A system and method for cultivating and deepening habits of children based on AI analysis feedback

Abstract

This invention relates to the field of educational management technology and discloses a system for cultivating and deepening children's habits based on AI analysis and feedback. The system includes: a profile building module, used to extract behavioral features associated with multiple preset habit dimensions from collected target children's behavioral data, and generate a structured initial habit profile based on the feature values; and a goal management module, used to generate a set of phased cultivation goals for the target children, including at least one main habit goal and its associated sub-goals, based on the initial habit profile and a pre-stored multi-dimensional habit association model. This invention overcomes the shortcomings of traditional timed reminder tools, such as one-way instruction transmission and inability to dynamically adjust. By constructing a closed loop of data collection, profile building, goal management, and real-time feedback, it can analyze and learn based on children's actual behavioral data, dynamically adjust feedback strategies and cultivation goals, thereby significantly improving the pertinence and long-term effectiveness of habit cultivation.

Description

Technical Field

[0001] This invention relates to the field of educational management technology, specifically to a system and method for cultivating and deepening children's habits based on AI analysis and feedback. Background Technology

[0002] In the field of cultivating children's habits, there are currently two main technical solutions. The first is timed reminder tools based on fixed rules (such as alarm clocks and simple schedule management apps). These solutions can only execute preset, one-way instructions. Their technical pain points are: lack of ability to collect, analyze and learn from individual behavioral data, inability to dynamically adjust according to children's actual behavior, single and mechanical feedback methods, difficulty in dealing with complex and ever-changing real-world scenarios, resulting in low user compliance and difficulty in sustaining the effect of habit cultivation.

[0003] The second type is educational apps that integrate some preset rules. Although they can record behavior, they usually adopt a "one-size-fits-all" approach to setting goals and providing feedback based on simple conditional judgments (such as rewarding users for completing daily check-ins). The technical pain points of this type of solution are: its "personalization" is mostly superficial, unable to build in-depth, multi-dimensional user profiles, and lacks cognitive models that simulate complex relationships between habits; the feedback strategy library is static and rigid, unable to self-calibrate and optimize based on users' historical interaction data; the entire system is open-loop, unable to complete the closed loop from behavior assessment to automatic iteration of cultivation strategies, and therefore cannot achieve the "deepening" and "transfer" of habits. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for cultivating and deepening children's habits based on AI analysis and feedback, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a system for cultivating and deepening children's habits based on AI analysis and feedback, comprising:

[0006] The profile building module is used to extract behavioral features associated with multiple preset habit dimensions from the collected target children's behavioral data, and generate a structured initial habit profile based on the feature values.

[0007] The goal management module is used to generate a set of phased training goals for target children based on the initial habit profile and the pre-stored multi-dimensional habit association model. These goals include at least one main habit goal and its associated sub-goals.

[0008] The intelligent feedback hub is connected to the target management module and accesses a feedback strategy rule base. The intelligent feedback hub is configured to: receive real-time behavioral data during the habit cultivation cycle, convert it into progress data corresponding to the cultivation target set, compare the progress data with the target threshold to determine the deviation status, and generate personalized feedback guidance instructions based on the deviation status and the condition-action pairs in the feedback strategy rule base.

[0009] The interaction adaptation output module is used to adapt and execute the corresponding feedback output action based on the feedback guidance instructions and the current interaction scene information obtained through the environment perception unit.

[0010] The evaluation and strategy iteration module is used to analyze real-time behavioral data to generate an evaluation report at the end of the training cycle, and to make coordinated adjustments to the training objective set and feedback strategy rule base for the next stage based on the evaluation report and the multi-dimensional habit association model.

[0011] As a preferred technical solution of the present invention, the multi-dimensional habit association model defines the positive promotion or negative interference relationship between different habit dimensions; the target management module selects the main habit target and the associated sub-targets for promoting the achievement of the main habit target based on the habit dimensions that are weak in the initial habit profile and with reference to the association relationship.

[0012] As a preferred technical solution of the present invention, the condition-action pairs in the feedback strategy rule base are pre-personalized based on the personality tendency tags of the target child in the initial habit profile and the historical feedback response efficiency; wherein, the triggering conditions and reminder intensity of the graded reminder actions are dynamically calibrated according to the average response delay time and ignore rate of the target child to historical reminders.

[0013] The content of targeted suggestion pushes is determined by the cognitive understanding level tags and interest preference tags in the initial habit profile, which together determine its expression and example type.

[0014] As a preferred embodiment of the present invention, when the interactive adaptation output module performs a feedback output action, its decision logic includes:

[0015] First priority judgment: Based on the device type and current task status in the current interaction scenario information, determine whether immediate strong interruption feedback is allowed; if not, store the feedback instruction in the delay queue, and automatically trigger it in the next allowed interaction scenario based on the urgency tag of the feedback guidance instruction.

[0016] Second modality matching: In scenarios where output is permitted, the environmental noise level, ambient light intensity, and recent visual fatigue estimation of the target child are combined with the environmental noise level, ambient light intensity, and recent visual fatigue estimation values ​​obtained by the environmental perception unit. Through a pre-trained output utility model, the modality combination with the least expected interactive interference and the highest information absorption rate is dynamically selected from multiple output modalities for output.

[0017] A method for cultivating and deepening children's habits based on AI analysis and feedback, applied to the system described in any one of the above, includes the following steps:

[0018] Step S101, Multi-dimensional habit profile construction: Collect historical behavioral data of the target children, extract quantitative features that are mapped to multiple preset habit dimensions, and form a structured initial habit profile;

[0019] Step S102, generation of associated target set: call the multi-dimensional habit association model, analyze the status and interrelationship of each habit dimension in the initial habit profile, and formulate a phased training target set containing one main habit target and at least one associated sub-target;

[0020] Step S103, Dynamic Behavior Perception and Comparison: During the cultivation cycle, continuously perceive and record real-time behaviors related to the cultivation target set, convert them into progress data, compare them with preset target thresholds, and output comparison results with status labels.

[0021] Step S104, Strategy-based feedback generation and output: Based on the comparison results with status markers, query the feedback strategy rule base and match the corresponding feedback strategy; combine the profile features of the target child and the real-time interaction scenario to generate and execute the appropriate feedback guidance action.

[0022] Step S105, Evaluation and Adaptive Deepening: After the cycle ends, comprehensively evaluate the achievement of the training goal set, analyze the trend of behavioral changes, and based on the multi-dimensional habit association model, conduct root cause analysis on the unachieved goals, deepen and upgrade the stable habits, thereby updating the training goal set and feedback strategy for the next cycle.

[0023] As a preferred technical solution of the present invention, the setting logic of the associated sub-target in step S102 is as follows: select the habit dimension that has a positive promoting relationship with the main habit target and has room for improvement in the initial habit profile as the target carrier.

[0024] As a preferred technical solution of the present invention, the matching and generation of feedback strategies in step S104 is based on the personalized tags of the target child in the initial habit profile; wherein, the urgency of reminder strategies is adjusted according to their response habits to historical reminders, and the specific expression of suggestion strategies is adapted and generated according to their cognitive level and interest preferences.

[0025] As a preferred technical solution of the present invention, the step S104 of generating and executing the adapted feedback guidance action specifically includes: firstly determining whether the current scenario is suitable for executing immediate feedback; if not, suspending the feedback task and marking its urgency, and automatically resuming execution when a suitable scenario is subsequently detected; if suitable, further dynamically selecting the optimal output modality combination based on real-time environmental parameters and personal state estimation values.

[0026] As a preferred technical solution of the present invention, step S105 involves deepening and upgrading the already stable habit, including: raising the target threshold of the habit, increasing the complexity of the maintenance scenario of the habit, or transforming the habit from a goal that needs to be deliberately cultivated into a background support factor for promoting other new habits.

[0027] As a preferred technical solution of the present invention, the method further includes a collaborative closed-loop step: in steps S103-S104, when the target child is detected to have achieved a key milestone, an achievement report containing a specific behavioral description is automatically generated and pushed to the monitoring terminal; at the same time, the confirmation or incentive response returned by the monitoring terminal is monitored, and this response is incorporated as a positive feedback factor into the subsequent feedback strategy generation logic.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. This invention overcomes the shortcomings of traditional timed reminder tools, such as one-way instruction transmission and inability to dynamically adjust. By constructing a closed loop of data collection, profile building, goal management and real-time feedback, it can analyze and learn based on children's actual behavioral data, dynamically adjust feedback strategies and training goals, thereby significantly improving the pertinence and long-term effectiveness of habit cultivation.

[0030] 2. This invention solves the technical problems of low personalization and rigid feedback mechanisms in existing educational apps. By establishing a personalized feedback strategy rule base based on multi-dimensional user profile tags and introducing historical response data to dynamically calibrate the feedback strategy, it realizes the transformation from simple condition judgment to deep personalized intelligent interaction, effectively enhancing the adaptability of feedback and user acceptance.

[0031] 3. This invention addresses the problem that existing solutions suffer from limited interaction methods and high interference in complex real-world scenarios. By employing a two-level intelligent decision-making mechanism, it comprehensively assesses the current task status and environmental parameters, autonomously selecting the optimal feedback timing and output mode combination. This enables effective guidance with minimal interference in appropriate scenarios, thereby improving the system's intelligent interaction level and user experience.

[0032] 4. This invention breaks through the limitations of existing systems that operate in an open loop and cannot be continuously optimized and deepened. Through periodic evaluation, strategy iteration based on the association model, and collaborative closed loop with the external monitoring terminal, a dynamic cultivation system capable of self-evolution and social integration is formed. This enables the system not only to evaluate and consolidate the habits that have been formed, but also to promote the habits to a higher level of deepening and migration, ultimately achieving the sustainability and adaptive growth of the habit cultivation process. Attached Figure Description

[0033] Figure 1 This is an overall flowchart of a method for cultivating and deepening children's habits based on AI analysis and feedback, according to the present invention.

[0034] Figure 2 This is a schematic diagram of the module structure of a children's habit cultivation and deepening system based on AI analysis and feedback according to the present invention;

[0035] Figure 3 This is a schematic diagram of the internal working logic of the intelligent feedback hub and interactive adaptation output module in a children's habit cultivation and deepening system based on AI analysis feedback according to the present invention. Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: Cultivating Regular Sleep Habits in a Family Setting

[0038] This embodiment uses 30 children aged 8-10 in Haidian District, Beijing as experimental subjects to implement AI-assisted cultivation of regular daily routines, and specifically demonstrates the system's operation process.

[0039] System configuration and data acquisition:

[0040] The system server is deployed in the cloud, and the user end consists of smart bracelets, smart home speakers (and a parent's mobile APP). Data collection includes: daily bedtime, wake-up time, and deep sleep ratio automatically recorded by the smart bracelet; dinner time, bedtime activities, and the child's self-assessment of alertness the next morning manually recorded by the parent through the APP. The alertness self-assessment uses a scale of 1 to 5.

[0041] Multi-dimensional habit profile construction:

[0042] The system pre-sets three core habit dimensions: regularity of daily routine, sufficiency of sleep, and pre-sleep preparation behaviors, based on the collected raw data:

[0043] Regularity dimension of daily routine: The standard deviation of each child's bedtime over two consecutive weeks is calculated as a quantitative feature. A standard deviation of less than 0.5 hours is marked as high regularity, and a standard deviation of more than 1.0 hour is marked as low regularity.

[0044] Sleep sufficiency dimension: characterized by the average total sleep duration and the average proportion of deep sleep per night, and graded according to the recommended value of 9 to 11 hours in the "Guidelines for Sleep Health of Chinese Children and Adolescents".

[0045] Bedtime preparation behavior dimension: No use of electronic screens in 1 hour before bedtime is defined as a valid behavior, and the percentage of days with valid behavior out of the total number of monitoring days is used as a feature value.

[0046] Ultimately, the system generates a structured initial habit profile for each child. For example, child A's profile has a low rating of 1.2 hours for regularity of routine, an insufficient rating of 8.5 hours for sleep duration, and a rating of 20% for bedtime preparation behavior that needs improvement.

[0047] Generation of associated target sets:

[0048] The system has a built-in multi-dimensional habit association model based on expert knowledge. This model clearly defines the relationship between each dimension. For example, improving bedtime preparation behavior will positively promote the improvement of regularity of work and rest and sufficient sleep.

[0049] Based on the profile of child A, the goal management module analysis revealed that regularity in daily routines is currently the weakest link. Therefore, the system sets a set of phased goals for a period of 4 weeks:

[0050] Main habit goal: Improve the regularity of daily routines, specifically by reducing the standard deviation of bedtime from 1.2 hours to less than 0.7 hours.

[0051] Related sub-goals: In order to effectively promote the achievement of the main goal, the effective percentage of improving bedtime preparation behavior is set to increase from 20% to 50%. This sub-goal is set directly based on the positive relationship between bedtime preparation behavior and the regularity of sleep schedule in the association model, which reflects the systematicness and relevance of goal setting.

[0052] Dynamic behavior perception and strategic feedback generation:

[0053] During the 4-week training period, the system continuously receives real-time data from the smart bracelet and parents. The intelligent feedback center operates daily, and its core workflow is as follows:

[0054] 1. Progress Comparison: Calculate the deviation between the current bedtime and the target range each day, as well as the percentage of days without screens before bedtime this week.

[0055] 2. Personalized Strategy Matching: The system accesses the feedback strategy rule base, which has been pre-personalized based on the initial profile of child A. The rule base takes into account the child's personality tendency tags and the effectiveness of historical feedback responses. Based on previous interactions, the system judges that child A has an encouragement-sensitive personality.

[0056] Regarding the reminder strategy: Based on the child A's past average response delay time of 15 minutes and ignore rate of 20%, the system dynamically calibrated the reminder trigger threshold and intensity. For example, the first reminder was triggered 10 minutes earlier than the target bedtime.

[0057] Regarding the suggested content: Based on the child's cognitive understanding level (labeled as "middle elementary school grade") and interest preference (labeled as "likes dinosaurs"), the system generates customized suggestions, such as: "Little dinosaur manager, protect your sleep energy so you'll have the strength to explore Jurassic Park tomorrow! Now get ready for bed."

[0058] Interactive Adaptation Output: After the feedback guidance command is generated, it is executed by the interactive adaptation output module, and its decision-making process includes two levels:

[0059] First-level output timing judgment: For example, when the system needs to issue a bedtime reminder one evening, it detects that the smart speaker is playing an English course and the current task status is "learning". The system judges that this is a scenario where immediate interruption is not allowed, so it stores the reminder instruction in the delay queue and marks its urgency label as medium. Five minutes after the English course ends, the system automatically retrieves it from the queue and triggers the instruction.

[0060] The second level of output format matching: When output is permitted, the environmental perception unit uses the smart speaker's microphone and light sensor to obtain the current ambient noise level (40 dB) and ambient light intensity (the main living room light is off, only the night light is on). Simultaneously, the system estimates that child A's visual fatigue is high based on the day's screen usage time. Combining these parameters, the system calls a pre-trained output utility model for decision-making. This model predicts that under the current low light and visual fatigue conditions, a bright screen prompt is ineffective and may cause discomfort, while a voice prompt is more suitable. Therefore, the system ultimately chooses to deliver feedback through the smart speaker with gentle voice broadcasting combined with slight vibrations from the wristband, aiming to minimize interactive interference and maximize information absorption.

[0061] Evaluation and Adaptive Deepening:

[0062] After the first four-week cycle ends, the evaluation and strategy iteration module will run automatically:

[0063] An assessment report was generated: The report shows that Child A's standard deviation of sleep pattern regularity has decreased to 0.65 hours, achieving the main goal; the proportion of bedtime preparation behaviors has increased to 55%, achieving the sub-goal; and the average sleep duration has increased to 9.2 hours. The report includes a behavioral trend graph.

[0064] Coordinated adjustment of goals and strategies: Based on the assessment results and the multi-dimensional habit association model, the system has determined that the regular work and rest habits have been initially stabilized and have entered the deepening stage.

[0065] Goal set update: The main goal for the new cycle is to improve sleep quality, specifically to increase the average deep sleep rate from 24% to 28%. At the same time, based on the newly introduced correlation in the model that moderate daytime exercise helps improve deep sleep quality, a new associated sub-goal is set: to engage in at least 3 daytime outdoor activities per week, each lasting more than 30 minutes.

[0066] Feedback strategy rule base update: Based on the historical data of 95% effectiveness of child A's good response to voice feedback, the system automatically increased the weight of the voice encouragement strategy in its personalized rule base, and added a trigger rule based on geolocation electronic fence for new outdoor activity goals.

[0067] Example 2: Cultivating Focused Learning Habits in a Collaborative School-Home Setting

[0068] This embodiment uses 25 fifth-grade students from a primary school in Xuhui District, Shanghai as subjects to demonstrate the application of the system in more complex scenarios and its collaborative closed-loop function.

[0069] System configuration and data acquisition:

[0070] In classroom scenarios, a smart teaching system supporting behavioral analysis is deployed, categorized as Huawei Smart Classroom. It collects anonymized data such as students' sitting posture and gaze direction through classroom cameras, without facial recognition, but only through posture analysis. In home scenarios, students use smart learning tablets with client software installed, model iFlytek T10, which collect application usage logs and focus time.

[0071] Generation of associated target sets:

[0072] For student B, whose initial profile shows short attention span (averaging 15 minutes per class) and significant procrastination on homework, the system invokes a correlation model. This model indicates a high correlation between classroom attention efficiency and homework completion fluency. A target set is then set for this student.

[0073] Main habit goal: Increase effective focus time in class to an average of 25 minutes per class.

[0074] Related sub-goal: Reduce the number of irrelevant app switching during homework time from an average of 8 times per half hour to less than 3 times.

[0075] Strategic feedback generation and collaborative closed loop:

[0076] 1. Real-time classroom feedback: When the system analyzes camera data and detects that student B's gaze deviates from the teaching focus area for more than 20 seconds, it is determined to be a potential distraction. The intelligent feedback center generates a command and sends it to the personal terminal touchpad on student B's desk, displaying a gentle, visible animation prompt, such as a gradually focusing lens. The prompt style can be finely adjusted based on the student's interests and preferences in their profile, such as liking aerospace design, to align with the target.

[0077] 2. Home-Scene Adaptation Output: During the designated homework time, the learning tablet client detects frequent switching to game applications. The interaction adaptation output module first determines the scenario: if the student is using the video problem-solving function, it is considered an acceptable, gentle interruption, triggering feedback. In the modal matching stage, the tablet's built-in sensors detect ambient noise exceeding 65 decibels. To avoid increasing auditory interference, the system displays a semi-transparent text prompt in the center of the screen, accompanied by a silent vibration of the tablet.

[0078] 3. Collaborative closed-loop step trigger: When the system detects that student B has achieved the classroom focus goal for five consecutive days, it is considered that he has achieved a key milestone. The system automatically generates a detailed achievement report, which includes specific behavioral data, the average focus time in math class this week reached 28 minutes, and a description of progress. The report is then immediately pushed to the app of his parents and homeroom teacher.

[0079] 4. External incentives incorporated into the system: Parents click "like" and send voice encouragement within the app, and the homeroom teacher awards a Classroom Focus Star electronic badge online. After the system detects these confirmations and incentive responses from the guardians, it records this event as a high-weight positive feedback factor in student B's personalized profile. In subsequent feedback generation logic, the system may reference this achievement, for example: "Your Focus Star is shining brightly! Keep it up and move towards the next star!" This forms a reinforced closed loop of system feedback, behavior improvement, external incentives, and reinforcement of system feedback.

[0080] Example 3: Comparison of Traditional Timed Reminder Methods

[0081] To objectively evaluate the effectiveness of the present invention, a control group was established, consisting of 30 children from the same region, age group, and with similar initial daily routines as those in Example 1.

[0082] Method: Using a regular electronic watch with only a fixed alarm clock function, parents verbally remind and subjectively evaluate each day, setting a uniform and rigid bedtime target of 21:30.

[0083] Process: There are no personalized habit profiles, no associated sub-goals, and the feedback is only the ringing of a bell at a fixed time or the parents' monotonous urging. It cannot be adjusted according to the scenario, nor is there periodic evaluation and strategy optimization.

[0084] Experimental data, analysis and conclusions

[0085] All experiments lasted for 8 weeks, and the key data are summarized and analyzed as follows:

[0086] Table 1: Quantitative Comparison of Core Effects of Habit Cultivation

[0087]

[0088] Table 2: Data on System Personalization and Intelligent Function Applications

[0089]

[0090] The experimental data in Table 1 provide the most direct evidence of the effectiveness of the technical solution of the present invention. In Example 1, the children who used the present invention to cultivate regular sleep patterns showed that the standard deviation of bedtime, a core observable indicator, decreased significantly from 1.15 hours to 0.52 hours within 8 weeks, a reduction of 54.8%, and 93.3% of the individuals consistently achieved the preset target. In Example 2, the effective attention span in the classroom for cultivating focused learning increased by 73.5%, and 88.0% of the individuals achieved the target. In contrast, the control group using the traditional timed reminder method showed an improvement of only 16.9% in the corresponding indicator, with a target achievement rate of only 36.7%.

[0091] This significant difference is not accidental; its root lies in the underlying logic of the technical solutions. The traditional method represented by the example is essentially a one-way, static, and undifferentiated transmission of instructions. Its effectiveness highly depends on the child's self-discipline and the parent's continuous supervision, lacking adaptation to individual differences and systematic intervention in the causes of behavior. In contrast, this invention constructs a data-driven, dynamically closed-loop, and adaptively optimized technical intervention system. Specifically:

[0092] 1. Initial profile construction transforms vague behavioral observations into structured, multi-dimensional quantitative features (such as standard deviation, proportion, and duration), providing data anchors for precise intervention.

[0093] 2. The generation of associated goal sets utilizes pre-stored knowledge models and multi-dimensional habit association models, shifting from isolated goal setting to systematic goal planning that considers the mutual influence between habits. For example, for the main problem of irregular work and rest, sub-goals to improve pre-sleep behavior are set simultaneously, intervening at the level of inducement. This reflects the deepening of problem-solving thinking from treating the symptoms to addressing the root cause.

[0094] 3. Strategic feedback and interaction adaptation are key differentiators in this solution. Feedback is no longer a simple prompt sound, but a composite information package based on individual historical response data such as average response delay and ignore rate, dynamically calibrating intensity, generating customized content based on cognitive and interest tags, and intelligently selecting the best output timing and modality according to real-time scenario task status and environmental parameters. This ensures that every interaction strives to maximize information reach and persuasive effect while minimizing user resistance. The 95% personalized reach rate and approximately 40% scenario adaptation intervention rate shown in Table 2 are proof that this step has been effectively implemented.

[0095] 4. Evaluation and adaptive deepening are the core of this solution to achieve long-term results. The system is not satisfied with achieving short-term goals, but generates evaluation reports through periodic evaluations, analyzes behavioral trends, and proposes higher deepening goals for stable habits based on the same set of correlation models, such as deepening from regular bedtime to improving sleep quality, or transforming them into background supporting factors for new habits. The habit stability index of the example group in Table 1 is as high as 0.85 or more, which is much higher than the 0.45 of the control group, which strongly confirms the decisive role of this deepening mechanism in habit solidification.

[0096] The necessity of multi-dimensional habit association models and association target sets:

[0097] Table 2 shows that the system sets associated sub-goals for 100% of individuals. This means that the model is not an optional module, but the cornerstone of the goal generation logic. In the example, the system uses the model to identify the positive promoting relationship between pre-sleep behavior and sleep patterns, thereby setting associated sub-goals. This goal binding based on inherent logical connections rather than subjective arbitrary combinations ensures the systematic and synergistic nature of the intervention measures. This is the fundamental logical innovation of this solution compared to the method of arbitrarily setting multiple isolated goals.

[0098] The personalized feedback proposed in this invention goes beyond simple grouping based on age and gender. Its innovation lies in constructing a multi-label fusion decision-making framework, including:

[0099] Personality traits and response history tags: used to dynamically calibrate interaction intensity, such as reminder thresholds, enabling the system to learn user interaction preferences and avoid insufficient or excessive feedback.

[0100] Cognitive level and interest preference tags: used to generate appropriate feedback content to ensure that the information is understandable and acceptable. The example of generating thematic contextual encouragement for dinosaur enthusiasts is proof of this. The personalized reach rate of over 92% in Table 2 proves that this multi-tag fusion rule generation mechanism is stable and can be applied on a large scale. The personalized feedback experience it generates is difficult for traditional rule engines to achieve.

[0101] The technological advancements of the two-level intelligent interactive adaptation output mechanism:

[0102] This mechanism resolves the fundamental contradiction between the timing and form of feedback triggering in the mobile internet environment, and its technological advancement is reflected in:

[0103] The first-level priority judgment introduces task awareness and queue management: by judging the current task status of the device, it decides whether to interrupt immediately or delay the processing, and uses urgency tags for queue scheduling. This mimics the situational judgment in advanced human communication, significantly reducing interference with the user's main activities and improving system etiquette and user experience.

[0104] The second-level modal matching realizes optimization decision-making based on environmental perception and utility models: it is not a simple if rule, but integrates multi-source data such as environmental noise, lighting, and user state estimates such as visual fatigue, and performs comprehensive calculations through a pre-trained output utility model to dynamically select the modal combination with the least expected interaction interference and the highest information absorption rate. This is essentially a real-time resource optimization and allocation algorithm for human-computer interaction channels, and its technical complexity and intelligence level are far higher than static strategies based on a single condition such as vibration at night.

[0105] The system's evolutionary and social integration capabilities are reflected in the assessment, strategy iteration, and collaborative closed loop.

[0106] The systematic nature of this solution is not only reflected in a single cycle, but also in its ability to self-evolve and socialize.

[0107] Self-evolution: Table 2 shows that the strategy is iterated more than once on average in each cultivation cycle, which proves that the system can proactively optimize subsequent goals and feedback rules based on the achievement and trend of the evaluation results, and realize the leap from executing preset procedures to continuously optimizing strategies based on data.

[0108] Social integration: Through collaborative closed-loop steps, the system transforms offline social incentives such as likes, badges, and verbal encouragement from parents and teachers into structured data that can be monitored and utilized by the system. This data is then incorporated as a positive feedback factor into the subsequent AI feedback generation logic. This creatively breaks down the barriers between AI systems and real social incentives, constructing a reinforcement loop that integrates artificial intelligence feedback with human social incentives. This greatly enriches the means and sustainability of behavioral reinforcement, something that purely technical systems cannot achieve.

[0109] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A system for cultivating and deepening children's habits based on AI analysis and feedback, characterized in that, include: The profile building module is used to extract behavioral features associated with multiple preset habit dimensions from the collected target children's behavioral data, and generate a structured initial habit profile based on the feature values. The goal management module is used to generate a set of phased training goals for target children based on the initial habit profile and the pre-stored multi-dimensional habit association model. These goals include at least one main habit goal and its associated sub-goals. The intelligent feedback hub is connected to the target management module and accesses a feedback strategy rule base. The intelligent feedback hub is configured to: receive real-time behavioral data during the habit cultivation cycle, convert it into progress data corresponding to the cultivation target set, compare the progress data with the target threshold to determine the deviation status, and generate personalized feedback guidance instructions based on the deviation status and the condition-action pairs in the feedback strategy rule base. The interaction adaptation output module is used to adapt and execute the corresponding feedback output action based on the feedback guidance instructions and the current interaction scene information obtained through the environment perception unit. The evaluation and strategy iteration module is used to analyze real-time behavioral data to generate an evaluation report at the end of the training cycle, and to make coordinated adjustments to the training objective set and feedback strategy rule base for the next stage based on the evaluation report and the multi-dimensional habit association model.

2. The system according to claim 1, characterized in that, The multi-dimensional habit association model defines the positive promotion or negative interference relationship between different habit dimensions; the target management module selects the main habit target and the associated sub-targets used to promote the achievement of the main habit target based on the weak habit dimensions in the initial habit profile and with reference to the association relationship.

3. The system according to claim 1, characterized in that, The condition-action pairs in the feedback strategy rule base are pre-personalized based on the target child's personality tendency tags and historical feedback response efficiency in the initial habit profile; wherein, the triggering conditions and reminder intensity of the graded reminder actions are dynamically calibrated based on the target child's average response delay time and ignore rate to historical reminders. The content of targeted suggestion pushes is determined by the cognitive understanding level tags and interest preference tags in the initial habit profile, which together determine its expression and example type.

4. The system according to claim 1, characterized in that, When the interactive adaptation output module executes the feedback output action, its decision logic includes: First priority judgment: Based on the device type and current task status in the current interaction scenario information, determine whether immediate strong interruption feedback is allowed; if not, store the feedback instruction in the delay queue, and automatically trigger it in the next allowed interaction scenario based on the urgency tag of the feedback guidance instruction. Second modality matching: In scenarios where output is permitted, the environmental noise level, ambient light intensity, and recent visual fatigue estimation of the target child are combined with the environmental noise level, ambient light intensity, and recent visual fatigue estimation values ​​obtained by the environmental perception unit. Through a pre-trained output utility model, the modality combination with the least expected interactive interference and the highest information absorption rate is dynamically selected from multiple output modalities for output.

5. A method for cultivating and deepening children's habits based on AI analysis and feedback, applied to the system as described in any one of claims 1-4, characterized in that, Includes the following steps: Step S101, Multi-dimensional habit profile construction: Collect historical behavioral data of the target children, extract quantitative features that are mapped to multiple preset habit dimensions, and form a structured initial habit profile; Step S102, generation of associated target set: call the multi-dimensional habit association model, analyze the status and interrelationship of each habit dimension in the initial habit profile, and formulate a phased training target set containing one main habit target and at least one associated sub-target; Step S103, Dynamic Behavior Perception and Comparison: During the cultivation cycle, continuously perceive and record real-time behaviors related to the cultivation target set, convert them into progress data, compare them with preset target thresholds, and output comparison results with status labels. Step S104, Strategy-based feedback generation and output: Based on the comparison results with status markers, query the feedback strategy rule base and match the corresponding feedback strategy; combine the profile features of the target child and the real-time interaction scenario to generate and execute the appropriate feedback guidance action. Step S105, Evaluation and Adaptive Deepening: After the cycle ends, comprehensively evaluate the achievement of the training goal set, analyze the trend of behavioral changes, and based on the multi-dimensional habit association model, conduct root cause analysis on the unachieved goals, deepen and upgrade the stable habits, thereby updating the training goal set and feedback strategy for the next cycle.

6. The method according to claim 5, characterized in that, The logic for setting associated sub-goals in step S102 is as follows: select habit dimensions that have a positive promoting relationship with the main habit goal and that still have room for improvement in the initial habit profile as the target carrier.

7. The method according to claim 5, characterized in that, In step S104, the matching and generation of feedback strategies are based on the personalized tags of the target child in the initial habit profile; among them, the urgency of reminder strategies is adjusted according to their response habits to historical reminders, and the specific expression of suggestion strategies is adapted and generated according to their cognitive level and interest preferences.

8. The method according to claim 5, characterized in that, The step S104 of generating and executing the adapted feedback guidance action specifically includes: first, determining whether the current scenario is suitable for executing immediate feedback; if not, suspending the feedback task and marking its urgency, and automatically resuming execution when a suitable scenario is detected later; if suitable, further dynamically selecting the optimal combination of output modes based on real-time environmental parameters and personal state estimation values.

9. The method according to claim 5, characterized in that, Step S105 involves deepening and upgrading the established habit, including: raising the target threshold of the habit, increasing the complexity of the maintenance scenario of the habit, or transforming the habit from a goal that needs to be deliberately cultivated into a background support factor for promoting other new habits.

10. The method according to claim 5, characterized in that, The method also includes a collaborative closed-loop step: in steps S103-S104, when the target child is detected to have reached a key milestone, an achievement report containing a specific behavioral description is automatically generated and pushed to the monitoring terminal; at the same time, the confirmation or incentive response returned by the monitoring terminal is monitored, and this response is incorporated as a positive feedback factor into the subsequent feedback strategy generation logic.

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