An artificial intelligence-based youth career development planning method and system

Abstract

The application discloses a kind of based on artificial intelligence's youth career development planning method and system, belong to educational informatization technical field, by the standardization processing and data fusion processing of the multi-source career related data of target user, construct the multi-source fusion, including multiple portrait dimensions target user career portrait, so that the target user career portrait that constructs is more accurate and comprehensive;And based on the target user career portrait of target user, target user career reasoning chain is constructed to form interpretable reasoning path, on the basis of target user career reasoning chain in target user career reasoning chain, each youth career development direction in the youth career development direction library is matched with target user career development direction and target user development career planning scheme generation, to make the target user development career planning scheme finally obtained more accurate, comprehensive, and have explainability.

Description

Technical Field

[0001] This invention belongs to the field of educational informatization technology, specifically relating to a method and system for adolescent career development planning based on artificial intelligence. Background Technology

[0002] Currently, technologies related to career planning for teenagers mainly fall into the following categories: The first category is teaching analysis and learning path planning technologies based on learning behavior or academic performance. For example, by analyzing data such as students' study time and homework completion, these technologies recommend learning content or adjust learning plans. These technologies primarily focus on short-term learning effects and lack systematic modeling of an individual's long-term development potential. The second category is educational resource recommendation technologies based on assessment results or interests. For example, based on the results of the Holland Occupational Themes Test or the MBTI personality test, these technologies recommend majors or career paths to users. These technologies rely solely on static assessment data from a single dimension, ignoring the comprehensive influence of factors such as ability, values, and environmental support, making it difficult to support complex career decisions. The third category is student profiling systems based on big data. These typically focus on educational data such as academic performance and school behavior to construct ability or behavioral profiles for teaching management. However, these profiles are often limited in dimensions and cannot comprehensively depict an individual's multidimensional characteristics such as interests, personality, and motivation, let alone be used for forward-looking career planning. The fourth category is one-off career counseling services that rely on human experience. Planners provide advice through interviews, questionnaires, and other methods. The results are highly dependent on individual experience, difficult to standardize and scale, and lack follow-up dynamic tracking and adjustment mechanisms.

[0003] Therefore, it can be seen that existing methods for career planning in adolescents rely on a single data source, mainly consisting of partial data such as grades and interests. This lack of multi-source integration makes it difficult to form a comprehensive understanding of individuals. Furthermore, the user profile dimensions are insufficient, failing to simultaneously depict user profiles across dimensions such as interests, personality, abilities, values, motivation, and environmental constraints, resulting in a weak decision-making foundation. In addition, the decision-making process for selecting career development directions for adolescents is opaque, often involving "black box" recommendations without an explainable reasoning path, making it difficult for users to understand and trust the recommendation results.

[0004] As mentioned above, how to provide an AI-based youth career development planning method and system that can achieve multi-source data fusion, depict multi-dimensional user profiles, and form accurate and interpretable youth career development planning schemes has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide an artificial intelligence-based method and system for adolescent career development planning, in order to solve the aforementioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an artificial intelligence-based method for adolescent career development planning, comprising: Acquire multi-source career-related data of the target user, and perform standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user, wherein the career profile of the target user includes multiple profile dimensions; Obtain a preset youth career development direction library, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction library based on the target user career reasoning chain to obtain the target user's career development direction. The key career development parameters of the target user are calculated based on the target user's career development direction. Based on the target user's career inference chain and the key career development parameters, a corresponding target user career planning scheme is generated for the target user.

[0007] In one possible design, multi-source career-related data of the target user is acquired, and the multi-source career-related data of the target user is standardized and fused to construct a career profile of the target user, including: The system collects target users' career assessment data, career interview data, career behavior process data, career achievement evidence data, and career environment support data from a youth career database. It also performs missing value verification and data format verification on the target users' career assessment data, career interview data, career behavior process data, career achievement evidence data, and career environment support data to obtain multi-source career-related data for the target users. The multi-source career-related data of the target user is evaluated for data quality to obtain the multi-source career-related data quality index of the target user. The multi-source career-related data of the target user is subjected to data standardization processing to map the multi-source career-related data to the [0,1] interval to obtain standard multi-source career-related data; Add corresponding dimension labels to each data point in the standard multi-source career-related data according to the data source of the standard multi-source career-related data; Based on the dimension labels of each data point in the standard multi-source career-related data, each data point in the standard multi-source career-related data is divided into multiple profile dimensions according to the corresponding dimension labels, so as to obtain the pre-target user career profile based on each profile dimension of the standard multi-source career-related data. Based on the multi-source career-related data quality indicators of the target user, corresponding data freshness weights and data reliability weights are generated for each data point in each profile dimension of the pre-target user career profile. Based on the data freshness weights and the data reliability weights, the corresponding dimensional data comprehensive weight is calculated for each data point. Based on the comprehensive weight of each data point in each profile dimension, the data points in each profile dimension are weighted and merged to obtain the comprehensive profile dimension data for each profile dimension. A career profile of the target user is constructed based on the quality indicators of the multi-source career-related data of the target user and the comprehensive profile dimension data of each profile dimension of the standard multi-source career-related data.

[0008] In one possible design, the multi-source career-related data of the target user is evaluated for data quality to obtain the multi-source career-related data quality indicators of the target user, including: Obtain the total number of preset key data fields, perform key data field statistics on the multi-source career-related data of the target user, obtain the number of key data fields collected, and calculate the data completeness index of the multi-source career-related data using the following formula (1): (1) in, The number of key data fields collected. The total number of the key data fields. This refers to the data completeness index of the multi-source career-related data; Obtain a preset time decay factor, sort the multi-source career-related data of the target user according to the collection time, and obtain multi-source career-related time series data. Then, perform decay weighted calculation on the multi-source career-related data using the following formula (2) to obtain the data freshness index of the multi-source career-related data: (2) in, The sorting of the multi-source career-related time series data, The total number of data points in the multi-source career-related time-series data. The first in the multi-source career-related time series data data, The time decay factor is... The first in the multi-source career-related time series data The time interval between the first and last data entries. is the base of the natural logarithm. This refers to the data freshness index of the multi-source career-related data; The reliability of each data source in the multi-source career-related data of the target user is obtained, and the proportion of valid data in the data corresponding to each data source is calculated. The reliability of the multi-source career-related data is calculated using the following formula (3) to obtain the data reliability index of the multi-source career-related data: (3) in, This refers to the index of the data source in the multi-source career-related data. This indicates the data source of the aforementioned multi-source career-related data. The data sources in the aforementioned multi-source career-related data The corresponding data source credibility, The data sources in the aforementioned multi-source career-related data The percentage of valid data in the corresponding data. This serves as a data reliability index for the aforementioned multi-source career-related data; The multi-source career-related data of the target user is statistically analyzed to obtain the total number of related data. Data consistency analysis is performed on the data from each data source in the multi-source career-related data of the target user to filter out conflicting data in each data source. The number of conflicting data is obtained by counting each conflicting data. Data consistency is calculated on the multi-source career-related data using the following formula (4) to obtain the data conflict rate index of the multi-source career-related data: (4) in, The number of conflicting data entries, The total number of the relevant data. This refers to the data conflict rate metric for the aforementioned multi-source career-related data; The data completeness index, data freshness index, data reliability index, and data conflict rate index of the multi-source career-related data are integrated as the multi-source career-related data quality index for the target user.

[0009] In one possible design, a corresponding target user career inference chain is constructed based on the target user's career profile, including: Obtain a preset user career reasoning graph structure, wherein the user career reasoning graph structure includes multiple user career reasoning nodes; The target user's career profile is input into the user career reasoning graph structure to form a corresponding target user career reasoning chain.

[0010] In one possible design, based on the target user's career reasoning chain, a matching analysis is performed on each youth career development direction in the youth career development direction database to obtain the target user's career development direction, including: Obtain a preset youth career development direction library, and select relevant youth career development directions from the preset career direction library as candidate target user career development directions through the target user career reasoning chain; For each of the candidate target user's career development directions, calculate the matching degree between the target user's career profile and each of the candidate target user's career development directions; Based on the matching degree between each candidate target user's career development direction and the target user's career profile, the candidate target user's career development direction with the highest matching degree is selected as the target user's career development direction.

[0011] In one possible design, key career development parameters are calculated for the target user's career development direction to obtain the target user's key career development parameters, including: For the career development direction of the target user, calculate the corresponding direction discrimination, direction consistency, and direction explanatory sufficiency. By integrating the direction differentiation, direction consistency, and direction explanatory sufficiency, key career development parameters for the target user corresponding to the target user's career development direction are formed.

[0012] In one possible design, based on the target user's career reasoning chain and key career development parameters, a corresponding career development plan is generated for the target user, including: Based on the target user's career reasoning chain and the target user's key career development parameters, the target user's career development direction is analyzed to obtain the development direction analysis results of the target user's career development direction. The development direction analysis results of the target user's career development direction include development direction score, development direction risk index, development direction stage feasibility, development direction dimension contribution and development direction risk warning. Obtain a preset career development planning configuration item comparison table, and use the career development direction analysis results of the target user's career development direction as the query condition to perform a condition query in the career development planning configuration item comparison table to obtain multiple corresponding target user career development configurations; Integrate the career development configurations of various target users to create a career development plan for each target user.

[0013] Secondly, the present invention provides an artificial intelligence-based career development planning system for teenagers, comprising: The user career profile building unit is used to acquire multi-source career-related data of the target user, and to perform standardization and data fusion processing on the multi-source career-related data of the target user in order to build a career profile of the target user. The career development direction analysis unit is used to obtain a preset youth career development direction database, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction database based on the target user career reasoning chain to obtain the target user's career development direction. The career planning scheme generation unit is used to calculate key career development parameters for the target user's career development direction, obtain key career development parameters for the target user, and generate a corresponding career planning scheme for the target user based on the target user's career inference chain and key career development parameters.

[0014] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the artificial intelligence-based youth career development planning method as described in the first aspect or any possible design of the first aspect.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the artificial intelligence-based youth career development planning method described in the first aspect or any possible design of the first aspect.

[0016] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the artificial intelligence-based youth career development planning method as described in the first aspect or any possible design of the first aspect.

[0017] Beneficial Effects: This invention provides an artificial intelligence-based method and system for adolescent career development planning, comprising: First, acquiring multi-source career-related data of the target user, and performing standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user; Second, acquiring a preset adolescent career development direction library, constructing a corresponding career inference chain for the target user based on the career profile of the target user, and performing matching analysis on each adolescent career development direction in the career development direction library based on the career inference chain to obtain the career development direction of the target user; Finally, calculating key career development parameters for the career development direction of the target user to obtain key career development parameters of the target user, and generating a corresponding career development planning scheme for the target user based on the career inference chain and the key career development parameters of the target user. By standardizing and fusion the multi-source career-related data of target users, a multi-source fusion career profile of target users with multiple profile dimensions is constructed, making the constructed career profile of target users more accurate and comprehensive. Furthermore, based on the career profile of target users, a career reasoning chain is constructed to form an interpretable reasoning path. On the basis of the career reasoning chain, the career development direction of target users is matched with each career development direction in the youth career development direction database, and a career development plan scheme for target users is generated, so that the final career development plan scheme for target users is more accurate, comprehensive, and interpretable. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the artificial intelligence-based youth career development planning method provided in this embodiment of the invention; Figure 2 A schematic diagram of the functional structure of an AI-based youth career development planning system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0020] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0021] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0022] Example: like Figure 1 As shown, the first aspect of this embodiment provides an artificial intelligence-based method for adolescent career development planning, which may include, but is not limited to, the following steps: S1. Obtain multi-source career-related data of the target user, and perform standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user, wherein the career profile of the target user includes multiple profile dimensions; In one possible implementation, step S1 involves acquiring multi-source career-related data of the target user, and performing standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user. This can be broken down into, but is not limited to, the following steps S11-S18, specifically including: S11. Collect user career assessment data, user career interview data, user career behavior process data, user career achievement evidence data, and user career environment support data of target users from the adolescent career database, and perform missing value verification and data format verification on the user career assessment data, user career interview data, user career behavior process data, user career achievement evidence data, and user career environment support data of target users to obtain multi-source career-related data of target users; S12. Perform a data quality assessment on the multi-source career-related data of the target user to obtain the multi-source career-related data quality index of the target user; S13. Perform data standardization processing on the multi-source career-related data of the target user to map the multi-source career-related data to the [0,1] interval to obtain standard multi-source career-related data; S14. Add corresponding dimension labels to each piece of data in the standard multi-source career-related data according to the data source of the standard multi-source career-related data; S15. Based on the dimension labels of each piece of data in the standard multi-source career-related data, divide each piece of data in the standard multi-source career-related data into multiple profile dimensions according to the corresponding dimension labels, so as to obtain the pre-target user career profile based on each profile dimension of the standard multi-source career-related data; S16. Based on the multi-source career-related data quality indicators of the target user, generate corresponding data freshness weights and data reliability weights for each data point in each profile dimension of the pre-target user career profile, and calculate the corresponding dimensional data comprehensive weight for each data point based on the data freshness weights and the data reliability weights. S17. Based on the comprehensive weight of each data point in each portrait dimension, the data points in each portrait dimension are weighted and fused to obtain the comprehensive portrait dimension data for each portrait dimension. S18. Based on the quality indicators of the multi-source career-related data of the target user and the comprehensive profile dimension data of each profile dimension of the standard multi-source career-related data, construct the career profile of the target user.

[0023] The target user career profile includes at least six dimensions: interests, personality, abilities, values, motivation, and supporting constraints. Specifically, interests, personality, abilities, and values ​​are the four core main dimensions, while motivation and supporting constraints are the two supporting dimensions.

[0024] In one possible implementation, step S12, which involves evaluating the data quality of the target user's multi-source career-related data to obtain the target user's multi-source career-related data quality index, can be broken down into, but is not limited to, the following steps S121-S12, specifically including: S121. Obtain the total number of preset key data fields, perform key data field statistics on the multi-source career-related data of the target user, obtain the number of key data fields collected, and calculate the data completeness index of the multi-source career-related data using the following formula (1): (1) in, The number of key data fields collected. The total number of the key data fields. This refers to the data completeness index of the multi-source career-related data; S122. Obtain a preset time decay factor, sort the multi-source career-related data of the target user according to the collection time, and obtain multi-source career-related time-series data. Then, perform decay-weighted calculation on the multi-source career-related data using the following formula (2) to obtain the data freshness index of the multi-source career-related data: (2) in, The sorting of the multi-source career-related time series data, The total number of data points in the multi-source career-related time-series data. The first in the multi-source career-related time series data data, The time decay factor is... The first in the multi-source career-related time series data The time interval between the first and last data entries. is the base of the natural logarithm. This refers to the data freshness index of the multi-source career-related data; S123. Obtain the data source credibility of each data source in the multi-source career-related data of the target user, and calculate the effective data ratio in the data corresponding to each data source. Calculate the data reliability of the multi-source career-related data using the following formula (3) to obtain the data reliability index of the multi-source career-related data: (3) in, This refers to the index of the data source in the multi-source career-related data. This indicates the data source of the aforementioned multi-source career-related data. The data sources in the aforementioned multi-source career-related data The corresponding data source credibility, The data sources in the aforementioned multi-source career-related data The percentage of valid data in the corresponding data. This serves as a data reliability index for the aforementioned multi-source career-related data; S124. Statistically analyze the multi-source career-related data of the target user to obtain the total number of related data, and perform data consistency analysis on the data from each data source in the multi-source career-related data of the target user to filter out conflicting data in the data from each data source, count each conflicting data to obtain the number of conflicting data entries, and calculate the data consistency of the multi-source career-related data using the following formula (4) to obtain the data conflict rate index of the multi-source career-related data: (4) in, The number of conflicting data entries, The total number of the relevant data. This refers to the data conflict rate metric for the aforementioned multi-source career-related data; S125. Integrate the data completeness index, data freshness index, data reliability index, and data conflict rate index of the multi-source career-related data as the multi-source career-related data quality index for the target user.

[0025] In practical application scenarios: like If the data completeness of the multi-source career-related data is deemed to meet the standard, the process can proceed to step S2. like If the data completeness of the multi-source career-related data is deemed to require supplementary collection, proceed to step S2 and mark the multi-source career-related data as "requiring supplementary collection". like If the data is deemed insufficient to generate a user profile, it is not recommended to generate a formal profile (do not proceed to step S2). Instead, only a "draft initial profile" will be generated for reference and will not be used for formal decision-making.

[0026] when In such cases, it is necessary to prompt the target user to supplement recent data to ensure the timeliness of the subsequent target user career profile; when If the multi-source career-related data of the target user is considered reliable, it can be used to generate an accurate and credible career profile of the target user; if... When this occurs, it indicates that there are serious contradictions among the various data sources in the target user's multi-source career-related data, and it is necessary to forcibly introduce a "conflict resolution" process in step S2; When performing data consistency analysis on the data from various data sources in the multi-source career-related data of the target user, the data from all data sources can be standardized to obtain standardized data values. The average of the standardized data values ​​from each data source can be calculated to obtain the standardized data mean. For each data source, the deviation ratio between its corresponding standardized data value and the fused standardized data mean can be analyzed one by one. When the deviation ratio exceeds a preset deviation ratio threshold (such as 0.3), it is regarded as a conflicting data.

[0027] In one possible implementation, step S1 further includes a preset conflict resolution mechanism. In step S1, a target user's career profile is constructed based on the target user's multi-source career-related data quality indicators and the comprehensive profile dimension data of each profile dimension of the standard multi-source career-related data. The data conflict rate metric is extracted from the multi-source career-related data quality metrics of the target users. Obtain a preset maximum data conflict rate threshold (which can be preset to 0.2), and determine whether the data conflict rate index is lower than the maximum data conflict rate threshold; If so, the multi-source career-related data of the target user are considered to be consistent, and the comprehensive profile dimension data of each profile dimension of the standard multi-source career-related data are used to construct the career profile of the target user. If not, the multi-source career-related data of the target user is considered inconsistent. The conflict resolution mechanism then uses its rules to resolve conflicts in the multi-source career-related data, resulting in consistent multi-source career-related data. This consistent data is then standardized to map to the [0,1] interval, yielding standard consistent multi-source career-related data. Finally, the comprehensive profile dimension data from each profile dimension of the standard consistent multi-source career-related data is used to construct the target user's career profile. The rules in the conflict resolution mechanism may include, but are not limited to, the following: 1) Behavioral data takes precedence over self-reported data; 2) Long-term data takes precedence over short-term data; 3) Data from high-reliability data sources (through formula (3) The calculated data takes precedence over data from sources with lower reliability.

[0028] S2. Obtain a preset youth career development direction library, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction library based on the target user's career reasoning chain to obtain the target user's career development direction. In one possible implementation, step S2, constructing a corresponding target user career inference chain based on the target user's career profile, can be, but is not limited to, decomposed into the following steps S21-S22, specifically including: S21. Obtain a preset user career reasoning graph structure, wherein the user career reasoning graph structure includes multiple user career reasoning nodes; S22. Input the target user's career profile into the user career reasoning graph structure to form a corresponding target user career reasoning chain.

[0029] It should be noted that the target user career reasoning chain described in this embodiment consists of multiple reasoning nodes (user career reasoning nodes) with causal relationships, in order to simulate the deduction process from individual characteristics to directional decisions.

[0030] The user career reasoning nodes include at least a profile input node, a target constraint node, a direction candidate generation node, a matching degree calculation node, a risk conflict identification node, an opportunity window identification node, a stage feasibility node, a path combination node, and a conclusion output node. Furthermore, predefined directed edges exist between each user career reasoning node in the user career reasoning graph structure. For example, the directed edge from the profile input node to the matching degree calculation node, the directed edge from the profile input node to the risk conflict node, the directed edge from the target constraint node to the stage feasibility node, the directed edge from the opportunity window identification node to the stage feasibility node, the directed edge from the matching degree calculation node to the direction candidate generation node, the directed edge from the risk conflict node to the conclusion output node, and the directed edge from the stage feasibility node to the conclusion output node.

[0031] In one possible implementation, step S2, based on the target user's career reasoning chain, performs a matching analysis on each youth career development direction in the youth career development direction database to obtain the target user's career development direction. This can be, but is not limited to, decomposed into the following steps S23-S25, specifically including: S23. Obtain a preset youth career development direction library, and select relevant youth career development directions from the preset career direction library as candidate target user career development directions through the target user career reasoning chain; S24. For each of the candidate target user career development directions, calculate the matching degree between the target user career profile and each of the candidate target user career development directions; S25. Based on the matching degree between each candidate target user's career development direction and the target user's career profile, select the candidate target user's career development direction with the highest matching degree as the target user's career development direction.

[0032] It should be noted that each youth career development direction in the youth career development direction database includes multiple dimensions (corresponding to the portrait dimensions of the target user's career profile). When calculating the matching degree, it is first necessary to calculate the similarity between each dimension of each candidate target user's career development direction and the portrait dimensions of the target user's career profile. Secondly, the similarity of each dimension is weighted and averaged to obtain the comprehensive similarity, which is used as the matching degree.

[0033] In one possible application, the target user's career development direction may include the target user's primary recommended career development direction and the target user's alternative career development directions; the target user's career development planning scheme may include the target user's phased goals, the target user's capability development path, the target user's career development resource allocation strategy, and the target user's career development risk contingency plan.

[0034] In one possible implementation, after calculating the matching degree between each candidate target user's career development direction and the target user's career profile, a conflict risk index (e.g., conflict between interests and abilities, conflict between values ​​and environment, conflict between goals and resources), a feasibility index (including ability gap, time resources, support and path availability), and a reasoning chain traceability index (used to characterize whether each direction can be traced back to the corresponding career-related data) can also be calculated between each candidate target user's career development direction and the target user's career profile.

[0035] S3. Calculate key career development parameters for the target user's career development direction to obtain key career development parameters for the target user, and generate a corresponding career development planning scheme for the target user based on the target user's career inference chain and key career development parameters.

[0036] In one possible implementation, step S3 involves calculating key career development parameters for the target user's career development direction to obtain the target user's key career development parameters. This can be, but is not limited to, decomposed into the following steps S31-S32, specifically including: S31. For the career development direction of the target user, calculate the corresponding direction discrimination, direction consistency and direction explanatory sufficiency. S32. Integrate the direction differentiation, direction consistency, and direction explanatory sufficiency to form the target user's key career development parameters corresponding to the target user's career development direction.

[0037] In one possible implementation, step S3, based on the target user's career reasoning chain and the target user's key career development parameters, generates a corresponding target user career planning scheme. This can be, but is not limited to, decomposed into the following steps S33-S35, specifically including: S33. Based on the target user's career reasoning chain and the target user's key career development parameters, analyze the target user's career development direction to obtain the development direction analysis results of the target user's career development direction. The development direction analysis results of the target user's career development direction include development direction score, development direction risk index, development direction stage feasibility, development direction dimension contribution, and development direction risk warning. S34. Obtain a preset career development planning configuration item comparison table, and use the career development direction analysis results of the target user's career development direction as the query condition to perform a condition query in the career development planning configuration item comparison table to obtain multiple corresponding target user career development configurations; S35. Integrate the career development configurations of various target users to form a career development plan for each target user.

[0038] In one possible implementation, after step S3, step S4 may also be included, but is not limited to: S4. Acquire stage feedback data of target users in real time, and dynamically update and record the target user career profile, target user career reasoning chain and target user career development plan in real time based on preset trigger thresholds.

[0039] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the artificial intelligence-based youth career development planning method described in the first aspect of the embodiment, including: The user career profile building unit is used to acquire multi-source career-related data of the target user, and to perform standardization and data fusion processing on the multi-source career-related data of the target user in order to build a career profile of the target user. The career development direction analysis unit is used to obtain a preset youth career development direction database, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction database based on the target user career reasoning chain to obtain the target user's career development direction. The career planning scheme generation unit is used to calculate key career development parameters for the target user's career development direction, obtain key career development parameters for the target user, and generate a corresponding career planning scheme for the target user based on the target user's career inference chain and key career development parameters.

[0040] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0041] like Figure 3 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the artificial intelligence-based youth career development planning method as described in the first aspect of the embodiment.

[0042] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0043] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0044] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0045] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the artificial intelligence-based youth career development planning method described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when the instructions are run on a computer, execute the artificial intelligence-based youth career development planning method as described in the first aspect of the embodiment.

[0046] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0047] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0048] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the artificial intelligence-based youth career development planning method as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0049] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for adolescent career development planning based on artificial intelligence, characterized in that, include: Acquire multi-source career-related data of the target user, and perform standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user, wherein the career profile of the target user includes multiple profile dimensions; Obtain a preset youth career development direction library, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction library based on the target user career reasoning chain to obtain the target user's career development direction. The key career development parameters of the target user are calculated based on the target user's career development direction. Based on the target user's career inference chain and the key career development parameters, a corresponding target user career planning scheme is generated for the target user.

2. The method for adolescent career development planning based on artificial intelligence according to claim 1, characterized in that, Acquire multi-source career-related data of the target user, and perform standardization and data fusion processing on the multi-source career-related data of the target user to construct a career profile of the target user, including: The system collects target users' career assessment data, career interview data, career behavior process data, career achievement evidence data, and career environment support data from a youth career database. It also performs missing value verification and data format verification on the target users' career assessment data, career interview data, career behavior process data, career achievement evidence data, and career environment support data to obtain multi-source career-related data for the target users. The multi-source career-related data of the target user is evaluated for data quality to obtain the multi-source career-related data quality index of the target user. The multi-source career-related data of the target user is subjected to data standardization processing to map the multi-source career-related data to the [0,1] interval to obtain standard multi-source career-related data; Add corresponding dimension labels to each data point in the standard multi-source career-related data according to the data source of the standard multi-source career-related data; Based on the dimension labels of each data point in the standard multi-source career-related data, each data point in the standard multi-source career-related data is divided into multiple profile dimensions according to the corresponding dimension labels, so as to obtain the pre-target user career profile based on each profile dimension of the standard multi-source career-related data. Based on the multi-source career-related data quality indicators of the target user, corresponding data freshness weights and data reliability weights are generated for each data point in each profile dimension of the pre-target user career profile. Based on the data freshness weights and the data reliability weights, the corresponding dimensional data comprehensive weight is calculated for each data point. Based on the comprehensive weight of each data point in each profile dimension, the data points in each profile dimension are weighted and merged to obtain the comprehensive profile dimension data for each profile dimension. A career profile of the target user is constructed based on the quality indicators of the multi-source career-related data of the target user and the comprehensive profile dimension data of each profile dimension of the standard multi-source career-related data.

3. The method for adolescent career development planning based on artificial intelligence according to claim 2, characterized in that, A data quality assessment is performed on the multi-source career-related data of the target user to obtain the multi-source career-related data quality indicators of the target user, including: Obtain the total number of preset key data fields, perform key data field statistics on the multi-source career-related data of the target user, obtain the number of key data fields collected, and calculate the data completeness index of the multi-source career-related data using the following formula (1): （1） in, The number of key data fields collected. The total number of the key data fields. This refers to the data completeness index of the multi-source career-related data; Obtain a preset time decay factor, sort the multi-source career-related data of the target user according to the collection time, and obtain multi-source career-related time series data. Then, perform decay weighted calculation on the multi-source career-related data using the following formula (2) to obtain the data freshness index of the multi-source career-related data: （2） in, The sorting of the multi-source career-related time series data, The total number of data points in the multi-source career-related time-series data. The first in the multi-source career-related time series data data, The time decay factor is... The first in the multi-source career-related time series data The time interval between the first and last data entries. is the base of the natural logarithm. This refers to the data freshness index of the multi-source career-related data; The reliability of each data source in the multi-source career-related data of the target user is obtained, and the proportion of valid data in the data corresponding to each data source is calculated. The reliability of the multi-source career-related data is calculated using the following formula (3) to obtain the data reliability index of the multi-source career-related data: （3） in, This refers to the index of the data source in the multi-source career-related data. This indicates the data source of the aforementioned multi-source career-related data. The data sources in the aforementioned multi-source career-related data The corresponding data source credibility, The data sources in the aforementioned multi-source career-related data The percentage of valid data in the corresponding data. This serves as a data reliability index for the aforementioned multi-source career-related data; The multi-source career-related data of the target user is statistically analyzed to obtain the total number of related data. Data consistency analysis is performed on the data from each data source in the multi-source career-related data of the target user to filter out conflicting data in each data source. The number of conflicting data is obtained by counting each conflicting data. Data consistency is calculated on the multi-source career-related data using the following formula (4) to obtain the data conflict rate index of the multi-source career-related data: （4） in, The number of conflicting data entries, The total number of the relevant data. This refers to the data conflict rate metric for the aforementioned multi-source career-related data; The data completeness index, data freshness index, data reliability index, and data conflict rate index of the multi-source career-related data are integrated as the multi-source career-related data quality index for the target user.

4. The method for adolescent career development planning based on artificial intelligence according to claim 1, characterized in that, Based on the target user's career profile, a corresponding target user career reasoning chain is constructed, including: Obtain a preset user career reasoning graph structure, wherein the user career reasoning graph structure includes multiple user career reasoning nodes; The target user's career profile is input into the user career reasoning graph structure to form a corresponding target user career reasoning chain.

5. The artificial intelligence-based youth career development planning method according to claim 1, characterized in that, Based on the target user's career reasoning chain, a matching analysis is performed on each youth career development direction in the youth career development direction database to obtain the target user's career development direction, including: Obtain a preset youth career development direction library, and filter relevant youth career development directions from the preset career direction library as candidate target user career development directions through the target user career reasoning chain; For each of the candidate target user's career development directions, calculate the matching degree between the target user's career profile and each of the candidate target user's career development directions; Based on the matching degree between each candidate target user's career development direction and the target user's career profile, the candidate target user's career development direction with the highest matching degree is selected as the target user's career development direction.

6. The method for adolescent career development planning based on artificial intelligence according to claim 1, characterized in that, The key career development parameters of the target user are calculated based on the target user's career development direction, and include: For the career development direction of the target user, calculate the corresponding direction discrimination, direction consistency, and direction explanatory sufficiency. By integrating the direction differentiation, direction consistency, and direction explanatory sufficiency, key career development parameters for the target user corresponding to the target user's career development direction are formed.

7. The method for adolescent career development planning based on artificial intelligence according to claim 1, characterized in that, Based on the target user's career reasoning chain and key career development parameters, a corresponding career development plan is generated for the target user, including: Based on the target user's career reasoning chain and the target user's key career development parameters, the target user's career development direction is analyzed to obtain the development direction analysis results of the target user's career development direction. The development direction analysis results of the target user's career development direction include development direction score, development direction risk index, development direction stage feasibility, development direction dimension contribution and development direction risk warning. Obtain a preset career development planning configuration item comparison table, and use the career development direction analysis results of the target user's career development direction as the query condition to perform a condition query in the career development planning configuration item comparison table to obtain multiple corresponding target user career development configurations; Integrate the career development configurations of various target users to create a career development plan for each target user.

8. An artificial intelligence-based career development planning system for teenagers, characterized in that, The method for youth career development planning based on artificial intelligence as described in any one of claims 1 to 7 includes: The user career profile building unit is used to acquire multi-source career-related data of the target user, and to perform standardization and data fusion processing on the multi-source career-related data of the target user in order to build a career profile of the target user. The career development direction analysis unit is used to obtain a preset youth career development direction database, construct a corresponding target user career reasoning chain based on the target user's career profile, and perform matching analysis on each youth career development direction in the youth career development direction database based on the target user career reasoning chain to obtain the target user's career development direction. The career planning scheme generation unit is used to calculate key career development parameters for the target user's career development direction, obtain key career development parameters for the target user, and generate a corresponding career planning scheme for the target user based on the target user's career inference chain and key career development parameters.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the artificial intelligence-based youth career development planning method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the artificial intelligence-based youth career development planning method as described in any one of claims 1 to 7.

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