Community cloud service intelligent comprehensive management system based on artificial intelligence

Abstract

The invention relates to the technical field of intelligent communities, and relates to an artificial intelligence-based community cloud service intelligent comprehensive management system, which comprises a user layer, an application layer, a data layer, a communication layer, a sensing layer and a base layer, and is characterized in that the data layer comprises a community offline data processing flow and a real-time data analysis flow; the system not only improves the efficiency of comprehensive application of community data, but also provides powerful support for upgrading and construction of a community, realizes collection, analysis and personalized management of resident health data, can efficiently predict resident health risks, provides a personalized health management scheme, remarkably improves the health level of residents, and improves the social benefits of the residents. And the method is excellent in the aspects of health risk prediction accuracy and resident satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of smart communities, and in particular to an intelligent integrated management system for community cloud services based on artificial intelligence. Background Art

[0002] As an emerging product of the Internet of Things + big data, smart community is an application that is closest to people's lives.

[0003] The construction of smart communities is essentially a transformation from traditional management to intelligent management, which is of great significance. In recent years, many cities in my country have made plans to advance towards smart homes and smart cities. Currently, the vast majority of residential communities have basically implemented offline facilities such as electronic access control and smart charging, which has also received widespread praise.

[0004] However, the community cloud service intelligent integrated management system still has the following problems:

[0005] Because each department in the community has independent information, forming "information islands", data collection efficiency is low and real-time performance is poor. There are also problems such as poor data coordination and high risks, as well as high processing delays.

[0006] The health management model in the community comprehensive management system often relies on offline services and periodic health checks, which makes it difficult to detect residents' health problems in a timely manner and cannot meet personalized and precise health management needs.

[0007] Based on this, an intelligent comprehensive management system for community cloud services based on artificial intelligence is proposed. Summary of the Invention

[0008] (1) Technical problems solved

[0009] In order to solve the above technical problems, the present invention provides an intelligent comprehensive management system for community cloud services based on artificial intelligence.

[0010] (2) Technical solution

[0011] Based on this, the present invention provides the following technical solutions: an artificial intelligence-based community cloud service intelligent integrated management system, comprising a user layer, an application layer, a data layer, a communication layer, a perception layer, and a basic layer. The user layer is mainly provided to related fields with needs such as community security, property civil defense system, and emergency departments. The application layer includes a human health monitoring layer, a command and dispatch layer, and an emergency plan layer. The communication layer interconnects the user layer, application layer, perception layer, and basic layer through a network to achieve real-time transmission of information. The perception layer is responsible for collecting data information and uploading data through Internet of Things technology for data analysis and storage, and then making corresponding decisions after judging the collected data. The basic layer contains the technology and hardware equipment required by the system, which provides basic technical and hardware support for the system.

[0012] The data layer includes a data integration layer, a data storage layer, a data processing layer, and a data application layer. The data integration layer is used to classify and integrate diverse and multi-source community data, which is divided into offline data and real-time data. Offline data is collected from various data sources in the community, and real-time data covers the community's immediate dynamics and is collected in real time through external systems with the help of the Flume data transmission framework. The data storage layer includes an HDFS module, an HBase (Hadoop DataBase) module, an HIVE (Hadoop Integrated View Environment) module, and a MySQL module, which are used to provide the required raw data support for the data processing layer and also store the results of data processing. The data processing layer adopts the high-performance Flink stream processing framework, supports stream and batch integration, provides excellent state management, fault tolerance mechanism, and flexible event-driven processing capabilities, and combines the ETL module based on the Flink computing engine to provide community managers with a complete processing closed loop to effectively manage and analyze large amounts of community data. The data application layer relies on the "Weiwang Shige" smart service platform, combines the SpringBoot microservice framework, SpringSecurity security framework, Redis distributed cache system, and Hadoop big data processing platform to build a comprehensive big data application layer.

[0013] Preferably, the transmission network of the communication layer includes 4G / 5G network, Bluetooth, wireless local area network, and wireless long-distance communication GPR.

[0014] Preferably, the sensing devices of the sensing layer usually include mobile terminal data input, electronic bracelet, laser scanner, APP information input, infrared detector, geomagnetic induction, etc.

[0015] Preferably, the offline data includes community grid information, community street view videos, community news, community street view images, and public sentiment information, and the real-time data includes street traffic data and community robot data.

[0016] Preferably, the Flume data transmission framework includes a Source module, a Channel module and a Sink module. The Source module is the starting point of data collection and is responsible for capturing data from various data sources (such as sensors and smart devices) in the smart community. The Channel module is used for temporary storage to ensure the security and reliability of data during transmission. The Sink module is responsible for sending the collected data to a designated storage system, such as (Hadoop Distributed File System) or a Kafka message queue system.

[0017] Preferably, the data storage layer is based on a stable Hadoop cluster, which is coordinated by the Zookeeper distributed coordination service, and uses the YARN (Yet Another Resource Negotiator) resource scheduling platform to implement resource and job scheduling, and uses the Kafka message queue system as the middleware data pipeline to ensure high throughput and scalability of data transmission.

[0018] Preferably, the data processing layer uses the efficient processing capabilities of the Flink framework for text files in HDFS, so that grid workers can execute ETL processes to ensure data quality and safely store the results back to HDFS; for the diverse files uploaded by community grid workers (such as .CSV, .Excel, .txt, etc.), the Flink framework also executes the ETL process and then maps them to the relational database to optimize data storage and enhance the data display and management capabilities of the smart service platform. For real-time data streams with higher timeliness, the data processing process relies more on automated operation. Community grid workers mainly monitor and manage task flows through the platform and components such as DolphinScheduler and Yarn; they capture and process data streams in real time, collect them through Flume, transmit them through Kafka, and use Flink for high-speed calculations, and visualize them through WebSocket and ECharts. Each link is seamlessly connected to build an efficient and automated analysis system. At the same time, persistent data storage is introduced to ensure data security and long-term value.

[0019] Preferably, the data application layer front-end adopts the Vue.js front-end framework and the Echarts data visualization library to achieve intuitive data visualization and large-screen display. The WebSocket real-time two-way communication protocol supports two-way communication and pushes community data in real time. The ECharts visualization library converts complex data into vivid charts to help managers and residents grasp community dynamics.

[0020] Preferably, the human health monitoring layer includes an information entry module, an age range information classification module, a health indicator collection module, a health indicator analysis module, a health indicator common management module, and a health monitoring terminal. The information entry module is used to collect basic health information of community residents, including age, gender, medical history and living habits, etc., to lay a solid foundation for personalized health management. The age range information classification module stores the data entered by the information entry module according to age groups and health characteristics, which accelerates the platform's identification of common health needs and risk factors for residents of different age groups and improves the accuracy and efficiency of data processing. The health monitoring terminal is installed in residents' homes or community service stations to capture key health indicators such as heart rate, blood pressure, body temperature, and blood oxygen in real time. The health indicator collection module is used to preliminarily organize monitoring data and upload it to the platform database. The health indicator common management module is used to deeply mine data and identify potential risks such as hypertension, diabetes, and heart disease. With the help of big data and machine learning technology, the platform can draw health portraits of residents, combine historical and real-time data, and generate personalized health assessment reports to safeguard residents' health management.

[0021] Preferably, the health indicator analysis module has a built-in health risk prediction algorithm:

[0022] The specific algorithm steps are as follows:

[0023] Assume the probability of health risk P is:

[0024]

[0025] Where X1, X2, ...X n Represents the health indicators of residents (such as age, blood pressure, blood sugar, weight, etc.); β0, β1, ...β n is the weight coefficient obtained from model training;

[0026] The regularization term \(R(\beta)\) is defined as:

[0027]

[0028] In the formula, λ is the parameter of regularization strength. By controlling the size of λ, the complexity and generalization ability of the model can be adjusted.

[0029] The loss function is defined as:

[0030]

[0031] Where y i is the actual label; P i is the predicted probability; N is the total number of samples. By minimizing the loss function, the model can obtain the optimal weight β parameter, thereby improving the accuracy of health risk prediction.

[0032] (3) Beneficial effects

[0033] Compared with the existing technology, the present invention provides an artificial intelligence-based community cloud service intelligent integrated management system with the following beneficial effects:

[0034] This AI-based community cloud service intelligent comprehensive management system has set up a data layer, including community offline data processing processes and real-time data analysis processes. The system not only improves the efficiency of comprehensive application of community data, but also provides strong support for community upgrades and construction. At the same time, it realizes the collection, analysis and personalized management of residents' health data, can efficiently predict residents' health risks, and provide personalized health management plans, significantly improving residents' health levels, and performing well in terms of health risk prediction accuracy and resident satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a system block diagram of the present invention;

[0036] Figure 2 This is a block diagram of the data layer system of the present invention;

[0037] Figure 3 This is a system block diagram of the human health monitoring layer of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] See also Figure 1-Figure 3 , an intelligent integrated management system for community cloud services based on artificial intelligence, including a user layer, an application layer, a data layer, a communication layer, a perception layer, and a basic layer. The user layer is mainly provided to related fields with needs such as community security, property civil defense system, and emergency departments. The application layer includes a human health monitoring layer, a command and dispatch layer, and an emergency plan layer; the communication layer connects the user layer, application layer, perception layer, and basic layer to each other through the network to achieve real-time transmission of information. The perception layer is responsible for collecting data information and uploading data through the Internet of Things technology for data analysis and storage, and then making corresponding decisions after judging the collected data. The basic layer contains the technology and hardware equipment required by the system, which provides basic technical support and hardware support for the system;

[0040] The data layer includes a data integration layer, a data storage layer, a data processing layer, and a data application layer. The data integration layer is used to classify and integrate diverse and multi-source community data, which is divided into offline data and real-time data. Offline data is collected from various data sources in the community, and real-time data covers the real-time dynamics of the community and is collected in real time through an external system with the help of the Flume data transmission framework. The data storage layer includes HDFS modules, HBase (Hadoop DataBase) modules, HIVE (Hadoop Integrated View Environment) module and MySQL module are used to provide the required raw data support for the data processing layer and also store the results of data processing. The data processing layer adopts the high-performance Flink stream processing framework, supports stream and batch integration, provides excellent state management, fault tolerance mechanism and flexible event-driven processing capabilities, and combines the ETL module based on the Flink computing engine to provide community managers with a complete processing closed loop to effectively manage and analyze large amounts of community data. The data application layer relies on the "Weiwang Shige" smart service platform, combined with the SpringBoot microservice framework, SpringSecurity security framework, Redis distributed cache system and Hadoop big data processing platform to build a comprehensive big data application layer. Compared with the traditional model, the system has made a qualitative leap in real-time, information exchange, data transmission, data analysis and data processing, and has a wider range of applications and richer functions. It can quickly respond to and solve common emergency management issues.

[0041] In some embodiments, the transmission network of the communication layer includes 4G / 5G network, Bluetooth, wireless local area network, and wireless long-distance communication GPR. The perception devices of the perception layer usually include mobile terminal data input, electronic bracelets, laser scanners, APP information input, infrared detectors, geomagnetic induction, etc. The offline data includes community grid information, community street view videos, community news, community street view images, and public sentiment information. The real-time data includes street traffic data and community robot data. The Flume data transmission framework includes a Source module, a Channel module, and a Sink module. The Source module is the starting point for data collection and is responsible for capturing data from various data sources (such as sensors and smart devices) in the smart community. The Channel module is used for temporary storage to ensure the security and reliability of data during transmission. The Sink module is responsible for sending the collected data to a designated storage system, such as (Hadoop Distributed File System) or a Kafka message queue system.

[0042] In this application, the data storage layer is based on a stable Hadoop cluster, which is coordinated by the Zookeeper distributed coordination service, and uses the YARN (Yet Another Resource Negotiator) resource scheduling platform to implement resource and job scheduling. The Kafka message queue system is used as the middleware data pipeline to ensure high throughput and scalability of data transmission. The data processing layer targets text files in HDFS and uses the efficient processing capabilities of the Flink framework. Grid workers can execute ETL processes to ensure data quality and store the results back to HDFS safely. For diversified files uploaded by community grid workers (such as .CSV, .Excel, .txt, etc.), the Flink framework also executes the ETL process and then maps it to a relational database to optimize data storage and enhance the data display and management capabilities of the smart service platform. For real-time data streams with higher timeliness, the data processing process relies more on automated operation. Community grid workers mainly use the platform and DolphinSched uler, Yarn and other components are used to monitor and manage task flows; data flows are captured and processed in real time, collected through Flume, transmitted through Kafka, and processed at high speed through Flink, and visualized through WebSocket and ECharts. Each link is seamlessly connected to build an efficient automated analysis system. At the same time, persistent data storage is introduced to ensure data security and long-term value. The front-end of the data application layer uses the Vue.js front-end framework and the Echarts data visualization library to achieve intuitive data visualization and large-screen display. The WebSocket real-time two-way communication protocol supports two-way communication and pushes community data in real time. The ECharts visualization library converts complex data into vivid charts to help managers and residents grasp community dynamics. The system architecture is clearly separated. The front-end focuses on business logic and interaction, and the back-end is responsible for efficient data transmission and processing. The design separates concerns to improve scalability, reliability and maintainability. The front-end focuses on UI (User Interface) and logic, and the back-end ensures data transmission efficiency and stability.

[0043] Flink also provides the main APIs for data processing: the DataStream API and the DataSet API, which are the standard choice for batch processing. They are designed for bounded datasets, which have an upper limit on the size and are completely collected before processing. Global optimizations such as sorting and deduplication can be applied before processing begins.

[0044] The DataStream API supports both unbounded and bounded data processing, offering low latency and high flexibility, making it a preferred solution for both real-time data streams and static data processing.

[0045] The processing steps are as follows: Static batch processing mainly focuses on one-time large-scale processing of existing data sets stored in HDFS. First, the readTextFile(inputPath) method is used to read the text data source from HDFS. Duplicate elements in the dataset are deduplicated by calling the distinct() function. Second, the deduplicated dataset is rewritten back to HDFS using the writeAsText(outputPath) method, ensuring overwriting. KeyedStream operators are used in conjunction with state management and window operations for deduplication. Dynamic stream processing focuses on processing continuously generated data streams and responding immediately to data updates. First, the fromSource operator is used to receive a real-time data stream from the data source. Second, the flatMap operator is used to process each line of text in the data stream, converting it into a two-tuple format. The first field serves as the deduplication key, and the second field retains the original text data. The keyBy operator is used to group the data, and a time window is set for each key group. Deduplication logic is implemented within this fixed time window. Only the first data record with a unique key is received and output within each time window. Therefore, data with the same key is output only once within the same time period, achieving time-based deduplication. Finally, the processed data stream is written to HDFS, completing the ETL operation of the real-time data stream.

[0046] Implementing data stream processing on the Flink platform and writing it to a MySQL database is another key part of the data pipeline design. This involves reading offline data uploaded by grid workers to HDFS or real-time data that has been persistently stored in HDFS, processing it, and effectively storing it in a relational database. The Flink data filtering and database writing task chain steps are as follows:

[0047] First, read the real-time data stream from the data source to ensure that continuously generated data can be captured and lay the foundation for subsequent processing steps; second, map the string data to the relevant fields of the database table. This conversion step is the key to achieving the transformation from the original data structure to the database structure. After the mapping conversion, the data stream is divided according to a unique field in the data (for example, the entity ID). During this process, the data corresponding to each unique key (ID) will be assigned to different tasks to facilitate parallel processing and mark whether the data of each unique field (ID) has been processed before. Based on this status, deduplication is performed to ensure that the data of each unique key (ID) is only processed once, achieving deduplication by key (ID). In addition, to further ensure data quality, the records in the data stream are checked and those containing null value fields are removed to ensure the integrity of the data transmitted to the database. The processed data is written to the MySQL database to ensure the number of retries that the system can perform in the event of temporary write failures, and to specify the number of records contained in each batch; optimize the performance of data batch processing and set the time window for batch processing;

[0048] Due to the timeliness of real-time community data, the ETL processing of real-time data streams does not rely on the distribution of processing tasks by community grid workers, but rather on task monitoring. Flink utilizes Kafka's partitioning strategy, allowing Flink to read data by partition; Flink consumes data according to Kafka partitions, ensuring efficient data processing and distributed parallelism.

[0049] When using the DataStream API, first connect to Kafka and create a new data stream source. Parse the data in the data stream to convert the raw data into a useful format. Next, group the data based on specific key fields (such as IDs), specify window types and lengths to define the data processing scope, and calculate the required metrics. Finally, write the processed data back to Kafka.

[0050] In some embodiments, the human health monitoring layer includes an information entry module, an age range information classification module, a health indicator collection module, a health indicator analysis module, a health indicator common management module, and a health monitoring terminal. The information entry module is used to collect basic health information of community residents, including age, gender, medical history and living habits, laying a solid foundation for personalized health management. The age range information classification module stores the data entered by the information entry module according to age groups and health characteristics, accelerating the platform's identification of common health needs and risk factors for residents of different age groups, and improving the accuracy and efficiency of data processing. The health monitoring terminal is installed in residents' homes or community service stations to capture key health indicators such as heart rate, blood pressure, body temperature, and blood oxygen in real time. The health indicator collection module is used to preliminarily organize monitoring data and upload it to the platform database. The health indicator common management module is used to deeply mine data and identify potential risks such as hypertension, diabetes, and heart disease. With the help of big data and machine learning technology, the platform can draw health portraits of residents, combine historical and real-time data, generate personalized health assessment reports, and escort residents' health management. The health indicator analysis module has a built-in health risk prediction algorithm:

[0051] The specific algorithm steps are as follows:

[0052] Assume the probability of health risk P is:

[0053]

[0054] Where X1, X2, ...X n Represents the health indicators of residents (such as age, blood pressure, blood sugar, weight, etc.); β0, β1, ...β n is the weight coefficient obtained from model training;

[0055] The regularization term \(R(\beta)\) is defined as:

[0056]

[0057] In the formula, λ is the parameter of regularization strength. By controlling the size of λ, the complexity and generalization ability of the model can be adjusted.

[0058] The loss function is defined as:

[0059]

[0060] Where y i is the actual label; P i is the predicted probability; N is the total number of samples. By minimizing the loss function, the model can obtain the optimal weight β parameter, thereby improving the accuracy of health risk prediction.

[0061] Example 2

[0062] To verify the effectiveness of the human health monitoring layer, the platform's performance was evaluated through tests of actual data collection, health risk prediction, and personalized health management. During the experiment, we randomly selected 500 residents from a community as a research sample and recorded and analyzed their health indicator data, including age, gender, blood pressure, blood sugar, BMI, etc. The goal was to test the accuracy of the platform's health risk prediction and the rationality of the personalized management plan. In the initial stage of the experiment, the platform collected residents' health data through smart devices, mobile applications, and community health monitoring points, and aggregated it into the health data center. Table 1 shows some of the collected health data samples.

[0063] Table 1 Healthy sample data

[0064]

[0065]

[0066] The main steps of the experiment include the following: First, data preprocessing: The collected health data is cleaned and standardized to eliminate noise and outliers. A regularization algorithm is used to set weights for key health indicators to ensure the stability of the model. Second, health risk prediction: Using the above-mentioned health risk prediction algorithm, residents' health data is analyzed to calculate the probability of each resident developing a chronic disease in the next year. The accuracy of the algorithm's predictions is verified by comparing with actual health conditions. Third, personalized health management plan recommendations: Based on the risk prediction results, the platform generates personalized health management plans, such as dietary recommendations and exercise plans. Residents with different risk levels are included in different health intervention plans, and the experiment analyzes how these plans improve residents' health. At the end of the experiment, we evaluated the platform's effectiveness based on prediction accuracy, resident satisfaction, and improvement in health status. Table 2 shows the main indicators of the experimental results.

[0067] Table 2 Experimental results

[0068] index Numerical Health risk prediction accuracy 88％ Satisfaction with personalized management solutions 93％ Health status improvement rate 78％

[0069] Analysis results showed that the platform's health risk prediction accuracy reached 88%, resident satisfaction with personalized management solutions reached 93%, and the health improvement rate was 78%. These data demonstrate that the human health monitoring layer can effectively improve the health management level of community residents, reduce health risks through personalized solutions, and enhance residents' overall health. Overall, this experiment validated the platform's effectiveness in practical applications and provided reliable data support and practical basis for subsequent community health management.

[0070] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based community cloud service intelligent integrated management system, characterized by: It includes user layer, application layer, data layer, communication layer, perception layer, and basic layer. The user layer is mainly provided to related fields with needs such as community security, property civil defense system, and emergency departments. The application layer includes human health monitoring layer, command and dispatch layer, and emergency plan layer. The communication layer connects the user layer, application layer, perception layer, and basic layer to each other through the network to achieve real-time transmission of information. The perception layer is responsible for collecting data information and uploading data through Internet of Things technology for data analysis and storage. It makes corresponding decisions after judging the collected data. The basic layer contains the technology and hardware equipment required by the system, which provides basic technical and hardware support for the system. The data layer includes a data integration layer, a data storage layer, a data processing layer and a data application layer. The data integration layer is used to classify and integrate diverse and multi-source community data, which is divided into offline data and real-time data. Offline data is collected from various data sources in the community. Real-time data covers the community's immediate dynamics and is collected in real time through an external system with the help of a Flume data transmission framework. The data storage layer includes an HDFS module, an HBase (Hadoop DataBase) module, a HIVE (Hadoop Integrated View Environment) module and a MySQL module, which are used to provide the required raw data support for the data processing layer. The data processing layer adopts a high-performance Flink stream processing framework. The data application layer relies on the "Micronet Grid" smart service platform, combined with the SpringBoot microservice framework, SpringSecurity security framework, Redis distributed cache system and Hadoop big data processing platform to build a comprehensive big data application layer.

2. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The transmission networks of the communication layer include 4G / 5G network, Bluetooth, wireless local area network, and wireless long-distance communication GPR.

3. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The sensing devices of the sensing layer usually include mobile terminal data input, electronic bracelet, laser scanner, APP information input, infrared detector, geomagnetic induction, etc.

4. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The offline data includes community grid information, community street view videos, community news, community street view images, and public sentiment information; the real-time data includes street traffic data and community robot data.

5. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The Flume data transmission framework includes the Source module, the Channel module and the Sink module. The Source module is the starting point of data collection and is responsible for capturing data from various data sources (such as sensors and smart devices) in the smart community. The Channel module is used for temporary storage to ensure the security and reliability of data during transmission. The Sink module is responsible for sending the collected data to the designated storage system.

6. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The data storage layer is based on a stable Hadoop cluster, which is coordinated by the Zookeeper distributed coordination service, and uses the YARN (Yet Another Resource Negotiator) resource scheduling platform to implement resource and job scheduling, and uses the Kafka message queue system as the middleware data pipeline.

7. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The data processing layer uses the efficient processing capabilities of the Flink framework for text files in HDFS. Grid operators can execute ETL processes, ensure data quality, and securely store the results back to HDFS. For the diverse files uploaded by community grid workers (such as .CSV, .Excel, .txt, etc.), the Flink framework also executes the ETL process and then maps them to the relational database to optimize data storage.

8. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The data application layer front-end adopts the Vue.js front-end framework and the Echarts data visualization library to achieve intuitive data visualization and large-screen display. The WebSocket real-time two-way communication protocol supports two-way communication and pushes community data in real time. The ECharts visualization library transforms complex data into vivid charts.

9. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 1, characterized in that: The human health monitoring layer includes an information entry module, an age range information classification module, a health indicator collection module, a health indicator analysis module, a health indicator common management module, and a health monitoring terminal. The information entry module is used to collect basic health information of community residents, including age, gender, medical history and living habits. The age range information classification module stores the data entered by the information entry module according to age groups and health characteristics. The health monitoring terminal is installed in residents' homes or community service stations to capture key health indicators such as heart rate, blood pressure, body temperature, and blood oxygen in real time. The health indicator collection module is used to preliminarily organize monitoring data and upload it to the platform database. The health indicator common management module is used to deeply mine data and identify potential risks such as hypertension, diabetes, and heart disease.

10. The community cloud service intelligent integrated management system based on artificial intelligence according to claim 9, characterized in that: The health indicator analysis module has a built-in health risk prediction algorithm: The specific algorithm steps are as follows: Assume the probability of health risk P is: Where X1, X2, ...X n Represents the health indicators of residents (such as age, blood pressure, blood sugar, weight, etc.); β0, β1, ...β n is the weight coefficient obtained from model training; The regularization term \(R(\beta)\) is defined as: In the formula, λ is the parameter of regularization strength. By controlling the size of λ, the complexity and generalization ability of the model can be adjusted. The loss function is defined as: Where y i is the actual label; P i is the predicted probability; N is the total number of samples.

Images