A health service production-teaching integration big data interaction method and system

Abstract

The application discloses a health service production and education integration big data interaction method and system, comprising constructing a digital public information platform, building a production and education cooperation and human resource scheduling full-chain management module; embedding a user service feedback port in the platform, setting a credit level public display module to display to users, colleges and enterprises in real time, forming a service digital trust mechanism, synchronously feeding back credit data to a grading evaluation system, carrying out full-occupation-cycle production and education integration based on the platform, collecting learning data in real time in the training process, updating the skill archives of employees and grading evaluation results in combination with enterprise practice examination results. The application solves the limitations of single subject local data processing, improves the pertinence of talent training and the matching degree of student probation and employment, solves the problems of difficult enterprise talent screening and employee skill lag, and improves the trust degree of users to health service providers.

Description

Technical Field

[0001] This invention relates to a big data interaction method and system, and more specifically, to a big data interaction method and system for the integration of industry and education in health services. Background Technology

[0002] With the rapid development of the health service industry, the demand for professional health service personnel continues to grow. The suitability of talent training quality to actual job requirements and the standardization of industry service quality have become key to the industry's development. Industry-education integration, as a core pathway to break down barriers between talent training and industry needs, requires the use of big data technology to achieve efficient collaboration among multiple stakeholders. Therefore, conducting big data interaction for industry-education integration in health services has become a solution to the industry's development challenges. Currently, existing technologies lack specialized big data interaction technology for industry-education integration in the health service field. There is a lack of an integrated platform that can systematically integrate information on practitioners, employer needs, service feedback data, and industry-education collaboration process data. Furthermore, a dynamic management and precise matching mechanism spanning the entire career lifecycle of talent has not been established. The application of related technologies is mostly limited to local data processing by a single entity, failing to achieve interoperability and deep linkage of multi-dimensional data. This results in several problems: firstly, universities struggle to obtain real-time job demand data from enterprises, leading to lags in professional curriculum design and practical teaching content behind industry development, insufficient targeted talent training, and low matching rates between student internships and employment; secondly, enterprises face inefficient talent selection and untimely skill updates for their workforce, making it difficult to quickly meet their needs for human resource allocation and technological upgrades; thirdly, users lack effective evaluation references for health service providers, resulting in a lack of quantitative supervision and dynamic optimization mechanisms for service quality, leading to insufficient trust; and fourthly, government departments are unable to grasp real-time trends in industry talent supply and demand, dynamic service quality, and industry-education matching, resulting in a lack of data support for industry supervision and policy formulation.

[0003] Therefore, those skilled in the art are dedicated to providing a health service industry-education integration big data interaction method and system that can effectively solve the above-mentioned technical problems. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a big data interaction method for industry-education integration in health services, comprising the following steps: constructing a digital public information platform, the platform including an active talent pool and an employer database; collecting information on the educational background, skill certificates, and practical experience of practitioners in the health service field, as well as data on the job requirements, scale, and service scope of employers; establishing standardized data entry and storage; setting grading indicators for practitioners' professional skills, service reputation, and professional qualities, as well as grading standards for employers' qualifications, service quality, and industry reputation based on intelligent algorithms, forming a grading assessment; and building a full-chain management module for industry-education collaboration and human resource scheduling. The platform embeds a user service feedback port to collect multi-dimensional feedback information, including service process ratings, effectiveness evaluations, and complaints / suggestions. This feedback information is quantified into credit evaluation indicators and linked to the grading and assessment results of practitioners and employers to establish dynamic credit files. A credit rating disclosure module is set up to display the results in real time to users, schools, and enterprises, forming a digital trust mechanism for services. Credit data is simultaneously fed back to the grading and assessment system to achieve dynamic linkage and adjustment of credit and grading. Based on the platform, industry-education integration is carried out throughout the entire career cycle. In the academic education stage, schools obtain real-time job demand data from employers through the platform and adjust professional curriculum settings and practical teaching content. The platform matches school students with enterprise internship positions, generates personalized internship plans, and simultaneously records student internship performance and feeds it back to schools as a basis for teaching evaluation. In the vocational training stage, the platform, in conjunction with schools and enterprises, develops customized training courses based on practitioners' credit ratings, job promotion needs, and enterprise technology updates. Learning data is collected in real time during training, and practitioners' skill files and grading and assessment results are updated in conjunction with enterprise practical assessment results. The platform aggregates and analyzes data from talent pools, employer databases, credit records, and industry-education integration processes in real time. It generates multi-dimensional data reports on industry talent supply and demand trends, service quality dynamics, and industry-education matching degrees. These reports are then pushed to relevant government departments, while simultaneously providing targeted supply and demand matching information, skills enhancement suggestions, and job postings to educational institutions, enterprises, and professionals. This achieves multi-entity data sharing and efficient interaction.

[0005] Furthermore, the intelligent algorithm is a fusion algorithm of the analytic hierarchy process (AHP) and the dynamic weighted fuzzy comprehensive evaluation method based on the characteristics of the health service industry.

[0006] Furthermore, the analytic hierarchy process (AHP) includes constructing a three-level evaluation index system. The first-level indicators are the comprehensive ability evaluation index of practitioners and the service suitability evaluation index of employers. The second-level indicators include practitioners' professional skills, service reputation, professional ethics, and employers' qualification level, job supply quality, and willingness to cooperate in the industry. The third-level indicators are the subdivided quantitative items under the second-level indicators. The initial weights of each indicator are determined by pairwise comparison judgment matrices. A dynamic feedback mechanism from health service industry experts is introduced to iteratively correct the initial weights. During the correction process, the fluctuation coefficient of industry talent demand and the service quality correlation factor are combined to ensure that the weight allocation matches the actual development status of the industry in real time.

[0007] Furthermore, the dynamic weighted fuzzy comprehensive evaluation method includes establishing a dynamic fuzzy evaluation matrix to address the fuzzy and time-sensitive characteristics of user feedback information in health service scenarios. This matrix transforms multi-dimensional feedback data, such as user service process ratings, satisfaction levels, complaint rectification rates, service response speed ratings, and professional advice effectiveness evaluations, into fuzzy evaluation vectors. A time decay factor is set to assign differentiated weights to feedback data from different time periods, with recent feedback data having a higher weight coefficient than older data. The formula, derived using an exponential function, is as follows:

[0008] in It is a natural constant; The attenuation coefficient; The current time; Feedback generation time.

[0009] Furthermore, the fusion algorithm includes performing matrix operations on the indicator weights determined by the optimized analytic hierarchy process and the fuzzy evaluation vectors obtained by the dynamic fuzzy comprehensive evaluation method to output the graded evaluation results of practitioners and employers; an abnormal data filtering module is embedded in the fusion process to remove extremely abnormal feedback data and indicator data through the 3σ criterion, and an evaluation result confidence verification mechanism is set up. When the confidence is lower than the preset threshold, the secondary correction of indicator weights and the re-collection of evaluation data are triggered.

[0010] Furthermore, the update frequency of the dynamic credit file is consistent with the collection frequency of user service feedback information, and the update results are synchronized to the grading and evaluation system in real time. The collection frequency of user service feedback information adopts an intelligent and flexible adaptation mechanism, which is dynamically adjusted based on the service type, service duration, and service target group characteristics of the health service scenario: for high-frequency interactive services such as emergency nursing and postoperative rehabilitation, the collection frequency is set to be collected immediately after each service link is completed; for low-frequency services such as health check-ups and health management consultations, the collection frequency is set to be collected in a targeted manner within 24 hours after the service is completed. At the same time, for special service targets such as elderly patients with chronic diseases and maternal and infant care, an additional secondary feedback collection node is added 72 hours after the service, forming a collection mode that includes immediate, delayed, and targeted supplementation.

[0011] Furthermore, the dynamic credit profile update process incorporates a dynamic feedback weight calibration module, meaning that not all collected feedback information is assigned the same update weight: the authenticity of the feedback information is verified through a triple verification process involving audio and video playback of service scenarios, service recipient identity verification, and cross-comparison of multi-source feedback; the specificity of the feedback content is quantified into general evaluation, specific description, problem identification, and improvement suggestions; the credit rating of the feedback subject is established based on the authenticity of historical feedback and the timeliness of feedback response, and is divided into A, B, and C. The system employs three levels: based on the authenticity verification results of feedback information, the specificity of the feedback content, and the credit rating of the feedback subject, the update weight coefficient of a single feedback is calculated in real time using the aforementioned fusion algorithm. The weight coefficient ranges from 0.3 to 1.5. Feedback information that has been verified as authentic through triple verification, contains specific problem identification and improvement suggestions, and whose feedback subject has a credit rating of A or above, has a weight coefficient of 1.2-1.5, and the corresponding credit profile update result has a correspondingly increased impact on the tiered rating system. Feedback information that has not passed authenticity verification, is only a general evaluation, or whose feedback subject has a credit rating of C or below, has a weight coefficient of 0.3-0.5, and must be combined with at least two other valid feedbacks from other users in the same scenario for joint updates.

[0012] Furthermore, the synchronization of the update results with the tiered assessment system adopts a tiered linkage trigger mechanism: when the update magnitude of the core credit indicators in the dynamic credit file exceeds a preset threshold, the immediate reassessment process of the tiered assessment system is triggered, and synchronous adjustments are made through the related indicators in the three-level evaluation indicator system; the core credit indicators include service quality compliance rate, complaint rectification completion rate, and user repurchase recommendation rate, with the positive threshold set at 15% and the negative threshold set at 10% in the preset threshold; the related indicators in the three-level evaluation indicator system are the long-term service stability tiered indicator under the service reputation tiered indicator for practitioners and the service quality sustainability tiered indicator under the job supply quality tiered indicator for employers; if the update magnitude does not reach the threshold, the updated data is temporarily stored in the dynamic cache module of the tiered assessment system, and after a cumulative total of 3 updates or an interval of 7 calendar days, batch calibration and adjustment are performed in conjunction with the cumulative update data during the period.

[0013] Furthermore, the personalized internship program is generated based on the student's academic level, major, skills, career development intentions, and the company's job requirements, internship skill requirements, and mentoring resource allocation. Internship performance records will be synchronized to the job matching system as a basis for assessing the student's job matching ability. The content updates of the customized training courses will be synchronized with the company's technological updates within a time lag of no more than 7 working days. The course content covers modules for updating theoretical knowledge, strengthening practical skills, and reviewing and discussing case studies. Course development will be tailored to the student's skill gaps and the company's job skill requirements analyzed in the job matching system. The data report generation cycle can be set to daily, weekly, or monthly according to user needs. The report content will include dimensions such as the employment matching trend of college students, the distribution of skill gaps, the professional decline rate, and the job prosperity rate.

[0014] A health service industry-education integration big data interactive system, the system comprising: The data collection and storage module is used to build a digital public information platform, collect information on the educational background, skills certificates, and practical experience of practitioners and students in the health service field, as well as data on the job requirements, scale, service scope, job skills requirements, and teaching resource allocation of employers, and establish a standardized data entry and storage mechanism. The two-way grading and evaluation module embeds a fusion algorithm based on the analytic hierarchy process (AHP) optimized for the characteristics of the health service industry and the dynamic weighted fuzzy comprehensive evaluation method. It sets grading indicators and standards for practitioners, college students, and employers, analyzes and processes the collected data, and outputs grading and evaluation results. The dynamic credit management module includes a user service feedback port and a credit file management unit. It collects multi-dimensional service feedback information and quantifies it into credit evaluation indicators. It establishes dynamic credit files by linking the grading assessment results. The feedback weight is determined by verifying the authenticity of the feedback, quantifying the specificity of the content, and assessing the credit rating of the subject, so as to realize the dynamic updating of the credit file and the linkage adjustment of the grading system. The industry-education integration and matching module is used to match college students with enterprise internship positions during the formal education stage, generate personalized internship plans and record feedback, and, during the vocational training stage, collaborate with colleges and enterprises to develop customized training courses, collect learning and assessment data, and update skills files and grading results. At the same time, it connects to a dedicated employment matching module to enable the implementation of tasks such as student employment matching, skills gap analysis, and curriculum optimization. The data aggregation, analysis, and push module aggregates and analyzes talent pools, college student databases, employer databases, credit files, industry-education integration process data, and employment matching data in real time, generating multi-dimensional data reports that are pushed to relevant government departments. At the same time, it accurately pushes supply and demand matching information, skills enhancement suggestions, job recruitment information, employment matching results, and training program suggestions to colleges, enterprises, practitioners, and college students. The dedicated job matching module relies on the platform's active talent pool and employer database, and connects to the health service industry-education integration job matching system. This enables interconnection and interoperability of data among students, schools, and enterprises. It synchronizes student information, job information, matching results, skills gaps, and training suggestions from the job matching system to the talent files, employer files, and industry-education integration process data on the public information platform. This provides data support for the platform to update practitioners' skills files, adjust grading and assessment results, optimize customized training courses, and improve the basis for school teaching evaluations. Simultaneously, it synchronizes practitioners' grading and assessment results, employer qualification levels, and credit records from the public information platform to the job matching system, improving the accuracy and scientific nature of job matching.

[0015] The present invention has the following beneficial effects: 1. This invention constructs a digital public information platform containing an active talent pool and an employer pool, integrating information on practitioners, employer needs, service feedback, and industry-education collaboration process data. Through data aggregation, analysis, and push modules, it enables data exchange among multiple entities, including government, universities, enterprises, and practitioners, thus overcoming the limitations of single-entity local data processing.

[0016] 2. The industry-education integration adaptation module of this invention allows colleges and universities to obtain real-time job requirements from employers through the platform, adjust course and practical teaching content, generate personalized internship plans based on student situation and enterprise needs, and provide feedback on internship performance to colleges and universities as a basis for teaching evaluation. This solves the problem of talent training being out of touch with industry needs and improves the relevance of talent training and the matching degree between students' internships and employment.

[0017] 3. This invention uses a two-way rating module to rate and evaluate practitioners and employers. The platform accurately pushes supply and demand matching and job recruitment information. At the same time, it collaborates with colleges and enterprises to develop customized training courses during the vocational training stage. The course content is synchronized with the enterprise's technology updates within 7 working days, which solves the problems of enterprises' difficulty in talent screening and employees' lagging skills.

[0018] 4. This invention embeds a user service feedback port, collects multi-dimensional feedback information and quantifies it into credit evaluation indicators, establishes and publicizes dynamic credit files, and ensures the reliability of the evaluation through mechanisms such as feedback authenticity verification and weight calibration. At the same time, credit data is dynamically linked with the hierarchical assessment, which solves the problems of lack of user evaluation reference and lack of service quality supervision, forms a digital trust mechanism, and enhances users' trust in health service providers.

[0019] 5. The platform of this invention summarizes and analyzes various types of data in real time, generates multi-dimensional data reports such as industry talent supply and demand trends, service quality dynamics, and industry-education matching degree, and pushes them to relevant government departments, solving the problem of the government's untimely grasp of industry dynamics. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the hierarchical evaluation system of the present invention; Figure 2 This is a schematic diagram illustrating the integration and adaptation of industry and education in the system. Figure 3 This is a schematic diagram of the entire career cycle of industry-education integration. Figure 4 This is a schematic flowchart of the core algorithm of the two-way hierarchical evaluation module; Figure 5 This is a schematic diagram of the student user guide; Figure 6 This is a schematic diagram of the employment direction interface in this invention.

[0021] Figure 7 This is a schematic diagram of the interface explaining the employment direction in this invention.

[0022] Figure 8 This is a diagram illustrating the user guide for the school.

[0023] Figure 9 This is a statistical diagram illustrating student profiles.

[0024] Figure 10 This is a schematic diagram of the query record analysis interface.

[0025] Figure 11 This is a schematic diagram of the training program suggestion interface.

[0026] Figure 12 This is a schematic diagram of the gap warning interface.

[0027] Figure 13 This is a diagram illustrating the mapping relationship between majors and job positions.

[0028] Figure 14 This is a diagram illustrating the enterprise-side user guide.

[0029] Figure 15 This is a schematic diagram of the job posting management interface.

[0030] Figure 16 This is a schematic diagram of the job matching query interface. Detailed Implementation

[0031] like Figures 1 to 16 As shown, a big data interaction method for industry-education integration in health services includes the following steps: Constructing a digital public information platform, which includes an active talent pool and an employer database; collecting information on the educational background, skill certificates, and practical experience of practitioners in the health service field, as well as data on the job requirements, scale, and service scope of employers; establishing standardized data entry and storage; setting grading indicators for practitioners' professional skills, service reputation, and professional qualities, and grading standards for employers' qualifications, service quality, and industry reputation based on intelligent algorithms, forming a grading assessment; and building a full-chain management module for industry-education collaboration and human resource scheduling; embedding a user service feedback port in the platform, collecting multi-dimensional feedback information such as service process ratings, effect evaluations, and complaints and suggestions; quantifying the feedback information into credit evaluation indicators; establishing dynamic credit files by linking them with the grading assessment results of practitioners and employers; setting up a credit rating publicity module to display the credit rating to users, schools, and enterprises in real time, forming a digital trust mechanism for services; and simultaneously feeding credit data back to the grading assessment system to achieve dynamic linkage and adjustment of credit and grading. Based on the platform, industry-education integration is carried out throughout the entire career cycle. In the academic education stage, colleges and universities obtain real-time job demand data from employers through the platform, adjust professional curriculum settings and practical teaching content, match college students with enterprise internship positions, generate personalized internship plans, and simultaneously record students' internship performance and provide feedback to colleges and universities as a basis for teaching evaluation. In the vocational training stage, the platform, based on practitioners' credit rating, job promotion needs, and enterprise technology update dynamics, collaborates with colleges and enterprises to develop customized training courses. Learning data is collected in real time during the training process, and the practitioner's skill file and grading assessment results are updated in combination with the enterprise's practical assessment results. The platform aggregates and analyzes data from talent pools, employer databases, credit records, and industry-education integration processes in real time. It generates multi-dimensional data reports on industry talent supply and demand trends, service quality dynamics, and industry-education matching degrees. These reports are then pushed to relevant government departments, while simultaneously providing targeted supply and demand matching information, skills enhancement suggestions, and job postings to educational institutions, enterprises, and professionals. This achieves multi-entity data sharing and efficient interaction.

[0032] The intelligent algorithm is a fusion algorithm of the analytic hierarchy process (AHP) and the dynamic weighted fuzzy comprehensive evaluation method, optimized based on the characteristics of the health service industry. The AHP includes constructing a three-level evaluation index system; The primary indicators are the comprehensive ability evaluation indicators for practitioners and the service suitability evaluation indicators for employers. Secondary indicators include practitioners' professional skills, service reputation, professional ethics, and employers' qualification levels, job supply quality, and willingness to collaborate within the industry. Tertiary indicators are the subdivided quantitative items under the secondary indicators. Initial weights for each indicator are determined through a pairwise comparison judgment matrix. A dynamic feedback mechanism from health service industry experts is introduced to iteratively correct the initial weights. During the correction process, the fluctuation coefficient of industry talent demand and the service quality correlation factor are considered to ensure that the weight allocation matches the actual development status of the industry in real time. The dynamic weighted fuzzy comprehensive evaluation method includes establishing a dynamic fuzzy evaluation matrix to address the fuzzy and time-sensitive characteristics of user feedback information in health service scenarios. This matrix transforms multi-dimensional feedback data, such as user service process ratings, effect satisfaction, complaint rectification rate, service response speed ratings, and professional advice effectiveness evaluations, into fuzzy evaluation vectors. A time decay factor is set to assign differentiated weights to feedback data from different time periods, with recent feedback data having a higher weight coefficient than older data. The formula, derived using an exponential function, is as follows:

[0033] in It is a natural constant; The attenuation coefficient; The current time; Feedback generation time.

[0034] The fusion algorithm involves performing matrix operations on the indicator weights determined by the optimized analytic hierarchy process (AHP) and the fuzzy evaluation vectors obtained by the dynamic fuzzy comprehensive evaluation method, outputting the grading results for practitioners and employers. During the fusion process, an abnormal data filtering module is embedded to remove extremely abnormal feedback and indicator data using the 3σ criterion. Simultaneously, a confidence verification mechanism for the evaluation results is set up; when the confidence level falls below a preset threshold, a secondary correction of the indicator weights and a re-collection of evaluation data are triggered. These methods ensure the accuracy and reliability of the grading results. The update frequency of the dynamic credit file is consistent with the collection frequency of user service feedback information, and the update results are synchronized to the grading and evaluation system in real time. The collection frequency of user service feedback information adopts an intelligent and flexible adaptation mechanism, which is dynamically adjusted based on the service type, service duration, and service target group characteristics of the health service scenario: for high-frequency interactive services such as emergency nursing and postoperative rehabilitation, the collection frequency is set to be collected immediately after each service link is completed; for low-frequency services such as health check-ups and health management consultations, the collection frequency is set to be collected in a targeted manner within 24 hours after the service is completed. At the same time, for special service targets such as elderly patients with chronic diseases and maternal and infant care, an additional secondary feedback collection node is added 72 hours after the service, forming a collection mode that includes immediate, delayed, and targeted supplementation.

[0035] The dynamic credit profile update process incorporates a dynamic feedback weight calibration module, meaning that not all collected feedback information is assigned the same update weight. The authenticity of the feedback information is verified through a triple verification process: service scenario audio / video playback, service recipient identity verification, and cross-comparison of multi-source feedback. The specificity of the feedback content is quantified into general evaluations, detailed descriptions, problem identification, and improvement suggestions. The credit rating of the feedback subject is established based on the authenticity of historical feedback and the timeliness of feedback response, and is divided into A, B, and C levels. The system employs three levels: based on the authenticity verification results of feedback information, the specificity of the feedback content, and the credit rating of the feedback provider, the update weight coefficient of each piece of feedback is calculated in real time using the aforementioned fusion algorithm. The weight coefficient ranges from 0.3 to 1.5. Feedback information that has been verified as authentic through triple verification, contains specific problem identification and improvement suggestions, and whose feedback provider has a credit rating of A or above, has a weight coefficient set at 1.2-1.5, and the corresponding credit profile update result has a correspondingly increased impact on the grading system. Feedback information that fails authenticity verification, provides only general evaluations, or whose feedback provider has a credit rating of C or below, has a weight coefficient set at 0.3-0.5, and must be combined with at least two other valid feedbacks from users in the same scenario for joint updates. This is to avoid interference from a single low-quality feedback on the credit profile and grading results.

[0036] The synchronization of the update results to the tiered evaluation system adopts a tiered linkage trigger mechanism: when the update magnitude of the core credit indicators in the dynamic credit file exceeds the preset threshold, the immediate re-evaluation process of the tiered evaluation system is triggered, and the relevant indicators in the three-level evaluation indicator system are adjusted synchronously; the core credit indicators include service quality compliance rate, complaint rectification completion rate, and user repurchase recommendation rate, with the positive threshold set at 15% and the negative threshold set at 10% in the preset threshold; the relevant indicators in the three-level evaluation indicator system are the long-term service stability indicator under the service reputation indicator of practitioners and the service quality sustainability indicator under the job supply quality indicator of employers. If the update magnitude does not reach the threshold, the updated data will be temporarily stored in the dynamic cache module of the grading system. After a cumulative total of 3 updates or an interval of 7 calendar days, batch calibration and adjustment will be performed based on the cumulative update data during the period. This invention ensures both the real-time linkage between credit data and grading results and avoids excessive fluctuations in the grading system caused by frequent small updates, achieving a balance between dynamic adjustment and stable operation.

[0037] The personalized internship program is generated based on the student's academic level, major, skills, career development intentions, and the company's job requirements, internship skill requirements, and mentoring resource allocation. Internship performance records will be synchronized to the job matching system as a basis for assessing the student's job matching ability. The content updates of the customized training courses will be synchronized with the company's technological updates within 7 working days. The course content covers modules for updating theoretical knowledge, strengthening practical skills, and case review and discussion. Course development will be tailored to the student's skill gaps and the company's job skill requirements analyzed in the job matching system. The data report generation cycle can be set to daily, weekly, or monthly according to user needs. The report content will include dimensions such as the employment matching trend of college students, the distribution of skill gaps, the professional decline rate, and the job prosperity rate.

[0038] A health service industry-education integration big data interactive system, the system comprising: The data collection and storage module is used to build a digital public information platform, collect information on the educational background, skills certificates, and practical experience of practitioners and students in the health service field, as well as data on the job requirements, scale, service scope, job skills requirements, and teaching resource allocation of employers, and establish a standardized data entry and storage mechanism. The two-way grading and evaluation module embeds a fusion algorithm based on the analytic hierarchy process (AHP) optimized for the characteristics of the health service industry and the dynamic weighted fuzzy comprehensive evaluation method. It sets grading indicators and standards for practitioners, college students, and employers, analyzes and processes the collected data, and outputs grading and evaluation results. The dynamic credit management module includes a user service feedback port and a credit file management unit. It collects multi-dimensional service feedback information and quantifies it into credit evaluation indicators. It establishes dynamic credit files by linking the grading assessment results. The feedback weight is determined by verifying the authenticity of the feedback, quantifying the specificity of the content, and assessing the credit rating of the subject, so as to realize the dynamic updating of the credit file and the linkage adjustment of the grading system. The industry-education integration and matching module is used to match college students with enterprise internship positions during the formal education stage, generate personalized internship plans and record feedback, and, during the vocational training stage, collaborate with colleges and enterprises to develop customized training courses, collect learning and assessment data, and update skills files and grading results. At the same time, it connects to a dedicated employment matching module to enable the implementation of tasks such as student employment matching, skills gap analysis, and curriculum optimization. The data aggregation, analysis, and push module aggregates and analyzes talent pools, college student databases, employer databases, credit files, industry-education integration process data, and employment matching data in real time, generating multi-dimensional data reports that are pushed to relevant government departments. At the same time, it accurately pushes supply and demand matching information, skills enhancement suggestions, job recruitment information, employment matching results, and training program suggestions to colleges, enterprises, practitioners, and college students. The dedicated job matching module relies on the platform's active talent pool and employer database, and connects to the health service industry-education integration job matching system. This enables interconnection and interoperability of data among students, schools, and enterprises. It synchronizes student information, job information, matching results, skills gaps, and training suggestions from the job matching system to the talent files, employer files, and industry-education integration process data on the public information platform. This provides data support for the platform to update practitioners' skills files, adjust grading and assessment results, optimize customized training courses, and improve the basis for school teaching evaluations. Simultaneously, it synchronizes practitioners' grading and assessment results, employer qualification levels, and credit records from the public information platform to the job matching system, improving the accuracy and scientific nature of job matching.

[0039] The Health Service Industry-Education Integration Employment Matching System, developed using Python and Streamlit, is a comprehensive employment service platform. The platform architecture comprises three main functional modules: student, school, and enterprise. The student module primarily handles functions such as personal information entry, employment direction query, viewing and scoring matching results, and understanding employment directions. The personal information entry function is located in the left sidebar of the page. The entry dimensions cover basic information, major information, school information, occupational information, skills and certificates, and other information. This personal information forms the core foundation for the system's employment matching. The completeness and accuracy of the information directly determine the accuracy of the matching results. Based on this information, the system analyzes the suitability of students and target positions from multiple dimensions, including skills matching, certificate matching, academic qualification matching, and major matching. Table 1 describes the fields to be entered in this step, clearly specifying the format requirements, mandatory fields, and entry instructions for each field. After completing the information and checking their employment direction, students can view the corresponding job matching results. The results are displayed in a variety of dimensions, including overall matching degree, matching level, matching / missing skills and certificates, reasons for recommendation, improvement suggestions, job requirements details, etc. The matching level is divided into four tiers based on the overall matching degree: 80% and above is very good match, 60%-79% is fairly good match, 40%-59% is average match, and below 40% requires improvement.

[0040] Field Categories Field Name Formatting requirements Is this field required? illustrate Basic Information gender Select from dropdown menu (Male / Female) yes Select your gender Basic Information age Number input (18-60) yes Enter your age Basic Information Education dropdown selection yes Choose your highest level of education, as education is a basic requirement for many job positions. Professional Information major Drop-down selection or manual input yes Select your major or choose "Other" and then enter it manually. Your academic background is an important factor in job matching. Professional Information Is it a sports-related major? checkboxes no A checkmark indicates a sports-related major, and students in sports-related majors have a natural advantage in the field of health services. School Information School approximate address Text input no Enter the region where the school is located; some companies may have geographical restrictions. Career Information Expected salary (RMB / month) Numeric input no Enter your expected monthly salary to help the system understand your salary expectations. Career Information Job Category Professional Skills Drop-down selection (Full-time / Part-time / Flexible scheduling) yes Select your desired job type; different job types correspond to different job requirements. Skills and Certificates Professional skills checkbox no Choosing and mastering professional skills is crucial; skills matching is the core dimension of job matching. Skills and Certificates Other skills Text input, separated by commas. no Enter skills that are not in the options, separating multiple skills with commas, to supplement personal strengths not listed by the system. Skills and Certificates Certificates obtained checkbox no Choose any certificates you have already obtained; certificates are important proof of professional competence. Skills and Certificates Other certificates Text input, separated by commas. no Enter certificates that are not in the options. Separate multiple certificates with commas. Supplement personal qualifications not listed by the system. Other information Training willingness dropdown selection no Students with a higher willingness to train are more likely to adapt to the requirements of new positions. Other information Experience (years) Numeric input no Input years of work experience; experience is an important consideration for many positions. Other information other Text input no Enter additional information to provide personal strengths not covered by the system. Table 1 Instructions for Student Employment Direction Inquiry Operations and Functions Students can perform job matching in the "Employment Direction Search" section of the main content area. They must first select an employment direction of interest (multiple selections are supported). After selecting, clicking the "Start Matching Analysis" button will initiate the analysis. Note that at least one employment direction must be selected; otherwise, the system will provide a prompt. The more complete the personal information provided, the more accurate the matching results will be. See [link to relevant documentation] for more information. Figure 6 After completing the matching analysis, students can view the matching results generated by the system based on their personal information and job requirements. These results help students clearly understand their suitability for the target position and obtain targeted suggestions for improving their skills, thus clarifying the direction for career planning and skills development. The matching results will display a comprehensive match score from 0-100%, with higher scores indicating stronger suitability. The score will be categorized into four matching levels: 80% and above is a very good match, meaning the student possesses the core competencies for the position and is an ideal candidate. They should actively apply and highlight their matching skills and certifications. 60%-79% is a fairly good match, indicating the student has some basic skills and can try applying, showcasing their learning and improvement potential in the interview and addressing any skill gaps before applying. 40%-59% is a moderate match, indicating a gap between the student and the position. The student can treat this position as a long-term goal, focusing on accumulating relevant experience and skills and developing a detailed learning plan in the short term. Below 40% indicates a need for improvement, with a significant gap between the student and the position. The student can use this position as a reference for industry requirements, starting with basic skills and systematically learning to gradually build a relevant knowledge base. The results will also clearly present matching skills and certifications, as well as missing skills and certifications. The former helps students identify their core competencies and highlight them in their resumes and interviews, while the latter pinpoints skill gaps and provides specific goals for learning and improvement. After viewing the matching results, students can rate them in the matching result rating section below each result, entering a score from 1 to 5, where 1 represents very little help and 5 represents very much help. After completing the rating, click the submit button. This rating data will be synchronized to the school for matching result analysis, helping the system continuously optimize the matching algorithm and recommendation quality. In addition, students can view detailed introductions to various career paths in the career direction description section at the bottom of the page, covering areas such as fitness instructor, group exercise instructor, swimming instructor, ball sports instructor, rehabilitation therapist, health manager, venue operation and management, and personal trainer. Related content can be found in [link to relevant information]. Figure 7 .

[0041] School-side core functions and data management instructions The school-side platform offers multiple functions, including data management, basic data analysis, student profile statistics, query record analysis, and curriculum suggestion. It can upload, refresh, and clear student data, and conduct multi-dimensional analysis on student numbers, academic qualifications, skills, certificates, gender, and other dimensions. Based on students' job search and matching data, it generates key training directions and certificate training suggestions. The system also features a gap warning system and introduces indicators such as professional decline rate and job prosperity rate to provide data support for the school's professional development and curriculum reform. The professional decline rate is calculated as the ratio of the number of students searching for non-mapped jobs to the total number of students searching for that major, multiplied by 100%. The job prosperity rate is calculated as the ratio of the number of students from non-traditional majors searching for that job to the total number of students searching for that job, multiplied by 100%. The data management function is located in the left sidebar. It supports uploading student data files in Excel format. Newly uploaded data will be merged with existing data. The system defaults to using the simulation data in health_service_system / data / sports_students_data600.xlsx as the demonstration data source. If schools want to obtain more realistic analysis results, they need to upload their own student data files through the student data file upload function. Clicking the refresh data button will clear the cache and reload the data, while clicking the clear data button will clear the uploaded data and restore the system to its default state. The uploaded Excel data file must contain the specified columns. The relevant column requirements can be found in Table 2. The column names can be adjusted according to the actual situation of the school, and the system will automatically recognize them.

[0042] Data categories Suggested list Formatting requirements illustrate Basic Information Gender Text (male / female) or numbers (0 / 1) The system will automatically convert Basic Information Age number Student age Basic Information Academic Qualifications / Education Text or numbers The system will automatically convert it to the standard educational level. Professional Information Major text Students' major Professional Information Sports-related majors / is_sports_major Text (Yes / No) or Boolean value The system will automatically convert Experience information Years of experience number Years of work experience Skills Information professional skills Text, multiple skills separated by delimiters Supports multiple delimiters Certificate Information Certificates obtained Text, multiple certificates separated by delimiters Supports multiple delimiters Job Intentions Expected Career text Students' expected job positions Table 2 The system in this invention possesses intelligent compatibility and error message capabilities for uploaded files. If there are file format issues, it will display detailed error explanations. It can also automatically process various types of data with different column names and data types without requiring manual adjustments. The basic data analysis functions for the school side are located in the corresponding tab, enabling multi-dimensional data statistical analysis, including the total number of students in the system, the number and percentage of students majoring in sports-related fields, and the ability to present student academic qualifications in pie charts, and the distribution characteristics of student skill mastery and certificate holdings in bar charts, as well as the gender ratio of students. Student profile statistics are presented in a dedicated tab (see...). Figure 9 The core display shows the top 10 professional skills and the top 10 certificates held by students. It also supports viewing a complete data table containing information on all students, providing a comprehensive overview of students' abilities and qualifications.

[0043] The query record analysis function is located in the corresponding tab (see...). Figure 10The system allows users to visually display the search popularity of various positions among students of different majors through a heatmap of major-job search trends. It also tracks the number of searches for each employment direction, analyzes the distribution of student ratings for matching results, and provides a detailed table of search records containing student information, searched positions, and matching scores. Furthermore, it supports downloading and saving search records as CSV files for convenient offline analysis. The curriculum suggestion function is located on a dedicated tab (see...). Figure 11 Based on the skills and certification gaps in students' expected positions, the system will provide corresponding key training directions and certification training suggestions. It will also display the student possession rates of the required skills and certifications for each position through charts, and comprehensively score the health status of the major from multiple dimensions. The system also has a gap warning system. When the proportion of skill or certification gaps exceeds 80%, a warning will be automatically issued in the sidebar to remind the school to pay attention in time.

[0044] The major decline rate is calculated as 100% of the ratio of students searching for non-mapped job positions to the total number of students searching for that major. When this ratio exceeds 80%, the system issues a warning. This indicator reflects a decline in students' interest in traditional career paths, requiring the school to consider adjusting the major's training direction. The major decline rate is a key indicator of the match between a major and market demand, helping schools identify discrepancies between talent cultivation and market needs, providing data support for major adjustments and curriculum reforms. When the major decline rate exceeds the standard, schools can make adjustments in several ways: In terms of major design, increase course content in emerging employment fields to align with students' employment needs; in terms of curriculum system, identify emerging fields of interest to students and add relevant skills training and practical components; in terms of industry-university cooperation, expand cooperation with emerging industry enterprises to build more internship and employment platforms for students; in terms of enrollment strategy, adjust enrollment plans for majors with continuously high decline rates, highlighting the connection between the major and emerging health service fields in promotional materials; and in terms of faculty development, introduce teachers with backgrounds in emerging fields and organize relevant training for existing teachers to update their knowledge structure. The system also includes a tiered early warning mechanism for professional decline rates. A decline rate of 60%-79% indicates a mild warning, suggesting a deviation from market demand that requires close monitoring; 80%-89% indicates a moderate warning, representing a significant deviation that necessitates timely adjustments; and 90% or higher indicates a severe warning, signifying a serious disconnect between the profession and market needs, requiring major reforms. For example, if the decline rate for physical education reaches 85%, it means 85% of students in this major have inquired about non-traditional job positions such as fitness instructors and health managers. This reflects a decline in the attractiveness of traditional physical education teacher employment or market saturation, with students showing increased interest in emerging health service fields. The university needs to adjust the major's training direction promptly, increasing relevant courses and practical components, while also establishing partnerships with local fitness clubs and health management institutions to provide students with more internship and employment opportunities.

[0045] The job prosperity rate is calculated by multiplying the number of non-traditional major students searching for the job by the total number of students searching for the job. When this value is ≥60%, it indicates that the job has strong cross-disciplinary appeal. This indicator is an important basis for measuring the market attractiveness and emerging nature of job positions. It reflects the degree of attention the job receives among students from non-traditional related majors. The higher the prosperity rate, the stronger the cross-disciplinary appeal of the job, and it is often in a stage of rapid development or demand expansion. Job availability rates are a crucial indicator for schools to identify new market opportunities and can guide curriculum reform and professional expansion. Schools can offer interdisciplinary micro-majors, minor courses, or workshops for jobs with high availability rates. For example, when the availability rate for health management positions is high, schools can collaborate with medical or public health schools to offer interdisciplinary courses in exercise, nutrition, and chronic disease management. In recruitment and advertising, schools should emphasize the connection between their programs and these high-potential, highly cross-disciplinary attractive positions to enhance the programs' relevance and appeal. Regarding faculty and resource investment, schools should guide teachers to focus on updating knowledge in emerging fields and invest resources in building relevant laboratories or training bases. For businesses, high job availability rates mean an expanded talent pool. Besides graduates from traditional sports majors, a large number of non-traditional professionals are interested in these positions, broadening recruitment channels. High cross-disciplinary interest also reflects the multifaceted nature of these positions, allowing businesses to optimize their recruitment strategies accordingly. The job descriptions and competency models should be refined. A tiered system based on job popularity rates is primarily used for opportunity identification: 60%-79% indicates mild interest, suggesting the job is showing cross-disciplinary appeal and deserves inclusion in the observation list; 80%-89% indicates moderate opportunity, representing a job that has become a cross-disciplinary hotspot, and the university should initiate relevant course research; 90% and above indicates significant opportunity, suggesting the job is highly likely to become an industry trend, and the university should prioritize resources and respond quickly to market demands. For example, if the popularity rate for a venue operation and management position reaches 85%, it means that 85% of the interested students come from non-traditional related majors such as social sports guidance and management, such as business administration, marketing, and computer science. This signals a growing demand for composite abilities in digital management, customer service, and business planning in modern sports venue operations. Based on this, the university can collaborate with the School of Economics and Management to develop a specialized training module focusing on smart sports venue operations.

[0046] The enterprise-side platform encompasses four core functions: enterprise information management, job posting management, job matching query, and matching result viewing. It allows for the input and posting of enterprise and job information. When a job is posted, the system generates a unique job code based on the core job information using the MD5 hash algorithm, achieving deduplication of job information. The job matching process combines education, experience, skills, and certification matching, employing TF-IDF, cosine similarity, and XGBoost models for multi-dimensional feature modeling to calculate a precise matching score. Matching results display key information such as job code, number of matches, job requirements, and requirement fulfillment status, providing data support for enterprise recruitment. The system requires Python 3.8 or later and depends on libraries such as Streamlit, Pandas, Scikit-learn, Plotly, Joblib, and XGBoost. It includes built-in simulated student and job data to meet functional demonstration needs, while also supporting schools uploading real student data and companies posting real job requirements, closely matching practical usage scenarios. The system's interface uses a sidebar and main content area layout, incorporating diverse interactive elements such as buttons, dropdown selection boxes, and checkboxes to enhance ease of use. Visual feedback includes green success prompts, yellow warning prompts, red error prompts, and loading animations, allowing users to monitor the operation status in real time. The chart display function supports interactive operations such as hovering to view details, clicking to filter data, and zooming to view details, making data viewing more flexible.

[0047] The enterprise information management function is located on the main page of the enterprise interface. Enterprises can fill in information such as enterprise name, venue name, venue address, name of the person filling in the information, contact information of the person filling in the information, services provided, unique services, number of coaches, coaches' areas of expertise, and supplementary coaches' areas of expertise. After completing the information, clicking the "Save Enterprise Information" button will synchronize the information to the system backend. The job posting management function is located on the secondary page of the enterprise interface (see...). Figure 15 Companies need to fill in job information such as job title, work location, salary range, education requirements, experience requirements, number of positions needed, required skills, required certificates, and job description on the page. After clicking the "Post Job" button, the system will generate a unique job code for the job. If the same job information is detected, the number of positions will be accumulated and stored to avoid data redundancy.

[0048] The unique job posting code (firm_post_code) generated by the system for each position is crucial for ensuring the uniqueness and traceability of job posting information, effectively preventing data redundancy caused by companies repeatedly posting the same job. The job posting code is not generated randomly, but calculated using the MD5 hash algorithm based on the core information of the job. This algorithm is an encrypted hash function that maps data of arbitrary length to a fixed-length (128-bit, usually represented as a 32-bit hexadecimal string) digest. Its core characteristic is that even slight changes in the input data will result in completely different output hash values, while the same input will generate the same output. The specific calculation steps are as follows: After the company submits job information, the system first extracts core attributes such as job name, work location, education requirements, experience requirements, required skills (sorted and concatenated), and required certificates (sorted and concatenated), and concatenates them into a standardized string. Then, the concatenated string is uniformly formatted, removing extra spaces and unifying capitalization to ensure consistent input for the same information. Next, the standardized string is used as input to call the hashlib.md5() function to perform MD5 hash calculation. Finally, the hexadecimal representation of the hash result is taken to obtain the final job posting code, firm_post_code.

Claims

1. A big data interaction method for integrating industry and education in health services, characterized in that, Includes the following steps: Construct a digital public information platform, which includes an active talent pool and an employer database. Collect information on the educational background, skills certificates, and practical experience of practitioners in the health service field, as well as data on the job requirements, scale, and service scope of employers, and establish standardized data entry and storage. Based on intelligent algorithms, the professional skills, service reputation, and professional qualities of practitioners are set as grading indicators, and the qualifications, service quality, and industry reputation of employers are set as grading standards, forming a grading assessment and building a full-chain management module for industry-education collaboration and human resource scheduling. The platform embeds a user service feedback port to collect multi-dimensional feedback information such as service process ratings, effect evaluations, and complaints and suggestions. This feedback information is quantified into credit evaluation indicators and linked with the grading and assessment results of practitioners and employers to establish dynamic credit files. A credit rating publicity module is set up to display the credit rating to users, schools, and enterprises in real time, forming a digital trust mechanism for services. Simultaneously, credit data is fed back to the grading and assessment system to achieve dynamic linkage and adjustment of credit and grading. Based on the platform, industry-education integration is carried out throughout the entire career cycle. In the academic education stage, colleges and universities obtain real-time job demand data from employers through the platform, adjust professional curriculum settings and practical teaching content, match college students with enterprise internship positions, generate personalized internship plans, and simultaneously record students' internship performance and provide feedback to colleges and universities as a basis for teaching evaluation. In the vocational training stage, the platform, in conjunction with colleges and enterprises, develops customized training courses based on practitioners' credit rating, job promotion needs, and enterprise technology update dynamics. Learning data is collected in real time during the training process, and the skills files and grading results of practitioners are updated in combination with the results of enterprise practical assessments. The platform summarizes and analyzes the data of the integration process in real time and generates reports.

2. The health service industry-education integration big data interaction method according to claim 1, characterized in that, The intelligent algorithm is a fusion algorithm based on the hierarchical analysis method and the dynamic weighted fuzzy comprehensive evaluation method optimized for the characteristics of the health service industry.

3. The health service industry-education integration big data interaction method according to claim 2, characterized in that, The analytic hierarchy process includes constructing a three-level evaluation index system; The primary indicators are the comprehensive ability evaluation indicators for practitioners and the service suitability evaluation indicators for employers. The secondary indicators include practitioners' professional skills, service reputation, professional ethics, as well as employers' qualification level, job supply quality, and willingness to cooperate in the industry. The tertiary indicators are the subdivided quantitative items under the secondary indicators. The initial weights of each indicator are determined by pairwise comparison judgment matrices. A dynamic feedback mechanism from health service industry experts is introduced to iteratively correct the initial weights. During the correction process, the fluctuation coefficient of industry talent demand and the service quality correlation factor are combined to ensure that the weight allocation matches the actual development status of the industry in real time.

4. The health service industry-education integration big data interaction method according to claim 2, characterized in that, The dynamic weighted fuzzy comprehensive evaluation method includes establishing a dynamic fuzzy evaluation matrix to address the characteristics of fuzziness and timeliness of user feedback information in health service scenarios. This matrix transforms multi-dimensional feedback data, such as user service process scores, effect satisfaction, complaint rectification rate, service response speed scores, and professional advice effectiveness evaluations, into fuzzy evaluation vectors. A time decay factor is set to assign differentiated weights to feedback data from different time periods, with a higher weight coefficient for recent feedback data than for older data. The formula is derived using an exponential function: ； in It is a natural constant; The attenuation coefficient; The current time; Feedback generation time.

5. The health service industry-education integration big data interaction method according to claim 2, characterized in that, The fusion algorithm includes performing matrix operations on the index weights determined by the optimized analytic hierarchy process and the fuzzy evaluation vectors obtained by the dynamic fuzzy comprehensive evaluation method, and outputting the graded evaluation results of practitioners and employers. An abnormal data filtering module is embedded in the fusion process to remove extremely abnormal feedback data and indicator data through the 3σ criterion. At the same time, an evaluation result confidence verification mechanism is set up. When the confidence level is lower than the preset threshold, the process of secondary correction of indicator weight and re-collection of evaluation data is triggered.

6. The health service industry-education integration big data interaction method according to claim 1, characterized in that, The update frequency of the dynamic credit file is consistent with the collection frequency of user service feedback information, and the update results are synchronized to the grading and evaluation system in real time. The collection frequency of user service feedback information adopts an intelligent and flexible adaptation mechanism, which is dynamically adjusted based on the service type, service duration, and service target group characteristics of the health service scenario: for high-frequency interactive services such as emergency nursing and postoperative rehabilitation, the collection frequency is set to be collected immediately after each service link is completed; for low-frequency services such as health check-ups and health management consultations, the collection frequency is set to be collected in a targeted manner within 24 hours after the service is completed. At the same time, for special service targets such as elderly patients with chronic diseases and maternal and infant care, an additional secondary feedback collection node is added 72 hours after the service, forming a collection mode that includes immediate, delayed, and targeted supplementation.

7. The health service industry-education integration big data interaction method according to claim 6, characterized in that, The dynamic credit profile update process incorporates a dynamic feedback weight calibration module, meaning that not all collected feedback information is assigned the same update weight: The authenticity verification of the feedback information is achieved through a triple verification process: service scenario audio and video playback, service recipient identity verification, and cross-comparison of multi-source feedback. The specificity of the feedback content is quantified into general evaluation, specific description, problem identification, and improvement suggestions. The credit rating of the feedback subject is established based on the authenticity of historical feedback and the timeliness of feedback response, and is divided into three levels: A, B, and C. Based on the authenticity verification results of the feedback information, the specificity of the feedback content, and the credit rating of the feedback subject, the update weight coefficient of a single feedback is calculated in real time using the fusion algorithm. The weight coefficient ranges from 0.3 to 1.

5. Feedback information that has been confirmed as authentic through triple verification, contains specific problem identification and improvement suggestions, and has a credit rating of A or above from the feedback subject, has a weight coefficient of 1.2-1.5, and the corresponding credit profile update result has a correspondingly increased impact on the grading system. Feedback information that fails the authenticity verification, is only a general evaluation, or has a credit rating of C or below from the feedback subject, has a weight coefficient of 0.3-0.5, and must be combined with at least two other valid feedbacks from other users in the same scenario for joint updates.

8. The health service industry-education integration big data interaction method according to claim 7, characterized in that, The synchronization of the update results to the tiered assessment system adopts a tiered linkage triggering mechanism: When the update magnitude of core credit indicators in the dynamic credit profile exceeds a preset threshold, the immediate reassessment process of the tiered evaluation system is triggered, and the relevant indicators in the three-level evaluation indicator system are adjusted synchronously. The core credit indicators include service quality compliance rate, complaint rectification completion rate, and user repurchase recommendation rate. The preset thresholds are set at 15% for positive and 10% for negative. The relevant indicators in the three-level evaluation indicator system are the long-term service stability indicator under the service reputation indicator of practitioners and the service quality sustainability indicator under the job supply quality indicator of employers. If the update magnitude does not reach the threshold, the updated data will be temporarily stored in the dynamic cache module of the grading and evaluation system. After a total of 3 updates or an interval of 7 calendar days, batch calibration and adjustment will be carried out in combination with the cumulative update data during the period.

9. The health service industry-education integration big data interaction method according to claim 1, characterized in that, The personalized internship program is generated based on the student's academic level, major, skills, career development intentions, and the company's job requirements, internship skill requirements, and mentoring resource allocation. Internship performance records will be synchronized to the job matching system as a basis for assessing the student's job matching ability. The content updates of the customized training courses will be synchronized with the company's technological updates within 7 working days. The course content covers modules for updating theoretical knowledge, strengthening practical skills, and case review and discussion. Course development will be tailored to the student's skill gaps and the company's job skill requirements analyzed in the job matching system. The data report generation cycle can be set to daily, weekly, or monthly according to user needs. The report content will include dimensions such as the employment matching trend of college students, the distribution of skill gaps, the professional decline rate, and the job prosperity rate.

10. A health service industry-education integration big data interactive system, characterized in that, The system includes: The data collection and storage module is used to build a digital public information platform, collect information on the educational background, skills certificates, and practical experience of practitioners and students in the health service field, as well as data on the job requirements, scale, service scope, job skills requirements, and teaching resource allocation of employers, and establish a standardized data entry and storage mechanism. The two-way grading and evaluation module embeds a fusion algorithm based on the analytic hierarchy process (AHP) optimized for the characteristics of the health service industry and the dynamic weighted fuzzy comprehensive evaluation method. It sets grading indicators and standards for practitioners, college students, and employers, analyzes and processes the collected data, and outputs grading and evaluation results. The dynamic credit management module includes a user service feedback port and a credit file management unit. It collects multi-dimensional service feedback information and quantifies it into credit evaluation indicators. It establishes dynamic credit files by linking the grading assessment results. The feedback weight is determined by verifying the authenticity of the feedback, quantifying the specificity of the content, and assessing the credit rating of the subject, so as to realize the dynamic updating of the credit file and the linkage adjustment of the grading system. The industry-education integration and matching module is used to match college students with enterprise internship positions during the formal education stage, generate personalized internship plans and record feedback, and, during the vocational training stage, collaborate with colleges and enterprises to develop customized training courses, collect learning and assessment data, and update skills files and grading results. At the same time, it connects to a dedicated employment matching module to enable the implementation of tasks such as student employment matching, skills gap analysis, and curriculum optimization. The data aggregation, analysis, and push module aggregates and analyzes talent pools, college student databases, employer databases, credit files, industry-education integration process data, and employment matching data in real time, generating multi-dimensional data reports that are pushed to relevant government departments. At the same time, it accurately pushes supply and demand matching information, skills enhancement suggestions, job recruitment information, employment matching results, and training program suggestions to colleges, enterprises, practitioners, and college students. The dedicated employment matching module relies on the platform's active talent pool and employer database to connect with the health service industry-education integration employment matching system. This module enables the interconnection of data among students, schools, and enterprises, synchronizing student information, job information, matching results, skills gaps, and training suggestions from the employment matching system to the talent files, employer files, and industry-education integration process data on the public information platform.

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