A community health service management system based on grid management

Abstract

The present invention proposes a community health service management system based on grid management. The grid-based community health service management system includes: a health management grid division module for establishing a health management grid and managing the health service status of community residents within the grid through the health management grid; a data collection module for collecting resident health information and establishing a resident health information database; a health service function module for establishing a daily health service platform and performing hierarchical and classified management of community residents based on their health status; and an information management and support module for uploading community residents' health data to a health cloud and automatically generating health management reports through the health cloud.

Description

Technical Field

[0001] The present invention proposes a community health service management system based on grid management, belonging to the technical field of community health management. Background Art

[0002] In recent years, with the acceleration of urbanization and the emergence of an aging population, community residents have faced increasingly serious health challenges. Traditional community health management models have numerous shortcomings. Under the current model, community health service resources are fragmented, information communication is poor, and it is difficult to accurately grasp and promptly respond to residents' health needs. Furthermore, the ability to collect, manage, and analyze resident health data is weak, making it impossible to provide residents with personalized, continuous health management services.

[0003] Therefore, there is an urgent need for an innovative community health service system that integrates the health information of community residents to improve the efficiency and quality of community health management and provide better services to community residents. Summary of the Invention

[0004] The present invention provides a community health service management system based on grid management to solve the above technical problems in the prior art. The technical solutions adopted are as follows:

[0005] A community health service management system based on grid management, comprising:

[0006] The health management grid division module is used to establish a health management grid and manage the health service status of community residents within the grid through the health management grid;

[0007] Data collection module, used to collect health information of community residents and establish a resident health information database;

[0008] The health service function module is used to establish a daily health service platform and carry out hierarchical and classified management of community residents based on their health status;

[0009] The information management and support module is used to upload the health data of community residents to the health cloud and automatically generate health management reports through the health cloud.

[0010] Furthermore, the execution steps of the health management grid division module include:

[0011] Extract the management radiation area of ​​the community health service management system;

[0012] Use geographic information systems to conduct spatial analysis and initial health management grid division of the radiation area of ​​the community health service management system based on community planning, and obtain the initial health management grid corresponding to the radiation area and the regional boundaries corresponding to the initial health management grid;

[0013] Extract the street management departments and community service departments within the management radiation area of ​​the community health service management system;

[0014] Taking the street management department as the regional node;

[0015] The regional nodes are used to integrate the initial health management grids corresponding to the radiation area within the regional boundary to obtain a health management grid.

[0016] Furthermore, the initial health management grid corresponding to the radiation area is integrated within the area boundary using the regional nodes to obtain a health management grid, including:

[0017] Connect every two regional nodes and obtain an angle between the connecting line between the two regional nodes and the horizontal direction that is no greater than 90° as a reference angle;

[0018] Extract the distance between two area nodes;

[0019] The correlation determination coefficient is obtained by using the distance between the connection line of two regional nodes and the angle between the connection line of the two regional nodes and the horizontal direction that is not greater than 90°;

[0020] The correlation coefficient is obtained by the following formula:

[0021]

[0022] Where s represents the correlation determination coefficient; L represents the distance between the two regional nodes; θ represents the angle between the line between the two regional nodes and the horizontal direction that is not greater than 90°; D 01 and D 02 They represent the maximum spanning distances of the regions corresponding to the initial health management grids corresponding to the two regional nodes;

[0023] Comparing the correlation determination coefficient with a preset coefficient threshold;

[0024] The initial health management grid is integrated according to the quantitative relationship between the correlation determination coefficient and a preset coefficient threshold to obtain a health management grid.

[0025] Furthermore, the initial health management grid is integrated according to the quantitative relationship between the correlation determination coefficient and a preset coefficient threshold to obtain a health management grid, including:

[0026] When the correlation determination coefficient exceeds a preset coefficient threshold, it is determined that the correlation between the two regional nodes is poor and the two regional nodes are used as the first regional node group;

[0027] When the correlation determination coefficient does not exceed a preset coefficient threshold, it is determined that the correlation between the two regional nodes is strong and the two regional nodes are used as a second regional node group;

[0028] comparing the number of the first regional node group and the number of the second regional node group;

[0029] When the number of the first regional node groups is not less than the number of the second regional node groups, the initial health management grid is not integrated and the initial health management grid is used as the health management grid;

[0030] When the number of the first regional node groups is less than the number of the second regional node groups, the number of health management users and the number of health consultation entries in each second regional node group are retrieved;

[0031] Obtaining a health management load coefficient corresponding to the second regional node group according to the number of health management users and the number of health consultation entries in the second regional node group;

[0032] The health management load coefficient is obtained by the following formula:

[0033]

[0034] Where f represents the health management load factor; C 01 and C 02 C represents the number of health management users corresponding to the two regional nodes of the second regional node group; k Indicates the preset reference value of the number of health management users; B 01 and B 02 Respectively represent the number of health consultation entries corresponding to the two regional nodes of the second regional node group; B k Indicates the preset reference value of health consultation terms;

[0035] Obtaining a combined determination coefficient using the health management load coefficient in combination with the associated determination coefficient corresponding to the second regional node group;

[0036] The combined determination coefficient is obtained by the following formula:

[0037]

[0038] Wherein, K represents the combined determination coefficient; f represents the health management load coefficient; s represents the correlation determination coefficient;

[0039] Comparing the combined determination coefficient with a preset determination coefficient threshold;

[0040] When the merging determination coefficient exceeds a preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are not merged;

[0041] When the combined determination coefficient does not exceed the preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are merged to form one health management grid.

[0042] Furthermore, the execution steps of the data acquisition module include:

[0043] Collect health data from residents who agree to have their health checkup reports collected, obtain residents' health information, and eliminate redundant data from the health information corresponding to each resident;

[0044] Generate a unique identifier ID for each resident;

[0045] The resident health information is associated and bound with the unique identifier ID, so that each resident's unique identifier ID has a call association relationship with its corresponding resident health information.

[0046] Furthermore, redundant data is removed from the health information of each resident, including:

[0047] Extract keywords corresponding to medical terms and degree adjectives from the resident health information corresponding to each resident; for example, medical terms may be "low-density lipoprotein cholesterol," "blood sugar," and "blood pressure," and degree adjectives may be "low," "high," "high," or "low."

[0048] Extract the contextual semantic segment of each key word and its location;

[0049] When there are multiple occurrence positions of a keyword, extracting the semantic correlation coefficient corresponding to the context semantic segment where the keyword is located;

[0050] Obtaining a standard deviation of the semantic correlation coefficient using the semantic correlation coefficient corresponding to each occurrence position of the keyword;

[0051] Comparing the standard deviation of the semantic relevance coefficient with a preset standard deviation threshold;

[0052] When the standard deviation of the semantic relevance coefficient is lower than a preset standard deviation threshold, the key words and their contexts whose standard deviation of the semantic relevance coefficient is lower than the preset standard deviation threshold are eliminated;

[0053] The key words and their contexts that are determined to be removed will be removed.

[0054] Furthermore, the health service function module includes:

[0055] The daily health service module is used to call the function type of health communication and establish a communication service platform interface according to the function type of health communication; wherein the function type of health communication includes health science popularization and health consultation;

[0056] The module for collecting and managing residents' health needs is used to classify community residents according to their health status and to manage them in a hierarchical manner according to the corresponding health classifications;

[0057] The risk assessment and intervention module is used to conduct health risk assessments on community residents and obtain health risk assessment results for community residents with different health classifications; it provides corresponding health intervention measures for people with different risk levels, such as disease screening, health education, lifestyle guidance, etc.

[0058] The temporary task management module is used to manage and track temporarily assigned health management tasks. The deputy building manager of health should complete them within the specified time and report the results on time according to the procedures to ensure timely completion and feedback of the tasks.

[0059] Furthermore, the execution steps of the daily health service module include:

[0060] Establish a communication service platform interface corresponding to the functions of health science popularization and health consultation;

[0061] Real-time monitoring of communication data between community residents and health management personnel within the communication service platform interface;

[0062] The health data information and health need information corresponding to the community residents are screened from the communication data information, and the health data information and health need information corresponding to the community residents are stored in the resident health information database, and a calling association relationship is directly established with the resident's unique identifier ID.

[0063] Furthermore, the execution steps of the resident health needs collection and management module include:

[0064] Retrieving the health data and health needs information corresponding to the community residents from the resident health information database using the unique identifier ID corresponding to the community residents;

[0065] Classifying the health status of community residents based on the health data information and health needs information to obtain a health category corresponding to each community resident;

[0066] Community residents are managed in a graded and classified manner according to their health categories.

[0067] Furthermore, the execution steps of the information management and support module include:

[0068] Establishing a wireless communication connection between the health service function module and the health cloud;

[0069] Upload the health data of community residents to the health cloud through wireless communication;

[0070] After receiving the health data of community residents, the health cloud automatically generates a health management report based on the health data of the community residents.

[0071] Beneficial effects of the present invention:

[0072] The community health service management system based on grid management proposed in the present invention aims to solve the problems existing in the existing community health management model. Through grid management, it can achieve the optimal allocation and efficient utilization of community health service resources, provide residents with comprehensive, convenient and personalized health management services, and improve the health level and quality of life of community residents. Through grid management, the system can more accurately locate and manage the health needs of community residents, thereby improving the pertinence and efficiency of health management. The establishment of a daily health service platform enables residents to obtain health services more conveniently, no matter where they are in the community. The establishment of a resident health information database and the application of a health cloud enable health data to be used more effectively. These data not only provide residents with personalized health management plans, but also provide medical service providers with valuable scientific research and clinical data support. By automatically generating health management reports, the system can promptly identify residents' health problems and issue early warnings, thereby helping to improve the health management level of the entire community. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a system block diagram of the system of the present invention;

[0074] Figure 2 Schematic diagram of the principle of the system of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present invention proposes a community health service management system based on grid management, such as Figure 1 and Figure 2 As shown, the community health service management system based on grid management includes:

[0077] The health management grid division module is used to establish a health management grid and manage the health service status of community residents within the grid through the health management grid;

[0078] Data collection module, used to collect health information of community residents and establish a resident health information database;

[0079] The health service function module is used to establish a daily health service platform and carry out hierarchical and classified management of community residents based on their health status;

[0080] The information management and support module is used to upload the health data of community residents to the health cloud and automatically generate health management reports through the health cloud.

[0081] The execution steps of the data acquisition module include:

[0082] Collect health data from residents who agree to have their health checkup reports collected, obtain residents' health information, and eliminate redundant data from the health information corresponding to each resident;

[0083] Generate a unique identifier ID for each resident;

[0084] The resident health information is associated and bound with the unique identifier ID, so that each resident's unique identifier ID has a call association relationship with its corresponding resident health information.

[0085] Specifically, redundant data of each resident's health information is eliminated, including:

[0086] Extract keywords corresponding to medical terms and degree adjectives from the resident health information corresponding to each resident; for example, medical terms may be "low-density lipoprotein cholesterol," "blood sugar," and "blood pressure," and degree adjectives may be "low," "high," "high," or "low."

[0087] Extract the contextual semantic segment of each key word and its location;

[0088] When there are multiple occurrence positions of a keyword, extracting the semantic correlation coefficient corresponding to the context semantic segment where the keyword is located;

[0089] The semantic relevance coefficient corresponding to the context semantic segment where the keyword is located is obtained by the following formula:

[0090]

[0091] Among them, K represents the semantic correlation coefficient corresponding to the position of the keyword; n represents the text fragment contained in the context semantic segment corresponding to the position of the keyword; H 01 Indicates the normalized redundant entropy value of the first text segment in the context semantic segment corresponding to the keyword position; H irepresents the normalized redundant entropy value corresponding to the key words contained in the i-th text segment, and the normalized redundant entropy value range is (-1, 1); Sim i represents the cosine similarity between the i-th text segment and its corresponding previous text segment;

[0092] Obtaining a standard deviation of the semantic correlation coefficient using the semantic correlation coefficient corresponding to each occurrence position of the keyword;

[0093] Comparing the standard deviation of the semantic relevance coefficient with a preset standard deviation threshold;

[0094] When the standard deviation of the semantic relevance coefficient is lower than a preset standard deviation threshold, the key words and their contexts whose standard deviation of the semantic relevance coefficient is lower than the preset standard deviation threshold are eliminated;

[0095] The key words and their contexts that are determined to be removed will be removed.

[0096] The technical effect of the above technical solution is: by extracting medical terms, degree adjective keywords and their contextual semantic segments, calculating the semantic correlation coefficient and standard deviation to judge and eliminate redundant data, it can effectively streamline residents' health information, remove duplicate or overly correlated data content, reduce the amount of data, and improve data storage and processing efficiency. Eliminating redundant data avoids the interference caused by information duplication, making the retained residents' health information more accurate, clear, and valuable, improving the quality of data, and facilitating subsequent analysis and utilization of health data, such as assisting disease diagnosis, health trend prediction, etc. Using the semantic correlation coefficient to measure the degree of correlation between keyword contextual semantic segments, judging whether the data is redundant at the semantic level, compared with simple data screening methods, it can more deeply understand the meaning of the data, help to explore the hidden relationships and patterns in health information, and improve the understanding and analysis of residents' health status.

[0097] By extracting key words corresponding to medical terms and degree adjectives and analyzing them in conjunction with contextual semantic segments, we can more accurately capture the key semantic content of residents' health information, enabling more accurate understanding and utilization of this information in subsequent processing, thereby improving the accuracy of semantic understanding. By calculating the semantic relevance coefficient and combining it with the standard deviation threshold for judgment, we can identify and eliminate redundant information that may cause semantic ambiguity, minimize ambiguity and misunderstanding in the semantic understanding process, and improve the accuracy of semantic processing.

[0098] At the same time, the above technical solution can calculate the semantic association coefficient based on the text fragments in different contextual semantic segments, so as to adapt to the semantic processing needs in different contexts, and can have stronger robustness and adaptability when processing complex and changeable residents' health information. In addition, by calculating physical quantities such as redundant entropy and cosine similarity, and combining the semantic association coefficient to eliminate redundant data, it can effectively resist noise interference in the data, further improve the stability and reliability of semantic processing, and reduce errors and deviations caused by noise interference. At the same time, the above technical solution automatically extracts key words, calculates semantic association coefficients and eliminates redundant data through algorithms, realizing an automated process for semantic processing. It greatly improves the efficiency of semantic processing and reduces the cost and time of manual intervention.

[0099] Specifically, when the standard deviation of the semantic relevance coefficient is lower than a preset standard deviation threshold, the keywords and their contexts whose standard deviation of the semantic relevance coefficient is lower than the preset standard deviation threshold are eliminated, including:

[0100] When the standard deviation of the semantic correlation coefficient is lower than a preset standard deviation threshold, the keyword corresponding to the semantic correlation coefficient standard deviation lower than the preset standard deviation threshold is used as the target keyword;

[0101] Extract all semantic correlation coefficients corresponding to the target keywords;

[0102] Obtain the semantic intensity coefficient corresponding to the target keyword using all semantic association coefficients corresponding to the target keyword;

[0103] The semantic strength coefficient is obtained by the following formula:

[0104]

[0105] Among them, S represents the semantic strength coefficient corresponding to the target keyword; m represents the total number of contextual semantic segments corresponding to the target keyword; A i represents the average attention weight of the target keyword to other words in the i-th context semantic segment; K i represents the semantic relevance coefficient of the contextual semantic segment of the target keyword at position i; λ represents the syntactic weight corresponding to the keyword; pi Represents the average syntactic weight of all other keywords contained in the context semantic segment of the i-th position of the target keyword; len() represents the length function, which is used to calculate the number of words or characters in the context semantic segment; R i Indicates the i-th context semantic segment corresponding to the target keyword;

[0106] The elimination status of the target keyword is determined based on the relationship between the semantic intensity coefficient and the semantic relevance coefficient in combination with the preset judgment conditions.

[0107] The determination conditions are as follows:

[0108]

[0109] Among them, Q represents the elimination status of the target keyword. When Q=1, it means that it does not need to be eliminated; when Q=0, it means that it needs to be eliminated; when Q=0.5, it means that the target keyword corresponding to the position where the semantic correlation coefficient is lower than the preset correlation coefficient threshold will be eliminated; S represents the semantic strength coefficient corresponding to the target keyword; Y 01 Indicates the preset intensity coefficient threshold; Y 02 Indicates the preset correlation coefficient threshold; K p Represents the average value of the semantic intensity coefficient corresponding to the target keywords.

[0110] The technical effect of the above technical solution is: by introducing the semantic strength coefficient and combining multiple judgment conditions, the solution can more accurately determine which key words and their contexts are redundant, thereby avoiding the accidental deletion of important information or the omission of redundant information. Using multiple information such as attention weight and syntactic weight to calculate the semantic strength coefficient helps to more deeply understand the semantic role of key words in the context and improve the accuracy of semantic processing. The solution automatically calculates the semantic relevance coefficient and the semantic strength coefficient through an algorithm, and performs redundant judgment in combination with the judgment conditions, thereby realizing an automated process for eliminating redundant information and improving data processing efficiency. The automated judgment process reduces the need for manual participation, reduces labor costs, and improves the consistency and accuracy of judgments. At the same time, the above-mentioned technical solution can combine semantic strength and semantic relevance in the judgment of memory redundant information, which can effectively improve the accuracy of semantic judgment. Moreover, when combining the dual standards of semantic strength and semantic relevance, the computing power load of semantic processing will inevitably increase. Therefore, through the above-mentioned technical solution combined with the mathematical model and judgment conditions of semantic strength coefficient and semantic relevance, under the condition of increasing the judgment elements of redundant information, the computing power load of semantic redundant judgment is greatly reduced, thereby greatly improving the efficiency of redundant information judgment while improving the accuracy of redundant judgment, and reducing the computing power resource occupancy of redundant information judgment to the greatest extent.

[0111] At the same time, the health service function module includes:

[0112] The daily health service module is used to call the function type of health communication and establish a communication service platform interface according to the function type of health communication; wherein the function type of health communication includes health science popularization and health consultation;

[0113] The module for collecting and managing residents' health needs is used to classify community residents according to their health status and to manage them in a hierarchical manner according to the corresponding health classifications;

[0114] The risk assessment and intervention module is used to conduct health risk assessments on community residents and obtain health risk assessment results for community residents with different health classifications; it provides corresponding health intervention measures for people with different risk levels, such as disease screening, health education, lifestyle guidance, etc.

[0115] The temporary task management module is used to manage and track temporarily assigned health management tasks. The deputy building manager of health should complete them within the specified time and report the results on time according to the procedures to ensure timely completion and feedback of the tasks.

[0116] Specifically, the execution steps of the daily health service module include:

[0117] Establish a communication service platform interface corresponding to the functions of health science popularization and health consultation;

[0118] Real-time monitoring of communication data between community residents and health management personnel within the communication service platform interface;

[0119] The health data information and health need information corresponding to the community residents are screened from the communication data information, and the health data information and health need information corresponding to the community residents are stored in the resident health information database, and a calling association relationship is directly established with the resident's unique identifier ID.

[0120] Specifically, the execution steps of the resident health needs collection and management module include:

[0121] Retrieving the health data and health needs information corresponding to the community residents from the resident health information database using the unique identifier ID corresponding to the community residents;

[0122] Classifying the health status of community residents based on the health data information and health needs information to obtain a health category corresponding to each community resident;

[0123] Community residents are managed in a graded and classified manner according to their health categories.

[0124] The health categories include healthy people, high-risk people and disease people;

[0125] Furthermore, the hierarchical and classified management methods corresponding to the healthy population, high-risk population, and disease population are as follows:

[0126] The healthy population management approach is to coordinate hospital expert resources, regularly conduct health education for healthy community residents, and enhance health management awareness;

[0127] The management approach for high-risk groups is: disease screening for high-risk groups to identify their corresponding risk factors; guiding residents to undergo regular physical examinations based on their risk factors to identify high-risk indicators in a timely manner; health intervention, guiding residents to adopt feasible control measures and lifestyle interventions;

[0128] The disease population management method is: one-stop chronic disease center follow-up management, connecting with the hospital's one-stop chronic disease center, and carrying out standardized management of the community disease population after the informed consent of the residents corresponding to the disease population to improve the level of chronic disease management of residents; one-stop medical services are provided on demand for disabled, elderly, and mobility-impaired residents based on the health needs of residents; health guidance provides residents with health education and health intervention guidance.

[0129] At the same time, the execution steps of the information management and support module include:

[0130] Establishing a wireless communication connection between the health service function module and the health cloud;

[0131] Upload the health data of community residents to the health cloud through wireless communication;

[0132] After receiving the health data of community residents, the health cloud automatically generates a health management report based on the health data of the community residents.

[0133] The working principle of the above technical solution is as follows: The above technical solution of this embodiment includes the following three core parts: The first core part is the grid management architecture.

[0134] This implementation uses the community as the basic unit and rationally divides the health management grid based on factors such as geography and population to ensure comprehensive coverage and comprehensive service coverage. A three-tiered team structure consisting of "hospital-community health grid leader-deputy health building leader" is established to clarify the responsibilities and tasks of grid personnel at all levels, achieving hierarchical management and coordinated operation of health services.

[0135] The second core part is the health service function module. It includes the following modules:

[0136] Daily health service module: includes health science popularization, health consultation, convenient medical treatment and other functions, and provides timely and professional health services through the interaction between the deputy building manager and residents.

[0137] Residents' health needs collection and management module: Through communication between the deputy building manager and residents, residents' health needs are collected, a resident health information database is established, and graded and classified management is carried out according to the residents' health status to develop personalized health management plans.

[0138] Among them, hierarchical and classified management includes:

[0139] Healthy population management: coordinate hospital expert resources, regularly conduct health education for healthy community residents, and enhance health management awareness;

[0140] Management of high-risk groups: Disease screening, targeting risk factors of the population, guiding residents to undergo regular physical examinations and promptly identify high-risk indicators; health intervention, guiding residents to adopt feasible control measures and lifestyle interventions;

[0141] Disease population: One-stop chronic disease center follow-up management, connected to the hospital's one-stop chronic disease center, after residents' informed consent, carry out standardized management of community disease populations to improve residents' chronic disease management level; one-stop medical services, according to residents' health needs, provide one-stop medical services on demand for disabled, elderly, and mobility-impaired residents; health guidance, provide residents with health education and health intervention guidance.

[0142] Risk assessment and intervention module: Conduct health risk assessments on residents and provide corresponding health intervention measures such as disease screening, health education, and lifestyle guidance for people at different risk levels.

[0143] Temporary task management module: manage and track temporarily assigned health management tasks. The deputy building manager of health should complete them within the specified time and report the results on time according to procedures to ensure timely completion and feedback of tasks.

[0144] The third core part is the information management and support module.

[0145] First, through the interaction between the deputy building manager and residents, as well as data connection with hospitals and other health service agencies, the residents' health data is collected. The data will be uploaded to the "Healthy Yungang" applet to provide data support for residents' health management.

[0146] Secondly, after the collected health data is entered into the Health Yungang system, the program will automatically generate a patient stage health management report. The deputy building manager of health will provide professional medical opinions on the residents' health management based on the health management report, provide a basis for community health management decisions, and provide residents with personalized health advice.

[0147] Finally, information sharing with hospitals, community committees and other relevant departments can be achieved, breaking down information silos and promoting the coordinated operation and optimal allocation of health service resources.

[0148] The benefits of the above technical solution are: through grid management, daily health services, and personalized health management plans, we can achieve the precise allocation and efficient utilization of health service resources, reduce resource waste, improve service efficiency, enhance residents' health awareness and self-management capabilities, and promote the development of healthy lifestyles. Through health risk assessment and intervention measures, and the provision of high-quality health services, we can timely identify and control health risks, prevent the occurrence and development of diseases, and improve residents' health. This will enhance residents' sense of belonging and satisfaction with the community, and promote the harmonious and stable development of the community.

[0149] The data collection module ensures data accuracy and traceability by collecting residents' health checkup reports and generating unique identifiers (IDs) for association and binding. This design improves data processing efficiency, enabling health information to be quickly organized and stored in the resident health information database. The daily health service module within the health service module monitors community residents' communication data within the communication service platform interface in real time and filters health data and health needs information from this data. This real-time data processing capability helps the system quickly respond to residents' health needs. Through the resident health needs collection and management module within the health service module, the system categorizes and manages community residents based on their health data and health needs. This personalized management approach helps meet the health needs of different residents and improves the targeted and effective nature of health management. The system provides different management options for residents in different health categories, such as health education for healthy individuals, disease screening and health intervention for high-risk individuals, and one-stop chronic disease center follow-up management for those with health problems. These measures help reduce disease incidence and improve residents' quality of life. The risk assessment and intervention module assesses health risks for community residents and provides appropriate health intervention measures based on the risk assessment results. This capability helps promptly identify health risks and take appropriate intervention measures, thereby reducing the impact of health risks on residents. The temporary task management module can manage and track temporarily assigned health management tasks, ensuring timely completion and feedback. This design improves the system's response speed and feedback efficiency, allowing residents' health needs to be met promptly. The information management and support module uploads community residents' health data to the health cloud via wireless communication and automatically generates health management reports. This design ensures the security and reliability of health data, allowing the data to be quickly accessed and utilized when needed.

[0150] In one embodiment of the present invention, the steps of executing the health management grid division module include:

[0151] Extract the management radiation area of ​​the community health service management system;

[0152] Use geographic information systems to conduct spatial analysis and initial health management grid division of the radiation area of ​​the community health service management system based on community planning, and obtain the initial health management grid corresponding to the radiation area and the regional boundaries corresponding to the initial health management grid;

[0153] Extract the street management departments and community service departments within the management radiation area of ​​the community health service management system;

[0154] Taking the street management department as the regional node;

[0155] The regional nodes are used to integrate the initial health management grids corresponding to the radiation area within the regional boundary to obtain a health management grid.

[0156] The initial health management grid corresponding to the radiation area is integrated within the area boundary using the regional nodes to obtain the health management grid, including:

[0157] Connect every two regional nodes and obtain an angle between the connecting line between the two regional nodes and the horizontal direction that is no greater than 90° as a reference angle;

[0158] Extract the distance between two regional nodes;

[0159] The correlation determination coefficient is obtained by using the distance between the connection line of two regional nodes and the angle between the connection line of the two regional nodes and the horizontal direction that is not greater than 90°;

[0160] The correlation coefficient is obtained by the following formula:

[0161]

[0162] Where s represents the correlation determination coefficient; L represents the distance between the two regional nodes; θ represents the angle between the line between the two regional nodes and the horizontal direction that is not greater than 90°; D 01 and D 02 They represent the maximum spanning distances of the regions corresponding to the initial health management grids corresponding to the two regional nodes;

[0163] Comparing the correlation determination coefficient with a preset coefficient threshold;

[0164] The initial health management grid is integrated according to the quantitative relationship between the correlation determination coefficient and a preset coefficient threshold to obtain a health management grid.

[0165] Specifically, the initial health management grid is integrated according to the quantitative relationship between the correlation determination coefficient and the preset coefficient threshold to obtain the health management grid, including:

[0166] When the correlation determination coefficient exceeds a preset coefficient threshold, it is determined that the correlation between the two regional nodes is poor and the two regional nodes are used as the first regional node group;

[0167] When the correlation determination coefficient does not exceed a preset coefficient threshold, it is determined that the correlation between the two regional nodes is strong and the two regional nodes are used as a second regional node group;

[0168] comparing the number of the first regional node group and the number of the second regional node group;

[0169] When the number of the first regional node groups is not less than the number of the second regional node groups, the initial health management grid is not integrated and the initial health management grid is used as the health management grid;

[0170] When the number of the first regional node groups is less than the number of the second regional node groups, the number of health management users and the number of health consultation entries in each second regional node group are retrieved;

[0171] Obtaining a health management load coefficient corresponding to the second regional node group according to the number of health management users and the number of health consultation entries in the second regional node group;

[0172] The health management load coefficient is obtained by the following formula:

[0173]

[0174] Where f represents the health management load factor; C 01 and C 02 C represents the number of health management users corresponding to the two regional nodes of the second regional node group; k Indicates the preset reference value of the number of health management users; B 01 and B 02 Respectively represent the number of health consultation entries corresponding to the two regional nodes of the second regional node group; B k Indicates the preset reference value of health consultation terms;

[0175] Obtaining a combined determination coefficient using the health management load coefficient in combination with the associated determination coefficient corresponding to the second regional node group;

[0176] The combined determination coefficient is obtained by the following formula:

[0177]

[0178] Wherein, K represents the combined determination coefficient; f represents the health management load coefficient; s represents the correlation determination coefficient;

[0179] Comparing the combined determination coefficient with a preset determination coefficient threshold;

[0180] When the merging determination coefficient exceeds a preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are not merged;

[0181] When the combined determination coefficient does not exceed the preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are merged to form one health management grid.

[0182] The working principle of the above technical solution is as follows: First, the specific area managed by the community health service management system is determined. A geographic information system is used to perform spatial analysis of the management coverage area. An initial health management grid is created according to the community plan, and the regional boundaries of each grid are determined. Street management departments within the management coverage area are extracted as regional nodes.

[0183] Then, the angle (reference angle) between the connecting line and the horizontal direction, no greater than 90°, and the distance between the connecting lines are calculated for each pair of regional nodes. The correlation determination coefficient is calculated using the connecting line distance, the reference angle, and the maximum span of the initial health management grid corresponding to the two regional nodes. Based on the comparison of the correlation determination coefficient with the preset coefficient threshold, the regional nodes are divided into two groups: those with strong correlation (the second regional node group) and those with weak correlation (the first regional node group). The number of nodes in the first and second regional nodes is compared. If the number of nodes in the first regional node group is not less than that in the second regional node group, the initial grid is maintained. If it is less than that, the health management load of the second regional node group is further analyzed. The health management load coefficient is calculated using a formula based on the number of health management users and the number of health consultation terms. Combining the health management load coefficient and the correlation determination coefficient, a combined determination coefficient is calculated using a formula.

[0184] Finally, based on the comparison result of the merging determination coefficient and the preset determination coefficient threshold, it is decided whether to merge the health management grids in the second regional node group.

[0185] The effect of the above technical solution is: through the geographic information system and a series of algorithms, the refined grid division of the management radiation area of ​​the community health service management system is realized, which helps to more accurately locate and manage the health service status within the grid. Through the calculation of the association determination coefficient and the health management load coefficient, the geographical location of the regional nodes, the number of health management users and the number of health consultation entries are comprehensively considered, and the initial health management grid is optimized and integrated to improve resource utilization efficiency. The optimized health management grid makes health service management more centralized and efficient, which helps to quickly respond to the health needs of residents and improve management efficiency. Through grid division and resource integration, it helps to achieve equal distribution of health services and ensure that residents in each grid can obtain corresponding health services. The algorithms and parameters in this technical solution can be adjusted and optimized according to actual conditions, which enhances the flexibility and adaptability of the system.

[0186] Furthermore, using a geographic information system (GIS) and a series of algorithms, this technical solution enables precise demarcation of the community health service management system's coverage area, ensuring clear and reasonable boundaries for each grid. This accuracy and rationality facilitate subsequent health service management and resource allocation. The accuracy of the grid demarcation can be assessed by comparing the actual demarcation results with the expected ones. Its rationality can be verified by analyzing factors such as the health service needs of residents within the grid, the number of health management users, and the volume of health consultation entries. This technical solution optimizes and integrates the initial health management grid by calculating the correlation coefficient and health management load coefficient, comprehensively considering factors such as the geographic location of regional nodes, the number of health management users, and the volume of health consultation entries. This integration approach helps improve resource utilization efficiency and ensures that each grid has access to sufficient health service resources. The effectiveness of resource integration can be assessed by comparing indicators such as health management service efficiency and resource allocation before and after integration. For example, changes in health service response time and health consultation satisfaction among residents within the grid before and after integration can be analyzed. The optimized health management grid makes health service management more centralized and efficient. Grid-based management enables real-time monitoring and rapid response to the health status of residents within the grid, improving management efficiency. Improvements in management efficiency can be assessed by comparing indicators such as management costs and efficiency before and after integration. For example, changes in health service demand response time, health service satisfaction, and management costs among residents within a grid before and after integration can be analyzed. By dividing the grid and integrating resources, this technical solution helps achieve equal distribution of health services. Residents within each grid can access appropriate health services, ensuring fairness and accessibility. The achievement of equal health service access can be assessed by comparing indicators such as health service access and health service satisfaction among residents within different grids. For example, changes in health service access rates and health service satisfaction among residents within different grids can be analyzed. The algorithms and parameters in this technical solution can be adjusted and optimized based on actual conditions, enhancing the system's flexibility and adaptability. Furthermore, as community health service management systems develop and improve, this technical solution also possesses good scalability, enabling it to meet future health service management needs.

[0187] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A community health service management system based on grid management, characterized by: The community health service management system based on grid management includes: The health management grid division module is used to establish a health management grid and manage the health service status of community residents within the grid through the health management grid; Data collection module, used to collect health information of community residents and establish a resident health information database; The health service function module is used to establish a daily health service platform and carry out hierarchical and classified management of community residents based on their health status; An information management and support module, used to upload the health data of community residents to the health cloud and automatically generate health management reports through the health cloud; The steps of executing the health management grid division module include: Extract the management radiation area of ​​the community health service management system; Use geographic information systems to conduct spatial analysis and initial health management grid division of the radiation area of ​​the community health service management system based on community planning, and obtain the initial health management grid corresponding to the radiation area and the regional boundaries corresponding to the initial health management grid; Extract the street management departments and community service departments within the management radiation area of ​​the community health service management system; Taking the street management department as the regional node; Integrating the initial health management grid corresponding to the radiation area within the area boundary using the regional nodes to obtain a health management grid; The initial health management grid corresponding to the radiation area is integrated within the area boundary using the regional nodes to obtain the health management grid, including: Connect every two regional nodes and obtain an angle between the connecting line between the two regional nodes and the horizontal direction that is no greater than 90° as a reference angle; Extract the distance between two area nodes; The correlation determination coefficient is obtained by using the distance between the connection line of two regional nodes and the angle between the connection line of the two regional nodes and the horizontal direction that is not greater than 90°; Comparing the correlation determination coefficient with a preset coefficient threshold; The initial health management grid is integrated according to the quantitative relationship between the correlation determination coefficient and a preset coefficient threshold to obtain a health management grid.

2. The community health service management system based on grid management according to claim 1 is characterized in that: Integrating the initial health management grid according to the quantitative relationship between the correlation determination coefficient and a preset coefficient threshold to obtain a health management grid includes: When the correlation determination coefficient exceeds a preset coefficient threshold, it is determined that the correlation between the two regional nodes is poor and the two regional nodes are used as the first regional node group; When the correlation determination coefficient does not exceed a preset coefficient threshold, it is determined that the correlation between the two regional nodes is strong and the two regional nodes are used as a second regional node group; comparing the number of the first regional node group and the number of the second regional node group; When the number of the first regional node groups is not less than the number of the second regional node groups, the initial health management grid is not integrated and the initial health management grid is used as the health management grid; When the number of the first regional node groups is less than the number of the second regional node groups, the number of health management users and the number of health consultation entries in each second regional node group are retrieved; Obtaining a health management load coefficient corresponding to the second regional node group according to the number of health management users and the number of health consultation entries in the second regional node group; Obtaining a combined determination coefficient using the health management load coefficient combined with the associated determination coefficient corresponding to the second regional node group; Comparing the combined determination coefficient with a preset determination coefficient threshold; When the merging determination coefficient exceeds a preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are not merged; When the combined determination coefficient does not exceed the preset determination coefficient threshold, the health management grids where the two regional nodes included in the second regional node group are located are merged to form one health management grid.

3. The community health service management system based on grid management according to claim 1 is characterized in that: The execution steps of the data acquisition module include: Collect health data from residents who agree to have their health checkup reports collected, obtain residents' health information, and eliminate redundant data from each resident's corresponding health information; Generate a unique identifier ID for each resident; The resident health information is associated and bound with the unique identifier ID, so that each resident's unique identifier ID has a call association relationship with its corresponding resident health information.

4. The community health service management system based on grid management according to claim 3 is characterized in that: Redundant data of each resident's health information is eliminated, including: Extracting key words corresponding to medical terms and degree adjectives from the resident health information corresponding to each resident; Extract the contextual semantic segment of each key word and its location; When there are multiple occurrence positions of a keyword, extracting the semantic correlation coefficient corresponding to the context semantic segment where the keyword is located; Obtaining a standard deviation of the semantic correlation coefficient using the semantic correlation coefficient corresponding to each occurrence position of the keyword; Comparing the standard deviation of the semantic relevance coefficient with a preset standard deviation threshold; When the standard deviation of the semantic relevance coefficient is lower than a preset standard deviation threshold, the key words and their contexts whose standard deviation of the semantic relevance coefficient is lower than the preset standard deviation threshold are eliminated; The key words and their contexts that are determined to be removed will be removed.

5. The community health service management system based on grid management according to claim 1 is characterized in that: Health service function modules include: The daily health service module is used to call the function type of health communication and establish a communication service platform interface according to the function type of health communication; wherein the function type of health communication includes health science popularization and health consultation; The module for collecting and managing residents' health needs is used to classify community residents according to their health status and to manage them in a hierarchical and classified manner according to the corresponding health classifications; The risk assessment and intervention module is used to conduct health risk assessments on community residents and obtain health risk assessment results for community residents of different health categories; The temporary task management module is used to manage and track temporarily assigned health management tasks.

6. The community health service management system based on grid management according to claim 5 is characterized in that: The execution steps of the daily health service module include: Establish a communication service platform interface corresponding to the functions of health science popularization and health consultation; Real-time monitoring of communication data between community residents and health management personnel within the communication service platform interface; The health data information and health need information corresponding to the community residents are screened from the communication data information, and the health data information and health need information corresponding to the community residents are stored in the resident health information database, and a calling association relationship is directly established with the resident's unique identifier ID.

7. The community health service management system based on grid management according to claim 5 is characterized in that: The execution steps of the resident health needs collection and management module include: Retrieving the health data and health needs information corresponding to the community residents from the resident health information database using the unique identifier ID corresponding to the community residents; Classifying the health status of community residents based on the health data information and health needs information to obtain a health category corresponding to each community resident; Community residents are managed in a graded and classified manner according to their health categories.

8. The community health service management system based on grid management according to claim 1 is characterized in that: The execution steps of the information management and support module include: Establishing a wireless communication connection between the health service function module and the health cloud; Upload the health data of community residents to the health cloud through wireless communication; After receiving the health data of community residents, the health cloud automatically generates a health management report based on the health data of the community residents.

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