Student health monitoring and intervention method based on multi-terminal cooperation and intelligent equipment linkage

Abstract

The invention discloses a student health monitoring and intervention method and system based on multi-terminal cooperation and intelligent equipment linkage. The method comprises the steps of authorizing and creating accounts according to levels, and completing student information storage in batches. Student historical health data, school basic information and special requirements are integrated, regional student health abnormity indexes are analyzed and identified, school hardware and priority requirements are matched, and a personalized screening list containing necessary and selective items and implementation suggestions is automatically generated. And a screener completes synchronous acquisition of health and environment data through a WeChat applet. And analyzing data through a health-environment association model, identifying high-risk association items, performing graded early warning, pushing optimization suggestions, risk reports and intervention guide, setting rectification time limit and acceptance standards, and forming closed-loop management. And optimizing the screening list in each quarter, establishing a follow-up file for abnormal students, establishing a home-school doctor collaborative follow-up module, and dynamically adjusting the intervention level. And the accuracy and effectiveness of student health management are improved.

Description

Technical Field

[0001] This invention belongs to the field of student health monitoring technology, and more specifically, relates to a student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage. Background Technology

[0002] The current student health monitoring and intervention work faces systemic challenges, with prominent issues of disconnect and inefficiency in various links, which seriously affect the actual effectiveness of health management.

[0003] From a collaborative perspective, student health management involves multiple stakeholders, including schools, disease control centers, community hospitals, and parents. However, these stakeholders lack a unified collaborative framework, and the division of authority is vague and lacks standardized norms. Key information such as student health data and school environment information held by different stakeholders is stored in a scattered manner, making efficient transfer and sharing difficult and resulting in poor coordination between different stages. For example, students' daily health records kept by schools cannot be synchronized to hospitals in a timely manner, and disease control centers struggle to integrate data from multiple schools within the region for overall analysis. As a result, each stakeholder can only work based on partial information, failing to form a cohesive management force.

[0004] At the data management level, there are two problems: "inefficient data collection" and "insufficient application." On the one hand, students' basic information and historical health data rely heavily on manual entry, which not only consumes a lot of manpower and time but is also prone to data errors due to operational mistakes. Furthermore, the lack of batch processing and verification mechanisms makes it difficult to build a complete and accurate health database. On the other hand, the collection of students' health indicators (such as vision and weight) and teaching environment factors (such as classroom lighting, cafeteria meals, and outdoor activity time) is independent. The data records are not synchronized or matched, making it impossible to explore the intrinsic relationship between the two and accurately identify the core factors affecting students' health. This means that health monitoring remains at the "data recording" level and cannot provide a scientific basis for subsequent interventions.

[0005] From the perspective of intervention and management, the existing model exhibits a "fragmented" characteristic. Early warnings for student health abnormalities often rely solely on a single health indicator, lacking a comprehensive assessment that incorporates environmental factors. This results in intervention recommendations that are too generic and fail to meet the specific needs of students in different regions and at different educational levels. Furthermore, after an early warning is issued, there is a lack of effective coordination among school rectification, parental cooperation, and follow-up by professional institutions. There are no clear deadlines or acceptance standards for rectification, and subsequent screening and management plans cannot be dynamically adjusted based on the intervention's effectiveness. This prevents the formation of a complete management loop, making it difficult to address student health issues continuously and accurately, thus hindering the overall improvement of health management quality. Summary of the Invention

[0006] This invention aims to address issues such as poor collaboration among multiple stakeholders, insufficient data collection and correlation, and lack of closed-loop intervention in student health monitoring and intervention. By constructing a multi-terminal collaboration mechanism, optimizing data collection and correlation analysis, and forming a "early warning-intervention-verification" closed loop, it enables efficient collaboration among stakeholders, accurately identifies health influencing factors, dynamically optimizes management plans, and improves the scientific rigor and effectiveness of student health management.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage, comprising: S1. Create accounts according to hierarchical authorization principles and complete batch entry of student information into the database; S2. Based on existing information including students' historical health data, basic school information, and user-supplemented specific needs, the system integrates and analyzes regional student health abnormality indicators, matches school hardware conditions, and combines priority needs to automatically generate a personalized screening list containing mandatory items, optional items, and implementation suggestions. S3. Screeners log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and scan the student's health check code to complete the information retrieval and automatic synchronization of test data; at the same time, for common disease monitoring tasks, they fill in environmental information including school drinking water, canteen, classrooms and other teaching facilities through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. S4. Automatically link and analyze student health data and teaching environment data, identify high-risk correlation items through the "health-environment correlation model" and trigger graded early warnings, complete the push of environmental optimization suggestions, regional risk reports and family intervention guidelines, and simultaneously set rectification deadlines and acceptance standards. After material upload and verification, a closed-loop management of "early warning-intervention-verification" is formed.

[0008] S5. Optimize the screening list for the next quarter based on full-process data each quarter; at the same time, generate follow-up files for students with abnormal health conditions, build a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjust the intervention level based on the effect evaluation indicators.

[0009] Furthermore, the hierarchical authorization principle in S1 is as follows: the municipal / district CDC creates management accounts for district / county CDCs, physical examination institutions, and schools in the system backend, and then the physical examination institutions create screening personnel accounts and configure operable schools, projects, and login time permissions.

[0010] Furthermore, the S2 student health abnormality indicators include: vision-related indicators used to determine whether students have problems including poor vision, myopia, and astigmatism; body shape indicators that can intuitively reflect students' overweight and obesity; physiological function indicators used to monitor students' cardiovascular function and lung ventilation capacity; oral health indicators used to provide a basis for oral hygiene intervention and caries prevention; blood health indicators used to screen students for anemia; skeletal development indicators used to identify students' skeletal development abnormalities at an early stage; and other health indicators used to screen for eye structure problems, hearing impairment, and potential skin health risks.

[0011] Furthermore, the classification method for mandatory items in S2 is as follows: The mandatory items to be checked must prioritize meeting three core requirements: "high health risk, strong policy requirements, and alignment with seasonal prevention and control priorities." Therefore, referring to the weighted summation model, by considering each influencing factor, Historical anomaly rate Seasonal risks Policy requires weight allocation The scores are then weighted and summed to obtain a comprehensive score, which directly reflects the degree of necessity for the project to be carried out. The classification logic is based on threshold selection, referencing element selection rules in set theory, and the comprehensive score is... Reaching the mandatory search threshold Project Included in the mandatory search set This ensures that all selected projects meet the "high necessity" standard and align with the screening objective of "prioritizing the resolution of core issues." , in, This represents the final set of items that must be searched.

[0012] Furthermore, the classification method for the selected items in S2 is as follows: The selection of projects needs to balance "feasibility" and "potential health risks" to avoid wasting resources due to "screening for the sake of screening." Therefore, a weighted summation model is also used to assess feasibility. and potential risks Assign weights The weighted summation yields a comprehensive score reflecting the cost-effectiveness of the project, making it suitable for projects with "non-common needs and flexible selection" in the selection process. , in, This represents the final set of selected items for investigation; This is a mandatory threshold. To select the lower limit threshold; To differentiate between mandatory and optional items, and to avoid including low-value items, a range classification method is used, with items having a comprehensive score ranging from [specific value] to [specific value]. Projects in the range Included in the selection set lower than Projects were excluded due to low feasibility or low risk, exceeding [a certain percentage]. Items deemed too necessary are automatically added to the mandatory query set.

[0013] Furthermore, the intelligent detection devices in S3 include: an intelligent vision chart, a computer optometry instrument, an eye biometer, a lung capacity meter, a Bluetooth height and weight meter, an electronic blood pressure monitor, and an electronic scoliosis measuring ruler.

[0014] Furthermore, the health-environment association model in S4 is specifically as follows: Let the first Class of health indicators in the first The standardized value of each subgroup is The higher the value, the more severe the health abnormality; Class II environmental indicators in the first The standardized value of the group A higher value indicates better environmental conditions; , indicating the first Quarterly In the group, the first Class 1 environmental indicators for the first The influence weights of health indicators satisfy ; It is used to correct the impact of physiological / behavioral differences among different groups on association relationships, and to ensure that the association analysis is consistent with the characteristics of the groups by calibration with previous epidemiological data; First, the original association strength is calculated using the Pearson correlation coefficient, and then dynamic weights are used. Correction, obtaining the single-factor corrected correlation. ; Adjusting the correlation for single factors , For environmental indicators with high correlation thresholds, a logistic regression model is used to calculate the probability of health abnormalities. Introducing a risk superposition coefficient Synergistic effects among quantitative indicators: , in, This is the intercept term in the logistic regression model, used to adjust the baseline probability of the model; This represents a set of environmental indicators that meet the high correlation threshold. It is the first in this set Environmental indicators; Based on the probability of combined association And the feasibility of intervention, using environmental indicators to improve costs measure, The lower the value, the higher the feasibility; calculate the intervention priority index. Identify the environmental factors that require priority rectification: , in, This is an intervention priority index.

[0015] Furthermore, the calculation method for the single-factor modified correlation degree is as follows: , in, This is the group fit coefficient; For this is the first Quarterly In the group, the first Class II environmental indicators and the first The original Pearson correlation coefficient of the health indicators; These are dynamic weighting coefficients; , in, Based on the weight of the previous quarter, To adjust the coefficient, It is the change in health indicators. It is the change in environmental indicators, and the weights are dynamically adjusted based on the relationship between changes in health and environmental indicators.

[0016] As a second aspect of the present invention, the present invention provides a student health monitoring and intervention system based on multi-terminal collaboration and intelligent device linkage, comprising: The hierarchical authorization and information entry unit is used to create accounts according to hierarchical authorization principles and complete the batch entry of student information into the database. The personalized screening checklist production unit is used to automatically generate a personalized screening checklist containing mandatory items, optional items, and implementation suggestions by integrating and analyzing existing information, including students' historical health data, basic school information, and user-supplemented specific needs, identifying abnormal health indicators of students in the region, matching school hardware conditions, and combining priority needs. The health and environmental data collection unit is used by screening personnel to log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and complete the information retrieval and automatic synchronization of test data by scanning the student's physical examination code. At the same time, for common disease monitoring tasks, environmental information including school drinking water, canteen, classrooms and other teaching facilities is filled in through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. The early warning and intervention closed-loop management unit is used to automatically correlate and analyze student health data and teaching environment data. Through the "health-environment correlation model", it identifies high-risk correlation items and triggers graded early warnings. It pushes environmental optimization suggestions, regional risk reports and family intervention guidelines, and sets rectification deadlines and acceptance standards at the same time. After material upload and verification, a "early warning-intervention-verification" closed-loop management is formed. The screening optimization and follow-up management unit is used to optimize the screening list for the next quarter based on full-process data every quarter; at the same time, it generates follow-up files for students with abnormal health conditions, builds a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjusts the intervention level based on the effect evaluation indicators.

[0017] As a third aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of any step of the student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage.

[0018] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage of the present invention utilizes an account and information management mechanism of "hierarchical authorization + batch data entry." Based on the principle of hierarchical authorization, it creates exclusive accounts for different entities while simultaneously enabling batch data entry of student information, thus constructing a standardized data foundation. This mechanism not only ensures the security of operational permissions for users at different levels (such as school administrators, screening personnel, and disease control personnel), avoiding data access chaos, but also reduces repetitive manual input and error rates through the batch data entry function. This ensures that core data such as basic student information and historical health data are efficiently and accurately entered into the system, providing complete and reliable data source support for subsequent monitoring and intervention processes.

[0019] 2. The student health monitoring and intervention method of the present invention, based on multi-terminal collaboration and intelligent device linkage, achieves synchronous collection of health data and environmental data by linking intelligent detection devices through a WeChat mini-program. After logging into the mini-program, screening personnel can connect to the intelligent detection device by scanning a code, quickly retrieve information by scanning the student's physical examination code, and automatically synchronize the test data. At the same time, information on the school's drinking water, canteen, classrooms, and other teaching environments can be filled in through the mini-program, breaking down the barriers between the separate collection of health data and environmental data. This method does not rely on complex hardware deployment. Relying on the lightweight characteristics of the mini-program and the convenient linkage of intelligent devices, it significantly improves data collection efficiency, ensures accurate correspondence between the two types of data in terms of time and object, and provides highly matched data samples for subsequent correlation analysis.

[0020] 3. The student health monitoring and intervention method of this invention, based on multi-terminal collaboration and intelligent device linkage, achieves a closed loop of risk warning, intervention, and management through a "health-environment correlation model" and a multi-terminal collaboration mechanism. Utilizing the correlation model to analyze collected health and environmental data, high-risk correlations are identified and tiered warnings are triggered. Targeted environmental optimization suggestions, regional risk reports, and family intervention guidelines are then pushed to schools, disease control centers, and parents. Simultaneously, the screening list is optimized quarterly based on full-process data, and a collaborative follow-up module involving "parents, school doctors, and community hospitals" is established for students with abnormal health conditions. This process transforms data analysis into implementable intervention measures, forming a complete closed loop of "early warning-intervention-verification-optimization" through multi-terminal collaboration. This ensures timely risk management and promotes continuous adaptation of monitoring and intervention plans to actual needs, improving the accuracy and effectiveness of student health management. Attached Figure Description

[0021] Figure 1 This is a flowchart of a student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating student information filing according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the screening task confirmation in an embodiment of the present invention; Figure 4 This is a schematic diagram of an intelligent data acquisition device according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the collection of environmental health influencing factors according to an embodiment of the present invention; Figure 6 This is a system unit diagram of an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0023] Example 1 Please refer to Figure 1 This embodiment 1 provides a student health monitoring and intervention method based on multi-terminal collaboration and smart device linkage, including: S1. Create accounts according to hierarchical authorization principles and complete batch entry of student information into the database; S2. Based on existing information including students' historical health data, basic school information, and user-supplemented specific needs, the system integrates and analyzes regional student health abnormality indicators, matches school hardware conditions, and combines priority needs to automatically generate a personalized screening list containing mandatory items, optional items, and implementation suggestions. S3. Screeners log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and scan the student's health check code to complete the information retrieval and automatic synchronization of test data; at the same time, for common disease monitoring tasks, they fill in environmental information including school drinking water, canteen, classrooms and other teaching facilities through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. S4. Automatically link and analyze student health data and teaching environment data, identify high-risk correlation items through the "health-environment correlation model" and trigger graded early warnings, complete the push of environmental optimization suggestions, regional risk reports and family intervention guidelines, and simultaneously set rectification deadlines and acceptance standards. After material upload and verification, a closed-loop management of "early warning-intervention-verification" is formed.

[0024] S5. Optimize the screening list for the next quarter based on full-process data each quarter; at the same time, generate follow-up files for students with abnormal health conditions, build a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjust the intervention level based on the effect evaluation indicators.

[0025] This embodiment 1 further elaborates on the above steps.

[0026] (1) Hierarchical authorization and information entry In student health monitoring and intervention, to address issues such as chaotic access control among multiple stakeholders and inefficient data entry, it is necessary to first establish a standardized access control and data infrastructure framework. The municipal / district CDC should coordinate the creation of management accounts for district / county CDCs, physical examination institutions, and schools at different levels through the system backend, clearly defining the core management permissions for different stakeholders to avoid overlapping or missing permissions in subsequent operations.

[0027] Based on this, the medical examination institutions, relying on their own management accounts, create screening agent accounts for the personnel who actually carry out screening work, and simultaneously configure refined operation permissions: delineate the specific school range that the account can operate, limit the screening items that can be performed, and set login time to ensure that screening agents can only carry out work within the authorized scope, thus ensuring system operation and data security.

[0028] Please refer to Figure 2 After completing the creation of accounts and the allocation of permissions at all levels, the system's batch import function was used to uniformly enter student basic information, efficiently completing the student information entry into the database. This process not only streamlined the collaborative permissions of multiple entities but also built a complete and accurate student basic database, laying a solid foundation for subsequent work such as generating personalized screening lists and collecting health and environmental data.

[0029] (2) Personalized screening checklist production After completing account permission configuration and student information entry, to ensure the accuracy and efficiency of subsequent screening work, a personalized screening list needs to be generated based on existing information. The process first integrates students' historical health data, basic school information, and specific user needs. Through comprehensive analysis, regional student health abnormality indicators are identified. These include vision-related indicators to determine if students have problems such as poor vision, myopia, and astigmatism; body morphology indicators that directly reflect student overweight and obesity; physiological function indicators to monitor students' cardiovascular function and lung ventilation capacity; oral health indicators to provide a basis for oral hygiene intervention and caries prevention; blood health indicators to screen for anemia; skeletal development indicators to identify early skeletal development abnormalities; and other health indicators to screen for eye structure problems, hearing impairment, and potential skin health risks. Simultaneously, the screening items are divided according to the hardware conditions of each school and priority requirements.

[0030] Please refer to Figure 3 When classifying projects, mandatory projects are categorized based on "high health risk, strong policy requirements, and alignment with seasonal prevention and control priorities." By assigning weights to historical abnormality rates, seasonal risks, and policy requirements and then weighting the scores, projects that reach the mandatory project threshold are included to ensure that core health issues are addressed first. Selected projects take into account both "feasibility of implementation" and "potential health risks." After weighting the scores of these two factors, projects with scores between the mandatory project threshold and the low-value threshold are selected, while projects with low feasibility or low risk are excluded.

[0031] In a preferred embodiment, the method for classifying mandatory items is as follows: The mandatory items to be checked must prioritize meeting three core requirements: "high health risk, strong policy requirements, and alignment with seasonal prevention and control priorities." Therefore, referring to the weighted summation model, by considering each influencing factor, Historical anomaly rate Seasonal risks Policy requires weight allocation The scores are then weighted and summed to obtain a comprehensive score, which directly reflects the degree of necessity for the project to be carried out. The classification logic is based on threshold selection, referencing element selection rules in set theory, and the comprehensive score is... Reaching the mandatory search threshold Project Included in the mandatory search set This ensures that all selected projects meet the "high necessity" standard and align with the screening objective of "prioritizing the resolution of core issues." , in, This represents the final set of items that must be searched.

[0032] In a preferred embodiment, the classification method for the selected items is as follows: The selection of projects needs to balance "feasibility" and "potential health risks" to avoid wasting resources due to "screening for the sake of screening." Therefore, a weighted summation model is also used to assess feasibility. and potential risks Assign weights The weighted summation yields a comprehensive score reflecting the cost-effectiveness of the project, making it suitable for projects with "non-common needs and flexible selection" in the selection process. , in, This represents the final set of selected items for investigation; This is a mandatory threshold. To select the lower limit threshold; To differentiate between mandatory and optional items, and to avoid including low-value items, a range classification method is used, with items having a comprehensive score ranging from [specific value] to [specific value]. Projects in the range Included in the selection set lower than Projects were excluded due to low feasibility or low risk, exceeding [a certain percentage]. Items deemed too necessary are automatically added to the mandatory query set.

[0033] This design avoids blind screening, ensuring that the program is tailored to actual needs, and provides clear guidance for subsequent screening staff through clear program classification, ensuring that limited resources are concentrated on screening key health issues and improving the overall screening efficiency and targeting.

[0034] (3) Health and environmental data collection Once the personalized screening checklist is finalized, screening staff enter the data collection phase, utilizing a WeChat mini-program and smart devices to conduct their work. Screening staff first log into the mini-program, select their affiliated school from the system, and then check the screening items they are responsible for according to their assigned tasks, thus clarifying their work scope.

[0035] Subsequently, the screening personnel connected to the smart testing device via the QR code scanning function of the mini-program. Please refer to... Figure 4 These devices include a smart vision chart, a computer-controlled optometry system, an eye biometer, a lung capacity meter, a Bluetooth height and weight scale, an electronic blood pressure monitor, and an electronic scoliosis measuring ruler. They can respectively detect multiple health indicators such as vision, cardiopulmonary function, body shape, and bone development. After the devices are connected, the screening staff scans the student's health check code, and the system automatically retrieves the student's basic information and historical health data. During the test, the vision test values, lung capacity data, height and weight values ​​generated by the devices are automatically synchronized to the mini-program in real time, eliminating the need for manual data entry.

[0036] While completing the health data collection, please refer to the requirements for monitoring common diseases. Figure 5 Screeners used a dedicated reporting module within the mini-program to record information about the school's drinking water quality and supply methods, the canteen's food procurement standards and disinfection frequency, and the classroom's lighting and ventilation, among other teaching environment information.

[0037] This operation not only achieves efficient and accurate health data collection, but also completes the synchronous correlation between health data and environmental influencing factor data, laying a solid data foundation for subsequent analysis of the intrinsic relationship between the two.

[0038] (4) Closed-loop management of early warning and intervention After completing the synchronous collection of health and environmental data, the system enters the stage of in-depth data analysis and closed-loop management, realizing the transformation from data to intervention through the "health-environment correlation model". In this stage, the two types of data are first integrated and correlated. The model will standardize the health indicators and environmental indicators of different subgroups respectively - the higher the health indicator value, the more severe the abnormality, and the higher the environmental indicator value, the better the conditions.

[0039] The analysis first calculates the original association strength between various environmental and health indicators. Then, it combines the population fit coefficient (calibrated based on previous epidemiological data to accommodate physiological and behavioral differences among different groups) and the dynamic weight coefficient (adjusted based on the previous quarter's weights and the changing relationship between current health and environmental indicators) to derive the single-factor corrected association degree. For environmental indicators with high association degrees, the system uses a logistic regression model to calculate the probability of health abnormalities, while also introducing a risk superposition coefficient to consider the synergistic effects among multiple environmental indicators. Finally, considering the cost of improving environmental indicators (lower costs indicate higher intervention feasibility), an intervention priority index is calculated to identify environmental factors requiring priority rectification.

[0040] In a preferred embodiment, the health-environment association model is specifically as follows: Let the first Class of health indicators in the first The standardized value of each subgroup is The higher the value, the more severe the health abnormality; Class II environmental indicators in the first The standardized value of the group A higher value indicates better environmental conditions; , indicating the first Quarterly In the group, the first Class 1 environmental indicators for the first The influence weights of health indicators satisfy ; It is used to correct the impact of physiological / behavioral differences among different groups on association relationships, and to ensure that the association analysis is consistent with the characteristics of the groups by calibration with previous epidemiological data; First, the original association strength is calculated using the Pearson correlation coefficient, and then dynamic weights are used. Correction, obtaining the single-factor corrected correlation. ; Adjusting the correlation for single factors , For environmental indicators with high correlation thresholds, a logistic regression model is used to calculate the probability of health abnormalities. Introducing a risk superposition coefficient Synergistic effects among quantitative indicators: , in, This is the intercept term in the logistic regression model, used to adjust the baseline probability of the model; This represents a set of environmental indicators that meet the high correlation threshold. It is the first in this set Environmental indicators; Based on the probability of combined association And the feasibility of intervention, using environmental indicators to improve costs measure, The lower the value, the higher the feasibility; calculate the intervention priority index. Identify the environmental factors that require priority rectification: , in, This is an intervention priority index.

[0041] In a preferred embodiment, the method for calculating the single-factor modified association degree is as follows: , in, This is the group fit coefficient; For this is the first Quarterly In the group, the first Class II environmental indicators and the first The original Pearson correlation coefficient of the health indicators; These are dynamic weighting coefficients; , in, Based on the weight of the previous quarter, To adjust the coefficient, It is the change in health indicators. It is the change in environmental indicators, and the weights are dynamically adjusted based on the relationship between changes in health and environmental indicators.

[0042] Based on the above analysis, the system triggers tiered early warnings, pushing environmental optimization suggestions, regional risk reports, and family intervention guidelines to relevant stakeholders, while simultaneously specifying rectification deadlines and acceptance standards. After completing the rectification, the relevant parties upload materials for verification through the system, forming a closed loop of "early warning-intervention-verification." This process makes the correlation analysis between health and the environment more realistic, ensuring that intervention measures accurately target high-risk issues, while closed-loop management guarantees the implementation of rectification, improving the systematicness and effectiveness of student health management.

[0043] (5) Screening optimization and follow-up management After the "early warning-intervention-verification" closed-loop management was implemented, the entire student health monitoring and intervention process entered the final stage of continuous iteration and optimization. At the same time, a management closed loop was formed through long-term follow-up, providing support and summarization for the whole process management.

[0044] At the end of each quarter, the system integrates complete data accumulated throughout the entire process—from initial student basic information and historical health data, to the implementation status of the personalized screening checklist in the middle stage, the results of health and environmental data collection, and then to the implementation of intervention measures, rectification verification feedback, and other information—to dynamically adjust the screening checklist for the next quarter. By analyzing the changing trends of student health problems reflected in the data, the adaptability of school hardware, and changes in priority needs, the system optimizes the division between mandatory and optional screening items, ensuring that the screening work always aligns with actual needs and achieving continuous improvement from "experience-based screening" to "data-driven screening."

[0045] Meanwhile, for students with health abnormalities, based on the initial intervention, the system automatically generates follow-up files containing abnormal indicators, intervention records, and re-examination results, and establishes a collaborative follow-up module involving parents, school doctors, and community hospitals. School doctors use this module to track students' health status at school, community hospitals provide professional medical re-examinations and guidance, and parents provide feedback on the implementation of home interventions; all three parties share information in real time. Based on effectiveness evaluation indicators such as the degree of improvement in students' abnormal health indicators and the implementation rate of intervention measures, the intervention level is dynamically adjusted—the intensity of intervention is reduced if indicators improve, and measures are upgraded and the plan is optimized if improvement is not satisfactory, forming a full-cycle management system for students with health abnormalities.

[0046] This process is both a summary and consolidation of all previous steps, using data feedback to optimize the screening process and create a virtuous cycle of "collection-analysis-intervention-optimization" throughout the monitoring process; and a collaborative follow-up to achieve long-term protection of students' health, ultimately achieving the student health management goal of "precise monitoring, scientific intervention, continuous optimization, and long-term management," allowing the value of multi-terminal collaboration and intelligent device linkage to run through the entire process, and effectively improving the scientific nature and effectiveness of student health management.

[0047] Example 2 Please refer to Figure 6This embodiment 2 provides a student health monitoring and intervention system based on multi-terminal collaboration and intelligent device linkage, including: The hierarchical authorization and information entry unit is used to create accounts according to hierarchical authorization principles and complete the batch entry of student information into the database. The personalized screening checklist production unit is used to automatically generate a personalized screening checklist containing mandatory items, optional items, and implementation suggestions by integrating and analyzing existing information, including students' historical health data, basic school information, and user-supplemented specific needs, identifying abnormal health indicators of students in the region, matching school hardware conditions, and combining priority needs. The health and environmental data collection unit is used by screening personnel to log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and complete the information retrieval and automatic synchronization of test data by scanning the student's physical examination code. At the same time, for common disease monitoring tasks, environmental information including school drinking water, canteen, classrooms and other teaching facilities is filled in through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. The early warning and intervention closed-loop management unit is used to automatically correlate and analyze student health data and teaching environment data. Through the "health-environment correlation model", it identifies high-risk correlation items and triggers graded early warnings. It pushes environmental optimization suggestions, regional risk reports and family intervention guidelines, and sets rectification deadlines and acceptance standards at the same time. After material upload and verification, a "early warning-intervention-verification" closed-loop management is formed. The screening optimization and follow-up management unit is used to optimize the screening list for the next quarter based on full-process data every quarter; at the same time, it generates follow-up files for students with abnormal health conditions, builds a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjusts the intervention level based on the effect evaluation indicators.

[0048] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can realize any step of the student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage.

[0049] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0051] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage, characterized in that, include: S1. Create accounts according to hierarchical authorization principles and complete batch entry of student information into the database; S2. Based on existing information including students' historical health data, basic school information, and user-supplemented specific needs, the system integrates and analyzes regional student health abnormality indicators, matches school hardware conditions, and combines priority needs to automatically generate a personalized screening list containing mandatory items, optional items, and implementation suggestions. S3. Screeners log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and scan the student's health check code to complete the information retrieval and automatic synchronization of test data; at the same time, for common disease monitoring tasks, they fill in environmental information including school drinking water, canteen, classrooms and other teaching facilities through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. S4. Automatically correlate and analyze student health data and teaching environment data, identify high-risk correlation items through the "health-environment correlation model" and trigger graded early warnings, complete the push of environmental optimization suggestions, regional risk reports and family intervention guidelines, and simultaneously set rectification deadlines and acceptance standards. After material upload and verification, a closed-loop management of "early warning-intervention-verification" is formed. S5. Optimize the screening list for the next quarter based on full-process data each quarter; at the same time, generate follow-up files for students with abnormal health conditions, build a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjust the intervention level in combination with the effect evaluation indicators.

2. The student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The hierarchical authorization principle in S1 is as follows: the municipal / district CDC creates management accounts for district / county CDCs, physical examination institutions, and schools in the system backend, and then the physical examination institutions create screening personnel accounts and configure operable schools, projects, and login time permissions.

3. The student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The S2 student health abnormality indicators include: vision-related indicators used to determine whether students have problems including poor vision, myopia, and astigmatism; body shape indicators that can intuitively reflect students' overweight and obesity; physiological function indicators used to monitor students' cardiovascular function and lung ventilation capacity; oral health indicators used to provide a basis for oral hygiene intervention and caries prevention; blood health indicators used to screen students for anemia; skeletal development indicators used to identify students' skeletal development abnormalities at an early stage; and other health indicators used to screen for eye structure problems, hearing impairment, and potential skin health problems.

4. The student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The classification method for mandatory items in S2 is as follows: The mandatory items to be checked must prioritize meeting three core requirements: "high health risk, strong policy requirements, and alignment with seasonal prevention and control priorities." Therefore, referring to the weighted summation model, by considering each influencing factor, Historical anomaly rate Seasonal risks Policy requires weight allocation The scores are then weighted and summed to obtain a comprehensive score, which directly reflects the degree of necessity for the project to be carried out. The classification logic is based on threshold selection, referencing element selection rules in set theory, and the comprehensive score is... Reaching the mandatory search threshold Project Included in the mandatory search set This ensures that all selected projects meet the "high necessity" standard and align with the screening objective of "prioritizing the resolution of core issues." ， in, This represents the final set of items that must be searched.

5. A student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The classification method for the selected items in S2 is as follows: The selection of projects needs to balance "feasibility" and "potential health risks" to avoid wasting resources due to "screening for the sake of screening." Therefore, a weighted summation model is also used to assess feasibility. and potential risks Assign weights The weighted summation yields a comprehensive score reflecting the cost-effectiveness of the project, making it suitable for the selection of projects with "non-common needs and flexible choices." ， in, This represents the final set of selected items for investigation; This is a mandatory threshold. To select the lower threshold; To differentiate between mandatory and optional items, and to avoid including low-value items, a range classification method is used, with items having a comprehensive score ranging from [specific value] to [specific value]. Projects in the range Included in the selection set lower than Projects were excluded due to low feasibility or low risk, exceeding [a certain percentage]. Items deemed too necessary are automatically added to the mandatory query set.

6. The student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The intelligent detection devices in S3 include: an intelligent vision chart, a computer optometry instrument, an eye biometer, a lung capacity meter, a Bluetooth height and weight meter, an electronic blood pressure monitor, and an electronic scoliosis measuring ruler.

7. A student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 1, characterized in that, The health-environment association model in S4 is specifically as follows: Let the first Class of health indicators in the first The standardized value of each subgroup is The higher the value, the more severe the health abnormality; Class II environmental indicators in the first The standardized value of the group A higher value indicates better environmental conditions; , indicating the first Quarterly In the group, the first Class 1 environmental indicators for the first The influence weights of health indicators satisfy ; It is used to correct the impact of physiological / behavioral differences among different groups on association relationships, and to ensure that the association analysis is consistent with the characteristics of the groups by calibration with previous epidemiological data; First, the original association strength is calculated using the Pearson correlation coefficient, and then dynamic weights are used. Correction, obtaining the single-factor corrected correlation. ; Adjusting the correlation for single factors , For environmental indicators with high correlation thresholds, a logistic regression model is used to calculate the probability of health abnormalities. Introducing a risk superposition coefficient Synergistic effects among quantitative indicators: ， in, This is the intercept term in the logistic regression model, used to adjust the baseline probability of the model; This represents a set of environmental indicators that meet the high correlation threshold. It is the first in this set Environmental indicators; Based on the probability of combined association And the feasibility of intervention, using environmental indicators to improve costs measure, The lower the value, the higher the feasibility; calculate the intervention priority index. Identify the environmental factors that require priority rectification: ， in, This is an intervention priority index.

8. A student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage according to claim 7, characterized in that, The method for calculating the single-factor modified correlation degree is as follows: ， in, This is the group fit coefficient; For this is the first Quarterly In the group, the first Class II environmental indicators and the first The original Pearson correlation coefficient of the health indicators; These are dynamic weighting coefficients; ， in, Based on the weight of the previous quarter, To adjust the coefficient, It is the change in health indicators. It is the change in environmental indicators, and the weights are dynamically adjusted based on the relationship between changes in health and environmental indicators.

9. A student health monitoring and intervention system based on multi-terminal collaboration and intelligent device linkage, characterized in that, include: The hierarchical authorization and information entry unit is used to create accounts according to hierarchical authorization principles and complete the batch entry of student information into the database. The personalized screening checklist production unit is used to automatically generate a personalized screening checklist containing mandatory items, optional items, and implementation suggestions by integrating and analyzing existing information, including students' historical health data, basic school information, and user-supplemented specific needs, identifying abnormal health indicators of students in the region, matching school hardware conditions, and combining priority needs. The health and environmental data collection unit is used by screening personnel to log in to the WeChat mini-program, select their school and the screening project they are responsible for, connect to the smart testing equipment by scanning the code, and complete the information retrieval and automatic synchronization of test data by scanning the student's physical examination code. At the same time, for common disease monitoring tasks, environmental information including school drinking water, canteen, classrooms and other teaching facilities is filled in through the mini-program to complete the synchronous collection of student health data and environmental influencing factor data. The early warning and intervention closed-loop management unit is used to automatically correlate and analyze student health data and teaching environment data. Through the "health-environment correlation model", it identifies high-risk correlation items and triggers graded early warnings. It pushes environmental optimization suggestions, regional risk reports and family intervention guidelines, and sets rectification deadlines and acceptance standards at the same time. After material upload and verification, a "early warning-intervention-verification" closed-loop management is formed. The screening optimization and follow-up management unit is used to optimize the screening list for the next quarter based on full-process data every quarter; at the same time, it generates follow-up files for students with abnormal health conditions, builds a collaborative follow-up module of "parents-school doctor-community hospital", and dynamically adjusts the intervention level in combination with the effect evaluation indicators.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by the processor as described in any one of claims 1-8: the student health monitoring and intervention method based on multi-terminal collaboration and intelligent device linkage.

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