which is determined as continuous random clicking. The interaction depth feature is used to evaluate the actual degree of interaction between the user and the card module, such as opening a knowledge card without any function triggering and a dwell time of less than 1 second. The environmental association feature is used to detect operation behaviors under specific device states, such as operation when the Learning Machine 100 is in parental control lock mode.

[0107] Furthermore, when user operation characteristics meet preset abnormal patterns, the system will mark the corresponding records as abnormal and filter them. For example, a user's operation of rapidly clicking multiple knowledge cards with a very short dwell time will be identified as an invalid record; operations within the knowledge card module that do not trigger core learning functions and have insufficient dwell time will be judged as shallow interactions; all operations of the PaiXueJi100 device in parental lock mode will be marked as permission abnormalities. Such abnormal records will not be included in subsequent statistical calculations.

[0108] In this embodiment, anomaly filtering effectively improves the statistical accuracy of valid records, avoiding sorting distortions caused by children's random clicking or accidental touches, thus enhancing data reliability and reducing popularity assessment errors. Furthermore, filtering invalid records effectively improves the efficiency of popularity calculation, preventing interface update lag caused by redundant data, ensuring smooth operation of the learning machine 100, and reducing system resource consumption.

[0109] Please see Figure 12 , Figure 12 This is a flowchart of a control method for an electronic device provided in another embodiment of this application. The step of statistically analyzing the number of times each knowledge card module is opened and the completion rate based on the usage records further includes the following steps:

[0110] S1034. Call the preset interface interaction logic configuration information library, which stores the completion judgment rules corresponding to each knowledge card module.

[0111] The aforementioned interface interaction logic configuration information database can be understood as a structured data storage unit preset locally or in the cloud by the Paixueji100. It is used to centrally store the set of interaction rules corresponding to each knowledge card module. Its essence is a configuration file cluster with queryable and updatable characteristics, providing standardized data support for the determination of the interaction behavior of each knowledge card module.

[0112] The aforementioned completion judgment rules can be understood as standardized judgment criteria customized for each knowledge card module in the interface interaction logic configuration information database, used to define whether the user's use of the knowledge card module has reached a state of complete learning or effective use.

[0113] Optionally, differentiated judgment conditions can be formulated based on the functional differences of different knowledge card modules to avoid statistical distortion caused by applying a single rule to all knowledge card modules. In one possible embodiment, the completion judgment rule for the popular science knowledge card module can be that the user's dwell time on the popular science function page is greater than or equal to 5 seconds. In another possible embodiment, the completion judgment rule for the story knowledge card module can be that the playback progress of the story audio or video is greater than or equal to 80%.

[0114] S1035. Based on the interface interaction records of each knowledge card module, determine the total number of interface interactions corresponding to each knowledge card module.

[0115] The aforementioned interface interaction records can be understood as a set of structured behavioral data related to the operation of the knowledge card module, captured and stored in real time by the front-end interaction monitoring module during the user's use of the knowledge card module by the Paixueji100 back-end system. This data is used to trace the quantifiable interactive behavior of the user within the knowledge card module's functional interface and serves as the core data basis for determining the effectiveness of the interaction and filtering the completion records.

[0116] The total number of interface interactions mentioned above can be understood as the cumulative number of times that meet the valid interaction criteria in the interface interaction records corresponding to a certain knowledge card module within the preset time domain analysis range, after validity screening, that is, the number of times the user has effectively used the knowledge card module.

[0117] S1036. For each knowledge card module, analyze its interface interaction records according to its completion judgment rules, and count the number of records that meet the completion conditions.

[0118] In this embodiment, within the preset time domain analysis range, for each knowledge card module, based on its corresponding completion judgment rule, the total number of valid records that meet the corresponding completion judgment rule is selected from the interface interaction records of the knowledge card module, providing core data for subsequent completion rate calculation.

[0119] S1037. Calculate the completion rate of each knowledge card module based on the number of records that meet the completion conditions and the total number of interface interactions.

[0120] In this embodiment, a standardized formula is used to convert the number of records meeting the completion criteria and the total number of interface interactions into a percentage indicator representing the effectiveness of user usage of the knowledge card module, providing core input parameters for subsequent usage popularity value calculation. In one possible embodiment, the completion rate can be calculated as: Completion Rate = (Number of records meeting the completion criteria / Total number of interface interactions) × 100%. Wherein, if the total number of interface interactions is 0, meaning that a certain knowledge card module has no effective usage records within the time domain analysis range, the completion rate is set to 0%.

[0121] In the application embodiment, the control method of the learning machine 100 calls a preset interface interaction logic configuration library to customize exclusive completion judgment rules for different knowledge card modules, and combines interface interaction records to distinguish between valid completion and invalid operations, so that the completion rate can truly reflect the user's acceptance and usage depth of the knowledge card module content, and accurately reflect the user's real usage needs.

[0122] In one optional embodiment of this application, before the step of analyzing the interface interaction records according to its completion judgment rules and counting the number of records that meet the completion conditions, a scene recognition-based dynamic adaptation mechanism for completion rules is further included, which is used to automatically adjust the completion judgment conditions according to the user's current usage scenario, so as to enhance the rationality and scene adaptability of the evaluation.

[0123] Specifically, the scene recognition module identifies the current usage scenario through at least one of the following dimensions: time scene dimension, which identifies the scenario corresponding to a specific time period based on the system time, for example, identifying 19:00-21:00 on weekdays as an after-school review scenario, and 9:00-11:00 on weekends as an interest-based learning scenario; device status scene dimension, which identifies the scenario based on the network connection status of the device, for example, when the device is connected to home WiFi, it is determined to be a fixed learning scenario, and when it is connected to a mobile hotspot, it is determined to be an outing scenario; task association scene dimension, which identifies the scenario based on the user's usage of other functional modules.

[0124] The control system of the learning machine 100 has a preset scene-completion judgment rule mapping table, which is used to dynamically adjust the completion judgment conditions according to different scenes. In one possible embodiment, when the scene is identified as an outing, the completion condition of the story knowledge card module is adjusted from "playing to 80%" to "playing to 50%" to adapt to the characteristics of fragmented learning. In this embodiment, the completion judgment rule of the story knowledge card module is adaptively adjusted in the outing scene, avoiding the decline in the popularity of knowledge cards due to insufficient time to complete the playback, ensuring that commonly used knowledge card modules are stably placed in front, adapting to fragmented learning scenes, and improving the applicability of the completion judgment rule.

[0125] Optionally, the operation record includes at least one of the following: a click operation, a swipe operation, a close operation, or a return operation for the knowledge card module. The interface interaction record includes at least one of the following: a function trigger record within the card or a task completion status.

[0126] The aforementioned click operation can be understood as the user clicking the virtual icon corresponding to the knowledge card element 120 on the touch screen 110 of the learning machine 100 using a finger or stylus. The aforementioned swipe operation can be understood as an interactive behavior characterized by continuous positional movement performed by the user on the touch screen 110 of the learning machine 100 using a finger or stylus, used to switch knowledge card modules or browse content within a knowledge card module. The aforementioned close operation can be understood as clicking the close button on the interface to exit the knowledge card module. The aforementioned return operation can be understood as clicking the return button to return to the previous interface.

[0127] In one possible implementation, a user is considered to have successfully opened the idiom knowledge card if they click on it, stay for more than 3 seconds, and then perform a swipe. In another possible implementation, if a user closes the idiom knowledge card within 1 second of clicking on it, the card is excluded from the statistics to avoid invalid data interfering with the accuracy of the opening count.

[0128] The aforementioned function trigger records can be understood as the core component of the interface interaction records. They are used to retain structured data representing the process where "after the user clicks on knowledge card element 120, the learning machine 100 control system automatically presents the core functions and content of that module." This core data characterizes the functional presentation status of the learning machine 100 control system in response to the card call command, and is the direct basis for determining whether the knowledge card module has been effectively invoked. The aforementioned task completion status can be understood as objective feedback data characterizing the user's usage status after the knowledge card module's functions are presented. This data is based on quantitative indicators such as content browsing completeness and dwell time, providing core verification support for the completion judgment rules.

[0129] In this embodiment, the operation log covers core interactive behaviors such as clicking, swiping, closing, and returning, encompassing the entire user operation process of the knowledge card module. Clicking precisely pinpoints the intent to invoke the knowledge card module, swiping adapts to switching between modules and content browsing needs, and closing or returning clearly identifies the termination point, effectively distinguishing between valid operations and accidental touches, providing a clear basis for subsequent valid record filtering. The interface interaction log is further subdivided into function trigger records and task completion statuses. Function trigger records directly corroborate the valid invocation of the knowledge card module, while task completion statuses use browsing completeness and dwell time as core indicators to objectively reflect the user's true usage depth, avoiding data statistical distortion and providing precise verification support for completion judgment rules. This ensures the completeness and accuracy of data collection, thereby guaranteeing the reliability of popularity value calculation and the rationality of card sorting, ultimately achieving a precise match between interface layout and user needs, improving operational convenience and product intelligence.

[0130] Optionally, determining the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate of each knowledge card module includes calculating the usage popularity value according to the calculation formula: usage popularity value = number of times opened × α + completion rate × β × k, where α is the weighting coefficient of the number of times opened, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantization conversion coefficient of the completion rate.

[0131] The above calculation formula uses weighted summation as its core logic, merging two statistical indicators from different dimensions into a single popularity value to ensure the uniformity and comparability of the evaluation dimensions. Here, α is the weighting coefficient for the number of views, representing the importance of usage frequency in popularity evaluation, and β is the weighting coefficient for the completion rate, representing the importance of usage effectiveness in popularity evaluation. The quantification conversion coefficient k is used to convert the percentage-based completion rate into a quantified value of the same dimension as the number of views, avoiding calculation biases caused by differences in indicator dimensions and ensuring that the two indicators can be directly weighted in the formula.

[0132] Optionally, the step of updating the sorting of knowledge card elements 120 corresponding to each knowledge card module in the electronic device interface display area according to the usage popularity value of each knowledge card module is triggered by at least one of the following triggering conditions: in response to the user ending the current session on any knowledge card module; based on a preset periodic update strategy; in response to the cumulative increment of the number of times any knowledge card module is opened reaching a preset threshold.

[0133] The aforementioned response to the user ending the current session on any knowledge card module can be understood as the user completing a single use of a knowledge card module and performing termination operations such as closing the module or returning to the previous interface. The learning machine 100 control system immediately triggers a sorting update. In this embodiment, each time the user completes a use of a knowledge card module, the system updates the popularity value based on the latest number of openings and completion rate, thereby adjusting the sorting to ensure that subsequent interface presentation directly reflects the user's latest usage preferences.

[0134] The aforementioned pre-configured periodic update strategy can be understood as the system being pre-configured with a fixed update cycle. Upon reaching a preset time point, the sorting update is automatically triggered without user intervention. In this implementation, it avoids excessive system resource consumption due to frequent user activity, while ensuring the sorting remains optimized through periodic updates, adapting to changes in long-term user preferences.

[0135] The aforementioned response, where the cumulative increase in the number of times any knowledge card module is opened reaches a preset threshold, can be understood as a preset threshold for the cumulative increase in the number of times a knowledge card module is opened. When the cumulative increase in the number of times a knowledge card module is opened within a statistical period reaches this threshold, the system triggers a sorting update. In this embodiment, if a user frequently opens a certain knowledge card module in a short period of time, the sorting adjustment can be triggered by the incremental threshold without waiting for the session to end or the periodic node to occur, ensuring that popular knowledge card modules are promptly prioritized and improving user acquisition efficiency.

[0136] In one optional embodiment of this application, the learning device 100 stores the latest sorting results of knowledge card elements 120 locally and simultaneously synchronizes them to a cloud server via an encrypted communication protocol. The learning device 100 is also associated with a parent control terminal, which is deployed independently as an application and establishes a connection with the learning device 100 via a cloud server. After logging into the same user account associated with the learning device 100, the parent control terminal has the function of viewing the sorting status of knowledge card elements 120 on the learning device 100 and supports remote intervention. For example, parents can manually set a specific knowledge card element 120 to be prioritized for display; this intervention command, after being synchronized to the cloud, will take effect in real time on the learning device 100.

[0137] Optionally, when the learning machine 100 automatically triggers a sorting adjustment due to local usage record updates, and this occurs simultaneously with a manual sorting command from the parent control terminal, the system prioritizes the command with the higher permission level. For example, the parent's manual adjustment permission can be set to be higher than the system's automatic sorting permission. In this embodiment, parents can remotely optimize their child's learning entry point through the application, and parents do not need to frequently check the learning machine 100 to understand their child's usage preferences, thus improving the ease of use of the learning machine 100.

[0138] Please see Figure 13 , Figure 13 This is a schematic diagram of the structure of the control device for the electronic device provided in this application embodiment. The control device for the electronic device includes:

[0139] The authorization management module 201 is used to control the electronic device to pop up an authorization request interface for the user and obtain the user's authorization for the collection of usage records of the knowledge card module;

[0140] The record acquisition module 202, after obtaining authorization from the user to collect the usage records of the knowledge card module, controls the background system of the electronic device to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records.

[0141] The popularity calculation module 203 determines the usage popularity value of each knowledge card module based on the number of times each knowledge card module is opened and the completion rate.

[0142] The sorting update module 204 updates the sorting of the knowledge card elements corresponding to each knowledge card module in the electronic device interface display area according to the usage popularity value of each knowledge card module.

[0143] Optionally, the knowledge card module includes at least two of the following: a science popularization card module, a Chinese character card module, an idiom card module, a word card module, and a story card module. The usage record includes at least one of the following: timestamp, operation record, interface dwell time, and interface interaction record.

[0144] Optionally, the record acquisition module 202 is further configured to: acquire a preset time domain analysis range; filter out valid records within the time domain analysis range from the operation records according to the timestamp; and count the number of valid records corresponding to each knowledge card module as the number of times the knowledge card module is opened.

[0145] Optionally, the record acquisition module 202 is further configured to: call a preset interface interaction logic configuration information library, the interface interaction logic configuration information library storing the completion judgment rules corresponding to each knowledge card module; determine the total number of interface interactions corresponding to each knowledge card module based on the interface interaction records of each knowledge card module; for each knowledge card module, analyze its interface interaction records according to its completion judgment rules, and count the number of records that meet the completion conditions; calculate the completion rate of each knowledge card module based on the number of records that meet the completion conditions and the total number of interface interactions.

[0146] Optionally, the operation record includes at least one of the following: click operation, swipe operation, close operation, or return operation for the knowledge card module; the interface interaction record includes at least one of the following: function trigger record within the card or task completion status.

[0147] Optionally, the popularity calculation module is further configured to calculate the usage popularity value according to the calculation formula, which is: usage popularity value = number of times opened × α + completion rate × β × k, where α is the weighting coefficient of the number of times opened, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantization conversion coefficient of the completion rate.

[0148] Optionally, updating the sorting of each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module is triggered by at least one of the following triggering conditions: in response to the user ending the current session with any knowledge card module; based on a preset periodic update strategy; in response to the cumulative increment of the number of times any knowledge card module is opened reaching a preset threshold.

[0149] Please see Figure 14 , Figure 14This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. This application also provides an electronic device, including: a memory 301, a processor 302, and a computer program stored in the memory and executable on the processor 302. When the processor 302 executes the computer program, it implements the steps in the control method of the electronic device provided in this embodiment of the invention.

[0150] Specifically, the electronic device can be the learning machine 100 itself, or a server that is communicatively connected to the learning machine 100. When the electronic device is a learning machine, its hardware structure also includes peripheral components such as a touch screen, speaker, microphone, physical buttons, and camera, used to realize functions such as interaction, voice acquisition, and image display.

[0151] When processor 302 runs the computer program for the control method of the electronic device stored in memory 301, it specifically performs the following steps:

[0152] The control device's front-end software displays an authorization request interface to the user to obtain the user's authorization for the collection of knowledge card module usage records;

[0153] After obtaining authorization from the user to collect usage records of the knowledge card module, the system controls the electronic device's backend system to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records.

[0154] The usage popularity value of each knowledge card module is determined based on the number of times it is opened and the completion rate.

[0155] Based on the usage popularity value of each knowledge card module, update the sorting of the knowledge card elements corresponding to each knowledge card module in the display area of ​​the electronic device interface.

[0156] Optionally, the knowledge card module includes at least two of the following: a science popularization card module, a Chinese character card module, an idiom card module, a word card module, and a story card module. The usage record includes at least one of the following: timestamp, operation record, interface dwell time, and interface interaction record.

[0157] Optionally, the step of the processor 302 executing the step of counting the number of times each knowledge card module is opened and the completion rate based on the usage records includes: obtaining a preset time domain analysis range; filtering out valid records within the time domain analysis range from the operation records based on the timestamp; and counting the number of valid records corresponding to each knowledge card module as the number of times that knowledge card module is opened.

[0158] Optionally, the step of the processor 302 executing the step of counting the number of times each knowledge card module is opened and the completion rate based on the usage records further includes: calling a preset interface interaction logic configuration information library, which stores the completion judgment rules corresponding to each knowledge card module; determining the total number of interface interactions corresponding to each knowledge card module based on the interface interaction records of each knowledge card module; analyzing the interface interaction records of each knowledge card module according to its completion judgment rules, and counting the number of records that meet the completion conditions for each knowledge card module; and calculating the completion rate of each knowledge card module based on the number of records that meet the completion conditions and the total number of interface interactions.

[0159] Optionally, the operation record includes at least one of the following: click operation, swipe operation, close operation, or return operation for the knowledge card module; the interface interaction record includes at least one of the following: function trigger record within the card or task completion status.

[0160] Optionally, the process executed by processor 302 to determine the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate of each knowledge card module includes calculating the usage popularity value according to the calculation formula: usage popularity value = number of times opened × α + completion rate × β × k, where α is the weighting coefficient of the number of times opened, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantization conversion coefficient of the completion rate.

[0161] Optionally, the step of updating the sorting of each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module, executed by the processor 302, is triggered by at least one of the following triggering conditions: in response to the user ending the current session with any knowledge card module; based on a preset periodic update strategy; in response to the cumulative increment of the number of times any knowledge card module is opened reaching a preset threshold.

[0162] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by the processor 302, it implements the various processes of the control method of the learning machine provided in this embodiment of the invention and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0164] In this application, the terms "embodiment" and "implementation" mean that a specific feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of this application. The appearance of these phrases in various locations throughout the specification does not necessarily refer to the same embodiment, nor are they independent or alternative embodiments mutually exclusive with other embodiments. Those skilled in the art will understand, explicitly and implicitly, that the embodiments described in this application can be combined with other embodiments. Furthermore, it should be understood that the features, structures, or characteristics described in the various embodiments of this application can be arbitrarily combined to form another embodiment that does not depart from the spirit and scope of the technical solution of this application, provided there is no contradiction between them.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of this application should not depart from the spirit and scope of the technical solutions of this application.

Claims

1. A control method for an electronic device, characterized in that, The method includes the following steps: Control the electronic device to display an authorization request interface to the user and obtain the user's authorization for the collection of usage records of the knowledge card module; After obtaining authorization from the user to collect usage records of the knowledge card module, the system controls the electronic device's backend system to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records. The usage popularity value of each knowledge card module is determined based on the number of times it is opened and the completion rate. Based on the usage popularity value of each knowledge card module, update the sorting of the knowledge card elements corresponding to each knowledge card module in the display area of ​​the electronic device interface.

2. The control method for an electronic device as described in claim 1, characterized in that, The knowledge card module includes at least two of the following: a science card module, a Chinese character card module, an idiom card module, a word card module, and a story card module. The usage record includes at least one of the following: timestamp, operation record, duration of time spent on the interface, and interface interaction record.

3. The control method for an electronic device as described in claim 2, characterized in that, The step of statistically analyzing the number of times each knowledge card module is opened and its completion rate based on the usage records includes: Obtain the preset time domain analysis range; Based on the timestamp, select valid records that fall within the time domain analysis range from the operation records; The number of valid records corresponding to each knowledge card module is counted as the number of times that knowledge card module is opened.

4. The control method for an electronic device as described in claim 2, characterized in that, The step of calculating the number of times each knowledge card module is opened and the completion rate based on the usage records also includes: The preset interface interaction logic configuration information library is invoked, which stores the completion judgment rules corresponding to each knowledge card module. Based on the interface interaction records of each knowledge card module, determine the total number of interface interactions corresponding to each knowledge card module; For each knowledge card module, analyze its interface interaction records according to its completion judgment rules, and count the number of records that meet the completion conditions; The completion rate of each knowledge card module is calculated based on the number of records that meet the completion criteria and the total number of interface interactions.

5. The control method for an electronic device as described in claim 2, characterized in that, The operation record includes at least one of the following: click operation, swipe operation, close operation, or return operation for the knowledge card module; The interface interaction record includes at least one of the following: function trigger record within the card or task completion status.

6. The control method for an electronic device as described in claim 1, characterized in that, The determination of the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate includes calculating the usage popularity value according to a calculation formula, which is: Popularity value = number of opens × α + completion rate × β × k, where α is the weighting coefficient of the number of opens, β is the weighting coefficient of the completion rate, and α + β = 1, and k is the quantification conversion coefficient of the completion rate.

7. The control method for an electronic device as described in claim 1, characterized in that, The step of updating the sorting of knowledge card elements corresponding to each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module is triggered by at least one of the following triggering conditions: Responds when the user ends the current session on any knowledge card module; Based on a pre-defined periodic update strategy; The cumulative increase in the number of times any knowledge card module is opened reaches a preset threshold.

8. A control device for an electronic device, characterized in that, The control device for the electronic device includes: The authorization management module is used to control electronic devices to pop up an authorization request interface for users and obtain user authorization for the collection of knowledge card module usage records; The record acquisition module, after obtaining authorization from the user to collect the usage records of the knowledge card module, controls the background system of the electronic device to collect the user's usage records for each knowledge card module, and calculates the number of times each knowledge card module is opened and the completion rate based on the usage records; The popularity calculation module determines the usage popularity value of each knowledge card module based on the number of times it is opened and the completion rate. The sorting update module updates the sorting of the knowledge card elements corresponding to each knowledge card module in the electronic device interface display area based on the usage popularity value of each knowledge card module.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the control method for an electronic device as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the control method for an electronic device as described in any one of claims 1 to 7.

Images