CN116008481B - Air pollutant monitoring method and device based on large-range ground monitoring station - Google Patents
Air pollutant monitoring method and device based on large-range ground monitoring station Download PDFInfo
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Abstract
The invention provides an air pollutant monitoring method and device based on a large-scale ground monitoring station, comprising the following steps: acquiring ground air monitoring station data and concentration monitoring data in a target area; giving geographic attributes to the ground monitoring site and calculating the loss rate; interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site; carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site; performing time scale segmentation on the monitoring data according to the time interval, fusing the monitoring data into a multi-scale data set, and then performing merging and filtering treatment; and carrying out statistical index calculation and analysis on the combined and filtered multi-scale data set to obtain the distribution data of the air pollutants. The method solves the data processing problem associated with the interpolation of air pollutant data and site coding of the ground monitoring station, realizes the site data segmentation on time and space scales, and has certain universality and adaptivity to the monitoring processing of the site monitoring air pollutant.
Description
Technical Field
The invention relates to the field of environmental monitoring and air pollution control, in particular to an air pollutant monitoring method and device based on a large-range ground monitoring station.
Background
Monitoring of standard air contaminant (PM 2.5、PM10、SO2、NO2, CO, and O 3) concentration values helps to understand the air quality conditions, as well as the source of air pollution and the effectiveness of control measures. Air pollution monitoring is performed on different scales, so that the air pollution condition of a local area can be known, and a basis is provided for air pollution control decision. There are two main ways of monitoring air pollutants in general: remote sensing monitoring using satellite or aircraft mounted sensors and monitoring using ground air monitoring sites. The remote sensing monitoring can provide large-scale coverage and long-time monitoring data, is favorable for quickly and intuitively knowing regional distribution and time change trend of air pollution, but the large-scale monitoring data such as remote sensing only count the concentration of pollutants in an atmospheric vertical column, is not suitable for urban areas with high population density, and the remote sensing sensor cannot accurately detect the concentration of the air pollutants in the near-surface atmosphere due to various natural factors, so that the remote sensing monitoring data may have a certain error. While ground monitoring can provide more accurate data of air pollutants in the near-surface atmosphere, which is helpful for more accurately evaluating the health influence of air pollution and the effectiveness of limiting measures, traditional ground monitoring measures only evaluate and measure for single sites, various defects can occur in observation values among the sites, processing algorithms for the sites lack universality and adaptability, the space and time characteristics among the monitoring sites are ignored, and proper segmentation scales are difficult to determine.
Disclosure of Invention
The invention provides an air pollutant monitoring method and device based on a large-scale ground monitoring station and a storage medium. The method solves the data processing problem associated with the interpolation of the ground monitoring air pollutant data and the site coding, realizes the site data segmentation on the time and space scales, improves the difficulty that the ground monitoring site data cannot be associated in large-scale regions, and improves the precision and the processing efficiency of the environment monitoring and air pollution monitoring data.
In order to achieve the above purpose, the specific technical scheme of the invention is as follows:
in a first aspect, there is provided an air contaminant monitoring method based on a wide-area ground monitoring site, the method comprising:
Acquiring ground air monitoring station data in a target area;
Acquiring air standard pollutant concentration monitoring data of an air monitoring station;
giving geographic attributes to the ground monitoring site;
Calculating the loss rate of the observation data of the ground monitoring station;
interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site;
Carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site;
performing time scale segmentation on the monitoring data according to the time interval;
Carrying out space-time fusion on the monitoring data after space scale segmentation and the monitoring data after time scale segmentation to obtain a multi-scale data set;
combining and filtering the multi-scale data set;
and carrying out statistical index calculation and analysis on the combined and filtered multi-scale data set to obtain the distribution data of the air pollutants.
In some implementations of the first aspect, assigning geographic attributes to the ground monitoring site includes:
Acquiring ground monitoring station information, wherein the ground monitoring station information comprises a monitoring point code, a monitoring point name, affiliated cities, longitude information and latitude information;
The monitoring point codes are used as indexes, the names of the monitoring points are stored in a JSON format, and the longitude and the latitude of the monitoring points belong to cities;
And traversing all the monitoring station codes, and fusing all the monitoring station information including longitude and latitude information to enable the ground monitoring station to have geographic attributes.
In some implementations of the first aspect, interpolating missing monitoring data as a function of a geographic attribute of a ground monitoring site includes:
According to ground monitoring station information, finding that the data observed by a single monitoring station in the same city at a certain moment has a missing value, if the data of other n surrounding stations are complete, calculating the distance D i between the data and other surrounding stations according to the geographic attribute of the stations, and interpolating the missing data by adopting an inverse distance weighting method (INVERSE DISTANCE WEIGHTING, IDW):
the distance D i is obtained by:
wherein x and y are the position information of the station;
Meanwhile, inverse distance weighted interpolation is carried out according to the distance D i between the stations:
Wherein Z 0 represents a monitoring station missing value; z i is the i (i=th) 1,2, 3. N) number monitoring contaminants of the sample site; p is a power of distance; d i is the distance between each adjacent site.
In some implementations of the first aspect, interpolating missing monitoring data as a function of a geographic attribute of a ground monitoring site includes:
If a plurality of sites in the same city have data missing at the same time and cannot meet the condition of spatial interpolation, carrying out interpolation filling on missing values by using a time sequence linear interpolation method or exponential smoothing interpolation on the data of a single site;
linear interpolation based on time series:
Where x 1 and x 2 are the abscissa of the known time point, y 1 and y 2 are the ordinate of the known time point, x is the abscissa of the missing time point, and y is the ordinate of the missing time point;
Exponential smoothing interpolation based on time series:
In the method, in the process of the invention, Is the missing value at time t, y t is the actual value at time t,/>Is the missing value of the t-1 time point, alpha is a smoothing factor, and the value is between 0 and 1.
In some implementations of the first aspect, spatially scale partitioning of the monitored data according to geographic attributes of the ground monitoring site includes:
Acquiring information containing site geographic attributes;
Defining a spatial scale, wherein the spatial scale refers to the size of a geographic range considered in the statistical analysis process;
calculating the distance between the monitored stations, and calculating the radian distance between the two points by using Euclidean distance between the two stations or using an inverse cosine function in a spherical coordinate system;
Establishing a spatial distance matrix, namely storing distance information between stations in the matrix, filling distance values into corresponding positions of the matrix, and setting the distance between non-adjacent stations to be infinity;
The sites are required to be distributed to different groups, all the distance values smaller than or equal to the spatial scale are taken out from the spatial distance matrix, and the corresponding sites are distributed to the same group;
And for the rest stations, continuously taking out the distance values smaller than or equal to the spatial scale, and distributing the corresponding stations into the new group until all stations are distributed.
In some implementations of the first aspect, time-scale segmentation of the monitored data according to time intervals includes:
acquiring a data set containing monitoring data of a ground monitoring station, wherein the data set contains monitoring numerical values and time information;
Defining a time scale, and defining a proper time scale according to the requirement to divide data into different groups according to the time;
and counting the intra-group information, calculating statistics of the intra-group information by using the ground monitoring information of the intra-group data, and completing time scale segmentation of the ground monitoring data.
In some implementations of the first aspect, the merging and filtering process for the multi-scale dataset includes:
Selecting a preset space scale, and selecting monitoring stations in the space scale according to geographic attributes of the stations to obtain a monitoring station code in a space area;
selecting a time interval, and merging monitoring values of all monitoring stations in the area in each hour;
the average monitoring value per hour of all monitoring sites in the area is calculated to produce a monitoring data set of the space-time scale pollutant.
Processing the continuous time series data of each site by using a Savitzky-Golay filter on the data set, improving the autocorrelation of the data, enhancing the change trend of pollutants with time and reducing the noise and error of observed data;
the Savitzky-Golay filter implementation algorithm is as follows:
Where y i is the smoothed data value at the ith time point, x i is the original data value at the ith time point, a j is the coefficient, n is the coefficient of the order Savitzky-Golay filter of the fitted polynomial a j is obtained by least squares fitting;
when Savitzky-Golay filtering is used, the order n of the fitted polynomial and the size m of the window need to be specified, where m = 2n+1;
for a given order n and window size m, the corresponding coefficient a j is found using the least squares method.
In a second aspect, there is provided an air contaminant monitoring device based on a wide area ground monitoring site, the device comprising:
The acquisition module is used for acquiring ground air monitoring station data in the target area;
The acquisition module is also used for acquiring air standard pollutant concentration monitoring data of the air monitoring station;
the processing module is used for endowing geographic attributes to the ground monitoring station;
The processing module is also used for calculating the loss rate of the observed data of the ground monitoring station;
the processing module is also used for interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site;
the processing module is also used for carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site;
The processing module is also used for carrying out time scale segmentation on the monitoring data according to the time interval;
the processing module is further used for carrying out space-time fusion on the monitoring data after the space scale segmentation and the monitoring data after the time scale segmentation to form a multi-scale data set;
The processing module is also used for carrying out merging and filtering processing on the multi-scale data set;
The processing module is also used for carrying out statistical index calculation and analysis on the combined and filtered multi-scale data set to obtain the distribution data of the air pollutants.
In some implementations of the second aspect, the apparatus further includes a memory module;
The acquisition module is further used for acquiring ground monitoring station information, wherein the ground monitoring station information comprises a monitoring point code, a monitoring point name, affiliated cities, longitude information and latitude information;
the storage module is also used for storing the names of the monitoring points, the affiliated cities, the longitudes and the latitudes in a JSON format by taking the monitoring point codes as indexes;
The processing module is also used for traversing all the monitoring station codes, fusing all the monitoring station information, including longitude and latitude information, and enabling the ground monitoring station to have geographic attributes.
In a third aspect, there is provided a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of the first aspect and some implementations of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
The method solves the problem of space-time interpolation of the ground air monitoring station monitoring data, improves the autocorrelation of the observation data, reduces the observation noise, and improves the precision and the processing efficiency of the environment monitoring and air pollution monitoring data.
The data processing algorithm has universality and self-adaptability, comprises the space and time characteristics among all monitoring stations, and gives out proper segmentation scale and category.
The method and the system realize site data segmentation on time and space scales, perfect the difficult problem that ground monitoring station data cannot be associated in large-scale regions, and solve the difficult problem that ground monitoring data is subjected to large-space-scale macroscopic observation.
The invention provides an air pollutant monitoring method, an air pollutant monitoring device and a storage medium based on a large-scale ground monitoring station, which aim at the multi-scale segmentation monitoring and data processing method of the air pollutant of the large-scale ground monitoring station to solve the data processing of the air pollutant monitoring segmentation and each station monitoring value under different space-time scales. By the aid of the technology, ground air monitoring stations can be monitored on a time scale to know the change trend of air pollutants in different time phases, and regional distribution of the pollutants can be known by monitoring each station on a large scale on a space scale.
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FIG. 1 is a schematic flow chart of an air pollutant monitoring method based on a large-scale ground monitoring station according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for monitoring air pollutants based on a large-scale ground monitoring station according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an air pollutant monitoring device based on a large-scale ground monitoring station according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. In this embodiment, steps 4-14 are not sequenced, and changing the sequence has no effect on the final result. All other embodiments, which can be made by those skilled in the art without the inventive effort, are intended to be within the scope of the present invention.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. In order to solve the technical problems in the background technology, the application provides an air pollutant monitoring method, an air pollutant monitoring device and a storage medium based on a large-range ground monitoring station.
Fig. 1 is a schematic flow chart of an air pollutant monitoring method based on a large-range ground monitoring station according to an embodiment of the present invention, and fig. 2 is a schematic flow chart of an air pollutant monitoring method based on a large-range ground monitoring station according to an embodiment of the present invention, where an execution subject of the method may be a server or other terminal devices with computing processing capability, and the air pollutant monitoring method based on a large-range ground monitoring station according to the present invention is described with reference to fig. 1 and 2.
As shown in fig. 1, an air contaminant monitoring method based on a wide-range ground monitoring site may include:
s101: acquiring ground air monitoring station data in a target area;
S102: acquiring air standard pollutant concentration monitoring data of an air monitoring station;
S103: giving geographic attributes to the ground monitoring site;
s104: calculating the loss rate of the observation data of the ground monitoring station;
s105: interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site;
S106: carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site;
S107: performing time scale segmentation on the monitoring data according to the time interval;
S108: carrying out space-time fusion on the monitoring data after space scale segmentation and the monitoring data after time scale segmentation to obtain a multi-scale data set;
s109: combining and filtering the multi-scale data set;
S110: and carrying out statistical index calculation and analysis on the combined and filtered multi-scale data set to obtain the distribution data of the air pollutants.
In S101, ground air monitoring station data in a target area is acquired, station information of an air quality monitoring station is acquired through an interface, and the station information includes: the site data includes: site code, site city, site longitude and latitude information.
In S102, air standard pollutant concentration monitoring data of air monitoring stations are acquired, and hour-by-hour surface air standard pollutant concentration monitoring data of the ground air quality monitoring stations are acquired through interfaces. Wherein the contaminant monitoring data comprises: PM2.5, PM10, SO2, NO2, O3 (concentration measure of μg/m 3), CO (concentration measure of mg/m 3) and AQI (air quality index).
In S103, assigning a geographic attribute to the ground monitoring site includes obtaining monitoring site information, the ground monitoring site information including: encoding a monitoring point; monitoring point names; belonging to cities; longitude information; latitude information. And storing the names, the affiliated cities, the longitudes and the latitudes of the monitoring points in a JSON format by taking the monitoring point codes as indexes. And traversing all the monitoring station codes, and fusing all the monitoring station information including longitude and latitude information to enable the monitoring station to have geographic attributes.
In S104, calculating the loss rate of the ground monitoring station observation data; in calculating the missing rate of the data set, statistics are needed first
The number of missing data in the dataset is then divided by the total data volume in the dataset to obtain the rate of missing:
In the formula, the number of missing values in the data is set as n, the total data amount of the data is set as m, and in the data processing flow, the number of missing data in the data set is counted by using a dataframe (). Sum () function in a Pandas library.
In S105, the present invention uses two different interpolation methods to fill in the missing values of the contaminants per hour in the process of interpolating the missing monitored data according to the geographical attributes of the site.
The interpolation method 1 comprises the steps of finding that missing values exist in data observed by a single monitoring station in the same city at a certain moment according to station information, but the data of other n surrounding stations are complete, calculating the distance D i between the data and other surrounding stations according to the geographic attribute (longitude and latitude) of the station, and interpolating the missing data by adopting an inverse distance weighting method (INVERSE DISTANCE WEIGHTING, IDW):
the distance D i between each adjacent station is obtained by the following formula:
where x and y are the location information of the station.
Meanwhile, inverse distance weighted interpolation is carried out according to the distance D i between the stations:
Wherein Z 0 represents a monitoring station missing value; z i is the i (i=th) 1,2, 3. N) number monitoring contaminants of the sample site; p is a power of distance; d i is the distance between each adjacent site.
And 2, if the data of a plurality of sites in the same city are missing at the same time and the condition of spatial interpolation cannot be met, performing interpolation filling on the missing value by using the data of a single site to perform a time sequence linear interpolation method or exponential smoothing interpolation.
Linear interpolation based on time series:
Where x 1 and x 2 are the abscissa of the known time point, y 1 and y 2 are the ordinate of the known time point, x is the abscissa of the missing time point, and y is the ordinate of the missing time point.
Exponential smoothing interpolation based on time series:
Wherein, in the formula, wherein, Is the missing value at time t, y t is the actual value at time t,/>Is the missing value of the t-1 time point, alpha is a smoothing factor, and the value is between 0 and 1.
In S106, performing spatial scale segmentation on the monitored data according to the geographic attribute of the ground monitoring site, including: acquiring a site requires preparing information containing geographical attributes of the site, such as longitude, latitude, etc.
Spatial dimensions are defined, which refers to the size of the geographic range considered in the statistical analysis process.
The distance between the monitored stations is calculated, either by calculating the euclidean distance between the two stations or by calculating the radian distance between the two points using an arccosine function in the spherical coordinate system.
A spatial distance matrix is established, i.e. distance information between stations is stored in the matrix. The distance values may be filled into corresponding positions of the matrix while the distance between non-adjacent sites is set to infinity.
The sites are required to be distributed to different groups, all the distance values smaller than or equal to the spatial scale are taken out from the spatial distance matrix, and the corresponding sites are distributed to the same group. And for the rest stations, continuously taking out the distance values smaller than or equal to the spatial scale, and distributing the corresponding stations into the new group until all stations are distributed. And (5) performing spatial scale segmentation on the geographic attributes of the sites.
In S107, performing time scale segmentation on the monitored data according to the time interval includes:
It is necessary to prepare a data set containing ground monitoring station monitoring data. The dataset should contain monitoring value and time information such as monitoring date, sensor time, etc.
Defining a time scale, defining a proper time scale according to the requirement, and dividing data into different groups according to the size of time. Specifically, the data may be divided into a plurality of time periods according to the size of the time scale, and the data in the same time period is allocated to the same group.
And counting the information in the group, and calculating statistics of the information in the group by using ground monitoring information of the data in the group, wherein the statistics, such as average value, median and the like, of the information in the group can be used for completing time scale segmentation of the ground monitoring data.
In S108, performing space-time fusion on the monitored data after the spatial scale segmentation and the monitored data after the time scale segmentation to a multi-scale data set, including: statistical methods, such as multiple linear regression analysis, time series analysis, etc., are used to explore the effects of time and space on data. So as to explore the effect of time scale changes on data or to conduct the effect of spatial scale changes on data. The combined effect of time scale and spatial scale can also be explored to gain a deeper understanding of the laws of contaminant variation.
In S109, the multi-scale dataset is combined and filtered, including: and selecting a proper space scale, and selecting monitoring stations in the space scale according to the geographical attribute of the stations to obtain all monitoring station codes in the space area.
Next, a study time interval is selected and the hourly monitoring values of all monitoring sites in the area are combined.
Finally, calculating the average monitoring value of all monitoring stations in the area per hour to manufacture a monitoring data set of the space-time scale pollutant.
Processing the continuous time series data of each site by using a Savitzky-Golay filter on the data set will improve the autocorrelation of the data and enhance the trend of the contaminant over time, reducing the noise and error of the observed data.
The Savitzky-Golay filter implementation algorithm is as follows:
Where y i is the smoothed data value at the ith time point, x i is the raw data value at the ith time point, a j is the coefficient, n is the coefficient of the order Savitzky-Golay filter of the fitted polynomial a j is obtained by least squares fitting.
When Savitzky-Golay filtering is used, the order n of the fitted polynomial and the size m of the window need to be specified, where m=2n+1. For a given order n and window size m, the corresponding coefficient a j can be found using the least squares method.
In S110, performing statistical index calculation and analysis on the combined and filtered multiscale dataset to obtain distribution data of air pollutants, including: common statistical indicators for multi-scale calculations include mean, median, mode, variance, standard deviation, skewness, kurtosis, etc. Different statistical indexes can be selected as analysis values according to the characteristics of the data and the analysis purpose. Finally, the statistical index obtained by calculation needs to be interpreted. Specifically, the data characteristics, such as whether the distribution of the data is biased, whether there is a peak value, etc., can be determined by comparing the sizes of the statistical indexes. And can also be compared with the statistical indexes of other data sets to further understand the rule of the data.
Corresponding to the method embodiment in fig. 1, the present invention also provides an air pollutant monitoring device based on a wide-range ground monitoring station, as shown in fig. 3, the device may include:
an acquisition module 301, configured to acquire ground air monitoring station data in a target area;
the acquisition module 301 is further configured to acquire air standard pollutant concentration monitoring data of an air monitoring station;
A processing module 302, configured to assign a geographic attribute to the ground monitoring station;
the processing module 302 is further configured to calculate a loss rate of observation data of the ground monitoring station;
the processing module 302 is further configured to interpolate the missing monitoring data according to the geographic attribute of the ground monitoring site;
The processing module 302 is further configured to perform spatial scale segmentation on the monitored data according to the geographic attribute of the ground monitoring site;
The processing module 302 is further configured to perform time scale segmentation on the monitored data according to the time interval;
the processing module 302 is further configured to space-time fuse the monitored data after the spatial scale segmentation and the monitored data after the time scale segmentation into a multi-scale data set;
the processing module 302 is further configured to perform merging and filtering processing on the multi-scale data set;
the processing module 302 is further configured to perform statistical index calculation and analysis on the combined and filtered multi-scale dataset to obtain distribution data of the air pollutants.
In some embodiments, the apparatus further comprises a storage module;
The acquiring module 301 is further configured to acquire ground monitoring station information, where the ground monitoring station information includes a monitoring point code, a monitoring point name, a affiliated city, longitude information, and latitude information;
The storage module is also used for storing the names, the subordinate cities, the longitudes and the latitudes of the monitoring points in a JSON format by taking the monitoring point codes as indexes;
the processing module 302 is further configured to traverse all the monitoring station codes, and fuse all the monitoring station information, including longitude and latitude information, so that the ground monitoring station has a geographic attribute.
Interpolating missing monitored data according to geographic attributes of a ground monitoring site, comprising:
According to ground monitoring station information, finding that the data observed by a single monitoring station in the same city at a certain moment has a missing value, if the data of other n surrounding stations are complete, calculating the distance D i between the data and other surrounding stations according to the geographic attribute of the stations, and interpolating the missing data by adopting an inverse distance weighting method (INVERSE DISTANCE WEIGHTING, IDW):
The distance D i is given by:
wherein x and y are the position information of the station;
Meanwhile, inverse distance weighted interpolation is carried out according to the distance D i between the stations:
Wherein Z 0 represents a monitoring station missing value; z i is the i (i=th) 1,2, 3. N) number monitoring contaminants of the sample site; p is a power of distance; d i is the distance between each adjacent site.
In some embodiments, the processing module 302 is further configured to, if there is a data loss at a plurality of sites in the same city at the same time, fail to satisfy the condition of spatial interpolation, perform a time-series linear interpolation method or exponential smoothing interpolation on the data of the single site to perform interpolation filling on the missing value;
linear interpolation based on time series:
Where x 1 and x 2 are the abscissa of the known time point, y 1 and y 2 are the ordinate of the known time point, x is the abscissa of the missing time point, and y is the ordinate of the missing time point;
Exponential smoothing interpolation based on time series:
In the method, in the process of the invention, Is the missing value at time t, yt is the actual value at time t/>Is the missing value of the t-1 time point, alpha is a smoothing factor, and the value is between 0 and 1.
In some embodiments, the obtaining module 301 is further configured to obtain information including a geographic attribute of a site;
The processing module 302 is further configured to define a spatial scale, where the spatial scale refers to a size of a geographic range considered in the statistical analysis process;
calculating the distance between the monitored stations, and calculating the radian distance between the two points by using Euclidean distance between the two stations or using an inverse cosine function in a spherical coordinate system;
Establishing a spatial distance matrix, namely storing distance information between stations in the matrix, filling distance values into corresponding positions of the matrix, and setting the distance between non-adjacent stations to be infinity;
The sites are required to be distributed to different groups, all the distance values smaller than or equal to the spatial scale are taken out from the spatial distance matrix, and the corresponding sites are distributed to the same group;
And for the rest stations, continuously taking out the distance values smaller than or equal to the spatial scale, and distributing the corresponding stations into the new group until all stations are distributed.
In some embodiments, the obtaining module 301 is further configured to obtain a data set including monitoring data of the ground monitoring station, where the data set includes monitoring values and time information;
The processing module 302 is further configured to define a time scale, and define an appropriate time scale according to the requirement, so that the data needs to be divided into different groups according to the time;
and counting the intra-group information, calculating statistics of the intra-group information by using the ground monitoring information of the intra-group data, and completing time scale segmentation of the ground monitoring data.
In some embodiments, the processing module 302 is further configured to select a preset spatial scale, and select a monitoring site in the spatial scale according to a geographic attribute of the site, so as to obtain a monitoring site code in the spatial area;
selecting a time interval, and merging monitoring values of all monitoring stations in the area in each hour;
the average monitoring value per hour of all monitoring sites in the area is calculated to produce a monitoring data set of the space-time scale pollutant.
Processing the continuous time series data of each site by using a Savitzky-Golay filter on the data set, improving the autocorrelation of the data, enhancing the change trend of pollutants with time and reducing the noise and error of observed data;
the Savitzky-Golay filter implementation algorithm is as follows:
Where y i is the smoothed data value at the ith time point, x i is the original data value at the ith time point, a j is the coefficient, n is the coefficient of the order Savitzky-Golay filter of the fitted polynomial a j is obtained by least squares fitting;
when Savitzky-Golay filtering is used, the order n of the fitted polynomial and the size m of the window need to be specified, where m = 2n+1;
for a given order n and window size m, the corresponding coefficient a j is found using the least squares method.
It can be understood that each module/unit in the air pollutant monitoring device based on the large-range ground monitoring station shown in fig. 3 has a function of implementing each step in the air pollutant monitoring method based on the large-range ground monitoring station provided by the embodiment of the application, and can achieve the corresponding technical effects, which are not described herein for brevity.
Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention. As shown in fig. 4, computing device 400 includes an input interface 401, a central processor 402, a memory 403, and an output interface 404. Wherein the input interface 401, the central processing unit 402, the memory 403, and the output interface 404 are connected to each other by a bus 410.
The computing device shown in fig. 4 may also be implemented as an implementation of an air contaminant monitoring method based on a wide range of ground monitoring sites, which may include: a processor and a memory storing computer-executable instructions; the processor can realize the air pollutant monitoring method based on the large-range ground monitoring station provided by the embodiment of the invention when executing the computer executable instructions.
Embodiments of the present invention also provide a computer readable storage medium having computer program instructions stored thereon; the computer program instructions, when executed by the processor, implement the air pollutant monitoring method based on the large-range ground monitoring station provided by the embodiment of the invention.
It should be clear that, all embodiments in this specification are described in a progressive manner, and the same or similar parts of all embodiments are referred to each other, so that for brevity, no further description is provided. The present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. The method processes of the present application are not limited to the specific steps described and shown, but various changes, modifications and additions, or the order between steps may be made by those skilled in the art after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific IntegratedCircuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor Memory devices, read-Only Memory (ROM), flash Memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be adjusted as needed, or several steps may be performed at the same time, different from the order in the embodiments.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of 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, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Furthermore, the foregoing description is only illustrative of the preferred embodiment of the invention, and is not to be construed as limiting the invention in any way, as modifications to the disclosed technology are possible to those skilled in the art. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (5)
1. An air contaminant monitoring method based on a wide-range ground monitoring site, the method comprising:
Acquiring ground air monitoring station data in a target area;
Acquiring air standard pollutant concentration monitoring data of an air monitoring station;
giving geographic attributes to the ground monitoring site;
Calculating the loss rate of the observation data of the ground monitoring station;
interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site;
Carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site;
performing time scale segmentation on the monitoring data according to the time interval;
Carrying out space-time fusion on the monitoring data after space scale segmentation and the monitoring data after time scale segmentation to obtain a multi-scale data set;
combining and filtering the multi-scale data set;
carrying out statistical index calculation and analysis on the combined and filtered multi-scale data set to obtain distribution data of air pollutants;
The interpolation of the missing monitoring data according to the geographic attribute of the ground monitoring station comprises the following steps:
According to ground monitoring station information, finding that the data observed by a single monitoring station in the same city at a certain moment has a missing value, if the data of other n surrounding stations are complete, calculating the distance D i between the data and other surrounding stations according to the geographic attribute of the stations, and interpolating the missing data by adopting an inverse distance weighting method:
the distance D i is obtained by:
wherein x and y are the position information of the station;
Meanwhile, inverse distance weighted interpolation is carried out according to the distance D i between the stations:
Wherein Z 0 represents a monitoring station missing value; z i is the monitoring of contaminants at the ith sample station, wherein i=1, 2,3··n; p is a power of distance; d i is the distance between each adjacent site;
If a plurality of sites in the same city have data missing at the same time and cannot meet the condition of spatial interpolation, carrying out interpolation filling on missing values by using a time sequence linear interpolation method or exponential smoothing interpolation on the data of a single site;
linear interpolation based on time series:
Where x 1 and x 2 are the abscissa of the known time point, y 1 and y 2 are the ordinate of the known time point, x is the abscissa of the missing time point, and y is the ordinate of the missing time point;
Exponential smoothing interpolation based on time series:
In the method, in the process of the invention, Is the missing value at time t, y t is the actual value at time t,/>Is the missing value of the t-1 time point, alpha is a smoothing factor, and the value is between 0 and 1;
The space scale segmentation of the monitored data according to the geographic attribute of the ground monitoring site comprises the following steps: acquiring information containing site geographic attributes; defining a spatial scale, wherein the spatial scale refers to the size of a geographic range considered in the statistical analysis process; calculating the distance between the monitored stations, and calculating the radian distance between the two points by using Euclidean distance between the two stations or using an inverse cosine function in a spherical coordinate system; establishing a spatial distance matrix, namely storing distance information between stations in the matrix, filling distance values into corresponding positions of the matrix, and setting the distance between non-adjacent stations to be infinity; the sites are required to be distributed to different groups, all the distance values smaller than or equal to the spatial scale are taken out from the spatial distance matrix, and the corresponding sites are distributed to the same group; for the rest stations, continuously taking out the distance values smaller than or equal to the spatial scale, and distributing the corresponding stations to the new group until all stations are distributed;
The time scale segmentation of the monitoring data according to the time interval comprises the following steps: acquiring a data set containing monitoring data of a ground monitoring station, wherein the data set contains monitoring numerical values and time information; defining a time scale, and defining a proper time scale according to the requirement to divide data into different groups according to the time; counting the intra-group information, calculating statistics of the intra-group information by using ground monitoring information of the intra-group data, and completing time scale segmentation of the ground monitoring data;
The merging and filtering processing of the multi-scale data set comprises the following steps: selecting a preset space scale, and selecting monitoring stations in the space scale according to geographic attributes of the stations to obtain a monitoring station code in a space area; selecting a time interval, and merging monitoring values of all monitoring stations in the area in each hour; calculating average monitoring values of all monitoring sites in the area per hour to manufacture a monitoring data set of the space-time scale pollutants; processing the continuous time series data of each site by using a Savitzky-Golay filter on the data set, improving the autocorrelation of the data, enhancing the change trend of pollutants with time and reducing the noise and error of observed data; the Savitzky-Golay filter implementation algorithm is as follows:
Where y i is the smoothed data value at the ith time point, x i is the original data value at the ith time point, a j is the coefficient, n is the coefficient of the order Savitzky-Golay filter of the fitted polynomial a j is obtained by least squares fitting;
when Savitzky-Golay filtering is used, the order n of the fitted polynomial and the size m of the window need to be specified, where m = 2n+1;
for a given order n and window size m, the corresponding coefficient a j is found using the least squares method.
2. The method of claim 1, wherein assigning geographic attributes to the ground monitoring site comprises:
acquiring ground monitoring site information, wherein the ground monitoring site information comprises a monitoring point code, a monitoring point name, a subordinate city, longitude information and latitude information;
The monitoring point codes are used as indexes, the names of the monitoring points are stored in a JSON format, and the longitude and the latitude of the monitoring points belong to cities;
And traversing all the monitoring station codes, and fusing all the monitoring station information including longitude and latitude information to enable the ground monitoring station to have geographic attributes.
3. An air contaminant monitoring device based on a wide-area ground monitoring site, the device comprising:
The acquisition module is used for acquiring ground air monitoring station data in the target area;
The acquisition module is also used for acquiring air standard pollutant concentration monitoring data of the air monitoring station;
the processing module is used for endowing geographic attributes to the ground monitoring station;
The processing module is also used for calculating the loss rate of the observed data of the ground monitoring station;
the processing module is also used for interpolating the missing monitoring data according to the geographic attribute of the ground monitoring site;
the processing module is also used for carrying out space scale segmentation on the monitoring data according to the geographic attribute of the ground monitoring site;
The processing module is also used for carrying out time scale segmentation on the monitoring data according to the time interval;
the processing module is further used for carrying out space-time fusion on the monitoring data after the space scale segmentation and the monitoring data after the time scale segmentation to form a multi-scale data set;
The processing module is also used for carrying out merging and filtering processing on the multi-scale data set;
the processing module is also used for calculating and analyzing statistical indexes of the combined and filtered multi-scale data sets to obtain distribution data of air pollutants;
The interpolation of the missing monitoring data according to the geographic attribute of the ground monitoring station comprises the following steps:
According to ground monitoring station information, finding that the data observed by a single monitoring station in the same city at a certain moment has a missing value, if the data of other n surrounding stations are complete, calculating the distance D i between the data and other surrounding stations according to the geographic attribute of the stations, and interpolating the missing data by adopting an inverse distance weighting method:
the distance D i is obtained by:
wherein x and y are the position information of the station;
Meanwhile, inverse distance weighted interpolation is carried out according to the distance D i between the stations:
Wherein Z 0 represents a monitoring station missing value; z i is the monitoring of contaminants at the ith sample station, wherein i=1, 2,3··n; p is a power of distance; d i is the distance between each adjacent site;
If a plurality of sites in the same city have data missing at the same time and cannot meet the condition of spatial interpolation, carrying out interpolation filling on missing values by using a time sequence linear interpolation method or exponential smoothing interpolation on the data of a single site;
linear interpolation based on time series:
Where x 1 and x 2 are the abscissa of the known time point, y 1 and y 2 are the ordinate of the known time point, x is the abscissa of the missing time point, and y is the ordinate of the missing time point;
Exponential smoothing interpolation based on time series:
In the method, in the process of the invention, Is the missing value at time t, y t is the actual value at time t,/>Is the missing value of the t-1 time point, alpha is a smoothing factor, and the value is between 0 and 1;
The space scale segmentation of the monitored data according to the geographic attribute of the ground monitoring site comprises the following steps: acquiring information containing site geographic attributes; defining a spatial scale, wherein the spatial scale refers to the size of a geographic range considered in the statistical analysis process; calculating the distance between the monitored stations, and calculating the radian distance between the two points by using Euclidean distance between the two stations or using an inverse cosine function in a spherical coordinate system; establishing a spatial distance matrix, namely storing distance information between stations in the matrix, filling distance values into corresponding positions of the matrix, and setting the distance between non-adjacent stations to be infinity; the sites are required to be distributed to different groups, all the distance values smaller than or equal to the spatial scale are taken out from the spatial distance matrix, and the corresponding sites are distributed to the same group; for the rest stations, continuously taking out the distance values smaller than or equal to the spatial scale, and distributing the corresponding stations to the new group until all stations are distributed;
The time scale segmentation of the monitoring data according to the time interval comprises the following steps: acquiring a data set containing monitoring data of a ground monitoring station, wherein the data set contains monitoring numerical values and time information; defining a time scale, and defining a proper time scale according to the requirement to divide data into different groups according to the time; counting the intra-group information, calculating statistics of the intra-group information by using ground monitoring information of the intra-group data, and completing time scale segmentation of the ground monitoring data;
The merging and filtering processing of the multi-scale data set comprises the following steps: selecting a preset space scale, and selecting monitoring stations in the space scale according to geographic attributes of the stations to obtain a monitoring station code in a space area; selecting a time interval, and merging monitoring values of all monitoring stations in the area in each hour; calculating average monitoring values of all monitoring sites in the area per hour to manufacture a monitoring data set of the space-time scale pollutants; processing the continuous time series data of each site by using a Savitzky-Golay filter on the data set, improving the autocorrelation of the data, enhancing the change trend of pollutants with time and reducing the noise and error of observed data; the Savitzky-Golay filter implementation algorithm is as follows:
Where y i is the smoothed data value at the ith time point, x i is the original data value at the ith time point, a j is the coefficient, n is the coefficient of the order Savitzky-Golay filter of the fitted polynomial a j is obtained by least squares fitting;
when Savitzky-Golay filtering is used, the order n of the fitted polynomial and the size m of the window need to be specified, where m = 2n+1;
for a given order n and window size m, the corresponding coefficient a j is found using the least squares method.
4. A monitoring device according to claim 3, further comprising a memory module;
the acquisition module is further used for acquiring ground monitoring site information, wherein the ground monitoring site information comprises a monitoring point code, a monitoring point name, affiliated cities, longitude information and latitude information;
the storage module is also used for storing the names of the monitoring points, the affiliated cities, the longitudes and the latitudes in a JSON format by taking the monitoring point codes as indexes;
The processing module is also used for traversing all the monitoring station codes, fusing all the monitoring station information, including longitude and latitude information, and enabling the ground monitoring station to have geographic attributes.
5. A computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of any of claims 1-2.
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