Intelligent community environment monitoring and regulation system

Abstract

The invention relates to the technical field of environment management, and discloses a smart community environment monitoring and regulation system which collects data such as air quality, noise, temperature and humidity, garbage overflow and energy consumption in real time through a plurality of monitoring points. The core of the system is a data processing center which comprises a communication interface layer, a service processing layer, a data access layer and a data storage layer, and comprehensive and efficient processing of data is achieved. The communication interface layer provides stable connection service through the gateway module; the service processing layer adopts a distributed architecture and supports the functions of data acquisition, preprocessing, fusion analysis and the like; the data storage layer is responsible for real-time caching and persistent storage of data; and the data access layer ensures the safety and stability of data read-write operation. The user interaction interface displays environment data in real time and supports a community manager to make scientific regulation and control decisions. According to the invention, the accuracy and real-time performance of environment monitoring are improved, the data management and processing capability is optimized, and the construction and development of a smart community are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management, and in particular to a smart community environmental monitoring and control system. Background Art

[0002] In recent years, the rapid development of information technologies such as the Internet of Things, big data, and cloud computing has provided strong technical support for the construction of smart communities. By integrating advanced sensor technology, wireless communication technology, and data processing technology, comprehensive, real-time, and high-precision monitoring of the community environment can be achieved. At the same time, the smart community environment monitoring and control system built based on these technologies can automatically collect, process, and analyze environmental data from multiple monitoring points, such as air quality, noise levels, temperature and humidity changes, garbage overflow, and energy consumption, thereby providing comprehensive data support for community managers, enabling them to grasp the community environment in real time and make scientific control decisions accordingly.

[0003] Most of the existing smart community environmental monitoring systems have problems such as unreasonable system architecture and limited data processing capabilities. For example, some systems lack a unified data processing center, resulting in decentralized data collection, processing and analysis processes, making it difficult to achieve efficient data fusion and strategy formulation; other systems are unable to meet the needs of large-scale community environmental monitoring and regulation due to unstable communication interfaces, poor scalability of the service processing layer or unreasonable design of the data storage layer, resulting in limited system performance. Summary of the invention

[0004] The purpose of the present invention is to provide a smart community environment monitoring and control system to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: A smart community environment monitoring and control system: comprising a user interaction interface and a data processing center for comprehensively processing environmental data, wherein the environmental data originates from multiple monitoring points in the community, including air quality monitoring stations, noise monitoring stations, temperature and humidity sensors, smart trash cans, and energy consumption monitoring equipment, and the data processing center provides comprehensive data support for the user interaction interface, so that users can grasp the community environment status in real time and make corresponding control decisions;

[0006] The data processing center includes a communication interface layer, a service processing layer, a data access layer and a data storage layer; the communication interface layer includes a gateway module, which is responsible for providing connection services to the user interaction interface and establishing a data transmission channel; the service processing layer adopts a distributed service architecture to provide data collection, preprocessing, fusion analysis, strategy formulation, business logic processing and basic service functions for the data processing center, and the gateway module locates the specific service instance in the service processing layer through a dynamic routing mechanism; the data storage layer is responsible for data storage, including real-time caching, persistent storage and column storage of data; the data access layer includes a connection pool management module, which is used to establish a connection channel between the service processing layer and the data storage layer so that the two can perform safe and stable data reading and writing operations.

[0007] Preferably, the service processing layer includes a data collection service group, a real-time analysis service group, an operation control service group and an infrastructure service group; the data collection service group is responsible for designing communication protocols with various environmental monitoring equipment and building a front-end adapter for receiving data; the real-time analysis service group is responsible for instant processing, in-depth analysis and storage of data received from the data collection service group; the operation control service group is used to formulate and implement environmental regulation strategies based on the analysis results of the real-time analysis service group; the infrastructure service group is responsible for maintaining the operational stability of the entire service processing layer and message transmission between services.

[0008] Preferably, the real-time analysis service group includes a data preprocessing module, a data fusion module, an intelligent analysis module and a data archiving module; the data preprocessing module is responsible for processing missing values ​​and abnormal values ​​in the environmental monitoring data; the data fusion module integrates the preprocessed data, including the supplement of environmental parameters and equipment status information, to generate a more accurate data set, the environmental parameters include air quality index, noise level, temperature and humidity values, garbage overflow status and energy consumption, the equipment status includes the working status and maintenance record of the sensor; the intelligent analysis module uses machine learning algorithms to construct a data analysis model, the data analysis model is used to analyze the comprehensive environmental conditions of the community output by the data fusion module, identify abnormal data points, predict environmental change trends, and evaluate the impact of the environment on residents' lives; the data archiving module is responsible for sending the fused data and analysis results to the data storage layer for preservation.

[0009] Preferably, the business control service group includes a user authority management module, a message push management module, an environmental parameter configuration module, an early warning rule setting module and a data analysis report module; the user authority management module is responsible for defining user accounts and their access rights; the message push management module sends environmental monitoring alarms in real time to assist users in quickly responding to environmental problems; the environmental parameter configuration module displays and allows adjustment of the configuration information of the monitoring equipment; the early warning rule setting module defines the conditions for triggering early warnings, and determines whether an early warning needs to be issued and the early warning level based on this during data analysis; the data analysis report module is used to output the analysis results in the form of a report file, including the current environmental quality assessment, abnormal early warnings and energy efficiency optimization suggestions.

[0010] Preferably, the infrastructure service group includes a microservice registration module, an automation management module and a log management module; the microservice registration module is used to provide a unified registration function for microservice instances, and monitor the status of microservice instances in real time, while providing a message bus communication function to enable message communication between microservices; the automation management module uses automation tools to automatically manage and schedule microservice instances, specifically including the deployment, start, stop and restart operations of microservices; the log management module is used to perform data monitoring and log management on microservices, and collect service operation data and log information.

[0011] Preferably, the data storage layer uses Redis as a cache database, PostgreSQL as a persistence database, and ClickHouse as a column storage database.

[0012] Preferably, the data storage layer is designed to adopt an active-active architecture, including an active node and a standby node. The two nodes back up each other when processing data. When the active node fails, requires maintenance or upgrade, the standby node takes over the service.

[0013] Preferably, the environmental control strategy formulated by the operation control service group includes: adjusting the ventilation system in public areas according to the air quality index; optimizing the layout of noise sources according to the noise level; adjusting the air-conditioning system in public areas according to the temperature and humidity values; arranging garbage removal according to the garbage overflow status, and making energy-saving suggestions based on energy consumption.

[0014] Preferably, the data acquisition service group includes a front-end adapter for various types of environmental monitoring equipment, and the front-end adapter is used to establish a communication mechanism to the specific corresponding monitoring equipment, including the formulation of the front-end communication protocol, the definition of the format of sending and receiving data, disconnection processing and status monitoring.

[0015] Preferably, the communication interface layer provides an HTTP API interface and a WebSocket real-time message push service for the user interaction interface to realize user interaction and instruction issuance.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] The system builds a unified data processing center, which includes a communication interface layer, a service processing layer, a data access layer, and a data storage layer, realizing centralized data collection, processing, and analysis. This architecture not only improves the efficiency of data processing, but also realizes efficient data fusion and strategy formulation. In particular, the distributed service architecture adopted by the service processing layer enhances the scalability and stability of the system, meeting the needs of large-scale community environmental monitoring and regulation.

[0018] By designing a stable communication interface layer, an efficient service processing layer, a flexible data access layer, and a reasonable data storage layer, the system achieves stable data transmission, efficient processing, and reliable storage. The gateway module is responsible for providing connection services to the user interface and locating specific service instances in the service processing layer through a dynamic routing mechanism, ensuring the reliability and real-time performance of data transmission.

[0019] The data storage layer combines real-time caching, persistent storage, and column storage to improve the speed and efficiency of data access. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic diagram of the service processing layer structure;

[0021] Figure 2 It is a schematic diagram of the overall structure of the present invention;

[0022] Figure 3 This is a schematic diagram of message bus communication between services in the microservice architecture. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0024] See also Figure 1-3 The present invention provides a technical solution: a smart community environmental monitoring and control system. The system mainly includes two core parts: a user interaction interface and a data processing center. The user interaction interface provides an intuitive operation interface for community managers to view the community environmental conditions in real time and issue control instructions. The data processing center is responsible for collecting, processing and analyzing environmental data from multiple monitoring points in the community, and providing comprehensive data support for users.

[0025] Multiple monitoring points are set up in the community, including air quality monitoring stations, noise monitoring stations, temperature and humidity sensors, smart trash cans and energy consumption monitoring equipment. These monitoring points are connected to the data processing center through wireless communication technology to upload environmental data in real time.

[0026] The data processing center is the core of this system, including the communication interface layer, service processing layer, data access layer and data storage layer.

[0027] Communication interface layer: includes a gateway module, which is responsible for providing connection services to the user interaction interface and establishing a stable data transmission channel. It adopts efficient communication protocols and dynamic routing mechanisms to ensure the real-time and reliability of data transmission. The communication interface layer also provides HTTP API interface and WebSocket real-time message push service for the user interaction interface to realize user interaction and command issuance.

[0028] Service processing layer: adopts a distributed service architecture to provide data collection, preprocessing, fusion analysis, strategy formulation, business logic processing and basic service functions. Through the dynamic routing mechanism, the gateway module can locate the specific service instance in the service processing layer to achieve efficient data processing and service call.

[0029] Data storage layer: responsible for data storage, including real-time data caching, persistent storage, and column storage. Efficient storage technology and optimization strategies are used to ensure data security and accessibility.

[0030] Data access layer: includes a connection pool management module, which is used to establish a connection channel between the service processing layer and the data storage layer, so that both can perform safe and stable data reading and writing operations. The isolation and encapsulation of the data access layer improves the scalability and maintainability of the system.

[0031] The workflow is:

[0032] ① Data collection: Each monitoring point collects environmental data in real time through sensors and uploads it to the data processing center through wireless communication technology.

[0033] ②Data preprocessing: The service processing layer preprocesses the received data, including data cleaning, format conversion, and anomaly detection, to ensure the accuracy and reliability of the data.

[0034] ③Data fusion analysis: The service processing layer performs fusion analysis on the pre-processed data, extracts useful information, and generates a comprehensive environmental report.

[0035] ④ Strategy formulation: Based on the results of the fusion analysis, the service processing layer formulates corresponding control strategies, such as adjusting the air-conditioning temperature, turning on the air purifier, etc.

[0036] ⑤User interaction: Users view comprehensive environmental reports and control strategies through the interactive interface and issue control instructions as needed.

[0037] ⑥Control and execution: The data processing center controls the corresponding equipment to execute control operations according to the user's control instructions, thereby realizing scientific control of the community environment.

[0038] The present invention will be further described below in conjunction with Examples 1 to 3:

[0039] Embodiment 1:

[0040] The service processing layer adopts a distributed service architecture and is built based on Spring Boot and Spring Cloud to improve the scalability and stability of the system. The service processing layer mainly includes data collection service group, real-time analysis service group, operation control service group and infrastructure service group. The specific implementation steps of the service processing layer include:

[0041] Step 1: Initialize the SpringBoot project and add Spring Cloud dependencies

[0042] Create a Spring Boot project with Spring Initializer. When creating a project, select the required Spring Boot version and add Spring Cloud related dependencies. These dependencies usually include Spring CloudStarter, Spring Cloud Netflix (for components such as Feign and Ribbon), Spring Cloud ConfigClient, etc.

[0043] Step 2: Configure Nacos service registration center

[0044] In the project's application.yml file, configure the connection information of the Nacos service registration center, including the address and port of the Nacos server and the name of the current service application.

[0045] Step 3: Create and configure microservices

[0046] Create separate Spring Boot projects for the data collection service group, real-time analysis service group, operation control service group, and infrastructure service group. Each project is configured to connect to the same Nacos service registry to achieve service discovery and registration.

[0047] Step 4: Implement microservice communication

[0048] Use Spring Cloud components such as Feign and Ribbon to enable communication between microservices. Feign is a declarative web service client that simplifies the processing of HTTP requests. Ribbon is a load balancer that distributes requests between multiple service instances.

[0049] In each microservice, define the Feign client interface and specify the service name to be called through the @FeignClient annotation. Then, where other services need to be called, inject this Feign client interface and call the remote service like calling a local method.

[0050] Step 5: Start the service registry and microservices

[0051] Start the Nacos service registration center and start each microservice in turn. After the microservice is started, it will automatically register with the Nacos service registration center and implement service discovery and load balancing through Nacos.

[0052] Step 6: Integrate circuit breakers, monitoring, and configuration management

[0053] In order to enhance the robustness and maintainability of the system, Spring Cloud's Hystrix (circuit breaker), Turbine (aggregated monitoring data), and Config (configuration management) components are integrated.

[0054] Hystrix: Used to handle call failures between microservices and avoid system avalanche through circuit breaking and degradation strategies.

[0055] Turbine: It is used to aggregate the monitoring data of each microservice and provide a health status view of the overall system.

[0056] Config: It is used to centrally manage the configuration information of microservices, support dynamic refresh configuration, and improve the flexibility of the system.

[0057] Step 7: Deploy the microservice system

[0058] Deploy the microservice system to a physical environment (such as a server cluster). When deploying, you need to ensure that each microservice has sufficient resources (such as CPU, memory, and storage) and that the network is configured correctly so that microservices can communicate smoothly.

[0059] The data collection service group is responsible for designing the communication protocol with various environmental monitoring devices and building the front-end adapter for receiving data. The front-end adapter is used to establish a communication mechanism with the specific corresponding monitoring equipment, including the formulation of the front-end communication protocol, the definition of the format of sending and receiving data, disconnection processing and status monitoring. In terms of specific implementation, MQTT (Message Queuing Telemetry Transport) is used as the communication protocol. It is a lightweight, publish / subscribe message transmission protocol suitable for the communication of IoT devices. The front-end adapter is written in Java and integrated with MQTT Broker using the Spring Boot framework to achieve data reception and preliminary processing. The real-time analysis service group is responsible for the immediate processing, in-depth analysis and storage of the data received from the data collection service group; the operation control service group is used to formulate and implement environmental control strategies based on the analysis results of the real-time analysis service group. The environmental control strategies formulated by the operation control service group include: adjusting the ventilation system in the public area according to the air quality index; optimizing the layout of the noise source according to the noise level; adjusting the air conditioning system in the public area according to the temperature and humidity values; arranging garbage removal according to the overflowing state of garbage, and making energy-saving suggestions based on energy consumption. The infrastructure service group is responsible for maintaining the operational stability of the entire service processing layer and message delivery between services.

[0060] The real-time analysis service group is responsible for receiving environmental monitoring data from the data acquisition service group and performing a series of processing, analysis and storage tasks. The real-time analysis service group is responsible for the data preprocessing module, data fusion module, intelligent analysis module and data archiving module.

[0061] The data preprocessing module is responsible for processing missing values ​​and outliers in environmental monitoring data. In terms of specific implementation, Python programming language is used, and Pandas data processing library is used for data cleaning. For missing values, interpolation or mean method is used to fill them according to the time series and correlation of the data; for outliers, statistical methods (including 3σ principle) are used to identify and eliminate them.

[0062] The data fusion module integrates the pre-processed data, including the supplement of environmental parameters and equipment status information, to generate a more accurate data set. Environmental parameters mainly include air quality index, noise level, temperature and humidity values, garbage overflow status and energy consumption, etc. These parameters are collected by various environmental monitoring devices. Equipment status information includes the working status and maintenance records of sensors, etc. Data fusion algorithms (such as Kalman filtering) are used to integrate this information to generate a more comprehensive and accurate data set.

[0063] The intelligent analysis module uses machine learning algorithms to build data analysis models and conduct in-depth analysis of the fused data. The Scikit-Learn machine learning library is used to build and train the model. The data analysis model is mainly used to analyze the comprehensive environmental conditions of the community, identify abnormal data points (such as sudden environmental pollution incidents), predict environmental change trends (such as air quality forecasts for the next few days), and evaluate the impact of the environment on residents' lives (such as the impact of noise on residents' sleep quality).

[0064] The data archiving module is responsible for sending the fused data and analysis results to the data storage layer for preservation. The Apache HBase distributed database is used for data storage and management to ensure efficient data access and scalability, and a complete data archiving strategy is designed, including data backup, recovery, cleaning and compression.

[0065] Embodiment 2:

[0066] The business control service group includes a user authority management module, a message push management module, an environmental parameter configuration module, an early warning rule setting module and a data analysis report module.

[0067] The user rights management module is responsible for defining user accounts and their access rights. It uses Spring Security or Apache Shiro security framework to implement user authentication and authorization functions. By configuring user roles and permissions, you can finely control the user's access rights to system functions and data to ensure system security and data confidentiality. At the same time, it provides functions such as user registration, login, and password reset to facilitate users to manage their account information.

[0068] The message push management module sends environmental monitoring alarms in real time to assist users in quickly responding to environmental issues. It integrates Kafka message push technology to push and receive real-time messages. When the monitoring system detects abnormal data or triggers an early warning rule, the module will immediately trigger an alarm message to the relevant users, including alarm type, level, location and other information.

[0069] The environmental parameter configuration module displays and allows adjustment of the configuration information of the monitoring device. Users can use this module to remotely configure and adjust the monitoring device, including sampling frequency, measurement range, calibration parameters, etc., to meet different monitoring needs.

[0070] The warning rule setting module defines the conditions for triggering warnings. During data analysis, it determines whether a warning is needed and the warning level. It also provides a flexible warning rule setting function. Users can set different warning conditions according to actual needs, such as the air quality index exceeds a certain threshold, the noise level reaches a certain level, etc. When the data analysis module detects data that meets the warning conditions, it will automatically trigger the warning rules and send an alarm message to the relevant users.

[0071] The data analysis report module is used to output the analysis results in the form of a report file, integrating the Apache POI report generation tool to format and output the data analysis results. The report content includes information such as the current environmental quality assessment, abnormal warnings and energy efficiency optimization suggestions, which are presented in the form of charts, tables and text descriptions.

[0072] The infrastructure service group is mainly responsible for the registration, management and log monitoring of microservices. The infrastructure service group includes the microservice registration module, the automation management module and the log management module.

[0073] The microservice registration module provides a unified registration function for microservice instances. The microservice registration module uses the Consul registration center to realize automatic registration and discovery of microservice instances. This module monitors the status of microservice instances in real time, including health checks, load conditions, etc., to ensure the availability of services, and provides message bus communication functions. Based on the Event Bus message middleware, it realizes asynchronous message communication between microservices to ensure timely delivery of information.

[0074] The automated management module uses automated tools to manage and schedule microservice instances. The automated management module uses Docker and Kubernetes containerization technologies to enable rapid deployment, start, stop, and restart of microservices. By defining Dockerfile and Kubernetes configuration files, microservice containers can be automatically built, deployed, and managed to improve the portability and elasticity of services. The module also integrates the Spring Cloud Config configuration management tool to achieve unified management and dynamic refresh of configurations, ensuring the flexibility and maintainability of services.

[0075] The log management module is responsible for data monitoring and log management of microservices. It uses ELK Stack (Elasticsearch, Logstash, Kibana) log collection, analysis and visualization tools to achieve centralized management of service operation data and log information. By configuring log collection strategies, you can collect microservice log data in real time, store, analyze and visualize it, which helps to quickly locate problems, analyze system performance and monitor service status, providing support for system operation and optimization.

[0076] Embodiment 3:

[0077] The data storage layer uses Redis as a cache database, PostgreSQL as a persistent database, and ClickHouse as a column storage database. Redis, as a cache database, is mainly used to store hot data and temporary data. Redis has the characteristics of high performance and low latency, and can support high-concurrency data access. By configuring Redis's persistence mechanisms, such as RDB and AOF, the reliability and recovery capabilities of the data can be ensured. PostgreSQL, as a persistent database, is mainly used to store the core business data and historical data of the system. PostgreSQL has strong data integrity protection, transaction support and complex query capabilities, which can meet the system's requirements for data consistency and reliability. ClickHouse, as a column storage database, is mainly used to store and analyze large-scale environmental monitoring data. ClickHouse uses column storage and vectorized computing technology, which can significantly improve the performance of data query and analysis.

[0078] The data storage layer is designed with a dual-active architecture, which includes an active node and a standby node. The two nodes back up each other when processing data to ensure data reliability and availability. The specific implementation is as follows:

[0079] The data consistency between the active node and the standby node is maintained through a data synchronization mechanism. When the active node receives a data write request, it will write the data to the standby node at the same time to ensure that the data status of the two nodes is consistent.

[0080] Regularly back up data to remote storage or cloud storage to prevent data loss and for disaster recovery.

[0081] When the active node fails, needs maintenance or upgrade, the standby node can automatically take over the service to ensure the continuity and availability of the system. By configuring the fault detection and switching mechanism, the real-time monitoring and fast switching of the active node status can be achieved. After taking over the service, the standby node will continue to process data requests and ensure the consistency and integrity of the data.

[0082] The active-active architecture can also achieve load balancing and performance optimization. By configuring a load balancer, data requests can be distributed to active nodes and standby nodes to achieve balanced load distribution.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0084] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Smart community environment monitoring and control system, characterized by: It includes a user interaction interface and a data processing center for comprehensive processing of environmental data. The environmental data is derived from multiple monitoring points in the community, including air quality monitoring stations, noise monitoring stations, temperature and humidity sensors, smart trash cans, and energy consumption monitoring equipment. The data processing center provides comprehensive data support for the user interaction interface, so that users can grasp the community environment status in real time and make corresponding regulatory decisions; The data processing center includes a communication interface layer, a service processing layer, a data access layer and a data storage layer; the communication interface layer includes a gateway module, which is responsible for providing connection services to the user interaction interface and establishing a data transmission channel; the service processing layer adopts a distributed service architecture to provide data collection, preprocessing, fusion analysis, strategy formulation, business logic processing and basic service functions for the data processing center, and the gateway module locates the specific service instance in the service processing layer through a dynamic routing mechanism; the data storage layer is responsible for data storage, including real-time caching, persistent storage and column storage of data; the data access layer includes a connection pool management module, which is used to establish a connection channel between the service processing layer and the data storage layer so that the two can perform safe and stable data reading and writing operations.

2. The smart community environment monitoring and control system according to claim 1 is characterized in that: The service processing layer includes a data collection service group, a real-time analysis service group, an operation control service group and an infrastructure service group; The data acquisition service group is responsible for designing the communication protocols with various environmental monitoring devices and building the front-end adapters for receiving data; The real-time analysis service group is responsible for the immediate processing, in-depth analysis and storage of data received from the data collection service group; the operation control service group is used to formulate and implement environmental control strategies based on the analysis results of the real-time analysis service group; the infrastructure service group is responsible for maintaining the operational stability of the entire service processing layer and the message transmission between services.

3. The smart community environment monitoring and control system according to claim 2 is characterized in that: The real-time analysis service group includes a data preprocessing module, a data fusion module, an intelligent analysis module and a data archiving module; The data preprocessing module is responsible for processing missing values ​​and outliers in environmental monitoring data; The data fusion module integrates the preprocessed data, including the supplement of environmental parameters and equipment status information, to generate a more accurate data set. Environmental parameters include air quality index, noise level, temperature and humidity values, garbage overflow status and energy consumption. Equipment status includes the working status and maintenance records of sensors. The intelligent analysis module uses machine learning algorithms to build a data analysis model. The data analysis model is used to analyze the comprehensive environmental conditions of the community output by the data fusion module, identify abnormal data points, predict environmental change trends, and evaluate the impact of the environment on residents' lives. The data archiving module is responsible for sending the fused data and analysis results to the data storage layer for preservation.

4. The smart community environment monitoring and control system according to claim 3 is characterized by: The business control service group includes a user authority management module, a message push management module, an environmental parameter configuration module, an early warning rule setting module and a data analysis report module; The user rights management module is responsible for defining user accounts and their access rights; the message push management module sends environmental monitoring alarms in real time to assist users in quickly responding to environmental issues; the environmental parameter configuration module displays and allows adjustment of the configuration information of the monitoring equipment; the warning rule setting module defines the conditions for triggering warnings, and determines whether a warning needs to be issued and the warning level based on this during data analysis; the data analysis report module is used to output the analysis results in the form of a report file, including the current environmental quality assessment, abnormal warnings and energy efficiency optimization suggestions.

5. The smart community environment monitoring and control system according to claim 4 is characterized in that: The infrastructure service group includes a microservice registration module, an automation management module and a log management module; The microservice registration module is used to provide a unified registration function for microservice instances, monitor the status of microservice instances in real time, and provide a message bus communication function to enable message communication between microservices; The automation management module uses automation tools to automatically manage and schedule microservice instances, including deployment, start, stop and restart operations of microservices; The log management module is used to perform data monitoring and log management on microservices and collect service operation data and log information.

6. The smart community environment monitoring and control system according to claim 5 is characterized by: The data storage layer uses Redis as the cache database, PostgreSQL as the persistence database, and ClickHouse as the column storage database.

7. The smart community environment monitoring and control system according to claim 6, characterized in that: The data storage layer is designed with an active-active architecture, including an active node and a standby node. The two nodes back up each other when processing data. When the active node fails, requires maintenance or upgrade, the standby node takes over the service.

8. The smart community environment monitoring and control system according to claim 2 is characterized in that: The environmental control strategies formulated by the operation control service group include: adjusting the ventilation system in public areas according to the air quality index; optimizing the layout of noise sources according to the noise level; adjusting the air conditioning system in public areas according to the temperature and humidity values; arranging garbage removal according to the garbage overflow status, and making energy-saving suggestions based on energy consumption.

9. The smart community environment monitoring and control system according to claim 2 is characterized in that: The data acquisition service group includes a front-end adapter for various environmental monitoring devices. The front-end adapter is used to establish a communication mechanism with the specific corresponding monitoring device, including the formulation of the front-end communication protocol, the definition of the format of sending and receiving data, disconnection processing and status monitoring.

10. The smart community environment monitoring and control system according to claim 9, characterized in that: The communication interface layer provides HTTP API interface and WebSocket real-time message push service for the user interaction interface to realize user interaction and instruction issuance.

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