Data processing system and method for data management

Abstract

The invention relates to the technical field of data processing, in particular to a data processing system and method for data management. A data extraction module dynamically generates a learning directory and adjusts difficulty; the data dynamic management module strengthens memory through multimedia explanation; the task grading generation module creates grading task exercise output capability; the error data processing module records and strengthens error exercise; the real-time feedback module provides excitation and correction; and the intelligent path adjustment module optimizes a learning path. Through interesting breakthrough, timely feedback and intelligent adjustment, learning difficulty is reduced, learning efficiency and interestingness are improved, and learners are helped to quickly master knowledge, so that the problem that an existing system lacks intelligent analysis ability based on user behavior data is solved, learning path adjustment depends on static rules, and learning efficiency is improved. And the system cannot dynamically adapt to the learning progress and the cognitive level of the user through an algorithm.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data processing system and method for data management. Background Art

[0002] Traditional systems have obvious deficiencies in the dynamic organization and adaptive adjustment of learning materials. They are unable to dynamically optimize data structures based on users' real-time learning performance (such as error rate, response time, etc.), resulting in a low match between learning content and user capabilities.

[0003] In addition, the mechanism for capturing and processing error data is not systematic enough, and the error frequency and type data are not effectively used for prioritization, making it difficult for users to review weak areas efficiently.

[0004] Due to the problem that the existing system lacks intelligent analysis capabilities based on user behavior data, learning path adjustments rely on static rules, which makes it impossible for the system to dynamically adapt to the user's learning progress and cognitive level through algorithms, affecting the effectiveness of system use. Summary of the Invention

[0005] The purpose of the present invention is to provide a data processing system and method for data management, aiming to solve the problem that the existing system lacks intelligent analysis capabilities based on user behavior data, the learning path adjustment relies on static rules, and the system cannot dynamically adapt to the user's learning progress and cognitive level through algorithms.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a data processing system for data management, comprising a data extraction module, a data dynamic management module, a task hierarchical generation module, an error data processing module, a real-time feedback module, and an intelligent path adjustment module, wherein the data extraction module, the data dynamic management module, the task hierarchical generation module, and the error data processing module are sequentially connected, the real-time feedback module is connected to the data dynamic management module, the task hierarchical generation module, and the intelligent path adjustment module, and the intelligent path adjustment module is connected to the data extraction module and the task hierarchical generation module;

[0007] The data extraction module dynamically filters learning materials from the database and generates a learning catalog that is suitable for the user's current stage;

[0008] The data dynamic management module collects historical performance data on user accuracy and time consumption, automatically divides the difficulty level of materials through clustering algorithms, groups materials according to logical relevance, and adapts to the needs of different task scenarios;

[0009] The task hierarchical generation module decomposes complex materials into operational components and generates step-by-step tasks from the operational components according to the learning stage;

[0010] The error data processing module captures user error data in real time, records error types and frequencies, and dynamically adjusts the task queue based on error weight data;

[0011] The real-time feedback module improves user learning motivation through high-frequency sound stimulation and visual reward mechanism;

[0012] The intelligent path adjustment module dynamically optimizes the learning path based on user answer data and AI algorithms.

[0013] Wherein, the data extraction module includes a vocabulary extraction unit, a difficulty grading unit and a dynamic grouping unit;

[0014] The vocabulary extraction unit is used to dynamically extract words from a preset vocabulary to generate a learning catalog;

[0015] The difficulty grading unit is used to automatically grade the difficulty of words according to user learning data;

[0016] The dynamic grouping unit is used to group words by theme or memory logic to adapt to different level-breaking tasks.

[0017] The data dynamic management module includes a word pronunciation unit, a storyline generation unit, an image display unit and a logic verification unit;

[0018] The word pronunciation unit is used to provide standard American / British pronunciation demonstrations to enhance user listening input;

[0019] The storyline generating unit is used to generate an associated storyline for each word to assist logical memory;

[0020] The image display unit is used to display visual images corresponding to words to enhance image memory;

[0021] The logic verification unit is used to verify the consistency of word spelling and story logic after the user completes the word filling task.

[0022] The task level generation module includes a story connection unit, a word splitting and memory unit, and a word spelling and clearance unit.

[0023] The story connection unit is used to bind words to key elements in the story to generate multiple-choice questions;

[0024] The word splitting memory unit is used to split a word into prefixes, roots, and suffixes, and prompt the splitting logic;

[0025] The word spelling unit is used to require the user to spell a complete word according to part of speech, English meaning or pronunciation.

[0026] The error data processing module includes an error question capturing unit, an error question cyclic sorting unit, an option clearing and reselecting unit, and an error question review control unit;

[0027] The error question capturing unit is used to record the incorrect words and error types in the user's answers in real time;

[0028] The wrong question circular sorting unit is used to sort the wrong questions according to the error frequency and forcibly add them to the current task queue;

[0029] The option clearing and reselecting unit is used to clear the user's wrong options and allow the user to re-answer until the answer is correct;

[0030] The wrong question review control unit is used to automatically trigger special exercises for wrong questions after the user completes the current task.

[0031] The real-time feedback module includes a standard pronunciation feedback unit, an immediate error prompt unit, a celebration interface generation unit, and a dopamine incentive unit;

[0032] The standard pronunciation feedback unit is used to play the standard pronunciation after each answer is submitted, so as to strengthen the listening memory simultaneously;

[0033] The instant error prompt unit is used to display the wrong answer and pop up a prompt box;

[0034] The celebration interface generation unit is used to generate dynamic animation, points reward interface and voice encouragement after successfully passing the level;

[0035] The dopamine incentive unit is used to stimulate users to continue learning through high-frequency positive feedback.

[0036] The intelligent path adjustment module includes a user behavior analysis unit, an adaptive learning engine and a personalized recommendation unit;

[0037] The user behavior analysis unit is used to collect data on answering time, error rate, and number of repetitions to generate a learning ability profile;

[0038] The adaptive learning engine is used to dynamically adjust the vocabulary difficulty, task type ratio and prompt strategy according to the user profile;

[0039] The personalized recommendation unit is used to recommend specialized training content based on the user's weaknesses.

[0040] In a second aspect, a data processing method for data management is provided, which is used in the data processing system for data management according to the first aspect, and comprises the following steps:

[0041] Dynamically extract words from the preset vocabulary to generate a learning catalog, and dynamically adjust word grouping and difficulty levels based on the user's learning progress and error rate;

[0042] Provides standard American / British pronunciation demonstrations for each word, explains the word splitting logic, corresponding Chinese vocabulary, and generates related storylines and visual images;

[0043] Break down complex materials into actionable components and generate step-by-step tasks from these components based on the learning stage;

[0044] Record the incorrect words and error types in the user's answers in real time, sort them by error frequency, and then force them to be added to the current task queue;

[0045] After each answer is submitted, the standard pronunciation is played to simultaneously strengthen listening memory. Wrong answers are displayed and a prompt box pops up to help users correct them in time.

[0046] Dynamically optimize learning paths based on user answer data and AI algorithms.

[0047] The data processing system for information management of the present invention solves the problems of the Feynman learning method in the prior art, such as the difficulty in applying it, the lack of standardized learning materials, the lack of interest, and the inability to quickly resolve learning difficulties. The system includes modules such as data extraction, dynamic data management, task hierarchical generation, error data processing, real-time feedback, and intelligent path adjustment. The data extraction module dynamically generates a learning catalog and adjusts the difficulty; the dynamic data management module strengthens memory through multimedia explanations; the task hierarchical generation module creates hierarchical tasks to train output capabilities; the error data processing module records and strengthens wrong question exercises; the real-time feedback module provides incentives and corrections; and the intelligent path adjustment module optimizes the learning path. This solution reduces the difficulty of learning, improves learning efficiency and interest, and helps learners quickly master knowledge through interesting challenges, timely feedback, and intelligent adjustments. It is particularly suitable for English word memorization and can significantly improve learning results and experience, thereby solving the problem that the existing system lacks intelligent analysis capabilities based on user behavior data, learning path adjustment relies on static rules, and the system cannot dynamically adapt to the user's learning progress and cognitive level through algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1It is a schematic diagram of a data processing system for information management provided by the present invention.

[0050] Figure 2 It is a schematic diagram of the data extraction module.

[0051] Figure 3 It is a schematic diagram of the data extraction module.

[0052] Figure 4 It is a schematic diagram of the task hierarchical generation module.

[0053] Figure 5 It is a schematic diagram of the error data processing module.

[0054] Figure 6 It is a schematic diagram of the real-time feedback module.

[0055] Figure 7 It is a schematic diagram of the intelligent path adjustment module.

[0056] Figure 8 It is a flow chart of a data processing method for data management provided by the present invention.

[0057] In the figure: 1-data extraction module, 2-data dynamic management module, 3-task classification generation module, 4-error data processing module, 5-real-time feedback module, 6-intelligent path adjustment module, 11-lexicon extraction unit, 12-difficulty classification unit, 13-dynamic grouping unit, 21-word pronunciation unit, 22-story line generation unit, 23-image display unit, 24-logic verification unit, 31-story connection unit, 32-word splitting and memory unit, 33-word spelling and clearance unit, 41-wrong question capture unit, 42-wrong question cyclic sorting unit, 43-option clearing and reselection unit, 44-wrong question review control unit, 51-standard pronunciation feedback unit, 52-instant error prompt unit, 53-celebration interface generation unit, 54-dopamine incentive unit, 61-user behavior analysis unit, 62-adaptive learning engine, 63-adaptive learning engine. DETAILED DESCRIPTION

[0058] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0059] See also Figures 1 to 7In a first aspect, the present invention provides a data processing system for data management, comprising a data extraction module 1, a data dynamic management module 2, a task hierarchical generation module 3, an error data processing module 4, a real-time feedback module 5, and an intelligent path adjustment module 6, wherein the data extraction module 1, the data dynamic management module 2, the task hierarchical generation module 3, and the error data processing module 4 are sequentially connected, the real-time feedback module 5 is connected to the data dynamic management module 2, the task hierarchical generation module 3, and the intelligent path adjustment module 6, and the intelligent path adjustment module 6 is connected to the data extraction module 1 and the task hierarchical generation module 3;

[0060] The data extraction module 1 dynamically filters learning materials from the database and generates a learning catalog that is adapted to the user's current stage;

[0061] The data dynamic management module 2 collects historical performance data on user accuracy and time consumption, automatically divides the difficulty level of the materials through clustering algorithms, groups the materials according to logical relevance, and adapts to the needs of different task scenarios;

[0062] The task hierarchical generation module 3 decomposes complex materials into operational components and generates step-by-step tasks from the operational components according to the learning stage;

[0063] The error data processing module 4 captures user error data in real time, records error types and frequencies, and dynamically adjusts the task queue based on error weight data;

[0064] The real-time feedback module 5 improves user learning motivation through high-frequency sound stimulation and visual reward mechanism;

[0065] The intelligent path adjustment module 6 dynamically optimizes the learning path based on user answer data and AI algorithms.

[0066] This embodiment addresses existing issues such as the Feynman learning method's difficulty in application, lack of standardized learning materials, lack of fun, and inability to quickly resolve learning difficulties. The system includes modules for data extraction, dynamic data management, hierarchical task generation, error data processing, real-time feedback, and intelligent path adjustment. Data extraction module 1 dynamically generates a learning catalog and adjusts difficulty; dynamic data management module 2 reinforces memory through multimedia explanations; hierarchical task generation module 3 creates hierarchical tasks to train output capabilities; error data processing module 4 records and reinforces incorrect practice; real-time feedback module 5 provides incentives and corrections; and intelligent path adjustment module 6 optimizes the learning path. This solution reduces learning difficulty, improves learning efficiency and fun, and helps learners quickly master knowledge through engaging challenges, timely feedback, and intelligent adjustments. It is particularly suitable for memorizing English vocabulary and can significantly improve learning outcomes and experience, thus addressing the existing system's lack of intelligent analysis capabilities based on user behavior data, the reliance on static rules for learning path adjustment, and the system's inability to dynamically adapt to the user's learning progress and cognitive level through algorithms.

[0067] Furthermore, the data extraction module 1 includes a vocabulary extraction unit 11, a difficulty grading unit 12 and a dynamic grouping unit 13;

[0068] The word library extraction unit 11 is used to dynamically extract words from a preset word library to generate a learning catalog;

[0069] The difficulty grading unit 12 is used to automatically grade the difficulty of words according to user learning data;

[0070] The dynamic grouping unit 13 is used to group words by theme or memory logic to adapt to different level-breaking tasks.

[0071] In this embodiment, the vocabulary extraction unit 11 dynamically extracts words from a preset vocabulary and generates a learning catalog. According to the user's learning progress and error rate, the word grouping and difficulty level are dynamically adjusted. The difficulty grading unit 12 analyzes the user's historical learning data, including the accuracy rate, answering time, and number of repeated learning times, and automatically divides the word difficulty level. The difficulty level is divided into multiple levels from easy to difficult to ensure that the learning content matches the user's current ability. The dynamic grouping unit 13 groups words according to the word's theme, part of speech, or memory logic. The grouped words are adapted to the requirements of different challenge tasks to ensure the systematic and targeted nature of the learning content.

[0072] Furthermore, the data dynamic management module 2 includes a word pronunciation unit 21, a storyline generation unit 22, an image display unit 23 and a logic verification unit 24;

[0073] The word pronunciation unit 21 is used to provide standard American / British pronunciation demonstrations to enhance user listening input;

[0074] The storyline generating unit 22 is used to generate an associated storyline for each word to assist logical memory;

[0075] The image display unit 23 is used to display visual images corresponding to words to enhance image memory;

[0076] The logic verification unit 24 is used to verify the consistency between the word spelling and the story logic after the user completes the word filling task.

[0077] In this embodiment, the word pronunciation unit 21 provides a standard American / British pronunciation demonstration for each word to strengthen the user's listening input. The user can click the pronunciation button at any time to listen to the pronunciation repeatedly to ensure that the pronunciation of the word is correctly mastered. The storyline generation unit 22 generates an associated storyline for each word to help the user remember the spelling and meaning of the word through logical memory. The storyline is usually associated with the word splitting logic and Chinese vocabulary, which enhances the fun and logic of memory. The image display unit 23 displays a visual image corresponding to the word to enhance the user's image memory. The image is usually related to the meaning or storyline of the word, helping the user to remember the word through visual association. After the user completes the word filling task, the logic verification unit 24 verifies the consistency between the word spelling and the story logic. If the user fills in the word correctly, the system will give positive feedback; if it is wrong, the system will prompt the user and provide the correct answer and explanation.

[0078] Furthermore, the task classification generation module 3 includes a story connection unit 31, a word splitting and memory unit 32, and a word spelling and clearance unit 33;

[0079] The story connection unit 31 is used to bind words to key elements in the story to generate multiple-choice questions;

[0080] The word splitting memory unit 32 is used to split a word into prefixes, roots, and suffixes, and prompt the splitting logic;

[0081] The word spelling unit 33 is used to require the user to spell a complete word according to part of speech, English meaning or pronunciation.

[0082] In this embodiment, the story connection unit 31 binds words to key elements in the story to generate multiple-choice questions. The user needs to select the correct word components according to the story line to complete the connection task. After the task is completed, the system will give immediate feedback. The word splitting memory unit 32 splits the word into prefixes, roots, and suffixes, and prompts the splitting logic. The user needs to recombine the words according to the splitting logic to complete the fill-in-the-blank task. The system will provide immediate error prompts and correct answers. The word spelling clearance unit 33 requires the user to spell the complete word according to the part of speech, English meaning or pronunciation. The user can click the pronunciation button to listen to the standard pronunciation, or view the image prompt to recall the story line of the word. After spelling correctly, the system will give congratulatory feedback.

[0083] Furthermore, the error data processing module 4 includes an error question capturing unit 41, an error question cyclic sorting unit 42, an option clearing and reselecting unit 43 and an error question review control unit 44;

[0084] The error question capturing unit 41 is used to record the incorrect words and error types in the user's answers in real time;

[0085] The wrong question circular sorting unit 42 is used to sort the wrong questions according to the error frequency and force them to be added to the current task queue;

[0086] The option clearing and reselecting unit 43 is used to clear the user's wrong options and allow the user to re-answer until the answer is correct;

[0087] The wrong question review control unit 44 is used to automatically trigger special exercises for wrong questions after the user completes the current task.

[0088] In this embodiment, the wrong question capture unit 41 records the wrong words and error types in the user's answers in real time. Error types include spelling errors, selection errors, pronunciation errors, etc. The wrong question circular sorting unit 42 sorts the wrong questions by priority based on the error frequency and error type, and forcibly adds them to the current task queue. Ensure that users give priority to reviewing weak points in subsequent learning. When the user answers a question incorrectly, the option clearing and reselection unit 43 clears the user's selection and allows the user to re-answer until the answer is correct. The system will provide immediate error prompts and correct answers. The wrong question review control unit 44 automatically triggers special exercises for wrong questions after the user completes the current task. Special exercises for wrong questions include reviewing wrong questions, intensive training and error analysis to help users thoroughly master their weaknesses.

[0089] Furthermore, the real-time feedback module 5 includes a standard pronunciation feedback unit 51, an immediate error prompt unit 52, a celebration interface generation unit 53 and a dopamine incentive unit 54;

[0090] The standard pronunciation feedback unit 51 is used to play the standard pronunciation after each answer is submitted, so as to strengthen the listening memory simultaneously;

[0091] The instant error prompt unit 52 is used to display the wrong answer and pop up a prompt box;

[0092] The celebration interface generation unit 53 is used to generate a dynamic animation, a points reward interface and voice encouragement after successfully passing a level;

[0093] The dopamine incentive unit 54 is used to stimulate the user to continue learning through high-frequency positive feedback.

[0094] In this embodiment, the standard pronunciation feedback unit 51 plays the standard pronunciation each time the user submits an answer, and simultaneously strengthens the user's listening memory. Regardless of whether the answer is correct or not, the system will provide a standard pronunciation demonstration. When the user answers a question incorrectly, the instant error prompt unit 52 will immediately display the wrong answer, pop up a prompt box, explain the reason for the error and provide the correct answer. The prompt box may include text descriptions, image prompts or video explanations. When the user successfully completes a task or passes a level, the celebration interface generation unit 53 will generate a dynamic animation, a points reward interface and voice encouragement. The celebration interface is designed to enhance the user's sense of accomplishment and gain, and motivate the user to continue learning. The dopamine incentive unit 54 stimulates users to continue learning through high-frequency positive feedback, such as point rewards, level upgrades and voice encouragement. The system will dynamically adjust the incentive mechanism according to the user's learning performance to maintain the user's enthusiasm for learning.

[0095] Furthermore, the intelligent path adjustment module 6 includes a user behavior analysis unit 61, an adaptive learning engine 62 and a personalized recommendation unit 63;

[0096] The user behavior analysis unit 61 is used to collect answering time, error rate, and repetition number data to generate a learning ability profile;

[0097] The adaptive learning engine 62 is used to dynamically adjust the vocabulary difficulty, task type ratio and prompt strategy according to the user profile;

[0098] The personalized recommendation unit 63 is used to recommend specialized training content based on the user's weaknesses.

[0099] In this embodiment, the user behavior analysis unit 61 collects data such as the user's answering time, error rate, number of repetitions, etc. to generate a learning ability profile of the user. The learning ability profile includes information such as the user's knowledge mastery, learning habits and weaknesses. The adaptive learning engine 62 dynamically adjusts the vocabulary difficulty, task type ratio and prompt strategy based on the user's learning ability profile. Ensure that the learning content always matches the user's current ability and provides appropriate challenges and learning support. The personalized recommendation unit 63 recommends special training content based on the user's learning ability profile and weaknesses. Special training content includes targeted word exercises, storyline reinforcement and image memory training to help users quickly improve their learning results.

[0100] See also Figure 8 In a second aspect, a data processing method for data management is provided, which is used in the data processing system for data management according to the first aspect, and comprises the following steps:

[0101] S1 dynamically extracts words from a preset vocabulary, generates a learning catalog, and dynamically adjusts word grouping and difficulty levels based on the user's learning progress and error rate;

[0102] Specifically, the vocabulary extraction unit 11 dynamically extracts words from a preset vocabulary and generates a learning catalog. According to the user's learning progress and error rate, the word grouping and difficulty level are dynamically adjusted. The difficulty grading unit 12 analyzes the user's historical learning data, including the accuracy rate, answering time and number of repeated learning times, and automatically divides the word difficulty level. The difficulty level is divided into multiple levels from easy to difficult to ensure that the learning content matches the user's current ability. The dynamic grouping unit 13 groups words according to the word's theme, part of speech or memory logic. The grouped words are adapted to the requirements of different challenge tasks to ensure the systematic and targeted nature of the learning content.

[0103] S2 provides standard American / British pronunciation demonstrations for each word, explains the word splitting logic, the corresponding Chinese vocabulary, and generates related storylines and visual images;

[0104] Specifically, the word pronunciation unit 21 provides a standard American / British pronunciation demonstration for each word to strengthen the user's listening input. The user can click the pronunciation button at any time to listen to the pronunciation repeatedly to ensure that the pronunciation of the word is correctly mastered. The storyline generation unit 22 generates an associated storyline for each word to help the user remember the spelling and meaning of the word through logical memory. The storyline is usually associated with the word splitting logic and Chinese vocabulary, which enhances the fun and logic of memory. The image display unit 23 displays a visual image corresponding to the word to enhance the user's image memory. The image is usually related to the meaning or storyline of the word, helping the user to remember the word through visual association. After the user completes the word filling task, the logic verification unit 24 verifies the consistency of the word spelling with the story logic. If the user fills in the word correctly, the system will give positive feedback; if it is wrong, the system will prompt the user and provide the correct answer and explanation.

[0105] S3 breaks down complex materials into actionable components and generates step-by-step tasks from these components based on the learning stage;

[0106] Specifically, the story connection unit 31 binds words to key elements in the story and generates multiple-choice questions. The user needs to select the correct word components according to the story line to complete the connection task. After the task is completed, the system will give immediate feedback. The word splitting memory unit 32 splits the word into prefixes, roots, and suffixes, and prompts the splitting logic. The user needs to recombine the words according to the splitting logic to complete the fill-in-the-blank task. The system will provide immediate error prompts and correct answers. The word spelling clearance unit 33 requires the user to spell the complete word according to the part of speech, English meaning or pronunciation. The user can click the pronunciation button to listen to the standard pronunciation, or view the image prompt to recall the story line of the word. After spelling correctly, the system will give congratulatory feedback.

[0107] S4 records the incorrect words and error types in the user's answers in real time, sorts them by error frequency, and then forces them to be added to the current task queue;

[0108] Specifically, the wrong question capture unit 41 records the wrong words and error types in the user's answers in real time. Error types include spelling errors, selection errors, pronunciation errors, etc. The wrong question circular sorting unit 42 sorts the wrong questions by priority based on the error frequency and error type, and forcibly adds them to the current task queue. Ensure that users give priority to reviewing weak points in subsequent learning. When the user answers a question incorrectly, the option clearing and reselection unit 43 clears the user's selection and allows the user to re-answer until the answer is correct. The system will provide instant error prompts and correct answers. The wrong question review control unit 44 automatically triggers special exercises for wrong questions after the user completes the current task. Special exercises for wrong questions include reviewing wrong questions, intensive training and error analysis to help users thoroughly master their weaknesses.

[0109] S5 plays the standard pronunciation after each answer is submitted, simultaneously strengthening listening memory, and displays incorrect answers with a pop-up prompt box to help users correct them in time;

[0110] Specifically, the standard pronunciation feedback unit 51 plays the standard pronunciation each time the user submits an answer, and simultaneously strengthens the user's listening memory. Regardless of whether the answer is correct or not, the system will provide a standard pronunciation demonstration. When the user answers a question incorrectly, the instant error prompt unit 52 will immediately display the wrong answer, pop up a prompt box, explain the reason for the error and provide the correct answer. The prompt box may include text descriptions, image prompts or video explanations. When the user successfully completes a task or passes a level, the celebration interface generation unit 53 will generate a dynamic animation, a points reward interface and voice encouragement. The celebration interface is designed to enhance the user's sense of accomplishment and gain, and motivate the user to continue learning. The dopamine incentive unit 54 stimulates users to continue learning through high-frequency positive feedback, such as point rewards, level upgrades and voice encouragement. The system will dynamically adjust the incentive mechanism according to the user's learning performance to maintain the user's enthusiasm for learning.

[0111] S6 dynamically optimizes learning paths based on user answer data and AI algorithms.

[0112] Specifically, the user behavior analysis unit 61 collects data such as the user's answering time, error rate, number of repetitions, etc., and generates a learning ability profile of the user. The learning ability profile includes information such as the user's knowledge mastery, learning habits and weaknesses. The adaptive learning engine 62 dynamically adjusts the vocabulary difficulty, task type ratio and prompt strategy according to the user's learning ability profile. Ensure that the learning content always matches the user's current ability and provides appropriate challenges and learning support. The personalized recommendation unit 63 recommends special training content based on the user's learning ability profile and weaknesses. Special training content includes targeted word exercises, storyline reinforcement and image memory training to help users quickly improve their learning results.

[0113] Beneficial effects:

[0114] First, through step-by-step output tasks, learners can understand the process of simultaneously outputting knowledge and inputting it, improving the efficiency of knowledge absorption. Learners can continuously consolidate what they have learned through practice, accelerating the internalization and mastery of knowledge.

[0115] Second, the level-by-level design makes the learning process similar to a game, gradually completing the output of knowledge and improving learners' acceptance and sense of achievement. Timely celebrations and rewards stimulate the brain's dopamine secretion, making learners feel happy and satisfied, and strengthening their learning enthusiasm.

[0116] 3. Timely reminders reduce learning difficulty, reduce learners' fear of difficulty, encourage them to try and fail, and develop a correct learning mindset. Learners can continue to try and make progress in a relaxed atmosphere.

[0117] 4. Through high-frequency stimulation of standard American pronunciation, create a native English learning environment, quickly improve English listening skills, activate the brain's sound memory area, and improve memory efficiency.

[0118] 5. Through intelligent data analysis, we automatically adjust the learning path and content of each learner to ensure that each learner learns at an appropriate level of difficulty and optimize learning outcomes. Learners can learn at their own pace and ability, achieving a better learning experience.

[0119] 6. Using vocabulary memorization as training material, using the same methods and paths to achieve standardized and systematic training of output ability, helping learners quickly break free from the difficulties of English learning. Learners can gradually improve their output ability through systematic training.

[0120] 7. Based on user behavior analysis and AI algorithms, dynamically adjust the learning path to ensure that the learning content always matches the user's current capabilities, continuously improve learning outcomes, and achieve personalized learning.

[0121] The above disclosure is merely a preferred embodiment of a data processing system and method for data management of the present invention. It is certainly not intended to limit the scope of the present invention. A person skilled in the art will understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention still fall within the scope of the invention.

Claims

1. A data processing system for data management, characterized in that: It includes a data extraction module, a data dynamic management module, a task classification generation module, an error data processing module, a real-time feedback module and an intelligent path adjustment module, wherein the data extraction module, the data dynamic management module, the task classification generation module and the error data processing module are connected in sequence, the real-time feedback module is connected to the data dynamic management module, the task classification generation module and the intelligent path adjustment module, and the intelligent path adjustment module is connected to the data extraction module and the task classification generation module; The data extraction module dynamically filters learning materials from the database and generates a learning catalog that is suitable for the user's current stage; The data dynamic management module collects historical performance data on user accuracy and time consumption, automatically divides the difficulty level of materials through clustering algorithms, groups materials according to logical relevance, and adapts to the needs of different task scenarios; The task hierarchical generation module decomposes complex materials into operational components and generates step-by-step tasks from the operational components according to the learning stage; The error data processing module captures user error data in real time, records error types and frequencies, and dynamically adjusts the task queue based on error weight data; The real-time feedback module improves user learning motivation through high-frequency sound stimulation and visual reward mechanism; The intelligent path adjustment module dynamically optimizes the learning path based on user answer data and AI algorithms.

2. The data processing system for data management according to claim 1, wherein: The data extraction module includes a vocabulary extraction unit, a difficulty grading unit and a dynamic grouping unit; The vocabulary extraction unit is used to dynamically extract words from a preset vocabulary to generate a learning catalog; The difficulty grading unit is used to automatically grade the difficulty of words according to user learning data; The dynamic grouping unit is used to group words by theme or memory logic to adapt to different level-breaking tasks.

3. The data processing system for data management according to claim 1, wherein: The data dynamic management module includes a word pronunciation unit, a storyline generation unit, an image display unit and a logic verification unit; The word pronunciation unit is used to provide standard American / British pronunciation demonstrations to enhance user listening input; The storyline generating unit is used to generate an associated storyline for each word to assist logical memory; The image display unit is used to display visual images corresponding to words to enhance image memory; The logic verification unit is used to verify the consistency of word spelling and story logic after the user completes the word filling task.

4. The data processing system for data management according to claim 1, wherein: The task classification generation module includes a story connection unit, a word splitting and memory unit, and a word spelling and level-clearing unit; The story connection unit is used to bind words to key elements in the story to generate multiple-choice questions; The word splitting memory unit is used to split a word into prefixes, roots, and suffixes, and prompt the splitting logic; The word spelling unit is used to require the user to spell a complete word according to part of speech, English meaning or pronunciation.

5. The data processing system for data management according to claim 1, wherein: The error data processing module includes an error question capturing unit, an error question cyclic sorting unit, an option clearing and reselecting unit and an error question review control unit; The error question capturing unit is used to record the incorrect words and error types in the user's answers in real time; The wrong question circular sorting unit is used to sort the wrong questions according to the error frequency and forcibly add them to the current task queue; The option clearing and reselecting unit is used to clear the user's wrong options and allow the user to re-answer until the answer is correct; The wrong question review control unit is used to automatically trigger special exercises for wrong questions after the user completes the current task.

6. The data processing system for data management according to claim 1, wherein: The real-time feedback module includes a standard pronunciation feedback unit, an immediate error prompt unit, a celebration interface generation unit and a dopamine incentive unit; The standard pronunciation feedback unit is used to play the standard pronunciation after each answer is submitted, so as to strengthen the listening memory simultaneously; The instant error prompt unit is used to display the wrong answer and pop up a prompt box; The celebration interface generation unit is used to generate dynamic animation, points reward interface and voice encouragement after successfully passing the level; The dopamine incentive unit is used to stimulate the user to continue learning through high-frequency positive feedback.

7. The data processing system for data management according to claim 1, wherein: The intelligent path adjustment module includes a user behavior analysis unit, an adaptive learning engine and a personalized recommendation unit; The user behavior analysis unit is used to collect data on answering time, error rate, and number of repetitions to generate a learning ability profile; The adaptive learning engine is used to dynamically adjust the vocabulary difficulty, task type ratio and prompt strategy according to the user profile; The personalized recommendation unit is used to recommend specialized training content based on the user's weaknesses.

8. A data processing method for data management, used in the data processing system for data management according to any one of claims 1 to 7, characterized in that: The following steps are involved: Dynamically extract words from the preset vocabulary to generate a learning catalog, and dynamically adjust word grouping and difficulty levels based on the user's learning progress and error rate; Provides standard American / British pronunciation demonstrations for each word, explains the word splitting logic, corresponding Chinese vocabulary, and generates related storylines and visual images; Break down complex materials into actionable components and generate step-by-step tasks from these components based on the learning stage; Record the incorrect words and error types in the user's answers in real time, sort them by error frequency, and then force them to be added to the current task queue; After each answer is submitted, the standard pronunciation is played to simultaneously strengthen listening memory. Wrong answers are displayed and a prompt box pops up to help users correct them in time. Dynamically optimize learning paths based on user answer data and AI algorithms.

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