CN117540229B - Atmospheric environment monitoring method based on clustering algorithm - Google Patents
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Abstract
The invention discloses an atmospheric environment monitoring method based on a clustering algorithm. The atmospheric environment monitoring method based on the clustering algorithm comprises the following steps: dividing an atmospheric environment clustering area; acquiring the atmospheric environment data collected by each monitoring device in each atmospheric environment clustering area, and simultaneously carrying out real-time quality control and pretreatment on the atmospheric environment data; analyzing environmental monitoring index data according to the atmospheric environmental data for each atmospheric environmental clustering area; calculating an environmental quality monitoring comprehensive index according to the environmental index data; and analyzing the environmental abnormal points of the atmospheric environmental clustering area according to the environmental quality monitoring index, and giving out an alarm. According to the invention, the atmospheric environment is monitored in the different regions, and the environmental quality monitoring comprehensive index is comprehensively analyzed according to the environmental monitoring index data obtained by monitoring, so that the atmospheric environment in the appointed region is comprehensively monitored, and the problem that the comprehensive environmental assessment is difficult to provide in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to an atmospheric environment monitoring method based on a clustering algorithm.
Background
The monitoring of the urban atmosphere is mainly managed by the environmental management department, and the pollutants in the atmosphere are measured and analyzed continuously at fixed points. After being stored and analyzed in real time according to the monitoring result, the air quality is reasonably evaluated, and a judgment basis for seeking a pollution source is provided.
For example, bulletin numbers: an atmospheric environment-based monitoring method, system and readable storage medium of CN115326661B patent publication, comprising: different coordinate positions of the preset area are respectively provided with a plurality of front-end monitoring devices; performing cluster analysis by adopting a density clustering algorithm based on the coordinate positions of the plurality of front-end monitoring devices, and calculating to obtain a plurality of cluster areas and corresponding cluster centers; based on each cluster area, selecting front-end monitoring equipment closest to a corresponding cluster center as main monitoring equipment; the front-end monitoring equipment monitors and acquires atmospheric environment data at the respective coordinate positions in real time; based on each clustering area, collecting atmospheric environment data monitored by other front-end monitoring equipment through the main monitoring equipment, collecting the atmospheric environment data, obtaining collected data of each clustering area, and reporting the collected data; and carrying out data analysis processing based on the collected data of each cluster area to obtain an atmospheric environment image.
For example, publication No.: the invention patent of CN110895526A discloses a method for correcting data abnormality in an atmosphere monitoring system, which comprises the following steps: preprocessing the feature data by using a minimum-maximum normalization method, calculating the abnormal probability of the sensor node according to a first-stage abnormal detection algorithm based on information entropy by combining the historical data of the node, executing a second-stage abnormal detection algorithm based on K-means when the abnormal probability of the node is higher than a threshold value, acquiring the feature vector of the node close to the node, clustering the node and the close node, calculating the distance between the feature vector of the node and the clustering center on the clustering result, and judging whether the sensor node has no abnormality.
However, in the process of implementing the technical scheme of the embodiment of the application, the inventor discovers that the above technology has at least the following technical problems:
In the prior art, because the atmospheric environmental conditions in different areas may have large differences, and the environmental conditions are affected by various factors, there is a problem that it is difficult to provide comprehensive environmental assessment.
Disclosure of Invention
The embodiment of the application solves the problem that the prior art is difficult to provide comprehensive environmental assessment by providing the atmospheric environment monitoring method based on the clustering algorithm, and realizes comprehensive monitoring of the atmospheric environment of the designated area.
The embodiment of the application provides an atmospheric environment monitoring method based on a clustering algorithm, which comprises the following steps of: carrying out cluster analysis on the coordinates of each monitoring device through a clustering algorithm in a designated area to be subjected to atmospheric environment monitoring to obtain different atmospheric environment clustering areas; acquiring the atmospheric environment data collected by each monitoring device in each atmospheric environment clustering area, and simultaneously carrying out real-time quality control and pretreatment on the atmospheric environment data; analyzing environmental monitoring index data according to atmospheric environmental data for each atmospheric environmental clustering area, wherein the environmental monitoring index data comprises a temperature and humidity index, an ultraviolet index, an atmospheric pressure index, a pollutant concentration index and a wind speed monitoring index; calculating an environmental quality monitoring comprehensive index according to the environmental index data; and analyzing the environmental abnormal points of the atmospheric environmental clustering area according to the environmental quality monitoring index, and giving out an alarm.
Further, the specific obtaining method of the atmospheric environment clustering area comprises the following steps: obtaining geographic coordinate position data of all monitoring devices in a designated area to be monitored in the atmospheric environment; determining a cluster radius and a density threshold; according to the geographic coordinate position data of each monitoring device, analyzing the coordinate positions of the monitoring devices by using a density clustering algorithm; and marking each cluster as an atmospheric environment area according to the result of the cluster analysis.
Further, the specific analysis method of the environment quality monitoring comprehensive index comprises the following steps: numbering each atmospheric environment clustering area in a designated area to be subjected to atmospheric environment monitoring; acquiring environment monitoring index data corresponding to each atmospheric environment clustering area; constructing an environment quality monitoring comprehensive index model formula; the specific environment quality monitoring comprehensive index model formula is as follows: wherein, ψ is the comprehensive index of environmental quality monitoring, e is a natural constant,/> And/>The method comprises the steps of respectively obtaining a temperature humidity index, an ultraviolet index, an atmospheric pressure index, a pollutant concentration index and an air velocity monitoring index corresponding to a Q-th atmospheric environment clustering area, wherein Q is the number of the atmospheric environment clustering area, q=1, 2, Q is the total number of the atmospheric environment clustering areas, epsilon 1,ε2,ε3,ε4 and epsilon 5 are weight coefficients of the temperature humidity index, the ultraviolet index, the atmospheric pressure index, the pollutant concentration index and the air velocity monitoring index in an environment quality monitoring comprehensive index, and zeta is a correction factor of the environment quality monitoring comprehensive index.
Further, the specific analysis method of the temperature and humidity index comprises the following steps: extracting temperature data and humidity data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the temperature data and the humidity data respectively; constructing a temperature and humidity index model formula; the specific temperature and humidity index model formula is as follows: in the/> I is the number of the temperature data, i=1, 2, & gt, I is the total number of the temperature data,/>, for the I-th temperature data in the q-th atmospheric environment cluster areaFor the jth humidity data in the qth atmospheric environment cluster area, J is the number of the humidity data, j=1, 2,..j, J is the total number of the humidity data, alpha 1 is the weight coefficient corresponding to the temperature value in the temperature humidity index, alpha 2 is the weight coefficient corresponding to the humidity value in the temperature humidity index, χ is the influence factor superposition value of the temperature and the humidity, and δ is the correction factor of the temperature humidity index.
Further, the specific analysis method of the ultraviolet index comprises the following steps: extracting solar radiation values, cloud coverage rate and atmospheric transparency in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the solar radiation values, the cloud coverage rate and the atmospheric transparency respectively; constructing an ultraviolet index model formula; the specific ultraviolet index model formula is as follows: in the/> For the mth solar radiation value in the qth atmospheric environment cluster region, M is the number of solar radiation values, m=1, 2,..m, M is the total number of solar radiation values, β 1 is the corresponding weight coefficient of solar radiation values in the ultraviolet index,/>For the nth cloud coverage in the qth atmospheric environment cluster region, N is the number of the cloud coverage, n=1, 2,..n, N is the total number of the cloud coverage, β 2 is the weight coefficient corresponding to the cloud coverage in the ultraviolet index,/>O is the number of the atmospheric transparency in the q-th atmospheric environment clustering area, o=1, 2,..o, O is the total number of the atmospheric transparency, β 3 is the weight coefficient corresponding to the atmospheric transparency in the ultraviolet index, and γ is the correction coefficient of the ultraviolet index.
Further, the specific analysis method of the atmospheric pressure index comprises the following steps: extracting altitude data and atmospheric pressure data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the altitude data and the atmospheric pressure data respectively; constructing an atmospheric pressure index model formula; the specific barometric pressure index model formula is: In the method, AH q is altitude data corresponding to the q-th atmospheric environment clustering area,/> For standard altitude data, phi 1 is the weight coefficient corresponding to the altitude data in the barometric pressure index, AP q is the barometric pressure data corresponding to the q-th atmospheric environment cluster area,/>As the standard atmospheric pressure data, phi 2 is the weight coefficient corresponding to the atmospheric pressure data in the atmospheric pressure index, and lambda is the correction coefficient of the atmospheric pressure index.
Further, the specific analysis method of the pollutant concentration index comprises the following steps: extracting and numbering a particulate matter concentration index, a sulfur oxide concentration value, a nitrogen oxide concentration value, a carbon monoxide concentration value and a volatile organic compound concentration value in atmospheric environment data corresponding to each atmospheric environment clustering area; constructing a pollutant concentration index model formula; the specific pollutant concentration index model formula is as follows: Wherein Θ q is the particle concentration index corresponding to the q-th atmospheric environment clustering region,/> For the weight coefficient of the particle concentration value corresponding to the pollutant concentration index, SO q、NOq and CO q are respectively the sulfur oxide concentration value, the nitrogen oxide concentration value and the carbon monoxide concentration value corresponding to the q-th atmospheric environment clustering region,/>The VO q is the concentration value of the volatile organic compound corresponding to the q-th atmospheric environment clustering area, and the concentration value of the volatile organic compound is/is the weight superposition coefficient corresponding to the concentration value of the sulfur oxide, the concentration value of the nitrogen oxide and the concentration value of the carbon monoxide in the pollutant concentration indexAnd theta is a correction coefficient of the pollutant concentration index, wherein the theta is a weight coefficient corresponding to the concentration value of the volatile organic compound in the pollutant concentration index.
Further, the specific analysis method of the particulate matter concentration index comprises the following steps: extracting PM2.5 particle concentration values and PM10 particle concentration values in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the PM2.5 particle concentration values and the PM10 particle concentration values respectively; constructing a particulate matter concentration index model formula; the specific particulate matter concentration index model formula is: in the/> For PM2.5 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 1 is a weight coefficient corresponding to the PM2.5 particulate matter concentration value in the particulate matter concentration index, and/(v)For the PM10 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 2 is a weight coefficient corresponding to the PM10 particulate matter concentration value in the particulate matter concentration index, and τ is a correction coefficient of the particulate matter concentration index.
Further, the specific analysis method of the wind speed monitoring index comprises the following steps: extracting the occupied area of an average wind speed building, the occupied area of a plain and the occupied area of a mountain in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the areas respectively; constructing a wind speed monitoring index model formula; the specific wind speed monitoring index model formula is as follows: in the/> For the average wind speed corresponding to the q-th atmospheric environment clustering area, mu 1 is a weight coefficient corresponding to the average wind speed in the wind speed monitoring index, SJR q、SPYq and SMT q are respectively the occupied area of a building, the occupied area of a plains and the occupied area of mountains corresponding to the q-th atmospheric environment clustering area, mu 2、μ3 and mu 4 are weight coefficients corresponding to the occupied area of the building, the occupied area of the plains and the occupied area of mountains in the wind speed monitoring index, and sigma is a correction coefficient of the wind speed monitoring index.
Further, the specific analysis method of the environment abnormal point comprises the following steps: defining an allowable threshold for the environmental monitoring index data, and marking and early warning the environmental monitoring index data after the environmental monitoring index data exceeds the allowable threshold; setting a reference value and a standard deviation of an environmental quality monitoring index in each atmospheric environment clustering area, and marking and early warning the environmental quality monitoring index when the difference value between the environmental quality monitoring index and the reference value is not lower than the standard deviation.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. The environmental quality monitoring comprehensive index is comprehensively analyzed according to the environmental monitoring index data obtained by monitoring, so that a plurality of index data are comprehensively evaluated, the atmospheric environment of a designated area is comprehensively monitored, and the problem that comprehensive environmental evaluation is difficult to provide in the prior art is effectively solved.
2. By performing real-time quality control and preprocessing after the atmospheric environmental data are acquired, the accuracy and reliability of the data are ensured, so that the data noise and error are reduced, the quality of the monitoring data is improved, and the reliability of the environmental monitoring result is improved.
3. Abnormal points in the atmospheric environment clustering area are analyzed through the environment quality monitoring comprehensive index, and an alarm is timely given, so that the environment problem is responded quickly, necessary measures are taken to improve the environment quality, and potential risks and hazards are reduced.
Drawings
Fig. 1 is a flowchart of an atmospheric environment monitoring method based on a clustering algorithm according to an embodiment of the present application.
Detailed Description
The embodiment of the application solves the problem that the prior art is difficult to provide comprehensive environmental assessment by providing the atmospheric environment monitoring method based on the clustering algorithm, and comprehensively monitors the atmospheric environment of a designated area by monitoring the atmospheric environment in regions and comprehensively analyzing the environmental quality monitoring comprehensive index according to the environmental monitoring index data obtained by monitoring.
The technical scheme in the embodiment of the application aims to solve the problem that comprehensive environmental assessment is difficult to provide, and the overall thought is as follows:
The atmospheric environment clustering area is divided; acquiring the atmospheric environment data collected by each monitoring device in each atmospheric environment clustering area, and simultaneously carrying out real-time quality control and pretreatment on the atmospheric environment data; analyzing environmental monitoring index data according to the atmospheric environmental data for each atmospheric environmental clustering area; calculating an environmental quality monitoring comprehensive index according to the environmental index data; environmental abnormal points of the atmospheric environment clustering area are analyzed according to the environmental quality monitoring index, and the atmospheric environment of the designated area is comprehensively monitored.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flowchart of an atmospheric environment monitoring method based on a clustering algorithm according to an embodiment of the present application is provided, and the method includes the following steps: carrying out cluster analysis on the coordinates of each monitoring device through a clustering algorithm in a designated area to be subjected to atmospheric environment monitoring to obtain different atmospheric environment clustering areas; acquiring the atmospheric environment data collected by each monitoring device in each atmospheric environment clustering area, and simultaneously carrying out real-time quality control and pretreatment on the atmospheric environment data; analyzing environmental monitoring index data according to the atmospheric environmental data for each atmospheric environmental clustering area, wherein the environmental monitoring index data comprises a temperature humidity index, an ultraviolet index, an atmospheric pressure index, a pollutant concentration index and a wind speed monitoring index; calculating an environmental quality monitoring comprehensive index according to the environmental index data; and analyzing the environmental abnormal points of the atmospheric environmental clustering area according to the environmental quality monitoring index, and giving out an alarm.
In this embodiment, real-time quality control refers to performing real-time data quality inspection and correction on the atmospheric environment data collected from each monitoring device to ensure accuracy and consistency of the data. It comprises the following aspects: data cleaning: identifying and correcting error values, missing values and abnormal values in the data to avoid interference of inaccurate data on an environment monitoring result; data calibration: correcting the data differences between the different monitoring devices to ensure that their data are comparable, the calibration including adjustments in temperature, humidity, air pressure, etc.; interpolation of data: missing data points are filled in to ensure that no information breaks caused by data missing, and when data at a certain time point or place is missing, the value of the missing point can be estimated or extrapolated from the values of the data points known around the point using interpolation methods. Preprocessing includes steps such as data cleaning, correction, normalization and interpolation to ensure that the monitored data is reliable and accurate in subsequent environmental index calculations and analyses. Through real-time quality control and preprocessing, the quality of data can be improved, and the accuracy and the credibility of environment monitoring are ensured.
Further, the specific acquisition method of the atmospheric environment clustering area comprises the following steps: obtaining geographic coordinate position data of all monitoring devices in a designated area to be monitored in the atmospheric environment; determining a cluster radius and a density threshold; according to the geographic coordinate position data of each monitoring device, analyzing the coordinate positions of the monitoring devices by using a density clustering algorithm; and marking each cluster as an atmospheric environment area according to the result of the cluster analysis.
In this embodiment, the Density clustering algorithm refers to DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and the algorithm can identify clusters by defining the Density of data points in the neighborhood, and can detect noise points at the same time, so that the geographic coordinate position of the atmospheric environment monitoring device is analyzed into different atmospheric environment areas, and cluster analysis is facilitated.
Further, the specific analysis method of the environment quality monitoring comprehensive index comprises the following steps: numbering each atmospheric environment clustering area in a designated area to be subjected to atmospheric environment monitoring; acquiring environment monitoring index data corresponding to each atmospheric environment clustering area; constructing an environment quality monitoring comprehensive index model formula; the specific environment quality monitoring comprehensive index model formula is as follows: wherein, ψ is the comprehensive index of environmental quality monitoring, e is a natural constant,/> And/>The method comprises the steps of respectively obtaining a temperature humidity index, an ultraviolet index, an atmospheric pressure index, a pollutant concentration index and an air velocity monitoring index corresponding to a Q-th atmospheric environment clustering area, wherein Q is the number of the atmospheric environment clustering area, q=1, 2, Q is the total number of the atmospheric environment clustering areas, epsilon 1,ε2,ε3,ε4 and epsilon 5 are weight coefficients of the temperature humidity index, the ultraviolet index, the atmospheric pressure index, the pollutant concentration index and the air velocity monitoring index in an environment quality monitoring comprehensive index, and zeta is a correction factor of the environment quality monitoring comprehensive index.
In this embodiment, the temperature and humidity index, the ultraviolet index, the atmospheric pressure index, the pollutant concentration index and the wind speed monitoring index are introduced into the environmental quality monitoring integrated index model to comprehensively consider a plurality of environmental indexes more comprehensively so as to reflect the overall condition of the atmospheric environment more accurately.
Further, the specific analysis method of the temperature and humidity index comprises the following steps: extracting temperature data and humidity data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the temperature data and the humidity data respectively; constructing a temperature and humidity index model formula; the specific temperature and humidity index model formula is as follows: in the/> I is the number of the temperature data, i=1, 2, & gt, I is the total number of the temperature data,/>, for the I-th temperature data in the q-th atmospheric environment cluster areaFor the jth humidity data in the qth atmospheric environment cluster area, J is the number of the humidity data, j=1, 2,..j, J is the total number of the humidity data, alpha 1 is the weight coefficient corresponding to the temperature value in the temperature humidity index, alpha 2 is the weight coefficient corresponding to the humidity value in the temperature humidity index, χ is the influence factor superposition value of the temperature and the humidity, and δ is the correction factor of the temperature humidity index.
In this embodiment, temperature and humidity are basic parameters in the atmosphere, which affect the comfort and quality of life of the human body. The temperature and humidity index can be used to evaluate the temperature and humidity conditions in the atmosphere to determine the impact of heat and humidity on the environment. Temperature and humidity changes can affect the diffusion and stability of contaminants in the atmosphere. Higher temperatures and humidities may improve air quality, while lower temperatures and humidities may cause contaminants to remain in the atmosphere, affecting air quality. Temperature and humidity are also key parameters for weather prediction. They affect cloud formation, precipitation, dew point and other meteorological conditions. High humidity is often associated with rainfall, while low humidity may lead to drought. In atmospheric environmental monitoring, temperature and humidity are incorporated into a comprehensive assessment model to more fully understand the condition of the atmospheric environment.
Further, the specific analysis method of the ultraviolet index is as follows: extracting solar radiation values, cloud coverage rate and atmospheric transparency in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the solar radiation values, the cloud coverage rate and the atmospheric transparency respectively; constructing an ultraviolet index model formula; the specific ultraviolet index model formula is as follows: in the/> For the mth solar radiation value in the qth atmospheric environment cluster region, M is the number of solar radiation values, m=1, 2,..m, M is the total number of solar radiation values, β 1 is the corresponding weight coefficient of solar radiation values in the ultraviolet index,/>For the nth cloud coverage in the qth atmospheric environment cluster region, N is the number of the cloud coverage, n=1, 2,..n, N is the total number of the cloud coverage, β 2 is the weight coefficient corresponding to the cloud coverage in the ultraviolet index,/>O is the number of the atmospheric transparency in the q-th atmospheric environment clustering area, o=1, 2,..o, O is the total number of the atmospheric transparency, β 3 is the weight coefficient corresponding to the atmospheric transparency in the ultraviolet index, and γ is the correction coefficient of the ultraviolet index.
In this embodiment, the ultraviolet radiation is part of the solar radiation, potentially damaging to the skin and eyes. The ultraviolet index reflects the intensity of ultraviolet radiation in the atmosphere, which is of great importance to both health and the environment, and, based on its provision of information about the level of ultraviolet radiation, helps people take sun protection measures to protect themselves. By considering solar radiation values, cloud coverage, and atmospheric transparency, the ultraviolet index model can more accurately reflect the intensity of ultraviolet radiation, helping to assess the potential risk of ultraviolet to the human body and the environment. A higher solar radiation value means more intense uv radiation, possibly resulting in a higher uv index, thus giving greater potential impact on the human body and the environment. Cloud coverage means the degree of coverage of the cloud layer in the sky. The cloud layer may absorb, scatter, or reflect ultraviolet radiation, thereby reducing the intensity of ultraviolet radiation at the surface. The cloud coverage has a significant impact on the uv index, and higher cloud coverage generally reduces uv radiation, resulting in a lower uv index. Atmospheric transparency means the degree of transparency of the atmosphere to light. The higher transparency atmosphere is better able to transmit ultraviolet radiation.
Further, the specific analysis method of the atmospheric pressure index is as follows: extracting altitude data and atmospheric pressure data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the altitude data and the atmospheric pressure data respectively; constructing an atmospheric pressure index model formula; the specific barometric pressure index model formula is: In the method, AH q is altitude data corresponding to the q-th atmospheric environment clustering area,/> For standard altitude data, phi 1 is the weight coefficient corresponding to the altitude data in the barometric pressure index, AP q is the barometric pressure data corresponding to the q-th atmospheric environment cluster area,/>As the standard atmospheric pressure data, phi 2 is the weight coefficient corresponding to the atmospheric pressure data in the atmospheric pressure index, and lambda is the correction coefficient of the atmospheric pressure index.
In this embodiment, altitude has an important impact on the atmospheric environment, because as altitude increases, barometric pressure and air temperature generally drop while oxygen concentration also decreases. Atmospheric pressure is typically affected by weather systems, such as cyclone and barometric systems. The changing atmospheric pressure can affect weather conditions and the direction of the wind. High air pressure generally means sunny weather, while low air pressure is generally accompanied by rainfall and storm.
Further, the specific analysis method of the pollutant concentration index comprises the following steps: extracting and numbering a particulate matter concentration index, a sulfur oxide concentration value, a nitrogen oxide concentration value, a carbon monoxide concentration value and a volatile organic compound concentration value in atmospheric environment data corresponding to each atmospheric environment clustering area; constructing a pollutant concentration index model formula; the specific pollutant concentration index model formula is as follows: Wherein Θ q is the particle concentration index corresponding to the q-th atmospheric environment clustering region,/> For the weight coefficient of the particle concentration value corresponding to the pollutant concentration index, SO q、NOq and CO q are respectively the sulfur oxide concentration value, the nitrogen oxide concentration value and the carbon monoxide concentration value corresponding to the q-th atmospheric environment clustering region,/>The VO q is the concentration value of the volatile organic compound corresponding to the q-th atmospheric environment clustering area, and the concentration value of the volatile organic compound is/is the weight superposition coefficient corresponding to the concentration value of the sulfur oxide, the concentration value of the nitrogen oxide and the concentration value of the carbon monoxide in the pollutant concentration indexAnd theta is a correction coefficient of the pollutant concentration index, wherein the theta is a weight coefficient corresponding to the concentration value of the volatile organic compound in the pollutant concentration index.
In the present embodiment, the particulate matter generally includes inhalable particulate matter (PM 10) and fine particulate matter (PM 2.5), which have a significant impact on the atmospheric environment and health. High concentrations of particulate matter can reduce air quality and cause harm to the respiratory and cardiovascular systems of the human body. Therefore, the concentration of particulate matter is critical for atmospheric environmental monitoring. Sulfur oxides are a type of pollutant in the atmosphere, typically from a combustion process. High sulfur oxide concentrations can lead to acid rain formation, negatively impact water and soil, and also cause harm to the respiratory system and environment of the human body. Nitrogen oxides are also pollutants in the atmosphere, sources including traffic exhaust and industrial emissions. High concentrations of nitrogen oxides are associated with air pollution and photochemical smog formation, which have adverse effects on the human body and vegetation. Carbon monoxide is a toxic gas mainly from combustion processes such as automobile exhaust. High carbon monoxide concentrations are detrimental to the human respiratory system and health and also affect the quality of the atmosphere. Volatile organic compounds are a diverse group of compounds including solvents and volatile organic compound emissions. They are associated with ozone generation and air pollution, and are a health and environmental hazard. By monitoring and integrating the concentration of these contaminants, the level of contamination in the atmospheric environment can be better understood, providing information about air quality and potential hazards. The pollutant concentration index is helpful for comprehensively evaluating the pollution condition of the atmospheric environment, and provides support for formulating pollution prevention and control policies and improving air quality.
Further, the specific analysis method of the particulate matter concentration index comprises the following steps: extracting PM2.5 particle concentration values and PM10 particle concentration values in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the PM2.5 particle concentration values and the PM10 particle concentration values respectively; constructing a particulate matter concentration index model formula; the specific particulate matter concentration index model formula is: in the/> For PM2.5 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 1 is a weight coefficient corresponding to the PM2.5 particulate matter concentration value in the particulate matter concentration index, and/(v)For the PM10 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 2 is a weight coefficient corresponding to the PM10 particulate matter concentration value in the particulate matter concentration index, and τ is a correction coefficient of the particulate matter concentration index.
In this embodiment, PM2.5 particles are fine particles that can be suspended in air for a longer period of time, with a greater impact on air quality and health. High concentrations of PM2.5 particulate matter can reduce air quality and pose a hazard to the respiratory and cardiovascular systems. PM10 particulate matter, including larger particles, also has a certain impact on air quality and health, especially on the respiratory system. These particulates are one of the major components of atmospheric pollution, with a direct impact on air quality and health. They are often associated with respiratory diseases, cardiovascular problems and other health effects. By monitoring and integrating the concentrations of the particulate matters, the pollution level of the particulate matters in the atmospheric environment can be better known, and support is provided for the quality assessment and environmental protection of the atmospheric environment.
Further, the specific analysis method of the wind speed monitoring index comprises the following steps: extracting the occupied area of an average wind speed building, the occupied area of a plain and the occupied area of a mountain in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the areas respectively; constructing a wind speed monitoring index model formula; the specific wind speed monitoring index model formula is as follows: in the/> For the average wind speed corresponding to the q-th atmospheric environment clustering area, mu 1 is a weight coefficient corresponding to the average wind speed in the wind speed monitoring index, SJR q、SPYq and SMT q are respectively the occupied area of a building, the occupied area of a plains and the occupied area of mountains corresponding to the q-th atmospheric environment clustering area, mu 2、μ3 and mu 4 are weight coefficients corresponding to the occupied area of the building, the occupied area of the plains and the occupied area of mountains in the wind speed monitoring index, and sigma is a correction coefficient of the wind speed monitoring index.
In this embodiment, the average wind speed has an important role in atmospheric environmental monitoring, since wind speed directly affects atmospheric mixing and contaminant diffusion. The higher average wind speed helps to disperse the contaminants and improve air quality. The distribution and density of the building can affect the atmospheric environment of the region, the building can change the flow mode of wind, and the convection of the atmosphere and the diffusion of pollutants are affected. Plain areas typically have flatter terrain and wind speeds may be high, helping air mixing and contaminant diffusion. Mountainous areas are often complex in topography, and wind speeds may be low, with a high likelihood of contaminant retention.
Further, the specific analysis method of the environment abnormal point comprises the following steps: defining an allowable threshold for the environmental monitoring index data, and marking and early warning the environmental monitoring index data after the environmental monitoring index data exceeds the allowable threshold; setting a reference value and a standard deviation of an environmental quality monitoring index in each atmospheric environment clustering area, and marking and early warning the environmental quality monitoring index when the difference value between the environmental quality monitoring index and the reference value is not lower than the standard deviation.
The technical scheme provided by the embodiment of the application at least has the following technical effects or advantages: relative to the bulletin number: according to the embodiment of the application, the atmospheric environment is monitored in regions, and the environmental quality monitoring comprehensive index is comprehensively analyzed according to the environmental monitoring index data obtained by monitoring, so that the atmospheric environment is evaluated by comprehensive temperature and humidity index, ultraviolet index, atmospheric pressure index, pollutant concentration index and wind speed monitoring index, and the comprehensive monitoring of the atmospheric environment in a designated region is realized; relative to publication No.: according to the method for correcting the data abnormality in the atmosphere monitoring system disclosed by the patent of CN110895526A, the embodiment of the application analyzes the abnormal points in the atmospheric environment clustering area through the comprehensive index of environment quality monitoring and timely gives an alarm, so that the environment problem is responded quickly, necessary measures are taken to improve the environment quality, and potential risks and hazards are reduced.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The atmospheric environment monitoring method based on the clustering algorithm is characterized by comprising the following steps of:
carrying out cluster analysis on the coordinates of each monitoring device through a clustering algorithm in a designated area to be subjected to atmospheric environment monitoring to obtain different atmospheric environment clustering areas;
acquiring the atmospheric environment data collected by each monitoring device in each atmospheric environment clustering area, and simultaneously carrying out real-time quality control and pretreatment on the atmospheric environment data;
analyzing environmental monitoring index data according to atmospheric environmental data for each atmospheric environmental clustering area, wherein the environmental monitoring index data comprises a temperature and humidity index, an ultraviolet index, an atmospheric pressure index, a pollutant concentration index and a wind speed monitoring index;
Calculating an environmental quality monitoring comprehensive index according to the environmental index data;
analyzing environmental abnormal points of the atmospheric environmental clustering area according to the environmental quality monitoring index, and giving an alarm;
the specific analysis method of the environment quality monitoring comprehensive index comprises the following steps:
numbering each atmospheric environment clustering area in a designated area to be subjected to atmospheric environment monitoring;
acquiring environment monitoring index data corresponding to each atmospheric environment clustering area;
constructing an environment quality monitoring comprehensive index model formula;
the specific environment quality monitoring comprehensive index model formula is as follows:
wherein, ψ is the comprehensive index of environmental quality monitoring, e is a natural constant, And/>The method comprises the steps that the temperature and humidity index, the ultraviolet index, the atmospheric pressure index, the pollutant concentration index and the wind speed monitoring index corresponding to a Q-th atmospheric environment clustering area are respectively provided, Q is the number of the atmospheric environment clustering area, q=1, 2,..q, Q is the total number of the atmospheric environment clustering areas, epsilon 1,ε2,ε3,ε4 and epsilon 5 are weight coefficients of the temperature and humidity index, the ultraviolet index, the atmospheric pressure index, the pollutant concentration index and the wind speed monitoring index in an environment quality monitoring comprehensive index respectively, and zeta is a correction factor of the environment quality monitoring comprehensive index;
The specific analysis method of the temperature and humidity index comprises the following steps:
Extracting temperature data and humidity data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the temperature data and the humidity data respectively;
constructing a temperature and humidity index model formula;
the specific temperature and humidity index model formula is as follows:
in the method, in the process of the invention, I is the number of the temperature data, i=1, 2, & gt, I is the total number of the temperature data,/>, for the I-th temperature data in the q-th atmospheric environment cluster areaFor the jth humidity data in the qth atmospheric environment cluster area, J is the number of the humidity data, j=1, 2,..j, J is the total number of the humidity data, alpha 1 is the weight coefficient corresponding to the temperature value in the temperature humidity index, alpha 2 is the weight coefficient corresponding to the humidity value in the temperature humidity index, χ is the influence factor superposition value of the temperature and the humidity, and δ is the correction factor of the temperature humidity index.
2. The atmospheric environment monitoring method based on the clustering algorithm as claimed in claim 1, wherein the specific obtaining method of the atmospheric environment clustering area is as follows:
Obtaining geographic coordinate position data of all monitoring devices in a designated area to be monitored in the atmospheric environment;
Determining a cluster radius and a density threshold;
According to the geographic coordinate position data of each monitoring device, analyzing the coordinate positions of the monitoring devices by using a density clustering algorithm;
and marking each cluster as an atmospheric environment area according to the result of the cluster analysis.
3. The atmospheric environment monitoring method based on the clustering algorithm as claimed in claim 2, wherein the specific analysis method of the ultraviolet index is as follows:
extracting solar radiation values, cloud coverage rate and atmospheric transparency in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the solar radiation values, the cloud coverage rate and the atmospheric transparency respectively;
constructing an ultraviolet index model formula;
the specific ultraviolet index model formula is as follows:
in the method, in the process of the invention, For the mth solar radiation value in the qth atmospheric environment cluster region, M is the number of solar radiation values, m=1, 2,..m, M is the total number of solar radiation values, β 1 is the corresponding weight coefficient of the solar radiation value in the ultraviolet index,For the nth cloud coverage in the qth atmospheric environment cluster region, N is the number of the cloud coverage, n=1, 2,..n, N is the total number of the cloud coverage, β 2 is the weight coefficient corresponding to the cloud coverage in the ultraviolet index,/>O is the number of the atmospheric transparency in the q-th atmospheric environment clustering area, o=1, 2,..o, O is the total number of the atmospheric transparency, β 3 is the weight coefficient corresponding to the atmospheric transparency in the ultraviolet index, and γ is the correction coefficient of the ultraviolet index.
4. The atmospheric environment monitoring method based on the clustering algorithm as claimed in claim 3, wherein the specific analysis method of the atmospheric pressure index is as follows:
extracting altitude data and atmospheric pressure data in atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the altitude data and the atmospheric pressure data respectively;
Constructing an atmospheric pressure index model formula;
The specific barometric pressure index model formula is:
Wherein AH q is the elevation data corresponding to the q-th atmospheric environment clustering area, For standard altitude data, phi 1 is the weight coefficient corresponding to the altitude data in the barometric pressure index, AP q is the barometric pressure data corresponding to the q-th atmospheric environment cluster area,/>As the standard atmospheric pressure data, phi 2 is the weight coefficient corresponding to the atmospheric pressure data in the atmospheric pressure index, and lambda is the correction coefficient of the atmospheric pressure index.
5. The atmospheric environment monitoring method based on the clustering algorithm as set forth in claim 4, wherein the specific analysis method of the pollutant concentration index is as follows:
Extracting and numbering a particulate matter concentration index, a sulfur oxide concentration value, a nitrogen oxide concentration value, a carbon monoxide concentration value and a volatile organic compound concentration value in atmospheric environment data corresponding to each atmospheric environment clustering area;
Constructing a pollutant concentration index model formula;
the specific pollutant concentration index model formula is as follows:
Wherein Θ q is the particle concentration index corresponding to the q-th atmospheric environment clustering area, For the weight coefficient of the particle concentration value corresponding to the pollutant concentration index, SO q、NOq and CO q are respectively the sulfur oxide concentration value, the nitrogen oxide concentration value and the carbon monoxide concentration value corresponding to the q-th atmospheric environment clustering region,/>The VO q is the concentration value of the volatile organic compound corresponding to the q-th atmospheric environment clustering area, and the concentration value of the volatile organic compound is/is the weight superposition coefficient corresponding to the concentration value of the sulfur oxide, the concentration value of the nitrogen oxide and the concentration value of the carbon monoxide in the pollutant concentration indexAnd theta is a correction coefficient of the pollutant concentration index, wherein the theta is a weight coefficient corresponding to the concentration value of the volatile organic compound in the pollutant concentration index.
6. The atmospheric environment monitoring method based on the clustering algorithm as set forth in claim 5, wherein the specific analysis method of the particulate matter concentration index is as follows:
extracting PM2.5 particle concentration values and PM10 particle concentration values in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the PM2.5 particle concentration values and the PM10 particle concentration values respectively;
constructing a particulate matter concentration index model formula;
The specific particulate matter concentration index model formula is:
in the method, in the process of the invention, For PM2.5 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 1 is a weight coefficient corresponding to the PM2.5 particulate matter concentration value in the particulate matter concentration index, and/(v)For the PM10 particulate matter concentration value corresponding to the q-th atmospheric environment clustering region, η 2 is a weight coefficient corresponding to the PM10 particulate matter concentration value in the particulate matter concentration index, and τ is a correction coefficient of the particulate matter concentration index.
7. The atmospheric environment monitoring method based on the clustering algorithm as claimed in claim 6, wherein the specific analysis method of the wind speed monitoring index is as follows:
Extracting the occupied area of an average wind speed building, the occupied area of a plain and the occupied area of a mountain in the atmospheric environment data corresponding to each atmospheric environment clustering area, and numbering the areas respectively;
Constructing a wind speed monitoring index model formula;
the specific wind speed monitoring index model formula is as follows:
in the method, in the process of the invention, For the average wind speed corresponding to the q-th atmospheric environment clustering area, mu 1 is a weight coefficient corresponding to the average wind speed in the wind speed monitoring index, SJR q、SPYq and SMT q are respectively the occupied area of a building, the occupied area of a plains and the occupied area of mountains corresponding to the q-th atmospheric environment clustering area, mu 2、μ3 and mu 4 are weight coefficients corresponding to the occupied area of the building, the occupied area of the plains and the occupied area of mountains in the wind speed monitoring index, and sigma is a correction coefficient of the wind speed monitoring index.
8. The atmospheric environment monitoring method based on the clustering algorithm as claimed in claim 1, wherein the specific analysis method of the environment abnormal points is as follows:
Defining an allowable threshold for the environmental monitoring index data, and marking and early warning the environmental monitoring index data after the environmental monitoring index data exceeds the allowable threshold;
setting a reference value and a standard deviation of an environmental quality monitoring index in each atmospheric environment clustering area, and marking and early warning the environmental quality monitoring index when the difference value between the environmental quality monitoring index and the reference value is not lower than the standard deviation.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003281671A (en) * | 2002-03-20 | 2003-10-03 | Nippon Telegr & Teleph Corp <Ntt> | Multi-sensing atmospheric environment monitoring system device and monitoring method |
| CN112232251A (en) * | 2020-10-24 | 2021-01-15 | 兰州旭阳祥辉科技有限公司 | Internet-based environment monitoring method, system, storage medium and equipment |
| CN113804829A (en) * | 2021-08-20 | 2021-12-17 | 重庆市生态环境监测中心 | Atmospheric pollution space-air-ground integrated real-time monitoring system and method |
| CN115326661A (en) * | 2022-10-12 | 2022-11-11 | 深圳市源清环境技术服务有限公司 | Atmospheric environment-based monitoring method and system and readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110410931B (en) * | 2019-08-06 | 2020-04-28 | 重庆大学 | Indoor air environment quality coordination control system and method based on multi-parameter evaluation |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003281671A (en) * | 2002-03-20 | 2003-10-03 | Nippon Telegr & Teleph Corp <Ntt> | Multi-sensing atmospheric environment monitoring system device and monitoring method |
| CN112232251A (en) * | 2020-10-24 | 2021-01-15 | 兰州旭阳祥辉科技有限公司 | Internet-based environment monitoring method, system, storage medium and equipment |
| CN113804829A (en) * | 2021-08-20 | 2021-12-17 | 重庆市生态环境监测中心 | Atmospheric pollution space-air-ground integrated real-time monitoring system and method |
| CN115326661A (en) * | 2022-10-12 | 2022-11-11 | 深圳市源清环境技术服务有限公司 | Atmospheric environment-based monitoring method and system and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于开源物联网平台的环境监测系统设计;陈凯;谭宋;朱恒;;电子技术与软件工程;20180810(15);第47-48页 * |
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