Multi-language AI (artificial intelligence)-based personalized ascending plan dynamic generation system

Abstract

The invention discloses a multi-language AI-based personalized ascending plan dynamic generation system, which comprises a multi-language semantic interaction module, a dynamic gradient prediction engine, a real-time policy adaptation system and a 1 + N double-track service module, and is characterized in that the multi-language semantic interaction module is used for processing Uyghur language and Kazakh language voice / text input; cross-language intention recognition is realized through a bidirectional LSTM neural network, and a Uighur-Chinese bilingual contrast policy lexicon covering college entrance examination, examination and research and doctor stages is constructed to realize multi-language policy semantic alignment; the dynamic gradient prediction engine is used for generating a volunteer scheme including three levels of sprint-adaptation-stability for college entrance examination, generating a sprint-adaptation-stability target scheme for the college entrance examination, and generating a core target-alternative target-bottom guarantee target scheme for the blog by fusing characteristic parameters of different learning stages on the basis of an XGBoost algorithm, and generating a college scheme including sprint-adaptation-stability for the college entrance examination, the college entrance examination, the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination and the college entrance examination. The real-time policy adapts the system.

Description

Technical Field

[0001] This invention relates to the field of college application planning technology, specifically a dynamic generation system for personalized college application planning based on multilingual AI. Background Technology

[0002] In today's education field, college planning plays a crucial role in students' future development. However, traditional college planning methods have many limitations and are unable to meet the increasingly diverse and personalized needs of students.

[0003] However, existing technologies have the following problems:

[0004] The current field of college application planning suffers from several pain points: traditional systems have weak multilingual support, making it difficult to effectively handle minority languages ​​such as Uyghur and Kazakh, leading to barriers for minority students in understanding policies and accessing information; personalized planning lacks precision, often overlooking students' interests, professional inclinations, research directions, and other multi-dimensional characteristics, and failing to design solutions for the differentiated needs of different stages such as college entrance examination, postgraduate entrance examination, and doctoral entrance examination; policy adaptability is poor, relying on manual updates and struggling to respond promptly to regional policy changes (such as minority bonus points, doctoral application-assessment system, etc.), easily leading to conflicts between planning and policy; service models are fragmented, often focusing on a single stage, with ineffective integration of online and offline resources and a lack of cross-stage support. Meanwhile, breakthroughs in artificial intelligence technology provide the technological foundation for precise planning, and the national strategic needs to promote educational equity, ethnic integration, and lifelong learning urgently require a comprehensive, multilingual, and highly accurate college application planning system, which existing systems struggle to meet. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a dynamic generation system for personalized college application planning based on multilingual AI, which has advantages such as diverse information acquisition channels and solves the problem of limited information acquisition channels.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, this invention provides the following technical solution: a multilingual AI-based personalized college entrance planning dynamic generation system, comprising a multilingual semantic interaction module, a dynamic gradient prediction engine, a real-time policy adaptation system, and a 1+N dual-track service module, wherein:

[0009] The multilingual semantic interaction module processes Uyghur and Kazakh speech / text input, achieves cross-language intent recognition through a bidirectional LSTM neural network, and constructs a Uyghur-Chinese bilingual policy lexicon covering the college entrance examination, postgraduate entrance examination, and doctoral degree stages to achieve multilingual policy semantic alignment. The dynamic gradient prediction engine, based on the XGBoost algorithm, integrates feature parameters from different stages of higher education, generating three-level application schemes ("sprint-suitable-safe") for the college entrance examination, "sprint-suitable-safe" for the postgraduate entrance examination, and "core target-alternative target-safe" for the doctoral degree. The real-time policy adaptation system uses web crawlers and BERT models to capture and analyze regional policies for the college entrance examination, postgraduate entrance examination, and doctoral degree stages in real time, automatically matching candidate qualifications and detecting conflicts between planning schemes and policies at each stage. The 1+N dual-track service module integrates an online intelligent platform and an offline doctoral workstation (including postgraduate entrance examination tutoring experts and doctoral supervisor advisory groups), realizing a closed-loop service throughout the entire higher education cycle through a data platform.

[0010] Preferably, in the multilingual semantic interaction module, the cross-language intent recognition accuracy is ≥98.3%, and the bilingual policy terminology contains ≥5000 policy terms, of which ≥2000 are for the college entrance examination stage, ≥1500 for the postgraduate entrance examination stage, and ≥1500 for the doctoral stage.

[0011] Preferably, the dynamic gradient prediction engine introduces an attention mechanism to assign weights to enrollment plans in different regions and at different stages of higher education, with the following prediction accuracy: ≥98% for the college entrance examination stage, ≥97% for the postgraduate entrance examination stage, and ≥96% for the doctoral stage.

[0012] Preferably, the policy response delay of the real-time policy adaptation system is ≤1 hour, and it supports automatic matching of more than 8 types of regional policies at various stages, such as the college entrance examination bonus points policy for ethnic minorities, the postgraduate entrance examination ethnic minority backbone program, and the doctoral application-assessment system.

[0013] Preferably, in the 1+N dual-track service module, the offline service team includes PhDs in the corresponding fields, postgraduate entrance examination tutoring experts, and a doctoral supervisor advisory group. Service resources are allocated to candidates through a dynamic matching algorithm. The basic service response time is ≤24 hours. An "expedited channel" is added one month before the postgraduate entrance examination re-examination and during the critical period of doctoral application, with a response time of ≤12 hours.

[0014] A method for implementing college application planning, characterized by: including:

[0015] S1. Multilingual data collection steps: Information on candidates at different stages of their academic journey is obtained through a bilingual interactive interface. For the college entrance examination, a 6-dimensional interest assessment is used to generate a candidate profile. For the postgraduate entrance examination, a "professional depth inclination assessment" is added. For the doctoral entrance examination, a "research direction fit assessment" is added. The data is cleaned using regularization methods, with an error rate of ≤0.7%.

[0016] S2. Dynamic Programming Generation Steps: Based on the candidate's corresponding stage scores and ranking data, combined with the admission data of universities at each stage, planning suggestions are generated. In the college entrance examination stage, based on the admission data of 3000+ universities over the years, ≥1968 sets of application suggestions are generated with a professional matching degree of ≥80%. In the postgraduate entrance examination stage, suggestions are generated by combining the application-to-admission ratio of the target university and the professional matching degree. In the doctoral stage, suggestions are generated by combining the matching degree of the supervisor's research direction and the enrollment plan.

[0017] S3. Policy Intelligent Adaptation Steps: Extract candidate characteristics to match regional policies for the college entrance examination, postgraduate entrance examination, and doctoral entrance examination, generate policy adaptation reports, and detect conflicts between planning schemes and policies. Support automatic matching of more than 8 types of policies at each stage.

[0018] S4. Dual-track service execution steps: Online, we provide phased intelligent tools (college entrance examination application simulation, postgraduate entrance examination re-examination simulation, doctoral research plan template library, etc.), and offline, we provide one-on-one consultation and course services (college entrance examination application guidance, postgraduate entrance examination re-examination simulation interview, doctoral application material refinement, etc.). Service records are synchronized to the cloud database to realize real-time interaction of online and offline data.

[0019] Preferably, in the multilingual data acquisition step, a "personal education record" is established through a data platform to achieve continuous correlation of data from the college entrance examination to the postgraduate and doctoral stages.

[0020] Preferably, in the dynamic programming generation step, the postgraduate entrance examination planning scheme includes matching information of three dimensions: university, major, and supervisor, and the doctoral planning scheme includes research direction fit analysis and supervisor enrollment quota evaluation.

[0021] Preferably, in the policy intelligent adaptation step, real-time push notifications are provided for adjustments to the re-examination score line and changes in the transfer process for postgraduate entrance examinations, and real-time push notifications are provided for changes in the number of supervisors to be recruited for doctoral programs.

[0022] Preferably, in the dual-track service execution steps, the offline service provides bridging guidance for cross-stage further education needs, including guidance on the correlation between post-college professional study planning and postgraduate entrance examination direction, and guidance on the correlation between master's stage research planning and doctoral application direction.

[0023] (III) Beneficial Effects

[0024] Compared with existing technologies, this invention provides a dynamic generation system for personalized college application planning based on multilingual AI, which has the following beneficial effects:

[0025] 1. This multilingual AI-based personalized college entrance planning dynamic generation system breaks through language barriers, protects the rights and interests of ethnic minority candidates, and relies on a multilingual semantic interaction module to achieve cross-language intent recognition between ethnic minority languages ​​such as Uyghur and Kazakh and Chinese (accuracy ≥ 98.3%). It also builds a bilingual policy lexicon containing 5,000+ terms, solving the problem of ethnic minority candidates' understanding of college entrance policies, ensuring their equal access to college entrance information, and accurately matching them with policy resources (such as ethnic minority bonus points, key talent programs, etc.).

[0026] 2. This AI-based multilingual personalized college application planning dynamic generation system boasts high accuracy in personalized planning, enhancing the success rate of college admissions. Its dynamic gradient prediction engine, combined with the XGBoost algorithm and attention mechanism, generates "gradient" plans for different stages of the college entrance examination, postgraduate entrance examination, and doctoral entrance examination, achieving prediction accuracy rates of over 98%, 97%, and 96%, respectively. Simultaneously, it generates precise student profiles through multi-dimensional assessments (interests, professional depth, research direction, etc.), and combines data from over 3000 universities to ensure a professional / research direction fit of ≥80%, significantly improving the matching degree between the planning scheme and the student's needs and reducing the risk of college application decisions.

[0027] 3. This multilingual AI-based personalized college application planning dynamic generation system offers real-time and efficient policy response, avoids planning conflicts, and utilizes web crawlers and BERT models to quickly capture and match regional policies (such as bonus points for ethnic minorities, doctoral application-assessment system, etc., more than 8 categories), with a response delay of ≤1 hour. It can automatically detect conflicts between the planning scheme and policies and generate adaptation reports, ensuring that candidates keep abreast of policy changes (such as adjustments to the re-examination score line, changes in the number of supervisors) and avoid planning errors caused by policy information lag.

[0028] 4. This AI-based multilingual personalized college application planning dynamic generation system provides a full-cycle dual-track service covering the entire college application process. The 1+N dual-track service module integrates online intelligent tools (application simulation, interview simulation, etc.) with offline professional teams (doctoral fellows, postgraduate entrance examination experts, and doctoral supervisor advisory groups) to achieve real-time data interaction between "online + offline". The basic service response time is ≤24 hours, and an "expedited channel" (response time ≤12 hours) is added during critical periods (such as before the postgraduate entrance examination interview and the doctoral application period). It also provides cross-stage connection guidance (such as the planning connection between the college entrance examination and the postgraduate entrance examination, and between the master's degree and the doctoral degree), forming a full-cycle service closed loop from data collection to college application implementation, improving service continuity and timeliness. Attached Figure Description

[0029] Figure 1 This is the main system flow deployment diagram of the multilingual AI-based personalized college entrance planning dynamic generation system proposed in this invention;

[0030] Figure 2This invention relates to a multilingual semantic interaction module for a dynamic generation system of personalized college application planning based on multilingual AI.

[0031] Figure 3 This invention presents a dynamic gradient prediction engine for a multilingual AI-based personalized college entrance planning dynamic generation system.

[0032] Figure 4 This invention relates to a real-time policy adaptation system for a multilingual AI-based personalized college application planning dynamic generation system.

[0033] Figure 5 This invention relates to a 1+N dual-track service module for a multilingual AI-based personalized college entrance planning dynamic generation system. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figures 1 to 5 This invention provides a technical solution: a dynamic generation system for personalized college application planning based on multilingual AI.

[0036] I. System Architecture Deployment Plan

[0037] (I) Multilingual Semantic Interaction Module

[0038] 1. Hardware configuration:

[0039] 1.1 Deploy a distributed server cluster and use GPU acceleration (such as NVIDIA A100) to improve the inference speed of the bidirectional LSTM model, ensuring that the response time for cross-language intent recognition is ≤0.5 seconds.

[0040] 1.2 Deploy edge computing nodes in ethnic minority areas such as Xinjiang and Tibet to localize voice / text input and reduce network latency.

[0041] 2. Software Implementation:

[0042] 2.1 Speech Recognition Engine: Integrates open-source ASR models for Uyghur and Kazakh languages ​​(such as DeepSpeech), and improves the accuracy of minority language recognition to over 97% through fine-tuning training.

[0043] 2.2 Bilingual Lexicon Construction:

[0044] We collected policy documents issued by the Ministry of Education and provincial education departments and established a bilingual database containing more than 5,000 terms (divided into 2000:1500:1500 for each stage of the college entrance examination, postgraduate entrance examination and doctoral entrance examination).

[0045] We use FastText to train bilingual word vectors to achieve semantic alignment of policy terms.

[0046] 2.3 Cross-language intent recognition:

[0047] Using a bidirectional LSTM+Attention architecture, input Uyghur / Kazakh policy consultation text, output Chinese intent vector.

[0048] Deploy a multi-turn dialogue management module to analyze complex inquiries using slot filling technology.

[0049] 3. Data update mechanism:

[0050] 3.1 Collect new policy documents monthly and update the bilingual lexicon through manual annotation and semi-supervised learning to ensure policy coverage ≥ 99%.

[0051] (II) Dynamic Gradient Prediction Engine

[0052] 1. Data Acquisition and Preprocessing

[0053] 1.1 Access the Ministry of Education's Sunshine College Entrance Examination Platform and the official websites of graduate schools of various universities to crawl admission data (including cut-off scores, enrollment numbers for each major, and transfer information) from over 3,000 universities over the past 10 years.

[0054] 1.2 For college entrance examination candidates, a candidate profile is generated through a 6-dimensional interest assessment (learning style, career orientation, subject ability, etc.); for postgraduate entrance examination candidates, a professional depth orientation assessment is added (such as programming ability test, academic paper analysis); for doctoral candidates, a research direction fit assessment is introduced (based on keyword matching of published papers).

[0055] 2. Model Training and Optimization

[0056] 2.1 Prediction of College Application Choices:

[0057] Feature engineering: Extract 20+ dimensions of features such as candidates' scores, rankings, regional preferences, and physical conditions, combined with the fluctuation trend of college admission scores over the past three years.

[0058] XGBoost model training: 5-fold cross-validation is used. The prediction unit is "university-major group". The output is a three-level application scheme of "sprint (admission probability 30%-50%)-suitable (50%-80%)-safe (80%-100%)". Each gradient generates ≥672 combinations (total ≥1968 combinations).

[0059] 2.2 Prediction of Postgraduate Entrance Examination Goals:

[0060] It integrates features such as the application-to-admission ratio of target universities, the degree of professional matching (by comparing the results of in-depth professional evaluation with the curriculum of the target major), and the research direction of the supervisor (by crawling the keywords of the supervisor's published papers).

[0061] An attention mechanism is introduced to assign weights to universities in different regions (e.g., a weight coefficient of 1.2 for "Double First-Class" universities and 0.8 for ordinary universities), generating a three-level plan of "sprint-adaptation-safety" that includes matching information in three dimensions: university, major, and mentor.

[0062] 2.3 Generation of Doctoral Entrance Examination Plan:

[0063] Construct a knowledge graph of supervisors and research directions, and calculate the cosine similarity between the candidate's research direction and the supervisor (threshold ≥ 0.8).

[0064] Taking into account dynamic factors such as the fluctuation of the supervisor's enrollment quota and the number of ongoing projects over the past three years, we output a "core goal - alternative goal - safety goal" plan, with each goal accompanied by "application success rate assessment" and "improvement suggestions".

[0065] 3. Real-time forecasting service

[0066] 3.1 Deploy a microservice architecture and use Kubernetes to achieve elastic scaling of the model, supporting tens of thousands of concurrent requests.

[0067] 3.2 The prediction results are pushed to the user terminal in real time via WebSocket, generating a visual report (such as the probability distribution chart of the volunteer program's "sprint-suitable-safe").

[0068] (III) Real-time policy adaptation system

[0069] 1. Policy Data Collection

[0070] 1.1 Deploy a distributed crawler cluster to regularly crawl policy information from the Ministry of Education, provincial education departments, and university websites, with a focus on:

[0071] 1.1.1 College Entrance Examination: Policies on bonus points for ethnic minorities, rules for independent enrollment, and special programs (such as national special programs and local special programs).

[0072] 1.1.2. Postgraduate Entrance Examination: Minority Backbone Program, Adjustment of Re-examination Score Line, Changes in Transfer Policy.

[0073] 1.1.3 Doctoral Programs: Detailed rules for the application-assessment system, changes in the number of supervisors to be recruited, and research project funding policies.

[0074] 2. Policy Analysis and Matching

[0075] 2.1 Text parsing:

[0076] 2.1.1 Use the BERT model to perform semantic understanding of policy texts and extract key entities (such as "ethnic minorities", "bonus points ratio", and "application requirements").

[0077] 2.1.2 Construct a policy knowledge graph to associate policy clauses with applicable objects (e.g., “minority candidates in the four prefectures of southern Xinjiang” corresponds to “10 points added to the college entrance examination”).

[0078] 2.2 Candidate Qualification Matching:

[0079] 2.2.1 Extract characteristics such as ethnicity, household registration, and special status (such as children of martyrs) from the candidate's file and automatically match applicable policies.

[0080] 2.2.2 For real-time information such as adjustments to the postgraduate entrance examination re-examination cutoff scores and changes in the number of doctoral supervisors, early warnings will be sent via SMS / APP (response delay ≤ 1 hour).

[0081] 3. Conflict detection and report generation

[0082] 3.1 Compare the planning scheme with policy provisions to identify potential conflicts (such as the candidate's intended university not accepting transfers from the Minority Backbone Program).

[0083] 3.2 Generate a "Policy Adaptation Report", which includes: a list of applicable policies, a compliance assessment of the planning scheme, risk warnings, and adjustment suggestions.

[0084] (iv) 1+N Dual-Track Service Module

[0085] 1. Online intelligent platform

[0086] 1.1 Front-end design:

[0087] 1.1.1 Develop multilingual responsive web applications (supporting Uyghur / Kazakh / Chinese language switching) and adapt them to PCs, tablets, mobile phones and other terminals.

[0088] 1.1.2. Provide phased tools: College Entrance Examination Application Simulation System (supports multiple modes such as "major priority" and "university priority"), Postgraduate Entrance Examination Interview Simulation System (includes English oral and professional interview question banks), and Doctoral Research Proposal Template Library (categorized by discipline, with AI-assisted generation function).

[0089] 1.2 Data Platform:

[0090] 1.2.1 Establish a "personal academic record" to store all data of candidates from the college entrance examination to the doctoral stage (scores, assessment results, service records, etc.) and support cross-stage data correlation analysis.

[0091] 1.2.2. Real-time online and offline data synchronization is achieved through Apache Kafka, ensuring that service records (such as offline consultation content) are updated to the cloud within 5 minutes.

[0092] 2. Offline Postdoctoral Workstation

[0093] 2.1 Team Building:

[0094] We are recruiting PhDs in relevant fields, postgraduate entrance examination tutoring experts (with more than 5 years of tutoring experience), and a doctoral supervisor advisory group (with associate professor or higher titles), grouped by discipline (such as humanities and social sciences, science and engineering).

[0095] 2.2 Service Process:

[0096] 2.2.1 Dynamic matching algorithm: Based on the needs of candidates (such as "postgraduate entrance examination interview tutoring" and "doctoral application planning") and the expertise of experts (such as "computer science" and "economics"), the optimal service resources are automatically allocated.

[0097] 2.2.2 Basic Services: One-on-one consultation is provided through the appointment system (response time ≤ 24 hours). During critical periods (1 month before the postgraduate entrance examination re-examination and the critical period of doctoral application), an "expedited channel" is opened to arrange expert consultation within 12 hours.

[0098] 3. Service closed-loop management

[0099] 3.1 Establish a service quality evaluation system, assess satisfaction through NPS (Net Promoter Score), and automatically trigger a re-evaluation process for services with negative reviews.

[0100] 3.2 Regularly analyze service data and optimize the configuration of the expert team (e.g., increase the proportion of experts in emerging disciplines).

[0101] II. Method Implementation Process

[0102] (I) Multilingual Data Acquisition Steps (S1)

[0103] 1. Bilingual interactive interface

[0104] 1.1 Supports voice input (Uyghur / Kazakh / Chinese) and text input (Uyghur / Kazakh / Chinese trilingual keyboard), automatically detects the input language and routes it to the corresponding processing module.

[0105] 1.2 For college entrance examination candidates, a profile report is generated by a 6-dimensional interest assessment (which takes about 15 minutes to complete), including their learning style, career inclination, and subject strengths.

[0106] 2. Data cleaning and standardization

[0107] 2.1. Use regularization methods to handle abnormal data (such as null scores or contradictory test answers), and keep the error rate below 0.7%.

[0108] 2.2 Standardize data from different sources (such as college entrance examination scores, postgraduate entrance examination transcripts, and scientific research achievements) into a unified format and store them in the data platform.

[0109] (II) Dynamic Programming Generation Steps (S2)

[0110] 1. College Entrance Examination Stage Planning

[0111] 1.1 Input the candidate's scores, rankings, regional preferences, and other data, and combine them with the historical admission data of 3000+ universities to generate ≥1968 sets of application suggestions.

[0112] 1.2 Calculate the professional fit of each volunteer combination (by comparing the results of interest assessment with the professional curriculum), and ensure that the average fit is ≥80%.

[0113] 2. Planning during the postgraduate entrance examination stage

[0114] 2.1 Based on the candidate's undergraduate institution level, academic performance, and research experience, combined with the application-to-admission ratio of the target institution (average of the past three years), a three-level plan of "ambitious-suitable-safe" is generated.

[0115] 2.2 For each option, provide "Mentor Matching Analysis" (based on the similarity of research directions) and "Improvement Suggestions" (such as "Suggest publishing 1 core journal paper").

[0116] 3. Doctoral Program Planning

[0117] 3.1 Analyze the compatibility between the candidate's research direction (through published papers and project participation keywords) and the supervisor's research direction, and generate a "core objective - alternative objective - backup objective" plan.

[0118] 3.2 Assess the stability of the supervisor's enrollment quota (enrollment fluctuation coefficient ≤ 0.3 in the past three years) and provide "suggestions on the best time to apply" (e.g., "This supervisor plans to expand enrollment in 2026, so it is recommended to apply first").

[0119] (III) Policy Intelligent Adaptation Steps (S3)

[0120] 1. Policy matching

[0121] Extract candidate characteristics (ethnicity, household registration, special status, etc.) and automatically match applicable regional policies (such as "5 points added to the college entrance examination for ethnic minority candidates in Xinjiang" and "reduced admission score for postgraduate entrance examination for ethnic minority backbone program").

[0122] 2. Conflict Detection

[0123] 2.1 Compare the planning scheme with the policy provisions to identify potential conflicts (such as "the university the candidate intends to apply to does not accept the transfer of students from the backbone program").

[0124] 2.2 For the postgraduate entrance examination stage, push out real-time information on adjustments to the re-examination score line (e.g., "The re-examination score line for the Computer Science major at XX University has increased by 15 points compared to last year"); for the doctoral stage, push out information on changes in the number of supervisors to be recruited (e.g., "Supervisor XX will stop recruiting in 2026").

[0125] 3. Report generation

[0126] 3.1 Generate a "Policy Adaptation Report", which includes:

[0127] 3.1.1 List of applicable policies and details of bonus / deduction points.

[0128] 3.1.2 Compliance assessment of the planning scheme (green √ / yellow △ / red ×).

[0129] 3.1.3 Risk warning and adjustment suggestions (e.g., "It is recommended to change XX University from a top choice to a safe choice").

[0130] (iv) Dual-track service execution steps (S4)

[0131] 1. Online services

[0132] 1.1 Provide phased intelligent tools:

[0133] 1.1.1 College Entrance Examination: College Application Simulation System (supports "one-click generation" and "custom adjustment"), Admission Probability Calculator.

[0134] 1.1.2 Postgraduate Entrance Examination: Real-time push of interview simulation system (including English oral test and professional interview question bank) and transfer information.

[0135] 1.1.3. Doctoral students: Research proposal template library (categorized by discipline), supervisor information search (including research direction and enrollment quota).

[0136] 2. Offline services

[0137] 2.1 After the Gaokao (National College Entrance Examination): Provide "guidance on the connection between professional study planning and postgraduate entrance examination direction", for example:

[0138] 2.1.1 Computer Science Major: It is recommended to take courses such as "Artificial Intelligence" and "Big Data" and participate in related research projects.

[0139] 2.1.2 Medical Majors: Provide guidance for those applying for a professional master's degree in "Clinical Medicine" and help them contact internship hospitals in advance.

[0140] 2.2 Master's Stage: Providing guidance on "the connection between research planning and doctoral application direction", for example:

[0141] 2.2.1 Guidance on publishing high-quality papers (such as top journals in the Chinese Academy of Sciences' journals).

[0142] 2.2.2 Assist in contacting overseas mentors to enhance the competitiveness of your application.

[0143] 3. Data synchronization

[0144] Offline service records (such as consultation content and course schedules) are synchronized to the cloud database in real time via mobile devices to update candidates' personal files.

[0145] III. Typical Application Scenarios

[0146] (I) College Entrance Examination Application in Xinjiang Uygur Autonomous Region

[0147] 1. Requirements Analysis

[0148] Uyghur candidate A, with a total score of 480 in the college entrance examination (Chinese language and literature), is a resident of one of the four prefectures in southern Xinjiang and hopes to apply for a finance-related major.

[0149] 2. System Processing Flow

[0150] 2.1 Multilingual Interaction: Candidates can input Uyghur voice.

[0151] 2.2 Dynamic Programming:

[0152] 2.2.1 The system identifies candidates whose registered residence is in one of the four prefectures in southern Xinjiang and automatically adds 10 points to their score (equivalent to 490 points).

[0153] 2.2.2 Generate a three-tiered application plan of "Aim High - Suitable - Safe", which includes finance and economics majors from universities such as Xinjiang University of Finance and Economics, Shihezi University, and Tarim University.

[0154] 2.3 Policy Adaptation:

[0155] 2.3.1 If the candidate is found to be eligible for the "Southern Xinjiang Special Plan", universities under this plan will be given priority in the application process.

[0156] 2.3.2 Generate a "Policy Adaptation Report" which indicates that "those applying to universities under the jurisdiction of the autonomous region can enjoy tuition reduction and exemption policies."

[0157] 2.4 Dual-track service:

[0158] 2.4.1 Online: Provide career prospects analysis and curriculum introduction for finance-related majors.

[0159] 2.4.2 Offline: Arrange one-on-one professional selection guidance from doctoral supervisors at Xinjiang University of Finance and Economics.

[0160] (II) Postgraduate Entrance Examination Planning for Mongolian Candidates

[0161] 1. Requirements Analysis

[0162] Mongolian candidate B, undergraduate student majoring in Computer Science at Inner Mongolia University, with a GPA of 3.5 / 4.0, hopes to apply for a master's program in Artificial Intelligence.

[0163] 2. System Processing Flow

[0164] 2.1 Multilingual Interaction: Candidates can input "I am a minority candidate and I would like to apply for the postgraduate entrance examination bonus policy for Artificial Intelligence major. What should I do?" in Chinese text input.

[0165] 2.2 Dynamic Programming:

[0166] 2.2.1 Based on the candidate's undergraduate institution level and academic performance, combined with the ranking of universities in the AI ​​field, a three-tiered plan of "ambitious-adaptive-safe" is generated (e.g., Tsinghua University, Beijing University of Posts and Telecommunications, Inner Mongolia University).

[0167] 2.2.2 Analyze the matching degree between the target supervisor's research direction (such as "computer vision" or "natural language processing") and the candidate's research experience.

[0168] 2.3 Policy Adaptation:

[0169] 2.2.1 Identify candidates who meet the criteria for the "Minority Backbone Program" and automatically select universities that accept candidates for the program.

[0170] 2.3.2 Real-time push notifications for "XX University's 2026 AI major re-examination cutoff score increased by 10 points compared to last year".

[0171] 2.4 Dual-track service:

[0172] 2.4.1 Online: Provide AI professional postgraduate entrance examination review materials package and interview English oral template.

[0173] 2.4.2 Offline: Arrange for PhDs in AI from Tsinghua University to provide guidance on research directions and help revise research plans.

[0174] (III) Application Plan for Tibetan Doctoral Students

[0175] 1. Requirements Analysis

[0176] Candidate C, a Tibetan, holds a Master's degree in Ethnology from Sichuan University and has published two CSSCI papers. He hopes to apply for a doctoral degree in Ethnology.

[0177] 2. System Processing Flow

[0178] 2.1 Multilingual Interaction: Candidates can input "I want to apply for a PhD in Ethnology. Which professors' research areas are most compatible with my dissertation?" in both Tibetan and Chinese.

[0179] 2.2 Dynamic Programming:

[0180] 2.2.1 Construct a vector of the candidate's research direction (based on the keywords in the paper: "Tibetan culture" and "religious anthropology") and calculate the cosine similarity with the research directions of more than 300 ethnology mentors in China.

[0181] 2.2.2 Generate a “core objective - alternative objective - minimum objective” scheme, such as mentor X from Minzu University of China (similarity 0.92) and mentor Y from Yunnan University (similarity 0.87).

[0182] 2.3 Policy Adaptation:

[0183] 2.3.1 The candidate was found to be eligible for the "High-level Talents Program for Ethnic Minorities" and was prompted to apply for funding under the program.

[0184] 2.3.2 Real-time push notifications for alerts such as "The number of students to be enrolled by mentor XX in 2026 has been reduced from 2 to 1".

[0185] 2.4 Dual-track service:

[0186] 2.4.1 Online: Provides templates for doctoral application materials (research plan, personal statement) and AI tools for revising dissertations.

[0187] 2.4.2 Offline: Arrange for doctoral supervisors from Minzu University of China to provide one-on-one refinement of application materials and guide them in contacting overseas cooperative mentors.

[0188] IV. System Integration and Optimization

[0189] 1. Data security and privacy protection

[0190] 1.1. Blockchain technology is used to store candidate data to ensure that it cannot be tampered with; sensitive information (such as ID number) is encrypted using the national cryptographic algorithm SM4.

[0191] 1.2 Establish a tiered data access permission system, whereby offline service personnel can only view candidate information within their authorized scope.

[0192] 2. System Iteration and Optimization

[0193] 2.1 Collect user feedback monthly and optimize the recommendation algorithm through A / B testing (e.g., adjust the threshold for the "sprint-suitable-safe" gradient division).

[0194] 2.2. Update college admission data and policy knowledge base annually, and introduce new features (such as college employment quality reports) to improve prediction accuracy.

[0195] 3. Cross-system integration

[0196] 3.1 Establish API interfaces with the Ministry of Education's "Sunshine College Entrance Examination" platform and the China Higher Education Student Information System (CHESICC) to obtain data such as changes in enrollment plans and admission results in real time.

[0197] 3.2 Connect with the graduate admissions system of universities to enable online submission of application materials and automatic push of interview notices.

[0198] 4. Multilingual technical support

[0199] 4.1 Establish localized operation and maintenance teams in Xinjiang, Tibet and other regions to regularly collect feedback from ethnic minority users and optimize the bilingual interactive experience.

[0200] 4.2 Develop a Uyghur / Kazakh / Chinese trilingual speech synthesis engine to support the voice broadcasting function of policy interpretation and volunteer suggestions.

[0201] Through the above implementation methods, this system can effectively address the pain points of existing college planning services, provide multilingual, full-cycle, and highly accurate college application support for ethnic minority candidates, and contribute to educational equity and talent cultivation.

[0202] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0203] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multilingual AI-based personalized college application planning dynamic generation system, characterized in that: It includes a multilingual semantic interaction module, a dynamic gradient prediction engine, a real-time policy adaptation system, and a 1+N dual-track service module, among which: The multilingual semantic interaction module processes Uyghur and Kazakh speech / text input, achieves cross-language intent recognition through a bidirectional LSTM neural network, and constructs a Uyghur-Chinese bilingual policy lexicon covering the college entrance examination, postgraduate entrance examination, and doctoral degree stages to achieve multilingual policy semantic alignment. The dynamic gradient prediction engine, based on the XGBoost algorithm, integrates feature parameters from different stages of higher education, generating three-level application schemes ("sprint-suitable-safe") for the college entrance examination, "sprint-suitable-safe" for the postgraduate entrance examination, and "core target-alternative target-safe" for the doctoral degree. The real-time policy adaptation system uses web crawlers and BERT models to capture and analyze regional policies for the college entrance examination, postgraduate entrance examination, and doctoral degree stages in real time, automatically matching candidate qualifications and detecting conflicts between planning schemes and policies at each stage. The 1+N dual-track service module integrates an online intelligent platform and an offline doctoral workstation (including postgraduate entrance examination tutoring experts and doctoral supervisor advisory groups), realizing a closed-loop service throughout the entire higher education cycle through a data platform.

2. The multilingual AI-based personalized college application planning dynamic generation system according to claim 1, characterized in that: In the multilingual semantic interaction module, the cross-language intent recognition accuracy is ≥98.3%, and the bilingual policy terminology contains ≥5000 policy terms, of which ≥2000 are for the college entrance examination stage, ≥1500 for the postgraduate entrance examination stage, and ≥1500 for the doctoral stage.

3. The multilingual AI-based personalized college application planning dynamic generation system according to claim 1, characterized in that: The dynamic gradient prediction engine introduces an attention mechanism to assign weights to enrollment plans in different regions and at different stages of higher education. The accuracy of prediction for college entrance examination plans is ≥98%, ≥97% for postgraduate entrance examination, and ≥96% for doctoral entrance examination.

4. The multilingual AI-based personalized college application planning dynamic generation system according to claim 1, characterized in that: The real-time policy adaptation system has a policy response delay of ≤1 hour and supports automatic matching of more than 8 types of regional policies at various stages, such as the college entrance examination bonus points policy for ethnic minorities, the postgraduate entrance examination ethnic minority backbone program, and the doctoral application-assessment system.

5. The multilingual AI-based personalized college application planning dynamic generation system according to claim 1, characterized in that: In the 1+N dual-track service module, the offline service team includes PhDs in the corresponding fields, postgraduate entrance examination tutoring experts, and doctoral supervisor advisors. Service resources are allocated to candidates through a dynamic matching algorithm. The basic service response time is ≤24 hours. An "expedited channel" is added one month before the postgraduate entrance examination re-examination and during the critical period of doctoral application, with a response time of ≤12 hours.

6. A method for implementing college entrance examination planning based on the system described in any one of claims 1-5, characterized in that: include: S1. Multilingual data collection steps: Information on candidates at different stages of their academic journey is obtained through a bilingual interactive interface. For the college entrance examination, a 6-dimensional interest assessment is used to generate a candidate profile. For the postgraduate entrance examination, a "professional depth inclination assessment" is added. For the doctoral entrance examination, a "research direction fit assessment" is added. The data is cleaned using regularization methods, with an error rate of ≤0.7%. S2. Dynamic Programming Generation Steps: Based on the candidate's corresponding stage scores and ranking data, combined with the admission data of universities at each stage, planning suggestions are generated. In the college entrance examination stage, based on the admission data of 3000+ universities over the years, ≥1968 sets of application suggestions are generated with a professional matching degree of ≥80%. In the postgraduate entrance examination stage, suggestions are generated by combining the application-to-admission ratio of the target university and the professional matching degree. In the doctoral stage, suggestions are generated by combining the matching degree of the supervisor's research direction and the enrollment plan. S3. Policy Intelligent Adaptation Steps: Extract candidate characteristics to match regional policies for the college entrance examination, postgraduate entrance examination, and doctoral entrance examination, generate policy adaptation reports, and detect conflicts between planning schemes and policies. Support automatic matching of more than 8 types of policies at each stage. S4. Dual-track service execution steps: Online, we provide phased intelligent tools (college entrance examination application simulation, postgraduate entrance examination re-examination simulation, doctoral research plan template library, etc.), and offline, we provide one-on-one consultation and course services (college entrance examination application guidance, postgraduate entrance examination re-examination simulation interview, doctoral application material refinement, etc.). Service records are synchronized to the cloud database to realize real-time interaction of online and offline data.

7. The method for implementing college entrance examination planning according to claim 6, characterized in that: In the multilingual data collection step, a "personal education record" is established through the data platform to achieve continuous correlation of data from the college entrance examination to the postgraduate and doctoral stages.

8. The method for implementing college entrance examination planning according to claim 6, characterized in that: In the dynamic programming generation step, the postgraduate entrance examination planning scheme includes matching information of three dimensions: university, major, and supervisor, while the doctoral planning scheme includes research direction fit analysis and supervisor enrollment quota evaluation.

9. The method for implementing college entrance examination planning according to claim 6, characterized in that: In the policy intelligent adaptation step, information on adjustments to the re-examination score line and changes in the transfer process is pushed in real time for the postgraduate entrance examination stage, and information on changes in the number of supervisors to be recruited is pushed in real time for the doctoral stage.

10. The method for implementing college entrance examination planning according to claim 6, characterized in that: In the implementation steps of the dual-track service, the offline service provides bridging guidance for cross-stage further education needs, including guidance on the connection between post-college major study planning and postgraduate entrance examination direction, and guidance on the connection between master's stage research planning and doctoral application direction.

Images