Air quality real-time monitoring method and monitoring system based on single-chip microcomputer

Abstract

The invention discloses an air quality real-time monitoring method and monitoring system based on a single-chip microcomputer, and relates to the technical field of air quality monitoring, and the method comprises the following steps: S10, emission data integration and model initialization; s20, monitoring area subdivision and monitoring point deployment are carried out, and air quality monitoring areas are intelligently divided according to geographical distribution of emission hot spots; s30, virtual area construction and interactive network construction are carried out based on the layout of the monitoring points; s40, real-time data monitoring and abnormity recognition are conducted, specifically, the monitoring points collect air quality data in real time, and the air quality data are compared with preset compliance standards; and S50, visualizing the abnormal region by using the interaction relationship of the virtual region model. According to the invention, through comprehensive data integration and model initialization, collection and integration of emission data of new pollutants and construction of a dynamic emission model, accurate positioning of emission hot spots and distribution of compliance standards are realized, and the accuracy and timeliness of data are ensured by using a high-precision sensor network and a real-time data transmission technology.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality monitoring, and in particular to a single-chip-based real-time air quality monitoring method and a monitoring system. Background Art

[0002] With the rapid development of industrialization and urbanization, air quality issues are becoming increasingly serious, posing a serious threat to public health and the ecological environment. In order to effectively monitor and control air pollution, it is necessary to establish an efficient and accurate real-time air quality monitoring system. However, traditional air quality monitoring methods have many shortcomings, such as limited monitoring range, untimely data updates, and weak abnormal data processing capabilities, which are difficult to meet the current urgent needs for air quality monitoring.

[0003] At present, although there are some air quality monitoring systems on the market, most of these systems are based on fixed monitoring sites, with limited monitoring range, and it is difficult to reflect the dynamic changes of air pollutants in real time. In addition, these systems also have shortcomings in data processing and anomaly identification, and often can only provide simple data display, but cannot conduct in-depth analysis of abnormal data and provide targeted processing strategies.

[0004] In order to overcome the shortcomings of traditional air quality monitoring systems, in recent years, the air quality real-time monitoring technology based on single-chip microcomputers has gradually attracted attention. Single-chip microcomputers have the characteristics of small size, low power consumption, and powerful functions, which are very suitable for building real-time monitoring systems. However, how to combine single-chip microcomputer technology with air quality monitoring to achieve efficient and accurate real-time monitoring is still a technical problem.

[0005] In terms of air quality monitoring, the integration of emission data and model initialization are key links. By collecting and integrating emission data of new pollutants, a dynamic emission model can be constructed to accurately locate emission hotspots and assign specific emission compliance standards to each hotspot. However, this process requires processing a large amount of data, and the accuracy and timeliness of the data have a crucial impact on the accuracy of the model.

[0006] In addition, the subdivision of monitoring areas and the deployment of monitoring points are also important factors in achieving efficient monitoring. Reasonable division of monitoring areas and deployment of monitoring points can ensure the comprehensiveness and accuracy of monitoring data, thereby more accurately reflecting the dynamic changes of air pollutants. However, this process requires comprehensive consideration of factors such as the geographical distribution of emission hotspots, the diffusion characteristics of pollutants, and monitoring costs, which is difficult to achieve.

[0007] In terms of data processing, traditional air quality monitoring systems can only provide simple data display, but cannot conduct in-depth analysis of abnormal data. In order to realize the automatic identification and processing of abnormal data, it is necessary to introduce advanced data processing algorithms and abnormality identification technologies. However, the introduction of these technologies also brings additional computational complexity and cost. Summary of the invention

[0008] The purpose of the present invention is to provide a method and system for real-time monitoring of air quality based on a single chip microcomputer, so as to solve the shortcomings of the background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a method for real-time monitoring of air quality based on a single chip microcomputer, comprising: S10: Emission data integration and model initialization: collect and integrate emission data of new pollutants, use these data to build a dynamic emission model, accurately locate emission hotspots in the model, and assign specific emission compliance standards to each hotspot; S20: Monitoring area segmentation and monitoring point deployment: intelligently divide the air quality monitoring area according to the geographical distribution of emission hotspots, and carefully deploy monitoring points in each segmented area to form a dense monitoring network to fully capture the dynamics of pollutants; S30: Virtual area construction and interactive network construction. Based on the layout of monitoring points, a series of interconnected virtual area models are constructed to form an intelligent monitoring network. Using emission data, dynamic interactive relationships between virtual areas are established to simulate the diffusion path of pollutants. S40: Real-time data monitoring and anomaly identification: monitoring points collect air quality data in real time and compare it with preset compliance standards, automatically identify and mark abnormal data that exceeds the standards, trigger an alarm mechanism, and integrate abnormal data into the relevant virtual area model; S50: Visualization of abnormal areas, using the interactive relationship of the virtual area model to dynamically display the areas containing abnormal data, and present the abnormal areas of pollutant emissions through an intuitive visualization interface.

[0010] Preferably, the emission data integration and model initialization includes: Collection of emission data and construction of three-dimensional models: using high-precision sensor networks and real-time data transmission technology to collect data on emission pathways, emission locations and related parameters of new pollutants. Based on the collected emission data, using geographic information system (GIS) technology and three-dimensional spatial modeling algorithms, a three-dimensional emission model is constructed that includes emission point locations, emission pathways and spatial distribution characteristics. Identification of emission points and marking of models: using preset algorithm logic and conditional screening criteria, potential emission points in the three-dimensional emission model are identified, and graphic marking technology is used to mark all identified emission points in the three-dimensional emission model, and detailed attribute information is provided for each mark; Configuration and dynamic management of emission restriction information: for each marked emission point, clear emission restriction information is configured according to environmental protection laws and regulations, industry standards and the results of environmental impact assessment.

[0011] Preferably, the monitoring area subdivision and monitoring point deployment include: Analysis of the geographical distribution of emission hotspots and intelligent division of monitoring areas: Using geographic information system (GIS) technology and big data analysis methods, we conduct in-depth analysis of the geographical distribution of emission hotspots. Based on the analysis results, we intelligently divide multiple air quality monitoring areas to ensure that each area can accurately reflect its unique emission characteristics and pollutant dynamics; Selection of monitoring range and sub-range planning: In each monitoring area, the corresponding new pollutant monitoring range is carefully selected according to the actual location and emission path of the emission point, and the selected monitoring range is further subdivided to plan multiple sub-ranges in order to more accurately capture the spatial distribution and dynamic changes of pollutants; Setting up monitoring feature network and deploying monitoring points: In each sub-range, a new pollutant monitoring feature network is set up according to the characteristics of the pollutants and monitoring requirements. Corresponding to each monitoring feature network, multiple monitoring points are carefully deployed to ensure that the monitoring points can fully cover the monitoring range and accurately capture the dynamic changes of pollutants; The relationship between monitoring points is established and sub-control points are generated. Based on the monitoring range of new pollutants, the relationship between multiple monitoring points is established to form a monitoring network.

[0012] Preferably, the virtual area construction and interactive network establishment include: Acquisition of monitoring point location information and construction of a layout network: Using positioning technology and geographic information system (GIS), the geographical location information of multiple monitoring points is accurately obtained based on the new pollutant monitoring range. Based on the acquired monitoring point location information, each monitoring point is connected in pairs to construct a layout network covering the entire monitoring range. The layout network can intuitively display the distribution and connection relationship of the monitoring points; Safety sphere model construction and monitoring point mapping: At the center of the network, a central point is set as the origin, which represents the center of the entire monitoring area. A three-dimensional safety sphere model is constructed with the origin as the center of the sphere. This model is used to simulate the diffusion and impact range of pollutants in the monitoring area. According to the geographical location information of the monitoring point, each monitoring point is mapped to the corresponding position on the safety sphere model, and the actual distance of each monitoring point from the origin is recorded; this step ensures the precise positioning of the monitoring point in three-dimensional space; Monitoring point information binding and association relationship construction. For each monitoring point, its mapping position on the safety ball model and the distance from the origin are used as verification information. Each monitoring point is bound to its corresponding verification information to form a unique identity of the monitoring point. Based on the verification information, association relationships between multiple monitoring points are established. These association relationships reflect the relative positions and mutual influences of the monitoring points in space.

[0013] Preferably, the real-time data monitoring and anomaly identification includes: District bureau model construction: According to the requirements of multiple new pollutant monitoring ranges, multiple independent district bureau models are constructed. In each district bureau model, the positions of multiple sub-control points are accurately marked according to actual needs, and corresponding cloud server resources are configured for each marked sub-control point position, thereby generating multiple virtual plate models corresponding to the district bureau models; Communication connection setting: establish communication connection between sub-control points and corresponding virtual plate models to ensure the reliability and stability of real-time data transmission. Through communication connection, sub-control points can collect pollutant information in each sub-area in real time and transmit this information to the corresponding virtual plate model; Information association and analysis: receiving and storing pollutant information transmitted from sub-control points, establishing associations between multiple virtual plate models based on these emission information, so as to conduct more comprehensive pollutant monitoring and analysis, comparing the received pollutant information with the preset emission threshold, identifying abnormal pollutant information that exceeds the threshold, and triggering the corresponding warning mechanism, integrating the identified abnormal information into the corresponding virtual plate model.

[0014] Preferably, the abnormal area visualization includes: Communication channel establishment: According to actual needs, a communication channel between the sub-control point and the virtual plate model is constructed; Information correspondence and verification: an information ball is set between the sub-control point and the communication channel. The information ball is consistent with the shape of the preset safety ball and is used to store and transmit data. The information ball is verified to ensure the integrity and accuracy of the data. After the verification is passed, the position in the information ball is used as the storage point, and the corresponding relationship between the storage point and the corresponding monitoring point is established for subsequent data processing and analysis; Connection and data transmission: Based on the information ball and communication channel, the sub-control point is connected to the virtual plate model. Through the communication channel, the new pollutant information collected by the monitoring point is transmitted to the corresponding virtual plate model in the form of an information ball. During the transmission process, the real-time and accuracy of the data must be ensured so as to reflect the air quality status in a timely manner. Abnormal information judgment and processing: In the virtual plate model, a judgment module is set up to receive and process new pollutant information transmitted from the monitoring point. The judgment module compares the new pollutant information with the preset emission limit information, and regards the new pollutant information that does not meet the emission limit as abnormal information, and marks and stores the abnormal information for subsequent analysis and processing; Data load and transmission optimization: Based on the verification information, the new pollutant information collected by the monitoring point is loaded into the corresponding storage point in the information ball in a specific format, and the information ball is compressed and optimized to reduce the bandwidth occupation of data transmission and improve the transmission efficiency. The optimized information ball is transmitted to the corresponding virtual plate model through the communication channel; Dynamic demonstration and decision support, using the correlation between virtual plate models, dynamically demonstrate models containing abnormal information. Through the demonstration results, the abnormal location, concentration, and change trend of pollutant emissions are intuitively displayed. Based on the demonstration results, decision support is provided for subsequent pollution control and governance, including formulating targeted emission reduction measures and optimizing the monitoring network layout.

[0015] Preferably, it also includes: an abnormal data identification unit, which is used to monitor and identify abnormal data exceeding a preset emission threshold in the data collected by the air quality monitoring point in real time; An abnormal data caching and analysis unit is used to cache the identified abnormal data in a dedicated memory and apply a preset data analysis algorithm to conduct an in-depth analysis of the abnormal data to determine the type, source, impact range and possible cause of the abnormal data; The exception handling strategy generation unit automatically generates targeted exception handling strategies based on the analysis results of the exception data, combined with environmental protection laws and regulations, industry standards and historical handling experience, including but not limited to pollution source investigation instructions, emission reduction measures or monitoring point adjustment plans; The processing effect evaluation and feedback unit is used to evaluate the effectiveness of the abnormal processing strategy after its implementation by comparing the air quality data before and after the processing, and present the evaluation results to the user in a visual manner. At the same time, the processing strategy is continuously optimized and adjusted based on user feedback.

[0016] Preferably, it also includes: system performance monitoring and evaluation, real-time monitoring and evaluation of the overall performance of the monitoring system, including key indicators such as data transmission rate, data processing capacity, system stability, etc., to ensure that the system can meet the needs of real-time monitoring; Technology iteration and function upgrade: Based on the results of system performance monitoring and evaluation, combined with the latest technology development trends and user needs, the monitoring system will be upgraded in terms of technology and function, including but not limited to sensor technology upgrades, data processing algorithm optimization, user interface improvements, etc.; Verification and testing of upgrade effects: comprehensive verification and testing of the upgraded monitoring system to ensure that the upgraded system can operate stably and that all performance indicators are improved; User training and document updates: Provide training and guidance to users on new functions and features after the upgrade to ensure that users can master and use the monitoring system proficiently. At the same time, update relevant technical documents and user manuals to provide users with comprehensive technical support and services.

[0017] A single-chip-based real-time air quality monitoring system, including: an emission data integration and model initialization module, which collects and integrates emission data of new pollutants, builds a dynamic emission model, accurately locates emission hotspots, and assigns emission compliance standards; Emission data refinement processing module, collects emission data, builds a three-dimensional emission model, identifies and marks emission points, and configures emission restriction information; Monitoring area segmentation and monitoring point deployment module, analyzes the geographical distribution of emission hotspots, intelligently divides the monitoring area, selects the monitoring range, plans the sub-range, sets up the monitoring feature network, deploys monitoring points, and establishes the association relationship between monitoring points; Virtual area construction and interactive network building module, obtain monitoring point location information, build deployment network and safety ball model, map monitoring points, bind monitoring point information, and build association relationships; Real-time data monitoring and anomaly identification module, builds district and bureau models and virtual plate models, sets up communication connections, receives and stores pollutant information, establishes associations, and identifies abnormal pollutant information; The abnormal area visualization and data processing module establishes communication channels, verifies information balls, transmits data, judges abnormal information, optimizes data transmission, dynamically demonstrates abnormal information, and provides decision support.

[0018] Abnormal data processing and strategy generation module, real-time monitoring and identification of abnormal data, cache and analysis of abnormal data, generation of abnormal processing strategy, evaluation of processing effect and feedback; The system performance monitoring and upgrade module monitors and evaluates system performance, performs technology iterations and functional upgrades, verifies and tests upgrade effects, provides training and guidance to users, and updates technical documentation and user manuals.

[0019] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention collects and integrates emission data of new pollutants through comprehensive data integration and model initialization, builds a dynamic emission model, realizes the precise positioning of emission hotspots and the allocation of compliance standards, and uses high-precision sensor networks and real-time data transmission technology to ensure the accuracy and timeliness of data.

[0020] 2. The present invention realizes precise monitoring by intelligently dividing air quality monitoring areas and deploying monitoring points according to the geographical distribution of emission hotspots. Monitoring points are carefully deployed in each monitoring area to form a dense monitoring network to comprehensively capture the dynamics of pollutants.

[0021] 3. The present invention constructs a virtual area model through virtual area construction and interactive network construction, forms an intelligent monitoring network, simulates the diffusion path of pollutants, uses positioning technology and GIS to accurately obtain the location information of monitoring points, and establishes the association relationship between monitoring points.

[0022] 4. The present invention monitors air quality data in real time through real-time data monitoring, anomaly identification and visualization, automatically identifies and marks abnormal data, triggers an alarm mechanism, and intuitively displays the abnormal location, concentration and change trend of pollutant emissions through dynamic demonstration of a virtual area model containing abnormal information. An abnormal data processing and strategy generation module is set to realize automatic identification of abnormal data, in-depth analysis and automatic generation of targeted processing strategies, thereby improving the efficiency and accuracy of abnormal processing. DETAILED DESCRIPTION

[0023] Embodiment 1: A method for real-time monitoring of air quality based on a single-chip microcomputer described in this embodiment includes: S10: Emission data integration and model initialization: collect and integrate emission data of new pollutants, use these data to build a dynamic emission model, accurately locate emission hotspots in the model, and assign specific emission compliance standards to each hotspot; In the S10 stage, the system first collects a wide range of emission data, including key parameters such as the emission pathways, emission locations, and emission rates of new pollutants. High-precision sensor networks and real-time data transmission technology are used to ensure the accuracy and timeliness of the data. Subsequently, a three-dimensional emission model containing detailed emission information is constructed based on the geographic information system (GIS) and three-dimensional spatial modeling algorithms. This model not only shows the specific location of the emission point, but also simulates the emission path and spatial distribution characteristics of the pollutants.

[0024] During the model initialization process, the system uses preset algorithm logic and conditional screening criteria to accurately identify potential emission points in the three-dimensional model, and assigns a unique tag and detailed attribute information to each identified emission point. These attribute information include the type of emission point, emission intensity, emission time, etc. Finally, according to environmental protection laws and regulations, industry standards and environmental impact assessment results, clear emission limit information is configured for each emission point to provide a basis for subsequent monitoring and compliance judgment.

[0025] S20: Monitoring area segmentation and monitoring point deployment: intelligently divide the air quality monitoring area according to the geographical distribution of emission hotspots, and carefully deploy monitoring points in each segmented area to form a dense monitoring network to fully capture the dynamics of pollutants; In the S20 stage, the system uses GIS technology and big data analysis to conduct in-depth analysis of the geographical distribution of emission hotspots. Based on the analysis results, multiple air quality monitoring areas are intelligently divided, each of which reflects its unique emission characteristics and pollutant dynamics.

[0026] In each monitoring area, the system carefully selects the corresponding new pollutant monitoring range based on the actual location and emission path of the emission point. Then, the selected monitoring range is further subdivided and multiple sub-ranges are planned to more accurately capture the spatial distribution and dynamic changes of pollutants. In each sub-range, the system sets up a new pollutant monitoring feature network based on the characteristics of the pollutants and monitoring needs, and carefully deploys multiple monitoring points. These monitoring points can fully cover the monitoring range and accurately capture the dynamic changes of pollutants.

[0027] S30: Virtual area construction and interactive network construction. Based on the layout of monitoring points, a series of interconnected virtual area models are constructed to form an intelligent monitoring network. Using emission data, dynamic interactive relationships between virtual areas are established to simulate the diffusion path of pollutants. In the S30 stage, the system first uses positioning technology and GIS to accurately obtain the geographical location information of multiple monitoring points. Then, based on the acquired location information of the monitoring points, a layout network covering the entire monitoring range is constructed. This layout network intuitively shows the distribution and connection relationship of the monitoring points.

[0028] Next, the system sets a center point as the origin at the center of the network and constructs a three-dimensional safety sphere model with the origin as the center of the sphere. This model is used to simulate the spread and impact range of pollutants in the monitoring area. According to the geographical location information of the monitoring point, each monitoring point is mapped to the corresponding position on the safety sphere model, and the actual distance of each monitoring point from the origin is recorded.

[0029] Finally, the system binds verification information (including mapping location and distance from the origin) to each monitoring point, and establishes associations between multiple monitoring points based on this information. These associations reflect the relative positions and mutual influences of the monitoring points in space.

[0030] S40: Real-time data monitoring and anomaly identification: monitoring points collect air quality data in real time and compare it with preset compliance standards, automatically identify and mark abnormal data that exceeds the standards, trigger an alarm mechanism, and integrate abnormal data into the relevant virtual area model; In the S40 stage, the system first builds multiple independent district models based on the needs of multiple new pollutant monitoring ranges. In each district model, the locations of multiple sub-control points are accurately marked, and corresponding cloud server resources are configured for each marked sub-control point location. These cloud server resources are used to store and process real-time data from monitoring points.

[0031] Then, the system establishes a communication connection between the sub-control point and the corresponding virtual plate model to ensure the reliability and stability of real-time data transmission. Through the communication connection, the sub-control point can collect pollutant information in each sub-area in real time and transmit this information to the corresponding virtual plate model.

[0032] In the virtual plate model, the system receives and stores pollutant information transmitted from the sub-control points, and establishes associations between multiple virtual plate models based on this information. Then, the system compares the received pollutant information with the preset emission threshold, identifies abnormal pollutant information that exceeds the threshold, and triggers the corresponding warning mechanism. Finally, the identified abnormal information is integrated into the corresponding virtual plate model.

[0033] S50: Visualization of abnormal areas, using the interactive relationship of the virtual area model to dynamically display the areas containing abnormal data, and present the abnormal areas of pollutant emissions through an intuitive visualization interface.

[0034] In the S50 stage, the system first builds a communication channel between the sub-control point and the virtual plate model for data transmission and command issuance. Then, an information ball is set between the sub-control point and the communication channel for data storage and transmission. The information ball is consistent with the shape of the preset safety ball, ensuring the integrity and accuracy of the data during transmission.

[0035] Next, the system verifies the information ball to ensure the integrity and accuracy of the data in it. After verification, the location in the information ball is used as a storage point, and the corresponding relationship between the storage point and the corresponding monitoring point is established. Then, based on the information ball and the communication channel, the sub-control point is connected to the virtual plate model for communication, and the new pollutant information collected by the monitoring point is transmitted to the corresponding virtual plate model in the form of an information ball through the communication channel.

[0036] In the virtual plate model, the system sets up a judgment module to receive and process new pollutant information transmitted from the monitoring point. The judgment module compares the new pollutant information with the preset emission limit information, and regards the new pollutant information that does not meet the emission limit as abnormal information, and marks and stores it.

[0037] Finally, the system uses the correlation between virtual plate models to dynamically demonstrate the models containing abnormal information. The demonstration results intuitively display the abnormal location, concentration, and change trend of pollutant emissions, providing decision support for subsequent pollution control and governance.

[0038] In this embodiment, the emission data integration and model initialization include: Collection of emission data and construction of three-dimensional models: using high-precision sensor networks and real-time data transmission technology to collect data on emission pathways, emission locations and related parameters of new pollutants. Based on the collected emission data, using geographic information system (GIS) technology and three-dimensional spatial modeling algorithms, a three-dimensional emission model is constructed that includes emission point locations, emission pathways and spatial distribution characteristics. Identification of emission points and marking of models: using preset algorithm logic and conditional screening criteria, potential emission points in the three-dimensional emission model are identified, and graphic marking technology is used to mark all identified emission points in the three-dimensional emission model, and detailed attribute information is provided for each mark; Configuration and dynamic management of emission restriction information: for each marked emission point, clear emission restriction information is configured according to environmental protection laws and regulations, industry standards and the results of environmental impact assessment; Through high-precision sensor networks and real-time data transmission technology, emission data of new pollutants can be collected quickly and accurately, providing a reliable data foundation for subsequent model building and monitoring work.

[0039] The three-dimensional emission model constructed using GIS technology and three-dimensional spatial modeling algorithms intuitively displays the location, emission path and spatial distribution characteristics of emission points, providing an intuitive visual reference for the identification of emission points and compliance judgment.

[0040] In this embodiment, the monitoring area subdivision and monitoring point deployment include: Analysis of the geographical distribution of emission hotspots and intelligent division of monitoring areas: Using geographic information system (GIS) technology and big data analysis methods, we conduct in-depth analysis of the geographical distribution of emission hotspots. Based on the analysis results, we intelligently divide multiple air quality monitoring areas to ensure that each area can accurately reflect its unique emission characteristics and pollutant dynamics; Selection of monitoring range and sub-range planning: In each monitoring area, the corresponding new pollutant monitoring range is carefully selected according to the actual location and emission path of the emission point, and the selected monitoring range is further subdivided to plan multiple sub-ranges in order to more accurately capture the spatial distribution and dynamic changes of pollutants; Setting up monitoring feature network and deploying monitoring points: In each sub-range, a new pollutant monitoring feature network is set up according to the characteristics of the pollutants and monitoring requirements. Corresponding to each monitoring feature network, multiple monitoring points are carefully deployed to ensure that the monitoring points can fully cover the monitoring range and accurately capture the dynamic changes of pollutants; Establishment of monitoring point association and generation of sub-control points. Based on the monitoring range of new pollutants, establish associations between multiple monitoring points to form a monitoring network. Through GIS technology and big data analysis, the geographical distribution of emission hotspots is deeply analyzed, and multiple air quality monitoring areas are intelligently divided to ensure that each area can accurately reflect its unique emission characteristics and pollutant dynamics.

[0041] In each monitoring area, the monitoring range is carefully selected and subdivided according to the actual location of the emission point and the emission route, multiple sub-ranges are planned, and multiple monitoring points are deployed, forming a dense monitoring network that can comprehensively capture the dynamic changes of pollutants.

[0042] In this embodiment, the virtual area construction and interactive network establishment include: Acquisition of monitoring point location information and construction of a layout network: Using positioning technology and geographic information system (GIS), the geographical location information of multiple monitoring points is accurately obtained based on the new pollutant monitoring range. Based on the acquired monitoring point location information, each monitoring point is connected in pairs to construct a layout network covering the entire monitoring range. The layout network can intuitively display the distribution and connection relationship of the monitoring points; Safety sphere model construction and monitoring point mapping: At the center of the network, a central point is set as the origin, which represents the center of the entire monitoring area. A three-dimensional safety sphere model is constructed with the origin as the center of the sphere. This model is used to simulate the diffusion and impact range of pollutants in the monitoring area. According to the geographical location information of the monitoring point, each monitoring point is mapped to the corresponding position on the safety sphere model, and the actual distance of each monitoring point from the origin is recorded; this step ensures the precise positioning of the monitoring point in three-dimensional space; Monitoring point information binding and association relationship building: For each monitoring point, its mapping position on the safety ball model and the distance from the origin are used as verification information. Each monitoring point is bound to its corresponding verification information to form a unique identity of the monitoring point. Based on the verification information, association relationships between multiple monitoring points are established. These association relationships reflect the relative positions and mutual influences of the monitoring points in space. By constructing a virtual area model and establishing the correlation between monitoring points, the simulation and prediction of the pollutant diffusion path in the monitoring area are realized, providing strong support for subsequent anomaly identification and visualization.

[0043] The use of safety sphere models and verification information ensures the precise positioning of monitoring points in three-dimensional space and the integrity of data, improving the accuracy and reliability of the monitoring system.

[0044] In this embodiment, the real-time data monitoring and anomaly identification include: District bureau model construction: According to the requirements of multiple new pollutant monitoring ranges, multiple independent district bureau models are constructed. In each district bureau model, the positions of multiple sub-control points are accurately marked according to actual needs, and corresponding cloud server resources are configured for each marked sub-control point position, thereby generating multiple virtual plate models corresponding to the district bureau models; Communication connection setting: establish communication connection between sub-control points and corresponding virtual plate models to ensure the reliability and stability of real-time data transmission. Through communication connection, sub-control points can collect pollutant information in each sub-area in real time and transmit this information to the corresponding virtual plate model; Information association and analysis: receiving and storing pollutant information transmitted from sub-control points, establishing associations between multiple virtual plate models based on these emission information, so as to conduct more comprehensive pollutant monitoring and analysis, comparing the received pollutant information with the preset emission threshold, identifying abnormal pollutant information exceeding the threshold, triggering the corresponding warning mechanism, and integrating the identified abnormal information into the corresponding virtual plate model; By establishing a communication connection between the sub-control points and the virtual plate model, real-time data transmission and processing are achieved, and abnormal data that exceeds the preset emission threshold can be discovered and identified in a timely manner.

[0045] The alarm mechanism is triggered and the abnormal data is integrated into the relevant virtual area model, providing timely and accurate information for subsequent abnormal processing and decision support.

[0046] In this embodiment, the abnormal area visualization includes: Communication channel establishment: According to actual needs, a communication channel between the sub-control point and the virtual plate model is constructed; Information correspondence and verification: an information ball is set between the sub-control point and the communication channel. The information ball is consistent with the shape of the preset safety ball and is used to store and transmit data. The information ball is verified to ensure the integrity and accuracy of the data. After the verification is passed, the position in the information ball is used as the storage point, and the corresponding relationship between the storage point and the corresponding monitoring point is established for subsequent data processing and analysis; Connection and data transmission: Based on the information ball and communication channel, the sub-control point is connected to the virtual plate model. Through the communication channel, the new pollutant information collected by the monitoring point is transmitted to the corresponding virtual plate model in the form of an information ball. During the transmission process, the real-time and accuracy of the data must be ensured so as to reflect the air quality status in a timely manner. Abnormal information judgment and processing: In the virtual plate model, a judgment module is set up to receive and process new pollutant information transmitted from the monitoring point. The judgment module compares the new pollutant information with the preset emission limit information, and regards the new pollutant information that does not meet the emission limit as abnormal information, and marks and stores the abnormal information for subsequent analysis and processing; Data load and transmission optimization: Based on the verification information, the new pollutant information collected by the monitoring point is loaded into the corresponding storage point in the information ball in a specific format, and the information ball is compressed and optimized to reduce the bandwidth occupation of data transmission and improve the transmission efficiency. The optimized information ball is transmitted to the corresponding virtual plate model through the communication channel; Dynamic demonstration and decision support: using the correlation between virtual plate models, models containing abnormal information are dynamically demonstrated. Through the demonstration results, the abnormal location, concentration, and change trend of pollutant emissions are intuitively displayed. Based on the demonstration results, decision support is provided for subsequent pollution control and governance, including the formulation of targeted emission reduction measures and optimization of monitoring network layout. By dynamically demonstrating a virtual plate model containing abnormal information, the abnormal location, concentration, and change trend of pollutant emissions are intuitively displayed, providing an intuitive visual reference for pollution control and governance. With the help of a visual interface, users can easily understand the monitoring results and make decisions quickly.

[0047] In this embodiment, it also includes: an abnormal data identification unit, which is used to monitor and identify abnormal data exceeding a preset emission threshold in the data collected by the air quality monitoring point in real time; An abnormal data caching and analysis unit is used to cache the identified abnormal data in a dedicated memory and apply a preset data analysis algorithm to conduct an in-depth analysis of the abnormal data to determine the type, source, impact range and possible cause of the abnormal data; The exception handling strategy generation unit automatically generates targeted exception handling strategies based on the analysis results of the exception data, combined with environmental protection laws and regulations, industry standards and historical handling experience, including but not limited to pollution source investigation instructions, emission reduction measures or monitoring point adjustment plans; The processing effect evaluation and feedback unit is used to evaluate the effectiveness of the abnormal processing strategy after its implementation by comparing the air quality data before and after the processing, and present the evaluation results to the user in a visual manner. At the same time, the processing strategy is continuously optimized and adjusted based on user feedback.

[0048] An abnormal data identification unit, an abnormal data caching and analysis unit, and an abnormal data processing strategy generation unit are set up to realize automatic identification of abnormal data, in-depth analysis, and automatic generation of targeted processing strategies.

[0049] Through the processing effect evaluation and feedback unit, the effectiveness of the processing strategy is evaluated, and continuous optimization and adjustment are carried out based on user feedback, thereby improving the efficiency and accuracy of exception handling.

[0050] In this embodiment, it also includes: system performance monitoring and evaluation, which monitors and evaluates the overall performance of the monitoring system in real time, including key indicators such as data transmission rate, data processing capacity, and system stability, to ensure that the system can meet the needs of real-time monitoring; Technology iteration and function upgrade: Based on the results of system performance monitoring and evaluation, combined with the latest technology development trends and user needs, the monitoring system will be upgraded in terms of technology and function, including but not limited to sensor technology upgrades, data processing algorithm optimization, user interface improvements, etc.; Verification and testing of upgrade effects: comprehensive verification and testing of the upgraded monitoring system to ensure that the upgraded system can operate stably and that all performance indicators are improved; User training and document updates: Provide training and guidance to users on new functions and features after the upgrade to ensure that users can master and use the monitoring system proficiently. At the same time, update relevant technical documents and user manuals to provide users with comprehensive technical support and services.

[0051] A single-chip-based real-time air quality monitoring system, including: an emission data integration and model initialization module, which collects and integrates emission data of new pollutants, builds a dynamic emission model, accurately locates emission hotspots, and assigns emission compliance standards; Emission data refinement processing module, collects emission data, builds a three-dimensional emission model, identifies and marks emission points, and configures emission restriction information; Monitoring area segmentation and monitoring point deployment module, analyzes the geographical distribution of emission hotspots, intelligently divides the monitoring area, selects the monitoring range, plans the sub-range, sets up the monitoring feature network, deploys monitoring points, and establishes the association relationship between monitoring points; Virtual area construction and interactive network building module, obtain monitoring point location information, build deployment network and safety ball model, map monitoring points, bind monitoring point information, and build association relationships; Real-time data monitoring and anomaly identification module, builds district and bureau models and virtual plate models, sets up communication connections, receives and stores pollutant information, establishes associations, and identifies abnormal pollutant information; The abnormal area visualization and data processing module establishes communication channels, verifies information balls, transmits data, judges abnormal information, optimizes data transmission, dynamically demonstrates abnormal information, and provides decision support.

[0052] Abnormal data processing and strategy generation module, real-time monitoring and identification of abnormal data, cache and analysis of abnormal data, generation of abnormal processing strategy, evaluation of processing effect and feedback; The system performance monitoring and upgrade module monitors and evaluates system performance, performs technology iterations and functional upgrades, verifies and tests upgrade effects, provides training and guidance to users, and updates technical documentation and user manuals.

[0053] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for real-time monitoring of air quality based on a single-chip microcomputer, characterized in that: include: S10: Emission data integration and model initialization: collect and integrate emission data of new pollutants, use these data to build a dynamic emission model, accurately locate emission hotspots in the model, and assign specific emission compliance standards to each hotspot; S20: Monitoring area segmentation and monitoring point deployment: intelligently divide the air quality monitoring area according to the geographical distribution of emission hotspots, and carefully deploy monitoring points in each segmented area to form a dense monitoring network to fully capture the dynamics of pollutants; S30: Virtual area construction and interactive network construction. Based on the layout of monitoring points, a series of interconnected virtual area models are constructed to form an intelligent monitoring network. Using emission data, dynamic interactive relationships between virtual areas are established to simulate the diffusion path of pollutants. S40: Real-time data monitoring and anomaly identification: monitoring points collect air quality data in real time and compare it with preset compliance standards, automatically identify and mark abnormal data that exceeds the standards, trigger an alarm mechanism, and integrate abnormal data into the relevant virtual area model; S50: Visualization of abnormal areas, using the interactive relationship of the virtual area model to dynamically display the areas containing abnormal data, and present the abnormal areas of pollutant emissions through an intuitive visualization interface.

2. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: The emission data integration and model initialization include: Collection of emission data and construction of three-dimensional models: using high-precision sensor networks and real-time data transmission technology to collect data on emission pathways, emission locations and related parameters of new pollutants. Based on the collected emission data, using geographic information system (GIS) technology and three-dimensional spatial modeling algorithms, a three-dimensional emission model is constructed that includes emission point locations, emission pathways and spatial distribution characteristics. Identification of emission points and marking of models: using preset algorithm logic and conditional screening criteria, potential emission points in the three-dimensional emission model are identified, and graphic marking technology is used to mark all identified emission points in the three-dimensional emission model, and detailed attribute information is provided for each mark; Configuration and dynamic management of emission restriction information: for each marked emission point, clear emission restriction information is configured according to environmental protection laws and regulations, industry standards and the results of environmental impact assessment.

3. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: The monitoring area segmentation and monitoring point deployment include: Analysis of the geographical distribution of emission hotspots and intelligent division of monitoring areas: Using geographic information system (GIS) technology and big data analysis methods, we conduct in-depth analysis of the geographical distribution of emission hotspots. Based on the analysis results, we intelligently divide multiple air quality monitoring areas to ensure that each area can accurately reflect its unique emission characteristics and pollutant dynamics; Selection of monitoring range and sub-range planning: In each monitoring area, the corresponding new pollutant monitoring range is carefully selected according to the actual location and emission path of the emission point, and the selected monitoring range is further subdivided to plan multiple sub-ranges in order to more accurately capture the spatial distribution and dynamic changes of pollutants; Setting up monitoring feature network and deploying monitoring points: In each sub-range, a new pollutant monitoring feature network is set up according to the characteristics of the pollutants and monitoring requirements. Corresponding to each monitoring feature network, multiple monitoring points are carefully deployed to ensure that the monitoring points can fully cover the monitoring range and accurately capture the dynamic changes of pollutants; The relationship between monitoring points is established and sub-control points are generated. Based on the monitoring range of new pollutants, the relationship between multiple monitoring points is established to form a monitoring network.

4. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: The virtual area construction and interactive network establishment include: Acquisition of monitoring point location information and construction of a layout network: Using positioning technology and geographic information system (GIS), the geographical location information of multiple monitoring points is accurately obtained based on the new pollutant monitoring range. Based on the acquired monitoring point location information, each monitoring point is connected in pairs to construct a layout network covering the entire monitoring range. The layout network can intuitively display the distribution and connection relationship of the monitoring points; Safety sphere model construction and monitoring point mapping: At the center of the network, a central point is set as the origin, which represents the center of the entire monitoring area. A three-dimensional safety sphere model is constructed with the origin as the center of the sphere. This model is used to simulate the diffusion and impact range of pollutants in the monitoring area. According to the geographical location information of the monitoring point, each monitoring point is mapped to the corresponding position on the safety sphere model, and the actual distance of each monitoring point from the origin is recorded; this step ensures the precise positioning of the monitoring point in three-dimensional space; Monitoring point information binding and association relationship construction. For each monitoring point, its mapping position on the safety ball model and the distance from the origin are used as verification information. Each monitoring point is bound to its corresponding verification information to form a unique identity of the monitoring point. Based on the verification information, association relationships between multiple monitoring points are established. These association relationships reflect the relative positions and mutual influences of the monitoring points in space.

5. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: The real-time data monitoring and anomaly identification include: District bureau model construction: According to the requirements of multiple new pollutant monitoring ranges, multiple independent district bureau models are constructed. In each district bureau model, the positions of multiple sub-control points are accurately marked according to actual needs, and corresponding cloud server resources are configured for each marked sub-control point position, thereby generating multiple virtual plate models corresponding to the district bureau models; Communication connection setting: establish communication connection between sub-control points and corresponding virtual plate models to ensure the reliability and stability of real-time data transmission. Through communication connection, sub-control points can collect pollutant information in each sub-area in real time and transmit this information to the corresponding virtual plate model; Information association and analysis: receiving and storing pollutant information transmitted from sub-control points, establishing associations between multiple virtual plate models based on these emission information, so as to conduct more comprehensive pollutant monitoring and analysis, comparing the received pollutant information with the preset emission threshold, identifying abnormal pollutant information that exceeds the threshold, and triggering the corresponding warning mechanism, integrating the identified abnormal information into the corresponding virtual plate model.

6. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: The abnormal area visualization includes: Communication channel establishment: According to actual needs, a communication channel between the sub-control point and the virtual plate model is constructed; Information correspondence and verification: an information ball is set between the sub-control point and the communication channel. The information ball is consistent with the shape of the preset safety ball and is used to store and transmit data. The information ball is verified to ensure the integrity and accuracy of the data. After the verification is passed, the position in the information ball is used as the storage point, and the corresponding relationship between the storage point and the corresponding monitoring point is established for subsequent data processing and analysis; Connection and data transmission: Based on the information ball and communication channel, the sub-control point is connected to the virtual plate model. Through the communication channel, the new pollutant information collected by the monitoring point is transmitted to the corresponding virtual plate model in the form of an information ball. During the transmission process, the real-time and accuracy of the data must be ensured so as to reflect the air quality status in a timely manner. Abnormal information judgment and processing: In the virtual plate model, a judgment module is set up to receive and process new pollutant information transmitted from the monitoring point. The judgment module compares the new pollutant information with the preset emission limit information, and regards the new pollutant information that does not meet the emission limit as abnormal information, and marks and stores the abnormal information for subsequent analysis and processing; Data load and transmission optimization: Based on the verification information, the new pollutant information collected by the monitoring point is loaded into the corresponding storage point in the information ball in a specific format, and the information ball is compressed and optimized to reduce the bandwidth occupation of data transmission and improve the transmission efficiency. The optimized information ball is transmitted to the corresponding virtual plate model through the communication channel; Dynamic demonstration and decision support, using the correlation between virtual plate models, dynamically demonstrate models containing abnormal information. Through the demonstration results, the abnormal location, concentration, and change trend of pollutant emissions are intuitively displayed. Based on the demonstration results, decision support is provided for subsequent pollution control and governance, including formulating targeted emission reduction measures and optimizing the monitoring network layout.

7. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: Also includes: An abnormal data identification unit, used to monitor in real time and identify abnormal data exceeding a preset emission threshold in the data collected by the air quality monitoring point; An abnormal data caching and analysis unit is used to cache the identified abnormal data in a dedicated memory and apply a preset data analysis algorithm to conduct an in-depth analysis of the abnormal data to determine the type, source, impact range and possible cause of the abnormal data; The exception handling strategy generation unit automatically generates targeted exception handling strategies based on the analysis results of the exception data, combined with environmental protection laws and regulations, industry standards and historical handling experience, including but not limited to pollution source investigation instructions, emission reduction measures or monitoring point adjustment plans; The processing effect evaluation and feedback unit is used to evaluate the effectiveness of the abnormal processing strategy after its implementation by comparing the air quality data before and after the processing, and present the evaluation results to the user in a visual manner. At the same time, the processing strategy is continuously optimized and adjusted based on user feedback.

8. The method for real-time air quality monitoring based on a single chip microcomputer according to claim 1, characterized in that: Also includes: System performance monitoring and evaluation: real-time monitoring and evaluation of the overall performance of the monitoring system, including key indicators such as data transmission rate, data processing capacity, and system stability, to ensure that the system can meet the needs of real-time monitoring; Technology iteration and function upgrade: Based on the results of system performance monitoring and evaluation, combined with the latest technology development trends and user needs, the monitoring system will be upgraded in terms of technology and function, including but not limited to sensor technology upgrades, data processing algorithm optimization, user interface improvements, etc.; Verification and testing of upgrade effects: comprehensive verification and testing of the upgraded monitoring system to ensure that the upgraded system can operate stably and that all performance indicators are improved; User training and document updates: Provide training and guidance to users on new functions and features after the upgrade to ensure that users can master and use the monitoring system proficiently. At the same time, update relevant technical documents and user manuals to provide users with comprehensive technical support and services.

9. A single chip microcomputer-based real-time air quality monitoring system, applied to the method described in any one of claims 1 to 8, characterized in that: include: Emission data integration and model initialization module, which collects and integrates emission data of new pollutants, builds dynamic emission models, accurately locates emission hotspots, and assigns emission compliance standards; Emission data refinement processing module, collects emission data, builds a three-dimensional emission model, identifies and marks emission points, and configures emission restriction information; Monitoring area segmentation and monitoring point deployment module, analyzes the geographical distribution of emission hotspots, intelligently divides the monitoring area, selects the monitoring range, plans the sub-range, sets up the monitoring feature network, deploys monitoring points, and establishes the association relationship between monitoring points; Virtual area construction and interactive network building module, obtain monitoring point location information, build deployment network and safety ball model, map monitoring points, bind monitoring point information, and build association relationships; Real-time data monitoring and anomaly identification module, builds district and bureau models and virtual plate models, sets up communication connections, receives and stores pollutant information, establishes associations, and identifies abnormal pollutant information; The abnormal area visualization and data processing module establishes communication channels, verifies information balls, transmits data, judges abnormal information, optimizes data transmission, dynamically demonstrates abnormal information, and provides decision support.

10. Abnormal data processing and strategy generation module, real-time monitoring and identification of abnormal data, cache and analyze abnormal data, generate abnormal processing strategies, evaluate processing effects and provide feedback; The system performance monitoring and upgrade module monitors and evaluates system performance, performs technology iterations and functional upgrades, verifies and tests upgrade effects, provides training and guidance to users, and updates technical documentation and user manuals.