Community health service management system based on grid management

Abstract

The invention provides a community health service management system based on grid management. The grid management-based community health service management system comprises a health management grid division module which is used for establishing a health management grid and managing health service states of community residents in the grid through the health management grid; the data acquisition module is used for acquiring resident health information of community residents and establishing a resident health information base; the health service function module is used for establishing a daily health service platform and performing grading and classification management on community residents according to health conditions; and the information management and support module is used for uploading the health data of the community residents to a health cloud and automatically generating a health management report through the health cloud.

Description

Technical Field

[0001] The 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, the aging problem has become prominent, and the health problems of community residents have become increasingly serious. The traditional community health management model has many shortcomings. In the existing model, community health service resources are scattered, information communication is not smooth, and it is difficult to accurately grasp and respond to residents' health needs in a timely manner. In addition, the ability to collect, manage and analyze residents' health data is weak, and it is impossible to provide residents with personalized and 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, the 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] The data collection module is used to collect the 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 classify and manage community residents according to their health conditions;

[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 system to conduct spatial analysis and initial health management grid division on the radiation area of ​​community health service management system according to community planning, and obtain the initial health management grid corresponding to the radiation area and the regional boundary corresponding to the initial health management grid;

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

[0014] Take the said street management department as the regional node;

[0015] Use the said regional node to integrate the initial health management grid corresponding to the radiation area within the regional boundary range to obtain the health management grid.

[0016] Further, using the said regional node to integrate the initial health management grid corresponding to the radiation area within the regional boundary range to obtain the health management grid, including:

[0017] Connect every two regional nodes, and obtain the included angle not greater than 90° between the connection line of every two regional nodes and the horizontal direction as the reference included angle;

[0018] Extract the connection line distance between two regional nodes;

[0019] Use the connection line distance between two regional nodes and the included angle not greater than 90° between the connection line of two regional nodes and the horizontal direction to obtain the correlation determination coefficient;

[0020] Among them, the said correlation determination coefficient is obtained through the following formula:

[0021]

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

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

[0024] Integrate the initial health management grid according to the quantitative relationship between the said correlation determination coefficient and the preset coefficient threshold to obtain the health management grid.

[0025] Further, integrate the initial health management grid according to the quantitative relationship between the said correlation determination coefficient and the preset coefficient threshold to obtain the health management grid, including:

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

[0027] When the correlation determination coefficient exceeds a preset coefficient threshold, it is determined that the correlation between two regional nodes is strong, and they are used as the second regional node group;

[0028] Compare the number of the first regional node groups with the number of the second regional node groups;

[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] Obtain the 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] Among them, the health management load coefficient is obtained through the following formula:

[0033]

[0034] Among them, f represents the health management load coefficient; C 01 and C 02 respectively represent the number of health management users corresponding to the two regional nodes of the second regional node group; C k represents a 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 represents a preset reference value of the number of health consultation entries;

[0035] Obtain the merging determination coefficient by combining the health management load coefficient with the correlation determination coefficient corresponding to the second regional node group;

[0036] Among them, the merging determination coefficient is obtained through the following formula:

[0037]

[0038] Among them, K represents the merging determination coefficient; f represents the health management load coefficient; s represents the correlation determination coefficient;

[0039] Compare the merging determination coefficient with a preset determination coefficient threshold;

[0040] When the merging determination coefficient exceeds the 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 combined to form one health management grid.

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

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

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

[0045] Associate and bind the residents' health information with the unique identifier ID, so that the unique identifier ID of each resident has a call association relationship with its corresponding residents' health information.

[0046] Furthermore, eliminating redundant data from the residents' health information corresponding to each resident includes:

[0047] Extract the key words corresponding to medical terms and degree adjectives from the residents' health information corresponding to each resident; for example, medical terms can be "low density lipoprotein cholesterol", "blood sugar", "blood pressure", etc.; degree terms can be on the low side, on the high side, relatively high, relatively low, etc.;

[0048] Extract the context semantic segments of each key word and its position;

[0049] When the occurrence position of the key word is multiple, extract the semantic correlation coefficient corresponding to the context semantic segment where the key word is located;

[0050] Obtain the standard deviation of the semantic correlation coefficients by using the semantic correlation coefficients corresponding to each occurrence position of the key word;

[0051] Compare the standard deviation of the semantic correlation coefficients with the preset standard deviation threshold;

[0052] When the standard deviation of the semantic correlation coefficients is lower than the preset standard deviation threshold, perform an elimination determination on the key words whose standard deviation of the semantic correlation coefficients is lower than the preset standard deviation threshold and their context;

[0053] Eliminate the determined key words and their context.

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

[0055] The daily health service module is used to retrieve the functional types of health communication and establish a communication service platform interface according to the functional types of health communication; wherein, the functional types of health communication include health popularization and health consultation;

[0056] The resident health needs collection and management module is used to classify the community residents according to their health status and conduct hierarchical classification management for the community residents corresponding to the health classification;

[0057] The risk assessment and intervention module is used to conduct health risk assessment on community residents and obtain the health risk assessment results of community residents with different health classifications; for the above-mentioned people with different risk levels, corresponding health intervention measures are provided, such as disease screening, health education, lifestyle guidance, etc.

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

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

[0060] Establish communication service platform interfaces corresponding to health popularization and health consultation for the functions of health popularization and health consultation;

[0061] Real-time monitor the communication data information between community residents and health management personnel in the communication service platform interface;

[0062] Screen the health data information and health needs information corresponding to the community residents from the communication data information, store the health data information and health needs information corresponding to the community residents in the resident health information database, and directly establish a call association relationship with the unique identifier ID of the residents.

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

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

[0065] Classify the health status of community residents according to the health data information and health needs information to obtain the health category corresponding to each community resident;

[0066] Conduct hierarchical classification management for community residents according to the health category.

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

[0068] Establish 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] Advantages of the present invention:

[0072] A community health service management system based on grid management proposed by the present invention aims to solve the problems existing in the existing community health management mode. Through grid management, the optimal allocation and efficient utilization of community health service resources are realized, providing comprehensive, convenient and personalized health management services for residents, and improving 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 the daily health service platform enables residents to obtain health services more conveniently, regardless of which corner of the community they are in. The establishment of the resident health information database and the application of the health cloud enable more effective utilization of health data. These data not only provide personalized health management solutions for residents, but also provide valuable research and clinical data support for medical service providers. By automatically generating a health management report, the system can timely detect the health problems of residents and give early warnings, thereby helping to improve the overall community health management level. Description of the drawings

[0073] Figure 1 It is the system block diagram of the system described in the present invention;

[0074] Figure 2 It is the schematic diagram of the principle of the system described in the present invention. Detailed implementation manners

[0075] The following describes the preferred embodiments of the present invention with reference to the 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] An embodiment of the present invention proposes a community health service management system based on grid management, as Figure 1 and Figure 2 shown. The community health service management system based on grid management includes:

[0077] A health management grid division module, 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] A data collection module, which is used to collect the health information of community residents and establish a resident health information database;

[0079] A health service function module, which is used to establish a daily health service platform and classify and manage community residents according to their health status;

[0080] An information management and support module, which is used to upload the health data of community residents to the health cloud and automatically generate a health management report through the health cloud.

[0081] Among them, the execution steps of the data collection module include:

[0082] Collect health data from residents who agree to collect health examination reports, obtain resident health information, and eliminate redundant data from the resident health information corresponding to each resident;

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

[0084] Associate and bind the resident health information with the unique identifier ID, so that the unique identifier ID of each resident has a call association with its corresponding resident health information.

[0085] Specifically, eliminating redundant data from the resident health information corresponding to each resident includes:

[0086] Extract the keyword phrases corresponding to medical terms and degree adjectives from the resident health information corresponding to each resident; for example, medical terms can be "low density lipoprotein cholesterol", "blood sugar", and "blood pressure", etc.; degree adjectives can be low, high, relatively high, relatively low, etc.;

[0087] Extract the context semantic segments of each keyword phrase and its position;

[0088] When the occurrence position of the keyword phrase is multiple, extract the semantic correlation coefficient corresponding to the context semantic segment where the keyword phrase is located;

[0089] Among them, the semantic correlation coefficient corresponding to the context semantic segment where the keyword phrase is located is obtained through the following formula:

[0090]

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

[0092] obtain the standard deviation of semantic correlation coefficients by using the semantic correlation coefficients corresponding to each occurrence position of the keyword;

[0093] compare the standard deviation of semantic correlation coefficients with a preset standard deviation threshold;

[0094] When the standard deviation of semantic correlation coefficients is lower than the preset standard deviation threshold, determine whether to eliminate the keyword whose standard deviation of semantic correlation coefficients is lower than the preset standard deviation threshold and its context;

[0095] Eliminate the determined keywords to be eliminated and their contexts.

[0096] The technical effects of the above technical solution are as follows: By extracting medical terms, degree adjectives keywords and their context semantic segments, calculating semantic correlation coefficients and standard deviations to judge and eliminate redundant data, it can effectively streamline residents' health information, remove duplicate or highly relevant data content, reduce the data volume, and improve data storage and processing efficiency. Eliminating redundant data avoids the interference caused by information repetition, makes the remaining residents' health information more accurate, clear and valuable, improves the quality of data, and facilitates subsequent analysis and utilization of health data, such as assisting disease diagnosis, predicting health trends, etc. Using semantic correlation coefficients to measure the correlation degree of the context semantic segments of keywords, judging whether the data is redundant from the semantic level, compared with simple data screening methods, can better understand the meaning of data in depth, helps to discover hidden relationships and laws in health information, and improves the understanding and analysis ability of residents' health conditions.

[0097] By extracting keywords corresponding to medical terms and degree adjectives and analyzing them in combination with context semantic segments, it is possible to more accurately capture the key semantic content in residents' health information, and be able to more accurately understand and utilize this information in subsequent processing, improving the accuracy of semantic understanding. By calculating semantic correlation coefficients and making judgments in combination with standard deviation thresholds, it is possible to identify and eliminate redundant information that may cause semantic ambiguity, and can minimize ambiguities and misunderstandings in the process of semantic understanding, improving the accuracy of semantic processing.

[0098] Meanwhile, the above technical solution can calculate the semantic correlation coefficient according to the text fragments in different context semantic segments, so as to adapt to the semantic processing requirements in different contexts, and has stronger robustness and adaptability when processing complex and changeable resident health information. Moreover, by calculating physical quantities such as redundant entropy value and cosine similarity, and combining with the semantic correlation coefficient to eliminate redundant data, it can effectively resist the noise interference in the data, further improve the stability and reliability of semantic processing, and reduce errors and deviations caused by noise interference. Meanwhile, the above technical solution realizes the automatic process of semantic processing by automatically extracting key words, calculating the semantic correlation coefficient and eliminating redundant data through algorithms. 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 correlation coefficient is lower than the preset standard deviation threshold, a determination of elimination is performed on the key words and their contexts whose standard deviation of the semantic correlation coefficient is lower than the preset standard deviation threshold, including:

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

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

[0102] Use all the semantic correlation coefficients corresponding to the target key words to obtain the semantic intensity coefficient corresponding to the target key words;

[0103] Among them, the semantic intensity coefficient is obtained through the following formula:

[0104]

[0105] Among them, S represents the semantic intensity coefficient corresponding to the target key word; m represents the total number of context semantic segments corresponding to the target key word; A i represents the average attention weight of the target key word to other words in the i-th context semantic segment; K i represents the semantic correlation coefficient corresponding to the i-th position of the context semantic segment of the target key word; λ represents the syntactic weight corresponding to the key word; λ pi represents the average syntactic weight of all other key words included in the i-th context semantic segment of the target key word; len() represents the length function, which is used to calculate the number of words or characters in the context semantic segment; R i represents the i-th context semantic segment corresponding to the target key word;

[0106] Determine the elimination status of the target keyword according to the relationship between the semantic intensity coefficient and the semantic correlation coefficient in combination with the preset determination conditions.

[0107] Among them, the determination conditions are as follows:

[0108]

[0109] Among them, Q represents the elimination status of the target keyword. When Q = 1, it means no elimination is required; when Q = 0, it means elimination is required; when Q = 0.5, it means eliminating the target keyword corresponding to the position where the semantic correlation coefficient is lower than the preset correlation coefficient threshold; S represents the semantic intensity coefficient corresponding to the target keyword; Y 01 represents the preset intensity coefficient threshold; Y 02 represents the preset correlation coefficient threshold; K p represents the average value of the semantic intensity coefficients corresponding to the target keyword.

[0110] The technical effects of the above technical solution are as follows: By introducing the semantic intensity coefficient and combining multiple determination conditions, this solution can more precisely determine which keywords and their contexts are redundant, thereby avoiding misdeleting important information or missing redundant information. Calculating the semantic intensity coefficient using multiple information such as attention weights and syntactic weights helps to more deeply understand the semantic role of keywords in the context and improve the accuracy of semantic processing. This solution automatically calculates the semantic correlation coefficient and semantic intensity coefficient through an algorithm and combines the determination conditions for redundancy determination, realizing an automated process for eliminating redundant information and improving data processing efficiency. The automated determination process reduces the need for manual participation, lowers labor costs, and at the same time improves the consistency and accuracy of determination. At the same time, through the above technical solution, it is possible to combine the semantic intensity and semantic correlation degree for dual redundancy information determination, which can effectively improve the accuracy of semantic determination. And when combining the dual criteria of semantic intensity and semantic correlation degree, it is bound to increase the computing power load of semantic processing. Therefore, through the dual combination of the mathematical model of the semantic intensity coefficient and semantic correlation and the determination conditions in the above technical solution, under the condition of increasing the determination elements of redundant information, the computing power load of semantic redundancy determination is greatly reduced. Furthermore, while improving the accuracy of redundancy determination, the efficiency of redundant information determination is greatly improved, and the occupancy rate of computing power resources for redundant information determination is minimized.

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

[0112] The daily health service module is used to retrieve the function types of health communication and establish a communication service platform interface according to the function types of the health communication; among them, the function types of the health communication include health popularization and health consultation;

[0113] A resident health needs collection and management module, which is used to classify the health of community residents according to their health status, and conduct hierarchical classification management for the community residents corresponding to the health classification.

[0114] A risk assessment and intervention module, which is used to conduct health risk assessment on community residents, and obtain the health risk assessment results of community residents with different health classifications; for the above-mentioned people with different risk levels, corresponding health intervention measures are provided, such as disease screening, health education, lifestyle guidance, etc.

[0115] A temporary task management module, which is used to manage and track the temporarily assigned health management tasks. The deputy building head of health should complete them within the specified time and report the results on time and in accordance with the procedures to ensure the 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 health popularization and health consultation for the functions of health popularization and health consultation.

[0118] Real-time monitor the communication data information between community residents and health management personnel in the communication service platform interface.

[0119] Screen the health data information and health needs information corresponding to the community residents from the communication data information, and store the health data information and health needs information corresponding to the community residents in the resident health information database, and directly establish a call association relationship with the unique identifier ID of the residents.

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

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

[0122] Classify the health status of community residents according to the health data information and health needs information to obtain the health category corresponding to each community resident.

[0123] Conduct hierarchical classification management for community residents according to the health category.

[0124] Among them, the health category includes healthy people, high-risk people and disease people.

[0125] And, the hierarchical classification management methods corresponding to the healthy people, high-risk people and disease people are as follows:

[0126] The management method for healthy people is as follows: coordinate hospital expert resources, regularly carry out health education for healthy residents in the community, and enhance the awareness of health management;

[0127] The management method for high-risk people is as follows: conduct disease screening on high-risk people to obtain the corresponding risk factors of high-risk people; and according to the risk factors of high-risk people, guide residents to have regular physical examinations to timely detect high-risk indicators; carry out health interventions and guide residents to take feasible control measures and lifestyle interventions;

[0128] The management method for diseased people is as follows: one-stop follow-up management of chronic disease centers, connect with the one-stop chronic disease centers in hospitals, and after obtaining the informed consent of the residents corresponding to the diseased people, carry out standardized management of the diseased people in the community to improve the level of chronic disease management of residents; one-stop medical service provides one-stop medical services for disabled, elderly, and inconvenient residents according to their health needs; provide health guidance, including health education and health intervention guidance for residents.

[0129] Meanwhile, the execution steps of the information management and support module include:

[0130] Establish 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 according to 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] In this embodiment, the community is used as the basic unit, and the health management grid is reasonably divided according to factors such as geography and population to ensure comprehensive grid coverage and no blind spots in services. At the same time, a three-level team system of "hospital - community health grid leader - deputy health building leader" is established to clarify the responsibilities and tasks of grid personnel at all levels and achieve 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: It includes functions such as health science popularization, health consultation, and convenient medical treatment, and provides timely and professional health services through the interaction between the deputy health building leader and residents.

[0137] Resident health needs collection and management module: Through the communication between the deputy health building leader and residents, collect residents' health needs, establish a resident health information database, and conduct hierarchical and classified management according to the health status of residents, and formulate personalized health management plans.

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

[0139] Healthy population management: Coordinate hospital expert resources, regularly carry out health education for healthy community residents, and enhance the awareness of health management;

[0140] High-risk population management: Disease screening, target the risk factors of the population, guide residents to have regular physical examinations, and timely detect high-risk indicators; Health intervention, guide residents to take feasible control measures and lifestyle interventions;

[0141] Disease population: One-stop follow-up management of chronic disease centers, connect with the one-stop chronic disease centers of hospitals, and after obtaining the informed consent of residents, carry out standardized management of community disease populations to improve the level of chronic disease management of residents; One-stop medical service, according to the health needs of residents, provide one-stop medical services for disabled, elderly, and inconvenient residents as needed; Health guidance, provide health education and health intervention guidance for residents.

[0142] Risk assessment and intervention module: Conduct health risk assessments on residents, and provide corresponding health intervention measures for the above-mentioned populations with different risk levels, such as disease screening, health education, lifestyle guidance, etc.

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

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

[0145] First, through the interaction between the health deputy building chiefs and residents, as well as data docking with health service institutions such as hospitals, collect residents' health data, and the data will be uploaded to the "Health Cloud Post" mini-program to provide data support for residents' health management.

[0146] Secondly, after the collected health data is input into the Health Cloud Post system, the program will automatically generate a stage health management report for patients. The health deputy building chiefs will put forward professional medical opinions on the health management of residents based on the health management report, provide a basis for community health management decision-making, and provide personalized health suggestions for residents.

[0147] Finally, realize information sharing with relevant departments such as hospitals and community neighborhood committees, break information silos, and promote the coordinated operation and optimal allocation of health service resources.

[0148] The effects of the above technical solutions are as follows: Through grid management, daily health services, and personalized health management programs, the precise allocation and efficient utilization of health service resources are achieved, reducing resource waste, improving service efficiency, enhancing residents' health awareness and self-management capabilities, and promoting the formation of healthy lifestyles. Through health risk assessment and intervention measures, and by providing high-quality health services, health risks can be promptly detected and controlled, preventing the occurrence and development of diseases, and improving the health level of residents. It enhances residents' sense of belonging and satisfaction with the community, and promotes the harmonious and stable development of the community.

[0149] On the other hand, the data collection module collects residents' health examination reports and generates a unique identifier ID for association and binding, ensuring the accuracy and traceability of the data. This design improves the efficiency of data processing, enabling health information to be quickly sorted and stored in the residents' health information database. The daily health service module in the health service function module can real-time monitor the communication data information of community residents within the communication service platform interface, and screen health data information and health demand information from it. This real-time data processing ability helps the system quickly respond to residents' health needs. Through the residents' health demand collection and management module in the health service function module, the system can conduct health classification and hierarchical classification management of community residents based on their health data information and health demand information. This personalized management method helps to meet the health needs of different residents and improve the pertinence and effectiveness of health management. For residents in different health categories, the system provides different management methods, such as health education for healthy people, disease screening and health intervention for high-risk groups, and one-stop follow-up management of chronic disease centers for disease patients. These measures help to reduce the incidence of diseases and improve the quality of life of residents. The risk assessment and intervention module can conduct health risk assessment on community residents and provide corresponding health intervention measures according to the risk assessment results. This ability helps to promptly detect health risks and take corresponding measures for intervention, thereby reducing the impact of health risks on residents. The temporary task management module can manage and track the temporarily assigned health management tasks, ensuring the timely completion and feedback of the tasks. This design improves the response speed and feedback efficiency of the system, enabling residents' health needs to be promptly met. The information management and support module uploads the health data of community residents to the health cloud through wireless communication and automatically generates a health management report. This design ensures the security and reliability of health data, enabling the data to be quickly accessed and utilized when needed.

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

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

[0152] Use a geographic information system to perform spatial analysis on the management radiation area of the community health service management system according to the community plan and divide the initial health management grid, so as to obtain the initial health management grid corresponding to the radiation area and the area boundary 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] Take the street management department as the regional node;

[0155] Use the regional node to integrate the initial health management grid corresponding to the radiation area within the area boundary to obtain the health management grid.

[0156] Among them, using the regional node to integrate the initial health management grid corresponding to the radiation area within the area boundary to obtain the health management grid includes:

[0157] Connect every two regional nodes to obtain the angle between the connection line between each two regional nodes and the horizontal direction that is not greater than 90°, and use it as the reference angle;

[0158] Extract the connection line distance between two regional nodes;

[0159] Use the connection line distance between two regional nodes and the angle between the connection line between two regional nodes and the horizontal direction that is not greater than 90° to obtain the association determination coefficient;

[0160] Among them, the association determination coefficient is obtained through the following formula:

[0161]

[0162] Among them, s represents the association determination coefficient; L represents the connection line distance between two regional nodes; θ represents the angle between the connection line between two regional nodes and the horizontal direction that is not greater than 90°; D 01 and D 02 respectively represent the maximum spanning distances of the areas corresponding to the initial health management grids of the two regional nodes;

[0163] Compare the association determination coefficient with the preset coefficient threshold;

[0164] Integrate the initial health management grid according to the quantitative relationship between the association determination coefficient and the preset coefficient threshold to obtain the health management grid.

[0165] Specifically, integrating the initial health management grid according to the quantitative relationship between the association determination coefficient and the preset coefficient threshold to obtain the health management grid includes:

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

[0167] When the correlation determination coefficient exceeds a preset coefficient threshold, it is determined that the correlation between two regional nodes is strong, and they are used as the second regional node group;

[0168] Compare the number of the first regional node groups with the number of the second regional node groups;

[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 lower 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] Obtain the 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] Among them, the health management load coefficient is obtained through the following formula:

[0173]

[0174] Among them, f represents the health management load coefficient; C 01 and C 02 respectively represent the number of health management users corresponding to the two regional nodes of the second regional node group; C k represents the reference value of the preset 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 represents the reference value of the preset number of health consultation entries;

[0175] Obtain the combined determination coefficient by combining the health management load coefficient with the correlation determination coefficient corresponding to the second regional node group;

[0176] Among them, the combined determination coefficient is obtained through the following formula:

[0177]

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

[0179] Compare the combined determination coefficient with the preset determination coefficient threshold;

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

[0181] When the merging determination coefficient does not exceed the preset determination coefficient threshold, the health management grids where the two area nodes included in the second area 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, determine the specific area managed by the community health service management system. Use the geographic information system to perform spatial analysis on the management radiation area, divide the initial health management grids according to the community plan, and determine the regional boundaries of each grid. Extract the street management department as an area node within the management radiation area.

[0183] Then, calculate the angle (reference angle) not greater than 90° between the connection line of each two area nodes and the horizontal direction and the connection line distance. Use the connection line distance, the reference angle, and the maximum spanning distance of the areas of the initial health management grids corresponding to the two area nodes to calculate the association determination coefficient through a formula. According to the comparison result of the association determination coefficient and the preset coefficient threshold, divide the area nodes into two groups: those with strong association (the second area node group) and those with weak association (the first area node group). Compare the numbers of the first area node group and the second area node group. If the number of the first area node group is not lower than that of the second area node group, keep the initial grid unchanged; if it is lower, further analyze the health management load of the second area node group. Calculate the health management load coefficient through a formula according to the number of health management users and the amount of health consultation entries. Combine the health management load coefficient and the association determination coefficient to calculate the merging determination coefficient through a formula.

[0184] Finally, according to the comparison result of the merging determination coefficient and the preset determination coefficient threshold, decide whether to merge the health management grids in the second area node group.

[0185] The effects of the above technical solution are as follows: Through the geographic information system and a series of algorithms, a 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. By calculating the correlation determination coefficient and the health management load coefficient, factors such as the geographical location of regional nodes, the number of health management users, and the quantity of health consultation entries are comprehensively considered, and the initial health management grid is optimized and integrated, improving the resource utilization efficiency. The optimized health management grid makes the health service management more centralized and efficient, helps to quickly respond to the health needs of residents, and improves the management efficiency. Through grid division and resource integration, it helps to achieve the equalized 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 the actual situation, enhancing the flexibility and adaptability of the system.

[0186] On the other hand, through the Geographic Information System (GIS) and a series of algorithms, this technical solution can achieve the precise division of the management radiation area of the community health service management system, ensuring that the boundaries of each grid are clear and reasonable. This accuracy and rationality contribute to subsequent health service management and resource allocation. The accuracy of grid division can be evaluated by comparing the differences between the actual division results and the expected division results. 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 determination coefficient and the health management load coefficient, comprehensively considering factors such as the geographical location of regional nodes, the number of health management users, and the volume of health consultation entries. This integration method helps to improve resource utilization efficiency and ensures that each grid can obtain sufficient health service resources. The efficiency of resource integration can be evaluated by comparing indicators such as the health management service efficiency and resource allocation before and after integration. For example, the changes in indicators such as the health service response time and health consultation satisfaction of residents within the grid before and after integration can be statistically analyzed. The optimized health management grid makes health service management more centralized and efficient. Through grid management, real-time monitoring and rapid response to the health status of residents within the grid can be achieved, improving management efficiency. The improvement of management efficiency can be evaluated by comparing indicators such as management costs and management efficiency before and after integration. For example, the changes in indicators such as the health service demand response time and health service satisfaction of residents within the grid before and after integration, as well as the changes in management costs, can be statistically analyzed. Through grid division and resource integration, this technical solution helps to achieve the equalized distribution of health services. Residents within each grid can obtain corresponding health services, ensuring the fairness and accessibility of health services. The realization of health service equalization can be evaluated by comparing indicators such as the health service access situation and health service satisfaction of residents in different grids. For example, the changes in indicators such as the health service access rate and health service satisfaction of residents in different grids can be statistically analyzed. The algorithms and parameters in this technical solution can be adjusted and optimized according to actual situations, enhancing the flexibility and adaptability of the system. At the same time, with the development and improvement of the community health service management system, this technical solution also has good scalability and can meet the needs of future health service management.

[0187] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A community health service management system based on grid management, characterized in that, The community health service management system based on grid management includes: A health management grid division module, which 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; A data collection module, which is used to collect the health information of community residents and establish a resident health information database; A health service function module, which is used to establish a daily health service platform and conduct hierarchical and classified management of community residents according to their health conditions; An information management and support module, which is used to upload the health data of community residents to the health cloud and automatically generate a health management report through the health cloud.

2. The community health service management system based on grid management according to claim 1, characterized in that The execution steps of the health management grid division module include: Extracting the management radiation area of the community health service management system; Using a geographic information system to conduct spatial analysis and initial health management grid division on the management radiation area of the community health service management system according to the community plan, and obtaining the corresponding initial health management grid of the radiation area and the area boundary corresponding to the initial health management grid; Extracting the street management department and community service department within the management radiation area of the community health service management system; Regarding the street management department as a regional node; Using the regional node to integrate the initial health management grid corresponding to the radiation area within the area boundary to obtain a health management grid.

3. The community health service management system based on grid management according to claim 2, wherein Using the regional node to integrate the initial health management grid corresponding to the radiation area within the area boundary to obtain a health management grid, including: Connecting every two regional nodes to obtain the included angle between the connection line between every two regional nodes and the horizontal direction that is not greater than 90°, and taking it as a reference included angle; Extracting the connection line distance between two regional nodes; Obtaining an association determination coefficient by using the connection line distance between two regional nodes and the included angle between the connection line between two regional nodes and the horizontal direction that is not greater than 90°; Comparing the association determination coefficient with a preset coefficient threshold; Integrating the initial health management grid according to the quantitative relationship between the association determination coefficient and the preset coefficient threshold to obtain a health management grid.

4. The community health service management system based on grid management according to claim 3, wherein Integrating the initial health management grid according to the quantitative relationship between the association determination coefficient and the preset coefficient threshold to obtain a health management grid, including: When the association determination coefficient exceeds the preset coefficient threshold, it is determined that the association between two regional nodes is poor, and it is used as the first regional node group; When the association determination coefficient exceeds the preset coefficient threshold, it is determined that the association between two regional nodes is strong, and it is used as the 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 group is not less than the number of the second regional node group, 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 group is less than the number of the second regional node group, the number of health management users and the number of health consultation entries in each second regional node group are retrieved; Obtain the health management load coefficient corresponding to the second regional node group according to the number of health management users and the quantity of health consultation entries in the second regional node group; Use the health management load coefficient and combine it with the associated determination coefficient corresponding to the second regional node group to obtain the combined determination coefficient; Compare the combined determination coefficient with a preset determination coefficient threshold; When the combined determination coefficient exceeds the preset determination coefficient threshold, do not merge the health management grids where the two regional nodes included in the second regional node group are located; When the combined determination coefficient does not exceed the preset determination coefficient threshold, merge the health management grids where the two regional nodes included in the second regional node group are located to form a health management grid.

5. The community health service management system based on grid management according to claim 1, characterized in that The execution steps of the data collection module include: Collect health data from residents who agree to collect health examination reports, obtain residents' health information, and eliminate redundant data for each resident's corresponding residents' health information; Generate a unique identifier ID for each resident; Associate and bind the residents' health information with the unique identifier ID, so that each resident's unique identifier ID has a call association relationship with its corresponding residents' health information.

6. The community health service management system based on grid management according to claim 5, characterized in that Eliminating redundant data for each resident's corresponding residents' health information includes: Extract the keyword phrases corresponding to medical terms and degree adjectives from each resident's corresponding residents' health information; Extract the context semantic segments of each keyword phrase and its location; When the occurrence position of the keyword phrase is multiple, extract the semantic correlation coefficients corresponding to the context semantic segments where the keyword phrase is located; Obtain the standard deviation of the semantic correlation coefficients using the semantic correlation coefficients corresponding to each occurrence position of the keyword phrase; Compare the standard deviation of the semantic correlation coefficients with a preset standard deviation threshold; When the standard deviation of the semantic correlation coefficients is lower than the preset standard deviation threshold, perform an elimination determination on the keyword phrases whose standard deviation of the semantic correlation coefficients is lower than the preset standard deviation threshold and their context; Eliminate the determined keyword phrases and their context.

7. The community health service management system based on grid management according to claim 1, wherein, The health service function module includes: The daily health service module is used to retrieve the function types of health communication and establish a communication service platform interface according to the function types of health communication; among them, the function types of health communication include health popularization and health consultation; The residents' health needs collection and management module is used to classify the community residents according to their health status and perform hierarchical classification management on the community residents corresponding to the health classification; The risk assessment and intervention module is used to perform health risk assessment on community residents and obtain the health risk assessment results of community residents with different health classifications; The temporary task management module is used to manage and track the temporarily assigned health management tasks.

8. The community health service management system based on grid management according to claim 7, wherein, The execution steps of the daily health service module include: Establish communication service platform interfaces corresponding to health popularization and health consultation for health popularization and health consultation; Real-time monitor the communication data information between community residents and health management personnel in the communication service platform interface; Screen the health data information and health demand information corresponding to community residents from the communication data information, store the health data information and health demand information corresponding to the community residents in the resident health information database, and directly establish a call association relationship with the unique identifier ID of the residents.

9. The community health service management system based on grid management according to claim 7, characterized in that, The execution steps of the resident health demand collection and management module include: Retrieve the health data information and health demand information corresponding to community residents from the resident health information database by using the unique identifier ID corresponding to the community residents; Classify the health status of community residents according to the health data information and health demand information to obtain the health category corresponding to each community resident; Carry out hierarchical classification management of community residents according to the health category.

10. The community health service management system based on grid management according to claim 1, characterized in that, The execution steps of the information management and support module include: Establish 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 according to the health data of the community residents.

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