CN112880751B - Air pollution condition obtaining method based on navigation monitoring - Google Patents
Air pollution condition obtaining method based on navigation monitoring Download PDFInfo
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Abstract
The application provides an air pollution condition obtaining method based on navigation monitoring, which comprises the following steps: acquiring historical meteorological data and historical pollutant data, wherein the historical meteorological data and the historical pollution data are obtained by monitoring through a navigation monitoring system; determining the sub-index of meteorological factors influencing the target pollutant in each factor interval according to the historical meteorological data and the historical pollutant data; acquiring live meteorological data corresponding to meteorological factors through a navigation monitoring system; and calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factors in each factor interval. According to the method and the device, the comprehensive pollution diagnosis index corresponding to the target pollutant is objectively and quantitatively calculated according to the historical meteorological data and the historical pollutant data, calculation can be performed on different pollutants, and the selection of meteorological factors and the weight determination of different meteorological factors in the whole calculation process do not depend on personal experience or literature research any more, so that the method and the device are more objective and accurate.
Description
The present application claims priority from a chinese patent application entitled "air monitor system" filed by the chinese intellectual property office at 03/04/2020, application number 202010260791.7, the entire contents of which are incorporated herein by reference.
Technical Field
The application belongs to the technical field of environmental monitoring, and particularly relates to an air pollution condition acquisition method based on navigation monitoring.
Background
Currently, the most common comprehensive diagnostic index in environmental weather forecasting practice is usually a calm weather index, which is calculated by superposition of relevant weather conditions.
In the related art, meteorological factors related to the formation of calm weather are usually screened according to the experience of forecasters or on the basis of statistical analysis of related data and combined with literature investigation. And determining the threshold value of each meteorological factor according to the effect and the physical significance of each meteorological factor in the calm weather. And determining the weight of each meteorological factor according to the size of the forming or continuous action of each meteorological factor on the calm weather in the corresponding threshold value range. And searching all meteorological factors falling within the threshold range, summing the weights of the meteorological factors, and finally obtaining the value of the calm weather index.
However, the application of the calm weather index usually takes cities as units, the locality of the calculation method is strong, and the types of the meteorological factors selected to participate in the index calculation and the weights of the meteorological factors are greatly different among different cities. And the selection of weather factors and the giving of weights depend to a large extent on the personal experience of the forecaster. Except for cities which have been studied for quiet weather indexes in the past, other areas require a large amount of meteorological observation data statistics. And the calm weather index does not aim at a certain specific pollutant, but describes a wide range of atmospheric pollution degrees, and has obvious limitation when being applied to refined atmospheric pollution treatment work.
Disclosure of Invention
The application provides an air pollution condition obtaining method based on navigation monitoring, a comprehensive pollution diagnosis index corresponding to a target pollutant is objectively and quantitatively calculated according to historical meteorological data and historical pollutant data, calculation can be performed on different pollutants, the whole calculation process does not depend on personal experience or literature research, and the method is more objective and accurate.
An embodiment of a first aspect of the present application provides an air pollution condition obtaining method based on air traffic monitoring, including:
acquiring historical meteorological data and historical pollutant data, wherein the historical meteorological data and the historical pollutant data are obtained by monitoring through a navigation monitoring system;
determining the sub-index of meteorological factors influencing the target pollutant in each factor interval according to the historical meteorological data and the historical pollutant data;
acquiring live meteorological data corresponding to the meteorological factors through the navigation monitoring system;
and calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval.
In some embodiments of the present application, said determining a fractional index of meteorological factors affecting a target pollutant within each factor interval based on said historical meteorological data and said historical pollutant data comprises:
counting the total polluted weather and the total non-polluted weather caused by the target pollutants according to the historical pollutant data;
according to the historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor are respectively divided;
and determining the sub-index of the meteorological factor influencing the target pollutant in each factor interval according to each factor interval corresponding to each meteorological factor, the total polluted weather caused by the target pollutant and the total non-polluted weather.
In some embodiments of the present application, said dividing, according to the historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor respectively includes:
acquiring all observed values of a first meteorological factor from the historical meteorological data, wherein the first meteorological factor is any meteorological factor included in the historical meteorological data;
sequencing all the observed values according to a preset sequence;
deleting the observation value of the preset proportion arranged at the front and deleting the observation value of the preset proportion arranged at the back;
and uniformly dividing the rest observed values into a first preset number of intervals to obtain the first preset number of factor intervals corresponding to the first meteorological factor.
In some embodiments of the present application, said determining the fractional index of the meteorological factor affecting the target pollutant within each factor interval according to each factor interval corresponding to each meteorological factor, the total number of polluted weather caused by the target pollutant, and the total number of non-polluted weather, comprises:
respectively determining the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor according to the total number of polluted weather and the total number of non-polluted weather caused by the target pollutants;
respectively calculating the sub-index of each meteorological factor in each factor interval according to the total polluted weather, the total non-polluted weather and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor;
and determining the sub-index of the meteorological factor affecting the target pollutant in each factor interval according to the sub-index of each meteorological factor in each factor interval.
In some embodiments of the present application, the calculating the sub-index of each weather factor in each factor interval according to the total number of the polluted weather, the total number of the non-polluted weather, and the number of the polluted weather samples and the number of the non-polluted weather samples in each factor interval corresponding to each weather factor respectively includes:
calculating the sub-index of each meteorological factor in each factor interval through a formula (1) according to the total polluted weather, the total non-polluted weather and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor;
in the formula (1), KinIs the fractional index of meteorological factor i in the nth factor interval, ainThe number of polluted weather samples in a factor interval n corresponding to the meteorological factor i, binThe number of the non-polluted weather samples in the factor interval n corresponding to the meteorological factor i is shown, wherein a is the total number of the polluted weather, and b is the total number of the non-polluted weather.
In some embodiments of the present application, determining the fractional index of the meteorological factor affecting the target pollutant in each factor interval according to the fractional index of each meteorological factor in each factor interval includes:
respectively calculating the difference between the maximum index and the minimum index corresponding to each meteorological factor;
carrying out correlation significance test on each meteorological factor to determine an autocorrelation factor in each meteorological factor;
the meteorological factor with the maximum index in the autocorrelation factors is reserved, and other meteorological factors except the meteorological factor with the maximum index in the autocorrelation factors are removed;
selecting a second preset number of meteorological factors with the largest difference from all the remaining meteorological factors;
and determining the sub-index of each meteorological factor in each factor interval as the sub-index of the meteorological factor affecting the target pollutant in each factor interval.
In some embodiments of the present application, the calculating a pollution comprehensive diagnostic index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval includes:
obtaining observation values of each meteorological factor affecting the target pollutant from the live meteorological data respectively;
respectively determining factor intervals to which the observed values of the meteorological factors belong;
calculating the sum of the partial indexes corresponding to the factor interval to which the observation value of each meteorological factor belongs;
and determining the calculated sum value as a pollution comprehensive diagnosis index corresponding to the target pollutant.
In some embodiments of the present application, the method further comprises:
detecting atmospheric volatile organic pollutants by a mass spectrometer comprised by the navigational monitoring system;
detecting particulate matter in the atmosphere through a laser radar included in the navigation monitoring system;
detecting the concentration of the polluted gas in the atmosphere by a boundary layer atmospheric composition hyperspectral scanning and analyzer AHSA included in the navigation monitoring system;
detecting wind speed and wind direction through a wind speed and wind direction sensor included in the navigation monitoring system;
and detecting the temperature and the humidity through an air temperature and humidity sensor included in the navigation monitoring system.
An embodiment of the second aspect of the present application provides an air pollution condition acquisition device based on air traffic monitoring, including:
the historical data acquisition module is used for acquiring historical meteorological data and historical pollutant data, and the historical meteorological data and the historical pollutant data are obtained by monitoring through a navigation monitoring system;
the determining module is used for determining the sub-index of the meteorological factor influencing the target pollutant in each factor interval according to the historical meteorological data and the historical pollutant data;
the live data acquisition module is used for acquiring live meteorological data corresponding to the meteorological factors through the navigation monitoring system;
and the calculation module is used for calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval.
Embodiments of the third aspect of the present application provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
in the embodiment of the application, each meteorological factor influencing a target pollutant and the sub-index of each meteorological factor in each factor interval are calculated through historical meteorological data and historical pollutant data which are obtained by monitoring the navigation monitoring system. And acquiring live meteorological data of meteorological factors during actual prediction, and objectively and quantitatively calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of each meteorological factor influencing the target pollutant in each factor interval. The calculation of the pollution comprehensive diagnosis index can be performed aiming at different pollutants, and the selection of the meteorological factors and the weight determination of different meteorological factors in the whole calculation process do not depend on personal experience or literature investigation any more, so that the calculation is more objective and accurate.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating an air pollution condition acquisition method based on air pollution monitoring according to an embodiment of the present application;
FIG. 2 is a block diagram of a navigation monitoring system according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating the arrangement of the devices on the navigation monitoring vehicle according to an embodiment of the present application;
FIG. 4 illustrates an air condition diagram displayed on a vehicle computer as provided by an embodiment of the present application;
fig. 5 shows another flowchart of an air pollution condition acquisition method based on air pollution monitoring according to an embodiment of the present application;
fig. 6 is a schematic structural diagram illustrating an air pollution condition acquisition device based on air pollution monitoring according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 8 is a schematic diagram of a storage medium provided in an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
An air pollution condition acquisition method based on air pollution monitoring according to an embodiment of the present application is described below with reference to the accompanying drawings.
The embodiment of the application provides an air pollution condition obtaining method based on navigation monitoring, the method provides a scheme for objectively and quantitatively calculating a meteorological pollution comprehensive diagnosis index, the scheme can quantitatively calculate the meteorological pollution comprehensive diagnosis index of air pollution caused by different pollutants through meteorological data aiming at different pollutant types, and the selection of meteorological factors and the weight determination of different meteorological factors in the whole calculation process do not depend on personal experience or literature research any more, so that the method is more objective and accurate.
Referring to fig. 1, the method specifically includes the following steps:
step 101: historical meteorological data and historical pollutant data are obtained, and the historical meteorological data and the historical pollution data are obtained through monitoring of the navigation monitoring system.
In the embodiment of the application, meteorological data and pollutant data can be obtained by monitoring through the navigation monitoring system. As shown in fig. 2, the navigation monitoring system includes: the system comprises a navigation monitoring vehicle, a vehicle-mounted power supply system, a vehicle-mounted computer, a positioning system, a mass spectrometer, a laser radar, a wind speed and wind direction sensor, an air temperature and humidity sensor and an AHSA (boundary layer atmospheric composition hyperspectral scanning and analysis meter), wherein the positioning system, the mass spectrometer, the laser radar, the wind speed and wind direction sensor, the air temperature and humidity sensor and the AHSA are respectively connected with the vehicle-mounted computer. The vehicle-mounted power supply system, the positioning system, the mass spectrometer, the laser radar, the wind speed and wind direction sensor, the air temperature and humidity sensor, the vehicle-mounted computer and the AHSA are arranged on the navigation monitoring vehicle. The vehicle-mounted power supply system is respectively electrically connected with the positioning system, the mass spectrometer, the laser radar, the wind speed and wind direction sensor, the air temperature and humidity sensor, the vehicle-mounted computer and the AHSA and used for supplying power to the positioning system, the mass spectrometer, the laser radar, the wind speed and wind direction sensor, the air temperature and humidity sensor, the vehicle-mounted computer and the AHSA.
The navigation monitoring system moves through a driving navigation monitoring vehicle, fixed-point monitoring and navigation monitoring are carried out through instruments such as a vehicle-mounted laser radar, a wind speed and wind direction sensor, an air temperature and humidity sensor and an AHSA, the laser radar can detect the condition of particles in the atmosphere, and the AHSA can detect the concentrations of pollutants such as NO2, SO2 and HCHO. The wind speed and direction sensor is used for detecting wind speed and wind direction, and the air temperature and humidity sensor is used for detecting air temperature and humidity. The positioning system is used for positioning the position information of the navigation monitoring vehicle in real time. The mass spectrometer is used for detecting the volatile organic pollutants in the atmosphere. The laser radar, the wind speed and wind direction sensor, the air temperature and humidity sensor, the mass spectrometer, the AHSA and the positioning system transmit the detected data to the vehicle-mounted computer.
The vehicle-mounted computer combines data collected by a laser radar, a wind speed and wind direction sensor, an air temperature and humidity sensor, an AHSA, a mass spectrometer, a positioning system and the like, summarizes and displays the data according to detection time, and superposes the data (including information data such as a GIS map, wind speed, wind direction, temperature, humidity, atmospheric volatile organic pollutants, monitoring station position and the like) to display a path of sailing and pollutant conditions on the path. For each piece of data collected by the AHSA, the vehicle-mounted computer carries out inversion, screening, derivation and plotting operations on the data, simultaneously superposes information such as a map, wind speed, wind direction, temperature, humidity, atmospheric volatile organic pollutants, monitoring station point positions and the like in real time, and displays a navigation path and the condition of the pollutants on the path.
In some embodiments of the present application, the cruise monitoring vehicle may use the model of the IVECO M1-30 as a vehicle for mounting various detection devices. As shown in fig. 3, the navigation monitoring vehicle is divided into a driving area, a test area and an equipment area, the test area and the driving area are separated by a partition wall, and a partition window is arranged on the partition wall. The mass spectrometer may be a PTR-TOF mass spectrometer, such as a Vocus Elf PTR-TOF sprite. The laser radar may be aerosol laser radar. Damping and fixing four instruments including the Vocus Elf PTR-TOF sprite, the AHSA, the aerosol laser radar and the vehicle-mounted computer, wherein a wind speed and wind direction sensor, an air temperature and humidity sensor and the AHSA are installed on the roof, and a positioning system, the Vocus Elf PTR-TOF sprite, the vehicle-mounted computer and the aerosol laser radar are installed in an equipment area in the vehicle. And modifying a vehicle circuit according to the instrument power, and configuring a vehicle-mounted power supply system. The vehicle-mounted Power supply System comprises a UPS (uninterrupted Power supply), namely an Uninterruptible Power System (UPS System).
The Vocus Elf PTR-TOF Elf, AHSA, aerosol laser radar and vehicle-mounted computer can collect and store data by themselves, and the navigation monitoring vehicle provided with the instruments can simultaneously meet VOCs monitoring, particulate matter monitoring, NO2, SO2 and HCHO monitoring, and can also record meteorological parameters such as wind speed, wind direction, temperature, humidity, atmospheric volatile organic pollutants, air pressure and the like.
Data acquired by the AHSA is a column of light intensity information, the vehicle-mounted computer inverts the light intensity information by using a differential absorption spectroscopy method to obtain the whole-layer column concentration information of NO2, SO2 and HCHO, screens the inverted data, eliminates abnormal values (null values), and then generates a histogram by using a MATLAB script. The process of generating the histogram comprises the following steps: firstly, a longitude and latitude range is set, namely the longitude and latitude range of an area passed by the current navigation, such as the longitude range [ 36.005, 36.065 ] and the latitude range [ 111.50, 111.575 ]. And then reading column concentration information from the inverted data, identifying by using different colors, wherein the darker the specified color (for example, yellow and red can be used) represents the higher the concentration value, and simultaneously displaying the maximum value, the minimum value and the average value of the column concentration in the data observation time period. Fig. 4 is a diagram showing an air pollution state on the vehicle computer in one embodiment.
In another embodiment, the navigation monitoring system further comprises an ozone detection device and a data server, wherein the ozone detection device is connected with the vehicle-mounted computer and is used for detecting the ozone concentration of the atmosphere. The ozone detection device is preferably an ozone lidar. The data server is connected with the vehicle-mounted computer in a two-way communication mode and used for storing preset data for the vehicle-mounted computer to call and receiving data transmitted by the vehicle-mounted computer. The data server adopts a RAID disk array, the vehicle-mounted computer calls preset data in the data server, the preset data is compared with the received data transmitted by the mass spectrometer, the laser radar, the AHSA and other equipment, and the position of the pollution source is positioned according to a position signal generated by the positioning system. In addition, in other embodiments, the navigational monitoring system may further comprise a carbon dioxide concentration sensor coupled to the onboard computer for detecting the concentration of carbon dioxide in the air.
The integrated AHSA that has of monitoring system that walks to navigate of this application embodiment, other monitoring instrument such as collocation aerosol radar, it is more comprehensive to atmospheric environment state's monitoring effect, information data such as stack map, wind speed, wind direction, temperature, humidity, monitoring station point position combine GIS geographic information show the route of navigating and the concentration distribution condition of pollutant on the route of navigating, can discover as early as possible and trace to the source pollution source.
The embodiment of the application monitors meteorological data and pollutant data through the navigation monitoring system, and can monitor once per hour, and can also monitor for specific hours every day, such as monitoring at 02, 08, 24 and 20 every day.
In the step, meteorological data and pollutant data monitored by the navigation monitoring system in the past preset time period are obtained, and the obtained meteorological data and the obtained pollutant data are respectively called historical meteorological data and historical pollutant data. The preset time period may be one year or two years.
Step 102: and determining the sub-index of the meteorological factors influencing the target pollutant in each factor interval according to the historical meteorological data and the historical pollutant data.
The embodiment of the application specifically determines the sub-index of the meteorological factor affecting the target pollutant in each factor interval through the following operations of steps S1-S3, including:
s1: and counting the total polluted weather and the total non-polluted weather caused by the target pollutants according to the historical pollutant data.
The historical pollutant data comprises pollutant concentrations of PM2.5, O3, NO2, SO2, HCHO and the like per hour monitored by the navigation monitoring system within a preset time period. The target pollutant can be any pollutant of PM2.5, O3, NO2, SO2, HCHO and the like. And respectively determining the number of polluted weather formed by the target pollutants in the preset time period corresponding to the historical pollutants according to the pollutant concentration corresponding to the target pollutants every day in the historical pollutant data, so as to obtain the total number of the polluted weather caused by the target pollutants. And subtracting the total number of the polluted weather from the total days included in the preset time period to obtain the total number of the non-polluted weather.
For example, assuming that the historical pollutant data is pollutant data in 2020 and the target pollutant is PM2.5, the total days in which the concentration of PM2.5 in 2020 is greater than 75ug/m3 are determined according to the concentration of PM2.5 in the historical pollutant data every day, and the total polluted weather number corresponding to PM2.5 is obtained. And subtracting the total polluted weather number by using the total days of 2020 to obtain the total non-polluted weather number corresponding to PM 2.5.
S2: according to historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor are respectively divided.
The historical meteorological data comprises meteorological parameters such as wind speed, wind direction, temperature, humidity and air pressure at a specific moment every hour or every day, which are monitored by the navigation monitoring system within a preset time period. The meteorological factors involved in the historical meteorological data include ground elements and overhead elements, and the ground elements may include 24-hour temperature change (deg.C), 24-hour pressure change (hPa), 2m relative humidity (%), sea level air pressure (hPa), 10m horizontal wind speed (m/s), 10m wind direction (deg.C), and the like. The height elements can be selected from 1000/925/850/700/500hPa and other heights, including relative humidity (%), horizontal wind component U, V (m/s), horizontal wind speed (m/s), vertical speed (Pa/s), divergence (s-1), mixed layer height (boundary layer height) and the like.
The operation of dividing the factor interval by each meteorological factor is the same, and the dividing mode of the factor interval is described in detail only by taking the first meteorological factor as an example in the step, wherein the first meteorological factor is any meteorological factor included in historical meteorological data.
Specifically, all observations of the first meteorological factor are obtained from historical meteorological data. And sequencing all the observed values according to a preset sequence, wherein the preset sequence can be from small to large or from large to small. And deleting the observation value of the preset proportion arranged at the forefront and deleting the observation value of the preset proportion arranged at the rearmost. The preset ratio may be 5% or 6%, etc. And uniformly dividing the rest observed values into a first preset number of intervals to obtain a first preset number of factor intervals corresponding to the first meteorological factor, wherein the number of the observed values of the first meteorological factor falling into each factor interval is basically the same. The first predetermined number may be 10 or 15, etc.
S3: and determining the sub-index of the meteorological factors influencing the target pollutants in each factor interval according to each factor interval corresponding to each meteorological factor, the total polluted weather caused by the target pollutants and the total non-polluted weather.
And respectively determining the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor according to the total number of polluted weather and the total number of non-polluted weather caused by the target pollutants. Specifically, it is determined which dates are polluted weather caused by the target pollutant and which dates are non-polluted weather in the preset time period corresponding to the historical pollutant data, monitoring dates corresponding to each observation value of the meteorological factor included in each factor interval are counted, if the monitoring date is the same as the date of a certain polluted weather, the number of polluted weather samples corresponding to the factor interval is increased by one, and if the monitoring date is the same as the date of a certain non-polluted weather, the number of non-polluted weather samples corresponding to the factor interval is increased by one. And counting the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor by the mode.
And then respectively calculating the sub-index of each meteorological factor in each factor interval by a formula (1) according to the total polluted weather, the total non-polluted weather and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor.
In the formula (1), KinIs the fractional index of meteorological factor i in the nth factor interval, ainThe number of polluted weather samples in a factor interval n corresponding to the meteorological factor i, binThe number of the non-polluted weather samples in the factor interval n corresponding to the meteorological factor i is shown, wherein a is the total number of the polluted weather, and b is the total number of the non-polluted weather.
After the sub-index of each factor interval corresponding to each meteorological factor related in the historical meteorological data is calculated through the formula (1), the sub-index of the meteorological factor affecting the target pollutant in each factor interval is determined according to the sub-index of each meteorological factor in each factor interval.
Specifically, the difference between the maximum and minimum index scores corresponding to each meteorological factor is calculated respectively. And sequencing each meteorological factor according to the sequence of the difference values from large to small, carrying out correlation significance test on each meteorological factor, and determining the autocorrelation factor in each meteorological factor. The autocorrelation factors may include two or more strongly correlated meteorological factors. And (4) retaining the meteorological factor with the maximum index in the autocorrelation factor, and removing other meteorological factors except the meteorological factor with the maximum index in the autocorrelation factor. For example, assuming that 850hPa rh is ranked at the 2 nd position and 700hPa rh is ranked at the 3 rd position according to the difference between the maximum and minimum indices, the significant correlation between 850hPa and 700hPa rh is determined by the correlation significance test, i.e., it is determined that 850hPa and 700hPa rh belong to the autocorrelation factor, so that the meteorological factor of 700hPa rh ranked at the next position is deleted.
And selecting a second preset number of meteorological factors with the largest difference from all the remaining meteorological factors. And determining the sub-index of each meteorological factor in each factor interval as the sub-index of the meteorological factor affecting the target pollutant in each factor interval. The second predetermined number may be 10 or 15, etc.
According to the method, the second preset number of meteorological factors which have the largest influence on the target pollutants are determined, and the sub-indexes of the second preset number of meteorological factors in each factor interval corresponding to the meteorological factors are determined. And storing the target pollutant, each determined meteorological factor, each factor interval corresponding to each meteorological factor and the mapping relation of the sub-index of each meteorological factor in each factor area.
After determining the fractional indices of the meteorological factors affecting the target pollutant within each factor interval in the above manner, the fractional indices of the meteorological factors within each factor interval can be used to predict the degree of the target pollutant's effect on the air pollution through the following operations of steps 103 and 104.
Step 103: and acquiring live meteorological data corresponding to the meteorological factors through the navigation monitoring system.
And acquiring meteorological data of a time period to be predicted through the navigation monitoring system. And acquiring the live meteorological data corresponding to each meteorological factor influencing the target pollutant from the acquired meteorological data, namely acquiring the actual observation value of each meteorological factor influencing the target pollutant in the period to be predicted.
Step 104: and calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factors in each factor interval.
Observations of each meteorological factor affecting a target pollutant are separately obtained from live meteorological data. Step 102 stores the target pollutant, each meteorological factor affecting the target pollutant, each factor interval corresponding to each meteorological factor, and the mapping relation of the sub-index of each meteorological factor in each factor area. And respectively determining the factor interval to which the observed value of each meteorological factor belongs according to each factor interval corresponding to each meteorological factor included in the mapping relation. And acquiring the sub-index corresponding to each determined factor interval from the mapping relation, calculating the sum of the sub-indexes corresponding to the factor interval to which the observation value of each meteorological factor belongs, and determining the calculated sum as the comprehensive pollution diagnosis index corresponding to the target pollutant. The pollution integrated diagnostic index is used to indicate the probability of air pollution due to the target pollutant.
In order to facilitate understanding of the methods provided by the embodiments of the present application, reference is made to the following description taken in conjunction with the accompanying drawings. As shown in fig. 5, N weather factors to be selected in the historical data of a certain place are input, each weather factor is equally divided into 10 intervals to obtain N × 10 weather factors of each interval, then the polluted weather and the non-polluted weather are divided according to the pollutant concentration, the number of samples of each interval of each weather factor falling on the polluted weather and the non-polluted weather is counted, the index of each interval of the weather factors is calculated, the interval indexes are sorted according to the difference between the maximum value and the minimum value of each interval index, the autocorrelation factors with the back ranking are removed, the weather factors with the top 10 bits are sorted again according to the sequence from the maximum value to the minimum value of the index, and the selected weather factor types, interval critical values and the corresponding index are recorded. And finally, inputting live meteorological factors and calculating a comprehensive diagnosis index.
In the embodiment of the application, each meteorological factor influencing a target pollutant and the sub-index of each meteorological factor in each factor interval are calculated through historical meteorological data and historical pollutant data which are obtained by monitoring the navigation monitoring system. And acquiring live meteorological data of meteorological factors during actual prediction, and objectively and quantitatively calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of each meteorological factor influencing the target pollutant in each factor interval. The calculation of the pollution comprehensive diagnosis index can be performed aiming at different pollutants, and the selection of the meteorological factors and the weight determination of different meteorological factors in the whole calculation process do not depend on personal experience or literature investigation any more, so that the calculation is more objective and accurate.
The embodiment of the application further provides an air pollution condition acquisition device based on the air traffic monitoring, and the device is used for executing the air pollution condition acquisition method based on the air traffic monitoring provided by any one of the embodiments. Referring to fig. 6, the apparatus includes:
the historical data acquisition module 601 is used for acquiring historical meteorological data and historical pollutant data, wherein the historical meteorological data and the historical pollution data are obtained by monitoring through a navigation monitoring system;
a determining module 602, configured to determine, according to the historical meteorological data and the historical pollutant data, a fractional index of a meteorological factor affecting the target pollutant within each factor interval;
the live data acquisition module 603 is configured to acquire live meteorological data corresponding to meteorological factors through the navigation monitoring system;
and the calculating module 604 is configured to calculate a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval.
A determining module 602, configured to count a total number of polluted weather and a total number of non-polluted weather caused by the target pollutant according to the historical pollutant data; according to historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor are respectively divided; and determining the sub-index of the meteorological factors influencing the target pollutants in each factor interval according to each factor interval corresponding to each meteorological factor, the total polluted weather caused by the target pollutants and the total non-polluted weather.
The determining module 602 is configured to obtain all observed values of a first meteorological factor from historical meteorological data, where the first meteorological factor is any meteorological factor included in the historical meteorological data; sequencing all the observed values according to a preset sequence; deleting the observation value of the preset proportion arranged at the front and deleting the observation value of the preset proportion arranged at the back; and uniformly dividing the rest observed values into a first preset number of intervals to obtain a first preset number of factor intervals corresponding to the first meteorological factor.
The determining module 602 is configured to determine, according to the total contaminated weather and the total non-contaminated weather caused by the target pollutant, the number of contaminated weather samples and the number of non-contaminated weather samples in each factor interval corresponding to each meteorological factor respectively; respectively calculating the sub-index of each meteorological factor in each factor interval according to the total polluted weather, the total non-polluted weather, and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor; and determining the sub-index of the meteorological factor influencing the target pollutant in each factor interval according to the sub-index of each meteorological factor in each factor interval.
The determining module 602 is configured to calculate a sub-index of each weather factor in each factor interval according to the total polluted weather, the total non-polluted weather, and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each weather factor by using a formula (1);
in the formula (1), KinIs the fractional index of meteorological factor i in the nth factor interval, ainThe number of polluted weather samples in a factor interval n corresponding to the meteorological factor i, binThe number of the non-polluted weather samples in the factor interval n corresponding to the meteorological factor i is shown, wherein a is the total number of the polluted weather, and b is the total number of the non-polluted weather.
A determining module 602, configured to calculate a difference between a maximum index score and a minimum index score corresponding to each meteorological factor respectively; carrying out correlation significance test on each meteorological factor to determine an autocorrelation factor in each meteorological factor; the meteorological factor with the maximum index in the autocorrelation factors is reserved, and other meteorological factors except the meteorological factor with the maximum index in the autocorrelation factors are removed; selecting a second preset number of meteorological factors with the largest difference from all the remaining meteorological factors; and determining the sub-index of each meteorological factor in each factor interval as the sub-index of the meteorological factor affecting the target pollutant in each factor interval.
A calculation module 604 for obtaining observation values of each meteorological factor affecting the target pollutant from the live meteorological data, respectively; respectively determining factor intervals to which the observed values of the meteorological factors belong; calculating the sum of the partial indexes corresponding to the factor interval to which the observed value of each meteorological factor belongs; and determining the calculated sum value as a pollution comprehensive diagnosis index corresponding to the target pollutant.
The device also includes: the navigation monitoring module is used for detecting the volatile organic pollutants in the atmosphere through a mass spectrometer included in the navigation monitoring system; detecting particulate matters in the atmosphere through a laser radar included in the navigation monitoring system; detecting the concentration of the polluted gas in the atmosphere by a boundary layer atmospheric composition hyperspectral scanning and analyzer AHSA (advanced high Performance analysis) included in the navigation monitoring system; detecting wind speed and wind direction through a wind speed and wind direction sensor included in the navigation monitoring system; the temperature and the humidity are detected by an air temperature and humidity sensor included in the navigation monitoring system.
The air pollution condition acquisition device based on the air traffic monitoring provided by the embodiment of the application and the air pollution condition acquisition method based on the air traffic monitoring provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as methods adopted, operated or realized by application programs stored in the device.
The embodiment of the application also provides electronic equipment for executing the air pollution condition obtaining method based on the navigation monitoring. Please refer to fig. 7, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 7, the electronic device 7 includes: the system comprises a processor 700, a memory 701, a bus 702 and a communication interface 703, wherein the processor 700, the communication interface 703 and the memory 701 are connected through the bus 702; the memory 701 stores a computer program that can be executed on the processor 700, and the processor 700 executes the air pollution condition obtaining method based on the air pollution condition monitoring provided by any one of the foregoing embodiments when executing the computer program.
The Memory 701 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the apparatus and at least one other network element is realized through at least one communication interface 703 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The processor 700 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 700. The Processor 700 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 701, and the processor 700 reads the information in the memory 701, and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the air pollution condition acquisition method based on the air traffic monitoring provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
The present embodiment further provides a computer-readable storage medium corresponding to the air pollution status acquiring method based on air pollution monitoring provided by the foregoing embodiment, please refer to fig. 8, which illustrates the computer-readable storage medium as an optical disc 30, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer program will execute the air pollution status acquiring method based on air pollution monitoring provided by any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the air pollution condition obtaining method based on the air pollution condition monitoring provided by the embodiment of the present application have the same inventive concept, and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
in the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted to reflect the following schematic: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. An air pollution condition obtaining method based on navigation monitoring is characterized by comprising the following steps:
acquiring historical meteorological data and historical pollutant data, wherein the historical meteorological data and the historical pollutant data are obtained by monitoring through a navigation monitoring system;
counting the total polluted weather and the total non-polluted weather caused by the target pollutants according to the historical pollutant data; according to the historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor are respectively divided; determining the sub-index of the meteorological factors influencing the target pollutants in each factor interval according to each factor interval corresponding to each meteorological factor, the total polluted weather caused by the target pollutants and the total non-polluted weather;
acquiring live meteorological data corresponding to the meteorological factors through the navigation monitoring system;
and calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval.
2. The method of claim 1, wherein said dividing a first predetermined number of factor intervals corresponding to each meteorological factor according to the historical meteorological data comprises:
acquiring all observed values of a first meteorological factor from the historical meteorological data, wherein the first meteorological factor is any meteorological factor included in the historical meteorological data;
sequencing all the observed values according to a preset sequence;
deleting the observation value of the preset proportion arranged at the front and deleting the observation value of the preset proportion arranged at the back;
and uniformly dividing the rest observed values into a first preset number of intervals to obtain the first preset number of factor intervals corresponding to the first meteorological factor.
3. The method of claim 1, wherein said determining a fractional index of meteorological factors affecting said target pollutant within each factor interval based on each factor interval corresponding to each meteorological factor, said total number of polluted weather caused by said target pollutant, and said total number of non-polluted weather, comprises:
respectively determining the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor according to the total number of polluted weather and the total number of non-polluted weather caused by the target pollutants;
respectively calculating the sub-index of each meteorological factor in each factor interval according to the total polluted weather, the total non-polluted weather and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor;
and determining the sub-index of the meteorological factor affecting the target pollutant in each factor interval according to the sub-index of each meteorological factor in each factor interval.
4. The method of claim 3, wherein the calculating the sub-index of each weather factor in each factor interval according to the total number of polluted weather, the total number of uncontaminated weather, and the number of polluted weather samples and the number of uncontaminated weather samples in each factor interval corresponding to each weather factor comprises:
calculating the sub-index of each meteorological factor in each factor interval through a formula (1) according to the total polluted weather, the total non-polluted weather and the number of polluted weather samples and the number of non-polluted weather samples in each factor interval corresponding to each meteorological factor;
in the formula (1), Kin is a sub-index of the meteorological factor i in the nth factor interval, ain is the number of polluted weather samples in the factor interval n corresponding to the meteorological factor i, bin is the number of non-polluted weather samples in the factor interval n corresponding to the meteorological factor i, a is the total number of polluted weather, and b is the total number of non-polluted weather.
5. The method of claim 3, wherein determining the fractional index of the meteorological factor affecting the target pollutant within each factor interval based on the fractional index of each meteorological factor within each factor interval comprises:
respectively calculating the difference between the maximum index and the minimum index corresponding to each meteorological factor;
carrying out correlation significance test on each meteorological factor to determine an autocorrelation factor in each meteorological factor;
the meteorological factor with the maximum index in the autocorrelation factors is reserved, and other meteorological factors except the meteorological factor with the maximum index in the autocorrelation factors are removed;
selecting a second preset number of meteorological factors with the largest difference from all the remaining meteorological factors;
and determining the sub-index of each meteorological factor in each factor interval as the sub-index of the meteorological factor affecting the target pollutant in each factor interval.
6. The method of claim 1, wherein calculating a composite pollution diagnostic index for the target pollutant from the live meteorological data and the fractional indices of the meteorological factors within each factor interval comprises:
obtaining observation values of each meteorological factor affecting the target pollutant from the live meteorological data respectively;
respectively determining factor intervals to which the observed values of the meteorological factors belong;
calculating the sum of the partial indexes corresponding to the factor interval to which the observation value of each meteorological factor belongs;
and determining the calculated sum value as a pollution comprehensive diagnosis index corresponding to the target pollutant.
7. The method according to any one of claims 1-6, further comprising:
detecting atmospheric volatile organic pollutants by a mass spectrometer comprised by the navigational monitoring system;
detecting particulate matter in the atmosphere through a laser radar included in the navigation monitoring system;
detecting the concentration of the polluted gas in the atmosphere by a boundary layer atmospheric composition hyperspectral scanning and analyzer AHSA included in the navigation monitoring system;
detecting wind speed and wind direction through a wind speed and wind direction sensor included in the navigation monitoring system;
and detecting the temperature and the humidity through an air temperature and humidity sensor included in the navigation monitoring system.
8. An air pollution situation acquisition device based on monitoring of sailing, comprising:
the historical data acquisition module is used for acquiring historical meteorological data and historical pollutant data, and the historical meteorological data and the historical pollutant data are obtained by monitoring through a navigation monitoring system;
the determining module is used for counting the total polluted weather and the total non-polluted weather caused by the target pollutants according to the historical pollutant data; according to the historical meteorological data, a first preset number of factor intervals corresponding to each meteorological factor are respectively divided; determining the sub-index of the meteorological factors influencing the target pollutants in each factor interval according to each factor interval corresponding to each meteorological factor, the total polluted weather caused by the target pollutants and the total non-polluted weather;
the live data acquisition module is used for acquiring live meteorological data corresponding to the meteorological factors through the navigation monitoring system;
and the calculation module is used for calculating a comprehensive pollution diagnosis index corresponding to the target pollutant according to the live meteorological data and the fractional index of the meteorological factor in each factor interval.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of claims 1-7.
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| CN112034108A (en) * | 2020-09-16 | 2020-12-04 | 上海市环境科学研究院 | Device and method for analyzing regional pollution condition and computer readable storage medium |
| CN112562041A (en) * | 2020-12-31 | 2021-03-26 | 江苏天瑞仪器股份有限公司 | Method for drawing concentration trend graph of navigation laser monitoring factor |
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| CN113379150A (en) * | 2021-06-24 | 2021-09-10 | 中科三清科技有限公司 | Method and device for identifying cause of current atmospheric situation, computer equipment and storage medium |
| CN113654590A (en) * | 2021-07-20 | 2021-11-16 | 江苏源远检测科技有限公司 | Navigation monitoring system |
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