Personalized adjustment method and device based on growth task, equipment and medium

Abstract

The invention relates to a personalized adjustment method and device based on growth tasks, equipment and a storage medium. The method comprises the following steps: synchronously collecting behavior and emotion information of a user in a growth task to obtain a user behavior data set and a user emotion data set, analyzing and processing user early-stage test data and previous difficulty adjustment records to obtain an individual feature correction value of the user, and calculating the individual feature correction value of the user according to the individual feature correction value. And performing weighted calculation according to the user behavior data set, the user emotion data set and the individual feature correction value to obtain a difficulty adjustment coefficient, matching the difficulty adjustment coefficient with a difficulty rule corresponding to a user cognition theory to obtain an adjustment parameter, and adjusting the growth task according to the adjustment parameter. According to the method and the device, task difficulty control which is more accurate and better meets the personalized requirements of the user is realized, the comprehensive state of the user in the task is comprehensively reflected, the individual difference of the user is also considered, the unique requirements of different users are met, and the accuracy of task adjustment is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for personalized adjustment based on growth-type tasks. Background Technology

[0002] Currently, in adjusting tasks related to children's growth, existing technologies mainly rely on single data points to adjust task difficulty, lacking in-depth consideration of children's individual characteristics. This approach has several problems: on the one hand, relying on single-dimensional data may lead to one-sided adjustments that fail to fully reflect children's true state in the task; on the other hand, ignoring the differences in children's individual information makes it difficult to adjust tasks to meet each child's unique needs.

[0003] Therefore, how to personalize growth-related tasks to improve the accuracy of task adjustments has become a technical problem urgently needing to be solved by those skilled in the art. It should be noted that the information disclosed in the background section above is only for enhancing the understanding of the background of this disclosure and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] In view of the above, this application provides a method, apparatus, device and storage medium for personalized adjustment based on growth-type tasks, the purpose of which is to solve the above-mentioned technical problems.

[0005] Firstly, this application provides a personalized adjustment method based on growth-related tasks, the method comprising:

[0006] Simultaneously collect user behavior and emotional information in growth-related tasks to obtain user behavior data sets and user emotion data sets;

[0007] Analyze and process the user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value;

[0008] The difficulty adjustment coefficient is obtained by weighting the user behavior data set, the user emotion data set, and the individual characteristic correction value.

[0009] The difficulty adjustment coefficient is matched with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters, and the growth-type tasks are adjusted according to the adjustment parameters.

[0010] Secondly, this application provides a personalized adjustment device based on growth-type tasks, the personalized adjustment device based on growth-type tasks comprising:

[0011] Collection Unit: Used to synchronously collect user behavior and emotional information in growth-related tasks, and obtain user behavior data set and user emotion data set;

[0012] Analysis Unit: Used to analyze and process user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value;

[0013] Calculation unit: used to perform weighted calculations based on the user behavior data set, the user emotion data set, and the individual characteristic correction value to obtain the difficulty adjustment coefficient;

[0014] Adjustment unit: used to match the difficulty adjustment coefficient with the difficulty rules corresponding to the user cognition theory, obtain adjustment parameters, and adjust the growth-type tasks according to the adjustment parameters.

[0015] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0016] Memory, used to store computer programs;

[0017] When a processor executes a program stored in memory, it implements the steps of the personalized adjustment method based on growth-type tasks as described in any embodiment of the first aspect.

[0018] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the personalized adjustment method based on growth-type tasks as described in any embodiment of the first aspect.

[0019] The technical solutions provided in this application have the following advantages compared with the prior art:

[0020] This application collects user behavior and emotional information synchronously, combines it with previous test data and past adjustment records for comprehensive analysis, calculates individual characteristic correction values, and adjusts task difficulty accordingly. This achieves more precise task difficulty control that better meets users' personalized needs. It not only comprehensively reflects the user's overall state in the task, improving task adaptability and learning effectiveness, but also takes into account individual differences and meets the unique needs of different users. At the same time, the introduction of individual characteristic correction values, which comprehensively consider the user's previous performance and historical adjustment records, provides a more comprehensive basis for task difficulty adjustment and improves the accuracy of task adjustment. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a preferred embodiment of the personalized adjustment method based on growth-related tasks in this application;

[0024] Figure 2 This is a schematic diagram of a preferred embodiment of the personalized adjustment device based on growth-type tasks in this application;

[0025] Figure 3 This is a schematic diagram of a preferred embodiment of the electronic device of this application;

[0026] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0028] It should be noted that the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0029] Reference Figure 1 The diagram shown is a flowchart illustrating an embodiment of the personalized adjustment method based on growth-related tasks according to this application. This method is executed by an electronic device, which can be implemented by a software system and / or a hardware system; for example, the electronic device can be a pet robot that interacts with the user. The personalized adjustment method based on growth-related tasks includes:

[0030] Step S10: Synchronously collect user behavior and emotion information in growth-related tasks to obtain user behavior data set and user emotion data set;

[0031] Step S20: Analyze and process the user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value;

[0032] Step S30: Perform a weighted calculation based on the user behavior data set, the user emotion data set, and the individual characteristic correction value to obtain the difficulty adjustment coefficient;

[0033] Step S40: Match the difficulty adjustment coefficient with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters, and adjust the growth-type tasks according to the adjustment parameters.

[0034] In user (e.g., child) development tasks, understanding the child's behavior and emotional state is crucial for appropriately adjusting the task difficulty. By synchronously collecting behavioral and emotional information, a comprehensive and real-time understanding of the child's overall state during the task can be obtained, thus providing a foundation for subsequent precise and personalized adjustments to the task difficulty. Specifically, the synchronous collection of user behavior and emotional information in development tasks yields user behavior data sets and user emotion data sets, including:

[0035] Determine the task data acquisition benchmark, which includes task type, task unit, and acquisition frequency;

[0036] Based on the aforementioned collection benchmark, user behavior information during the task process is collected in real time to obtain a user behavior data set.

[0037] Based on the user's facial features during the task, a set of user emotion data is identified.

[0038] When collecting behavioral data, we focused on the core behavioral indicators of children's task operations and set six key behavioral parameters based on children's motor characteristics, including operation accuracy, reaction speed, task focus, error type, task completion consistency, and ability improvement rate. Taking operation accuracy as an example, we counted the percentage of times children correctly completed task steps, with a sampling frequency of once per task unit completed, such as the number of correctly assembled pieces in a jigsaw puzzle game / the number of correctly answered questions in a math exercise / the number of questions in a math exercise.

[0039] Furthermore, the user emotion data set identified based on the user's facial features during the task includes:

[0040] The ViT model is used to extract facial features of users during the task process;

[0041] The user's initial emotion data is identified based on the user's facial features;

[0042] The initial emotion data is verified based on the user's physiological signals to obtain a set of user emotion data.

[0043] To address the directness and distinctive facial expressions of children's emotions, a "facial expression recognition + physiological signal assistance" approach is used to collect emotional data. On one hand, a lightweight ViT model extracts key facial feature points, categorizing emotions into five types: "pleasure," "calm," "frustration," "irritability," and "anxiety," achieving an accuracy rate of ≥92%. On the other hand, if the device supports this (e.g., a children's smartwatch), heart rate data is used for auxiliary judgment. When the emotion is identified as "frustration" and the heart rate is >110 beats / min, the emotional feedback is confirmed as valid, avoiding misjudgments due to "false expressions." The duration of the emotional state is also recorded. For example, if "frustration" lasts >30 seconds, it is considered a "valid negative emotion" and requires priority response; if "irritability" occurs only briefly (<5 seconds), it is considered "emotional fluctuation," and the difficulty is not adjusted.

[0044] Because each child has different individual characteristics, such as age and cognitive type, these characteristics affect their adaptability to task difficulty and their learning pace. Furthermore, past records of difficulty adjustments can reflect a child's reactions and adaptation in different task situations. By comprehensively considering these individual characteristics and historical records, adjusting the difficulty can better align with the child's individual needs. Specifically, before a child first encounters a developmental task system, a series of basic task tests are organized, covering cognitive and operational content across multiple domains, such as simple image recognition, basic mathematical operations, and puzzles. The child's performance data in each test is recorded, including completion time, accuracy, and error types, to gain an initial understanding of the child's cognitive level and developmental characteristics.

[0045] This study examines the changes in children's performance after each difficulty adjustment when they previously used this system or other similar systems. These changes include variations in accuracy, emotional responses, and the time spent completing tasks. The analysis aims to assess the long-term impact of these adjustments on children's learning outcomes and emotional state.

[0046] Different correction weights are assigned based on the child's age: -50 for children aged 3-6 (easier to reduce difficulty), and +20 for children aged 7-12 (difficulty can be moderately increased). Based on the child's cognitive type determined by previous testing, if they are "kinesthetic" (reliant on manipulative learning), the correction value is -30 (easier to reduce difficulty when errors occur). Combining past adjustment records, if the child exhibits negative emotions after the last three adjustments, the correction value is -40 (reducing the magnitude of subsequent adjustments). All these correction values ​​are summed to obtain the final individual characteristic correction value.

[0047] Adjusting task difficulty solely based on behavioral or emotional data may be one-sided, while individual characteristic correction values ​​reflect children's individual differences. By constructing a weighted calculation model that comprehensively considers these three factors, a more accurate reflection of a child's current task adaptation status can be obtained, leading to a more reasonable difficulty adjustment coefficient and achieving precise task difficulty control. Specifically, the difficulty adjustment coefficient is obtained through weighted calculation based on the child's behavioral data set, the child's emotional data set, and the individual characteristic correction values, including:

[0048] After scoring each behavioral parameter in the children's behavior dataset, a weighted sum is performed to obtain the total score for the behavioral dimension.

[0049] After scoring the emotion types and durations in the children's emotion dataset, a weighted sum is performed to obtain the total score for the emotion dimension.

[0050] The difficulty adjustment coefficient is obtained by substituting the total score of the behavioral dimension, the total score of the emotional dimension, and the individual characteristic correction value into a preset formula.

[0051] The weighting can be determined as follows: Behavioral dimensions account for 60% of the weighting, with operational accuracy at 25%, rate of skill improvement at 20%, error type at 10%, and reaction speed, task focus, and completion consistency each at 5%. Emotional dimensions account for 30% of the weighting, with negative emotions weighted higher than positive emotions: frustration at 15%, irritability / anxiety at 10%, pleasure at 3%, and calmness at 2%. Individual characteristic correction values ​​account for 10%. For behavioral indicators, taking operational accuracy as an example, the score = (actual accuracy - target accuracy) × 100, where the target accuracy is set according to the child's age group (60%-70% for 3-6 years old, 70%-80% for 7-12 years old). For emotional indicators, if "frustration" lasts for 30-60 seconds, the score is -80; if it lasts for >60 seconds, the score is -150. The individual characteristic correction value is simply the previously calculated value. The difficulty adjustment coefficient K is obtained by multiplying the scores of each behavioral indicator by their respective weights, the scores of each emotional indicator by their respective weights, and then adding the individual characteristic correction value multiplied by its weight. Where K∈[-2, 2], the difficulty decreases when K<-0.5, the difficulty increases when K>0.5, and the difficulty remains the same when -0.5≤K≤0.5.

[0052] Children of different ages have different levels of cognitive development and learning abilities. Setting appropriate difficulty rules based on user cognitive theories (such as Piaget's cognitive development theory) can ensure that the difficulty of the task matches the child's cognitive stage. It is neither too easy, causing the child to lose interest, nor too difficult, causing the child to feel frustrated, thereby promoting the child's effective growth under an appropriate cognitive load.

[0053] Based on children's age and cognitive theories, task difficulty is divided into 5 levels (L1-L5). For example, L1 level for children aged 3-6 consists of single-step, concrete tasks (such as matching numbers to objects within 10, simple shape puzzles), while L5 level consists of more complex multi-step concrete tasks. For children aged 7-12, L1-L5 correspond to more complex task content, such as L5 level tasks involving mixed operations within 100, logic tasks with distracting information, etc. At the same time, an upper limit for the difficulty adjustment rate is specified for each age group: ≤1 level / day for 3-6 years old, ≤2 levels / day for 7-12 years old, and a continuous increase must be spaced at least 2 hours apart.

[0054] Based on the calculated difficulty adjustment coefficient K, determine the corresponding adjustment direction and magnitude. If K < -0.5, the difficulty needs to be reduced. According to the difficulty level of the current task and the difficulty rules corresponding to the child's age group, determine the specific difficulty level and task parameters after the reduction, such as reducing from level L3 to level L2, narrowing the range of numbers in the math problem, reducing the number of steps, etc. If K > 0.5, increase the difficulty according to the rules. If -0.5 ≤ K ≤ 0.5, maintain the current difficulty.

[0055] Based on the matching results, specific adjustment parameters are generated, such as the numerical range of the task, the number of steps, and the type of interference information. Then, the system makes real-time adjustments to growth-type tasks. For example, the number of pieces in a jigsaw puzzle task can be reduced from 20 to 15, edge color hints can be added, or the type of problem in a math exercise can be changed from simple addition to addition with simple interference terms.

[0056] For example, taking a 6-year-old child named Xiaoqiang's ongoing jigsaw puzzle task as an example, based on his behavioral data (3 consecutive incorrect attempts, accuracy rate 33%, target 60%; error type is cognitive, unable to understand the puzzle logic; concentration dropped to 50%), emotional data ("frustration" lasting 50 seconds, drooping corners of the mouth), and individual characteristic correction value (age correction value -50), the difficulty adjustment coefficient K is calculated to be -26.5 (see Part 3 for the specific calculation process). Matching K with the difficulty rules corresponding to user cognitive theory, it is found that the difficulty needs to be reduced. According to the difficulty rules corresponding to Xiaoqiang's age, the current task difficulty is L2 level (15 puzzle pieces). The difficulty is reduced by 1 level to L1 level (10 puzzle pieces). At the same time, a magnetic edge design for the puzzle pieces is added (to reduce action-related errors), an animation is played to encourage each piece that is correctly completed (emotional soothing), and the task goal is changed from "completing the entire puzzle" to "completing 5 pieces correctly" (reducing short-term goal pressure). By adjusting the task in accordance with cognitive theory, the difficulty level can be made more consistent with Xiaoqiang's current cognitive level and emotional state, thereby better promoting his growth and development in the jigsaw puzzle task.

[0057] In one embodiment, the analysis and processing of the user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value includes:

[0058] Obtain individual user characteristic data;

[0059] Based on the individual characteristic data, the amplitude is calculated and summed according to the first rule to obtain the user's basic characteristic correction score;

[0060] Obtain the emotional feedback records corresponding to the user's past difficulty adjustment records;

[0061] Based on the aforementioned emotional feedback records, values ​​are assigned and summed according to the second rule to obtain the user's past adjustment record correction score;

[0062] The sum of the basic feature correction score and the past adjustment record correction score is calculated as the user's individual feature correction value.

[0063] The children's preliminary test data is screened to extract key information related to individual characteristic correction values: age group (3-6 years old / 7-12 years old) and cognitive type (visual / auditory / kinesthetic), resulting in basic individual characteristic data for children (providing a basis for subsequent basic correction score calculation). Based on the children's basic individual characteristic data, values ​​are assigned and summed according to preset rules, for example: 3-6 year old children are assigned -50, 7-12 year old children are assigned +20; visual cognition is assigned 0, auditory cognition is assigned 0, and kinesthetic cognition is assigned -30, and these are summed to obtain the basic characteristic correction score.

[0064] Retrieve the child's emotional feedback records after the last three difficulty adjustments, and analyze whether negative emotions such as "frustration / irritability / anxiety" occurred after each adjustment. Assess the overall emotional feedback results of the last three adjustments to obtain the child's past adjustment emotional feedback results (providing a basis for subsequent past record correction score calculations). Based on the child's past adjustment emotional feedback results, assign values ​​according to preset rules: if negative emotions occurred after all three adjustments, assign a value of -40; if negative emotions occurred only 1-2 times or not at all after the three adjustments, assign a value of 0, thus obtaining the past adjustment record correction score.

[0065] The sum of the basic feature correction score and the past adjustment record correction score is calculated to obtain the user's individual feature correction value, which is used for subsequent difficulty adjustment coefficient calculation.

[0066] In one embodiment, matching the difficulty adjustment coefficient with the difficulty rules corresponding to user cognitive theory to obtain adjustment parameters includes:

[0067] The direction of difficulty adjustment is determined based on the aforementioned difficulty adjustment coefficient;

[0068] The adjustment range is determined by matching the adjustment direction with the difficulty rules corresponding to user cognitive theory.

[0069] The adjustment parameters are determined based on the direction and magnitude of the difficulty adjustment.

[0070] Based on the "zone of proximal development" theory of children's cognition, five difficulty levels (L1-L5) are defined, and the corresponding task parameters for each level are determined (e.g., L1 is single-step addition and subtraction within 10, L3 is multi-step addition and subtraction within 20 with carrying and borrowing, and L5 is mixed operations within 100 including interference information), forming a difficulty level rule table. Referring to the numerical range of the difficulty adjustment coefficient K (K∈[-2, 2]), the following rules are applied: K<-0.5 is judged as "reducing difficulty", K>0.5 is judged as "increasing difficulty", and -0.5≤K≤0.5 is judged as "maintaining difficulty", outputting the adjustment direction result. Based on the adjustment direction result, referring to the difficulty level rule table, the magnitude is determined according to the "step-by-step fine-tuning" rule (adjusting only ±1 level at a time to avoid sudden changes in difficulty across levels), such as "increasing difficulty" corresponding to a magnitude of "+1 level" and "reducing difficulty" corresponding to a magnitude of "-1 level", obtaining the adjustment magnitude result. The adjustment direction and magnitude results are integrated with the "adaptation period" (a 10-minute observation period after adjustment) and "difficulty rollback" mechanisms (if negative emotions intensify after adjustment, the original difficulty will be rolled back within 2 minutes) to form preliminary adjustment parameters that include "target difficulty level, adaptation period duration, and rollback trigger conditions".

[0071] In one embodiment, after obtaining adjustment parameters and adjusting growth-type tasks according to the adjustment parameters, the method further includes:

[0072] The target adjustment parameters are obtained by optimizing the adjustment parameters and the feedback data.

[0073] The target adjustment parameters are stored in the user's personalized parameter library.

[0074] After initially adjusting the parameters, record the user's behavioral data (such as operation accuracy and rate of skill improvement) and emotional data (such as duration of negative emotions) for the next two task units to obtain an adjustment feedback dataset. Based on the adjustment feedback dataset, determine whether the user's behavioral data meets the target (such as whether the accuracy has improved to the target accuracy) and whether the emotional data has improved (such as whether frustration has decreased) to obtain an effect judgment result (good / poor effect). If the effect judgment result is "good effect", retain the core content of the initial adjustment parameters, such as difficulty level and adaptation period; if it is "poor effect", re-analyze the adjustment feedback dataset (such as whether any task type mismatch factors have been omitted), change "reducing difficulty" to "changing task format", etc., to obtain the preliminary optimized parameters. Combine the preliminary optimized parameters with three types of safeguards for correction: limit the difficulty according to the cognitive threshold of the age group (such as the upper limit of difficulty for 3-6 years old is a single-step task), adjust the range according to the difficulty-interest balance (pause increasing difficulty if participation drops by more than 30%), and correct the scenario parameters according to parental collaborative feedback (such as reducing the difficulty by 1 level in kindergarten scenarios) to obtain the revised parameter version. The revised parameters are used as the final target adjustment parameters and stored in the user's personalized parameter library for direct use in subsequent similar growth-related task scenarios, thus completing the determination and storage of target adjustment parameters.

[0075] Reference Figure 2 The diagram shown is a functional module schematic of the personalized adjustment device 100 based on growth-type tasks in this application.

[0076] The personalized adjustment device 100 based on growth-type tasks described in this application is installed in an electronic device. Depending on the functions implemented, the personalized adjustment device 100 based on growth-type tasks includes a data acquisition unit 110, an analysis unit 120, a calculation unit 130, and an adjustment unit 140. These modules can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0077] In this embodiment, the functions of each module / unit are as follows:

[0078] Collection unit 110: used to synchronously collect user behavior and emotion information in growth-related tasks, and obtain user behavior data set and user emotion data set;

[0079] Analysis Unit 120: Used to analyze and process children's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value;

[0080] Calculation unit 130: used to perform weighted calculation based on the child behavior data set, the child emotion data set, and the individual characteristic correction value to obtain a difficulty adjustment coefficient;

[0081] Adjustment unit 140: is used to match the difficulty adjustment coefficient with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters and adjust the growth-type tasks according to the adjustment parameters.

[0082] The specific implementation of the personalized adjustment device based on growth tasks in this application is largely the same as the specific implementation of the personalized adjustment method based on growth tasks described above, and will not be repeated here.

[0083] Reference Figure 3 The diagram shown is a schematic representation of a preferred embodiment of the electronic device of this application.

[0084] The electronic device includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0085] Memory 113 is used to store computer programs, such as personalized adjustment programs based on growth-type tasks;

[0086] In some embodiments, the processor 111 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 111 is typically used to control the overall operation of the electronic device, such as performing data interaction or communication-related control and processing. In this embodiment, the processor 111 is used to run program code stored in the memory 113 or process data.

[0087] The communication interface 112 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The communication interface 112 may also be used to establish a communication connection between the electronic device and other electronic devices.

[0088] The memory 113 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 113 may be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the memory 113 may also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. of the electronic device. Of course, the memory 113 may include both internal storage units and external storage devices of the electronic device. In this embodiment, the memory 113 is typically used to store the operating system and various computer programs installed on the electronic device, such as program code for personalized adjustment programs based on growth tasks. In addition, the memory 113 may also be used to temporarily store various types of data that have been output or will be output.

[0089] Figure 3 Only an electronic device with components 111-114 is shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0090] In one embodiment of this application, when the processor 111 executes a program stored in the memory 113, it implements the personalized adjustment method based on growth-type tasks provided in any of the foregoing method embodiments, including:

[0091] Simultaneously collect user behavior and emotional information in growth-related tasks to obtain user behavior data sets and user emotion data sets;

[0092] By analyzing and processing the children's previous test data and past difficulty adjustment records, individual characteristic correction values ​​for users can be obtained;

[0093] The difficulty adjustment coefficient is obtained by weighting the child behavior data set, the child emotion data set, and the individual characteristic correction value.

[0094] The difficulty adjustment coefficient is matched with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters, and the growth-type tasks are adjusted according to the adjustment parameters.

[0095] For a detailed explanation of the above steps, please refer to the above. Figure 1A flowchart illustrating an embodiment of a personalized adjustment method based on growth-related tasks.

[0096] Furthermore, this application also proposes a computer-readable storage medium that is both non-volatile and volatile. This computer-readable storage medium is any one or any combination of several of the following: hard disk, multimedia card, SD card, flash memory card, SMC, read-only memory (ROM), erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, etc. The computer-readable storage medium includes a data storage area and a program storage area. The program storage area stores a personalized adjustment program based on growth-type tasks. When executed by a processor, the personalized adjustment program based on growth-type tasks performs the following operations:

[0097] Simultaneously collect user behavior and emotional information in growth-related tasks to obtain user behavior data sets and user emotion data sets;

[0098] By analyzing and processing the children's previous test data and past difficulty adjustment records, individual characteristic correction values ​​for users can be obtained;

[0099] The difficulty adjustment coefficient is obtained by weighting the child behavior data set, the child emotion data set, and the individual characteristic correction value.

[0100] The difficulty adjustment coefficient is matched with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters, and the growth-type tasks are adjusted according to the adjustment parameters.

[0101] The specific implementation of the computer-readable storage medium in this application is largely the same as the specific implementation of the personalized adjustment method based on growth-type tasks described above, and will not be repeated here.

[0102] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware simulation platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0104] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A personalized adjustment method based on growth-related tasks, characterized in that, The method includes: Simultaneously collect user behavior and emotional information in growth-related tasks to obtain user behavior data sets and user emotion data sets; Analyze and process the user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value; The difficulty adjustment coefficient is obtained by weighting the user behavior data set, the user emotion data set, and the individual characteristic correction value. The difficulty adjustment coefficient is matched with the difficulty rules corresponding to the user cognition theory to obtain adjustment parameters, and the growth-type tasks are adjusted according to the adjustment parameters.

2. The personalized adjustment method based on growth-related tasks as described in claim 1, characterized in that, The process of synchronously collecting user behavior and emotional information in growth-related tasks yields user behavior data sets and user emotion data sets, including: Determine the task data acquisition benchmark, which includes task type, task unit, and acquisition frequency; Based on the aforementioned collection benchmark, user behavior information during the task process is collected in real time to obtain a user behavior data set. Based on the user's facial features during the task, a set of user emotion data is identified.

3. The personalized adjustment method based on growth-related tasks as described in claim 2, characterized in that, The user emotion data set identified based on the user's facial features during the task includes: The ViT model is used to extract facial features of users during the task process; The user's initial emotion data is identified based on the user's facial features; The initial emotion data is verified based on the user's physiological signals to obtain a set of user emotion data.

4. The personalized adjustment method based on growth-related tasks as described in claim 1, characterized in that, The process of analyzing and processing the user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value includes: Obtain individual user characteristic data; Based on the individual characteristic data, the amplitude is calculated and summed according to the first rule to obtain the user's basic characteristic correction score; Obtain the emotional feedback records corresponding to the user's past difficulty adjustment records; Based on the aforementioned emotional feedback records, values ​​are assigned and summed according to the second rule to obtain the user's past adjustment record correction score; The sum of the basic feature correction score and the past adjustment record correction score is calculated as the user's individual feature correction value.

5. The personalized adjustment method based on growth-related tasks as described in claim 1, characterized in that, The step of calculating the difficulty adjustment coefficient by weighting the user behavior data set, the user emotion data set, and the individual characteristic correction value includes: After scoring each behavior parameter in the user behavior dataset, a weighted sum is performed to obtain the total score for the behavior dimension. After scoring the emotion type and duration in the user emotion data set, a weighted sum is performed to obtain the total score for the emotion dimension; The difficulty adjustment coefficient is obtained by substituting the total score of the behavioral dimension, the total score of the emotional dimension, and the individual characteristic correction value into a preset formula.

6. The personalized adjustment method based on growth-related tasks as described in claim 1, characterized in that, The step of matching the difficulty adjustment coefficient with the difficulty rules corresponding to user cognitive theory to obtain adjustment parameters includes: The direction of difficulty adjustment is determined based on the aforementioned difficulty adjustment coefficient; The adjustment range is determined by matching the adjustment direction with the difficulty rules corresponding to user cognitive theory. The adjustment parameters are determined based on the direction and magnitude of the difficulty adjustment.

7. The personalized adjustment method based on growth-related tasks as described in claim 1, characterized in that, The method further includes obtaining adjustment parameters and adjusting growth-related tasks according to those parameters: The target adjustment parameters are obtained by optimizing the adjustment parameters and the feedback data. The target adjustment parameters are stored in the user's personalized parameter library.

8. A personalized adjustment device based on growth-related tasks, characterized in that, The device includes: Collection Unit: Used to synchronously collect user behavior and emotional information in growth-related tasks, and obtain user behavior data set and user emotion data set; Analysis Unit: Used to analyze and process user's previous test data and past difficulty adjustment records to obtain the user's individual characteristic correction value; Calculation unit: used to perform weighted calculations based on the user behavior data set, the user emotion data set, and the individual characteristic correction value to obtain the difficulty adjustment coefficient; Adjustment unit: used to match the difficulty adjustment coefficient with the difficulty rules corresponding to the user cognition theory, obtain adjustment parameters, and adjust the growth-type tasks according to the adjustment parameters.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the personalized adjustment method based on growth-type tasks as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the personalized adjustment method based on growth-type tasks as described in any one of claims 1 to 7.

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