A hierarchical cognitive navigation system and method oriented to life stage progression

Abstract

The present application relates to the field of artificial intelligence and intelligent education technology, and discloses a hierarchical cognitive navigation system and method for life stage advancement, which is configured with a time interval division module to divide multiple age growth time intervals, comb the interval level growth relationship and output time sequence items; a literacy benchmark collection module collects literacy items according to four categories of psychological construction, physical reserve, habit formation and ability improvement, and generates literacy benchmark units; a time literacy matching module completes two-way accurate matching; a node arrangement module arranges cultivation nodes and combs node level logic; and a behavior collection and analysis module collects corresponding growth data, completes benchmark mapping and generates structured growth behavior records. The present application effectively solves the drawbacks of traditional growth cultivation without hierarchy, time sequence and quantification, and greatly improves the scientificity and dynamic adaptability of growth cultivation.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and intelligent education technology, and more specifically, to a hierarchical cognitive navigation system and method for advancing through life stages. Background Technology

[0002] Cognitive guidance and literacy cultivation at different life stages are important means to help individuals grow and develop. Existing growth management platforms can carry out basic growth planning and literacy recording based on age stages. They can divide different growth stages into intervals and list corresponding cultivation content, thus initially realizing the online retention and management of growth information.

[0003] However, existing technologies still have significant shortcomings. They only provide simple stage divisions and content listings, without clarifying the hierarchical relationships of positive flow, parallel intersection, and dynamic jumps between each growth stage, thus failing to form a systematic hierarchical navigation logic. At the same time, the classification of literacy content is scattered, failing to achieve precise matching between time intervals and literacy benchmarks, and the arrangement of cultivation nodes lacks hierarchical connection and cross-domain extension design. Furthermore, they can only passively record growth data, lacking the ability to collect multi-source behavioral data, perform feature analysis, and quantitatively map literacy, making it impossible to evaluate the cultivation effect in real time, or dynamically optimize node connection and initiation rules based on growth status. Overall, cultivation guidance lacks quantitative basis and adaptive adjustment capabilities.

[0004] Therefore, it is necessary to design a hierarchical cognitive navigation system and method for advancing through life stages, in order to solve the problems of chaotic logic in the existing technology growth stages, low matching degree between time sequence and literacy, unreasonable layout of cultivation nodes, inability to quantify and assess growth status and lack of dynamic control capabilities. Summary of the Invention

[0005] In view of this, the present invention proposes a hierarchical cognitive navigation system and method for life stage advancement, aiming to solve the problems of disordered growth stage logic, low matching degree between time sequence and literacy, unreasonable layout of cultivation nodes, inability to quantify and evaluate growth status and lack of dynamic control ability in existing technologies.

[0006] In one aspect, this invention proposes a hierarchical cognitive navigation system oriented towards life stage progression, comprising: The time series interval division module is configured to divide multiple age growth time series intervals, sort out the hierarchical growth relationships of the forward flow, parallel intersection, and dynamic jump of each growth time series interval, and output time series entries; The competency benchmark collection module is electrically connected to the time interval division module. The competency benchmark collection module is configured to collect growth competency items into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and integrate them to generate competency benchmark units. The temporal literacy matching module is electrically connected to the temporal interval division module and the literacy benchmark collection module, respectively. The temporal literacy matching module is configured to receive the temporal entries and literacy benchmark units, perform bidirectional precise association matching between the temporal interval and the literacy benchmark, and generate temporal literacy matching entries. The stage cultivation node arrangement module is electrically connected to the time-series literacy matching module. The stage cultivation node arrangement module is configured to receive time-series literacy matching entries, arrange corresponding cultivation nodes for each growth time-series interval according to the time-series literacy matching entries, and sort out the hierarchical logic of the successive connection and cross-domain extension between each cultivation node. The growth behavior collection and analysis module is electrically connected to the stage cultivation node arrangement module. The growth behavior collection and analysis module is configured to collect growth behavior data corresponding to each cultivation node, associate the data with literacy benchmarks to complete data mapping, and generate structured growth behavior records.

[0007] Furthermore, the stage cultivation node arrangement module includes: The hierarchical association unit is configured to identify the positive flow, parallel intersection, and dynamic jump relationships between each growth time interval, and thereby construct a directed connection graph between nodes; A node attribute labeling unit is electrically connected to the hierarchical association unit. The node attribute labeling unit is configured to label the required literacy type, target achievement threshold and recommended cultivation duration for each cultivation node according to the time-series literacy matching entries. The succession optimization unit is electrically connected to the hierarchical association unit and the node attribute labeling unit, respectively. The succession optimization unit is configured to dynamically adjust the start timing and resource allocation weight of subsequent cultivation nodes according to the achievement status of the previous cultivation node.

[0008] Furthermore, the growth behavior collection and analysis module includes: The multi-source data access unit is configured to receive growth behavior data from wearable devices, educational applications, and subjective self-assessment questionnaires. The behavior feature extraction unit is electrically connected to the multi-source data access unit. The behavior feature extraction unit is configured to extract behavior feature vectors according to four fixed categories: psychological construction, physical reserves, habit formation, and ability improvement. The literacy mapping analysis unit is electrically connected to the behavior feature extraction unit and the literacy benchmark collection module, respectively. The literacy mapping analysis unit is configured to perform similarity matching between the behavior feature vector and the literacy benchmark unit to generate the literacy achievement probability of each cultivation node.

[0009] Furthermore, when the literacy mapping analysis unit performs similarity matching between the behavioral feature vector and the literacy benchmark unit, it includes: The competency mapping analysis unit is also configured to substitute behavioral feature vectors into a pre-established competency maturity model and obtain an initial maturity score. The literacy mapping analysis unit is also configured to obtain the growth deviation rate between the current growth behavior data and the growth behavior data of adjacent historical periods, and to determine the growth compensation coefficient based on the growth deviation rate. The competency mapping analysis unit is also configured to correct the initial maturity score based on the growth compensation coefficient, and to determine the corrected maturity score as the competency achievement probability.

[0010] Furthermore, when the literacy mapping analysis unit pre-establishes a literacy maturity model, it includes: The competency mapping analysis unit is also configured to acquire an experimental dataset of growth behavior samples and corresponding expert-annotated competency maturity samples. The competency mapping analysis unit is also configured to take growth behavior samples from the experimental dataset as input, take the corresponding competency maturity samples as reference benchmarks, train the physical response model, and establish a competency maturity model based on the training results.

[0011] Furthermore, when the competency mapping analysis unit acquires the growth deviation rate between the current growth behavior data and the historical growth behavior data of adjacent time periods, and determines the growth compensation coefficient based on the growth deviation rate, it includes: The competency mapping analysis unit is also configured to determine whether to correct the initial maturity score based on the relationship between each growth deviation rate and a preset deviation threshold, and to calculate a growth compensation coefficient when correction is required. When all growth deviation rates are less than the preset deviation threshold, the competency mapping analysis unit determines not to correct the initial maturity score. When any growth deviation rate is greater than or equal to a preset deviation threshold, the competency mapping analysis unit determines to correct the initial maturity score and determines the growth compensation coefficient based on the relationship between each growth deviation rate and the preset deviation threshold.

[0012] Furthermore, when the competency mapping analysis unit determines the growth compensation coefficient based on the relationship between each growth deviation rate and a preset deviation threshold, it includes: The literacy mapping analysis unit is also configured to obtain the deviation difference between each growth deviation rate and a preset deviation threshold. The literacy mapping analysis unit is also configured to perform linear mapping processing based on the deviation difference, and to obtain the degree of deviation between each growth deviation rate and the preset deviation threshold based on the deviation difference after mapping processing. The competency mapping analysis unit is also configured to determine a growth compensation coefficient based on the relationship between the degree of deviation and a first preset deviation threshold and a second preset deviation threshold. When the degree of deviation is less than the first preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the first compensation coefficient. When the degree of deviation is greater than or equal to the first preset deviation threshold and less than the second preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the second compensation coefficient. When the degree of deviation is greater than or equal to the second preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the third compensation coefficient. Among them, the first preset deviation threshold is less than the second preset deviation threshold, and the third compensation coefficient is greater than the second compensation coefficient, the second compensation coefficient is greater than the first compensation coefficient, and the first compensation coefficient is greater than 1.

[0013] Furthermore, when the competency mapping analysis unit optimizes the succession logic of the cultivation nodes based on the revised maturity score, it includes: The competency mapping analysis unit is also configured to obtain the deviation ratio between the achievement time of the current cultivation node and the standard planned duration, and to determine the optimized node exit conditions based on the relationship between the current maturity score and the deviation ratio. The competency mapping analysis unit is also configured to obtain the average value of the prerequisite competency dependency weights for subsequent training nodes; The literacy mapping analysis unit is also configured to obtain the difference between the mean of the prerequisite literacy dependency weights and the preset dependency target value, and to determine the final node activation condition based on the relationship between the difference value and the preset difference threshold. When the difference value is less than or equal to the preset difference threshold, the literacy mapping analysis unit determines the current node's activation condition as the final activation condition. When the difference value is greater than the preset difference threshold, the literacy mapping analysis unit determines an adjustment factor based on the relationship between the difference value and the preset difference threshold, and determines the node activation condition adjusted according to the adjustment factor as the final activation condition.

[0014] Further, when determining the adjustment factor based on the relationship between the difference value and the preset difference threshold, the following steps are included: The literacy mapping analysis unit is also configured to obtain the ratio between the difference value and a preset difference threshold, and to determine the adjustment range of the adjustment factor based on the relationship between the ratio and a first preset ratio and a second preset ratio. When the ratio is less than the first preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the first adjustment range; When the ratio is greater than or equal to the first preset ratio and less than the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the second adjustment range. When the ratio is greater than or equal to the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the third adjustment range; Among them, the first preset ratio is less than the second preset ratio, and the third adjustment range is greater than the second adjustment range, and the second adjustment range is greater than the first adjustment range.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By dividing the developmental timeline into multiple age segments and clarifying the hierarchical growth relationships of these segments—including their forward flow, parallel intersections, and dynamic transitions—a systematic logic for the progression of developmental timelines was constructed. This streamlined the progressive relationships across all life stages of development, avoiding the problems of fragmented and logically chaotic stage divisions. Standardized competency benchmarks were created by uniformly categorizing competency items into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability enhancement. This achieved unified and standardized standards for cultivating developmental competencies. Furthermore, the precise two-way correlation and matching between time segments and competency benchmarks ensured a high degree of alignment between the content and needs of each age group. A coherent and complete developmental chain capable of cross-domain linkage was constructed through the hierarchical layout of developmental nodes, their seamless connection, and the logical flow of cross-domain extension. In addition, through the collection of multi-source developmental behavior data and the mapping and analysis of competency benchmark data, structured developmental behavior records were generated, enabling quantitative tracking and dynamic evaluation of individual developmental status. This allows for adaptive optimization of the connection logic between developmental nodes based on actual developmental status, effectively improving the accuracy, systematicity, and dynamic adaptability of cognitive navigation and competency cultivation across life stages.

[0016] On the other hand, this application also provides a hierarchical cognitive navigation method for life stage advancement, including the following steps: Divide the age growth time series into multiple segments, sort out the hierarchical growth relationships of positive flow, parallel intersection, and dynamic jump in each of the growth time series segments, and output the time series entries; The growth literacy items are categorized into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and then integrated to generate literacy benchmark units. Receive the time-series entries and literacy benchmark units, perform bidirectional precise correlation matching between time-series intervals and literacy benchmarks, and generate time-series literacy matching entries; Receive the time-series literacy matching entries, deploy corresponding cultivation nodes for each of the growth time-series intervals according to the time-series literacy matching entries, and sort out the hierarchical logic of the successive connection and cross-domain extension between each of the cultivation nodes. Collect growth behavior data corresponding to each of the aforementioned cultivation nodes, correlate them with literacy benchmarks to complete data mapping, and generate structured growth behavior records.

[0017] It is understandable that the aforementioned hierarchical cognitive navigation system and method for advancing through life stages have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A functional block diagram of a hierarchical cognitive navigation system for life stage progression provided in an embodiment of the present invention; Figure 2 A flowchart of a hierarchical cognitive navigation method for life stage progression provided in an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Reference Figure 1 In some embodiments of this application, a hierarchical cognitive navigation system for life stage progression includes: a time interval division module, a literacy benchmark collection module, a time-series literacy matching module, a stage cultivation node arrangement module, and a growth behavior collection and analysis module.

[0021] Specifically, the time-series interval division module is configured to divide multiple age-related growth time-series intervals, analyze the hierarchical growth relationships of positive flow, parallel intersection, and dynamic jumps within each growth time-series interval, and output time-series entries. The competency benchmark collection module, electrically connected to the time-series interval division module, is configured to collect growth competency entries into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and integrate them to generate competency benchmark units. The time-series competency matching module, electrically connected to both the time-series interval division module and the competency benchmark collection module, is configured to receive time-series entries and competency benchmark units, and then... The system achieves precise bidirectional correlation and matching between time-series intervals and competency benchmarks, generating time-series competency matching entries. A stage-based development node arrangement module, electrically connected to the time-series competency matching module, is configured to receive time-series competency matching entries, arrange corresponding development nodes for each growth time-series interval based on these entries, and organize the hierarchical logic of continuous connection and cross-domain extension between development nodes. A growth behavior collection and analysis module, electrically connected to the stage-based development node arrangement module, is configured to collect growth behavior data corresponding to each development node, correlate it with competency benchmarks to complete data mapping, and generate structured growth behavior records.

[0022] Specifically, the time-series interval division module divides the human growth process from 0 to 22 years old into the sensorimotor period (0-3 years old), the pre-operational period (4-6 years old), the concrete operational period (7-12 years old), the early formal operational period (13-15 years old), the complete formal operational period (16-18 years old), and the social adaptation transition period (19-22 years old). The forward flow refers to entering the next interval sequentially from the previous interval. The parallel crossover refers to being in two or more intervals at the same time (e.g., there is an overlap in the development window of logical reasoning ability between 13-15 years old and 16-18 years old). The dynamic jump refers to skipping a certain interval and directly entering the subsequent interval according to individual differences (e.g., children with outstanding creativity can jump directly from 4-6 years old to the project-based learning node of 7-12 years old). The time-series entries include interval identifiers, starting age thresholds, ending age thresholds, and a list of preceding intervals that allow jumps.

[0023] Specifically, in the competency benchmark collection module, the psychological construction covers emotion recognition and regulation, self-efficacy, resilience, empathy, and growth mindset; the physical fitness reserve covers cardiorespiratory endurance, muscle strength, flexibility, body coordination, and fine motor control; the habit formation covers regularity of work and rest, hygiene habits, attention allocation habits, knowledge organization habits, and reflection and review habits; the ability improvement covers observation and classification ability, logical reasoning ability, creative problem-solving ability, metacognitive monitoring ability, and social collaboration ability; the competency benchmark unit encodes the above items into a four-dimensional vector space according to four fixed categories, and each dimension is further subdivided into three levels of maturity description (enlightenment level, application level, and transfer level), and is accompanied by typical performance examples of each competency for each age group.

[0024] Specifically, after receiving the time-series entries and competency benchmark units, the time-series competency matching module employs a bidirectional weighted matching algorithm: on the one hand, using the time-series interval as the primary key, it retrieves the competency benchmark entries that should be prioritized for development within that interval and their target maturity levels (e.g., for the specific operational period of 7-12 years old, "logical reasoning ability" must reach the application level, with a target achievement probability of no less than 0.75); on the other hand, using the competency benchmark entries as the primary key, it reversely locates the most suitable combination of time-series intervals for initiation, reinforcement, and consolidation (e.g., "growth mindset" should be initiated at 4-6 years old, reinforced at 7-12 years old, and consolidated at 13-18 years old); the bidirectional precise association matching ultimately generates several time-series competency matching entries, each containing a time-series interval ID, competency entry ID, suggested development sequence number, and pre- and post-dependent constraints (e.g., "emotion recognition" must be initiated before "resilience").

[0025] The above embodiments, by dividing the growth sequence into multiple age-related time intervals and organizing the hierarchical growth relationships of positive flow, parallel intersection, and dynamic jumps between intervals, construct a systematic logic for the advancement of growth sequence, and regulate the progressive relationship of growth stages throughout the entire life cycle, avoiding the problems of fragmented and logically chaotic division of growth stages. By uniformly collecting and integrating literacy items according to four fixed categories—psychological construction, physical fitness reserves, habit formation, and ability improvement—standard literacy benchmark units are formed, achieving unified standardization of growth literacy cultivation standards. At the same time, through the two-way precise correlation and matching between time intervals and literacy benchmarks, a high degree of adaptability between the cultivation content and growth needs of each age stage is ensured. Relying on the hierarchical cultivation node layout and node continuation connection, and the logical organization of cross-domain extension, a coherent, complete, and cross-domain-linkable growth cultivation link is constructed. Furthermore, by collecting multi-source growth behavior data and mapping and analyzing literacy benchmark data, structured growth behavior records can be formed, enabling quantitative tracing and dynamic evaluation of individual growth status. Based on the actual growth status, the logic of connecting cultivation nodes can be adaptively optimized, effectively improving the accuracy, systematicness, and dynamic adaptability of cognitive navigation and literacy cultivation at life stages.

[0026] Specifically, the stage-based training node arrangement module includes: a hierarchical association unit, configured to identify the positive flow, parallel intersection, and dynamic jump relationships between each growth time interval, and to construct a directed connection graph between nodes based on this; a node attribute labeling unit, electrically connected to the hierarchical association unit, configured to label the required quality type, target achievement threshold, and suggested training duration for each training node based on time-series quality matching items; and a succession optimization unit, electrically connected to both the hierarchical association unit and the node attribute labeling unit, configured to dynamically adjust the start timing and resource allocation weight of subsequent training nodes based on the achievement status of the previous training node.

[0027] Specifically, after identifying the positive flow, parallel crossover, and dynamic jump relationships between each growth time series interval, the hierarchical association unit maps each growth time series interval to a nurturing node and assigns a directed edge to each relationship: the positive flow corresponds to a unidirectional sequential edge with an edge weight between 0.8 and 1.0; the parallel crossover corresponds to a bidirectional interconnection edge with an edge weight between 0.4 and 0.7; the dynamic jump corresponds to a conditional jump edge with an edge weight between 0.2 and 0.5; each node in the directed connection graph records the set of incoming edges and the set of outgoing edges, where the incoming edges represent the number of preceding dependent nodes (not less than 1 and not more than 5), and the outgoing edges represent the number of subsequent nodes that can be started.

[0028] Specifically, the node attribute labeling unit labels each cultivation node with the following attributes based on the literacy type, target maturity level, and associated time interval recorded in the time-series literacy matching entries: required literacy type, including one or more sub-category codes selected from four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement; target achievement threshold, represented by a probability value between 0 and 1, where the minimum achievement threshold corresponding to the enlightenment level is 0.60, the minimum achievement threshold corresponding to the application level is 0.75, and the minimum achievement threshold corresponding to the transfer level is 0.90; recommended cultivation duration, measured in weeks, where the cultivation duration for basic literacy nodes (such as habit formation) is 4 to 12 weeks, and the cultivation duration for composite literacy nodes (such as ability improvement) is 8 to 24 weeks, with a 25% fluctuation allowed based on individual differences.

[0029] Specifically, the successive connection optimization unit obtains the actual achievement probability and achievement time of the previous training node from the hierarchical association unit, and obtains the target achievement threshold and suggested training duration of the subsequent training node from the node attribute labeling unit, and dynamically performs the following adjustments: if the achievement probability of the previous node is greater than or equal to its target achievement threshold, the start time of the subsequent node is advanced, with the advancement not exceeding 20% ​​of the suggested training duration; if the achievement probability of the previous node is less than its target achievement threshold but greater than 70% of the target achievement threshold, the original start time is maintained; if the achievement probability of the previous node is less than 70% of the target achievement threshold, the start time of the subsequent node is delayed, with the delay ranging from 10% to 30% of the suggested training duration, and the resource allocation weight of the subsequent node is increased from the baseline value of 1.0 to between 1.2 and 1.5. The resource allocation weight is used to guide the training frequency, single training duration, and tutoring investment intensity.

[0030] Understandably, the hierarchical association unit first analyzes the positive flow, parallel intersection, and dynamic jump relationships between each growth time interval, and constructs a node network graph consisting of nurturing nodes and directed edges accordingly. The node attribute labeling unit then matches the time-series literacy items to label each node in the graph with the literacy category to be developed, the maturity probability value to be achieved, and the standard nurturing cycle calculated in weeks. The succession optimization unit monitors the actual achievement probability and achievement time of the previous node in real time. When the achievement value exceeds the threshold, the subsequent node is started in advance and its nurturing cycle is appropriately compressed. When the achievement value is lower than the threshold by a certain proportion, the subsequent node is started in a delayed manner and its resource allocation weight is increased, thereby forming an adaptive, interconnected node arrangement and dynamic adjustment mechanism.

[0031] Specifically, the growth behavior collection and analysis module includes: a multi-source data access unit, configured to receive growth behavior data from wearable devices, educational applications, and subjective self-assessment questionnaires; a behavior feature extraction unit, electrically connected to the multi-source data access unit, configured to extract behavior feature vectors according to four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement; and a literacy mapping analysis unit, electrically connected to both the behavior feature extraction unit and the literacy benchmark collection module, configured to perform similarity matching between the behavior feature vectors and the literacy benchmark unit to generate the literacy achievement probability for each development node.

[0032] Specifically, the multi-source data access unit receives heart rate, acceleration, and skin conductance data from wearable devices, answer accuracy, task completion time, and knowledge review interval data from educational applications, as well as mood self-rating scores (out of 1 to 5) and energy self-rating levels (out of 1 to 10) from subjective self-assessment questionnaires, within a fixed time window (each 24 hours is a collection cycle, and data is summarized weekly). The wearable device data is tagged according to sleep time (22:00 to 06:00 the next day) and daytime activity time. The educational application data is stored in categories according to subject area and task type (practice, exploration, and collaboration). The subjective self-assessment questionnaire is pushed out every two weeks, and a reminder to fill in the missing information is triggered when the completion rate is below 80%.

[0033] Specifically, the behavioral feature extraction unit cleans and normalizes the raw data received by the multi-source data access unit. Normalization uses a linear mapping method to compress each indicator to the 0-1 range. Then, behavioral feature vectors are extracted according to the four fixed categories: For the psychological construct category, emotional fluctuation amplitude (standard deviation of daytime heart rate variability), positive emotion proportion (ratio of self-rating scores ≥4 to total self-ratings), and self-efficacy response delay (seconds of waiting for the first operation when facing a difficult task); for the physical reserve category, duration of moderate-intensity activity (cumulative minutes with heart rate greater than 1.5 times resting heart rate), sleep efficiency (ratio of actual sleep duration to bed rest duration), and motor coordination... The system extracts the following metrics: bias (correlation coefficient of triaxial changes in accelerometer); habit formation metrics (daily standard deviation of sleep time), task persistence (percentage of tasks not abandoned within the specified time), and review interval optimization (reciprocal of the ratio of actual review interval to Ebbinghaus optimal interval); and ability improvement metrics (divergence of problem-solving strategies (number of attempts at different solutions for the same knowledge point), metacognitive monitoring frequency (number of times proactive checks are triggered before submitting answers), and collaborative dialogue rounds (ratio of user speech to total team speech in team tasks). The behavioral feature vector is a floating-point array with a fixed dimension of 64, with 16 dimensions assigned to each fixed category.

[0034] Specifically, the competency mapping analysis unit pre-stores standard feature templates (also 64-dimensional floating-point arrays) for each competency item in the competency benchmark unit. It then performs cosine similarity matching between the behavioral feature vectors output by the behavioral feature extraction unit and each standard feature template. The cosine similarity calculation formula is the dot product divided by the modulus product, with the result ranging from 0 to 1. For each cultivation node, the competency mapping analysis unit further selects one or more corresponding competency items from the similarity matching results based on the competency type associated with that node. A weighted average (the weights are determined by the dependency constraint coefficients in the time-series competency matching items, with a coefficient range of 0.3 to 1.0) is taken as the original achievement probability for that node. Finally, the competency mapping analysis unit introduces a time decay factor (in weeks; if the most recent effective behavioral collection is more than two weeks old, the decay coefficient is 0.85; if it is more than four weeks old, it is 0.70) to correct the original achievement probability. The corrected probability is the competency achievement probability and is output to the stage cultivation node arrangement module for subsequent connection and optimization.

[0035] Understandably, the multi-source data access unit collects multimodal growth behavior data from wearable devices, educational applications, and subjective self-assessment questionnaires in real time at fixed intervals and performs standardized aggregation. The behavior feature extraction unit cleans and normalizes the raw data, and extracts corresponding quantitative behavior feature vectors for each of the four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement. The literacy mapping analysis unit performs cosine similarity matching on these behavior feature vectors with the standard feature templates of each literacy item in the pre-stored literacy benchmark unit, calculates the original achievement probability by combining the literacy type and dependency weight associated with each cultivation node, and then performs weighted correction on the recent and long-term behavior data through a time decay factor, finally generating an accurate and dynamically updated literacy achievement probability for each cultivation node, providing a quantitative basis for the optimization of subsequent node arrangement and connection.

[0036] Specifically, when the competency mapping analysis unit performs similarity matching between behavioral feature vectors and competency benchmark units, it includes: The competency mapping analysis unit is also configured to input behavioral feature vectors into a pre-established competency maturity model and obtain an initial maturity score. The competency mapping analysis unit is also configured to obtain the growth deviation rate between the current growth behavior data and the growth behavior data of adjacent historical periods, and to determine the growth compensation coefficient based on the growth deviation rate. The competency mapping analysis unit is also configured to correct the initial maturity score based on the growth compensation coefficient and determine the corrected maturity score as the competency achievement probability.

[0037] Specifically, when the competency mapping analysis unit pre-establishes a competency maturity model, it includes: The competency mapping analysis unit is also configured to acquire experimental datasets of growth behavior samples and corresponding expert-annotated competency maturity samples. The competency mapping analysis unit is also configured to take growth behavior samples from the experimental dataset as input, use the corresponding competency maturity samples as reference benchmarks, train the physical response model, and establish a competency maturity model based on the training results.

[0038] Specifically, when the literacy mapping analysis unit substitutes the behavioral feature vector into the pre-established literacy maturity model, the model adopts a five-layer fully connected neural network structure. The number of nodes in the input layer is equal to the dimension of the behavioral feature vector (64 dimensions), the hidden layers have 128, 256, and 128 nodes respectively, the output layer has 1 node, and the output value is mapped to the interval between 0 and 1 by the Sigmoid function as the initial maturity score. The learning rate during model training is set to 0.001, the batch size is 32, the maximum number of training rounds is 200, and the early stopping rounds are 10. The growth deviation rate between the current growth behavior data and the growth behavior data of adjacent historical periods refers to the absolute difference calculated dimension-wise between the mean of each category of behavioral features extracted in the current period (the last 7 days) and the corresponding mean of the previous period, divided by the sum of the current period's mean and 0.01, resulting in a growth deviation rate vector (dimension 64). The Euclidean norm of this vector is taken as the comprehensive growth deviation rate, with a value range from 0 to positive infinity, typically between 0.05 and 0.45. The growth compensation coefficient is determined using a piecewise linear function based on the comprehensive growth deviation rate: when the comprehensive growth deviation rate is less than 0.10, the compensation coefficient is 1.00; when it is between 0.10 and 0.30, the compensation coefficient increases linearly from 1.00 to 1.25; when it is between 0.30 and 0.60, the compensation coefficient increases linearly from 1.25 to 1.60; and when it exceeds 0.60, the compensation coefficient is clamped to 1.60. The correction method involves multiplying the initial maturity score by the growth compensation coefficient, and then taking the minimum value of the result and 1.0 to ensure that the corrected maturity score does not exceed 1.0. This corrected value is the probability of achieving the required competency.

[0039] Specifically, when the competency mapping analysis unit pre-establishes a competency maturity model, the experimental dataset contains at least 5000 samples. Each sample consists of a growth behavior record and competency maturity scores independently annotated by three domain experts. The median of the expert scores is used as the reference benchmark for that sample. The growth behavior samples come from individuals of different ages (at least 100 samples from each age group from 0 to 22 years old), different genders, and different educational backgrounds, and the collection period lasts for at least 6 months. The physical response model uses a gradient boosting decision tree as the base model, with 100 trees, a maximum depth of 6, a minimum number of leaf node samples of 5, and a learning rate of 0.05. During training, five-fold cross-validation is used to evaluate the model's generalization ability, and mean squared error is used as the loss function. Training stops when the validation set loss no longer decreases in 5 consecutive iterations. After training, the model parameters are fixed and saved, and the model is named the competency maturity model, which is used to infer the initial maturity score in real time for new input growth behavior data.

[0040] Understandably, firstly, by using a large number of pre-collected growth behavior samples and corresponding maturity scores labeled by experts, a literacy maturity model is established by training a physical response model that can map behavioral features to maturity scores. During real-time matching, the current behavioral feature vector is substituted into the model to obtain an initial maturity score. At the same time, the growth deviation rate between the current and previous period's behavioral data is calculated, and the corresponding growth compensation coefficient is determined segment by segment based on the magnitude of the deviation rate. Finally, the initial score is corrected using the compensation coefficient, and the corrected result is the literacy achievement probability of that cultivation node.

[0041] Specifically, when the competency mapping analysis unit obtains the growth deviation rate between current growth behavior data and historical growth behavior data from adjacent periods, and determines the growth compensation coefficient based on the growth deviation rate, it includes: The competency mapping analysis unit is also configured to determine whether to correct the initial maturity score based on the relationship between each growth deviation rate and a preset deviation threshold, and to calculate the growth compensation coefficient when correction is required. When all growth deviation rates are less than the preset deviation threshold, the competency mapping analysis unit determines not to correct the initial maturity score. When any growth deviation rate is greater than or equal to the preset deviation threshold, the competency mapping analysis unit determines to correct the initial maturity score and determines the growth compensation coefficient based on the relationship between each growth deviation rate and the preset deviation threshold.

[0042] Specifically, the preset deviation threshold is set to 0.15. This threshold is derived from the statistical distribution analysis of 500 typical growth trajectory samples and represents the upper limit of natural fluctuations in behavioral characteristics between adjacent time periods. Each growth deviation rate refers to calculating the relative deviation of 64 dimensions of behavioral characteristics extracted from the current time period (the last 7 days) under four fixed categories: psychological construction, physical reserves, habit formation, and ability improvement, with the corresponding characteristics from the previous adjacent time period (going back 7 days). The relative deviation calculation formula is the absolute value of the current value minus the historical value, divided by the sum of the historical value and 0.01, thus obtaining 64 growth deviation rates.

[0043] Specifically, when all 64 growth deviation rates are less than 0.15, the growth trajectory is considered to be continuous and stable, and the current initial maturity score can accurately reflect the actual competence level. Therefore, the initial maturity score is not corrected and is directly used as the probability of competence achievement.

[0044] Specifically, when any one of the growth deviation rates is greater than or equal to 0.15, it indicates a significant change in behavioral pattern in at least one category recently (such as a sharp drop in physical activity or a jump in psychological self-assessment score), requiring a correction to the initial maturity score. The growth compensation coefficient is determined based on the statistical characteristics of each growth deviation rate: first, all deviation rates greater than or equal to 0.15 are selected, and the median of these deviation rates is calculated; then, the compensation coefficient is determined based on the range in which the median falls—1.10 for a median between 0.15 and 0.30; 1.25 for a median between 0.30 and 0.50; and 1.45 for a median above 0.50. The correction method involves multiplying the initial maturity score by this compensation coefficient and comparing it with the upper limit of 1.0, taking the smaller value as the corrected probability of competency achievement, to reflect the positive or negative moderating effect of significant behavioral changes on competency maturity.

[0045] Understandably, the growth deviation rate of the current period and the adjacent historical periods are compared with the preset deviation threshold in each behavioral dimension. If the deviation rate of all dimensions is lower than the threshold, the growth trajectory is considered stable and the initial maturity score is not corrected. If the deviation rate of any dimension reaches or exceeds the threshold, it is considered that the recent behavior has changed significantly and the correction process needs to be initiated. The corresponding growth compensation coefficient is calculated based on the statistical characteristics of those deviation rates that exceed the threshold (such as the interval of the median) and used to correct the initial maturity score in the future.

[0046] Specifically, when the competency mapping analysis unit determines the growth compensation coefficient based on the relationship between each growth deviation rate and a preset deviation threshold, it includes: The competency mapping analysis unit is also configured to obtain the deviation difference between each growth deviation rate and a preset deviation threshold; The competency mapping analysis unit is also configured to perform linear mapping processing based on the difference between each deviation, and to obtain the degree of deviation between each growth deviation rate and the preset deviation threshold based on the difference between each deviation after mapping processing. The competency mapping analysis unit is also configured to determine the growth compensation coefficient based on the relationship between the degree of deviation and a first preset deviation threshold and a second preset deviation threshold. When the degree of deviation is less than the first preset deviation threshold, the competency mapping analysis unit determines the growth compensation coefficient as the first compensation coefficient. When the degree of deviation is greater than or equal to the first preset deviation threshold and less than the second preset deviation threshold, the competency mapping analysis unit determines the growth compensation coefficient as the second compensation coefficient. When the degree of deviation is greater than or equal to the second preset deviation threshold, the competency mapping analysis unit determines the growth compensation coefficient as the third compensation coefficient. Among them, the first preset deviation threshold is less than the second preset deviation threshold, and the third compensation coefficient is greater than the second compensation coefficient, the second compensation coefficient is greater than the first compensation coefficient, and the first compensation coefficient is greater than 1.

[0047] Specifically, the deviation difference refers to the difference obtained by subtracting a preset deviation threshold (0.15) from each growth deviation rate. If the deviation rate is less than the threshold, the difference is negative; positive differences are only calculated for dimensions with deviation rates greater than or equal to the threshold. The linear mapping process maps each positive difference to the interval between 0 and 1 using the function f(d) = d / (d + 0.10). This function maps to 0 when the difference is 0 and to 1 when the difference approaches infinity. 0.10 is a half-saturation constant. The degree of deviation refers to the arithmetic mean of the values ​​obtained after linear mapping of all positive differences. The result ranges from 0 to 1, reflecting the overall level of deviation from the threshold. The closer the degree of deviation is to 1, the more severe the deviation.

[0048] Specifically, the first preset deviation threshold is set to 0.35, and the second preset deviation threshold is set to 0.70. The first compensation coefficient is set to 1.05, the second compensation coefficient is set to 1.20, and the third compensation coefficient is set to 1.45. When the deviation is less than 0.35, the growth deviation is considered minor, and only a 5% compensation increase is given; when the deviation is greater than or equal to 0.35 and less than 0.70, the growth deviation is considered moderate, and a 20% compensation increase is given; when the deviation is greater than or equal to 0.70, the growth deviation is considered significant, and a 45% compensation increase is given. All compensation coefficients are greater than 1 and increase with the degree of deviation to ensure that the corrected maturity score can promptly reflect drastic changes in behavioral patterns.

[0049] Understandably, the positive difference value of each growth deviation rate exceeding the preset deviation threshold is first calculated, and then these differences are transformed to the 0 to 1 interval through a linear mapping function and the average value is taken to obtain the degree of deviation. Finally, based on the comparison results of the degree of deviation with the first and second preset deviation thresholds, the first, second or third compensation coefficients are selected in stages.

[0050] Specifically, when the competency mapping analysis unit optimizes the succession logic of training nodes based on the revised maturity score, it includes: The competency mapping analysis unit is also configured to obtain the deviation ratio between the achievement time of the current cultivation node and the standard planned duration, and determine the optimized node exit conditions based on the relationship between the current maturity score and the deviation ratio. The competency mapping analysis unit is also configured to obtain the mean of the prerequisite competency dependency weights for subsequent training nodes; The literacy mapping analysis unit is also configured to obtain the difference between the mean of the prior literacy dependency weights and the preset dependency target value, and to determine the final node activation condition based on the relationship between the difference value and the preset difference threshold. When the difference value is less than or equal to the preset difference threshold, the literacy mapping analysis unit determines the current node's activation condition as the final activation condition. When the difference value is greater than the preset difference threshold, the literacy mapping analysis unit determines the adjustment factor based on the relationship between the difference value and the preset difference threshold, and determines the node activation condition adjusted according to the adjustment factor as the final activation condition.

[0051] Specifically, the achievement time of the current cultivation node refers to the actual time taken from the initial initiation of the node to the current moment, measured in days; the standard planned duration is the suggested cultivation duration pre-calibrated by the node attribute calibration unit, also measured in days; the deviation ratio is (actual time minus standard planned duration) divided by the standard planned duration, with a value range between -0.50 and 2.00, where a negative value indicates early completion and a positive value indicates delay. The current maturity score is the probability of achieving the skill, with a value range of 0 to 1. The optimized node exit conditions are set as follows: when the current maturity score is greater than or equal to 0.85 and the deviation ratio does not exceed 0.30, normal exit is allowed; when the current maturity score is greater than or equal to 0.75 but less than 0.85, the deviation ratio must not exceed 0.15 for exit to be allowed; when the current maturity score is less than 0.75, exit is not allowed regardless of the deviation ratio, and the node must continue to be nurtured and the standard plan duration extended by 20% before reassessment; when the deviation ratio exceeds 0.50 and the maturity score is still less than 0.70, the node replanning process is triggered, the current node is marked as "requires backtracking reinforcement" and resources are reallocated.

[0052] Specifically, the mean weight of prerequisite competency dependencies for subsequent training nodes refers to the arithmetic mean of the weights of all prerequisite dependent nodes recorded in the time-series competency matching entries for that subsequent node. Each prerequisite dependency weight is determined by the dependency constraint coefficient in the time-series competency matching entry, with the coefficient ranging from 0.30 to 1.00, representing the importance of the preceding node to the following node. The preset dependency target value is set to 0.75, representing the baseline expectation of the overall achievement level of prerequisite competencies required by subsequent nodes. The difference value is the mean weight of prerequisite competency dependencies minus the preset dependency target value, with the result ranging from -0.45 to 0.25. A positive value indicates that the overall prerequisite competency has been exceeded, while a negative value indicates that it has not been met.

[0053] Specifically, the preset difference threshold is set to 0.10. When the difference value is less than or equal to 0.10, it is considered that the prerequisite competencies have met the start-up requirements, and the current node start-up condition (i.e., the subsequent node start-up timing specified in the aforementioned optimized node exit condition) is directly used as the final start-up condition. When the difference value is greater than 0.10, it is considered that the prerequisite competencies have been exceeded or not met (the difference value exceeding 0.10 may be positive or negative, specifically referring to the absolute value). The adjustment factor is determined based on the relationship between the difference value and the preset difference threshold: if the difference value is between 0.10 and 0.20, the adjustment factor is 1.15; if the difference value is greater than 0.20, the adjustment factor is 1.30; if the difference value is negative and the absolute value is between 0.10 and 0.25, the adjustment factor is 0.85. The adjusted node start-up condition is: multiply the maturity score threshold in the original start-up condition by the adjustment factor (the upper limit is no more than 1.0, and the lower limit is no less than 0.60), and the time deviation ratio requirement is correspondingly relaxed or tightened (multiplied by the reciprocal of the subsequent adjustment factor). The final activation condition is the combination of the adjusted maturity score threshold and the deviation ratio requirement.

[0054] Understandably, the optimization exit conditions for a node (such as a score threshold and an allowable delay limit) are dynamically determined first based on the deviation ratio between the actual achievement time of the current nurturing node and the standard planned duration, combined with the node's current maturity score. Then, the average weight of all preceding nodes on which subsequent nurturing nodes depend is calculated, and this average is subtracted from the preset dependency target value to obtain the difference value, which is then compared with the preset difference threshold. If the difference value does not exceed the threshold, the original start conditions remain unchanged; if it exceeds the threshold, an adjustment factor is calculated based on the magnitude of the difference value to correct the maturity score threshold and time deviation requirements in the original start conditions, thereby forming the final adaptive node start conditions.

[0055] Specifically, when determining the adjustment factor based on the relationship between the difference value and the preset difference threshold, the following are included: The literacy mapping analysis unit is also configured to obtain the ratio between the difference value and a preset difference threshold, and to determine the adjustment magnitude of the adjustment factor based on the relationship between the ratio and a first preset ratio and a second preset ratio. When the ratio is less than the first preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the first adjustment range; When the ratio is greater than or equal to the first preset ratio and less than the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the second adjustment range. When the ratio is greater than or equal to the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the third adjustment range; Among them, the first preset ratio is less than the second preset ratio, and the third adjustment range is greater than the second adjustment range, and the second adjustment range is greater than the first adjustment range.

[0056] Specifically, the ratio between the difference value and the preset difference threshold refers to the quotient obtained by dividing the difference value (the absolute value of the average weight of prerequisite competency dependence minus the preset dependence target value; since the absolute value must be greater than 0.10 when the difference value is greater than the preset difference threshold) by the preset difference threshold (which has a value of 0.10). The ratio ranges from slightly greater than 1.0 to 3.0 and above. The first preset ratio is 1.0, and the second preset ratio is 2.0. The adjustment magnitude of the adjustment factor refers to the additional proportional value added to the baseline adjustment factor (1.0) when multiplicatively adjusting the maturity score threshold in the original node activation conditions. The adjustment magnitude itself is a value greater than 0, and its relationship with the adjustment factor is: adjustment factor = 1.0 + adjustment magnitude.

[0057] Specifically, a ratio between 1.0 and 2.0 corresponds to the second adjustment range, ≥2.0 corresponds to the third adjustment range, and less than 1.0 corresponds to the first adjustment range. The preset difference threshold is 0.10, and a ratio of 1.2 is calculated for an absolute difference value of 0.12. The first preset ratio is 1.5, and the second is 2.5. The ratio = absolute difference value / preset difference threshold, with the first preset ratio = 1.2 and the second preset ratio = 1.8. The corresponding adjustment ranges are 0.05, 0.15, and 0.30, respectively. The preset difference threshold is 0.10, the first preset ratio is 1.2, and the second preset ratio is 1.8. The first adjustment range is 0.05, corresponding to an adjustment factor of 1.05; the second adjustment range is 0.15, corresponding to an adjustment factor of 1.15; and the third adjustment range is 0.30, corresponding to an adjustment factor of 1.30. When the ratio of the absolute value of the difference to the preset difference threshold is less than 1.2, the excess is considered minor, and the maturity score threshold in the original node activation condition is increased by only 5%; when the ratio is greater than or equal to 1.2 and less than 1.8, the excess is considered moderate, and the threshold is increased by 15%; when the ratio is greater than or equal to 1.8, the excess is considered significant, and the threshold is increased by 30%. If the difference value is negative (i.e., the average weight of prerequisite competency dependence is lower than the preset dependence target value), the adjustment magnitude is negative, that is, the maturity score threshold is lowered accordingly: 5% when the ratio is less than 1.2, 15% when the ratio is between 1.2 and 1.8, and 30% when the ratio is greater than or equal to 1.8, and the lowered threshold is not lower than 0.60. The adjustment factor is calculated from the adjustment magnitude (1.0 plus or minus the adjustment magnitude) and is used in the maturity score threshold in the final adjustment node initiation condition.

[0058] Understandably, the ratio of the difference value to the preset difference threshold is first calculated, and then the ratio is compared with the first and second preset ratios in turn. If the ratio is less than the first preset ratio, the first adjustment range is used; if the ratio is between the first and second preset ratios, the second adjustment range is used; if the ratio reaches or exceeds the second preset ratio, the third adjustment range is used. Thus, the adjustment range of the adjustment factor is determined according to the degree of exceedance.

[0059] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a hierarchical cognitive navigation method for life stage advancement, including the following steps: Step S100: Divide the age growth time series into multiple segments, sort out the hierarchical growth relationships of positive flow, parallel intersection, and dynamic jump in each growth time series segment, and output the time series entries; Step S200: Group the growth literacy items into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and integrate them to generate literacy benchmark units; Step S300: Receive time-series entries and literacy benchmark units, perform bidirectional precise correlation matching between time-series intervals and literacy benchmarks, and generate time-series literacy matching entries; Step S400: Receive the time-series literacy matching entries, set up corresponding cultivation nodes for each growth time-series interval according to the time-series literacy matching entries, and sort out the hierarchical logic of the continuous connection and cross-domain extension between each cultivation node. Step S500: Collect growth behavior data corresponding to each nurturing node, correlate with literacy benchmarks to complete data mapping, and generate structured growth behavior records.

[0060] It is understandable that the aforementioned hierarchical cognitive navigation system and method for advancing through life stages have the same beneficial effects, and will not be elaborated further here.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A hierarchical cognitive navigation system for life stage progression, characterized in that, include: The time series interval division module is configured to divide multiple age growth time series intervals, sort out the hierarchical growth relationships of the forward flow, parallel intersection, and dynamic jump of each growth time series interval, and output time series entries. The competency benchmark collection module is electrically connected to the time interval division module. The competency benchmark collection module is configured to collect growth competency items into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and integrate them to generate competency benchmark units. The temporal literacy matching module is electrically connected to the temporal interval division module and the literacy benchmark collection module, respectively. The temporal literacy matching module is configured to receive the temporal entries and literacy benchmark units, perform bidirectional precise association matching between the temporal interval and the literacy benchmark, and generate temporal literacy matching entries. The stage cultivation node arrangement module is electrically connected to the time-series literacy matching module. The stage cultivation node arrangement module is configured to receive time-series literacy matching entries, arrange corresponding cultivation nodes for each growth time-series interval according to the time-series literacy matching entries, and sort out the hierarchical logic of the successive connection and cross-domain extension between each cultivation node. The growth behavior collection and analysis module is electrically connected to the stage cultivation node arrangement module. The growth behavior collection and analysis module is configured to collect growth behavior data corresponding to each cultivation node, associate the data with literacy benchmarks to complete data mapping, and generate structured growth behavior records.

2. The hierarchical cognitive navigation system for life stage progression according to claim 1, characterized in that, The stage cultivation node arrangement module includes: The hierarchical association unit is configured to identify the positive flow, parallel intersection, and dynamic jump relationships between each growth time interval, and thereby construct a directed connection graph between nodes; A node attribute labeling unit is electrically connected to the hierarchical association unit. The node attribute labeling unit is configured to label the required literacy type, target achievement threshold and recommended cultivation duration for each cultivation node according to the time-series literacy matching entries. The succession optimization unit is electrically connected to the hierarchical association unit and the node attribute labeling unit, respectively. The succession optimization unit is configured to dynamically adjust the start timing and resource allocation weight of subsequent cultivation nodes according to the achievement status of the previous cultivation node.

3. The hierarchical cognitive navigation system for life stage progression according to claim 2, characterized in that, The growth behavior collection and analysis module includes: The multi-source data access unit is configured to receive growth behavior data from wearable devices, educational applications, and subjective self-assessment questionnaires. The behavior feature extraction unit is electrically connected to the multi-source data access unit. The behavior feature extraction unit is configured to extract behavior feature vectors according to four fixed categories: psychological construction, physical reserves, habit formation, and ability improvement. The literacy mapping analysis unit is electrically connected to the behavior feature extraction unit and the literacy benchmark collection module, respectively. The literacy mapping analysis unit is configured to perform similarity matching between the behavior feature vector and the literacy benchmark unit to generate the literacy achievement probability of each cultivation node.

4. The hierarchical cognitive navigation system for life stage progression according to claim 3, characterized in that, When the literacy mapping analysis unit performs similarity matching between the behavioral feature vector and the literacy benchmark unit, it includes: The competency mapping analysis unit is also configured to substitute behavioral feature vectors into a pre-established competency maturity model and obtain an initial maturity score. The literacy mapping analysis unit is also configured to obtain the growth deviation rate between the current growth behavior data and the growth behavior data of adjacent historical periods, and to determine the growth compensation coefficient based on the growth deviation rate. The competency mapping analysis unit is also configured to correct the initial maturity score based on the growth compensation coefficient, and to determine the corrected maturity score as the competency achievement probability.

5. The hierarchical cognitive navigation system for life stage progression according to claim 4, characterized in that, When the literacy mapping analysis unit pre-establishes a literacy maturity model, it includes: The competency mapping analysis unit is also configured to acquire an experimental dataset of growth behavior samples and corresponding expert-annotated competency maturity samples. The competency mapping analysis unit is also configured to take growth behavior samples from the experimental dataset as input, take the corresponding competency maturity samples as reference benchmarks, train the physical response model, and establish a competency maturity model based on the training results.

6. The hierarchical cognitive navigation system for life stage progression according to claim 4, characterized in that, When the competency mapping analysis unit acquires the growth deviation rate between the current growth behavior data and the historical growth behavior data of adjacent periods, and determines the growth compensation coefficient based on the growth deviation rate, it includes: The competency mapping analysis unit is also configured to determine whether to correct the initial maturity score based on the relationship between each growth deviation rate and a preset deviation threshold, and to calculate a growth compensation coefficient when correction is required. When all growth deviation rates are less than the preset deviation threshold, the competency mapping analysis unit determines not to correct the initial maturity score. When any growth deviation rate is greater than or equal to a preset deviation threshold, the competency mapping analysis unit determines to correct the initial maturity score and determines the growth compensation coefficient based on the relationship between each growth deviation rate and the preset deviation threshold.

7. The hierarchical cognitive navigation system for life stage progression according to claim 6, characterized in that, When the competency mapping analysis unit determines the growth compensation coefficient based on the relationship between each growth deviation rate and a preset deviation threshold, it includes: The literacy mapping analysis unit is also configured to obtain the deviation difference between each growth deviation rate and a preset deviation threshold. The literacy mapping analysis unit is also configured to perform linear mapping processing based on the deviation difference, and to obtain the degree of deviation between each growth deviation rate and the preset deviation threshold based on the deviation difference after mapping processing. The competency mapping analysis unit is also configured to determine a growth compensation coefficient based on the relationship between the degree of deviation and a first preset deviation threshold and a second preset deviation threshold. When the degree of deviation is less than the first preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the first compensation coefficient. When the degree of deviation is greater than or equal to the first preset deviation threshold and less than the second preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the second compensation coefficient. When the degree of deviation is greater than or equal to the second preset deviation threshold, the literacy mapping analysis unit determines the growth compensation coefficient as the third compensation coefficient. Among them, the first preset deviation threshold is less than the second preset deviation threshold, and the third compensation coefficient is greater than the second compensation coefficient, the second compensation coefficient is greater than the first compensation coefficient, and the first compensation coefficient is greater than 1.

8. The hierarchical cognitive navigation system for life stage progression according to claim 7, characterized in that, When the competency mapping analysis unit optimizes the succession logic of the cultivation nodes based on the revised maturity score, it includes: The competency mapping analysis unit is also configured to obtain the deviation ratio between the achievement time of the current cultivation node and the standard planned duration, and to determine the optimized node exit conditions based on the relationship between the current maturity score and the deviation ratio. The competency mapping analysis unit is also configured to obtain the average value of the prerequisite competency dependency weights for subsequent training nodes; The literacy mapping analysis unit is also configured to obtain the difference between the mean of the prerequisite literacy dependency weights and the preset dependency target value, and to determine the final node activation condition based on the relationship between the difference value and the preset difference threshold. When the difference value is less than or equal to the preset difference threshold, the literacy mapping analysis unit determines the current node's activation condition as the final activation condition. When the difference value is greater than the preset difference threshold, the literacy mapping analysis unit determines an adjustment factor based on the relationship between the difference value and the preset difference threshold, and determines the node activation condition adjusted according to the adjustment factor as the final activation condition.

9. The hierarchical cognitive navigation system for life stage progression according to claim 8, characterized in that, When determining the adjustment factor based on the relationship between the difference value and the preset difference threshold, the following are included: The literacy mapping analysis unit is also configured to obtain the ratio between the difference value and a preset difference threshold, and to determine the adjustment range of the adjustment factor based on the relationship between the ratio and a first preset ratio and a second preset ratio. When the ratio is less than the first preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the first adjustment range; When the ratio is greater than or equal to the first preset ratio and less than the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the second adjustment range. When the ratio is greater than or equal to the second preset ratio, the literacy mapping analysis unit determines the adjustment range of the adjustment factor to be the third adjustment range; Among them, the first preset ratio is less than the second preset ratio, and the third adjustment range is greater than the second adjustment range, and the second adjustment range is greater than the first adjustment range.

10. A hierarchical cognitive navigation method for advancing through life stages, characterized in that, A hierarchical cognitive navigation system for life stage progression, as described in any one of claims 1 to 9, comprises the following steps: Divide the age growth time series into multiple segments, sort out the hierarchical growth relationships of positive flow, parallel intersection, and dynamic jump in each of the growth time series segments, and output the time series entries; The growth literacy items are categorized into four fixed categories: psychological construction, physical fitness reserves, habit formation, and ability improvement, and then integrated to generate literacy benchmark units. Receive the time-series entries and literacy benchmark units, perform bidirectional precise correlation matching between time-series intervals and literacy benchmarks, and generate time-series literacy matching entries; Receive the time-series literacy matching entries, deploy corresponding cultivation nodes for each of the growth time-series intervals according to the time-series literacy matching entries, and sort out the hierarchical logic of the successive connection and cross-domain extension between each of the cultivation nodes. Collect growth behavior data corresponding to each of the aforementioned cultivation nodes, correlate them with literacy benchmarks to complete data mapping, and generate structured growth behavior records.

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