A method and system for testing the professional personality of college graduates

Abstract

The present invention relates to the field of psychological assessment technology, and specifically to a method and system for testing the professional personality of college graduates. By integrating big data analysis and graph neural networks, the accuracy and scientificity of the test are significantly improved. The method uses advanced data processing technology to conduct in-depth analysis of the psychological and professional tendency data of college graduates, and encrypts the collected data by adopting a polynomial-based homomorphic encryption algorithm to ensure the security of the data during storage and transmission. This method allows encrypted data to be processed without decryption, thereby reducing the risk of data leakage. Through real-time analysis and model prediction, subsequent test questions are dynamically adjusted according to the answers and behavioral characteristics of graduates. This personalized test process can better adapt to the characteristics of different graduates and improve the accuracy and effectiveness of the test. Dynamically adjusted questions can more accurately evaluate the personality traits and professional tendencies of graduates.

Description

Technical Field

[0001] The present invention relates to the technical field of psychological assessment, and in particular to a method and system for testing the professional personality of college graduates. Background Art

[0002] Most of the career personality testing methods currently available on the market are based on traditional psychological theories. These methods are usually evaluated through questionnaires and interviews. However, due to the lack of full utilization of big data and artificial intelligence technology, these methods result in insufficient accuracy and scientificity of the test results. In addition, most existing career personality tests only focus on the current personality characteristics of graduates, ignoring the dynamic process of personality development and change, and lack long-term tracking and analysis of graduates' career personality. Therefore, how to accurately evaluate graduates' career personality and help them find a career that suits them has become a problem that needs to be solved urgently.

[0003] Existing career personality testing methods have many technical difficulties. First, these methods lack comprehensive processing and in-depth mining of multi-dimensional data during data collection and analysis, and cannot provide comprehensive personality assessments. Second, existing technologies make it difficult to accurately identify individual career adaptability issues and provide improvement suggestions. In addition, existing methods have a low level of intelligence in career matching and cannot provide personalized career advice based on individual characteristics.

[0004] Third, existing professional personality testing methods and systems usually lack effective data encryption mechanisms. The data collected during the test, including graduates' answers and behavior records, are often stored and managed in plain text. This storage method is extremely vulnerable to external attacks, resulting in data leakage or illegal acquisition.

[0005] Fourth, the existing professional personality test questions are usually fixed, and the follow-up questions cannot be adjusted in real time according to the graduates' answers and behaviors. This means that no matter how the graduates answer, they face the same questions, and there is no way to dynamically adjust according to individual differences.

[0006] Due to the fixed nature of the test questions, it is impossible to conduct detailed analysis of the test results. Each graduate's answers and behavior patterns are unique, and fixed questions cannot capture these subtle differences, resulting in insufficient refinement of the test results. The lack of personalized test program design means that the test results can only provide a general assessment of professional personality, but cannot deeply explore the potential traits and career tendencies of graduates.

[0007] Therefore, there is an urgent need for a new method and system for testing the professional personality of college graduates, which can make full use of big data analysis and artificial intelligence technology, especially graph neural network technology, to conduct a comprehensive and scientific assessment of the professional personality of graduates and provide accurate career matching and development suggestions to solve the above-mentioned problems in existing technologies. Summary of the invention

[0008] Based on the above purpose, the present invention provides a method and system for testing the professional personality of college graduates.

[0009] A method for testing the professional personality of college graduates comprises the following steps:

[0010] S1: Collect basic information of college graduates, including name, gender, age, major, graduation year, family background, educational experience, social practice experience during school, and relevant certificates and honors obtained;

[0011] S2: Conduct preliminary psychological assessments to obtain preliminary psychological characteristics of graduates through questionnaires and interviews. The questionnaires dynamically adjust test questions through artificial intelligence, and adjust follow-up questions in real time according to the graduates' answers and behaviors;

[0012] S3: Use big data analysis technology to organize and analyze preliminary psychological assessment data and generate a preliminary personality assessment report;

[0013] S4: Based on the preliminary personality assessment report, design a personalized test plan, including personality test questions and career orientation test questions;

[0014] S5: execute the personalized test plan in S4 and collect test result data;

[0015] S6: Use graph neural networks to perform deep learning analysis on the test result data, and generate a detailed career personality analysis report by building a graph model related to career characteristics;

[0016] S7: Match the career personality analysis report with the career database and recommend suitable career directions;

[0017] S8: Encrypted storage and management of the career personality test data results of college graduates to ensure the security, integrity and non-tamperability of the data, while protecting the privacy of graduates. The encrypted storage and management includes encrypted data, blockchain storage preparation, blockchain writing, access control and authority management, data access and decryption, and data integrity verification.

[0018] Optionally, the S1 specifically includes:

[0019] S11: Create a user account through the online platform and require college graduates to fill in basic information such as name, gender, age, major, and graduation year. The online platform receives the data entered by the user through a preset form interface to ensure the standardization and consistency of the information;

[0020] S12: Use advanced encryption standard technology to encrypt basic information input by users to ensure the security and privacy of data during transmission and storage;

[0021] S13: Confirm the authenticity of the filled-in information through a verification mechanism, including sending a mobile phone verification code or an email verification link. The verification mechanism performs two-way verification with the contact information entered by the user to prevent the submission of false information;

[0022] S14: After confirming the authenticity of the basic information, collect extended information, including family background, internship experience, hobbies, professional achievements, extracurricular activities, and awards. The extended information is collected through multiple-choice and open input boxes on the online platform;

[0023] S15: All collected information is stored in a cloud database, which uses a distributed storage architecture to classify and manage information;

[0024] S16: Use data cleaning techniques to clean and format the collected information. Data cleaning techniques include deduplication, filling missing values ​​and data normalization.

[0025] Optionally, the S2 specifically includes:

[0026] S21: Design a standardized psychological assessment questionnaire, which includes multiple aspects of personality traits, career interests, coping styles, and emotional states;

[0027] S22: Publish the psychological assessment questionnaire through the online platform;

[0028] S221: Psychological assessment questionnaire classification and annotation: Collect a large number of questions and situation descriptions related to professional personality, and establish an assessment questionnaire question bank, which is classified according to personality traits such as extroversion, responsibility, openness, professional fields such as finance, education, and technology, and question types such as situational questions, multiple-choice questions, and short-answer questions;

[0029] There are N questions in the question bank, each question Belong to a certain personality trait and career fields , each question is represented as a vector:

[0030] =( , , )

[0031] in {scenario questions, multiple choice questions, short answer questions}, It is the specific content of the problem;

[0032] Classification label: Label each question to form a label set L:

[0033] ,in For the problem, For personality traits, For the professional field;

[0034] S222: Initial question presentation: At the beginning of the test, a set of basic questions covering multiple personality traits and career areas are randomly selected from the question bank and presented to the graduates;

[0035] Randomly extract questions: Randomly extract M questions from the question bank as the initial test question set S:

[0036] ,in For is from A randomly selected index in

[0037] S223: Data preprocessing and feature extraction: preprocess the collected answers and behavior data, extract keywords, semantic information and emotional tendency features from the answer content; extract posture changes and facial expression features from the behavior data;

[0038] S224: Real-time analysis and model prediction: The extracted features are input into a pre-trained artificial intelligence model, which can evaluate the personality traits of graduates in real time based on these features. The model predicts the next most suitable question based on the evaluation results and preset rules.

[0039] S225: Follow-up question adjustment and presentation: Select and adjust follow-up test questions from the question bank based on the model’s predictions; present the adjusted questions and continue to collect graduates’ responses and behavior data;

[0040] S23: Collect questionnaire answer data, use data encryption technology to ensure the security of the answer data, and upload the questionnaire data to the cloud database for storage in real time;

[0041] S24: Arrange one-on-one interviews through a video conference system. The interviews are hosted by professional psychological assessors and adopt a semi-structured interview method, focusing on asking graduates about their career experiences, stress coping methods, and career plans;

[0042] S25: During the interview, the graduate’s responses and the interviewer’s evaluation were recorded. The interview records were converted in real time using speech-to-text technology and stored in a cloud database;

[0043] S26: Integrate the questionnaire data and interview data, analyze the interview records using natural language processing technology, and extract relevant information and psychological characteristics;

[0044] S27: Based on the results of the questionnaire survey and interview, a preliminary psychological characteristic data report is generated. The report content includes preliminary assessment results of personality traits, career interests, and emotional states in multiple dimensions.

[0045] Optionally, the S3 specifically includes:

[0046] S31: Import the collected preliminary psychological assessment data into the data warehouse, which adopts a distributed architecture;

[0047] S32: Use data preprocessing techniques to organize the imported data, including data cleaning, deduplication, missing value filling and data standardization;

[0048] S33: Apply data mining techniques to cluster the collated data and use the K-means clustering algorithm to divide the data into different psychological characteristic groups to identify the main personality types;

[0049] S34: Use association rule mining technology to analyze the associations between different psychological characteristic groups. Specifically, use the Apriori algorithm to mine frequent item sets and association rules to reveal the potential relationships between personality traits.

[0050] S35: Use principal component analysis to reduce the dimension of the data and extract the most representative features for personality assessment, including personality traits, career interests, coping styles, and emotional states, to reduce the data dimension;

[0051] S36: Use the machine learning model to classify and predict the extracted features, select the support vector machine algorithm for classification model training and prediction, and generate preliminary personality assessment results;

[0052] S37: Based on the results of cluster analysis, association rule mining and classification prediction, a preliminary personality assessment report is generated. The report includes a description of each personality type and an analysis of the associations between personality traits.

[0053] Optionally, the S4 specifically includes:

[0054] S41: Determine the personality traits and career orientations that require further testing based on the results of the preliminary personality assessment report, which includes descriptions of each personality type and correlation analysis between personality traits;

[0055] S42: Select test questions related to the determined personality traits from a predefined question bank, which covers multiple aspects of personality traits, career interests, coping styles, and emotional states;

[0056] S43: Design personality test questions, which are in the form of multiple-choice questions, scenario simulation questions and behavioral response questions to assess the personality performance of graduates in different situations;

[0057] S44: Design career orientation test questions, which include career interest questions, career value questions and career ability questions, to assess graduates' preferences and adaptability to different career environments and positions;

[0058] S45: Generate a test process of the online test platform according to the design of the personalized test plan.

[0059] Optionally, the S5 specifically includes:

[0060] S51: Upload the personalized test plan to the online test platform, which adopts a modular design and supports flexible combination and display of multiple test question types;

[0061] S52: Notify college graduates to participate in personalized testing through an online testing platform, including sending test invitations and access links via email or text message;

[0062] S53: After the graduates log in to the online testing platform, their personalized test plan is automatically loaded to ensure that the test content and sequence of each graduate matches their preliminary personality assessment results;

[0063] S54: During the online test, the graduate's answering progress and reaction time are monitored in real time, and the answering status and time data of each question are recorded;

[0064] S55: After the test, all test result data are automatically collected and stored, including the answer to each question, reaction time, and emotional fluctuation multi-dimensional information;

[0065] S56: Use data encryption technology to encrypt the collected test result data to ensure the security and privacy of the data, and upload it to the cloud database for storage.

[0066] Optionally, the S6 specifically includes:

[0067] S61: Extract the complete test result data from the cloud database, including the answer, reaction time and emotional fluctuation multi-dimensional information of each test question, as input data for graph neural network analysis;

[0068] S62: Construct a graph model related to career characteristics, where nodes represent test participants, edges represent similarities or correlations between participants, and node characteristics include information about personal career interests and personality traits from test results;

[0069] S63: Apply graph neural network to train the graph model. Graph neural network learns the comprehensive feature representation of each node by aggregating the information of nodes on the graph;

[0070] S64: Using the feature representation learned by the graph neural network, we conduct an in-depth analysis of the career personality of each test participant to identify their potential successful career paths and potential career adaptability issues;

[0071] S65: Based on the results of the graph neural network analysis, a career personality analysis report is generated, which includes an assessment of each participant’s career adaptability, recommended career paths, and potential development personality traits.

[0072] Optionally, the S64 specifically includes:

[0073] S641: taking the node feature representation obtained by training the graph neural network as input, the node feature representation including the comprehensive features of the career interests and personality traits of each test participant;

[0074] S642: Classify and predict each node feature representation, and use the soft maximization function to calculate the probability distribution of each test participant on different career paths. The formula of the soft maximization function is expressed as: ,in, Indicates test participants Categorized as a career path The probability of Indicates test participants The characteristic representation of Indicates career path The weight vector of represents the total number of career paths;

[0075] S643: Based on the probability distribution of career paths, identify the best successful career path for each test participant, and select the career path with the highest probability as the best recommended career path. The formula for the best recommended career path is: .in, Indicates test participants Recommended career paths is a function that represents all possible career paths In, find The largest path ;

[0076] S644: Analyze potential career adaptability issues in the feature representation and identify potential issues that affect career adaptability by calculating the career adaptability score. The formula for the career adaptability score is: ,in, Indicates test participants The career adaptability score represents the weight of the adaptive problem, Indicates test participants In Features The score on Represents the total number of features.

[0077] Optionally, the S7 specifically includes:

[0078] S71: extracting a career personality analysis report from the cloud database, the analysis report including the career adaptability assessment, recommended career path, and potential career adaptability issues of the test participant;

[0079] S72: Establish a career database, which contains information on various industries and positions, including job responsibilities, required skills, working environment, and career development paths;

[0080] S73: matching the recommended career path in the career personality analysis report with the job information in the career database, and determining the most matching career direction using a similarity calculation method;

[0081] S74: Generate a recommended career direction list based on the similarity calculation results; and provide the recommended career direction list to the test participants. The recommended career direction list is displayed through an online platform, and detailed job information and matching reasons are provided;

[0082] The S8 specifically includes: S81: Encrypted data: using a polynomial-based homomorphic encryption algorithm, the encryption function is expressed as;

[0083] ;

[0084] in is plain text data, is the public key, and the encryption function consists of two large prime numbers and composition, is a random number;

[0085] S82: Blockchain storage preparation: Combine the encrypted ciphertext and data fingerprint into a data block, and add timestamp and data version number metadata to it. Add timestamp T and data version number V, and the metadata of the data block is expressed as: M=(T,V);

[0086] S83: Blockchain writing: Write the prepared data blocks into the blockchain through the consensus mechanism of the blockchain network; each data block is connected to the previous data block through a hash pointer to form an unalterable chain structure. With its previous data block Connected by hash pointers;

[0087] =(H( ),Data,MetaData);

[0088] Where H( ) is the hash value of the previous data block;

[0089] S84: Access control and authority management: Establish an access control mechanism so that only authorized users, including graduates themselves and specific recruiting units, can obtain access to data; authorities are defined and managed through smart contracts;

[0090] S85: Data access and decryption: When an authorized user requests access to data, first verify their permissions; if the permissions are passed, read the encrypted data block from the blockchain and decrypt it using the key provided by the data owner. The decryption function is:

[0091] ;

[0092] is the key, , p and q are The two prime factors of ; = ;

[0093] S86: Data integrity verification: Use the previously generated data fingerprint to verify the integrity of the decrypted data to ensure that the data has not been tampered with during storage and transmission.

[0094] A system for testing the professional personality of college graduates, used to implement the above-mentioned method for testing the professional personality of college graduates, includes the following modules:

[0095] Basic information collection module: used to collect basic information of college graduates, including name, gender, age, major, and graduation year, and store it in the data warehouse;

[0096] Preliminary psychological assessment module: obtain preliminary psychological characteristics data of graduates through questionnaires and interviews, and transmit the data to the big data analysis module;

[0097] Psychological feature extraction module: extract key psychological features from questionnaire and interview data; use natural language processing to analyze text data and extract sentiment and attitude features;

[0098] Feature selection module: select the most useful features for career personality prediction and use feature selection to determine the most important features;

[0099] Big data analysis module: Use big data analysis technology to organize and analyze preliminary psychological assessment data, generate preliminary personality assessment reports, and transmit the reports to the personalized test design module;

[0100] Personalized test design module: design a personalized test plan based on the preliminary personality assessment report, including personality test questions and career orientation test questions, and upload the test plan to the online testing platform;

[0101] Online testing platform: used to execute personalized testing plans, collect test result data, and transmit the data to the graph neural network analysis module;

[0102] Graph neural network analysis module: Uses graph neural network to perform deep learning analysis on test result data, generates detailed career personality analysis report by building graph models related to career characteristics, and transmits the report to the career matching module;

[0103] Data encryption and security module: All collected data is encrypted during transmission and storage using a polynomial homomorphic encryption algorithm for data transmission and data storage encryption;

[0104] Data verification module: Verify the integrity and accuracy of input data, implement front-end and back-end data verification, and prevent the input of invalid or malicious data;

[0105] Career matching module: matches the career personality analysis report with the career database, recommends suitable career directions, and generates a list of recommended career directions;

[0106] Report generation module: Generate a detailed professional personality assessment report based on the analysis results, and use the report generation tool to automatically generate an easy-to-understand report;

[0107] Feedback collection module: collect user feedback on test results; collect user opinions through online questionnaires or direct feedback mechanisms.

[0108] Beneficial effects of the present invention:

[0109] The present invention significantly improves the accuracy and scientificity of the test by integrating big data analysis and graph neural network technology. The method uses advanced data processing technology to conduct in-depth analysis of the psychological and career tendency data of college graduates and comprehensively evaluate their career personality characteristics, which not only includes accurate measurement of the current personality state, but also can predict the potential development of personality, thereby providing a scientific basis for graduates' long-term career planning.

[0110] The present invention, through career matching technology, combined with the career characteristic graph model, can recommend the career path that best suits each participant's personality and interests. This personalized recommendation takes into account the comprehensive career adaptability of the participant, making the career advice more targeted and practical. Through this method, graduates obtain in-depth analysis of possible career paths and their adaptability issues, and thus make more informed career choices;

[0111] The collected data is encrypted by using a polynomial-based homomorphic encryption algorithm to ensure the security of the data during storage and transmission. This method allows the encrypted data to be processed without decryption, thereby reducing the risk of data leakage. At the same time, the use of blockchain to store data and its tamper-proof characteristics further enhances the integrity and security of the data. Only authorized users can access and decrypt the data, effectively protecting the personal privacy of graduates.

[0112] Through real-time analysis and model prediction, subsequent test questions are dynamically adjusted according to the graduates' answers and behavioral characteristics. This personalized test process can better adapt to the characteristics of different graduates and improve the accuracy and effectiveness of the test. Dynamically adjusted questions can more accurately assess the personality traits and career tendencies of graduates, providing recruiters with more accurate talent assessments.

[0113] By deeply analyzing each graduate’s unique responses and behavior patterns, this technical solution can reveal more detailed personality differences and potential traits. This refined analysis helps to discover the graduates’ potential abilities and career interests, providing more valuable information for personal career development planning and recruitment unit selection.

[0114] This technical solution enables the system to adapt to different testing needs and scenarios by establishing an expandable question bank and flexible testing process. Whether it is specific testing in different career fields or comprehensive assessment of different personality traits, the system can provide corresponding support.

[0115] Data access rights are defined and managed through smart contracts to ensure that only authorized users can access relevant data. This approach not only ensures the legal use of data, but also improves the transparency and fairness of the entire testing process. All operations are traceable and easy to supervise and audit.

[0116] The application of blockchain technology not only ensures short-term data security, but also provides the possibility for long-term data management and analysis. Over time, the accumulated data can be used for deeper research such as trend analysis and pattern recognition, bringing new insights and developments to the field of professional personality assessment.

[0117] This technical solution combines advanced encryption technology, artificial intelligence analysis and blockchain to not only solve the data security and test question fixity issues in existing professional personality test methods, but also improves the refinement of test results, enhances the flexibility and adaptability of the system, promotes fairness and transparency, and supports long-term data management and analysis. These beneficial effects jointly promote the development of professional personality assessment technology and provide more scientific and effective tools for personal career planning and organizational talent management. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0119] Figure 1 A schematic diagram of a method for testing the professional personality of college graduates according to an embodiment of the present invention;

[0120] Figure 2 A schematic diagram of a system for testing the professional personality of college graduates according to an embodiment of the present invention;

[0121] Figure 3 This is a test data diagram for extracting a professional personality test for college graduates according to an embodiment of the present invention. DETAILED DESCRIPTION

[0122] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0123] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", and "some embodiments" in the specification indicate that the embodiments include specific features, structures, or characteristics, but not every embodiment may include the specific features, structures, or characteristics. In addition, when a specific feature, structure, or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure, or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0124] In general, a term is understood, at least in part, from its use in context. For example, the term "one or more" as used herein is used to describe any feature, structure, or characteristic in the singular sense, or to describe a combination of features, structures, or characteristics in the plural sense, depending, at least in part, on the context. Additionally, the term "based on" is understood to not necessarily be intended to convey an exclusive set of factors, but instead, depending, at least in part on the context, allows for the presence of other factors that are not necessarily explicitly described.

[0125] like Figure 1 As shown, a method for testing the professional personality of college graduates comprises the following steps:

[0126] S1: Collect basic information of college graduates, including name, gender, age, major, graduation year, family background, educational experience, social practice experience during school, and relevant certificates and honors obtained;

[0127] S2: Conduct preliminary psychological assessments to obtain preliminary psychological characteristics of graduates through questionnaires and interviews. The questionnaires dynamically adjust test questions through artificial intelligence, and adjust follow-up questions in real time according to the graduates' answers and behaviors;

[0128] S3: Use big data analysis technology to organize and analyze preliminary psychological assessment data and generate a preliminary personality assessment report;

[0129] S4: Based on the preliminary personality assessment report, design a personalized test plan, including personality test questions and career orientation test questions;

[0130] S5: execute the personalized test plan in S4 and collect test result data;

[0131] S6: Use graph neural networks to perform deep learning analysis on the test result data, and generate a detailed career personality analysis report by building a graph model related to career characteristics;

[0132] S7: Match the career personality analysis report with the career database and recommend suitable career directions;

[0133] S8: Encrypted storage and management of the career personality test data results of college graduates to ensure the security, integrity and non-tamperability of the data, while protecting the privacy of graduates. The encrypted storage and management includes encrypted data, blockchain storage preparation, blockchain writing, access control and authority management, data access and decryption, and data integrity verification.

[0134] S1 specifically includes:

[0135] S11: Create a user account through the online platform and require college graduates to fill in basic information such as name, gender, age, major, and graduation year. The online platform receives the data entered by the user through a preset form interface to ensure the standardization and consistency of the information;

[0136] S12: Use Advanced Encryption Standard (AES) technology to encrypt basic information entered by users to ensure the security and privacy of data during transmission and storage;

[0137] S13: Confirm the authenticity of the filled-in information through a verification mechanism, including sending a mobile phone verification code or an email verification link. The verification mechanism performs two-way verification with the contact information entered by the user to prevent the submission of false information;

[0138] S14: After confirming the authenticity of the basic information, collect extended information, including family background, internship experience, hobbies, professional achievements, extracurricular activities, and awards. The extended information is collected through multiple-choice and open input boxes on the online platform to ensure the completeness of the information;

[0139] S15: All collected information is stored in a cloud database, which uses a distributed storage architecture to classify and manage information to ensure high availability and reliability of data;

[0140] S16: Use data cleaning technology to clean and format the collected information. Data cleaning technology includes deduplication, filling missing values ​​and data normalization to ensure the integrity, consistency and accuracy of the data. Through the above steps, the basic information of college graduates can be collected comprehensively and accurately, ensuring the security and authenticity of the information, and providing a solid data foundation for subsequent professional personality tests. The use of online platforms and encryption technology not only improves the efficiency of information collection, but also protects the privacy of users, which helps to improve the reliability and effectiveness of test results.

[0141] S2 specifically includes:

[0142] S21: Design a standardized psychological assessment questionnaire, which includes personality traits, career interests, coping styles, and emotional states. The questionnaire design is based on psychological theories and career personality assessment standards;

[0143] S22: Publish the psychological assessment questionnaire through an online platform and use responsive design to ensure the accessibility and user experience of the questionnaire on different devices;

[0144] S221: Psychological assessment questionnaire classification and annotation: Collect a large number of questions and situation descriptions related to professional personality, and establish an assessment questionnaire question bank, which is classified according to personality traits such as extroversion, responsibility, openness, professional fields such as finance, education, and technology, and question types such as situational questions, multiple-choice questions, and short-answer questions;

[0145] There are N questions in the question bank, each question Belong to a certain personality trait and career fields , each question is represented as a vector:

[0146] =( , , )

[0147] in {scenario questions, multiple choice questions, short answer questions}, It is the specific content of the problem;

[0148] Classification label: Label each question to form a label set L:

[0149] ,in For the problem, For personality traits, For the professional field;

[0150] S222: Initial question presentation: At the beginning of the test, a set of basic questions covering multiple personality traits and career areas are randomly selected from the question bank and presented to the graduates;

[0151] Randomly extract questions: Randomly extract M questions from the question bank as the initial test question set S:

[0152] ,in For is from A randomly selected index in

[0153] S223: Data preprocessing and feature extraction: preprocess the collected answers and behavior data, extract keywords, semantic information and emotional tendency features from the answer content; extract posture changes and facial expression features from the behavior data;

[0154] S224: Real-time analysis and model prediction: The extracted features are input into a pre-trained artificial intelligence model, which can evaluate the personality traits of graduates in real time based on these features. The model predicts the next most suitable question based on the evaluation results and preset rules.

[0155] S225: Follow-up question adjustment and presentation: Based on the model’s predictions, select and adjust follow-up test questions from the question bank; present the adjusted questions and continue to collect graduates’ answers and behavior data

[0156] S23: Collect questionnaire answer data, use data encryption technology to ensure the security of the answer data, and upload the questionnaire data to the cloud database for storage in real time;

[0157] S24: Arrange one-on-one interviews through a video conference system. The interviews are hosted by professional psychological assessors and adopt a semi-structured interview method, focusing on asking graduates about their career experiences, stress coping methods, and career plans;

[0158] S25: During the interview, the graduate’s responses and the interviewer’s evaluation were recorded. The interview records were converted in real time using speech-to-text technology and stored in a cloud database;

[0159] S26: Integrate the questionnaire data and interview data, analyze the interview records using natural language processing (NLP) technology, and extract relevant information and psychological characteristics;

[0160] S27: Based on the results of the questionnaire survey and the interview, a preliminary psychological characteristics data report is generated. The report content includes preliminary evaluation results of personality traits, career interests, and emotional states in multiple dimensions. Through the above steps, a preliminary psychological assessment of college graduates can be carried out systematically and comprehensively. The combination of online questionnaires and video interviews not only improves the efficiency of data collection, but also enhances the comprehensiveness and depth of the assessment. The use of standardized questionnaires and semi-structured interviews ensures the scientificity and accuracy of the assessment. The application of data encryption and natural language processing technology ensures data security and the accuracy of information extraction, thereby providing reliable basic data for subsequent career personality analysis.

[0161] S3 specifically includes:

[0162] S31: Import the collected preliminary psychological assessment data into the data warehouse, which adopts a distributed architecture to support large-scale data storage and processing;

[0163] S32: Use data preprocessing techniques to organize the imported data. Preprocessing techniques include data cleaning, deduplication, missing value filling and data standardization to ensure data integrity and consistency;

[0164] S33: Apply data mining technology to perform cluster analysis on the sorted data, and use the K-means clustering algorithm to divide the data into different psychological characteristic groups to identify the main personality types. The objective function expression of the K-means clustering algorithm is: ,in, represents the clustering objective function, represents the number of clusters, Indicates that it belongs to a cluster Psychological assessment data points, Representation Cluster The centroid of

[0165] S34: Use association rule mining technology to analyze the associations between different psychological characteristic groups. Specifically, use the Apriori algorithm to mine frequent item sets and association rules to reveal the potential relationship between personality traits. The support formula of frequent item sets is: ,in, Representing Item Sets The support of Indicates the included itemset The number of transactions, Represents the total number of transactions; the confidence formula of the association rule is: ,in, Representation Rules Derivation The confidence level, Indicates that it also contains and The number of transactions;

[0166] S35: Use principal component analysis (PCA) technology to reduce the dimension of the data and extract the most representative features for personality assessment, including personality traits (such as extraversion, responsibility, openness, agreeableness, and emotional stability), career interests (such as research, artistic, social, entrepreneurial, and traditional), coping styles (such as active coping and avoidant coping), and emotional states (such as stress level and satisfaction), reducing data dimensions and improving analysis efficiency;

[0167] S36: Use the machine learning model to classify and predict the extracted features, select the support vector machine (SVM) algorithm for classification model training and prediction, and generate preliminary personality assessment results; the decision function expression of the support vector machine classification is: ,in, represents the classification decision function, represents the weight vector, represents the input feature vector, Indicates bias;

[0168] S37: Based on the results of cluster analysis, association rule mining and classification prediction, a preliminary personality assessment report is generated. The report content includes the description of each personality type and the correlation analysis between personality traits. Through the above steps, the preliminary psychological assessment data can be systematically organized and analyzed to generate an accurate preliminary personality assessment report. The application of distributed data warehouse and data preprocessing technology ensures the integrity and consistency of the data. Through K-means clustering, Apriori association rule mining, PCA dimensionality reduction and SVM classification prediction, the potential patterns and correlation relationships in the data are deeply mined, which improves the accuracy and reliability of personality assessment. The comprehensive application of these steps provides scientific and comprehensive basic data for subsequent personalized career matching.

[0169] S4 specifically includes:

[0170] S41: Determine the personality traits and career orientations that require further testing based on the results of the preliminary personality assessment report, which includes descriptions of each personality type and correlation analysis between personality traits;

[0171] S42: Select test questions related to the determined personality traits from a predefined question bank, which covers multiple aspects of personality traits, career interests, coping styles, and emotional states;

[0172] S43: Design personality test questions, which are in the form of multiple-choice questions, scenario simulation questions and behavioral response questions to assess the personality performance of graduates in different situations;

[0173] S44: Design career orientation test questions, which include career interest questions, career value questions and career ability questions, to assess graduates' preferences and adaptability to different career environments and positions;

[0174] S45: Based on the design of the personalized test plan, generate the test process of the online test platform to ensure the logic and consistency of the test; through the above steps, it is possible to scientifically design a personalized test plan based on the preliminary personality assessment report, including personality test questions and career orientation test questions. Through the predefined question bank and online testing platform, the standardization and flexibility of the test are ensured. The design of personality test questions and career orientation test questions can comprehensively and deeply evaluate the personality traits and career orientations of graduates, and provide more accurate and detailed data support for subsequent career matching. The application of pre-testing and feedback mechanism further enhances the effectiveness and scientificity of the test plan.

[0175] S5 specifically includes:

[0176] S51: Upload the personalized test plan to the online test platform, which adopts a modular design and supports flexible combination and display of multiple test question types;

[0177] S52: Notify college graduates to participate in personalized testing through an online testing platform, including sending test invitations and access links via email or text message;

[0178] S53: After the graduates log in to the online testing platform, their personalized test plan is automatically loaded to ensure that the test content and sequence of each graduate matches their preliminary personality assessment results;

[0179] S54: During the online test, the graduate's answering progress and reaction time are monitored in real time, and the answering status and time data of each question are recorded;

[0180] S55: After the test, all test result data are automatically collected and stored, including the answer to each question, reaction time, and emotional fluctuation multi-dimensional information;

[0181] S56: Use data encryption technology to encrypt the collected test result data to ensure the security and privacy of the data, and upload it to the cloud database for storage; through the above steps, the personalized test plan in S4 can be executed efficiently and accurately, and the test result data can be comprehensively collected. The modular design and real-time monitoring function of the online test platform ensure the smoothness of the test process and the richness of the data. The application of data encryption and cloud database ensures the security and privacy of the data. Through preliminary sorting, the integrity and accuracy of the test result data are ensured, providing a reliable data foundation for subsequent in-depth analysis.

[0182] S6 specifically includes:

[0183] S61: Extract the complete test result data from the cloud database, including the answer, reaction time and emotional fluctuation multi-dimensional information of each test question, as input data for graph neural network analysis;

[0184] S62: Construct a graph model related to occupational characteristics, where nodes represent test participants, edges represent similarities or correlations between participants, and node characteristics include information about personal occupational interests and personality traits from test results. The specific formula for constructing the graph model is: ,in, Represents a graph model, represents a set of nodes, represents a set of test participants, represents a set of edges, indicating the similarity or correlation between participants, Represents a node feature set, including career interests and personality trait information;

[0185] S63: Apply graph neural network to train the graph model. The graph neural network learns the comprehensive feature representation of each node (test participant) by aggregating the information of nodes on the graph. The operation representation formula of the graph neural network is: ,in, Indicates Layer Node The characteristic representation of represents the test participants, represents the activation function, Representation Node The set of neighbor nodes of Other relevant participants, represents the normalization constant, represents neighbor nodes, Indicates The weight matrix of the layer, Indicates Layer Neighbor Node Characteristic representation of , including career interests and personality trait information;

[0186] S64: Using the feature representation learned by the graph neural network, we conduct an in-depth analysis of the career personality of each test participant to identify their potential successful career paths and potential career adaptability issues;

[0187] S65: Generate a career personality analysis report based on the results of the graph neural network analysis, which includes an assessment of each participant’s career adaptability, recommended career paths, and potential personality traits for development. Through the above steps, the graph neural network can be fully utilized to conduct in-depth analysis of complex test result data. The constructed career characteristic-related graph model and the application of the graph neural network make it possible to accurately analyze and predict each graduate’s career personality and career adaptability, and provide personalized career development suggestions. The detailed career personality analysis report not only helps graduates understand their own career potential, but also provides scientific data support for colleges and universities and career guidance services.

[0188] S64 specifically includes:

[0189] S641: Taking the node feature representation obtained by training the graph neural network (GNN) as input, the node feature representation includes the comprehensive features of each test participant's career interests and personality traits;

[0190] S642: Perform classification prediction on each node feature representation and use the softmax function to calculate the probability distribution of each test participant on different career paths. The formula of the softmax function is expressed as: ,in, Indicates test participants Categorized as a career path The probability of Indicates test participants The characteristic representation of Indicates career path The weight vector of represents the total number of career paths;

[0191] S643: Based on the probability distribution of career paths, identify the best successful career path for each test participant, and select the career path with the highest probability as the best recommended career path. The formula for the best recommended career path is: .in, Indicates test participants Recommended career paths is a function that represents all possible career paths In, find The largest path ;

[0192] S644: Analyze potential career adaptability issues in the feature representation and identify potential issues that affect career adaptability by calculating the career adaptability score. The formula for the career adaptability score is: ,in, Indicates test participants The career adaptability score represents the weight of the adaptive problem, Indicates test participants In Features The score on Represents the total number of features; through the above steps, we can make full use of the feature representation learned by the graph neural network, conduct in-depth career personality analysis on each test participant, calculate the probability distribution of career paths through the soft maximization function, accurately identify the most likely successful career paths, and calculate the career adaptability score to help identify potential career adaptability problems and provide comprehensive guidance and suggestions for career development.

[0193] S7 specifically includes:

[0194] S71: extracting a career personality analysis report from the cloud database, the analysis report including the career adaptability assessment, recommended career path, and potential career adaptability issues of the test participant;

[0195] S72: Establish a career database, which contains information on various industries and positions, including job responsibilities, required skills, working environment, and career development paths;

[0196] S73: Match the recommended career path in the career personality analysis report with the job information in the career database, and use a similarity calculation method to determine the most matching career direction; the similarity calculation formula is:

[0197] ,in, Indicates test participants Career paths and positions The similarity of Indicates career path Features The value of Indicates position Features The value of Indicates the total number of features;

[0198] S74: Based on the similarity calculation results, a list of recommended career directions is generated, which includes recommended career positions and their detailed information; and the list of recommended career directions is provided to the test participants. The list of recommended career directions is displayed on an online platform, and detailed position information and matching reasons are provided. Through the above steps, the career personality analysis report can be accurately matched with the career database, and suitable career directions can be recommended. The combination of detailed position information in the career database and the similarity calculation method ensures the scientificity and reliability of the recommendation results. The generated list of recommended career directions helps test participants understand the career path that suits them, and provides detailed position information and matching reasons, thereby improving the satisfaction and practicality of career recommendations.

[0199] The S8 specifically includes: S81: Encrypted data: using a polynomial-based homomorphic encryption algorithm, the encryption function is expressed as;

[0200] ;

[0201] in is plain text data, is the public key, and the encryption function consists of two large prime numbers and composition, is a random number;

[0202] S82: Blockchain storage preparation: Combine the encrypted ciphertext and data fingerprint into a data block, and add timestamp and data version number metadata to it. Add timestamp T and data version number V, and the metadata of the data block is expressed as: M=(T,V);

[0203] S83: Blockchain writing: Write the prepared data blocks into the blockchain through the consensus mechanism of the blockchain network; each data block is connected to the previous data block through a hash pointer to form an unalterable chain structure. With its previous data block Connected by hash pointers;

[0204] =(H( ),Data,MetaData);

[0205] Where H( ) is the hash value of the previous data block;

[0206] S84: Access control and authority management: Establish an access control mechanism so that only authorized users, including graduates themselves and specific recruiting units, can obtain access to data; authorities are defined and managed through smart contracts;

[0207] S85: Data access and decryption: When an authorized user requests access to data, first verify their permissions; if the permissions are passed, read the encrypted data block from the blockchain and decrypt it using the key provided by the data owner. The decryption function is:

[0208] ;

[0209] is the key, , p and q are The two prime factors of ; = ;

[0210] S86: Data integrity verification: Use the previously generated data fingerprint to verify the integrity of the decrypted data to ensure that the data has not been tampered with during storage and transmission.

[0211] like Figure 2 As shown, a system for testing the professional personality of college graduates is used to implement the above-mentioned method for testing the professional personality of college graduates, and includes the following modules:

[0212] Basic information collection module: used to collect basic information of college graduates, including name, gender, age, major, and graduation year, and store it in the data warehouse;

[0213] Preliminary psychological assessment module: obtain preliminary psychological characteristics data of graduates through questionnaires and interviews, and transmit the data to the big data analysis module;

[0214] Psychological feature extraction module: extract key psychological features from questionnaire and interview data; use natural language processing to analyze text data and extract sentiment and attitude features;

[0215] Feature selection module: select the most useful features for career personality prediction and use feature selection to determine the most important features;

[0216] Big data analysis module: uses big data analysis technology to organize and analyze preliminary psychological assessment data, generate preliminary personality assessment reports, and transmit the reports to the personalized test design module;

[0217] Personalized test design module: design a personalized test plan based on the preliminary personality assessment report, including personality test questions and career orientation test questions, and upload the test plan to the online testing platform;

[0218] Online testing platform: used to execute personalized testing plans, collect test result data, and transmit the data to the graph neural network analysis module;

[0219] Graph neural network analysis module: Uses graph neural network to perform deep learning analysis on test result data, generates detailed career personality analysis report by building graph models related to career characteristics, and transmits the report to the career matching module;

[0220] Data encryption and security module: All collected data is encrypted during transmission and storage using a polynomial homomorphic encryption algorithm for data transmission and data storage encryption;

[0221] Data verification module: Verify the integrity and accuracy of input data, implement front-end and back-end data verification, and prevent the input of invalid or malicious data;

[0222] Career matching module: matches the career personality analysis report with the career database, recommends suitable career directions, and generates a list of recommended career directions;

[0223] Report generation module: Generate a detailed professional personality assessment report based on the analysis results, and use the report generation tool to automatically generate an easy-to-understand report;

[0224] Feedback collection module: collect user feedback on test results; collect user opinions through online questionnaires or direct feedback mechanisms;

[0225] This system collects basic information and psychological characteristics of graduates, and uses data analysis, machine learning, natural language processing and other technologies to conduct in-depth mining and analysis of the data to assess the career personality of graduates, match them with the career database, and recommend suitable career directions for them. At the same time, data encryption and verification are used to ensure data security and accuracy, and user feedback is collected to continuously optimize the system.

[0226] Workflow:

[0227] The basic information collection module first collects the basic information of college graduates, such as name, gender, age, major, graduation year, etc., and stores this information in the data warehouse.

[0228] The preliminary psychological assessment module obtains the preliminary psychological characteristic data of graduates through questionnaires and interviews, and then transmits these data to the big data analysis module.

[0229] The psychological feature extraction module extracts key psychological features from questionnaire and interview data, uses natural language processing technology to analyze text data, and extracts features such as emotions and attitudes.

[0230] The feature selection module uses feature selection methods to screen out the most valuable features for career personality prediction.

[0231] The big data analysis module uses big data analysis technology to organize and analyze preliminary psychological assessment data, generate preliminary personality assessment reports, and transmit them to the personalized test design module.

[0232] The personalized test design module carefully designs a personalized test plan including personality test questions and career orientation test questions based on the preliminary personality assessment report, and uploads it to the online testing platform.

[0233] The online testing platform executes personalized testing plans, collects test result data, and then transmits the data to the graph neural network analysis module.

[0234] The graph neural network analysis module uses graph neural networks to perform deep learning analysis on the test result data, builds a graph model related to occupational characteristics, generates a detailed occupational personality analysis report, and then transmits the report to the occupation matching module.

[0235] During the data transmission and storage process, the data encryption and security module uses a polynomial homomorphic encryption algorithm to encrypt all collected data to ensure data security.

[0236] The data verification module verifies the integrity and accuracy of the input data, implements data verification on the front end and back end respectively, and prevents the input of invalid or malicious data.

[0237] The career matching module matches the career personality analysis report with the career database, recommends suitable career directions, and generates a list of recommended career directions.

[0238] The report generation module uses the report generation tool to automatically generate a detailed and easy-to-understand professional personality assessment report based on the final analysis results.

[0239] The feedback collection module collects users' opinions and feedback on test results through online questionnaires or direct feedback mechanisms.

[0240] Through the mutual coordination and linkage of the above modules, this system can comprehensively and accurately evaluate the professional personality characteristics of college graduates and provide personalized career advice.

[0241] Experimental example

[0242] 1. Purpose of the experiment

[0243] Verify the accuracy and effectiveness of the artificial intelligence-based professional personality testing method for college graduates.

[0244] 2. Experimental subjects

[0245] 100 graduating college students were selected, covering different majors and backgrounds.

[0246] 3. Experimental Procedure

[0247] S1: Basic information collection

[0248] User accounts are created through an online platform, and graduates are required to fill in basic information such as name, gender, age, major, and year of graduation.

[0249] Use Advanced Encryption Standard technology to encrypt basic information entered by users to ensure the security and privacy of data during transmission and storage.

[0250] S2: Preliminary Psychological Assessment

[0251] Design a standardized psychological assessment questionnaire covering multiple aspects such as personality traits, career interests, coping styles, and emotional states.

[0252] Publish psychological assessment questionnaires through online platforms and adjust follow-up questions in real time.

[0253] Arrange one-on-one interviews, which will be conducted by professional psychological assessors and adopt a semi-structured interview method.

[0254] S3: Big Data Analytics

[0255] Import the collected preliminary psychological assessment data into the data warehouse and organize the data using data preprocessing techniques.

[0256] Data mining technology is used to perform cluster analysis, association rule mining and classification prediction on the sorted data.

[0257] S4: Personalized test plan design

[0258] Based on the results of the preliminary personality assessment report, determine the personality traits and career tendencies that require further testing.

[0259] Design personality and career aptitude tests by selecting test questions related to identified personality traits from a predefined question bank.

[0260] S5: Execute personalized test plan

[0261] Upload the personalized test plan to the online testing platform and notify graduates to participate in the test.

[0262] During the online test, the graduates’ answering progress and reaction time are monitored in real time, and the answer status and time data for each question are recorded.

[0263] S6: Graph Neural Network Analysis

[0264] The complete test result data is extracted from the cloud database as input data for graph neural network analysis.

[0265] Construct a graph model related to occupational characteristics and apply graph neural network to train the graph model.

[0266] Using the feature representation learned by graph neural networks, an in-depth analysis of the professional personality of each test participant is conducted.

[0267] S7: Career Matching

[0268] Extract the occupational personality analysis report from the cloud database and establish an occupational database.

[0269] Match the recommended career paths in the career personality analysis report with the job information in the career database to generate a list of recommended career directions.

[0270] S8: Data encryption and security

[0271]

[0272] The data is encrypted using a polynomial-based homomorphic encryption algorithm and written into the blockchain through the consensus mechanism of the blockchain network.

[0273] Establish access control mechanisms so that only authorized users can gain access to data.

[0274] Experimental data of 10 people selected from 100 graduates’ career personality test

[0275] Experimental analysis

[0276] Through the professional personality test of 100 graduates, we observed the following phenomena:

[0277] 1. Accuracy: The personality assessment results of most graduates are consistent with their actual performance, indicating that the test method has a high accuracy.

[0278] 2. Personalization: Personalized testing programs designed according to the characteristics of different graduates can dig deeper into their potential traits.

[0279] 3. Security: The application of data encryption and blockchain technology effectively protects the privacy and data security of graduates.

[0280] Experimental Results

[0281] Accuracy: 95% of graduates believe that the test results accurately reflect their personality traits.

[0282] Satisfaction: 90% of graduates were satisfied with the testing process and results.

[0283] Recommendation effect: 85% of graduates believe that the recommended career direction matches their interests and abilities.

[0284] in conclusion

[0285] The AI-based professional personality test method for college graduates can not only accurately assess the professional personality of graduates, but also provide personalized test plans and career recommendations, which has high practical value and application prospects.

[0286] The present invention encompasses any substitution, modification, effective method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0287] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for testing the professional personality of college graduates, characterized in that: The following steps are involved: S1: Collect basic information of college graduates, including name, gender, age, major, graduation year, family background, educational experience, social practice experience during school, and relevant certificates and honors obtained; S2: Conduct preliminary psychological assessments to obtain preliminary psychological characteristics of graduates through questionnaires and interviews. The questionnaires dynamically adjust test questions through artificial intelligence, and adjust follow-up questions in real time according to the graduates' answers and behaviors; The S2 specifically includes: S21: Design a standardized psychological assessment questionnaire, which includes multiple aspects of personality traits, career interests, coping styles, and emotional states; S22: Publish the psychological assessment questionnaire through the online platform; S221: Psychological assessment questionnaire classification and annotation: Collect a large number of questions and situation descriptions related to professional personality, and establish an assessment questionnaire question bank, which is classified according to personality traits such as extroversion, responsibility, openness, professional fields such as finance, education, and technology, and question types such as situational questions, multiple-choice questions, and short-answer questions; There are N questions in the question bank, each question Belong to a certain personality trait and career fields , each question is represented as a vector: =( , , ) in {scenario questions, multiple choice questions, short answer questions}, It is the specific content of the problem; Classification label: Label each question to form a label data set L: ,in For the problem, For personality traits, S222: Initial question presentation: At the beginning of the test, a set of basic questions covering multiple personality traits and career fields are randomly selected from the question bank and presented to the graduates; Randomly extract questions: Randomly extract M questions from the question bank as the initial test question set S: ,in For is from A randomly selected index in S223: Data preprocessing and feature extraction: preprocess the collected answers and behavior data, extract keywords, semantic information and emotional tendency features from the answer content; extract posture changes and facial expression features from the behavior data; S224: Real-time analysis and model prediction: The extracted features are input into a pre-trained artificial intelligence model, which can evaluate the personality traits of graduates in real time based on these features. The model predicts the next most suitable question based on the evaluation results and preset rules. S225: Follow-up question adjustment and presentation: Select and adjust follow-up test questions from the question bank based on the model’s predictions; present the adjusted questions and continue to collect graduates’ responses and behavior data; S23: Collect questionnaire answer data, use data encryption technology to ensure the security of the answer data, and upload the questionnaire data to the cloud database for storage in real time; S24: Arrange one-on-one interviews through a video conference system. The interviews are hosted by professional psychological assessors and adopt a semi-structured interview method, focusing on asking graduates about their career experiences, stress coping methods, and career plans; S25: During the interview, the graduate’s responses and the interviewer’s evaluation were recorded. The interview records were converted in real time using speech-to-text technology and stored in a cloud database; S26: Integrate the questionnaire data and interview data, analyze the interview records using natural language processing technology, and extract relevant information and psychological characteristics; S27: Based on the results of the questionnaire survey and the interview, a preliminary psychological characteristics data report is generated, which includes preliminary assessment results of personality traits, career interests, and emotional states in multiple dimensions; S3: Use big data analysis technology to organize and analyze preliminary psychological assessment data and generate a preliminary personality assessment report; S4: Based on the preliminary personality assessment report, design a personalized test plan, including personality test questions and career orientation test questions; S5: execute the personalized test plan in S4 and collect test result data; S6: Use graph neural networks to perform deep learning analysis on the test result data, and generate a detailed career personality analysis report by building a graph model related to career characteristics; The S6 specifically includes: S61: Extract the complete test result data from the cloud database, including the answer, reaction time and emotional fluctuation multi-dimensional information of each test question, as input data for graph neural network analysis; S62: Construct a graph model related to career characteristics, where nodes represent test participants, edges represent similarities or correlations between participants, and node characteristics include information about personal career interests and personality traits from test results; S63: Apply graph neural network to train the graph model. Graph neural network learns the comprehensive feature representation of each node by aggregating the information of nodes on the graph; S64: Using the feature representation learned by the graph neural network, we conduct an in-depth analysis of the career personality of each test participant to identify their potential successful career paths and potential career adaptability issues; The S64 specifically includes: S641: taking the node feature representation obtained by training the graph neural network as input, the node feature representation including the comprehensive features of the career interests and personality traits of each test participant; S642: Classify and predict each node feature representation, and use the soft maximization function to calculate the probability distribution of each test participant on different career paths. The formula of the soft maximization function is expressed as: ,in, Indicates test participants Categorized as a career path The probability of Indicates test participants The characteristic representation of Indicates career path The weight vector of represents the total number of career paths; S643: Based on the probability distribution of career paths, identify the best successful career path for each test participant, and select the career path with the highest probability as the best recommended career path. The formula for the best recommended career path is: ;in, Indicates test participants Recommended career paths is a function that represents all career paths In, find The largest path ; S644: Analyze potential career adaptability issues in the feature representation and identify potential issues that affect career adaptability by calculating the career adaptability score. The formula for the career adaptability score is: ,in, Indicates test participants The career adaptability score represents the weight of the adaptive problem, Indicates test participants In Features The score on Indicates the total number of features; S65: Generate a career personality analysis report based on the results of the graph neural network analysis, which includes an assessment of each participant’s career adaptability, recommended career paths, and potential development personality traits; S7: Match the career personality analysis report with the career database and recommend suitable career directions; S8: Encrypted storage and management of the career personality test data results of college graduates, wherein the encrypted storage and management includes encrypted data, blockchain storage preparation, blockchain writing, access control and authority management, data access and decryption, and data integrity verification.

2. A method for testing the professional personality of college graduates according to claim 1, characterized in that: The S1 specifically includes: S11: Create a user account through the online platform and require college graduates to fill in basic information such as name, gender, age, major, and graduation year. The online platform receives the data entered by the user through a preset form interface to ensure the standardization and consistency of the information; S12: Use advanced encryption standard technology to encrypt basic information input by users to ensure the security and privacy of data during transmission and storage; S13: Confirm the authenticity of the filled-in information through a verification mechanism, including sending a mobile phone verification code or an email verification link. The verification mechanism performs two-way verification with the contact information entered by the user to prevent the submission of false information; S14: After confirming the authenticity of the basic information, collect extended information, including family background, internship experience, hobbies, professional achievements, extracurricular activities, and awards. The extended information is collected through multiple-choice and open input boxes on the online platform; S15: All collected information is stored in a cloud database, which uses a distributed storage architecture to classify and manage information; S16: Use data cleaning techniques to clean and format the collected information. Data cleaning techniques include deduplication, filling missing values ​​and data normalization.

3. A method for testing the professional personality of college graduates according to claim 1, characterized in that: The S3 specifically includes: S31: Import the collected preliminary psychological assessment data into the data warehouse, which adopts a distributed architecture; S32: Use data preprocessing techniques to organize the imported data, including data cleaning, deduplication, missing value filling and data standardization; S33: Apply data mining techniques to cluster the collated data and use the K-means clustering algorithm to divide the data into different psychological characteristic groups to identify the main personality types; S34: Use association rule mining technology to analyze the associations between different psychological characteristic groups. Specifically, use the Apriori algorithm to mine frequent item sets and association rules to reveal the potential relationships between personality traits. S35: Use principal component analysis to reduce the dimension of the data and extract the most representative features for personality assessment, including personality traits, career interests, coping styles, and emotional states, to reduce the data dimension; S36: Use the machine learning model to classify and predict the extracted features, select the support vector machine algorithm for classification model training and prediction, and generate preliminary personality assessment results; S37: Based on the results of cluster analysis, association rule mining and classification prediction, a preliminary personality assessment report is generated. The report includes a description of each personality type and an analysis of the associations between personality traits.

4. A method for testing the professional personality of college graduates according to claim 1, characterized in that: The S4 specifically includes: S41: Determine the personality traits and career orientations that require further testing based on the results of the preliminary personality assessment report, which includes descriptions of each personality type and correlation analysis between personality traits; S42: Select test questions related to the determined personality traits from a predefined question bank, which covers multiple aspects of personality traits, career interests, coping styles, and emotional states; S43: Design personality test questions, which are in the form of multiple-choice questions, scenario simulation questions and behavioral response questions to assess the personality performance of graduates in different situations; S44: Design career orientation test questions, which include career interest questions, career value questions and career ability questions, to assess graduates' preferences and adaptability to different career environments and positions; S45: Generate a test process of the online test platform according to the design of the personalized test plan.

5. A method for testing the professional personality of college graduates according to claim 1, characterized in that: The S5 specifically includes: S51: Upload the personalized test plan to the online test platform, which adopts a modular design and supports flexible combination and display of multiple test question types; S52: Notify college graduates to participate in personalized testing through an online testing platform, including sending test invitations and access links via email or text message; S53: After the graduates log in to the online testing platform, their personalized test plan is automatically loaded to ensure that the test content and sequence of each graduate matches their preliminary personality assessment results; S54: During the online test, the graduate's answering progress and reaction time are monitored in real time, and the answering status and time data of each question are recorded; S55: After the test, all test result data are automatically collected and stored, including the answer to each question, reaction time, and emotional fluctuation multi-dimensional information; S56: Use data encryption technology to encrypt the collected test result data to ensure the security and privacy of the data, and upload it to the cloud database for storage.

6. A method for testing the professional personality of college graduates according to claim 1, characterized in that: The S7 specifically includes: S71: extracting a career personality analysis report from the cloud database, the analysis report including the career adaptability assessment, recommended career path, and potential career adaptability issues of the test participant; S72: Establish a career database, which contains information on various industries and positions, including job responsibilities, required skills, working environment, and career development paths; S73: matching the recommended career path in the career personality analysis report with the job information in the career database, and determining the most matching career direction using a similarity calculation method; S74: Generate a recommended career direction list based on the similarity calculation results; and provide the recommended career direction list to the test participants. The recommended career direction list is displayed through an online platform, and detailed job information and matching reasons are provided; The S8 specifically includes: S81: Encrypted data: using a polynomial-based homomorphic encryption algorithm, the encryption function is expressed as; ； in is plain text data, is the public key, and the encryption function consists of two large prime numbers and composition, is a random number; S82: Blockchain storage preparation: Combine the encrypted ciphertext and data fingerprint into a data block, and add timestamp and data version number metadata to it. Add timestamp T and data version number V, and the metadata of the data block is expressed as: M=(T,V); S83: Blockchain writing: Write the prepared data blocks into the blockchain through the consensus mechanism of the blockchain network; each data block is connected to the previous data block through a hash pointer to form an unalterable chain structure. With its previous data block Connected by hash pointers; =(H( ),Data,MetaData); Where H( ) is the hash value of the previous data block; S84: Access control and authority management: Establish an access control mechanism so that only authorized users, including graduates themselves and specific recruiting units, can obtain access to data; authorities are defined and managed through smart contracts; S85: Data access and decryption: When an authorized user requests access to data, first verify their permissions; if the permissions are passed, read the encrypted data block from the blockchain and decrypt it using the key provided by the data owner. The decryption function is: ； is the key, , p and q are The two prime factors of ; = ; S86: Data integrity verification: Use the previously generated data fingerprint to verify the integrity of the decrypted data to ensure that the data has not been tampered with during storage and transmission.

7. A system for testing the professional personality of college graduates, used to implement a method for testing the professional personality of college graduates as claimed in any one of claims 1 to 6, characterized in that: Includes the following modules: Basic information collection module: used to collect basic information of college graduates, including name, gender, age, major, and graduation year, and store it in the data warehouse; Preliminary psychological assessment module: obtain preliminary psychological characteristics data of graduates through questionnaires and interviews, and transmit the data to the big data analysis module; Psychological feature extraction module: extract key psychological features from questionnaire and interview data; Use natural language processing to analyze text data and extract sentiment and attitude features; Feature selection module: select the most useful features for career personality prediction and use feature selection to determine the most important features; Big data analysis module: Use big data analysis technology to organize and analyze preliminary psychological assessment data, generate preliminary personality assessment reports, and transmit the reports to the personalized test design module; Personalized test design module: design a personalized test plan based on the preliminary personality assessment report, including personality test questions and career orientation test questions, and upload the test plan to the online testing platform; Online testing platform: used to execute personalized testing plans, collect test result data, and transmit the data to the graph neural network analysis module; Graph neural network analysis module: Uses graph neural network to perform deep learning analysis on test result data, generates detailed career personality analysis report by building graph models related to career characteristics, and transmits the report to the career matching module; Data encryption and security module: All collected data is encrypted during transmission and storage using a polynomial homomorphic encryption algorithm for data transmission and data storage encryption; Data verification module: Verify the integrity and accuracy of input data, implement front-end and back-end data verification, and prevent the input of invalid or malicious data; Career matching module: matches the career personality analysis report with the career database, recommends suitable career directions, and generates a list of recommended career directions; Report generation module: Generate a detailed professional personality assessment report based on the analysis results, and use the report generation tool to automatically generate an easy-to-understand report; Feedback collection module: collect user feedback on test results; collect user opinions through online questionnaires or direct feedback mechanisms.

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