Data analysis method and system based on environment element intelligent identification monitoring system

Abstract

The invention provides a data analysis method and system based on an environment element intelligent identification monitoring system, and relates to the technical field of environment monitoring. A large amount of monitoring data can be automatically processed through the AI algorithm, and the method is faster and more efficient than a traditional method. Data can be analyzed in real time, useful modes, trends and anomalies are extracted from the data, manual intervention or manual processing is not needed, the AI algorithm can process complex data modes and relevance, and the precision and accuracy of environmental element monitoring data are improved. Through deep learning and machine learning technologies, the AI can identify and correct noise in the data and provide a more reliable result, and the AI can quickly respond to environmental changes to realize real-time monitoring and early warning functions. For example, when a natural disaster occurs, the AI algorithm can quickly analyze a large amount of data and provide timely early warning information, thereby facilitating loss reduction and personnel safety protection.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly to a data analysis method and system based on an intelligent identification and monitoring system for environmental elements. Background Art

[0002] Environmental element monitoring can help evaluate whether there are harmful substances in the environment such as air, water, soil, etc., detect and reduce potential hazards to human health in a timely manner. For example, monitoring particulate matter and toxic gases in the air can warn of air pollution, protect public health, and protect the ecosystem; monitoring biological elements such as biodiversity, water quality, and vegetation health can evaluate the state of the ecosystem, detect threats or damage to the ecosystem at an early stage, and help take protective measures to maintain ecological balance and biodiversity; environmental management and policy making: environmental element monitoring provides scientific basis and data support for environmental management and policy making. The government and relevant departments can adjust environmental protection policies and regulations based on the monitoring results, formulate more effective environmental management measures, and ensure the sustainable use of resources and the continuous improvement of the environment; coping with environmental disasters and incidents: monitoring can help warn of environmental disasters (such as floods, droughts, earthquakes, etc.) and sudden environmental incidents (such as chemical leaks, nuclear accidents, etc.), and take response measures in a timely manner to reduce losses and impacts; scientific research and education: monitoring data provides important experimental data and cases for environmental scientific research, promotes the development of environmental protection technologies and methods. At the same time, the monitoring results can also be used for public education to enhance the society's awareness and attention to environmental protection. Monitoring environmental elements is for important purposes in many aspects such as protecting human health, maintaining ecological balance, supporting environmental management and policy making, coping with environmental disasters, promoting scientific research and education, etc. Through monitoring, a comprehensive understanding and effective management of the environmental situation can be achieved, and it can be ensured that future generations can continue to enjoy a clean and safe natural environment.

[0003] However, existing traditional methods usually require a large amount of human resources for data collection, processing and analysis. For example, it is necessary to send personnel to monitoring points regularly to collect data, which is not only time-consuming and laborious, but also may be limited by the capabilities and work efficiency of the personnel. Traditional monitoring methods have a slow response speed and lack flexibility when facing emergencies or rapidly changing environmental conditions. For example, when natural disasters occur, traditional methods may not be able to quickly provide real-time monitoring data and warning information. Therefore, technicians in this field have provided a data analysis method and system based on an intelligent identification and monitoring system for environmental elements to solve the problems raised in the above background art. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] Traditional methods usually require a large amount of human resources for data collection, processing, and analysis. For example, it is necessary to dispatch personnel to monitoring points regularly to collect data, which is not only time-consuming and laborious but may also be limited by the capabilities and work efficiency of the personnel. Traditional monitoring methods have a slow response speed and lack flexibility when faced with emergencies or rapidly changing environmental conditions.

[0006] (2) Technical solutions

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: The present invention provides a data analysis method and system based on an intelligent environmental factor identification and monitoring system, including system feedback, and is characterized in that it includes the following steps:

[0008] S1. Data preprocessing. The data preprocessing includes data cleaning. Data cleaning: Processing missing values, outliers, and duplicate data to ensure data quality. Data transformation: Such as smoothing, standardizing, or normalizing the data so that different data can be comparable. Feature selection: Selecting the most relevant and representative features to reduce data dimensions and noise;

[0009] S2. Time series analysis. The time series analysis includes trend analysis. Using trend lines and regression analysis methods to identify long-term trends. Periodic analysis: Using periodic models such as seasonal decomposition methods for periodic analysis, analyzing and predicting periodic changes. Anomaly detection: Identifying and analyzing the abnormal points existing in the data, including but not limited to emergencies or faults in the environment;

[0010] S3. Environmental analysis. The environmental analysis uses geographic information system (GIS) analysis: Combining environmental data with geographical location information to analyze spatial distribution and correlation;

[0011] S4. Spatial series analysis. The spatial series analysis includes trend analysis: Using methods such as trend lines and regression analysis to identify long-term trends; Periodic analysis: Using periodic models such as seasonal decomposition and Fourier transform to analyze and predict periodic changes; Anomaly detection: Identifying and analyzing the abnormal points in the data, which may be emergencies or faults in the environment;

[0012] S5. Machine learning and data mining. The machine learning and data mining include classification and clustering: Classifying or clustering environmental data to identify different environmental states or categories; Regression analysis: Predicting the relationships between environmental factors or future trends; Anomaly detection: Using unsupervised learning methods to detect anomalies or abnormal patterns in the environment;

[0013] S6, Visualization Report. The visualization report includes the use of charts and maps, presenting time series and spatial data using line charts, bar charts, heat maps, etc., designing interactive radar charts to monitor the environmental status and trends in real time; Reporting and Push: Generate regular reports or real-time alerts to notify decision-makers and relevant personnel of environmental changes and important events.

[0014] Preferably, the time series analysis adopts the seasonal decomposition method. For time series data showing seasonal variations, the seasonal decomposition method is used to decompose the data into trend, seasonal, and residual components for analyzing and predicting seasonal change trends.

[0015] Preferably, the core of the machine learning and data mining is to analyze and mine the obtained environmental data, spatial data, and time data through AI algorithms. The machine learning is based on the Ensemble Learning model.

[0016] Preferably, the spatial series analysis explores the spatial patterns in the dataset by calculating the autocorrelation over the entire space, examines the spatial correlation within a specific region, and analyzes the complex interactions between spatial and time variables. Through the spatial series analysis, a prediction model with the interaction of time and spatial variables can be developed.

[0017] Preferably, the system includes a sensor network, a data acquisition and transmission system, a data processing and analysis platform, intelligent recognition and monitoring algorithms, an application and user interface, and an early warning and decision support system.

[0018] Preferably, the sensor network is used for sensors deployed in various environments, such as air quality sensors and water quality monitoring sensors, to collect environmental data. The data acquisition and transmission system includes functions such as data storage, data cleaning, data analysis, and visualization, and processes and analyzes a large amount of real-time data through algorithms.

[0019] Preferably, the intelligent recognition and detection algorithms use machine learning and artificial intelligence technologies to identify and monitor the collected environmental data in real time, monitoring abnormal situations or key trends.

[0020] Preferably, the application and user interface presents the analysis results to the user in a visual manner, such as a monitoring panel, report generation, and mobile applications, facilitating the user to view the environmental status in real time and take necessary actions.

[0021] Preferably, the early warning and decision support system generates warning information based on the analysis results, supporting decision-makers to respond and take measures when environmental problems occur to protect public environmental health.

[0022] Preferably, the system collects data obtained from sensors in real time, such as temperature, humidity, air pressure in the atmosphere, pollutant concentration in the air, pH value and dissolved oxygen content of water bodies, etc. These data are transmitted through the network to the central processing unit or cloud server for processing and analysis.

[0023] Preferably, the intelligent identification and monitoring system of environmental elements relies on a variety of sensors, such as meteorological sensors, air quality sensors, water quality sensors, sound sensors, etc. These sensors are distributed at different locations to collect data in the environment.

[0024] Preferably, the system uses intelligent algorithms to identify and monitor environmental data, identifying various elements and abnormal events in the environment. For example, detecting that the concentration of harmful gases in the air exceeds the standard, warning of abnormal water quality, identifying fires or pollution sources in the natural environment, etc.

[0025] Preferably, the application scope of the intelligent identification and monitoring system of environmental elements is extensive, including industrial production environments, urban environments, nature reserves, etc., which can effectively improve the accuracy and efficiency of environmental monitoring, protect environmental resources, and reduce the impact of environmental pollution on humans and the ecosystem.

[0026] Preferably, the data analysis methods include statistical analysis, time series analysis and spatial analysis. Statistical analysis is used to analyze the distribution characteristics and trend changes of water quality monitoring data, such as calculating the mean, variance and correlation of pollutant concentrations.

[0027] Preferably, mathematical models and machine learning algorithms are used to analyze large datasets, such as climate models, neural network models, etc. Through data mining techniques, patterns and correlations in climate data are extracted to predict climate change trends and impacts in the next few years or decades.

[0028] Preferably, geographic information system (GIS) and spatial analysis techniques are used to process data collected by devices such as water quality sensors and flow sensors.

[0029] (III) Beneficial effects

[0030] The present invention provides a data analysis method and system based on an intelligent identification and monitoring system of environmental elements.

[0031] It has the following beneficial effects:

[0032] 1. In the present invention, AI algorithms can automatically process a large amount of monitoring data, which is faster and more efficient than traditional methods. They can analyze data in real time, extract useful patterns, trends, and anomalies from it. Without manual intervention or manual processing, AI algorithms can handle complex data patterns and correlations, improving the accuracy and precision of environmental element monitoring data. Through deep learning and machine learning technologies, AI can identify and correct noise in the data and provide more reliable results. AI can quickly respond to environmental changes and achieve real-time monitoring and early warning functions. For example, during natural disasters, AI algorithms can quickly analyze a large amount of data and provide timely early warning information, which helps reduce losses and protect the safety of personnel.

[0033] 2. In the present invention, AI algorithms can integrate multi-source data and conduct comprehensive analysis to provide a more comprehensive and in-depth environmental element monitoring report, which helps decision-makers better understand the overall picture of environmental changes and formulate more effective response strategies and measures. The application of AI technology in environmental monitoring helps promote the achievement of sustainable development goals. By providing accurate environmental data and predictions, AI can support decision-making in environmental protection and resource management and drive society towards a more sustainable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic framework diagram of the data analysis method of the environmental element intelligent recognition and monitoring system of the present invention;

[0035] Figure 2 It is a schematic flow diagram of the data analysis system of the environmental element intelligent recognition and monitoring system of the present invention;

[0036] Figure 3 It is a schematic diagram of the data analysis system of the environmental element intelligent recognition and monitoring system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1: In the detection and analysis of forest air quality, the data analysis method of the environmental intelligent recognition and monitoring system can help identify and evaluate various elements and changes in forest air quality. Through sensor data collection: The environmental intelligent monitoring system will use various sensors to detect PM2.5, PM10, O3, etc., temperature and humidity sensors, wind speed and direction sensors, etc., to monitor the meteorological and air quality parameters in the forest in real time.

[0039] Among them, for air quality index calculation: Using the collected air quality data, the system can calculate the Air Quality Index (AQI), handle missing values, outliers, and duplicate data to ensure data quality. Data transformation: Such as smoothing, standardizing, or normalizing the data to make different data comparable. Feature selection: Select the most relevant and representative features to reduce data dimensions and noise.

[0040] Among them, use trend lines and regression analysis methods to identify long-term trends. Periodic analysis: Use periodic models such as seasonal decomposition methods for periodic analysis, analyze and predict periodic changes. Use statistical methods to analyze historical data, explore seasonal changes, long-term trends, and changes under different weather conditions of air quality. The analysis can help understand the basic characteristics of forest air quality. Use periodic models such as seasonal decomposition, Fourier transform, etc., to analyze and predict periodic changes. Anomaly detection: Identify and analyze outliers in the data, which may be sudden events or failures in the environment.

[0041] Among them, classify or cluster environmental data to identify different environmental states or categories. Regression analysis: Predict the relationships between environmental factors or future trends. Anomaly detection: Use unsupervised learning methods to detect anomalies or abnormal patterns in the environment.

[0042] Among them, combine with Geographic Information System (GIS) to analyze the air quality differences in different areas of the forest and explore the spatial distribution characteristics of air pollutants within the forest.

[0043] Among them, real-time monitoring and early warning: The system can monitor air quality in real time. Once a decline or abnormal situation in air quality is detected, it can issue early warning signals in a timely manner to help relevant departments take measures to protect the forest environment.

[0044] Among them, display the analysis results through data visualization, such as generating trend charts, heat maps, or spatial distribution maps, so that decision-makers and the public can intuitively understand the status and changes of forest air quality. Use line charts, bar charts, heat maps, etc. to display time series and spatial data, design interactive radar charts to monitor environmental status and trends in real time; Reporting and pushing: Generate regular reports or real-time alerts to notify decision-makers and relevant personnel of environmental changes and important events.

[0045] Example 2: When the Ambient Intelligence Recognition and Monitoring System detects the presence of a danger or potential danger in the environment, its data analysis method can play a crucial role in helping with timely warnings, taking measures, and providing support. The data analysis method can analyze the monitored data in real time, such as meteorological data, air quality data, geological data, etc., to identify possible dangerous situations, such as storms, forest fires, geological disasters, etc. Once an anomaly or dangerous situation is detected, the system can send out a warning signal to notify relevant departments and the public to take appropriate countermeasures. By analyzing historical data and real-time data, the system can evaluate the risk level of a certain disaster occurring in the current environment. For example, based on the analysis of data such as weather, geology, and vegetation conditions, it can predict the likelihood and potential impact range of natural disasters such as forest fires or debris flows. The data analysis method can provide support for decision-makers by predicting the effects of different countermeasures through models and algorithms, helping to formulate response plans and emergency response strategies. For example, according to the fire spread model, it can predict the direction of the fire spread and guide the prioritization of fire-fighting tasks and resource allocation.

[0046] Among them, in dangerous situations such as fires or floods, the system can analyze real-time data to optimize the scheduling and allocation of resources, ensuring that rescue teams, supplies, and equipment can respond quickly and be effectively deployed to the places where they are most needed. The Ambient Intelligence Recognition and Monitoring System can not only detect dangers but also monitor the implementation effects of countermeasures in real time. Data analysis can evaluate the implementation situation and effects of countermeasures and adjust strategies in a timely manner to cope with changing environmental conditions.

[0047] Among them, through the analyzed and sorted data results, the system can generate easy-to-understand warning messages and educational content to convey dangerous situations and correct coping methods to the public, improving the public's emergency awareness and capabilities.

[0048] Among them, the data analysis method of the Ambient Intelligence Recognition and Monitoring System can provide various applications when a danger is detected. From warnings and predictions to real-time decision support and public education, it helps to protect the environment and people's safety.

[0049] Example 3: First, determine the specific requirements and goals of the system. This includes the types of environmental problems to be identified (such as air quality, water quality monitoring, natural disaster warnings, etc.), the expected functions of the system (real-time monitoring, data analysis, prediction capabilities, etc.), and the target users (government departments, research institutions, the public, etc.). Collect and integrate environmental-related data sources. These data can come from sensor networks, weather stations, satellite remote sensing, geographic information systems (GIS), environmental monitoring stations, etc. Ensuring the accuracy, integrity, and timeliness of the data is crucial. Preprocess the collected raw data, including data cleaning, denoising, filling in missing values, data standardization or normalization, and other processing steps. This step ensures the data quality and prepares the data for subsequent analysis and modeling.

[0050] Among them, design and establish a data storage architecture suitable for the scale, such as a relational database, a time series database, or a big data storage system, to ensure that the system can efficiently store and manage a large amount of real-time and historical data. Apply appropriate data analysis techniques and modeling methods to deeply analyze the data. Commonly used techniques include statistical analysis, machine learning algorithms (such as regression analysis, classification, clustering, time series analysis, etc.), artificial neural networks, etc. These methods can help discover patterns, trends, and anomalies in the data.

[0051] Among them, based on the results of analysis and modeling, the system can predict environmental change trends, disaster risks, etc. These prediction results provide a scientific basis for decision-makers to support them in formulating response measures and emergency response plans. Implement the designed data analysis system into the actual environment and conduct system deployment and integration. Ensure that the system can operate stably and is available to users and relevant stakeholders. After deployment, regularly monitor the running status and data quality of the system, conduct necessary maintenance and updates, and continuously improve the performance and functions of the system to adapt to the changing environment and technological requirements. Provide training and support for the end-users of the system to ensure that they can effectively use the functions and data of the system and enhance the ability and efficiency to respond to environmental problems.

[0052] Through the implementation of the above steps, the data analysis system of the environmental intelligent recognition and monitoring system can more effectively support environmental monitoring, early warning, and response work, and improve the level of environmental safety and protection.

[0053] Proportional example: The data analysis method of the air quality monitoring system, air quality monitoring stations, sensor networks, meteorological data, etc.

[0054] Among them, data types: include air pollutant concentration data such as PM2.5, PM10, SO2, NO2, etc., and at the same time, there are also environmental parameters such as temperature, humidity, wind speed, etc. Remove outliers and incorrect data to ensure the accuracy of the data. Process missing data and use interpolation methods to fill in the missing values to ensure the integrity and continuity of the data.

[0055] Among them, data transformation and standardization: Normalize or standardize the data for subsequent analysis and processing. Conduct statistical summaries of historical data, such as mean, variance, trend analysis, etc., to understand the change trends of pollutant concentrations. Analyze the time series characteristics of air quality data, explore seasonal and periodic changes on different time scales. Use supervised learning or unsupervised learning algorithms, such as regression analysis, clustering analysis, etc., to establish a relationship model between air quality and environmental parameters, predict future air quality conditions, and based on the established model, predict the change trends of future air quality and possible pollution events.

[0056] Among them, the analysis results are provided to government departments or the public to support decision-making such as formulating and adjusting environmental protection policies and issuing early warning information. The analysis results are integrated into the actual air quality monitoring system to ensure the continuity of real-time monitoring and data analysis, regularly monitor the system operation status and data quality, conduct system maintenance and updates, and provide training and technical support for end-users such as government departments and research institutions to ensure that they can effectively use the system analysis results.

[0057] Among them, for the comparative analysis method, the comparative analysis method can be the comparison between the traditional rule-based analysis method and the modern machine learning-based analysis method:

[0058] Among them, the traditional method (rule-based): The traditional method usually relies on expert knowledge and prior rules, such as the threshold division and warning system based on the Air Quality Index (AQI). These methods may be easier to explain and understand, but they are not flexible enough for complex data patterns and changes.

[0059] Among them, the modern method (machine learning-based): The modern method uses machine learning algorithms to process a large amount of data and can discover hidden data patterns and complex correlation relationships. For example, by analyzing atmospheric environment data through a deep learning model, the accuracy and timeliness of air quality prediction can be improved.

[0060] By comparing these two methods, it can be seen that the modern method has greater advantages in dealing with complex environmental data, can provide more accurate and real-time environmental monitoring and early warning services, and thus more effectively support environmental protection decision-making and emergency response.

[0061] Working principle: The intelligent environmental element identification and monitoring system first collects environmental data through various sensors (such as meteorological sensors, pollutant sensors, image sensors, etc.). These sensors can be installed at different locations and on different devices to capture various parameters and situations in the environment. The collected data is processed and encoded, and then transmitted through the network to the data processing center or cloud storage. Such a design can ensure that the data can be stored long-term and can be remotely accessed and managed. Before data analysis, preprocessing operations are usually required, including data cleaning, denoising, outlier detection, and data imputation, etc. These steps help improve the quality and accuracy of the data. Data analysis is the core part of the system, and various technologies and methods can be used, such as statistical analysis, machine learning, deep learning, etc. The main purpose is to extract useful information and patterns from the massive data, identify different environmental elements, change trends, and abnormal situations in the environment. Based on the analysis results, the system can intelligently identify different environmental elements, such as weather conditions, air quality, noise levels, etc. These identification results can be used to monitor and evaluate the environmental status in real-time and to make further responses and controls. According to the analysis results and the identified environmental elements, the system can generate feedback control signals or provide decision support information to decision-makers. These information can be used to adjust the operation of environmental control devices, optimize resource utilization, or formulate environmental protection policies, etc. In the intelligent environmental element identification and monitoring system, it is used to describe and summarize data characteristics, such as mean, standard deviation, correlation, etc., and to conduct trend analysis and periodic analysis. By training models to identify and predict environmental elements, such as using supervised learning algorithms for classification (such as weather type classification), regression (such as air quality prediction), etc., it is especially suitable for processing complex environmental data, such as image recognition (for monitoring natural landscapes or environmental changes), speech recognition (for noise monitoring), etc. Model and predict time-related data, such as analyzing seasonal changes, periodic trends, and the impact of emergencies. Combine geographic information system (GIS) technology to analyze the distribution and correlation of spatial data, such as analyzing the distribution characteristics of pollutants in the city. Select appropriate technologies and tools according to specific monitoring requirements and environmental characteristics to achieve efficient and accurate environmental element identification and monitoring.

[0062] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of the present invention as defined by the appended claims and their equivalents.

Claims

1. A data analysis method based on an intelligent recognition and monitoring system for environmental elements, characterized in that: The following steps are included: S1, data preprocessing, the data preprocessing includes data cleaning, data cleaning: processing missing values, outliers and duplicate data to ensure data quality, data transformation: such as smoothing, standardizing or normalizing the data to make different data comparable, feature selection: selecting the most relevant and representative features to reduce data dimension and noise; S2, time series analysis, which includes trend analysis, using trend lines and regression analysis methods to identify long-term trends, periodic analysis: using periodic models such as seasonal decomposition methods to conduct periodic analysis, analyze and predict periodic changes, anomaly detection, identifying and analyzing anomalies in the data, including but not limited to emergencies or failures in the environment; S3, environmental analysis, the environmental analysis uses geographic information system (GIS) analysis: combining environmental data with geographic location information to analyze spatial distribution and correlation; S4, spatial sequence analysis, the spatial sequence analysis includes trend analysis: using trend line and regression analysis to identify long-term trends, periodic analysis: using periodic models such as seasonal decomposition, Fourier transform, etc. to analyze and predict periodic changes, anomaly detection: identifying and analyzing abnormal points in the data, which may be emergencies or failures in the environment; S5, machine learning and data mining, including classification and clustering: classifying or clustering environmental data to identify different environmental states or categories, regression analysis: predicting the relationship between environmental elements or future trends, anomaly detection: using unsupervised learning methods to detect anomalies or abnormal patterns in the environment; S6, visual report, the visual report includes the use of charts and maps, using line charts, bar charts, heat maps, etc. to display time series and spatial data, design interactive radar charts, and monitor environmental status and trends in real time; Reporting and push: Generate regular reports or real-time alerts to notify decision makers and relevant personnel of environmental changes and important events.

2. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 1 is characterized in that: The time series analysis adopts a seasonal decomposition method. By using the seasonal decomposition method on the time series data showing seasonal changes, the data is decomposed into trend, seasonal and residual parts, so as to analyze and predict the seasonal change trend.

3. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 1 is characterized in that: The core of the machine learning and data mining is to analyze and mine the obtained environmental data, spatial data and temporal data through AI algorithms, and the machine learning is based on an ensemble learning model (Ensemble Learning).

4. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 1 is characterized in that: The spatial series analysis explores the spatial patterns in the data set by calculating the autocorrelation in the overall space, examines the spatial correlation within a specific area, and analyzes the complex interactions between spatial and temporal variables. Through spatial series analysis, a prediction model with the interaction of temporal and spatial variables can be developed.

5. A data analysis system based on an intelligent recognition and monitoring system for environmental elements, characterized in that: A data analysis method based on an intelligent recognition and monitoring system for environmental elements as described in any one of claims 1 to 4 above is applied, wherein the system includes a sensor network, a data acquisition and transmission system, a data processing and analysis platform, an intelligent recognition and monitoring algorithm, an application and user interface, and an early warning and decision support system.

6. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 5 is characterized in that: The sensor network is used to deploy sensors in various environments, such as air quality sensors and water quality monitoring sensors, to collect environmental data. The data acquisition and transmission system is used to include functions such as data storage, data cleaning, data analysis and visualization, and processes and analyzes large amounts of real-time data through algorithms.

7. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 5 is characterized in that: The intelligent identification and detection algorithm uses machine learning and artificial intelligence technology to identify and monitor the collected environmental data in real time to monitor abnormal situations or key trends.

8. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 5 is characterized in that: The applications and user interfaces shown present the analysis results to users in a visual manner, such as monitoring panels, report generation, and mobile applications, making it easy for users to view the environment status in real time and take necessary actions.

9. The data analysis method based on the environmental element intelligent recognition and monitoring system according to claim 5 is characterized in that: The early warning and decision support system generates early warning information based on the analysis results to support decision makers in responding and taking measures when environmental problems occur, so as to protect environmental public health.

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