A building indoor air quality detection method, system, medium and program product

Abstract

A building indoor air quality detection method, system, medium, and program product relate to the field of indoor air quality detection. The method comprises: acquiring air quality detection data from multiple detection points within a target building to generate a detection data set; when pollution warning data appears in the detection data set, sending a data sharing request to surrounding buildings within a preset communication range and receiving a verification data set; calculating the pollution confidence level of the pollution warning data and the pollution correlation coefficient of pollutant concentrations between buildings based on the detection data set, the verification data set, and ambient meteorological data; determining the pollution source type of the pollution warning data based on the pollution confidence level and the pollution correlation coefficient; and generating an air pollution warning and pollution prevention and control plan based on the pollution warning data and the pollution source type. Implementing this application can improve the accuracy of building indoor air quality detection and avoid false alarms from equipment.

Description

[0001] The present application relates to the field of indoor air quality detection, and in particular to a method, system, medium and program product for detecting indoor air quality in buildings. Background Art

[0002] With accelerating urbanization and increasing building density, indoor air quality in buildings is receiving increasing attention. Indoor air quality directly impacts people's health and work efficiency, and the transmission of pollutants between buildings further complicates air quality management. Therefore, accurately identifying pollution sources and implementing effective prevention and control measures have become crucial issues in building air quality management.

[0003] In the prior art, indoor air quality monitoring methods for buildings primarily use a single-point fixed-point detection method. Independent monitoring equipment is installed within the building to monitor indoor air quality by collecting real-time data on parameters such as temperature, humidity, and particulate matter. If abnormal data is detected, the system issues an alarm and activates appropriate ventilation equipment for air purification.

[0004] However, the related technology is difficult to perform data self-inspection by relying solely on detection data from a fixed area, and may cause false alarms in the equipment. Summary of the Invention

[0005] The present application provides a method, system, medium and program product for detecting indoor air quality in a building, which are used to improve the accuracy of indoor air quality detection in a building and avoid false alarms of equipment.

[0006] In the first aspect, the present application provides a method for detecting indoor air quality in a building, which is applied to an air quality detection system. The method includes: obtaining air quality detection data from multiple detection points inside a target building to generate a detection data set; when pollution warning data appears in the detection data set, sending a data sharing request to surrounding buildings within a preset communication range, and receiving a verification data set returned by the surrounding buildings; calculating the pollution confidence of the pollution warning data and the pollution correlation coefficient of the pollutant concentration between buildings based on the detection data set, the verification data set and the environmental meteorological data; determining the pollution source type of the pollution warning data based on the pollution confidence and the pollution correlation coefficient; the pollution source types include no pollution, endogenous pollution and exogenous pollution; generating air pollution prompts and pollution prevention and control plans based on the pollution warning data and the pollution source type.

[0007] In the above embodiment, the air quality detection system establishes a triple verification mechanism of data collection at multiple detection points, data verification of surrounding buildings, and analysis of environmental meteorological data. Combined with the calculation of pollution confidence and pollution correlation coefficient, it accurately determines the type of pollution source, effectively avoiding the false alarm problem that may be caused by single-point detection. It can distinguish between three types of pollution: no pollution, endogenous pollution, and exogenous pollution and generate prevention and control plans, thereby improving the accuracy of air quality detection and the effectiveness of prevention and control measures.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the steps of obtaining air quality detection data from multiple detection points inside the target building and generating a detection data set specifically include: obtaining air quality detection data from multiple detection points inside the target building; obtaining growth monitoring images of multiple target plants in the target building; the target plants are biological indicators for air quality detection; and generating a detection data set based on the air quality detection data and the growth monitoring images.

[0009] In the above embodiment, the air quality detection system introduces target plants as biological indicators, combines traditional physical sensor data with biological monitoring data, and forms a dual verification mechanism. Since biological indicators have a cumulative response characteristic to air pollution, they can reflect long-term pollution conditions, supplement the limitations of real-time detection data, and improve the reliability of detection results.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating a detection data set based on air quality detection data and growth monitoring images, the method also includes: extracting the apparent characteristics of the target plants in the growth monitoring images; and generating pollution warning data when the apparent characteristics meet the air pollution conditions.

[0011] In the above embodiment, the air quality detection system establishes a biological response warning mechanism by analyzing the correlation between the apparent characteristics of the target plants and air pollution. It can timely capture the potential pollution risks reflected by the apparent changes of plants, realize early warning of pollution, and provide a basis for the timely initiation of prevention and control measures.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the steps of calculating the pollution confidence of pollution warning data and the pollution correlation coefficient of pollutant concentrations between buildings based on the detection data set, the verification data set and the environmental meteorological data specifically include: constructing a pollutant concentration distribution heat map of the target building based on the detection data set; constructing a pollutant diffusion distribution heat map of the surrounding buildings based on the verification data set; obtaining environmental meteorological data including wind direction, wind speed, temperature and humidity within the target time period; calculating the impact path of environmental meteorological data on pollutant diffusion based on the pollutant concentration distribution heat map, the pollutant diffusion distribution heat map and the environmental meteorological data, and generating pollution confidence; when the pollution confidence is higher than the preset confidence threshold, calculating the pollution correlation coefficient of the pollutant concentration between buildings based on the impact path.

[0013] In the above embodiment, the air quality detection system uses heat map visualization technology to display the distribution of pollutants, combines environmental meteorological data to analyze the diffusion path of pollutants, constructs a dynamic tracking model for pollutants, and calculates the pollution confidence and pollution correlation coefficient to achieve precise positioning of pollution sources and accurate prediction of pollution diffusion trends.

[0014] In combination with some embodiments of the first aspect, in some embodiments, before obtaining air quality detection data from multiple detection points inside the target building and generating a detection data set, the method also includes: constructing an air circulation network based on the structural characteristics, usage functions and surrounding environment of the target building; dividing the target building into multiple detection areas based on the air circulation network, and determining multiple preset detection points in each detection area; binding data collection frequency and pollution alarm threshold to the preset detection points according to the building orientation, floor height and population density of the detection area.

[0015] In the above embodiment, the air quality detection system constructs an air circulation network based on the structural characteristics, usage functions and surrounding environment of the building, realizes the layout of detection points, and improves the targetedness and resource utilization efficiency of the detection system by setting differentiated data collection frequencies and alarm thresholds for different areas.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the steps of binding data collection frequency and pollution alarm threshold for preset detection points according to the building orientation, floor height and population density of the detection area specifically include: calculating the natural ventilation coefficient of the detection area based on the building orientation and the floor height of the detection area; determining the man-made pollution coefficient of the population density and air quality changes in the detection area based on historical detection data; binding detection priority for each preset detection point in the detection area based on the natural ventilation coefficient and the man-made pollution coefficient; and adjusting the data collection frequency and pollution alarm threshold of the preset detection point according to the detection priority.

[0017] In the above embodiment, the air quality detection system establishes a priority management mechanism for detection points by calculating the natural ventilation coefficient and the man-made pollution coefficient, and dynamically adjusts the detection strategy according to the actual environmental characteristics, thereby ensuring the monitoring intensity of key areas and avoiding the waste of detection resources.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating air pollution prompts and pollution prevention and control plans based on pollution warning data and pollution source types, the method also includes: collecting pollutant concentration change data at each detection point to generate an execution score of the prevention and control measures; and adjusting the pollution prevention and control plan based on the pollutant concentration change data and the execution score.

[0019] In the above embodiment, the air quality detection system establishes an effectiveness evaluation and dynamic optimization mechanism for prevention and control measures. By tracking the changes in pollutant concentrations in real time, it evaluates the effectiveness of the implementation of prevention and control measures and adjusts the prevention and control plan accordingly, forming a closed-loop optimized prevention and control system.

[0020] In a second aspect, an embodiment of the present application provides an air quality detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the air quality detection system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on an air quality detection system, the above-mentioned air quality detection system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an air quality detection system, the air quality detection system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understood that the air quality detection system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. Due to the linkage mechanism of multi-point detection data collection, surrounding building data verification and environmental meteorological data analysis, the source is tracked by calculating the pollution confidence and pollution correlation coefficient. Therefore, it can accurately identify and distinguish between the three types of pollution: no pollution, endogenous pollution and exogenous pollution. It effectively solves the problem of single-point fixed detection in related technologies that cannot perform data self-inspection and is prone to false alarms, thereby achieving high reliability of detection results and accuracy of prevention and control measures, improving the accuracy of indoor air quality detection in buildings, and providing reliable data support for subsequent pollution prevention and control.

[0026] 2. By adopting a biological monitoring mechanism based on the apparent characteristics of target plants, and using changes in plant growth status as biological indicators for pollution early warning, combined with computer vision technology for feature extraction and analysis, it is possible to promptly detect abnormal changes in plant apparent characteristics and generate early warning signals. This effectively solves the problem in related technologies that it is difficult to achieve continuous cumulative effect monitoring by relying solely on physical sensors, thereby achieving an organic combination of early warning and long-term monitoring of pollution, and providing supplementary verification of the biological dimension for the comprehensive assessment of air quality.

[0027] 3. Due to the calculation of the natural ventilation coefficient based on the building orientation and floor height, and the analysis of the man-made pollution coefficient based on historical data, differentiated detection priorities are set for the detection points through these two key parameters. Therefore, the rational allocation of detection resources and priority monitoring of key areas can be achieved, which effectively solves the problems of lack of scientific basis for the configuration of detection points and unreasonable allocation of monitoring resources in related technologies, thereby realizing the improvement of the operating efficiency of the detection system, ensuring the monitoring quality of key areas and avoiding waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for detecting indoor air quality in a building according to an embodiment of the present application;

[0029] Figure 2 This is another flow chart of the building indoor air quality detection method in an embodiment of the present application;

[0030] Figure 3 It is a schematic diagram of the structure of a physical device of the air quality detection system in the embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] With accelerating urbanization, high-density office buildings are increasingly populating. For example, in one business district, numerous office buildings over 30 stories high are densely packed, with only 20-30 meters between them. Due to the high building density and limited ventilation, indoor air quality issues frequently occur. After renovations, formaldehyde levels in one office building exceeded standards. Traditional single-point fixed detection equipment sounded an alarm, but management was unable to determine whether it was a false alarm or pinpoint the source of the pollution. Multiple tenants complained of symptoms like dizziness and eye discomfort, but the detection equipment displayed intermittent data, making it impossible to implement effective prevention and control measures. This situation is common in densely built-up areas, and an accurate and reliable air quality detection method is urgently needed.

[0035] In related technologies, automated indoor air quality monitoring can be achieved through real-time monitoring using single-point fixed detection equipment. Sensors collect parameters such as temperature, humidity, and particulate matter. When these data exceed preset thresholds, alarms are triggered and ventilation equipment is activated. The following describes scenarios where these related technologies for indoor air quality monitoring in buildings can be used.

[0036] In a certain office building complex, the property management company uses a traditional single-point fixed detection method for air quality monitoring. Two to four detection devices are installed on each floor, including sensors for temperature, humidity, PM2.5, and formaldehyde. When formaldehyde levels exceed the standard on a particular floor, the system automatically triggers an alarm and activates the fresh air system. However, due to the lack of a data verification mechanism, false alarms often occur. For example, when a cleaner uses a formaldehyde-containing detergent, the sensor briefly triggers an excessive-standard alarm, triggering emergency ventilation for the entire floor. However, the actual, persistent pollution is overlooked due to the fluctuations in the single-point data. Furthermore, when pollution is detected, it is impossible to determine whether it originates from internal renovations or external diffusion, resulting in blind prevention and control measures that waste resources and are ineffective.

[0037] The building indoor air quality detection method in the embodiment of this application, by establishing a triple verification mechanism of data collection from multiple detection points, data verification from surrounding buildings, and analysis of ambient meteorological data, can accurately locate and classify pollution sources. This not only avoids false alarms but also enables the development of targeted prevention and control plans based on the type of pollution source. The following describes scenarios in which the building indoor air quality detection method in this application is used.

[0038] After implementing this solution, an office building established a multi-dimensional air quality monitoring network. The system divided each floor into multiple monitoring zones based on the building's structural characteristics, deploying differentiated monitoring points. Ornamental plants were also placed in key areas as bio-indicators. When formaldehyde levels exceeded the standard, the system automatically requested data sharing from surrounding buildings and conducted a comprehensive analysis based on ambient meteorological data. For example, when formaldehyde levels exceeded the standard in the southwest corner, the system immediately analyzed that the surrounding data was normal and that the area had recently been renovated. It accurately identified the pollution as an internal source and promptly initiated targeted ventilation measures, avoiding false alarms and improving prevention and control efficiency.

[0039] It can be seen that the air quality detection system in the embodiment of the present application can not only accurately detect indoor air quality, but also effectively solve the false alarm problem caused by single-point detection and the problem of difficulty in locating pollution sources, thereby achieving high reliability of detection results and accuracy of prevention and control measures.

[0040] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a building indoor air quality detection method in an embodiment of the present application.

[0041] S101. Acquire air quality detection data from multiple detection points inside a target building to generate a detection data set.

[0042] Among them, the target building refers to a specific building that needs to undergo air quality testing; the testing point refers to a fixed monitoring location set up inside the building for collecting air quality data; the air quality testing data is used to represent a numerical set of air quality-related parameters including temperature, humidity, particulate matter concentration, carbon dioxide concentration, etc.; the testing data set refers to a structured data set formed by integrating the air quality testing data collected from multiple testing points in a time series.

[0043] The air quality monitoring system continuously collects data during the building's daily operations. Specifically, it first acquires real-time air quality parameter data, including temperature, humidity, and particulate matter concentration, from sensors at each monitoring point. It then cleans and preprocesses the collected raw data to remove outliers and redundant data. Finally, the processed data is organized and stored based on dimensions such as timestamp and monitoring point, forming a standardized monitoring dataset.

[0044] In some embodiments, the data collection and processing process can be implemented in a variety of ways: Optionally, the air quality detection system adopts a distributed data collection architecture, each detection point is equipped with an independent data collection unit, and the data is uploaded to the central processing server in real time via a wireless network. The server performs data cleaning and integration to generate a detection data set; Optionally, the air quality detection system adopts a centralized data collection architecture, all detection points are connected to a central controller via a wired network, and the controller uniformly collects and processes data to directly generate a detection data set. It is understandable that other network topologies and data processing methods can also be used to implement data collection and integration, which are not limited here.

[0045] S102: When pollution warning data appears in the detection data set, a data sharing request is sent to surrounding buildings within a preset communication range, and a verification data set is received from the surrounding buildings.

[0046] Among them, pollution warning data refers to abnormal data in the detection dataset that exceeds the preset threshold; the preset communication range is the coverage of surrounding buildings determined based on the diffusion characteristics of pollutants, usually a specific radius range centered on the target building; the data sharing request is used to represent the data exchange application initiated to the surrounding buildings; the verification dataset refers to the air quality data set obtained from the surrounding buildings for cross-validation.

[0047] The air quality monitoring system initiates a data verification process when it detects abnormal data. Specifically, when data exceeding a preset pollution threshold appears in the monitoring dataset, the system first identifies all buildings within the preset communication range that have data sharing capabilities. It then sends a data sharing request containing information such as the time period and data type to these buildings. After obtaining authorization from the buildings, it establishes an encrypted data transmission channel. Finally, it receives and verifies the integrity and validity of verification datasets returned by surrounding buildings, and incorporates these verified datasets into subsequent analysis.

[0048] In some embodiments, data sharing and verification can be implemented in a variety of ways: Optionally, the air quality monitoring system can use blockchain technology to build a data sharing network between buildings, automating data exchange and verification through smart contracts to ensure the security and credibility of data sharing. Optionally, the air quality monitoring system can establish an instant messaging network between buildings based on the Internet of Things protocol, using asymmetric encryption to protect data transmission security and verifying the reliability of the data source through digital signatures. It is understood that other data sharing protocols and security authentication mechanisms can also be used to implement data exchange and verification between buildings, and these are not limited here.

[0049] S103: Calculate the pollution confidence of the pollution warning data and the pollution correlation coefficient of the pollutant concentration between buildings based on the detection data set, the verification data set, and the environmental meteorological data.

[0050] Among them, environmental meteorological data represents external environmental factors that affect the diffusion of pollutants, including wind direction, wind speed, temperature, humidity, etc.; pollution confidence refers to the reliability score of pollution warning data; the pollution correlation coefficient is used to represent the correlation index of pollutant concentrations between different buildings; pollutant concentration represents the content level of specific pollutants in the air.

[0051] After acquiring complete data, the air quality monitoring system conducts a comprehensive analysis. Specifically, it first constructs a spatiotemporal distribution model of pollutants by combining the monitoring and validation datasets. It then incorporates ambient meteorological data to establish a pollutant diffusion prediction model that considers meteorological factors. Based on the results of these two models, a multi-dimensional assessment method is used to calculate the pollution confidence level. Finally, through correlation analysis, the temporal variation pattern of pollutant concentrations between buildings is calculated to derive the pollution correlation coefficient.

[0052] It should be noted that the pollutant diffusion prediction model utilizes a hybrid architecture combining physical models and deep learning. The physical model describes the diffusion of pollutants within a building complex based on the Navier-Stokes equations, including the continuity equation, momentum equation, and pollutant concentration transport equation. The deep learning component utilizes a graph neural network (GNN) architecture to optimize parameters in the physical model and handle the complex spatial relationships between buildings. Training data includes: 3D building geometry data, historical pollution diffusion records, meteorological observations, and more. Model training utilizes an end-to-end approach, with the physical model providing baseline predictions and the GNN learning to correct parameters by minimizing prediction errors. Validation standards require an average relative error of less than 20% for diffusion predictions within 15 minutes and less than 30% for predictions within 30 minutes.

[0053] In some embodiments, data analysis and calculation processes can be implemented in a variety of ways: Optionally, the air quality monitoring system employs machine learning methods, using deep neural network models to learn pollution characteristics and diffusion patterns from historical data, combining them with real-time data to predict pollutant diffusion trends and calculate various indicators. Optionally, the air quality monitoring system employs numerical simulation methods based on fluid dynamics models and atmospheric diffusion models to calculate the diffusion process of pollutants within a building complex and evaluate various indicators. It is understood that other mathematical models and calculation methods can also be used to analyze and evaluate pollution data, and these are not limited here.

[0054] S104. Determine the pollution source type of the pollution warning data according to the pollution confidence level and the pollution correlation coefficient.

[0055] Among them, the types of pollution sources include no pollution, endogenous pollution and exogenous pollution.

[0056] Among them, the pollution source type refers to the source category that causes air pollution, including no pollution, internal pollution and exogenous pollution; internal pollution refers to the pollution generated inside the building; exogenous pollution is used to refer to pollution introduced from the external environment.

[0057] After obtaining the assessment indicators, the air quality monitoring system determines the pollution source. Specifically, it first analyzes the pollution confidence level to confirm the authenticity of the pollution event; then, it assesses the spatial correlation of pollutant diffusion based on the pollution correlation coefficient; then, it matches the two indicators with the preset judgment rules; and finally, it synthesizes the various judgment results to reach a final conclusion on the pollution source type. During this judgment process, the system considers the characteristic manifestations of different pollution source types. For example, internal pollution is generally characterized by localized high concentrations, while external pollution is characterized by regional distribution.

[0058] It should be noted that the default judgment rules here are multi-level decision trees built based on pollutant diffusion dynamics models and historical data analysis. During the rule construction process, the typical characteristic patterns of different types of pollution sources are first extracted through feature cluster analysis of historical pollution events. Then, based on the diffusion patterns of pollutants in building complexes, a discriminant function is established that includes multiple dimensions such as time series, spatial distribution, and concentration gradients. Finally, machine learning methods are used to optimize the discrimination threshold to form a complete set of judgment rules. This set of rules can accurately identify the characteristic manifestations of pollution source types. For example, when a sudden high-concentration pollution occurs at a detection point, while the detection values ​​of surrounding buildings are normal, the system will determine it as endogenous pollution based on this local characteristic. When the detection values ​​of multiple buildings show a concentration gradient consistent with the wind direction, it will be determined as exogenous pollution.

[0059] In some embodiments, the pollution source determination process can be implemented in a variety of ways: Optionally, the air quality detection system can use a decision tree algorithm to establish multi-level determination rules, gradually analyzing the numerical characteristics of pollution confidence and correlation coefficients to ultimately determine the pollution source type. Optionally, the air quality detection system can use pattern recognition technology to compare the characteristic patterns of historical pollution events, identify the type characteristics of the current pollution event, and determine the pollution source. It is understood that other determination algorithms and identification methods can also be used to determine the pollution source type, and this is not limited here.

[0060] S105. Generate air pollution warnings and pollution prevention and control plans based on pollution warning data and pollution source types.

[0061] Among them, air pollution warnings refer to warning information issued to building users; pollution prevention and control plans refer to a collection of response measures formulated for specific types of pollution sources; and prevention and control measures are used to represent specific execution actions to control and eliminate pollution.

[0062] After determining the pollution source type, the air quality monitoring system formulates a response strategy. Specifically, it first selects an appropriate prevention and control measure template based on the pollution source type. It then adjusts the execution parameters of the prevention and control measures based on the specific parameters of the pollution warning data. It then generates a warning message containing a description of the pollution status, the scope of impact, and protective recommendations. Finally, it integrates the system into a complete pollution prevention and control plan, disseminates the warning information through multiple channels, and initiates appropriate prevention and control measures.

[0063] It should be noted that the specific parameters of pollution warning data are a multi-dimensional data feature set that includes quantitative and qualitative indicators that describe the pollution status. In the quantitative dimension, it includes numerical parameters such as the multiples of pollutant concentration exceeding the standard, the duration of pollution, and the pollution diffusion rate; in the spatial dimension, it includes spatial distribution characteristics such as the pollution impact range and the gradient distribution of pollutant concentration; in the temporal dimension, it includes temporal characteristics such as the trend of changes in pollutant concentration and the time of peak occurrence. By conducting a comprehensive analysis of these parameters, the air quality detection system can accurately assess the severity and development trend of pollution, thereby providing accurate data support for the formulation of prevention and control plans. For example, when it is detected that the concentration of a harmful gas exceeds the standard by 2 times and shows a continuous upward trend, the system will correspondingly increase the execution level and response speed of the prevention and control measures.

[0064] In addition, the air quality monitoring system can adopt a hierarchical information release mechanism, achieving differentiated push notifications based on the different roles and responsibilities of the information recipients. At the building management level, the system pushes detailed pollution data and prevention and control instructions to managers through the building's intelligent management platform; at the equipment control level, the system issues automatic adjustment instructions to ventilation, air conditioning, and other equipment through the industrial control network; and at the user service level, the system pushes personalized protection recommendations to building users through the building automation system. This multi-level information release mechanism ensures that all relevant parties can obtain timely pollution warning information that meets their needs. For example, when it detects that indoor formaldehyde exceeds the standard, the system will simultaneously activate automatic adjustment of the ventilation equipment, send a detailed detection report to the management staff, and push precautions to the users.

[0065] In some embodiments, the generation and execution of prevention and control plans can be achieved through a variety of methods: Optionally, the air quality monitoring system uses expert system technology to match the most appropriate combination of prevention and control measures through a rule engine and dynamically adjust the execution strategy based on real-time feedback; Optionally, the air quality monitoring system uses scenario-based deduction technology to optimize the specific implementation steps of the prevention and control plan by simulating the implementation effects of different prevention and control measures. It is understood that other decision support methods can also be used to achieve the development and optimization of prevention and control plans, which are not limited here.

[0066] In the above embodiment, the air quality monitoring system effectively improves detection accuracy by building a multi-layered data verification system. In practical applications, the system can dynamically adjust detection strategies based on the building's structural characteristics, usage functions, and environmental conditions, achieving optimal resource allocation and improved detection efficiency. The following supplements the scenarios of this embodiment.

[0067] Based on the continuous optimization of this solution, a business district has achieved regional-level collaborative air quality management. The detection systems of various buildings are interconnected through a secure data sharing mechanism, building a pollutant diffusion early warning network covering the entire area. When pollution is detected in a building, the system automatically analyzes the real-time and historical data of surrounding buildings, and combines it with wind direction and wind speed data from the weather station to accurately predict the path of pollutant diffusion. For example, in a renovation pollution incident, the system not only initiated prevention and control measures for the pollution source building, but also notified downwind buildings that may be affected in advance to adjust their fresh air strategies, achieving regional joint prevention and control and minimizing the scope of pollution impact.

[0068] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the building indoor air quality detection method in an embodiment of the present application.

[0069] S201. Construct an air circulation network based on the structural characteristics, usage functions and surrounding environment of the target building.

[0070] Among them, structural characteristics represent the physical construction characteristics of the building, such as the spatial layout, door and window distribution, and ventilation system; usage function refers to the usage and activity types of different areas within the building; the surrounding environment is used to represent environmental factors such as the terrain, vegetation, and nearby buildings outside the building; the air circulation network represents the topological structure model that describes the air flow paths and characteristics inside the building.

[0071] Before planning testing points, the air quality monitoring system first constructs an air circulation network. Specifically, the system first obtains the building's CAD drawings and BIM models to extract structural information. It then determines the air quality management requirements for each area based on its functional attributes. It then analyzes the impact of the building's surroundings on natural ventilation. Finally, it simulates air flow paths using a computational fluid dynamics model to create an air circulation network model consisting of nodes and connections, where nodes represent key spatial locations and connections represent air circulation channels.

[0072] It should be noted that the computational fluid dynamics model uses a set of nonlinear partial differential equations to describe the air flow characteristics in the construction of the air circulation network. The model first establishes a mathematical expression of the airflow field inside the building based on the Navier-Stokes equations, which includes the momentum equation, continuity equation, and energy equation; then the continuous domain is discretized into grid cells through the finite volume method, and the conservation law is applied to each cell; then the SIMPLE algorithm is used to solve the pressure-velocity coupling equation, and the steady-state airflow field distribution is obtained through iterative calculation. In the boundary condition setting, the geometric characteristics and pressure boundaries of the building's doors, windows, and vents are considered, as well as the influence of heat and cold sources on the airflow. For example, when a building has an atrium space, the model will pay special attention to the vertical airflow movement caused by temperature stratification and the chimney effect, so as to accurately describe the flow path and characteristics of the air in the building.

[0073] In some embodiments, the construction of an air circulation network can be achieved through various methods: Optionally, the air quality detection system uses graph theory to construct a network model, abstracting spatial locations as nodes and air circulation paths as edges, optimizing the network structure through a minimum spanning tree algorithm, and calculating key nodes and paths; Optionally, the air quality detection system uses a computational fluid dynamics model to calculate the airflow field distribution inside the building through numerical simulation methods, identify airflow organization characteristics, and construct a network model that reflects the actual air flow patterns. It is understood that other network construction methods can also be used to achieve modeling of air circulation characteristics, which are not limited here.

[0074] S202: Divide the target building into multiple detection areas based on the air circulation network, and determine multiple preset detection points in each detection area.

[0075] Among them, the detection area refers to the independent monitoring unit divided according to the air circulation characteristics; the preset detection point refers to the fixed monitoring position set in the detection area.

[0076] After constructing the air circulation network, the air quality monitoring system divides the air circulation into zones and deploys monitoring points. Specifically, the system first identifies natural air circulation zones based on the network topology; then refines these zones based on spatial function and usage characteristics; calculates the area, volume, and ventilation conditions of each zone; and finally, determines the number and specific locations of monitoring points in each zone based on regulatory requirements and actual needs, ensuring complete and representative monitoring coverage.

[0077] In some embodiments, regional division and point layout can be achieved in a variety of ways: Optionally, the air quality detection system uses a cluster analysis method to divide the space into multiple independent areas based on air circulation characteristics, and then determines the optimal detection point layout through a coverage optimization algorithm; Optionally, the air quality detection system is based on zoning control theory, by establishing a multi-objective optimization model, comprehensively considering detection effect and cost factors, to generate the optimal regional division and point layout plan. It is understandable that other optimization algorithms can also be used to achieve detection area division and point layout, which are not limited here.

[0078] S203. Bind the data collection frequency and pollution alarm threshold to the preset detection points according to the building orientation, floor height and population density of the detection area.

[0079] Among them, the building orientation refers to the angle between the main facade of the building and the north direction; the floor height refers to the vertical position of the detection area; the population density is used to indicate the number of people per unit area; the data collection frequency refers to the sampling time interval of the detection data; the pollution alarm threshold refers to the critical value of the pollutant concentration that triggers the pollution alarm.

[0080] After the air quality monitoring system is deployed, its parameters are configured. Specifically, the system first analyzes the impact of building orientation on natural ventilation; then calculates the vertical temperature difference effect based on floor height; and then estimates the anthropogenic pollution load based on occupancy density. Finally, based on these factors, it sets the appropriate data collection frequency and pollution alarm threshold for each monitoring point, achieving differentiated allocation of monitoring resources.

[0081] It should be noted that the dynamic adjustment of data collection frequency is based on adaptive sampling theory. The system first establishes a state assessment model that incorporates factors such as the rate of change of pollutant concentration, the intensity of human activity, and fluctuations in environmental parameters. The system then uses the Shannon sampling theorem to determine the minimum sampling rate required for signal reconstruction. A Kalman filter is then used to predict the changing trend of state variables, dynamically adjusting the sampling interval based on the prediction error. When rapid changes or abnormal trends are detected, the system automatically increases the sampling frequency to capture more detailed information. For example, during lunch breaks in office areas, due to reduced human activity, the system will appropriately reduce the sampling frequency to conserve resources. However, during periods of drastic changes in environmental parameters, such as when the air conditioning system is started or stopped, the system will increase the sampling frequency to accurately track parameter changes.

[0082] In some embodiments, the configuration of detection parameters can be achieved in a variety of ways: optionally, the air quality detection system adopts a fuzzy logic control method, establishes a multi-factor evaluation model, dynamically adjusts the detection parameters, and realizes intelligent allocation of detection resources; optionally, the air quality detection system uses statistical methods to analyze the pollution characteristics in historical data to establish a parameter adaptive adjustment mechanism to optimize detection efficiency. It is understood that other parameter optimization methods can also be used to achieve dynamic adjustment of detection configuration, which is not limited here.

[0083] In some embodiments, the air quality detection system will be set up based on historical data, that is, first, the natural ventilation coefficient of the detection area is calculated based on the building orientation and the floor height of the detection area; then, based on the historical detection data, the human pollution coefficient of the population density and air quality changes in the detection area is determined; then, based on the natural ventilation coefficient and the human pollution coefficient, the detection priority is bound to each preset detection point in the detection area; finally, according to the detection priority, the data collection frequency and pollution alarm threshold of the preset detection point are adjusted.

[0084] Among them, the natural ventilation coefficient indicates the degree of influence of the building's orientation and height on air circulation; the human pollution coefficient refers to the intensity of the impact of human activities on air quality; the detection priority is used to indicate the monitoring importance level of the detection point; the historical detection data refers to the air quality monitoring records accumulated in the past; the data collection frequency refers to the sampling time interval of the detection data.

[0085] After completing the deployment of monitoring points, the air quality monitoring system optimizes parameter configuration. Specifically, the system first obtains the building's geometric parameters and historical monitoring data; then establishes a multi-factor assessment model that incorporates natural ventilation and human activity; calculates the environmental characteristic coefficients for each monitoring area; and then prioritizes the monitoring points based on these coefficients. Finally, personalized monitoring parameters are configured for each monitoring point based on priority, achieving differentiated allocation of monitoring resources.

[0086] In some embodiments, parameter configuration optimization can be achieved through a variety of methods: Optionally, the air quality detection system constructs a time series prediction model based on a multi-layer neural network, identifies air quality variation patterns through deep learning of historical data, and adaptively adjusts the monitoring parameter configuration; Optionally, the air quality detection system adopts a fuzzy comprehensive evaluation method, establishes a multi-level indicator system, comprehensively evaluates the weights of various influencing factors, and dynamically optimizes the monitoring configuration scheme. It is understood that other intelligent optimization algorithms can also be used to achieve dynamic configuration of detection parameters, which is not limited here.

[0087] S204: Acquire air quality detection data at multiple detection points inside the target building.

[0088] Among them, air quality detection data refers to the air environment parameters collected by sensors; sensor type refers to professional equipment used to detect different pollutants, including particulate matter sensors, gas sensors, etc.; data collection cycle is used to represent the time interval between two consecutive data collections; raw data refers to the unprocessed sensor output value.

[0089] The air quality monitoring system continuously performs data collection tasks during daily operation. Specifically, the system first checks the operating status of sensors at each detection point; then reads data from the sensors at a preset collection frequency; then calibrates the collected data in real time to eliminate system errors such as sensor errors; then performs a data integrity check, identifying and marking outliers; and finally, packages the calibrated data in a unified format, adding timestamps and location identifiers to form standardized test data records.

[0090] In some embodiments, the data collection process can be implemented in a variety of ways: optionally, the air quality detection system adopts a distributed collection architecture, deploying edge computing units at each detection point to implement local data preprocessing and quality control, and then transmitting the processed data to a central server; optionally, the air quality detection system is based on Internet of Things technology, and by establishing a sensor mesh network, it implements multi-path data transmission and mutual backup mechanisms to improve the reliability of data collection. It is understood that other data collection solutions can also be used to obtain detection data, which are not limited here.

[0091] S205: Acquire growth monitoring images of multiple target plants in the target building.

[0092] Among them, the target plants are biological indicators used for air quality detection, that is, indicator plants that are specially selected and bred to be sensitive to specific pollutants; growth monitoring images refer to high-definition picture data that record the growth status of plants; biological indicators are used to represent organisms that can reflect environmental quality through morphological changes; image acquisition equipment refers to professional camera equipment used to capture the growth status of plants.

[0093] The air quality monitoring system monitors plants at regular intervals. Specifically, the system first activates the image acquisition device and adjusts focus and exposure parameters. It then captures high-definition images of the target plant from multiple angles. The images are then preprocessed, including lighting correction and noise removal. Morphological parameters of the plants, such as leaf area, leaf color, and stem inclination, are then extracted. Finally, these biometric data are correlated with environmental parameters to establish plant response indicators.

[0094] In some embodiments, plant monitoring can be implemented in a variety of ways: Alternatively, the air quality monitoring system can employ computer vision technology to analyze plant images using deep learning models, automatically identifying plant health and growth anomalies, and providing early warning of pollution. Alternatively, the air quality monitoring system can utilize spectral analysis technology to capture plant reflectance spectral characteristics using a multispectral camera, establish a plant stress response model, and assess air quality. It is understood that other biomonitoring methods can also be employed to implement plant-based air quality assessment, which is not limited here.

[0095] Image analysis of plant growth characteristics utilizes deep learning computer vision methods. The system first extracts plant morphological features, including leaf shape, area, and texture, using a multi-scale convolutional neural network. It then uses image segmentation algorithms to separate plant organs and establish a quantitative description of morphological parameters. It then analyzes plant growth changes through temporal comparisons to construct a plant growth curve. Finally, it integrates with an expert knowledge base to classify and identify growth anomalies. The model utilizes transfer learning, allowing it to quickly adapt to the feature extraction requirements of different plant species using a pre-trained network. For example, when abnormal morphologies such as chlorosis or curling are detected in plant leaves, the system combines the temporal and spatial evolution of these visual features to assess potential air pollution risks.

[0096] S206: Generate a detection data set based on the air quality detection data and the growth monitoring image.

[0097] Among them, the detection dataset refers to a comprehensive dataset that integrates physical sensor data and biological monitoring data; data relevance refers to the correspondence between physical parameters and biological responses; data timeliness is used to indicate the effective use period of the data; and data reliability refers to the accuracy and confidence of the data.

[0098] After completing data collection, the air quality monitoring system performs data integration. Specifically, the system first aligns the physical sensor data and plant monitoring data in time series; then analyzes the correlation between the two types of data and establishes a parameter mapping relationship; then performs data fusion, converting discrete data points into a continuous state curve; then, it conducts a quality assessment of the fused data to determine its reliability level; and finally, it generates a comprehensive detection dataset containing multi-dimensional information.

[0099] In some embodiments, the data integration process can be implemented in a variety of ways: Optionally, the air quality detection system can employ a data fusion algorithm, using methods such as Kalman filtering to optimally combine data from different sources to improve data accuracy and reliability; Optionally, the air quality detection system can utilize time series analysis methods to establish a dynamic correlation model for multi-source data to achieve intelligent data integration and anomaly identification. It is understood that other data processing methods can also be employed to achieve comprehensive analysis of detection data, and these are not limited herein.

[0100] In some embodiments, the air quality detection system extracts the apparent features of the target plant in the growth monitoring image; and generates pollution warning data when the apparent features meet the air pollution conditions.

[0101] Among them, apparent characteristics refer to the observable characteristics of the external morphology of plants; air pollution conditions refer to the pollutant concentration threshold that causes abnormal plant morphology; pollution warning data are used to represent pollution warning information issued based on biological indicators; image feature parameters represent numerical indicators that quantify plant morphological changes; and biological response patterns refer to the typical reaction characteristics of plants to specific pollutants.

[0102] The air quality monitoring system assesses plant status at regular intervals. Specifically, the system first preprocesses and enhances the captured plant images; then extracts plant morphological features, including leaf area, leaf color, and stem angle; compares and analyzes these features against a standard morphological baseline; and then assesses whether feature changes have reached a pollution response threshold. Finally, if an anomaly is detected, it generates alert data containing the pollution type and severity and initiates the corresponding verification process.

[0103] In some embodiments, plant monitoring and early warning can be achieved through a variety of methods: Optionally, the air quality detection system constructs a plant health status assessment model based on a deep convolutional neural network, rapidly adapting to the feature extraction requirements of different plant species through transfer learning methods to accurately identify plant anomalies; Optionally, the air quality detection system uses spatiotemporal sequence analysis methods to establish a dynamic model of plant growth, capturing the temporal characteristics of morphological changes and assessing the cumulative effects of pollution exposure. It is understood that other biological monitoring methods can also be used to achieve plant-based pollution early warning, which is not limited here.

[0104] S207: When pollution warning data appears in the detection data set, a data sharing request is sent to surrounding buildings within a preset communication range, and a verification data set is received from the surrounding buildings.

[0105] Referring to step S102 , the air quality detection system obtains a verification data set when pollution warning data appears.

[0106] S208. Calculate the pollution confidence level of the pollution warning data and the pollution correlation coefficient of the pollutant concentration between buildings based on the detection data set, the verification data set, and the environmental meteorological data.

[0107] Referring to step S103 , the air quality detection system calculates the pollution confidence and the pollution correlation coefficient.

[0108] It should be noted that the impact assessment of environmental meteorological data uses a multivariate time series analysis method. The system first constructs a state vector containing meteorological elements such as wind speed, wind direction, temperature, and humidity; then uses a vector autoregression model to describe the dynamic correlation between meteorological elements; then uses the Granger causality test to analyze the degree of influence of meteorological conditions on pollutant diffusion; and finally, uses a state-space model to predict pollutant diffusion trends under specific meteorological conditions. The model pays special attention to the lag effect and nonlinear interaction of meteorological elements, and uses methods such as wavelet transform to process the impact characteristics of multiple time scales. For example, when an increase in pollutant concentration is observed in upwind buildings, the system will combine wind field data to predict the transmission path and arrival time of the pollutants to provide support for prevention and control decisions.

[0109] In some embodiments, the air quality detection system will construct a heat map to perform modeling calculations, that is, the air quality detection system will construct a pollutant concentration distribution heat map of the target building based on the detection data set; construct a pollutant diffusion distribution heat map of the surrounding buildings based on the verification data set; obtain environmental meteorological data including wind direction, wind speed, temperature and humidity within the target time period; based on the pollutant concentration distribution heat map, the pollutant diffusion distribution heat map and the environmental meteorological data, calculate the impact path of the environmental meteorological data on the pollutant diffusion and generate a pollution confidence level; when the pollution confidence level is higher than the preset confidence threshold, calculate the pollution correlation coefficient of the pollutant concentration between buildings based on the impact path.

[0110] Among them, the heat map represents a visualization model of the spatial distribution of pollutant concentrations; the pollutant diffusion distribution refers to the migration pattern of pollutants in space; the impact path is used to represent the way in which environmental factors affect the movement of pollutants; the confidence threshold represents the credibility standard for pollution judgment; and the correlation coefficient refers to the strength of the correlation between the pollution levels between buildings.

[0111] The air quality monitoring system performs comprehensive modeling and analysis after acquiring multi-source data. Specifically, the system first spatially interpolates the monitoring and verification data to generate a continuous concentration distribution field. It then combines this with ambient meteorological data to construct a pollutant transport model. Numerical simulations are then used to calculate pollutant diffusion trajectories. Consistency assessments of the multi-source data are then used to determine the pollution confidence level. Finally, when the confidence level meets the required level, diffusion path analysis is used to establish pollution correlations between buildings.

[0112] In some embodiments, pollution diffusion modeling can be implemented in a variety of ways: Alternatively, the air quality monitoring system can employ computational fluid dynamics to simulate the diffusion of pollutants in complex building environments by solving the Navier-Stokes equations to assess the impact of environmental factors. Alternatively, the air quality monitoring system can construct a pollutant transport model adapted to urban environments by incorporating topographic and meteorological correction factors based on a Gaussian diffusion model. It is understood that other numerical simulation methods can also be employed to accurately describe pollution diffusion characteristics, and these are not intended to be limiting herein.

[0113] S209: Determine the pollution source type of the pollution warning data based on the pollution confidence level and the pollution correlation coefficient.

[0114] Referring to step S104 , the air quality detection system determines the type of pollution source.

[0115] It should be noted that the application of pattern recognition technology for pollution source identification is based on a multi-level feature extraction and classification framework. First, the system decomposes the pollution data in the time-frequency domain to extract key characteristic parameters such as concentration change rate, periodic characteristics, and spatial distribution gradients. Principal component analysis is then used to reduce the dimensionality of the feature space, retaining the most discriminative feature combinations. A deep learning network is then used to construct a pollution source classification model. This model learns the characteristic expressions of different pollution source types through multiple layers of nonlinear transformations. The input layer of the model receives preprocessed feature vectors, the hidden layer captures the complex correlations between features through activation functions, and the output layer provides a probability distribution of pollution source types.

[0116] In practical applications, the model continuously optimizes network parameters through a backpropagation algorithm to improve classification accuracy. For example, when an abnormal formaldehyde concentration is detected in a certain area, the system analyzes the characteristics of its concentration curve. If it exhibits a step-like rise followed by an exponential decay, with significant spatial localization, the model will identify it as indoor pollution caused by releases from renovation materials. If the concentration changes exhibit a gradual diffusion pattern with the direction of airflow, it is more likely to be an external pollution source. The application of pattern recognition technology in pollution source identification is based on a multi-layered feature extraction and classification recognition framework.

[0117] First, the system decomposes the pollution data in the time-frequency domain to extract key feature parameters including concentration change rate, periodic characteristics, and spatial distribution gradient; then, the principal component analysis method is used to reduce the dimension of the feature space and retain the most discriminative feature combination; then, a pollution source classification model is constructed using a deep learning network. The model learns the characteristic expressions of different pollution source types through multi-layer nonlinear transformations. The input layer of the model receives the preprocessed feature vector, the hidden layer captures the complex correlation between features through activation functions, and the output layer gives the probability distribution of pollution source types.

[0118] In practical applications, the model continuously optimizes network parameters through a backpropagation algorithm to improve classification accuracy. For example, when an abnormal formaldehyde concentration is detected in a certain area, the system analyzes the characteristics of its concentration change curve. If it shows an exponential decay characteristic after a step-like rise, and has obvious spatial localization, the model will identify it as indoor pollution caused by the release of decoration materials. If the concentration change shows a gradual diffusion characteristic with the direction of airflow, it is more likely to be judged as an external pollution source.

[0119] S210: Generate air pollution warnings and pollution prevention and control plans based on pollution warning data and pollution source types.

[0120] Referring to step S105, the air quality detection system will generate air pollution prompts and pollution prevention and control plans.

[0121] It should be noted that the emergency response level classification utilizes a multi-criteria decision-making approach based on fuzzy comprehensive evaluation. The system first constructs a decision matrix encompassing multiple evaluation indicators, including the number of times pollutant concentration exceeds the standard, the scope of impact, duration, and health risks. The system then uses the analytic hierarchy process to determine the weight coefficients for each indicator and establishes a weighted scoring model. Each indicator is then standardized and applied to a fuzzy membership function to calculate a comprehensive evaluation score. Finally, the corresponding response level is determined based on the score range. This evaluation system uses fuzzy mathematics to address the uncertainty and interactions between indicators, enabling a scientific classification of response levels. For example, if the concentration of a hazardous gas exceeds the standard value by 5 times, the impact area covers an entire office floor, and persists for more than 30 minutes, the system calculates a higher comprehensive risk score, triggering a higher level of emergency response and initiating a series of mandatory prevention and control measures, including evacuation and emergency ventilation. Dynamic adjustment of the response level also takes into account pollution trends and control effectiveness to ensure the timeliness and effectiveness of prevention and control measures.

[0122] In some embodiments, the air quality detection system collects pollutant concentration change data at each detection point and generates an execution score for the prevention and control measures; and adjusts the pollution prevention and control plan based on the pollutant concentration change data and the execution score.

[0123] Among them, the execution score represents the evaluation index of the implementation effect of prevention and control measures; the pollutant concentration change data refers to the monitoring results reflecting the prevention and control effect; the prevention and control measures refer to the specific actions used to control and eliminate pollution; the adjustment strategy is used to represent the modification method for optimizing the prevention and control plan; the implementation effect refers to the degree of effect of prevention and control measures on pollution control.

[0124] The air quality monitoring system continuously evaluates the effectiveness of prevention and control measures during their implementation. Specifically, the system first establishes a pollutant concentration monitoring network encompassing multiple timescales; then calculates pollutant concentration trends before and after the implementation of prevention and control measures; evaluates the effectiveness of the measures based on these trends and control targets; and finally, scores the measures based on the evaluation results. Finally, the system dynamically adjusts prevention and control strategies based on the performance scores, optimizing resource allocation and implementation plans.

[0125] In some embodiments, prevention and control effectiveness evaluation can be achieved through various methods: Optionally, the air quality monitoring system can employ reinforcement learning methods to establish a state-action-reward model to learn optimal prevention and control strategies and achieve adaptive optimization of prevention and control plans. Optionally, the air quality monitoring system can dynamically adjust the implementation plan of prevention and control measures by establishing a comprehensive evaluation system based on multi-objective decision-making theory that considers control effectiveness and resource consumption. It is understood that other optimization algorithms can also be used to achieve dynamic adjustment of prevention and control plans, which are not limited here.

[0126] In the embodiment of the present application, due to the use of technologies such as air circulation network construction based on building structural characteristics, multi-dimensional data collection and verification, and intelligent pollution source identification, it is possible to achieve a scientific layout of detection points, accurate verification of pollution data, and generation of prevention and control plans, effectively solving the problems of many detection blind spots, high false alarm rates, and difficulty in tracing the source in traditional methods, thereby achieving high precision and high reliability in building indoor air quality detection. This solution establishes a triple verification mechanism of multi-detection point data collection, surrounding building data verification, and environmental meteorological data analysis, combined with long-term cumulative effect monitoring of biological indicators, which can not only accurately distinguish between the three types of pollution: no pollution, endogenous pollution, and exogenous pollution, but also formulate targeted prevention and control plans based on the characteristics of the pollution sources, significantly improving the scientific nature and effectiveness of air quality management. The system's adaptive optimization mechanism ensures the rational allocation of detection resources and the continuous improvement of prevention and control measures, and realizes coordinated prevention and control at the scale of building complexes.

[0127] The following describes the air quality detection system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the physical device structure of the air quality detection system in an embodiment of the present application.

[0128] It should be noted that Figure 3 The structure of the air quality detection system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0129] like Figure 3As shown, the air quality detection system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0130] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0131] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0132] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0134] Specifically, the air quality detection system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the building indoor air quality detection method provided by the above embodiment is implemented.

[0135] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the air quality detection system described in the above embodiments, or may exist independently and not be incorporated into the air quality detection system. The storage medium carries one or more computer programs, which, when executed by a processor of the air quality detection system, enable the air quality detection system to implement the building indoor air quality detection method provided in the above embodiments.

[0136] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0137] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0138] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for detecting indoor air quality of a building, characterized in that: Applied to an air quality detection system, the method includes: Construct an air circulation network based on the structural characteristics, usage functions and surrounding environment of the target building; Dividing the target building into a plurality of detection areas based on the air circulation network, and determining a plurality of preset detection points in each of the detection areas; Binding the data collection frequency and pollution alarm threshold to the preset detection points according to the building orientation, the floor height and the population density of the detection area; the step of binding the data collection frequency and pollution alarm threshold to the preset detection points according to the building orientation, the floor height and the population density of the detection area specifically includes: calculating the natural ventilation coefficient of the detection area based on the building orientation and the floor height of the detection area; determining the man-made pollution coefficient of the population density and air quality changes in the detection area based on historical detection data; binding the detection priority to each preset detection point in the detection area based on the natural ventilation coefficient and the man-made pollution coefficient; adjusting the data collection frequency and pollution alarm threshold of the preset detection point according to the detection priority; Obtain air quality test data from multiple test points inside the target building and generate a test data set; When pollution warning data appears in the detection data set, a data sharing request is sent to surrounding buildings within a preset communication range, and a verification data set is received back from the surrounding buildings; Calculating the pollution confidence of the pollution warning data and the pollution correlation coefficient of the pollutant concentration between buildings based on the detection data set, the verification data set and the environmental meteorological data; the step of calculating the pollution confidence of the pollution warning data and the pollution correlation coefficient of the pollutant concentration between buildings based on the detection data set, the verification data set and the environmental meteorological data specifically includes: constructing a pollutant concentration distribution heat map of the target building based on the detection data set; constructing a pollutant diffusion distribution heat map of the surrounding buildings based on the verification data set; obtaining environmental meteorological data including wind direction, wind speed, temperature and humidity within a target time period; calculating the impact path of the environmental meteorological data on pollutant diffusion based on the pollutant concentration distribution heat map, the pollutant diffusion distribution heat map and the environmental meteorological data, and generating a pollution confidence; when the pollution confidence is higher than a preset confidence threshold, calculating the pollution correlation coefficient of the pollutant concentration between buildings based on the impact path; Determining the pollution source type of the pollution warning data according to the pollution confidence and the pollution correlation coefficient; the pollution source type includes no pollution, endogenous pollution and exogenous pollution; Generate air pollution warnings and pollution prevention and control plans based on the pollution warning data and the pollution source types.

2. The method according to claim 1, characterized in that The step of obtaining air quality detection data from multiple detection points inside the target building and generating a detection data set specifically includes: Obtain air quality test data from multiple test points inside the target building; Acquiring growth monitoring images of a plurality of target plants in the target building; the target plants are biological indicators used for air quality detection; A detection data set is generated based on the air quality detection data and the growth monitoring image.

3. The method according to claim 2, characterized in that After the step of generating a detection data set based on the air quality detection data and the growth monitoring image, the method further includes: extracting the apparent features of the target plant in the growth monitoring image; When the apparent characteristics meet air pollution conditions, pollution warning data is generated.

4. The method according to claim 1, wherein After the step of generating an air pollution warning and a pollution prevention and control plan based on the pollution warning data and the pollution source type, the method further includes: Collect pollutant concentration change data at each detection point and generate implementation scores for prevention and control measures; Adjust the pollution prevention and control plan based on the pollutant concentration change data and the execution score.

5. An air quality detection system, characterized in that: The air quality detection system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the air quality detection system to perform the method described in any one of claims 1 to 4.

6. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the air quality detection system, the air quality detection system is caused to execute the method according to any one of claims 1 to 4.

7. A computer program product, characterized in that When the computer program product is run on an air quality detection system, the air quality detection system is enabled to perform the method according to any one of claims 1 to 4.

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