Ideological and political and employment fusion-oriented college student occupational planning auxiliary system

Abstract

The invention relates to the field of occupational planning, and discloses an ideological and political and employment fusion-oriented college student occupational planning auxiliary system, which realizes personalized occupational path planning through an optimization algorithm, a Bellman optimality principle and a dynamic feedback mechanism. The system comprises a data acquisition module, an occupational planning module, an optimal control module, a dynamic feedback module and a user interaction platform, can acquire data such as interests, capabilities and social responsibility of students in real time, and performs dynamic adjustment in combination with changes of an external environment. By integrating the demands of students and the trend of social development, the occupational path is ensured to meet the personal goal, and the performance of social responsibility can be promoted. The system provides a flexible, accurate and sustainable occupational development scheme for students, and solves the problem that a traditional occupational planning system cannot flexibly cope with student changes and external environment fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of career planning, and in particular to a career planning auxiliary system for college students oriented towards the integration of ideological and political education and employment. Background Art

[0002] With the development of society and the improvement of education levels, career planning for college students has become increasingly personalized and diversified. Most existing career planning systems use fixed approaches, relying heavily on students' initial interests, professional background, and general information about the job market. However, these traditional systems often suffer from significant shortcomings.

[0003] First, existing career planning systems typically only consider students' initial interests and professional backgrounds. In many cases, this information fails to fully reflect a student's true evolving interests and growth potential. As students progress through their studies, their interests, abilities, and goals frequently evolve. However, traditional systems fail to effectively adapt these paths. Many systems fail to update students' career plans in a timely manner, resulting in a mismatch between students' career development directions and their actual needs, impacting the quality of their career choices and their adaptability for future career development.

[0004] Secondly, most traditional career planning systems have limitations when it comes to addressing students' individual needs. These systems rely on pre-set models and conventional career paths, ignoring individual differences. For example, the impact of students' sense of social responsibility, professional ethics, and ideals on career planning is often not fully considered. Most existing technologies treat students as single individuals, without deeply exploring their needs in terms of social responsibility, values, and other aspects. This results in a lack of comprehensiveness in students' career path planning, making it difficult to meet the diverse needs of their long-term career development.

[0005] Furthermore, existing systems generally lack the ability to respond responsively to changes in the external environment. In most traditional career planning systems, career path planning relies solely on fixed job market data, failing to promptly reflect the impact of external factors such as industry shifts, fluctuations in social demand, and policy guidance. Students' career choices often fail to keep pace with socioeconomic trends. As market demands constantly shift, many existing systems are unable to quickly adapt to these changes, potentially causing students to miss out on more suitable career opportunities.

[0006] Furthermore, existing career planning systems often neglect the interactive nature of the system between students and the system. Traditional planning systems typically rely on students inputting data and then providing recommendations, but this process lacks real-time feedback mechanisms. This prevents students from fully adjusting and optimizing their career paths during use. The system's inability to adapt to student changes in real time leads to delayed career planning updates. Even some systems attempt to incorporate feedback mechanisms, but they still cannot accurately identify changes in student needs or real-time changes in the external environment.

[0007] Therefore, the present invention proposes a career planning auxiliary system for college students that integrates ideological and political education with employment to solve the shortcomings of the existing technology. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a career planning assistance system for college students that integrates ideological and political education with employment. This system addresses the shortcomings of existing career planning systems, such as their inability to dynamically adjust students' career paths in real time, their neglect of individual differences, and their failure to incorporate social responsibility. By incorporating optimization algorithms, dynamic feedback mechanisms, and real-time updates of external environmental data, this system generates personalized career paths for each student and automatically optimizes and adjusts these paths as student needs and the external environment change.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a career planning auxiliary system for college students oriented towards the integration of ideological and political education and employment, the system comprising:

[0010] A data collection module is used to collect students' personal information, career goals, skill levels, interests and hobbies, and social responsibility data in real time, and update it in real time based on external environmental data, such as industry needs and social trends;

[0011] A career planning module, which constructs a student's career planning model through an optimization algorithm and generates a career development path that meets the student's personalized needs based on the data provided by the data acquisition module;

[0012] Dynamic feedback module, which automatically adjusts students' career paths based on their real-time feedback and changes in the external environment, ensuring they remain optimal.

[0013] The optimal control module is used to solve the optimal control problem in students' career path planning through the optimal control algorithm, maximizing career satisfaction, growth potential and social responsibility;

[0014] The user interaction platform is used to display career planning paths and dynamic adjustment results, and provide students with real-time feedback on career planning to ensure path optimization and adjustment.

[0015] Preferably, the objective function solved by the optimization algorithm includes career satisfaction, growth potential and social responsibility, and the objective function is expressed in the form of a weighted sum, where the weight coefficient of each part can be adjusted according to the individual needs of the students to meet the career planning needs of different students.

[0016] Preferably, the career planning module constructs a student planning model through a career satisfaction function, a growth potential function, and a social responsibility function, wherein the career satisfaction function is based on the matching degree of students' interests and skills, the growth potential function is based on the development space of students in their chosen careers, and the social responsibility function is based on the impact of ideological and political education on students' social responsibility.

[0017] Preferably, the optimal control module adopts the Bellman optimality principle to recursively solve the optimal control problem to generate the best selection plan for the student's career path, and dynamically adjusts the student's career path through the optimal solution.

[0018] Preferably, the dynamic feedback module adopts a system dynamics model based on students' immediate feedback and changes in the external environment, and adjusts students' career paths in real time through an adaptive mechanism to ensure that career planning always meets current personal needs and external changes.

[0019] Preferably, the data acquisition module includes big data technology and machine learning means to collect students' academic performance, skill levels, interest changes, employment trends, industry needs and policy change data in real time, and process and store them to support subsequent career planning and dynamic adjustments.

[0020] Preferably, the career planning module models the student's career path based on an optimization algorithm, wherein the optimization algorithm includes nonlinear optimization, stochastic control, and gradient descent method, and is used to solve a complex objective function with constraints.

[0021] Preferably, the user interaction platform includes a web platform and a mobile application, through which students can input their career goals, view career path suggestions and provide real-time feedback. The system adjusts the career development plan based on the students' feedback results and displays the adjusted optimal path.

[0022] Preferably, after receiving the students' feedback data, the dynamic feedback module automatically adjusts the career path according to the students' self-assessment and changes in external factors, and provides new career path suggestions to the users to ensure that career planning is always in the optimal state.

[0023] Preferably, a career planning assistance system method for college students oriented towards the integration of ideological and political education and employment includes the following steps:

[0024] Collect students' personal information, career goals, skill levels, interests and hobbies, and social responsibility data, and update them in real time based on external environmental data;

[0025] Based on the data, an optimization algorithm is used to construct a career planning model for students and generate a personalized career path plan;

[0026] Solve optimal control problems through optimal control algorithms to maximize students' career satisfaction, growth potential, and social responsibility;

[0027] Dynamically adjust students' career paths based on their real-time feedback and changes in the external environment to ensure they meet current personal needs and external changes;

[0028] The optimized career planning path will be displayed through the user interaction platform, and real-time feedback and adjustments will be provided.

[0029] The present invention provides a career planning auxiliary system for college students oriented towards the integration of ideological and political education and employment.

[0030] It has the following beneficial effects:

[0031] 1. This invention combines an optimization algorithm with the Bellman Optimality Principle to achieve the goal of providing students with personalized career paths. Compared to the more rigid career path planning methods used in existing technologies, this invention dynamically adjusts career plans based on multi-dimensional data such as students' interests, skills, and social responsibility, ensuring that the planned path consistently meets students' needs and changes in the social environment. This addresses the inability of existing methods to adapt to students' changes in real time.

[0032] 2. This invention combines a dynamic feedback mechanism with a system dynamics model to optimize career planning in real time based on student feedback and changes in the external environment. Compared to traditional static planning models, this invention can adjust career planning in real time based on student feedback and changes in the external environment during career development, ensuring a consistently optimal career path. This overcomes the limitation of existing technologies that prevent flexible adjustment of planned paths.

[0033] 3. This invention utilizes a technical solution that combines a data acquisition module with an external data interface, achieving efficient collection and real-time updating of student and external environment data. Compared to existing methods that rely on a single data source, this invention integrates dynamic student data and real-time changes in the external environment into career planning, resolving the issues of traditional methods that ignore external changes or experience delayed data updates.

[0034] 4. This invention uses a technical solution that seamlessly integrates the user interaction platform with other modules, achieving seamless interaction and real-time feedback between students and the system. Compared to traditional career planning systems where student-system interaction is relatively one-way, this invention uses the platform to enable real-time student feedback, planning path adjustment, and external environment data sharing, resolving the issues of delayed student feedback and untimely path adjustment in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The architecture diagram of the career planning assistance system for college students oriented towards the integration of ideological and political education and employment;

[0036] Figure 2 Flowchart of the career planning assistance system method for college students oriented towards the integration of ideological and political education and employment. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Please see the attached Figure 1 The embodiment of the present invention provides a career planning assistance system for college students that is oriented towards the integration of ideological and political education and employment. The various modules of the system of the present invention are described in detail below.

[0039] Data acquisition module

[0040] In this embodiment, the main function of the data acquisition module is to collect and process relevant data such as students' personalized information, career goals, interests and hobbies, skill levels, social responsibility, etc. in real time, and to update external environmental data (such as industry demand, social trends, employment market dynamics, etc.) in real time. By inputting this multi-dimensional data into the system, the data acquisition module provides data support and foundation for subsequent career planning, dynamic adjustment, and optimal control modules. The data acquisition module is an indispensable part of the system. Other modules such as the career planning module, dynamic feedback module, and optimal control module all rely on the data provided by this module to optimize students' career development paths.

[0041] In this embodiment, the data acquisition module achieves comprehensive data collection through multiple subsystems and technical means. These subsystems include a student information collection subsystem, an external data interface subsystem, and a data update mechanism. This module not only acquires and stores static student information but also collects dynamic data in real time based on changes in student behavior. The data acquisition module is the source of system data processing, and its accuracy and real-time performance directly impact the performance of subsequent modules.

[0042] Student information collection subsystem

[0043] Generally, the student information collection subsystem primarily collects basic student information and demand data through various means. This information may include, but is not limited to, academic performance, interests, career goals, skill levels, and social responsibility. Students can provide this information by completing personal profiles or regularly updating questionnaires. In one possible implementation, the system regularly pushes questionnaires to students to collect self-assessment data on evolving interests, career preferences, and social responsibility.

[0044] Specifically, students' interests and skill levels can be assessed through psychological testing, skill assessment tools, or automated analysis systems based on past academic performance. For example, a student's career goals and sense of social responsibility can be determined based on information such as their self-selected job preferences, career aspirations, and social activity participation. All of this data is automatically collected and stored in the system's database, and subsequently used by the career planning module to generate personalized career path recommendations.

[0045] In one possible implementation, after students enter their personal information through the platform, the system will automatically match relevant career paths and perform adaptive optimization through algorithms to ensure that the data is consistent with actual conditions.

[0046] External data interface subsystem

[0047] The data collection module also needs to obtain information from the external environment, such as industry demand, social trends, and job market dynamics. This data has a crucial impact on career planning. To this end, the external data interface subsystem connects to multiple external data sources, integrating with industry reports, government policy releases, corporate recruitment information, employment data, and other sources to obtain real-time information on changes in the external environment.

[0048] In some embodiments, external environmental data includes industry trend reports, national development plans, industry recruitment information, and market demand changes. This data is automatically imported into the system via an API interface, where it can be combined with students' personal data to provide dynamically updated information support for the career planning module. Specifically, students' career path planning must not only be based on their personal interests and abilities, but also take into account current social needs and market trends. This data is regularly updated into the system via external interfaces.

[0049] Data update mechanism

[0050] Data update mechanisms are crucial for ensuring the system remains up-to-date. In some implementations, the system regularly updates students' dynamic data (e.g., academic performance, internship experience, career goals, etc.) and automatically adjusts career planning paths with each update. Furthermore, external environmental data is constantly updated and automatically reflected in the system's decision-making.

[0051] In practice, the data update mechanism includes two modes: automatic and manual. Automatic update mode automatically acquires and updates data through scheduled tasks, while manual update mode is triggered manually by students or system administrators. These two modes ensure that the system's data is both up-to-date and fully considers changes in students' preferences.

[0052] Student data processing and optimization algorithm input

[0053] After student data is collected, it undergoes a preprocessing process to cleanse and standardize it for consistency and accuracy. This standardized data is then fed into the career planning module as input to the optimization algorithm. This data includes students' interests, skills, academic performance, and other self-assessed information related to their career goals. Regarding students' sense of social responsibility, the system scores them based on factors such as their involvement in social activities and volunteer work experience, which serves as a key factor influencing career planning.

[0054] In some embodiments, the system can also use machine learning algorithms to model students' historical data and predict future trends in their interests. These predictions are then fed into subsequent modules as part of the external data to provide more accurate career development paths.

[0055] In this embodiment, it is assumed that the optimization goal of the student's career planning path is achieved through the following weighted objective function:

[0056]

[0057] Among them, u1(x(t)) is the career satisfaction function based on students' interests and skills, which describes the students' satisfaction with their career path; u2(x(t)) is the students' growth potential function in their career path, which describes the growth opportunities that students can obtain from their career; v(x(t)) is the students' social responsibility function, which describes the students' social responsibility performance in their career path; α1, α2, and α3 are the weight coefficients of each goal, reflecting the importance of students' interests, growth potential, and social responsibility.

[0058] The student data collected through the data collection module is used to estimate and optimize the parameters in the objective function above, thereby generating the most suitable career path for the student. Specifically, the student's interest data influences the calculation of u1(x(t)), the skill level data affects u2(x(t)), and the social responsibility data directly affects v(x(t)).

[0059] The data acquisition module collects and processes students' personal information and external environmental data in real time, providing essential input for subsequent career planning and dynamic adjustments. By accurately capturing data on students' interests, skills, career goals, and other aspects, as well as external data such as industry needs and social trends, the system generates personalized, real-time optimized career development paths.

[0060] The implementation method of this module can ensure that students' career planning is always highly consistent with their interests, abilities and social needs, and can adapt to future changes and improve the quality of students' career development.

[0061] Career Planning Module

[0062] The Career Planning module is the core of the system. Based on students' personal data and external environmental information, it generates personalized career development paths through an optimization algorithm. This module closely integrates data provided by the Data Collection module and dynamically plans based on student needs and social development trends. It not only focuses on students' interests, skills, and career goals, but also incorporates social responsibility and ideological and political education into career planning, ensuring that students' career growth is aligned with social development.

[0063] In some embodiments, the primary goal of the career planning module is to maximize students' career satisfaction, growth potential, and social responsibility. To achieve this, the system uses an optimization algorithm to comprehensively analyze various student data and generate a career development path that is tailored to the student's individual needs.

[0064] In this embodiment, the core function of the career planning module is to use an optimization algorithm to generate a career path that matches the student's interests, abilities, and social needs based on the student information and external environment data provided by the data collection module. Specifically, the career planning module weights the student's career goals, interests, hobbies, and social responsibility to generate the optimal career path that meets the student's needs.

[0065] In this embodiment, the career planning module uses an optimization algorithm to solve the problem of planning students' career paths. The goal is to achieve a balance between multiple objective functions through the optimization process and find an optimal career path solution. The objective functions of the career planning module generally include career satisfaction, growth potential, and social responsibility, and are specifically defined as follows:

[0066]

[0067] Among them, u1(x(t)) is the career satisfaction function, which reflects the student's satisfaction with a certain career status and is usually closely related to the student's interest and skill matching; (x(t)) is the growth potential function, which describes the student's growth space in the career path; v(x(t)) is the social responsibility function, which measures the social responsibility assumed by the student in the career; α1, α2, α3 are weight coefficients, corresponding to the relative importance of career satisfaction, growth potential, and social responsibility, respectively.

[0068] By using an optimization algorithm, the system finds a balance between these three objectives and generates the best career path tailored to the student's individual development. In practice, the objective function is modeled based on factors such as students' interests, abilities, and personality, ensuring that the career path meets both the student's individual needs and the demands of society and the market.

[0069] Typically, optimization algorithms employ nonlinear optimization techniques because career planning problems involve multiple objectives and constraints, often nonlinear. In one possible implementation, the system employs common nonlinear optimization methods such as genetic algorithms, particle swarm optimization (PSO), or gradient descent to solve career planning problems.

[0070] Specifically, genetic algorithms simulate the mechanisms of natural selection and genetic variation to select the optimal solution from multiple possible career paths. Particle swarm optimization simulates the foraging process of birds to search for the optimal solution, while gradient descent gradually approaches the optimal solution by adjusting various parameters of the career path. Each algorithm has different advantages and disadvantages in practical applications, and the specific choice can be flexibly adjusted according to system requirements.

[0071] The core of the career planning module is how to transform factors such as students' interests, skills, and social responsibility into a mathematical model. In this embodiment, the calculation of the career satisfaction function u1(x(t)) and the growth potential function u2(x(t)) is mainly based on the matching degree between students' interests and skills. For example, suppose the relationship between students' interests and career skills can be represented by a linear function:

[0072]

[0073] Where β1 is the student’s interest influencing factor, and x(t) is the choice of career path.

[0074] In specific implementations, students' interests can be quantified through questionnaires or behavioral data (such as projects participated in, courses selected, etc.).

[0075] On the other hand, the growth potential function u2(x(t)) reflects the student's growth potential in the chosen career. In this embodiment, growth potential is closely related to the student's skill improvement and job promotion opportunities in a specific career path, and is usually expressed as an exponential function:

[0076]

[0077] Where γ1 is the growth potential influence coefficient, and x(t) is the choice of career path.

[0078] The social responsibility function v(x(t)) is a key part of ideological and political education, describing whether students can meet the requirements of social responsibility when choosing a career path. It can usually be represented by the following logical function:

[0079]

[0080] Where β2 is the social responsibility influencing factor. Through this function, the system can quantify students' social responsibility and optimize their career paths in combination with other factors.

[0081] In some embodiments, the weighting coefficients α1, α2, and α3 can be adjusted based on the individual needs of students. For example, for students who prioritize career satisfaction, the system can adjust the value of α1 to increase the impact of career satisfaction. Similarly, for students with a strong sense of social responsibility, the value of α3 can be increased accordingly to emphasize the importance of social responsibility in career planning. In one possible implementation, the system can dynamically adjust these weighting coefficients based on student self-assessment and feedback, ensuring that career planning always meets students' needs and goals.

[0082] The Career Planning Module relies on student information and external environmental data provided by the Data Acquisition Module. After preprocessing and standardization, this data serves as input for the optimization algorithm. The output of the Career Planning Module is fed back to the Dynamic Feedback Module, which adjusts the system based on real-time student feedback and changes in the external environment. Simultaneously, the Optimal Control Module uses the Bellman Optimality Principle and other algorithms to further optimize students' career paths and ensure they remain optimal throughout their careers.

[0083] The career planning module in this embodiment uses an optimization algorithm to combine factors such as students' interests, skills, and social responsibility to generate personalized career development paths for students. Through dynamic adjustments, weighting coefficient adjustments, and the integration of social responsibility, the system can provide students with precise career planning advice and optimize the path in real time based on their needs and changes in the external environment. This flexible and dynamic planning approach will greatly improve students' career development efficiency, ensuring that they can achieve their personal goals in the workplace and contribute to society.

[0084] Dynamic feedback module

[0085] The dynamic feedback module plays a crucial role in the entire system. As a feedback mechanism, it is closely connected to the aforementioned career planning module and optimal control module. This module aims to automatically adjust career planning paths based on real-time student feedback and changes in the external environment, ensuring that students' career development remains on the optimal trajectory. Unlike other modules, the dynamic feedback module not only processes student feedback but also integrates dynamic changes in the external environment. In this way, the system can continuously adapt to the changing needs of students and changes in the market environment.

[0086] In the aforementioned career planning module, the optimization algorithm generates preliminary career path recommendations, while the dynamic feedback module continuously refines these recommendations through real-time feedback and external data updates. Therefore, the dynamic feedback module is a crucial component in enabling the system to self-adjust and improve the quality of the career path. Through it, students' career paths can continuously reflect the latest changes in interests and abilities, as well as the dynamic demands of the social environment.

[0087] In this embodiment, the dynamic feedback module relies on the output of the aforementioned data acquisition module and career planning module. It combines student feedback on career planning paths with external environmental data and uses a system dynamics model to make real-time adjustments to career paths. This ensures that career paths remain consistent with students' individual needs and changes in the external environment.

[0088] Generally, students can enter their feedback through the online platform, covering information such as satisfaction with the current career path, whether it aligns with personal interests, changes in skills and career goals, etc. In some embodiments, the system quantifies the student feedback data and converts it into a processable data form. The content of student feedback may include the following aspects:

[0089] Overall satisfaction with current career path;

[0090] changes in personal interests or goals;

[0091] Changes in the importance of social responsibility in career choices, etc.

[0092] Specifically, student feedback is fed into the dynamic feedback module, which uses this feedback data to adjust career paths in real time. Based on changes in this feedback data, the system dynamically reassesses the current career plan and generates updated recommendations.

[0093] Changes in the external environment can also influence career path selection. Typically, these changes include shifts in industry demand, policy adjustments, and shifting social trends. In some embodiments, the system automatically updates external environment data periodically or based on triggering events (e.g., changes in industry recruitment data). This data is collected from the external data interface subsystem and combined with the student's personalized data to form a comprehensive career path optimization system.

[0094] In one possible implementation, external data isn't limited to industry needs and employment trends; it could also include social policies, technological development trends, and more. By analyzing and processing this data, the system can adjust students' career paths in a timely manner, preventing them from straying into inappropriate paths during their career development.

[0095] In this embodiment, the dynamic feedback module uses a system dynamics model. This model predicts and simulates career paths, making adjustments based on real-time student feedback and changes in the external environment. This model allows the system to consider the influence of multiple factors and make adaptive dynamic adjustments.

[0096] Specifically, assuming that the student's career path at time point t is x(t), the student's feedback information is δ(t), and the external environment changes are η(t), then the adjustment process of the dynamic feedback module can be expressed as follows:

[0097] x(t+1)=f(x(t),δ(t),η(t))

[0098] Here, f(x(t), δ(t), η(t)) is an adjustment function based on the current career path, student feedback, and changes in the external environment; δ(t) represents student feedback on the career path; and η(t) represents changes in the external environment, such as industry trends and changes in social policies.

[0099] In the above formula, x(t+1) represents the career path that the system updates based on feedback and external environment data.

[0100] As an option, the dynamic feedback module also uses an adaptive mechanism to further optimize career paths. This mechanism can quickly adjust career plans based on student feedback or changes in the external environment to ensure that the path is always optimal.

[0101] The core of the adaptive mechanism is to optimize the parameters of the objective function through continuous analysis of feedback and external data. For example, if a student expresses low satisfaction with a particular career path over a period of time, the system will incorporate this information into the next round of career planning, dynamically adjusting the career path generation process. As the system gradually collects more feedback data, it will adjust its own adjustment parameters to make the path adjustment more precise.

[0102] During implementation, the dynamic feedback module is closely integrated with the data collection module and the career planning module. The data collection module provides real-time updates on students' basic data and external environments, while the career planning module generates preliminary career path recommendations based on this data. The dynamic feedback module, through continuous student feedback and changes in external data, ensures that career paths are constantly adjusted and optimized.

[0103] Specifically, the initial path provided by the career planning module becomes more precise and aligned with students' current needs after adjustments made by the dynamic feedback module. The collaborative work of the feedback module and the optimal control module helps the system adapt to students' evolving needs and external environmental changes in real time, ensuring that career paths remain aligned with reality and student needs.

[0104] The dynamic feedback module in this embodiment, through integration with student feedback mechanisms, external environmental data, and system dynamics models, enables real-time adjustments to students' career planning paths. This module ensures that career planning can be adaptively optimized based on changing student needs and fluctuations in the social environment. This flexibility and real-time nature enable the career path planning system to consistently provide students with personalized and optimal career development recommendations, thereby maximizing the quality of their career development.

[0105] Optimal control module

[0106] The optimal control module plays an important role in the present invention. It connects the data acquisition module, the career planning module and the dynamic feedback module. Unlike the static planning of the aforementioned modules, the core function of the optimal control module is to continuously adjust and optimize based on students' real-time feedback and changes in the external environment. Through optimal control, the system can accurately guide students' career paths and ensure that they are in the optimal state at all times. The optimal control module not only ensures the accuracy of dynamic adjustment, but also enhances the adaptability of the entire system to uncertain factors. This module ensures the long-term optimization of career paths and avoids unstable or deviated plans caused by external fluctuations and changes in personal needs.

[0107] In this embodiment, the optimal control module is designed based on the Bellman Optimality Principle, which recursively solves the optimal control problem to generate the optimal career path. Specifically, based on the preliminary career path output by the aforementioned career planning module, the optimal control module optimizes the student's career development direction at each moment, dynamically adjusting this path to adapt to changes in student interests, market demand, and other fluctuations in the external environment.

[0108] Typically, the optimal control module uses the Bellman Optimality Principle to solve the optimal control problem for students' career paths. The Bellman Optimality Principle is a recursive optimal decision-making method that primarily solves the problem in stages to find the global optimal solution. In this embodiment, the optimal control module gradually adjusts students' career paths based on system feedback.

[0109] Specifically, the system solves the optimal control problem through the above super H formula

[0110]

[0111] Among them, V(t,x) is the optimal value function starting from time t and state x, which represents the optimal utility of the student's career path at the current moment; u1(x(t)), u2(x(t)), v(x(t)) represent career satisfaction, growth potential, and social responsibility functions, respectively. The specific definitions and calculation methods are given in the career planning module; α1, α2, α3 are the weight coefficients of the objective function, which indicate the degree of influence of each objective on the optimal path; u(t) is the career path decision, which represents the student's career path choice at time t.

[0112] In this model, the system uses recursive solutions to continuously adjust career paths based on student feedback and changes in the external environment, ensuring that career planning is optimal at every moment in the future.

[0113] In some embodiments, the optimal control module may be implemented using a dynamic programming approach, which progressively calculates the optimal career path at each moment. This algorithm recursively optimizes each decision-making stage and ultimately arrives at the global optimal solution. This ensures that students' career paths are always optimal within given constraints.

[0114] In one possible implementation, the system adjusts the current optimal decision based on historical feedback and future forecasts. For example, if demand in a particular industry is growing rapidly, the system will incorporate this change into the current career path plan, thereby optimizing the student's career development path. Whenever the external environment or the student's personal needs change, the optimal control module reassesses the current career path and makes adjustments based on the new information.

[0115] Generally, the optimal control module and the dynamic feedback module work in tandem. The dynamic feedback module adjusts career paths based on real-time student feedback and changes in the external environment, while the optimal control module ensures these adjustments always align with the optimal path. Within the system, the optimal control module is the "final decision maker" for further optimizing the results of dynamic feedback adjustments, effectively preventing inappropriate career path deviations.

[0116] In some embodiments, the optimal control module adjusts the weighting coefficients in the optimization process based on the magnitude and frequency of adjustments in response to dynamic feedback. For example, if a student's interests change dramatically over a period of time, the system will dynamically adjust the career path to better suit the student's new needs, while the optimal control module will ensure that this adjustment does not lead to extreme deviations in the career path. In this way, the system provides flexible adjustments while ensuring that career planning remains globally optimal.

[0117] The optimal control module relies on the aforementioned career planning module and dynamic feedback module. The career planning module uses an optimization algorithm to provide students with a preliminary career path, while the optimal control module further adjusts and optimizes this path. A tight feedback loop connects the optimal control module and the dynamic feedback module. Real-time student feedback and changes in the external environment are transmitted to the optimal control module through the dynamic feedback module. Based on these inputs, the optimal control module adjusts the optimal control strategy and optimizes the career path.

[0118] In one possible implementation, the optimal control module dynamically adjusts decisions at each stage based on input data (such as the career planning module's output and student feedback) to generate a final career plan. In this way, the optimal control module ensures a fully optimized career path, enabling students to continuously benefit throughout their career development.

[0119] The optimal control module in this embodiment uses the Bellman optimality principle and dynamic programming methods to optimize career paths based on student feedback and changes in the external environment. It not only makes real-time adjustments based on student needs and fluctuations in the external environment, but also uses adaptive mechanisms to ensure that career paths at each stage consistently develop in the optimal direction. The optimal control module, combined with the career planning module and dynamic feedback module, ensures the long-term development and optimization of students' career paths, enabling the system to provide students with a personalized, flexible, and highly adaptable career planning solution.

[0120] User interaction platform

[0121] The user interaction platform is a key module in the present invention, which plays a connecting role in the system. In conjunction with the aforementioned modules (such as the data acquisition module, the career planning module, the dynamic feedback module, etc.), the user interaction platform provides students with a window for interacting with the system, allowing students to input feedback, view planning results, and adjust their career paths according to their personal circumstances. The design and functional implementation of the platform are crucial for enhancing student participation and system adaptability. Through this platform, students can not only view the progress of their personal career plans in real time, but also make regular or immediate adjustments to their career paths based on the feedback mechanism.

[0122] In this embodiment, the user interaction platform is designed as both a web platform and a mobile application, enabling students to interact with the system anytime, anywhere. Whether using a computer or a mobile phone, students can conveniently access the platform to input data and view planning results. The core purpose of this platform is to provide a convenient user interface, making the interaction between students and the system smoother and more intuitive.

[0123] Generally speaking, the core functions of the user interaction platform include career path review, personalized feedback input, and path adjustment suggestions. Students can use the platform to review the career paths generated by the Career Planning Module and the Optimal Control Module and submit feedback based on their individual circumstances. Through these functions, the system can monitor students' changing needs in real time and adjust career paths based on this feedback.

[0124] As an option, the platform is not limited to a single function, it can also dynamically adjust the recommended content based on student feedback input. Specifically, students input the following types of feedback data through the platform:

[0125] Satisfaction evaluation of career path;

[0126] Personal perception of current career plans;

[0127] Changes in expectations and interests regarding future career development.

[0128] The system processes this feedback and, based on changes in the external environment, adjusts future career path planning. For example, if a student is dissatisfied with a particular career path, the platform will guide them through adjustment options to optimize their career plan.

[0129] In some embodiments, the user interaction platform is closely integrated with the data collection module. Students' personal data (such as interests, abilities, and social responsibility) is immediately updated to the data collection module after being input through the platform. This data serves as input to the career planning module, which generates a personalized career development path.

[0130] Specifically, the real-time updates of the data collection module ensure timely input into the platform. Whether it's changes in student personal information or updates to external environmental data, the platform is promptly reflected. By integrating with the career planning module, the platform automatically provides students with the latest career path planning results, ensuring they always see recommended paths that meet their current needs.

[0131] The platform is also connected to a dynamic feedback module. When students provide feedback, it's immediately passed on to the module, which then adjusts their career paths based on this data. This iterative mechanism ensures that students' career plans are constantly updated in real time based on their needs and the changing external environment.

[0132] In some embodiments, the platform not only focuses on functionality, but also emphasizes user experience design. The platform provides a simple and intuitive operation interface to ensure that students can quickly understand and use the various functions of the system.

[0133] Specifically, the platform design includes the following:

[0134] Visual display of career paths: Students can clearly view their career planning paths through charts, flow charts, etc., including future job development, required skills improvement and other information.

[0135] Feedback input interface: Provides an easy-to-fill feedback form where students can quickly select their satisfaction with their career path, the direction they would like to adjust, etc.

[0136] Adjustment suggestions: Based on the feedback input by students, the platform will provide adjustment suggestions for career paths, and students can choose whether to adopt these suggestions.

[0137] In one possible implementation, the platform also includes an intelligent recommendation system that automatically generates new career path options based on student input. By analyzing students' career goals and evolving interests, the system combines external data to propose new career development plans.

[0138] The platform not only allows students to review and provide feedback on their career paths, but also collaborates with the Optimal Control Module to ensure that students' career paths are always optimal. Whenever students provide new feedback or make adjustments, this information is passed to the Optimal Control Module and used to adjust career path planning.

[0139] Specifically, the optimal control module adjusts the weight coefficients of each path based on student feedback. For example, if a student expresses greater interest in a particular area, the optimal control module will adjust the weight of that part of the plan accordingly to ensure that their interest is more fully reflected. In this way, the optimal control module continuously adjusts career paths based on feedback input from the platform, thereby optimizing the entire career planning process.

[0140] The synergy between the platform and the dynamic feedback module is also crucial. The feedback data students input through the platform not only directly influences the optimal control module but also feeds into the dynamic feedback module. This module adaptively adjusts career paths based on this feedback data. Whenever the external environment (such as changes in industry demands) or student needs change, the dynamic feedback module reassesses the career path and makes necessary adjustments. The user interaction platform serves as a bridge in this process, ensuring the immediacy and accuracy of information transmission.

[0141] The user interaction platform in this embodiment provides students with a convenient way to interact with the system. Through the platform, students can not only view their personal career planning paths but also adjust them based on real-time feedback. The platform is interconnected with the data acquisition module, career planning module, optimal control module, and dynamic feedback module to ensure that the system can continuously optimize career paths and adjust them according to changes in students' needs and fluctuations in the external environment. Through a simple interface design and intelligent recommendation mechanism, the platform enhances the system's flexibility and personalization, greatly enhancing students' sense of participation and control in their career development process.

[0142] Please see the attached Figure 2 The present invention also provides a career planning assistance system method for college students that is oriented towards the integration of ideological and political education and employment. The following describes the specific implementation methods of each step in combination with the workflow of the method of the present invention.

[0143] Collect students' personal information, career goals, skill levels, interests and hobbies, and social responsibility data, and update them in real time based on external environmental data;

[0144] Based on the data, an optimization algorithm is used to construct a career planning model for students and generate a personalized career path plan;

[0145] Solve optimal control problems through optimal control algorithms to maximize students' career satisfaction, growth potential, and social responsibility;

[0146] Dynamically adjust students' career paths based on their real-time feedback and changes in the external environment to ensure they meet current personal needs and external changes;

[0147] The optimized career planning path will be displayed through the user interaction platform, and real-time feedback and adjustments will be provided.

[0148] The method of this embodiment can be used to execute the above system embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0149] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A career planning assistance system for college students oriented towards the integration of ideological and political education and employment, characterized by: The system includes: A data collection module is used to collect students' personal information, career goals, skill levels, interests and hobbies, and social responsibility data in real time, and update it in real time based on external environmental data, such as industry needs and social trends; A career planning module, which constructs a student's career planning model through an optimization algorithm and generates a career development path that meets the student's personalized needs based on the data provided by the data acquisition module; Dynamic feedback module, which automatically adjusts students' career paths based on their real-time feedback and changes in the external environment, ensuring they remain optimal. The optimal control module is used to solve the optimal control problem in students' career path planning through the optimal control algorithm, maximizing career satisfaction, growth potential and social responsibility; The user interaction platform is used to display career planning paths and dynamic adjustment results, and provide students with real-time feedback on career planning to ensure path optimization and adjustment.

2. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The objective function solved by the optimization algorithm includes career satisfaction, growth potential and social responsibility, and the objective function is expressed in the form of a weighted sum, where the weight coefficient of each part can be adjusted according to the personalized needs of students to meet the career planning needs of different students.

3. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 2 is characterized in that: The career planning module constructs a student planning model through career satisfaction function, growth potential function, and social responsibility function, where the career satisfaction function is based on the matching degree of students' interests and skills, the growth potential function is based on the development space of students in their chosen careers, and the social responsibility function is based on the impact of ideological and political education on students' social responsibility.

4. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The optimal control module adopts the Bellman optimality principle to recursively solve the optimal control problem to generate the best selection plan for students' career paths, and dynamically adjusts students' career paths through the optimal solution.

5. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The dynamic feedback module adopts a system dynamics model based on students' immediate feedback and changes in the external environment, and adjusts students' career paths in real time through an adaptive mechanism to ensure that career planning always meets current personal needs and external changes.

6. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The data collection module includes big data technology and machine learning methods, which are used to collect students' academic performance, skill levels, interest changes, employment trends, industry needs and policy change data in real time, and process and store them to support subsequent career planning and dynamic adjustments.

7. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The career planning module models students' career paths based on an optimization algorithm, which includes nonlinear optimization, stochastic control, and gradient descent methods to solve complex objective functions with constraints.

8. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: The user interaction platform includes a web platform and a mobile application. Students can use the platform to input their career goals, view career path suggestions, and provide real-time feedback. The system adjusts their career development plans based on the students' feedback and displays the optimal path after adjustment.

9. The career planning assistance system for college students oriented towards the integration of ideological and political education and employment according to claim 1 is characterized in that: After receiving students' feedback data, the dynamic feedback module automatically adjusts the career path based on the students' self-assessment and changes in external factors, and provides new career path suggestions to users to ensure that career planning is always in the optimal state.

10. A career planning assistance system method for college students oriented towards the integration of ideological and political education and employment, applied to the system according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: Collect students' personal information, career goals, skill levels, interests and hobbies, and social responsibility data, and update them in real time based on external environmental data; Based on the data, an optimization algorithm is used to construct a career planning model for students and generate a personalized career path plan; Solve optimal control problems through optimal control algorithms to maximize students' career satisfaction, growth potential, and social responsibility; Dynamically adjust students' career paths based on their real-time feedback and changes in the external environment to ensure they meet current personal needs and external changes; The optimized career planning path will be displayed through the user interaction platform, and real-time feedback and adjustments will be provided.

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