Big data-based multi-dimensional assessment intelligent occupational planning platform

Abstract

The invention relates to the field of occupational planning, and particularly discloses a multi-dimensional evaluation intelligent occupational planning platform based on big data, comprising a multi-channel data acquisition module used for integrating a natural language processing technology through an intelligent user interaction interface, realizing intelligent analysis and structured storage of a text input by a user, and obtaining a multi-dimensional evaluation result; external data docking expansion and intelligent data capture innovation are carried out; through the multi-channel data acquisition module, the platform can collect and integrate the personal background, education experience, working experience and other multi-dimensional information of the user, and a complete and accurate user data portrait is constructed. Therefore, a solid foundation is provided for subsequent evaluation and planning. Through the evaluation model constructed by using advanced technologies such as machine learning and deep learning, the platform can accurately evaluate the occupational development potential of the user and predict the future occupational development trend of the user.

Description

Technical Field

[0001] The present invention belongs to the field of career planning, and specifically relates to a multi-dimensional evaluation intelligent career planning platform based on big data. Background Art

[0002] Career planning refers to the process in which individuals and organizations combine to determine the best career goal based on the measurement, analysis and summary of the subjective and objective conditions of their careers, and make effective arrangements to achieve this goal.

[0003] The evaluation models used by some current career planning platforms are rudimentary, and they only conduct analysis based on a limited number of dimensions, such as simply considering users' interests, hobbies, professional skills, and other factors. This one-sided model cannot conduct a comprehensive evaluation of users' career development conditions, and it is very easy to miss key influencing factors such as psychological state and family background, which in turn leads to deviations in the evaluation results. Moreover, these platforms generally lack personalized analysis capabilities, use unified and rigid evaluation standards and models, and generalize all users, and are unable to tailor career planning suggestions for each user that fit their actual situation. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a multi-dimensional evaluation intelligent career planning platform based on big data to solve the problem that the platforms in the prior art generally lack personalized analysis capabilities, adopt unified and rigid evaluation standards and models, generalize all users, and are unable to tailor career planning suggestions for each user that fits their actual situation.

[0005] A multi-dimensional evaluation intelligent career planning platform based on big data, including:

[0006] Multi-channel data collection module: used to integrate natural language processing technology through an intelligent user interaction interface to achieve intelligent analysis and structured storage of user input text, as well as to collect and integrate users' personal background, education experience, work experience, professional interests, value orientation, social media information and market research data through external data docking expansion and intelligent data capture innovation;

[0007] Data cleaning and integration module: Use data cleaning algorithms to perform deep deduplication, denoising, and formatting of raw data, and use data fusion technology to organically integrate data from different channels to build a complete user data portrait;

[0008] Multi-dimensional evaluation model construction module: including the design of an evaluation indicator system with comprehensive improvement and dynamic enhancement, and an evaluation model constructed by using machine learning model integration innovation, deep learning model application expansion and model optimization algorithm innovation, which is used to accurately evaluate the user's career development potential;

[0009] Intelligent recommendation and planning module: Through collaborative filtering recommendation optimization, content recommendation innovation and hybrid recommendation algorithm integration, we design recommendation algorithms, tailor personalized career planning plans based on user evaluation results, establish a dynamic planning adjustment mechanism, regularly track user career development status and external environment changes, update and optimize career planning plans; at the same time, we provide interactive planning tools and user feedback collection and processing optimization functions to enhance user interaction experience;

[0010] System architecture and security module: The system architecture is designed using distributed architecture optimization and microservice architecture innovation to improve system scalability and reliability; through data encryption transmission enhancement, data storage encryption innovation, access control and permission management optimization, and data backup and recovery improvements, user data security and privacy protection are fully guaranteed.

[0011] Preferably, the multi-channel data collection module further comprises:

[0012] User interaction interface design integrates natural language processing technology to achieve intelligent analysis and structured storage of user input text;

[0013] External data interface, connecting with multiple external data sources such as universities, enterprises, and online education platforms to automatically collect user-related data;

[0014] Intelligent data capture function uses web crawler technology to accurately capture information closely related to the user's career planning from the Internet.

[0015] Preferably, the data cleaning and integration module further includes:

[0016] Data cleaning algorithm, used to perform deep deduplication, denoising, and formatting of raw data;

[0017] Data fusion technology organically integrates data from different channels, builds a complete user data portrait, and generates a multi-dimensional user feature vector.

[0018] Preferably, the multi-dimensional evaluation model building module further includes:

[0019] The evaluation index system covers basic dimensions such as personal factors, educational background and experience, social factors, family factors, professional relationships, professional market and competition, and refines and expands the indicators under each dimension;

[0020] The evaluation model integrates a variety of advanced machine learning algorithms and deep learning models, and uses model optimization algorithms such as parameter tuning and feature selection to improve the accuracy and robustness of the evaluation results.

[0021] Preferably, the intelligent recommendation and planning module further includes:

[0022] Recommendation algorithm, combining collaborative filtering recommendation, content recommendation and hybrid recommendation algorithms, to provide users with accurate and diverse recommendation results;

[0023] Career planning program formulation: tailor-made personalized career planning programs based on user evaluation results, and establish a dynamic planning adjustment mechanism;

[0024] User interaction tools provide an interactive user interface that allows users to independently adjust their career planning plans and provide professional feedback and suggestions in real time;

[0025] Process user feedback, establish a user feedback module, and continuously optimize the recommendation algorithm and planning scheme generation logic.

[0026] Preferably, the system architecture and security module further includes:

[0027] Distributed architecture distributes data storage, computing, and application services across multiple server nodes to improve system scalability and reliability;

[0028] Microservice architecture divides system functional modules into multiple independent microservices to improve the flexibility of system development and maintenance;

[0029] Data security protection, using advanced encryption protocols and encryption algorithms, establishing strict access control and authority management mechanisms to ensure user data security and privacy protection;

[0030] Data backup and recovery adopts a strategy that combines incremental backup and full backup to ensure data integrity and consistency.

[0031] Preferably, the accuracy of the multi-dimensional evaluation model is evaluated, and the accuracy evaluation formula of the multi-dimensional evaluation model is as follows:

[0032]

[0033] Among them, A 模型 is the accuracy evaluation of the multi-dimensional evaluation model; λ is the coefficient of adjusting the influence of the error term; K is the number of evaluation dimensions; y k is the actual evaluation value of the kth dimension; is the predicted evaluation value of the kth dimension; σ k is the standard deviation of the evaluation value of the kth dimension; α and β are the parameters of the Sigmoid function, which are used to map the evaluation value to the (0,1) interval.

[0034] Range explanation: A 模型 The value range of is (0,1), and the closer the value is to 1, the higher the accuracy of the model.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Through the multi-channel data collection module, the platform can collect and integrate multi-dimensional information such as the user's personal background, education experience, work experience, etc., to build a complete and accurate user data portrait. This provides a solid foundation for subsequent evaluation and planning;

[0037] By using the evaluation model built by advanced technologies such as machine learning and deep learning, the platform can accurately evaluate the user's career development potential and predict their future career development trends. This provides users with valuable reference information to help them make more informed career choices;

[0038] Based on the user's evaluation results, the platform can tailor a personalized career planning plan and establish a dynamic adjustment mechanism. This means that users can obtain career planning suggestions that meet their own characteristics and needs, and they can be continuously optimized and adjusted as their personal circumstances and external environment change. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1 As shown:

[0042] Embodiment 1: The present invention provides a multi-dimensional evaluation intelligent career planning platform based on big data, including:

[0043] Multi-channel data collection module: used to integrate natural language processing technology through an intelligent user interaction interface to achieve intelligent analysis and structured storage of user input text, as well as to collect and integrate users' personal background, education experience, work experience, professional interests, value orientation, social media information and market research data through external data docking expansion and intelligent data capture innovation;

[0044] Among them, the multi-channel data collection technology solution:

[0045] (1) Carefully design an intelligent user interaction interface and integrate natural language processing technology to achieve intelligent analysis and structured storage of user input text. For example, when a user enters "I actively participated in many club activities during college, such as various activities organized by the Student Union and charity activities organized by the Volunteer Association", the system can automatically identify and accurately extract key information such as "club activities", "student union", and "volunteer association" with advanced algorithms, and store it in an orderly and structured manner as user activity experience data in school, laying a solid foundation for subsequent in-depth analysis. At the same time, through intelligent guidance, the user's educational background is collected in detail, covering information such as the major studied, academic level, academic achievements, etc., to accurately grasp their academic foundation; the user's work experience is comprehensively sorted out, including internship experience, part-time work, full-time work, etc., and professional experience and job responsibilities are carefully recorded; professional evaluation questionnaires and interactive questions are used to deeply understand the user's career interests, and with the help of career interest assessments, their preferences for different career fields are accurately understood; a value scale is used to evaluate the user's value orientation, and to clarify the factors they value in their work, such as salary, work environment, development space, etc.; the user's public career-related information on social media is cleverly captured, and their career orientation and interests are deeply analyzed; active connection with market research institutions, real-time collection of current employment market job requirements, salary levels, industry development trends and other information, to provide accurate reference for career planning; accurate records of the training courses the user has participated in, the professional certifications obtained, etc., to truthfully reflect their skills Improvement; The system collects the user's personal portfolio, covering project experience, work cases, etc., to fully demonstrate their actual abilities and achievements; Through convenient selection and filling, the user's geographical location preferences are understood, and their preferred choice of work location, including cities, regions, etc.; Lifestyle questionnaires are used to fully understand the user's lifestyle and interests, and scientifically evaluate their compatibility with career choices; With the help of professional personality assessment tools, such as the MBTI career personality test (which divides personality into 16 types to help identify suitable career directions) and the Big Five personality test (which accurately assesses the five dimensions of extroversion, agreeableness, conscientiousness, emotional stability, and openness), the user's personality traits are deeply analyzed to clearly grasp their adaptability in different professional environments; The mental health scale is used to pay close attention to the user's emotions and stress management capabilities to ensure the sustainability of career choices, while assessing the user's mental health level and identifying potential psychological problems in a timely manner.

[0046] (2) External data connection and expansion: Pioneeringly building a stable data interface with multiple external data sources such as universities, enterprises, and online education platforms to automatically collect data such as user academic performance, course selection details, club activity records, internship evaluations, and professional certificate information. In addition, it further connects with professional career assessment institutions to obtain users' MBTI career personality test results, Big Five personality test results, career anchor test results, etc., to gain in-depth insights into users' personality traits and career value orientations; at the same time, it reaches cooperation with social media platforms to legally and compliantly analyze users' public career-related information on social media to obtain their career tendencies and interests; it establishes contacts with market research institutions to collect information such as job requirements, salary levels, and industry development trends in the current job market to provide accurate references for career planning.

[0047] (3) Innovation in intelligent data capture: By cleverly using web crawler technology, we can accurately capture information closely related to the user's career planning from the vast amount of Internet information, covering key content such as industry dynamics, career development trends, and corporate recruitment information. By reasonably setting keywords and rigorous crawling rules, we regularly update the career information database on the platform to provide users with cutting-edge and reference-worthy information for their career choices. For example, by setting keywords such as "latest development trends in the artificial intelligence industry" and "hot career needs in the big data field", we can crawl the latest and most valuable information from authoritative websites and professional forums, allowing users to keep abreast of the industry's pulse. In addition, we can also capture information related to various types of professional skills training and certification to help users understand their own skill improvement paths.

[0048] Data cleaning and integration module: Use data cleaning algorithms to perform deep deduplication, denoising, and formatting of raw data, and use data fusion technology to organically integrate data from different channels to build a complete user data portrait;

[0049] Among them, data cleaning and integration technical solutions:

[0050] (1) Data cleaning algorithm optimization: Use cutting-edge data cleaning algorithms to perform deep deduplication, denoising, formatting and other sophisticated processing on the collected raw data. For example, use rule-based cleaning algorithms to remove duplicate user information records, and use outlier detection and repair technology in machine learning algorithms to fill in missing data items, thereby comprehensively improving data accuracy and consistency and ensuring reliable data quality.

[0051] (2) Data fusion technology innovation: Flexibly use data fusion technology to organically integrate data from different channels and carefully construct a complete user data portrait. With the help of association rule mining technology, deeply analyze the intrinsic correlation between different data, such as closely linking the user's academic performance with the performance of community activities to comprehensively evaluate their comprehensive quality; combining the results of career interest assessment with market demand data to explore suitable career directions; integrating personality trait analysis with work experience, professional values, etc. to judge the user's adaptability in different professional environments. At the same time, make full use of the results of data fusion to generate multi-dimensional user feature vectors, providing a solid foundation for subsequent evaluation analysis and intelligent recommendation. For example, the multi-dimensional data such as the user's academic performance, community activity participation, internship performance, career interest assessment score, personality trait assessment results, etc. are integrated to generate a comprehensive ability score as one of the core indicators for evaluating the user's career development potential.

[0052] Multi-dimensional evaluation model construction module: including the design of an evaluation indicator system with comprehensive improvement and dynamic enhancement, and an evaluation model constructed by using machine learning model integration innovation, deep learning model application expansion and model optimization algorithm innovation, which is used to accurately evaluate the user's career development potential;

[0053] Among them, the technical solution for building a multi-dimensional evaluation model:

[0054] Improvements in the design of the evaluation index system:

[0055] (1) Comprehensive improvement: In the newly constructed evaluation index system, not only basic dimensions such as personal factors, educational background and experience, social factors, family factors, professional relationships, and professional market and competition are widely covered, but also indicators under each dimension are further refined and expanded. For example, in the personal factor dimension, the "career adaptability" indicator is innovatively added to provide accurate evaluation by deeply analyzing the adaptability and stability of users in different professional environments; in the social factor dimension, the "career culture fit" indicator is introduced to scientifically measure the degree of match between users and the target professional culture, providing a more practical reference for career choice. At the same time, the educational background is subdivided into the industry adaptability of the major studied, the competitiveness of the academic level, and the advantage field of academic performance; work experience is classified and evaluated according to internship, part-time, and full-time, considering the accumulation effect of different stages on career development; career interests are further analyzed into long-term stable interests and short-term exploratory interests to more accurately guide career directions; value orientations are closely linked to career choices, and the degree of realization of different value orientations in various career fields is analyzed; for the career orientations mined from social network data, credibility weights are set and integrated into the overall evaluation system.

[0056] (2) Enhanced dynamism: The evaluation index system is designed to be a dynamic and flexible structure that can be updated and optimized in real time according to the changing external environment and the real-time changes in the user's own situation. For example, when a major change occurs in an industry, such as a disruptive breakthrough in new technology or a major adjustment in policies and regulations, the system can respond quickly and adjust the career market and competition indicators related to the industry in a timely manner, re-evaluate the user's career development potential in the industry, and ensure that the planning keeps pace with the times. At the same time, it continuously tracks the user's changes in career interests, skill improvement, work experience accumulation, etc., and dynamically updates indicators related to personal factors; based on the fluctuations in market demand data, it optimizes the career recommendation direction and strategy in real time.

[0057] Improvements in building the evaluation model:

[0058] (1) Innovation in machine learning model integration: Ingeniously integrate multiple advanced machine learning algorithms (such as decision trees, random forests, support vector machines, etc.) to build a comprehensive evaluation model. For example, use the decision tree model to deeply analyze the user's career choice preferences, use the random forest model to accurately evaluate the user's competitiveness in different professional positions, and rely on the support vector machine model to scientifically predict the user's career development stability. Through model integration, the unique advantages of each model are fully utilized, greatly improving the accuracy and robustness of the evaluation results, so that the evaluation results can stand the test of practice.

[0059] (2) Expansion of deep learning model applications: Make full use of neural network models (such as convolutional neural networks, recurrent neural networks, etc.) to conduct deep learning and mining of users’ complex data features. For example, the recurrent neural network model can be used to carefully analyze the user’s career development path and accurately predict their future career development trends; the convolutional neural network model can be used to deeply analyze the user’s career interests and skills, explore their potential career advantages and disadvantages, and provide strong support for personalized career planning.

[0060] (3) Innovation in model optimization algorithms: We use cutting-edge model optimization algorithms such as parameter tuning and feature selection to further improve the performance and efficiency of the evaluation model. For example, we use the grid search method to fine-tune the model parameters and find the optimal parameter combination, which significantly improves the prediction accuracy of the model. We use the feature selection algorithm to select the features that have the greatest impact on career evaluation, effectively reduce the data dimension, and greatly improve the running speed of the model, making the evaluation process both accurate and efficient.

[0061] Intelligent recommendation and planning module: Through collaborative filtering recommendation optimization, content recommendation innovation and hybrid recommendation algorithm integration, we design recommendation algorithms, tailor personalized career planning plans based on user evaluation results, establish a dynamic planning adjustment mechanism, regularly track user career development status and external environment changes, update and optimize career planning plans; at the same time, we provide interactive planning tools and user feedback collection and processing optimization functions to enhance user interaction experience;

[0062] Among them, intelligent recommendation and planning technology solutions

[0063] Recommended algorithm design improvements:

[0064] (1) Collaborative filtering recommendation optimization: Based on the traditional collaborative filtering recommendation algorithm, the time series information and context information of user behavior are innovatively introduced to greatly improve the accuracy and real-time performance of recommendations. For example, the user's career interests in different time periods and career needs in different scenarios are fully considered to accurately recommend career directions and positions that better meet their current actual needs, making the recommendation results more considerate and practical. The recommended content is dynamically adjusted based on the training courses that the user has recently participated in, the new certifications obtained, and the career topics that the user has followed on social media.

[0065] (2) Innovation in content recommendation: Closely integrating the user's career planning needs and goals, the content recommendation algorithm is cleverly used to recommend highly relevant learning resources, vocational training courses, and cutting-edge career development information to users. For example, based on the skills that the user needs to focus on improving and the established career development direction, the corresponding online courses and learning materials are intelligently recommended, and dynamically adjusted according to the user's learning progress and feedback to ensure that learning resources always adapt to user needs. At the same time, based on the user's personality traits, lifestyle, and interests and hobbies, matching career environments and corporate culture-related information are recommended to enhance career adaptability.

[0066] (3) Hybrid recommendation algorithm fusion: Collaborative filtering recommendation and content recommendation are organically combined to form a powerful hybrid recommendation algorithm. Through this fusion method, the user's behavioral characteristics and content characteristics can be comprehensively considered to provide more accurate and diverse recommendation results. For example, for a user who has a strong interest in data analysis and has a certain programming foundation, the system can recommend both high-quality internship positions related to data analysis and courses to improve data analysis skills, fully meeting the user's career development needs. It can also recommend career opportunities and resources in the local or intended area based on the user's geographical location preferences.

[0067] Improvements in career planning generation:

[0068] (1) Optimization of personalized career planning: Based on the multi-dimensional evaluation results of the user, closely combined with their career goals and expectations, a personalized career planning plan is tailored. The plan covers a wide range of content, including career positioning, career development path, skill improvement plan, internship and job search strategies. For example, for a user who demonstrates strong innovation ability and scientific research potential, we carefully plan his career path to engage in scientific research, provide detailed suggestions for participating in relevant scientific research projects and academic exchange opportunities, and help the user show his talents in the field of scientific research. Based on the user's educational background, we recommend matching scientific research institutions, university positions or corporate R&D directions; based on work experience, we plan a growth path from basic research to project leader; based on market demand, we guide him to improve his skills in a targeted manner, such as mastering the latest scientific research tools and publishing high-level papers.

[0069] (2) Dynamic planning adjustment innovation: Establish a sensitive dynamic planning adjustment mechanism, regularly track the user's career development status and changes in the external environment, and timely update and optimize the career planning plan. For example, when a user performs well during an internship and obtains a new career opportunity, the system can quickly re-evaluate and flexibly adjust his or her career plan based on his or her latest professional capabilities and market trends, open up new career development directions for him or her, and provide practical suggestions. If there are emerging technology trends in the user's industry, relevant training courses will be recommended in a timely manner to help him or her keep up with the times; if market demand changes, the career advancement path will be replanned to explore potential job transfer opportunities.

[0070] User interaction and feedback improvements:

[0071] (1) Innovation of interactive planning tools: We have made every effort to develop an interactive user interface, which allows users to flexibly adjust the key elements of their career planning plans, such as career goals and skill improvement plans, through convenient drag and drop, click and other operations. At the same time, the system provides professional feedback and practical suggestions in real time to help users thoroughly understand the advantages and disadvantages of different planning options. For example, when a user adjusts his or her career goal, the system will immediately provide a feasibility analysis report and targeted suggestions for the career goal based on his or her current ability level and market situation, so that every decision made by the user is more informed.

[0072] (2) User feedback collection and processing optimization: A special user feedback module is set up, where users can objectively evaluate and provide feedback on career planning plans and recommendation results. Based on user feedback, the platform continuously optimizes the recommendation algorithm and the generation logic of planning plans, steadily improving service quality and user satisfaction. For example, when a user reports that a recommended career direction is very different from his or her expectations, the system will quickly and in-depth analyze the reasons for the feedback, accurately adjust the relevant parameters and rules in the recommendation algorithm, and provide users with recommendation results that are more in line with their expectations, allowing users to feel the platform's dedication and professionalism.

[0073] System architecture and security module: Distributed architecture optimization and microservice architecture are used to innovate the system architecture to improve system scalability and reliability; through data encryption transmission enhancement, data storage encryption innovation, access control and permission management optimization, and data backup and recovery improvements, user data security and privacy protection are fully guaranteed;

[0074] Among them, system architecture and security technology solutions

[0075] Improvements in system architecture design:

[0076] (1) Distributed architecture optimization: We decisively adopt a distributed system architecture to reasonably distribute data storage, computing, and application services on multiple server nodes, greatly improving the scalability and reliability of the system. For example, we use distributed databases to properly store massive amounts of user data and rich professional information, and use distributed computing frameworks to efficiently perform large-scale data processing and analysis, ensuring that the system operates stably under the harsh conditions of high concurrency and large amounts of data, and providing users with uninterrupted high-quality services.

[0077] (2) Microservice architecture innovation: The functional modules of the system are finely divided into multiple independent microservices, each of which is responsible for a specific function, such as data collection service, evaluation and analysis service, intelligent recommendation service, etc. The microservice architecture makes the development, deployment and maintenance of the system more flexible and efficient, and facilitates rapid iteration and upgrading. For example, when the evaluation and analysis service needs to be optimized and improved, it can be modified and tested independently without affecting the normal operation of other services, which greatly improves the maintainability and adaptability of the system.

[0078] Improvements in data security and privacy protection:

[0079] (1) Enhanced data encryption transmission: Adopt higher-level encryption protocols, such as TLS1.3, to protect user data during transmission, prevent data from being maliciously intercepted and tampered with during transmission, and ensure the security and confidentiality of data transmission.

[0080] (2) Data storage encryption innovation: Encrypt sensitive data stored in the database, such as user personal information and carefully customized career planning plans. Using more secure and reliable encryption algorithms, such as AES-256, only authorized users and systems can decrypt and access data, fully protecting the privacy and security of user data.

[0081] (3) Optimization of access control and authority management: Establish a more rigorous access control mechanism and implement sophisticated authority management for different users and system roles. For example, ordinary users can only view and modify their own relevant data within the scope of authorization, while administrators have higher-level management authority. Each of them performs their duties, eliminates the risk of unauthorized operations, and ensures the security and orderliness of system data.

[0082] (3) Improvement of data backup and recovery: Adopt more efficient data backup strategies, such as a scientific method that combines incremental backup and full backup, and regularly back up system data. When encountering emergencies such as data loss or system failure, data can be quickly restored to effectively ensure the normal operation of the system. For example, incremental backups are performed daily and full backups are performed weekly to ensure data integrity and consistency, so that user data is safe.

[0083] From the above, we can see that this platform integrates multiple modules such as multi-channel data collection, data cleaning and integration, multi-dimensional evaluation model construction, intelligent recommendation and planning, and system architecture and security; through the intelligent user interaction interface and natural language processing technology, the platform can collect and integrate multi-dimensional information such as users' personal background, educational experience, work experience, etc., and build a complete user data portrait; using the evaluation model constructed by advanced technologies such as machine learning and deep learning, the platform can accurately evaluate the user's career development potential; at the same time, based on the user's evaluation results, the platform can tailor personalized career planning plans, and establish a dynamic adjustment mechanism to regularly track and optimize planning plans; in addition, the platform also adopts a distributed architecture and microservice design to ensure the high scalability and reliability of the system, and through multiple encryption and access control methods, it fully protects the user's data security and privacy.

[0084] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that the multi-channel data collection module further includes:

[0085] User interaction interface design integrates natural language processing technology to achieve intelligent analysis and structured storage of user input text;

[0086] External data interface, connecting with multiple external data sources such as universities, enterprises, and online education platforms to automatically collect user-related data;

[0087] Intelligent data capture function uses web crawler technology to accurately capture information closely related to the user's career planning from the Internet.

[0088] Specifically, the data cleaning and integration module further includes:

[0089] Data cleaning algorithm, used to perform deep deduplication, denoising, and formatting of raw data;

[0090] Data fusion technology organically integrates data from different channels, builds a complete user data portrait, and generates a multi-dimensional user feature vector.

[0091] Specifically, the multi-dimensional evaluation model building module further includes:

[0092] The evaluation index system covers basic dimensions such as personal factors, educational background and experience, social factors, family factors, professional relationships, professional market and competition, and refines and expands the indicators under each dimension;

[0093] The evaluation model integrates a variety of advanced machine learning algorithms and deep learning models, and uses model optimization algorithms such as parameter tuning and feature selection to improve the accuracy and robustness of the evaluation results.

[0094] Specifically, the intelligent recommendation and planning module further includes:

[0095] Recommendation algorithm, combining collaborative filtering recommendation, content recommendation and hybrid recommendation algorithms, to provide users with accurate and diverse recommendation results;

[0096] Career planning program formulation: tailor-made personalized career planning programs based on user evaluation results, and establish a dynamic planning adjustment mechanism;

[0097] User interaction tools provide an interactive user interface that allows users to independently adjust their career planning plans and provide professional feedback and suggestions in real time;

[0098] Process user feedback, establish a user feedback module, and continuously optimize the recommendation algorithm and planning scheme generation logic.

[0099] Specifically, the system architecture and security module further includes:

[0100] Distributed architecture distributes data storage, computing, and application services across multiple server nodes to improve system scalability and reliability;

[0101] Microservice architecture divides system functional modules into multiple independent microservices to improve the flexibility of system development and maintenance;

[0102] Data security protection, using advanced encryption protocols and encryption algorithms, establishing strict access control and authority management mechanisms to ensure user data security and privacy protection;

[0103] Data backup and recovery adopts a strategy that combines incremental backup and full backup to ensure data integrity and consistency.

[0104] As can be seen from the above, this embodiment refines and enhances modules such as multi-channel data collection, data cleaning and integration, multi-dimensional evaluation model construction, intelligent recommendation and planning, and system architecture and security; in terms of data collection, it not only optimizes the user interaction interface design, but also adds external data interfaces and intelligent data capture functions to more comprehensively obtain user career planning related information; the data cleaning and integration module uses advanced data cleaning algorithms and data fusion technologies to build a more accurate user data portrait; the multi-dimensional evaluation model construction module covers a wider range of evaluation dimensions and integrates a variety of machine learning algorithms and deep learning models to improve the accuracy and robustness of the evaluation results; the intelligent recommendation and planning module provides users with more personalized career planning solutions by optimizing recommendation algorithms and providing interactive user interfaces; at the same time, the system architecture and security modules have also been upgraded, adopting distributed architecture and microservice architecture, and strengthening data security protection measures to ensure the security and privacy of user data.

[0105] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that it further includes evaluating the accuracy of the multi-dimensional evaluation model. The accuracy evaluation formula of the multi-dimensional evaluation model is as follows:

[0106]

[0107] Among them, A 模型 is the accuracy evaluation of the multi-dimensional evaluation model; λ is the coefficient of adjusting the influence of the error term; K is the number of evaluation dimensions; y k is the actual evaluation value of the kth dimension; is the predicted evaluation value of the kth dimension; σ k is the standard deviation of the evaluation value of the kth dimension; α and β are the parameters of the Sigmoid function, which are used to map the evaluation value to the (0,1) interval.

[0108] Range explanation: A 模型 The value range of is (0,1), and the closer the value is to 1, the higher the accuracy of the model;

[0109] The specific application process of the above formula is as follows:

[0110] 1. Determine the evaluation dimensions and collect data

[0111] Clarify the evaluation dimensions:

[0112] According to actual needs, determine the number of dimensions K that need to be evaluated. These dimensions can include personal factors, educational background, work experience, professional interests, value orientations, and other aspects.

[0113] Data Collection:

[0114] For each evaluation dimension, collect the actual evaluation value y k These values ​​can be obtained through questionnaire surveys, user interviews, data analysis, etc.

[0115] At the same time, collect the model's prediction evaluation value These values ​​are calculated by a multidimensional assessment model based on the input data.

[0116] Calculate the standard deviation:

[0117] For each evaluation dimension, calculate the standard deviation σ of the actual evaluation value k The standard deviation reflects the dispersion of the data and helps to assess the prediction error of the model.

[0118] 2. Set Sigmoid function parameters

[0119] Determine the slope parameter α and the intercept parameter β:

[0120] According to the evaluation requirements and data characteristics, select the appropriate slope parameter α and intercept parameter β. These parameters determine the shape and position of the Sigmoid function, thus affecting the mapping range of the evaluation value.

[0121] 3. Calculate the adjustment coefficient λ

[0122] Determine the adjustment coefficient λ:

[0123] The adjustment coefficient λ is used to balance the influence of the error term. A suitable λ value can be determined through experiments or experience to make the evaluation results more accurate and reliable.

[0124] 4. Apply the formula for evaluation

[0125] Calculate the denominator:

[0126] According to the denominator in the formula, calculate This reflects the comprehensive performance of each evaluation dimension under the mapping of the Sigmoid function.

[0127] Calculate the numerator part:

[0128] calculate This reflects the relative difference between the model prediction error and the actual evaluation value.

[0129] Then, multiply the above result by the adjustment coefficient λ and take the exponential function Get the molecular part.

[0130] Calculate the final evaluation value:

[0131] Divide the numerator by the denominator to get the final multi-dimensional evaluation model accuracy assessment value A 模型 .

[0132] V. Interpretation and Application of Evaluation Results

[0133] Explain the evaluation value:

[0134] According to A 模型 The value range of is (0,1), explaining the evaluation results. The closer the value is to 1, the higher the accuracy of the model; the closer the value is to 0, the lower the accuracy of the model.

[0135] As can be seen from the above, the formula disclosed in this embodiment involves the coefficient of adjusting the influence of the error term, the number of evaluation dimensions, the actual and predicted evaluation values ​​of each dimension and their standard deviations, and the mapping parameters of the Sigmoid function; this formula limits the evaluation value to the interval (0,1), and obtains the accuracy evaluation value of the model by calculation; the closer the value is to 1, the higher the accuracy of the model; by introducing this evaluation link, the model performance can be comprehensively measured, providing a scientific basis for model optimization, thereby further improving the accuracy and practicality of the model.

[0136] Embodiment 4:

[0137] The specific application process of the present invention is:

[0138] 1. Data collection phase:

[0139] (1) User input: Design a user-friendly interactive interface, built with front-end technologies such as HTML5, CSS3, and JavaScript to ensure compatibility with multiple terminals such as computers, tablets, and mobile phones. After the user opens the platform, he or she will follow the eye-catching guidance prompts to enter personal information step by step. For example, when entering educational background in the text box, a drop-down menu will automatically pop up to assist in selecting the educational level. After entering the major, the common course system of the major will be displayed to facilitate the user to check the academic performance input; when describing work experience, a timeline is used to guide the user to fill in internship, part-time, and full-time experience. Clicking on each stage can expand the detailed description of job responsibilities and achievements. Using the part-of-speech tagging and named entity recognition algorithms in natural language processing technology (NLP), the user input text is parsed and stored in a structured manner in the corresponding table of the MySQL relational database. For example, identify "served as a marketing assistant in XX company in 2023, responsible for competitive product research and promotion plan planning" and extract key information for storage.

[0140] (2) External data connection: On the server side, a data connection module is built based on the Python Django framework. When connecting with the university's academic affairs system, a secure HTTPS protocol is used to interact with the API interface provided by the university. The university's data format specifications are followed, and data conversion scripts are used to accurately extract and map information such as grades and courses. Connect to the company's HR system to obtain internship evaluations and professional certificate information, and use OAuth2.0 authorization and authentication to ensure the legitimacy of the data. Cooperate with online education platforms to exchange data in XML or JSON format based on the data sharing agreement agreed by both parties, and update user training and certification records in real time.

[0141] (3) Intelligent data crawling: Deploy the Scrapy crawler framework and configure multi-threaded crawling tasks. For industry dynamics, set keyword combinations such as "industry name + news / trend / policy", such as "new energy vehicle industry news", crawl information from authoritative information websites and industry association official websites, and store it in the MongoDB non-relational database to facilitate flexible storage of semi-structured data; for career development trends, focus on hot topics on professional forums, LinkedIn and other workplace social platforms, analyze post popularity and keyword frequency, and extract key insights; for corporate recruitment information crawling, target mainstream recruitment websites, accurately collect information based on job classification, salary range and other screening conditions, and update it daily to ensure data timeliness.

[0142] 2. Data cleaning and integration stage:

[0143] (1) Cleaning algorithm execution: On the data processing server, run the data cleaning program based on the Python Pandas library and the Scikit-l earn machine learning library. Use the drop_duplicates function of Pandas to remove duplicate records based on user ID and key information fields; by setting a reasonable threshold, use the isolation forest algorithm to detect outliers, such as data with salaries that are too high and deviate from the industry average, and combine linear regression to fill in missing data items such as grades and working hours to improve data accuracy.

[0144] (2) Data fusion process: With the help of Apache Spark’s data fusion tool, data from different data sources are loaded into distributed memory. By associating user IDs, the April algorithm is used to mine the association rules between academic performance and club activity performance. For example, it is found that users with excellent academic performance and rich experience in club leadership have strong comprehensive abilities. The association results are converted into feature vectors and stored in the Hi ve data warehouse in Parquet format to provide a unified data source for subsequent evaluation and analysis.

[0145] 3. Multi-dimensional evaluation model construction stage:

[0146] (1) Refinement of the indicator system: The indicator system framework is constructed based on R language, and the education background indicator is further decomposed. For example, the industry adaptability of the major is determined by cross-analysis of the employment popularity data of the major and the industry, and the competitiveness is measured by the average salary and promotion cycle data of the corresponding positions of the academic level. The academic performance advantage area is evaluated by the percentage of grade ranking. For career interests, combined with Holland's career interest theory, a questionnaire survey is used to quantify the scores of long-term stable interests and short-term exploratory interests, and the weights are dynamically adjusted to integrate into the evaluation.

[0147] (2) Model fusion training: In a GPU cluster environment, the TensorFlow deep learning framework is used to build a model fusion architecture. The decision tree, random forest, and support vector machine models are combined in a stacking integration manner. The decision tree is first used to preliminarily classify the user's career choice preference categories. The random forest is used to refine the job competitiveness assessment on this basis. The support vector machine predicts the stability of career development. The model is trained with a large amount of historical user data, and the hyperparameters are optimized to improve the prediction accuracy. At the same time, the convolutional neural network (CNN) is used to extract features from the user's portfolio images and text descriptions to analyze skill advantages; the recurrent neural network (RNN) is used to learn the user's career development trajectory based on time series and predict future trends.

[0148] 4. Intelligent recommendation and planning stage:

[0149] (1) Recommendation algorithm operation: On the recommendation system server, the improved collaborative filtering algorithm is combined with the real-time stream processing framework Flink to track user behavior logs in real time, such as browsing career information and learning course records. Recent behaviors are given higher weights based on the time decay function, and combined with the content recommendation algorithm. Content recommendation is based on user portraits and knowledge graph technology to build a professional skills-course-information knowledge graph. For example, if a user has a demand for data analysis skills, relevant Python programming, data mining courses and industry reports are recommended. When making mixed recommendations, the scores of the two algorithms are weighted and integrated to accurately push internship positions, training courses, etc., and A / B testing is used to continuously optimize the recommendation effect.

[0150] (2) Planning scheme generation: Based on the evaluation results, a personalized plan is generated based on the rule engine. For example, for users with scientific research potential, combined with the talent needs of scientific research institutions and the project resources of university mentors, a promotion path is planned starting from a scientific research assistant, participating in cutting-edge projects, and gradually publishing high-level papers; using dynamic programming algorithms, regularly scan the user's career development status and market changes, such as the emergence of new positions in the industry's emerging technologies, timely adjust the user's career direction, and recommend skill transformation courses.

[0151] 5. User interaction and feedback stage:

[0152] (1) Interactive interface design: The React Native cross-platform framework is used to develop the mobile interactive interface to ensure a smooth interactive experience. Users can adjust the direction by touching and dragging the career goal module, and click on the skill improvement plan to automatically pop up learning resource recommendations. The system calls the backend evaluation model in real time, and based on current user data and market trends, displays feasibility analysis and suggestions in visual charts and concise text, such as a bar chart comparing the target job salary with the expected gap, and a line chart showing the difficulty trend of the skill improvement path.

[0153] (2) Feedback processing loop: Build a Fl ask microservice on the back end to process user feedback and store the feedback in categories. If the recommended job does not meet expectations, analyze the keywords in the feedback text and backtrack the recommendation algorithm parameters, such as adjusting the interest similarity weight and filtering out jobs that do not match the region; based on user improvement suggestions, trigger model retraining, plan the program revision process, and regularly visit user satisfaction to form a closed-loop optimization.

[0154] (ii) Equipment, device or component

[0155] 1. Server architecture:

[0156] The overall hybrid cloud architecture is adopted, combining the elastic resources of public clouds with the data security advantages of private clouds. The core data storage uses the highly reliable distributed file system Ceph, which is redundantly stored across multiple physical nodes to ensure that data is not lost; the computing nodes are equipped with high-performance CPUs and GPUs, configured in cluster mode, and use Kubernetes container orchestration technology to achieve dynamic resource scheduling, such as automatic capacity expansion during large data volume cleaning and model training. The network uses software-defined network (SDN) technology to ensure high-speed and stable data transmission, optimize internal traffic trends, and reduce latency.

[0157] 2. Data acquisition equipment (virtual):

[0158] Smart interactive terminal: simulates multiple terminal forms, including computer web page, mobile phone APP, and tablet. The computer web page is compatible with mainstream browsers and adopts responsive design to ensure beautiful interface and convenient operation under different screen sizes; the mobile phone APP is developed based on iOS and Android native SDK, and deeply integrates mobile phone hardware functions, such as taking photos to upload portfolios and using GPS positioning to obtain geographic location preferences; the tablet optimizes large-screen interaction and supports stylus annotation input, which is convenient for users to describe their career experience and expectations in detail.

[0159] 3. Safety protection equipment (module):

[0160] (1) Encrypted transmission module: Deploy SSL / TLS acceleration devices at the network edge to support the latest TLS1.3 protocol, encrypt and decrypt incoming and outgoing data to ensure transmission confidentiality; combine with intrusion detection system (IDS) to monitor traffic anomalies in real time to prevent man-in-the-middle attacks and data leakage risks.

[0161] (2) Data storage encryption module: A hardware security module (HSM) is used to store encryption keys. Sensitive data stored in the database, such as user ID numbers and career planning program texts, is encrypted using the AES-256 algorithm when written and authorized decryption is verified when read to ensure data privacy.

[0162] (3) Access control module: Based on the identity authentication platform, it combines multi-factor authentication (MFA), such as password + SMS verification code, fingerprint recognition, etc., to identify the user's identity; at the application layer, it uses the role-based access control (RBAC) model to assign different permissions to ordinary users, administrators, data analysts, etc., and finely control data viewing, modification, and deletion operations. The log audit system records the entire operation trajectory to facilitate tracing of anomalies.

[0163] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0164] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0165] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0166] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0167] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0168] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0169] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-dimensional evaluation intelligent career planning platform based on big data, characterized by: include: Multi-channel data collection module: used to integrate natural language processing technology through an intelligent user interaction interface to achieve intelligent analysis and structured storage of user input text, as well as to collect and integrate users' personal background, education experience, work experience, professional interests, value orientation, social media information and market research data through external data docking expansion and intelligent data capture innovation; Data cleaning and integration module: Use data cleaning algorithms to perform deep deduplication, denoising, and formatting of raw data, and use data fusion technology to organically integrate data from different channels to build a complete user data portrait; Multi-dimensional evaluation model construction module: including the design of an evaluation indicator system with comprehensive improvement and dynamic enhancement, and an evaluation model constructed by using machine learning model integration innovation, deep learning model application expansion and model optimization algorithm innovation, which is used to accurately evaluate the user's career development potential; Intelligent recommendation and planning module: Through collaborative filtering recommendation optimization, content recommendation innovation and hybrid recommendation algorithm integration, we design recommendation algorithms, tailor personalized career planning plans based on user evaluation results, establish a dynamic planning adjustment mechanism, regularly track user career development status and external environment changes, update and optimize career planning plans; at the same time, we provide interactive planning tools and user feedback collection and processing optimization functions to enhance user interaction experience; System architecture and security module: Adopt distributed architecture optimization and microservice architecture to innovate the design of system architecture and improve system scalability and reliability; Through enhanced data encryption transmission, innovative data storage encryption, optimized access control and permission management, and improved data backup and recovery, we fully guarantee user data security and privacy protection.

2. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 1, characterized in that: The multi-channel data collection module further includes: User interaction interface design integrates natural language processing technology to achieve intelligent analysis and structured storage of user input text; External data interface, connecting with multiple external data sources such as universities, enterprises, and online education platforms to automatically collect user-related data; Intelligent data capture function uses web crawler technology to accurately capture information closely related to the user's career planning from the Internet.

3. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 2, characterized in that: The data cleaning and integration module further includes: Data cleaning algorithm, used to perform deep deduplication, denoising, and formatting of raw data; Data fusion technology organically integrates data from different channels, builds a complete user data portrait, and generates a multi-dimensional user feature vector.

4. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 3 is characterized by: The multi-dimensional evaluation model building module further includes: The evaluation index system covers basic dimensions such as personal factors, educational background and experience, social factors, family factors, professional relationships, professional market and competition, and refines and expands the indicators under each dimension; The evaluation model integrates a variety of advanced machine learning algorithms and deep learning models, and uses model optimization algorithms such as parameter tuning and feature selection to improve the accuracy and robustness of the evaluation results.

5. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 4, characterized in that: The intelligent recommendation and planning module further includes: Recommendation algorithm, combining collaborative filtering recommendation, content recommendation and hybrid recommendation algorithms, to provide users with accurate and diverse recommendation results; Career planning program formulation: tailor-made personalized career planning programs based on user evaluation results, and establish a dynamic planning adjustment mechanism; User interaction tools provide an interactive user interface that allows users to independently adjust their career planning plans and provide professional feedback and suggestions in real time; Process user feedback, establish a user feedback module, and continuously optimize the recommendation algorithm and planning scheme generation logic.

6. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 5, characterized in that: The system architecture and security module further includes: Distributed architecture distributes data storage, computing, and application services across multiple server nodes to improve system scalability and reliability; Microservice architecture divides system functional modules into multiple independent microservices to improve the flexibility of system development and maintenance; Data security protection, using advanced encryption protocols and encryption algorithms, establishing strict access control and authority management mechanisms to ensure user data security and privacy protection; Data backup and recovery adopts a strategy that combines incremental backup and full backup to ensure data integrity and consistency.

7. The multi-dimensional evaluation intelligent career planning platform based on big data as claimed in claim 1, characterized in that: It also includes evaluating the accuracy of the multi-dimensional evaluation model, and the accuracy evaluation formula of the multi-dimensional evaluation model is as follows: Among them, A 模型 is the accuracy evaluation of the multi-dimensional evaluation model; λ is the coefficient of adjusting the influence of the error term; K is the number of evaluation dimensions; y k is the actual evaluation value of the kth dimension; is the predicted evaluation value of the kth dimension; σ k is the standard deviation of the evaluation value of the kth dimension; α and β are the parameters of the Sigmoid function, which are used to map the evaluation value to the (0,1) interval. Range explanation: A 模型 The value range of is (0,1), and the closer the value is to 1, the higher the accuracy of the model.

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