An environmental monitoring system, method and device based on AI recognition

Abstract

The present invention provides an environmental monitoring system, method and device based on AI recognition, which relates to the technical field of environmental monitoring. The environmental monitoring platform controls the sensor group and the camera in real time to collect environmental data in the target monitoring area; obtains the environmental quality evaluation parameters in the target monitoring area by using the environmental data in the target monitoring area, and determines whether environmental anomaly alarm needs to be carried out according to the environmental quality evaluation parameters in the target monitoring area; the environmental monitoring platform monitors the data collection operation quality of the environmental data collected by the sensor group and the camera in real time, and when the data collection operation quality is abnormal, data collection quality anomaly alarm is carried out. By integrating the sensor group, the camera and AI technology, efficient real-time monitoring and intelligent analysis and recognition of the parameters in the target monitoring area are realized.

Description

Technical Field

[0001] The present invention provides an environmental monitoring system, method and device based on AI recognition, which relates to the technical field of environmental monitoring, and particularly to the technical field of environmental monitoring based on AI recognition. Background Art

[0002] Traditional environmental monitoring methods mainly rely on manual sampling, laboratory analysis and on-site monitoring. These methods have problems such as long monitoring cycles, poor data real-time performance, and high labor costs. At the same time, with the rapid development of industrialization and urbanization, environmental pollution problems are becoming increasingly serious, posing higher requirements for the accuracy, real-time performance and intelligent level of environmental monitoring. Although certain progress has been made in the existing technology, there are still some challenges and problems in the field of environmental monitoring. For example, how to achieve the fusion and collaborative processing of multi-source environmental data, how to improve the acquisition accuracy and real-time performance of environmental data, and how to achieve rapid response and early warning for environmental abnormal events. Summary of the Invention

[0003] The present invention provides an environmental monitoring system, method and device based on AI recognition to solve some challenges and problems existing in the field of environmental monitoring. For example, how to achieve the fusion and collaborative processing of multi-source environmental data, how to improve the acquisition accuracy and real-time performance of environmental data, and how to achieve rapid response and early warning for environmental abnormal events:

[0004] An environmental monitoring system, method and device based on AI recognition proposed by the present invention, the environmental monitoring method based on AI recognition includes:

[0005] The environmental monitoring platform controls the sensor group and the camera in real time to collect environmental data in the target monitoring area;

[0006] Use the environmental data in the target monitoring area to obtain the environmental quality evaluation parameters in the target monitoring area, and determine whether environmental abnormal alarm needs to be carried out according to the environmental quality evaluation parameters in the target monitoring area;

[0007] The environmental monitoring platform monitors the data acquisition operation quality of the environmental data collected by the sensor group and the camera in real time, and performs data acquisition quality abnormal alarm when the data acquisition operation quality is abnormal.

[0008] Further, the environmental monitoring platform controls the sensor group and the camera in real time to collect environmental data in the target monitoring area, including:

[0009] The environmental monitoring platform controls the sensor group to collect air parameter data in the target monitoring area in real time; wherein, the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data and ozone concentration data;

[0010] The environmental monitoring platform controls the sensor group in real time to collect the noise parameter data of the target monitoring area in real time;

[0011] The environmental monitoring platform controls the camera in real time to collect the water area images of the target monitoring area, and identifies the water area images to obtain the foreign object coverage area parameter and transparency in the water area.

[0012] Furthermore, the environmental quality evaluation parameters in the target monitoring area are obtained by using the environmental data in the target monitoring area, and it is determined whether environmental anomaly alarm is required according to the environmental quality evaluation parameters in the target monitoring area, including:

[0013] Extract the air parameter data in the target monitoring area;

[0014] Judge whether the air parameter data in the target monitoring area exceeds the preset air parameter standard range;

[0015] Extract the noise parameter data in the target monitoring area;

[0016] Judge whether the noise parameter data in the target monitoring area exceeds the preset noise parameter standard range;

[0017] Extract the foreign object coverage area parameter and transparency in the target monitoring area;

[0018] For transparency judgment, it is determined whether the foreign object coverage area parameter and transparency in the target monitoring area exceed their corresponding preset foreign object coverage area thresholds, and the transparency is lower than the preset transparency threshold;

[0019] When any one of the air parameter data, noise parameter data, foreign object coverage area parameter and transparency does not meet the corresponding parameter threshold requirements, it is determined that the environment in the target monitoring area is abnormal, and environmental anomaly alarm is carried out.

[0020] Furthermore, the environmental monitoring platform monitors the data collection operation quality of the sensor group and the camera for collecting environmental data in real time, and when there is an anomaly in the data collection operation quality, data collection quality anomaly alarm is carried out, including:

[0021] The environmental monitoring platform monitors the data collection response delay parameter of the sensors included in the sensor group in real time;

[0022] The operation quality evaluation parameter of the sensor group is obtained by using the data collection response delay parameter, and the operation quality evaluation parameter is obtained by the following formula:

[0023]

[0024]

[0025] Among them, f 01 represents the operation quality evaluation parameter of the sensor group; n represents the number of unit time periods experienced by the operation of the sensor group; P ti represents the data acquisition delay rate corresponding to the i-th unit time period; N represents the number of sensors with data acquisition delay in the i-th unit time period; m represents the total number of sensors included in the sensor group; λ i represents the response delay dynamic coefficient corresponding to the i-th unit time period; T si represents the average data acquisition response duration of the i-th sensor included in the sensor group; T i represents the theoretical data acquisition response duration corresponding to the i-th sensor; T max represents the maximum data acquisition response delay duration of the sensors included in the sensor group; T min represents the minimum data acquisition response delay duration of the sensors included in the sensor group; T p represents the average data acquisition response delay duration of the sensors included in the sensor group;

[0026] The environmental monitoring platform monitors the change information of the signal-to-noise ratio of the image data collected by the camera in real time;

[0027] The shooting quality evaluation parameter of the camera is obtained by using the change information of the signal-to-noise ratio of the image data, where the shooting quality evaluation parameter is obtained through the following formula:

[0028]

[0029]

[0030] Among them, f 02 represents the shooting quality evaluation parameter of the camera; B x represents the signal-to-noise ratio corresponding to the latest image data collected by the camera; B h represents the signal-to-noise ratio corresponding to the first image data collected by the camera; B e represents the preset signal-to-noise ratio threshold; α represents the shooting quality evaluation compensation coefficient; k represents the number of image data collected by the camera; B i represents the signal-to-noise ratio corresponding to the i-th image data; B max represents the maximum signal-to-noise ratio value of the image data collected by the camera;

[0031] When any one of the operation quality evaluation parameters of the sensor group and the shooting quality evaluation parameters of the camera is lower than its corresponding preset quality evaluation parameter threshold, it is determined that there is an abnormal operation in the current data collection, and a data collection quality anomaly alarm is issued.

[0032] Further, the AI recognition-based environmental monitoring system includes:

[0033] An environment collection module for the environmental monitoring platform to control the sensor group and the camera to collect environmental data in the target monitoring area in real time;

[0034] An environment alarm module for obtaining the environmental quality evaluation parameters in the target monitoring area by using the environmental data in the target monitoring area, and determining whether an environmental anomaly alarm needs to be issued according to the environmental quality evaluation parameters in the target monitoring area;

[0035] A data alarm module for the environmental monitoring platform to monitor the data collection operation quality of the sensor group and the camera for collecting environmental data in real time, and issue a data collection quality anomaly alarm when there is an abnormality in the data collection operation quality.

[0036] Further, the environment collection module includes:

[0037] An air data collection module for the environmental monitoring platform to control the sensor group to collect air parameter data in the target monitoring area in real time; wherein, the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data and ozone concentration data;

[0038] A noise data collection module for the environmental monitoring platform to control the sensor group to collect noise parameter data in the target monitoring area in real time;

[0039] A camera collection module for the environmental monitoring platform to control the camera to collect water area images in the target monitoring area in real time, and identify the water area images to obtain the foreign object coverage area parameter and transparency in the water area.

[0040] Further, the environment alarm module includes:

[0041] An air parameter acquisition module for extracting the air parameter data in the target monitoring area;

[0042] An air data analysis module for judging whether the air parameter data in the target monitoring area exceeds the preset air parameter standard range;

[0043] A noise parameter acquisition module for extracting the noise parameter data in the target monitoring area;

[0044] A noise data analysis module, which is used to determine whether the noise parameter data in the target monitoring area exceeds the preset noise parameter standard range;

[0045] A water quality parameter acquisition module, which is used to extract the foreign object coverage area parameter and transparency in the target monitoring area;

[0046] A water quality data analysis module, which is used to determine whether the foreign object coverage area parameter and transparency in the target monitoring area exceed their corresponding preset foreign object coverage area thresholds, and the transparency is lower than the preset transparency threshold;

[0047] An evaluation and alarm module, which is used to determine that the environment of the target monitoring area is abnormal and give an environmental anomaly alarm when any one of the air parameter data, noise parameter data, foreign object coverage area parameter and transparency does not meet the corresponding parameter threshold requirements.

[0048] Further, the data alarm module includes:

[0049] An operating parameter acquisition module, which is used to monitor the data acquisition response delay parameter of the sensors included in the sensor group in real time by the environmental monitoring platform;

[0050] Obtain the operation quality evaluation parameter of the sensor group by using the data acquisition response delay parameter, where the operation quality evaluation parameter is obtained through the following formula:

[0051]

[0052]

[0053] where, f 01 represents the operation quality evaluation parameter of the sensor group; n represents the number of unit times experienced by the sensor group during operation; P ti represents the data acquisition delay rate corresponding to the i-th unit time; N represents the number of sensors with data acquisition delay in the i-th unit time; m represents the total number of sensors included in the sensor group; λ i represents the response delay dynamic coefficient corresponding to the i-th unit time; T si represents the average data acquisition response duration of the i-th sensor included in the sensor group; T i represents the theoretical data acquisition response duration corresponding to the i-th sensor; T max represents the maximum data acquisition response delay duration of the sensors included in the sensor group; T min represents the minimum data acquisition response delay duration of the sensors included in the sensor group; T p represents the average data acquisition response delay duration of the sensors included in the sensor group;

[0054] A shooting parameter acquisition module, configured to monitor the change information of the signal-to-noise ratio of the image data collected by the camera in real time for the environmental monitoring platform;

[0055] Obtain the shooting quality evaluation parameter of the camera by using the change information of the signal-to-noise ratio of the image data, wherein the shooting quality evaluation parameter is obtained by the following formula:

[0056]

[0057]

[0058] Where f 02 Represents the shooting quality evaluation parameter of the camera; B x Represents the signal-to-noise ratio corresponding to the latest image data collected by the camera; B h Represents the signal-to-noise ratio corresponding to the first image data collected by the camera; B e Represents a preset signal-to-noise ratio threshold; α represents a shooting quality evaluation compensation coefficient; k represents the number of image data collected by the camera; B i Represents the signal-to-noise ratio corresponding to the i-th image data; B max Represents the maximum signal-to-noise ratio of the image data collected by the camera;

[0059] An alarm judgment module, configured to determine that there is an abnormal operation quality in the current data acquisition and perform an alarm for the abnormal data acquisition quality when any one of the operation quality evaluation parameter of the sensor group and the shooting quality evaluation parameter of the camera is lower than its corresponding preset quality evaluation parameter threshold.

[0060] Further, the environmental monitoring device based on AI recognition includes an environmental monitoring platform, a sensor group and a camera; the environmental monitoring platform is data-connected to the sensor group and the camera through a wireless communication method.

[0061] Further, the sensor group includes a sulfur dioxide sensor, a nitrogen dioxide sensor, a carbon monoxide sensor, an ozone sensor and a sound sensor for noise monitoring.

[0062] Advantages of the present invention: The present invention collects, processes, and analyzes environmental data in the target monitoring area in real time, ensuring timely response and handling of environmental changes. Through the collaborative work of the sensor group and the camera, as well as the preprocessing and quality control of the data, the environmental monitoring platform can provide accurate and reliable environmental quality evaluation parameters. The environmental monitoring platform has an intelligent alarm function, which can automatically judge whether the environmental quality is abnormal and trigger the corresponding alarm mechanism. This greatly improves the efficiency and accuracy of environmental management. The environmental monitoring platform adopts a modular design, which can easily expand new sensors and cameras to meet the environmental monitoring needs of different regions and scenarios. Through the data display module, the environmental monitoring platform can intuitively display the monitored environmental parameters and data change trends. The environmental monitoring platform can monitor the operation quality of data collection in real time and alarm and handle it in time when abnormalities are found. Description of the Drawings

[0063] Figure 1 It is a schematic diagram of an environmental monitoring method based on AI recognition;

[0064] Figure 2 It is a schematic diagram of an environmental monitoring system based on AI recognition. Detailed Embodiments

[0065] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0066] In an embodiment of the present invention, the present invention provides an environmental monitoring system, method, and device based on AI recognition. The environmental monitoring method based on AI recognition includes:

[0067] The environmental monitoring platform controls the sensor group and the camera to collect environmental data in the target monitoring area in real time;

[0068] Obtain environmental quality evaluation parameters in the target monitoring area by using the environmental data in the target monitoring area, and determine whether environmental abnormality alarm is required according to the environmental quality evaluation parameters in the target monitoring area;

[0069] The environmental monitoring platform monitors the operation quality of data collection of the sensor group and the camera for collecting environmental data in real time, and when the operation quality of data collection is abnormal, alarms for abnormal data collection quality.

[0070] The working principle of the above technical solution is as follows: Real-time data collection is carried out on the target monitoring area through the sensor group and the camera. The sensor group is responsible for collecting environmental data such as temperature, humidity, air quality, etc.; while the camera provides visual information to assist in the analysis and judgment of environmental data. The collected data is first preprocessed, including steps such as data cleaning and format conversion, to ensure the accuracy and consistency of the data. Subsequently, the environmental monitoring platform will use these data to calculate environmental quality evaluation parameters, which can comprehensively reflect the environmental quality status of the target monitoring area. According to the calculated environmental quality evaluation parameters, the environmental monitoring platform will conduct an environmental quality evaluation. During the evaluation process, the preset environmental quality standards or thresholds will be referred to determine whether the environmental quality of the target monitoring area meets the standards. If the environmental quality evaluation parameters show that the environmental quality of the target monitoring area does not meet the standards or is abnormal, the environmental monitoring platform will immediately trigger an environmental anomaly alarm. The environmental monitoring platform will also monitor the data collection operation quality of the sensor group and the camera in real time. This includes monitoring aspects such as the operating status of the sensors and cameras, the data collection frequency, and the data transmission stability. If an abnormality in the data collection operation quality is found (such as sensor failure, data transmission interruption, etc.), the environmental monitoring platform will issue an alarm for abnormal data collection quality.

[0071] The effects of the above technical solution are as follows: Real-time collection, processing, and analysis of environmental data in the target monitoring area ensure timely response and handling of environmental changes. Through the collaborative work of the sensor group and the camera, as well as the preprocessing and quality control of the data, the environmental monitoring platform can provide accurate and reliable environmental quality evaluation parameters. The environmental monitoring platform has an intelligent alarm function, which can automatically judge whether the environmental quality is abnormal and trigger the corresponding alarm mechanism. This greatly improves the efficiency and accuracy of environmental management. The environmental monitoring platform adopts a modular design, which can be easily expanded with new sensors and cameras to meet the environmental monitoring needs of different regions and scenarios. Through the data display module, the environmental monitoring platform can intuitively display the monitored environmental parameters and data change trends. The environmental monitoring platform can monitor the data collection operation quality in real time and issue alarms and handle them in a timely manner when abnormalities are found.

[0072] In an embodiment of the present invention, the environmental monitoring platform controls the sensor group and the camera to collect environmental data in the target monitoring area in real time, including:

[0073] The environmental monitoring platform controls the sensor group to collect air parameter data in the target monitoring area in real time; wherein, the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data, and ozone concentration data;

[0074] The environmental monitoring platform controls the sensor group to collect noise parameter data in the target monitoring area in real time;

[0075] The environmental monitoring platform controls the camera in real time to collect the water area images of the target monitoring area in real time, and identifies the water area images to obtain the foreign object coverage area parameter and transparency in the water area.

[0076] The working principle of the above technical solution is as follows: The air parameter data in the target monitoring area is collected by controlling the sensor group in real time. These sensors can respectively detect and measure the concentration data of sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone. The sensors convert the detected data into electrical signals or digital signals and transmit them to the data acquisition module of the environmental monitoring platform in real time. The noise parameter data of the target monitoring area is collected by the sensors in real time. Such sensors can sense the sound intensity or decibel value in the environment and convert it into a digital signal that can be processed. The water area images of the target monitoring area are collected by controlling the camera in real time. The collected image data is transmitted to the image processing module, and the image is processed and analyzed using image recognition algorithms. The image processing module can identify the foreign object coverage area in the water area and calculate the transparency, so as to evaluate the cleanliness and transparency of the water area.

[0077] The effects of the above technical solution are as follows: The system can collect and transmit data in real time, ensuring instant perception and response to environmental changes. The system not only collects air parameter data, but also covers noise parameter data and water area image data, and can comprehensively evaluate the environmental conditions of the target monitoring area. The system can accurately measure and identify various parameters in the environment, improving the accuracy of the data. It can automatically process and analyze the water area images collected by the camera, reducing the tediousness and subjectivity of manual operations. The system can automatically judge whether the environment is abnormal according to preset rules and standards, improving the intelligent level of environmental management. The system can monitor the environmental quality in real time and issue early warnings in time when abnormalities are found, which helps to take countermeasures in advance and avoid the further deterioration of environmental problems.

[0078] In an embodiment of the present invention, environmental quality evaluation parameters in the target monitoring area are obtained by using the environmental data in the target monitoring area, and it is determined whether environmental anomaly alarm needs to be carried out according to the environmental quality evaluation parameters in the target monitoring area, including:

[0079] Extract the air parameter data in the target monitoring area;

[0080] Judge whether the air parameter data in the target monitoring area exceeds the preset air parameter standard range;

[0081] Extract the noise parameter data in the target monitoring area;

[0082] Judge whether the noise parameter data in the target monitoring area exceeds the preset noise parameter standard range;

[0083] Extract the foreign object coverage area parameter and transparency in the target monitoring area;

[0084] For transparency judgment, determine whether the foreign object coverage area parameter and transparency in the target monitoring area exceed their corresponding preset foreign object coverage area thresholds, and the transparency is lower than the preset transparency threshold;

[0085] When any one of the air parameter data, noise parameter data, foreign object coverage area parameter, and transparency does not meet the requirements of its corresponding parameter threshold, it is determined that the environment of the target monitoring area is abnormal, and an environmental anomaly alarm is issued.

[0086] The working principle of the above technical solution is as follows: The air parameter data of the target monitoring area are extracted in real time from the sensor group. These data include sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, etc. The noise parameter data of the target monitoring area are also extracted from the noise sensor. The water area image collected by the camera is processed by an image recognition algorithm to extract the foreign object coverage area parameter and transparency of the water area. The system first extracts relevant environmental parameter data from the target monitoring area in real time or regularly, including air parameter data (such as PM2.5, temperature, humidity, etc.), noise parameter data (such as decibel value), and data on foreign object coverage area and transparency. The system then compares the collected data with the preset standard ranges (thresholds) of various parameters. These standard ranges or thresholds are set based on environmental quality standards, safety regulations, or user requirements. If any parameter exceeds its corresponding threshold range during the comparison process (such as poor air quality, excessive noise, excessive foreign object coverage area, or too low transparency), the system will immediately make an anomaly judgment. When the system determines that the environment of the target monitoring area is abnormal, it will trigger an alarm mechanism to notify relevant personnel for processing or taking necessary measures..

[0087] The effects of the above technical solution are as follows: By simultaneously monitoring multiple environmental elements such as air, noise, and water area, the system can comprehensively evaluate the environmental quality of the target monitoring area and provide more comprehensive environmental information. Real-time monitoring and data analysis can promptly detect environmental quality anomalies and immediately trigger an alarm mechanism, which helps to take timely countermeasures to prevent the further deterioration of environmental problems. The system has a high degree of automation, which can reduce the complexity and subjectivity of manual operations and improve the efficiency and accuracy of environmental management. The provided data and evaluation results can provide decision-making support for environmental management and help formulate more scientific and reasonable environmental management strategies. The system adopts a modular design and can expand new monitoring elements and sensors as needed to meet the environmental monitoring requirements in different scenarios.

[0088] In an embodiment of the present invention, the environmental monitoring platform monitors the data acquisition operation quality of the environmental data collected by the sensor group and the camera in real time, and when the data acquisition operation quality is abnormal, performs an alarm for abnormal data acquisition quality, including:

[0089] The environmental monitoring platform monitors the data acquisition response delay parameters of the sensors included in the sensor group in real time;

[0090] Obtain the operation quality evaluation parameter of the sensor group by using the data acquisition response delay parameter, wherein the operation quality evaluation parameter is obtained by the following formula:

[0091]

[0092]

[0093] where f 01 represents the operation quality evaluation parameter of the sensor group; n represents the number of unit times experienced by the operation of the sensor group; P ti represents the data acquisition delay rate corresponding to the i-th unit time; N represents the number of sensors with data acquisition delay in the i-th unit time; m represents the total number of sensors included in the sensor group; λ i represents the response delay dynamic coefficient corresponding to the i-th unit time; T si represents the average data acquisition response duration of the i-th sensor included in the sensor group; T i represents the theoretical data acquisition response duration corresponding to the i-th sensor; T max represents the maximum data acquisition response delay duration of the sensors included in the sensor group; T min represents the minimum data acquisition response delay duration of the sensors included in the sensor group; T p represents the average data acquisition response delay duration of the sensors included in the sensor group;

[0094] The environmental monitoring platform monitors the change information of the signal-to-noise ratio of the image data collected by the camera in real time;

[0095] Obtain the shooting quality evaluation parameter of the camera by using the change information of the signal-to-noise ratio of the image data, wherein the shooting quality evaluation parameter is obtained by the following formula:

[0096]

[0097]

[0098] where f 02 represents the shooting quality evaluation parameter of the camera; B xRepresents the signal-to-noise ratio corresponding to the latest image data collected by the camera; B h Represents the signal-to-noise ratio corresponding to the first image data collected by the camera; B e Represents a preset signal-to-noise ratio threshold; α represents a shooting quality evaluation compensation coefficient; k represents the number of image data collected by the camera; B i Represents the signal-to-noise ratio corresponding to the i-th image data; B max Represents the maximum signal-to-noise ratio that appears in the image data collected by the camera;

[0099] When any one of the operation quality evaluation parameters of the sensor group and the shooting quality evaluation parameters of the camera is lower than its corresponding preset quality evaluation parameter threshold, it is determined that there is an abnormal operation in the current data collection, and a data collection quality anomaly alarm is issued.

[0100] The working principle of the above technical solution is as follows: Real-time monitor the data acquisition response delay parameters of the sensors included in the sensor group. These parameters reflect the response speed and stability of the sensors during the data acquisition process. According to factors such as the data acquisition delay rate of each sensor per unit time, the number of sensors with delays, and a preset response delay dynamic coefficient, calculate the operation quality evaluation parameter (f 01 ). The average response duration of data acquisition (T si ), the theoretical response duration (T i ), the maximum and minimum response delay durations (T max and T min ), and the average response delay duration (T p ) and other factors are all included in the calculation process to ensure the accuracy and comprehensiveness of the evaluation parameters. The environmental monitoring platform real-time monitors the change information of the signal-to-noise ratio of the image data collected by the camera. The signal-to-noise ratio is an important indicator to measure the image quality and reflects the ratio of the signal to the noise in the image. By comparing the signal-to-noise ratio of the latest image data and the initial image data collected by the camera, as well as the preset signal-to-noise ratio threshold, combined with factors such as the shooting quality evaluation compensation coefficient and the number of image data, calculate the shooting quality evaluation parameter (f 02 ) of the camera, which can real-time evaluate the shooting quality of the camera and timely detect possible image quality problems. If the operation quality evaluation parameter (f 01 ) of the sensor group or the shooting quality evaluation parameter (f 02 ) of the camera is lower than its corresponding preset quality evaluation parameter threshold, it is determined that there is an abnormal operation in the current data collection. Once an anomaly occurs, it will trigger a data collection quality anomaly alarm mechanism, and notify relevant personnel through a preset alarm method (such as text message, email, APP push, etc.) so that they can take measures to solve the problem in a timely manner. Through the The average value of the ratio of the actual average response time to the theoretical response time can be calculated. Through the The proportionality coefficient of the difference between the maximum and minimum values of the response delay time to the average value can be calculated. Through The proportion of the number of sensors with data delay in the total number of sensors can be calculated. Through The coefficient for monitoring the response delay of sensors can be calculated. Relatively speaking, the larger, the smaller, then f 01 the smaller. Through the The ratio of the change difference of the signal-to-noise ratio of multiple image data to the threshold can be calculated. Through the The average value of the ratio of the actual signal-to-noise ratio to the preset threshold can be calculated. Relatively speaking, B i the larger, then the larger. Through The ratio of the maximum signal-to-noise ratio to the threshold can be calculated.

[0101] The effects of the above technical solutions are as follows: The operation quality of the sensor group and the camera is monitored in real time, and abnormal situations in the data acquisition process can be detected in time to ensure the accuracy and reliability of the data. By comprehensively considering various factors (such as the data acquisition delay rate, signal-to-noise ratio, etc.) and the preset threshold, the calculation of the evaluation parameters is more accurate and comprehensive, and can more truly reflect the operation status of the equipment and the system. Timely detection and handling of abnormal operation quality in the data acquisition process can avoid the occurrence of potential problems and improve the stability and reliability of the environmental monitoring system. Through the automated monitoring and alarm mechanism, the frequency and cost of manual inspections can be reduced, and the efficiency and effect of environmental monitoring can be improved. The provided data acquisition quality evaluation parameters can provide decision-making support for system maintenance and management, help relevant personnel understand the operation status of the equipment and the system, and formulate more reasonable maintenance and management strategies.

[0102] In one embodiment of the present invention, the AI recognition-based environmental monitoring system includes:

[0103] An environment acquisition module for the environmental monitoring platform to control the sensor group and the camera to collect environmental data in the target monitoring area in real time;

[0104] An environment alarm module for obtaining environmental quality evaluation parameters in the target monitoring area by using the environmental data in the target monitoring area, and determining whether environmental anomaly alarm is required according to the environmental quality evaluation parameters in the target monitoring area;

[0105] The data alarm module is used to monitor the data acquisition operation quality of the environmental monitoring platform for the environmental data collected by the sensor group and the camera in real time, and perform data acquisition quality anomaly alarm when the data acquisition operation quality is abnormal.

[0106] The working principle of the above technical solution is as follows: The environmental acquisition module performs real-time data acquisition on the target monitoring area through the sensor group and the camera. The sensor group is responsible for collecting environmental data, such as temperature, humidity, air quality, etc.; while the camera provides visual information to assist in the analysis and judgment of environmental data. The environmental alarm module first preprocesses the collected data, including steps such as data cleaning and format conversion, to ensure the accuracy and consistency of the data. Subsequently, the environmental monitoring platform will use these data to calculate environmental quality evaluation parameters, which can comprehensively reflect the environmental quality status of the target monitoring area. According to the calculated environmental quality evaluation parameters, the environmental monitoring platform will conduct an environmental quality evaluation. During the evaluation process, the preset environmental quality standards or thresholds will be referred to determine whether the environmental quality of the target monitoring area meets the standards. If the environmental quality evaluation parameters show that the environmental quality of the target monitoring area does not meet the standards or is abnormal, the environmental monitoring platform will immediately trigger an environmental anomaly alarm. The data alarm module monitors the data acquisition operation quality of the sensor group and the camera in real time. This includes monitoring aspects such as the operating status of the sensors and the camera, the data acquisition frequency, and the data transmission stability. If it is found that the data acquisition operation quality is abnormal (such as sensor failure, data transmission interruption, etc.), the environmental monitoring platform will perform a data acquisition quality anomaly alarm.

[0107] The effects of the above technical solution are as follows: The environmental acquisition module collects, processes, and analyzes the environmental data of the target monitoring area in real time, ensuring timely response and handling of environmental changes. Through the collaborative work of the sensor group and the camera, as well as the preprocessing and quality control of the data, the environmental monitoring platform can provide accurate and reliable environmental quality evaluation parameters. The environmental monitoring platform has an intelligent alarm function, which can automatically judge whether the environmental quality is abnormal and trigger the corresponding alarm mechanism. This greatly improves the efficiency and accuracy of environmental management. The environmental monitoring platform adopts a modular design, which can easily expand new sensors and cameras to meet the environmental monitoring needs of different regions and scenarios. Through the data display module, the environmental monitoring platform can intuitively display the monitored environmental parameters and data change trends. The environmental monitoring platform can monitor the data acquisition operation quality in real time and perform alarm and processing in a timely manner when abnormalities are found.

[0108] In an embodiment of the present invention, the environmental acquisition module includes:

[0109] An air data acquisition module is used for the environmental monitoring platform to control the sensor group in real time to collect air parameter data in the target monitoring area; wherein, the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data, and ozone concentration data;

[0110] A noise data acquisition module is used for the environmental monitoring platform to control the sensor group in real time to collect noise parameter data in the target monitoring area;

[0111] A camera acquisition module is used for the environmental monitoring platform to control the camera in real time to collect water area images in the target monitoring area, and identify the water area images to obtain the foreign object coverage area parameter and transparency in the water area.

[0112] The working principle of the above technical solution is as follows: The air parameter data in the target monitoring area is collected by controlling the sensor group in real time. These sensors can respectively detect and measure the concentration data of sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone. The sensors convert the detected data into electrical signals or digital signals and transmit them to the data acquisition module of the environmental monitoring platform in real time. The noise parameter data in the target monitoring area is collected by the sensors in real time. Such sensors can sense the sound intensity or decibel value in the environment and convert it into a digital signal that can be processed. The water area images in the target monitoring area are collected by controlling the camera in real time. The collected image data is transmitted to the image processing module, and the image is processed and analyzed using image recognition algorithms. The image processing module can identify the foreign object coverage area in the water area and calculate the transparency, so as to evaluate the cleanliness and transparency of the water area.

[0113] The effects of the above technical solution are as follows: The system can collect and transmit data in real time, ensuring instant perception and response to environmental changes. The system not only collects air parameter data, but also covers noise parameter data and water area image data, and can comprehensively evaluate the environmental conditions of the target monitoring area. The system can accurately measure and identify various parameters in the environment, improving the accuracy of the data. It can automatically process and analyze the water area images collected by the camera, reducing the tediousness and subjectivity of manual operations. The system can automatically judge whether the environment is abnormal according to preset rules and standards, improving the intelligent level of environmental management. The system can monitor the environmental quality in real time and issue early warnings in case of abnormalities, which helps to take countermeasures in advance and avoid the further deterioration of environmental problems.

[0114] In an embodiment of the present invention, the environmental alarm module includes:

[0115] An air parameter acquisition module is used to extract the air parameter data in the target monitoring area;

[0116] An air data analysis module, configured to determine whether the air parameter data in the target monitoring area exceeds a preset air parameter standard range;

[0117] A noise parameter acquisition module, configured to extract the noise parameter data in the target monitoring area;

[0118] A noise data analysis module, configured to determine whether the noise parameter data in the target monitoring area exceeds a preset noise parameter standard range;

[0119] A water quality parameter acquisition module, configured to extract the foreign object coverage area parameter and transparency in the target monitoring area;

[0120] A water quality data analysis module, configured to determine whether the foreign object coverage area parameter and transparency in the target monitoring area exceed their corresponding preset foreign object coverage area thresholds, and the transparency is lower than a preset transparency threshold;

[0121] An evaluation and alarm module, configured to determine that the environment of the target monitoring area is abnormal and perform an environmental anomaly alarm when any one of the air parameter data, noise parameter data, foreign object coverage area parameter, and transparency does not meet the corresponding parameter threshold requirements.

[0122] The working principle of the above technical solution is as follows: The air parameter data of the target monitoring area is extracted in real time from the sensor group, and these data include sulfur dioxide concentration, nitrogen dioxide concentration, carbon monoxide concentration, ozone concentration, etc. The noise parameter data of the target monitoring area is also extracted from the noise sensor. The water area image collected by the camera is processed by an image recognition algorithm to extract the foreign object coverage area parameter and transparency of the water area. The system first extracts relevant environmental parameter data from the target monitoring area in real time or periodically, including air parameter data (such as PM2.5, temperature, humidity, etc.), noise parameter data (such as decibel value), and data on foreign object coverage area and transparency. The system then compares the collected data with preset various parameter standard ranges (thresholds). These standard ranges or thresholds are set based on environmental quality standards, safety regulations, or user requirements. If any parameter exceeds its corresponding threshold range during the comparison process (such as poor air quality, excessive noise, excessive foreign object coverage area, or too low transparency), the system will immediately make an anomaly determination. When the system determines that the environment of the target monitoring area is abnormal, it will trigger an alarm mechanism to notify relevant personnel for processing or taking necessary measures..

[0123] The effects of the above technical solution are as follows: By simultaneously monitoring multiple environmental elements such as air, noise, and water areas, the system can comprehensively evaluate the environmental quality of the target monitoring area and provide more comprehensive environmental information. Real-time monitoring and data analysis can promptly detect abnormal environmental quality and immediately trigger an alarm mechanism, which helps to take timely countermeasures to prevent the further deterioration of environmental problems. The system has a high degree of automation, which can reduce the complexity and subjectivity of manual operations and improve the efficiency and accuracy of environmental management. The provided data and evaluation results can provide decision-making support for environmental management and help formulate more scientific and reasonable environmental management strategies. The system adopts a modular design and can expand new monitoring elements and sensors as needed to meet the environmental monitoring requirements in different scenarios.

[0124] In one embodiment of the present invention, the data alarm module includes:

[0125] An operating parameter acquisition module, configured to monitor in real time by the environmental monitoring platform the data acquisition response delay parameters of the sensors included in the sensor group;

[0126] Obtain the operation quality evaluation parameters of the sensor group by using the data acquisition response delay parameters, where the operation quality evaluation parameters are obtained through the following formula:

[0127]

[0128]

[0129] where f 01 represents the operation quality evaluation parameter of the sensor group; n represents the number of unit times experienced by the operation of the sensor group; P ti represents the data acquisition delay rate corresponding to the i-th unit time; N represents the number of sensors with data acquisition delay in the i-th unit time; m represents the total number of sensors included in the sensor group; λ i represents the response delay dynamic coefficient corresponding to the i-th unit time; T si represents the average data acquisition response duration of the i-th sensor included in the sensor group; T i represents the theoretical data acquisition response duration corresponding to the i-th sensor; T max represents the maximum data acquisition response delay duration of the sensors included in the sensor group; T min represents the minimum data acquisition response delay duration of the sensors included in the sensor group; T p represents the average data acquisition response delay duration of the sensors included in the sensor group;

[0130] A shooting parameter acquisition module, configured to monitor in real time by the environmental monitoring platform the signal-to-noise ratio change information of the image data collected by the camera;

[0131] Obtain the shooting quality evaluation parameter of the camera by using the signal-to-noise ratio change information of the image data, wherein the shooting quality evaluation parameter is obtained by the following formula:

[0132]

[0133]

[0134] wherein, f 02 represents the shooting quality evaluation parameter of the camera; B x represents the signal-to-noise ratio corresponding to the latest image data collected by the camera; B h represents the signal-to-noise ratio corresponding to the first image data collected by the camera; B e represents the preset signal-to-noise ratio threshold; α represents the shooting quality evaluation compensation coefficient; k represents the number of image data collected by the camera; B i represents the signal-to-noise ratio corresponding to the i-th image data; B max represents the maximum signal-to-noise ratio that appears in the image data collected by the camera;

[0135] An alarm judgment module, which is used to determine that there is an abnormal operation quality in the current data acquisition and perform an alarm for the abnormal data acquisition quality when any one of the operation quality evaluation parameter of the sensor group and the shooting quality evaluation parameter of the camera is lower than its corresponding preset quality evaluation parameter threshold.

[0136] The working principle of the above technical solution is as follows: Real-time monitor the data acquisition response delay parameters of the sensors included in the sensor group. These parameters reflect the response speed and stability of the sensors during the data acquisition process. According to factors such as the data acquisition delay rate of each sensor per unit time, the number of sensors with delays, and the preset response delay dynamic coefficient, calculate the operation quality evaluation parameter of the sensor group (f 01 ). The average response duration of data acquisition (T si ), the theoretical response duration (T i ), the maximum and minimum response delay durations (T max and T min ), and the average response delay duration (T p ) and other factors are all included in the calculation process to ensure the accuracy and comprehensiveness of the evaluation parameter. The environmental monitoring platform real-time monitors the signal-to-noise ratio change information of the image data collected by the camera. The signal-to-noise ratio is an important indicator for measuring image quality and reflects the ratio of signal to noise in the image. By comparing the signal-to-noise ratios of the latest image data and the initial image data collected by the camera, as well as the preset signal-to-noise ratio threshold, combined with factors such as the shooting quality evaluation compensation coefficient and the number of image data, calculate the shooting quality evaluation parameter of the camera (f 02) can evaluate the shooting quality of the camera in real time and detect possible image quality problems in a timely manner. If any one of the operation quality evaluation parameters (f 01 ) of the sensor group or the shooting quality evaluation parameter (f 02 ) of the camera is lower than its corresponding preset quality evaluation parameter threshold, it is determined that there is an abnormal operation quality in the current data acquisition. Once an abnormality occurs, a data acquisition quality abnormality alarm mechanism will be triggered, and relevant personnel will be notified through preset alarm methods (such as text messages, emails, APP push, etc.) so that measures can be taken in a timely manner to solve the problem.

[0137] The effects of the above technical solution are as follows: The operation quality of the sensor group and the camera is monitored in real time, and abnormal situations in the data acquisition process can be detected in a timely manner, ensuring the accuracy and reliability of the data. By comprehensively considering various factors (such as data acquisition delay rate, signal-to-noise ratio, etc.) and preset thresholds, the calculation of the evaluation parameters is more accurate and comprehensive, and can more truly reflect the operation status of the equipment and the system. Timely detection and handling of abnormal operation quality in the data acquisition process can avoid the occurrence of potential problems and improve the stability and reliability of the environmental monitoring system. Through the automated monitoring and alarm mechanism, the frequency and cost of manual inspections can be reduced, and the efficiency and effect of environmental monitoring can be improved. The provided data acquisition quality evaluation parameters can provide decision-making support for system maintenance and management, help relevant personnel understand the operation status of the equipment and the system, and formulate more reasonable maintenance and management strategies.

[0138] In an embodiment of the present invention, the AI recognition-based environmental monitoring device includes an environmental monitoring platform, a sensor group, and a camera; the environmental monitoring platform is data-connected to the sensor group and the camera through a wireless communication method. The sensor group includes a sulfur dioxide sensor, a nitrogen dioxide sensor, a carbon monoxide sensor, an ozone sensor, and a sound sensor for noise monitoring.

[0139] The working principle of the above technical solution is as follows: The environmental monitoring device system based on AI recognition mainly consists of an environmental monitoring platform, a sensor group, and a camera. They are connected for data through wireless communication methods (such as Wi-Fi, LoRa, Zigbee, or 4G / 5G networks, etc.). The concentration of sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone in the target environment is monitored in real time through sulfur dioxide sensors, nitrogen dioxide sensors, carbon monoxide sensors, and ozone sensors. The sound sensor is used to monitor the noise level in the environment in real time. These sensors send the collected data (such as concentration values, noise decibel values, etc.) to the environmental monitoring platform through wireless communication methods. The camera is responsible for capturing the image data of the target monitoring area in real time. The image data can be directly transmitted to the environmental monitoring platform wirelessly, or first transmitted to the local storage device and then uploaded to the monitoring platform regularly. The environmental monitoring platform receives the data from the sensor group and the camera. For the sensor data, the platform will conduct real-time analysis and may combine historical data and AI algorithms for prediction and early warning. For the image data captured by the camera, the platform will use AI image recognition technology to identify and analyze the coverage area, transparency, or other relevant indicators of foreign objects in the water area. The AI algorithm in the environmental monitoring platform will analyze and identify the received data and extract useful information. The AI algorithm can identify whether the concentration of pollutants in the air exceeds the standard, whether the noise reaches the noise pollution standard, and whether there are abnormal covers or a decrease in transparency in the water area. When it is detected that the environmental parameters exceed the preset threshold, the environmental monitoring platform will trigger an alarm mechanism and notify relevant personnel through methods such as text messages, emails, and APP push.

[0140] The effects of the above technical solution are as follows: Through wireless communication technology and real-time data analysis, the system can monitor the environmental conditions of the target area in real time and respond quickly when abnormalities are found. Professional sensors and cameras are used for data collection, combined with AI algorithms for data analysis and identification, improving the accuracy and reliability of the data. Using AI technology to intelligently analyze environmental data can automatically identify abnormal conditions in the environment and give early warnings, reducing the frequency and cost of manual inspections. The system design is flexible and more sensors and cameras can be added according to needs to expand the monitoring range and monitoring indicators. The provided real-time monitoring data and AI analysis results can provide decision-making support for environmental management and help relevant departments formulate more scientific and reasonable environmental management strategies.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. An environmental monitoring method based on AI recognition, characterized in that: The environmental monitoring method based on AI recognition includes: The environmental monitoring platform controls the sensor group and camera in real time to collect environmental data in the target monitoring area; Acquire environmental quality evaluation parameters in the target monitoring area using the environmental data in the target monitoring area, and determine whether an environmental abnormality alarm is required according to the environmental quality evaluation parameters in the target monitoring area; The environmental monitoring platform monitors the data collection operation quality of the environmental data collected by the sensor group and the camera in real time, and issues an abnormal data collection quality alarm when the data collection operation quality is abnormal; The environmental monitoring platform monitors the data collection operation quality of the sensor group and the camera in real time, and issues an abnormal data collection quality alarm when the data collection operation quality is abnormal, including: The environmental monitoring platform monitors the data acquisition response delay parameters of the sensors included in the sensor group in real time; The data acquisition response delay parameter is used to obtain the operation quality evaluation parameter of the sensor group, wherein the operation quality evaluation parameter is obtained by the following formula: in, f 01 represents the operating quality evaluation parameter of the sensor group; n Indicates the number of unit time experienced by the sensor group operation; P ti Indicates i Data collection delay rate corresponding to unit time; N Indicates i The number of sensors with data collection delay per unit time; m Indicates the total number of sensors included in the sensor group; λ i Indicates i The dynamic coefficient of response delay corresponding to unit time; T si Indicates the sensor group contained in i Average response time of data collection of sensors; T i Indicates i The theoretical response time of data collection corresponding to each sensor; T max Indicates the maximum response delay time of the data collection of the sensors included in the sensor group; T min Indicates the minimum response delay time of the data collection of the sensors included in the sensor group; T p Indicates the average response delay time of data collection of sensors included in the sensor group; The environmental monitoring platform monitors the signal-to-noise ratio change information of the image data collected by the camera in real time; The shooting quality evaluation parameter of the camera is obtained by using the signal-to-noise ratio change information of the image data, wherein the shooting quality evaluation parameter is obtained by the following formula: in, f 02 Indicates the camera's shooting quality evaluation parameters; B x Indicates the signal-to-noise ratio of the latest image data collected by the camera; B h Indicates the signal-to-noise ratio of the first image data collected by the camera; B e Indicates the preset signal-to-noise ratio threshold; α Indicates the shooting quality evaluation compensation coefficient; k Indicates the number of image data collected by the camera; B i Indicates i The signal-to-noise ratio corresponding to the image data; B max Indicates the maximum signal-to-noise ratio of the image data collected by the camera; When any one of the operation quality evaluation parameter of the sensor group and the shooting quality evaluation parameter of the camera is lower than the corresponding preset quality evaluation parameter threshold, it is determined that the current data collection has an operation quality abnormality, and an abnormal data collection quality alarm is issued; Data collection through sensor groups; The response delay dynamic coefficient corresponding to the unit time is calculated by the total number of sensors, the average response time of data acquisition, the theoretical response time of data acquisition, the maximum response delay time of data acquisition, the minimum response delay time of data acquisition and the average response delay time of data acquisition; The operation quality evaluation parameters of the sensor group are calculated by the response delay dynamic coefficient, the number per unit time, the data acquisition delay rate, the number of sensors with data acquisition delay per unit time and the total number of sensors; The shooting quality evaluation compensation coefficient is calculated according to the number of image data, the signal-to-noise ratio corresponding to the i-th image data, the maximum signal-to-noise ratio and the signal-to-noise ratio threshold; The shooting quality evaluation parameter is calculated by using the shooting quality evaluation compensation coefficient, the signal-to-noise ratio corresponding to the latest image data, the signal-to-noise ratio corresponding to the first image data, and the signal-to-noise ratio threshold.

2. The environmental monitoring method based on AI recognition according to claim 1 is characterized in that: The environmental monitoring platform controls the sensor group and camera in real time to collect environmental data in the target monitoring area, including: The environmental monitoring platform controls the sensor group in real time to collect air parameter data in the target monitoring area in real time; wherein the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data and ozone concentration data; The environmental monitoring platform controls the sensor group in real time to collect noise parameter data of the target monitoring area; The environmental monitoring platform controls the camera in real time to collect water images of the target monitoring area, and identifies the water images to obtain the coverage area parameters and transparency of foreign objects in the water area.

3. The environmental monitoring method based on AI recognition according to claim 1, characterized in that: Acquiring environmental quality evaluation parameters in the target monitoring area by using environmental data in the target monitoring area, and determining whether an environmental abnormality alarm is required according to the environmental quality evaluation parameters in the target monitoring area, including: Extracting air parameter data in the target monitoring area; Determining whether the air parameter data in the target monitoring area exceeds a preset air parameter standard range; Extracting noise parameter data in the target monitoring area; Determining whether the noise parameter data in the target monitoring area exceeds a preset noise parameter standard range; Extracting the coverage area parameters and transparency of foreign matter in the target monitoring area; The transparency is judged by using the foreign matter coverage area parameter and the transparency in the target monitoring area to determine whether it exceeds the corresponding preset foreign matter coverage area threshold, and the transparency is lower than the preset transparency threshold; When any one of the air parameter data, noise parameter data, foreign matter coverage area parameter and transparency does not meet the corresponding parameter threshold requirement, it is determined that the environment of the target monitoring area is abnormal, and an environmental abnormality alarm is issued.

4. A system for implementing the AI-based environmental monitoring method according to claim 1, characterized in that: The system of the environment monitoring method based on AI recognition includes: The environment acquisition module is used by the environment monitoring platform to control the sensor group and camera in real time to collect environmental data in the target monitoring area; An environmental alarm module is used to obtain environmental quality evaluation parameters in the target monitoring area using the environmental data in the target monitoring area, and determine whether an environmental abnormality alarm is required according to the environmental quality evaluation parameters in the target monitoring area; The data alarm module is used for the environmental monitoring platform to monitor in real time the data collection operation quality of the sensor group and the camera to collect environmental data, and to issue an abnormal data collection quality alarm when there is an abnormality in the data collection operation quality.

5. The system of the environment monitoring method based on AI recognition according to claim 4 is characterized in that: The environment acquisition module includes: An air data acquisition module is used for the environmental monitoring platform to control the sensor group in real time to collect air parameter data in the target monitoring area in real time; wherein the air parameter data includes sulfur dioxide concentration data, nitrogen dioxide concentration data, carbon monoxide concentration data and ozone concentration data; Noise data acquisition module, used for the environmental monitoring platform to control the sensor group to collect noise parameter data of the target monitoring area in real time; The camera acquisition module is used by the environmental monitoring platform to control the camera in real time to acquire the water area image of the target monitoring area, identify the water area image, and obtain the coverage area parameters and transparency of foreign objects in the water area.

6. The system of the environment monitoring method based on AI recognition according to claim 4 is characterized in that: The environmental alarm module includes: An air parameter acquisition module, used to extract air parameter data in the target monitoring area; An air data analysis module, used to determine whether the air parameter data in the target monitoring area exceeds a preset air parameter standard range; A noise parameter acquisition module, used to extract noise parameter data in the target monitoring area; A noise data analysis module, used to determine whether the noise parameter data in the target monitoring area exceeds a preset noise parameter standard range; A water quality parameter acquisition module, used to extract the foreign matter coverage area parameters and transparency in the target monitoring area; A water quality data analysis module, used for transparency judgment, using the foreign matter coverage area parameter and the transparency in the target monitoring area to determine whether it exceeds the corresponding preset foreign matter coverage area threshold, and the transparency is lower than the preset transparency threshold; The evaluation alarm module is used to determine that the environment in the target monitoring area is abnormal and issue an environmental abnormality alarm when any one of the air parameter data, noise parameter data, foreign matter coverage area parameter and transparency does not meet the corresponding parameter threshold requirements.

7. The system of the environment monitoring method based on AI recognition according to claim 4 is characterized in that: The data alarm module includes: An operating parameter acquisition module is used for the environmental monitoring platform to monitor the data acquisition response delay parameters of the sensors included in the sensor group in real time; The data acquisition response delay parameter is used to obtain the operation quality evaluation parameter of the sensor group, wherein the operation quality evaluation parameter is obtained by the following formula: in, f 01 represents the operating quality evaluation parameter of the sensor group; n Indicates the number of unit time experienced by the sensor group operation; P ti Indicates i Data collection delay rate corresponding to unit time; N Indicates i The number of sensors with data collection delay per unit time; m Indicates the total number of sensors included in the sensor group; λ i Indicates i The dynamic coefficient of response delay corresponding to unit time; T si Indicates the number of sensors included in the sensor group. i Average response time of data collection of sensors; T i Indicates i The theoretical response time of data collection corresponding to each sensor; T max Indicates the maximum response delay time of the data collection of the sensors included in the sensor group; T min Indicates the minimum response delay time of the data collection of the sensors included in the sensor group; T p Indicates the average response delay time of data collection of sensors included in the sensor group; A shooting parameter acquisition module is used for the environmental monitoring platform to monitor the signal-to-noise ratio change information of the image data collected by the camera in real time; The shooting quality evaluation parameter of the camera is obtained by using the signal-to-noise ratio change information of the image data, wherein the shooting quality evaluation parameter is obtained by the following formula: in, f 02 Indicates the camera's shooting quality evaluation parameters; B x Indicates the signal-to-noise ratio of the latest image data collected by the camera; B h Indicates the signal-to-noise ratio of the first image data collected by the camera; B e Indicates the preset signal-to-noise ratio threshold; α Indicates the shooting quality evaluation compensation coefficient; k Indicates the number of image data collected by the camera; B i Indicates i The signal-to-noise ratio corresponding to the image data; B max Indicates the maximum signal-to-noise ratio of the image data collected by the camera; The alarm judgment module is used to determine that the current data acquisition has an operation quality abnormality and issue a data acquisition quality abnormality alarm when any one of the operation quality evaluation parameters of the sensor group and the shooting quality evaluation parameters of the camera is lower than the corresponding preset quality evaluation parameter threshold.

8. A device for implementing the AI-based environmental monitoring method according to claim 1, characterized in that: The equipment of the environmental monitoring method based on AI recognition includes an environmental monitoring platform, a sensor group and a camera; the environmental monitoring platform is data-connected with the sensor group and the camera via wireless communication.

9. The device of the environment monitoring method based on AI recognition according to claim 8, characterized in that: The sensor group includes a sulfur dioxide sensor, a nitrogen dioxide sensor, a carbon monoxide sensor, an ozone sensor, and a sound sensor for noise monitoring.

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